Geomatics doi: 10.3390/geomatics5030036

Authors: Paula Andrea Contreras Rojas Felipe de Lucia Lobo Wesley J. Moses Gilberto Loguercio Collares Lino Sander de Carvalho

The Mirim–Patos–Mangueira coastal lagoon system provides a wide range of ecosystem services. However, its vast territorial extent and the political boundaries that divide it hinder integrated assessments, especially during extreme hydrological events. This study is divided into two parts. First, we assessed the spatial and temporal patterns of water quality in the lagoon system using Sentinel-3/OLCI satellite imagery. Atmospheric correction was performed using ACOLITE, followed by spectral grouping and classification into optical water types (OWTs) using the Sentinel Applications Platform (SNAP). To explore the behavior of water quality parameters across OWTs, Chlorophyll-a and turbidity were estimated using semi-empirical algorithms specifically designed for complex inland and coastal waters. Results showed a gradual increase in mean turbidity from OWT 2 to OWT 6 and a rise in chlorophyll-a from OWT 2 to OWT 4, with a decline at OWT 6. These OWTs correspond, in general terms, to distinct water masses: OWT 2 to clearer waters, OWT 3 and 4 to intermediate/mixed conditions, and OWT 6 to turbid environments. In the second part, we analyzed the response of the Patos Lagoon to flooding in Rio Grande do Sul during an extreme weather event in May 2024. Satellite-derived turbidity estimates were compared with in situ measurements, revealing a systematic underestimation, with a negative bias of 2.6%, a mean relative error of 78%, and a correlation coefficient of 0.85. The findings highlight the utility of OWT classification for tracking changes in water quality and support the use of remote sensing tools to improve environmental monitoring in data-scarce regions, particularly under extreme hydrometeorological conditions.

Geomatics doi: 10.3390/geomatics5030035

Authors: Andrew J. Lew Timothy Perkins Ethan Brewer Paul Corlies Robert Sundberg

Integrating diverse image datasets acquired from different satellites is challenging. Converting images from one sensor to another, like from WorldView-3 (WV) to SuperDove (SD), involves both changing image channel wavelengths and per-band intensity scales because different sensors can acquire imagery of the same scene at different wavelengths and intensities. A parametrized convolutional network approach has shown promise converting across sensor domains, but it introduces distortion artefacts when objects are in motion. The cause of spectral distortion is due to temporal delays between sequential multispectral band acquisitions. This can result in spuriously blurred images of moving objects in the converted imagery, and consequently misaligned moving object locations across image bands. To resolve this, we propose an enhanced model, the Physics-Informed Gaussian-Enforced Separated-Band Convolutional Conversion Network (PIGESBCCN), which better accounts for known spatial, spectral, and temporal correlations between bands via band reordering and branched model architecture.

Geomatics doi: 10.3390/geomatics5030034

Authors: Kazi Aminul Islam Omar Abul-Hassan Hongfang Zhang Victoria Hill Blake Schaeffer Richard Zimmerman Jiang Li

Traditional bathymetry measures require a large number of human hours, and many bathymetry records are obsolete or missing. Automated measures of bathymetry would reduce costs and increase accessibility for research and applications. In this paper, we optimized a recent machine learning model, named CatBoostOpt, to estimate bathymetry based on high-resolution WorldView-2 (WV-2) multi-spectral optical satellite images. CatBoostOpt was demonstrated across the Florida Big Bend coastline, where the model learned correlations between in situ sound Navigation and Ranging (Sonar) bathymetry measurements and the corresponding multi-spectral reflectance values in WV-2 images to map bathymetry. We evaluated three different feature transformations as inputs for bathymetry estimation, including raw reflectance, log-linear, and log-ratio transforms of the raw reflectance value in WV-2 images. In addition, we investigated the contribution of each spectral band and found that utilizing all eight spectral bands in WV-2 images offers the best solution for handling complex water quality conditions. We compared CatBoostOpt with linear regression (LR), support vector machine (SVM), random forest (RF), AdaBoost, gradient boosting, and deep convolutional neural network (DCNN). CatBoostOpt with log-ratio transformed reflectance achieved the best performance with an average root mean square error (RMSE) of 0.34 and coefficient of determination (R2) of 0.87.

Geomatics doi: 10.3390/geomatics5030033

Authors: Nico Van de Weghe Lars De Sloover Jana Verdoodt Haosheng Huang

Capturing the interactions between moving objects is vital in traffic analysis, sports, and animal behavior, but remains challenging because of subtle spatiotemporal dynamics. This paper introduces HAIMO (Hybrid Analysis of the Interaction of Moving Objects), a conceptual framework that combines knowledge-driven AI for interpretable, symbolic interaction representations with data-driven models trained through self-supervised learning (SSL) on large sets of unlabeled trajectory data. In HAIMO, we propose using transformer architectures to model complex spatiotemporal dependencies while maintaining interpretability through symbolic reasoning. To illustrate the feasibility of this hybrid approach, we present a basic proof-of-concept using elite tennis rallies, where the knowledge-driven component identifies interaction patterns between players and the ball, and we outline how SSL-enhanced transformer models could support and strengthen movement prediction. By bridging symbolic reasoning and self-supervised data-driven learning, HAIMO provides a conceptual foundation for future GeoAI and spatiotemporal analytics, especially in applications where both pattern discovery and explainability are crucial.

Geomatics doi: 10.3390/geomatics5030032

Authors: Jeyavanan Karthigesu Toshiaki Owari Satoshi Tsuyuki Takuya Hiroshima

Individual tree crown detection (ITCD) and tree species classification are critical for forest inventory, species-specific monitoring, and ecological studies. However, accurately detecting tree crowns and identifying species in structurally complex forests with overlapping canopies remains challenging. This study was conducted in a complex mixed conifer–broadleaf forest in northern Japan, aiming to improve ITCD and species classification by employing two machine learning models and different combinations of metrics derived from very high-resolution (2.5 cm) UAV red–green–blue (RGB) and multispectral (MS) imagery. We first enhanced ITCD by integrating different combinations of metrics into multiresolution segmentation (MRS) and DeepForest (DF) models. ITCD accuracy was evaluated across dominant forest types and tree density classes. Next, nine tree species were classified using the ITCD outputs from both MRS and DF approaches, applying Random Forest and DF models, respectively. Incorporating structural, textural, and spectral metrics improved MRS-based ITCD, achieving F-scores of 0.44–0.58. The DF model, which used only structural and spectral metrics, achieved higher F-scores of 0.62–0.79. For species classification, the Random Forest model achieved a Kappa value of 0.81, while the DF model attained a higher Kappa value of 0.91. These findings demonstrate the effectiveness of integrating UAV-derived metrics and advanced modeling approaches for accurate ITCD and species classification in heterogeneous forest environments. The proposed methodology offers a scalable and cost-efficient solution for detailed forest monitoring and species-level assessment.

Geomatics doi: 10.3390/geomatics5030031

Authors: Enrico Corrado Borgogno-Mondino

Recently, geomatics-related data, products, services and applications have proven to significantly support many actions in environmental (land, water, extra-terrestrial) analysis, management and protection, often answering to political instances [...]

Geomatics doi: 10.3390/geomatics5030030

Authors: Martin Enrique Romero-Sanchez Antonio Gonzalez-Hernandez Efraín Velasco-Bautista Arian Correa-Diaz Alma Delia Ortiz-Reyes Ramiro Perez-Miranda

Estimating aboveground biomass (AGB) is crucial for understanding the carbon cycle in terrestrial ecosystems, particularly within the context of climate change. Therefore, it is essential to research and compare different methods of AGB estimation to achieve acceptable accuracy. This study modelled AGB in temperate forests of central Mexico using active and passive remote sensing data combined with machine learning techniques (Random Forest and XGBoost) and compared the estimations against a traditional method, such as linear regression. The main goal was to evaluate the performance of machine learning techniques against linear regression in AGB estimation and then validate against an independent forest inventory database. The models obtained acceptable performance in all cases, but the machine learning algorithm Random Forest outperformed (R2cv = 0.54; RMSEcv = 19.17) the regression method (R2cv = 0.41; RMSEcv = 25.76). The variables that made significant contributions, in both Random Forest and XGBoost modelling, were NDVI, kNDVI (Landsat OLI sensor), and the HV polarisation from ALOS-Palsar. For validation, the Machine learning ensemble had a higher Spearman correlation (r = 0.68) than the linear regression (r = 0.50). These findings highlight the potential of integrating machine learning techniques with remote sensing data to improve the reliability of AGB estimation in temperate forests.

Geomatics doi: 10.3390/geomatics5030029

Authors: Sercan Gülci Michael Wing Abdullah Emin Akay

The use of Google Earth Engine (GEE), a cloud-based computing platform, in spatio-temporal evaluation studies has increased rapidly in natural sciences such as forestry. In this study, Sentinel-2 satellite imagery and Shuttle Radar Topography Mission (SRTM) elevation data and image classification algorithms based on two machine learning techniques were examined. Random Forest (RF) and Classification and Regression Trees (CART) were used to classify land use and land cover (LULC) in western Oregon (USA). To classify the LULC from the spectral bands of satellite images, a composition consisting of vegetation difference indices NDVI, NDWI, EVI, and BSI, and a digital elevation model (DEM) were used. The study area was selected due to a diversity of land cover types including research forest, botanical gardens, recreation area, and agricultural lands covered with diverse plant species. Five land classes (forest, agriculture, soil, water, and settlement) were delineated for LULC classification testing. Different spatial points (totaling 75, 150, 300, and 2500) were used as training and test data. The most successful model performance was RF, with an accuracy of 98% and a kappa value of 0.97, while the accuracy and kappa values for CART were 95% and 0.94, respectively. The accuracy of the generated LULC maps was evaluated using 500 independent reference points, in addition to the training and testing datasets. Based on this assessment, the RF classifier that included elevation data achieved an overall accuracy of 92% and a kappa coefficient of 0.90. The combination of vegetation difference indices with elevation data was successful in determining the areas where clear-cutting occurred in the forest. Our results present a promising technique for the detection of forests and forest openings, which was helpful in identifying clear-cut sites. In addition, the GEE and RF classifier can help identify and map storm damage, wind damage, insect defoliation, fire, and management activities in forest areas.

Geomatics doi: 10.3390/geomatics5030028

Authors: Mohamed Abdelazeem Hussain A. Kamal Amgad Abazeed Amr M. Wahaballa

This research examines the performance of the affordable Xiaomi 11T smartphone in static positioning mode. Static Global Navigation Satellite System (GNSS) measurements are acquired over a two-hour period with a known reference point, spanning three consecutive days. The acquired data are processed, employing both real-time and post-processing Precise Point Positioning (PPP) solutions using GPS-only, Galileo-only, and the combined GPS/Galileo datasets. To correct the satellite and clock errors, the instantaneous Centre National d’Études Spatiales (CNES), the final Le Groupe de Recherche de Géodésie Spatiale (GRG), GeoForschungsZentrum (GFZ), and Wuhan University (WUM) products were applied. The results demonstrate that sub-30 cm positioning accuracy is achieved in the horizontal direction using real-time and final products. Additionally, sub-50 cm positioning accuracy is attained in the vertical direction for the real-time and post-processed solutions. Furthermore, the real-time products achieved three-dimensional (3D) position accuracies of 40 cm, 29 cm, and 20 cm using GPS-only, Galileo-only, and the combined GPS/Galileo observations, respectively. The final products achieved 3D position accuracies of 24 cm, 26 cm, and 28 cm using GPS-only, Galileo-only, and the combined GPS/Galileo measurements, respectively. The attained positioning accuracy can be used in some land use and urban planning applications.

Geomatics doi: 10.3390/geomatics5030027

Authors: Rohit Singh Mahesh Pal Mantosh Biswas

With the continuous advancement of remote sensing technology and its growing importance, the need for ready-to-use data has increased exponentially. Satellite platforms such as Sentinel-2, which carries the Multispectral Instrument (MSI) sensor, known for their cost-effectiveness, capture valuable information about Earth in the form of images. However, they encounter a significant challenge in the form of clouds and their shadows, which hinders the data acquisition and processing for regions of interest. This article undertakes a comprehensive literature review to systematically analyze the critical cloud-related challenges. It explores the need for accurate cloud detection, reviews existing datasets, and evaluates contemporary cloud detection methodologies, including their strengths and limitations. Additionally, it highlights the inaccuracies introduced by varying atmospheric and environmental conditions, emphasizing the importance of integrating advanced techniques that can utilize local and global semantics. The review also introduces a structured intercomparison framework to enable standardized evaluation across binary and multiclass cloud detection methods using both qualitative and quantitative metrics. To facilitate fair comparison, a conversion mechanism is highlighted to harmonize outputs across methods with different class granularities. By identifying gaps in current practices and datasets, the study highlights the importance of innovative, efficient, and scalable solutions for automated cloud detection, paving the way for unbiased evaluation and improved utilization of satellite imagery across diverse applications.

Geomatics doi: 10.3390/geomatics5020026

Authors: Kristian Breili Carl William Lund

Horizontal dilution of precision (HDOP) is a widely used quality indicator of Global Navigation Satellite System (GNSS) positioning, considering only satellite geometry. In this study, HDOP was simulated using GNSS almanacs and high-resolution digital surface models (DSMs) along three European road sections: Oslo— Svinesund Bridge (Norway); Hamburg city center (Germany); and Rotterdam—Dutch–German border (Netherlands). This study was accomplished as part of the MODI project, which is a cross-border initiative to accelerate Cooperative, Connected, and Automated Mobility (CCAM). Our analysis revealed excellent or good overall GNSS performance in the study areas, particularly on highway sections with 99–100% of study points having a median HDOP that is categorized as excellent (HDOP < 2) or good (HDOP < 5). However, the road section in Hamburg’s city center presents challenges. When GPS is used alone, 8% of the study points experience weak or poor HDOP, and there are study points where the system is available (HDOP < 5) less than 50% of the time. Combining GNSS constellations significantly improved system availability, reaching 95% for 99% of the study points in Hamburg. To validate our simulations, we compared results with GNSS observations from a survey vehicle in Hamburg. Initial low correlation was attributed to the reception of signals from non-line-of-sight satellites. By excluding satellites with low signal-to-noise ratios, the correlation increased significantly, and reasonable agreement was obtained. We also examined the impact of using a 10 m DSM instead of a 1 m DSM in Hamburg. While the coarser spatial resolution offers computational benefits, it may miss critical details for accurate assessment of satellite visibility.

Geomatics doi: 10.3390/geomatics5020025

Authors: Siyandza M. Dlamini Yashon O. Ouma

The automation in image acquisition and processing using UAV drones has the potential to acquire terrain data that can be utilized for the accurate production of 2D and 3D digital data. In this study, the DJI Phantom 4 drone was employed for large-scale topographical mapping, and based on the photogrammetric Structure-from-Motion (SfM) algorithm, drone-derived point clouds were used to generate the terrain DSM, DEM, contours, and the orthomosaic from which the topographical map features were digitized. An evaluation of the horizontal (X, Y) and vertical (Z) coordinates of the UAV drone points and the RTK-GNSS survey data showed that the Z-coordinates had the highest MAE(X,Y,Z), RMSE(X,Y,Z) and Accuracy(X,Y,Z) errors. An integrated georeferencing of the UAV drone imagery using the mobile RTK-GNSS base station improved the 2D and 3D positional accuracies with an average 2D (X, Y) accuracy of <2 mm and height accuracy of −2.324 mm, with an overall 3D accuracy of −4.022 mm. Geometrically, the average difference in the perimeter and areas of the features from the RTK-GNSS and UAV drone topographical maps were −0.26% and −0.23%, respectively. The results achieved the recommended positional accuracy standards for the production of digital geospatial data, demonstrating the cost-effectiveness of low-cost UAV drones for large-scale topographical mapping.

Geomatics doi: 10.3390/geomatics5020024

Authors: Petr Vaní?ek Pavel Novák Marcelo Santos

The determination of the shape of the Earth has been one of the fundamental problems geodesy was supposed to solve; it has been and possibly still is the main geodetic problem. It is thus appropriate for geodesists to look at this problem periodically, and this is what the authors of this paper aim to do. About 50 years ago, geodesists started using satellites as a new and very powerful tool. Many problems that were either impossible to solve or that presented almost unsurmountable hurdles to solutions have now been solved relatively simply, so much so that in the eyes of some people, satellites can solve all geodetic problems, and attempts are being made to show that this is indeed the case. We feel that the time has come to show that even satellites have their limitations, the main one being that for them to remain in their orbit, they must fly quite high, typically at several hundred kilometres. The gravitational field of the Earth (and that of any celestial body) smoother as one gets higher and higher. In other words, the gravitational field at the satellite orbit altitude loses detailed information that one can see at the surface of the Earth. In this contribution, we shall try to explain what satellites have contributed to the study of the shape of the Earth and what issues remain to be sorted out.

Geomatics doi: 10.3390/geomatics5020023

Authors: William Ritchie Ali Shahzad Scott R. Gallagher Wolfgang Hall

The pursuit of mergers and acquisitions (M&A) is often an acclaimed strategy for firm growth, resource sharing, and extended reach into new market segments. However, in the healthcare marketplace, there are two very different perspectives related to M&A. On the one hand, the American Hospital Association commends M&A activity as a tool to reduce healthcare costs, drive quality, and serve rural markets. On the other hand, a recent United States’ Presidential executive order suggests that M&A in the healthcare space is harmful to healthcare due to its restrictions on competition and adverse impacts on patients. These conflicting perspectives reflect differing M&A views in mainstream management research, as well. The purpose of the current study is twofold. First, we aim to explore these two seemingly paradoxical perspectives by examining the degree of hospital network geographic dispersion that results from M&A activity. Second, we contribute to the broader M&A literature by drawing attention to the importance of considering geographic influences on M&A performance. Using a spatial analysis of 147 nationwide hospital networks comprising 1713 hospitals, we propose and find support for the notion that the degree of network dispersion, as measured by actual driving distances in healthcare networks, are correlated with patient experiences. Using ordinary least squares (OLS) regression to examine relationships between patient experiences and overall hospital network geographic dispersion, we found support for the hypothesis that more spatially dispersed healthcare networks are associated with lower overall performance outcomes, as measured by customer (patient) satisfaction. The implications of these findings suggest that growth strategies that involve M&A activity should carefully consider the spatial influences on M&A entity selection. Our exploratory findings also provide a foundation for future research to bridge the gap between industry and governmental perspectives on healthcare M&A practices.

Geomatics doi: 10.3390/geomatics5020022

Authors: Noor Marji Michal Kohout

This article presents a comprehensive evaluation of image segmentation methods for monitoring morphological changes in refugee camps, comparing five distinct approaches: ESRI Landviewer clustering, K-means clustering, U-Net segmentation, Meta’s Segment Anything Model (SAM) and ArcGIS segmentation. Using high-resolution satellite imagery from Al-Azraq refugee camp in Jordan (2014–2023) as a case study, this research systematically assesses each method’s performance in detecting and quantifying settlement pattern changes. The evaluation framework incorporates multiple validation metrics, including overall accuracy, the Kappa coefficient, F1-score and computational efficiency. The results demonstrate that ArcGIS’s ISO clustering and classification approach achieves superior performance, with 99% overall accuracy and a Kappa coefficient of 0.95, significantly outperforming the other tested methods. While Meta SAM shows promise in object detection, its performance degrades with aerial imagery, achieving only 75% accuracy in settlement pattern recognition. The study establishes specific parameter optimization guidelines for humanitarian contexts, with spectral detail values of 3.0–7.0 and spatial detail values of 14.0–18.0, yielding optimal results for refugee settlement analysis. These findings provide crucial methodological guidance for monitoring refugee settlement evolution and transition, contributing to more effective humanitarian response planning and settlement management through integrating remote sensing and machine learning technologies.

Geomatics doi: 10.3390/geomatics5020021

Authors: Wladyslaw Koc

This article addresses the issue of designing compound curves, i.e., a geometric system consisting of two (or more) circular arcs of different radii, pointing in the same direction and directly connected to each other. Nowadays, compound curves are mainly used on tram lines; they also occur on railways (e.g., on mountain lines), but new ones are generally no longer being built there. Therefore, in relation to railway lines, the aim is to be able to recreate (i.e., model) the existing geometric layout with compound curves, so that it is then possible to correct this layout. An analytical method for designing track geometric systems was used, adapted to the mobile satellite measurement technique, in which calculations are carried out in the appropriate local Cartesian coordinate system. The basis of this system is the symmetrically arranged adjacent main directions of the route, and the beginning is located at the point of intersection of these directions. A number of detailed issues have been clarified and basic characteristic quantities have been determined, and the computational algorithm described in the paper leads to the solution of the problem in a sequential manner. The obtained possibilities of modeling the compound curves are illustrated by the provided calculation example.

Geomatics doi: 10.3390/geomatics5020020

Authors: Forrest Hisey Valerie Lin Tingting Zhu

Higher education institutions have played a central role in building sustainability awareness. However, current models only show an effect on students’ knowledge about sustainable development, with a large gap in transformative solutions that shift from understanding problems towards solutions. This case study explores a new model that integrates sustainability reflections in a Geographic Information Science (GIS) Capstone Project course. Through collaborations with external partners and reflections on sustainability modules, students analyzed complex problems and developed sustainability competencies. The assessment tool adopted in this study combines reflective writing, scenario testing, performance observation, and self-assessment. Based on the set of key competencies in sustainability, half of the students developed systems-thinking and strategies-thinking, while a quarter of the students developed futures-thinking and values-thinking. Their development of sustainability competencies went beyond simply acquiring knowledge, also critically evaluating different perspectives and implementing or integrating the concepts when addressing the problems. Geospatial information tackles three key aspects of sustainability, which are relational, distributional, and directional, making it ideal in analyzing sustainability issues and providing insights for informed decisions. This study fills another important gap of integrating sustainability competency development in GIS education.

Geomatics doi: 10.3390/geomatics5020019

Authors: Rozymario Fagundes Luiz Patric Kayser Lúcio de Paula Amaral Ana Caroline Benedetti édson Luis Bolfe Taya Cristo Parreiras Manuela Ramos-Ospina Alejandro Marulanda-Tobón

Spectral indices such as NDRE (Normalized Difference Red Edge Index), CCCI (Canopy Chlorophyll Content Index), and IRECI (Inverted Red Edge Chlorophyll Index), derived from the Red Edge band of MSI/Sentinel-2A (B05, B06, B07), are critical for coffee monitoring. To align the Red Edge band (20 m resolution) with the NIR band (10 m resolution), the nearest neighbor, bilinear, cubic and Lanczos resampling methods were used, available in the Terra package in the R software(4.4.0). This study evaluates these methods using two original B05 images from 24 November 2023, and 21 September 2023, covering the “Ouro Verde” (15 ha) and “Canto do Rio” (45 ha) farms in Bahia, Brazil. A total of 500 random points were analyzed using PSF, linear models, and cross-validation with R2, MAE, and RMSE. PSF analysis confirmed data integrity, and the cubic method demonstrated the best performance (R2 = 0.996, MAE = 0.008 and RMSE = 0.012 in the “Ouro Verde” Farm and R2 = 0.995, MAE = 0.007 and RMSE = 0.011 in the “Canto do Rio” Farm). The results highlight the importance of selecting appropriate resampling methods for precise remote sensing in coffee cultivation, ensuring accurate digital processing aligned with study objectives.

Geomatics doi: 10.3390/geomatics5020018

Authors: Shaeden Gokool Alistair Clulow Nadia A. Araya

The cultivation of Moringa oleifera Lam. (M. oleifera) has steadily increased over the past few decades, and interest in the crop continues to rise due to its unique multi-purpose properties. However, knowledge pertaining to its water use to guide decision-making in relation to the growth and management of this crop remains fairly limited. Since acquiring such information can be challenging using traditional in situ or remote sensing-based methods, particularly in resource-poor regions, this study aims to explore the potential of using the cloud-based Earth Engine Evapotranspiration Flux (EEFlux) model to quantify the water use of M. oleifera in a semi-arid region of South Africa. For this purpose, EEFlux estimates were acquired and compared with eddy covariance measurements between November 2022 and May 2023. The results of these comparisons demonstrated that EEFlux unsatisfactorily estimated ET, producing root mean square error, mean absolute error, and R2 values of 2.03 mm d−1, 1.63 mm d−1, and 0.24, respectively. The poor performance of this model can be attributed to several factors such as the quantity and quality of the in situ data as well as inherent model limitations. While these results are less than satisfactory, EEFlux affords users a quick and convenient approach to extracting crucial ET and ancillary data. Subsequently, with further refinement and testing, EEFlux can potentially serve to provide a wide variety of users with an invaluable tool to guide and inform decision-making with regards to agricultural water use management, particularly those in resource-constrained environments.

Geomatics doi: 10.3390/geomatics5020017

Authors: Kevin Musungu Moreblessings Shoko Julian Smit

The Cape Floristic Region (CFR) boasts rich biodiversity but faces threats from invasive species and land-use changes. Fynbos wetland vegetation within the CFR is under-mapped despite its crucial role in supporting biodiversity and maintaining hydrological cycles. This study assessed the potential of UAV VIS-NIR data, gathered during Spring and Summer, to identify the spectral characteristics of eleven Fynbos wetland species in a seep wetland. Spectral distances derived from reflectance data revealed distinct spectral clustering of plant species, highlighting which species could be distinguished from each other. UAV data also captured differences in reflectance across spectral bands for both dates. Spectral statistics indicated that certain species could be more accurately classified in Spring than in Summer, and vice versa. These findings underscore the efficacy of UAV multispectral data in analyzing the reflectance patterns of fynbos wetland species. Additionally, the sensitivity of UAV multispectral data to foliar pigment composition across different seasonal stages was confirmed. Lastly, species classification results demonstrated that a random forest classifier is well suited, with relative producer and user accuracies aligning with the derived spectral distances. The results highlight the potential of UAV imagery for monitoring these endemic species and creating opportunities for scalable mapping of Fynbos seep wetlands.

Geomatics doi: 10.3390/geomatics5020016

Authors: Konstantinos Vantas Vasiliki Mirkopoulou

Positional accuracy in cadastral data is fundamental for secure land tenure and efficient land administration. However, many land administration systems (LASs) experience difficulties to meet accuracy standards, particularly when data come from various sources or historical maps, leading to disruptions in land transactions. This study investigates the use of unsupervised clustering algorithms to identify and characterize systematic spatial error patterns in cadastral maps. We compare Fuzzy c-means (FCM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Gaussian Mixture Models (GMMs) in clustering error vectors using two different case studies from Greece, each with different error origins. The analysis revealed distinctly different error structures: a systematic rotational pattern surrounding a central random-error zone in the first, versus localized gross errors alongside regions of different discrepancies in the second. Algorithm performance was context-dependent: GMMs excelled, providing the most interpretable partitioning of multiple error levels, including gross errors; DBSCAN succeeded at isolating the dominant systematic error from noise. However, FCM struggled to capture the complex spatial nature of errors in both cases. Through the automated identification of problematic regions with different error characteristics, the proposed approach provides actionable insights for targeted, cost-effective cadastral renewal. This aligns with fit-for-purpose land administration principles, supporting progressive improvements towards more reliable cadastral data and offering a novel methodology applicable to other LASs facing similar challenges.

Geomatics doi: 10.3390/geomatics5010015

Authors: Georgia Ray Minerva Singh

Tropical forests are essential ecosystems recognized for their carbon sequestration and biodiversity benefits. As the world undergoes a simultaneous data revolution and climate crisis, accurate data on the world’s forests are increasingly important. Completely novel in approach, this study proposes a methodology encompassing two bespoke deep learning models: (1) a single encoder, double decoder (SEDD) model to generate a species segmentation map, regularized by a distance map in training, and (2) an XGBoost model that estimates the diameter at breast height (DBH) based on tree species and crown measurements. These models operate sequentially: RGB images from the ReforesTree dataset undergo preprocessing before species identification, followed by tree crown detection using a fine-tuned DeepForest model. Post-processing applies the XGBoost model and custom allometric equations alongside standard carbon accounting formulas to generate final sequestration estimates. Unlike previous approaches that treat individual tree identification as an isolated task, this study directly integrates species-level identification into carbon accounting. Moreover, unlike traditional carbon estimation methods that rely on regional estimations via satellite imagery, this study leverages high-resolution, drone-captured RGB imagery, offering improved accuracy without sacrificing accessibility for resource-constrained regions. The model correctly identifies 67% of trees in the dataset, with accuracy rising to 84% for the two most common species. In terms of carbon accounting, this study achieves a relative error of just 2% compared to ground-truth carbon sequestration potential across the test set.

Geomatics doi: 10.3390/geomatics5010014

Authors: Gachie Baraka Guido D’Urso Oscar Belfiore

The desert locust is documented as one of the most destructive polyphagous plant pests that require preventive or proactive management practices due to its phase polyphenism, rapid breeding, transnational migration, and heavy feeding behaviour. Desert locust situation analysis, forecasting and early warning are complex due to the systemic interaction of biological, meteorological, and geographical factors that play different roles in facilitating the survival, breeding and migration of the pest. This article seeks to elucidate the factors that affect desert locust distribution and review the application of earth observation (EO) data in explaining the pest’s infestations and impact. The review presents details concerning the application of EO data to understand factors that affect desert locust breeding and migration, elaborates on impact assessment through vegetation change detection and discusses modelling techniques that can support the effective management of the pest. The review reveals that the application of EO technology is inclined in favour of desert locust habitat suitability assessment with a limited financial quantification of losses. The review also finds a progressive advancement in the use of multi-modelling approaches to address identified gaps and reduce computational errors. Moreover, the review recognises great potential in applications of EO tools, products and services for anticipatory action against desert locusts to ensure resource use efficiency and environmental conservation.

Geomatics doi: 10.3390/geomatics5010013

Authors: Miguel Julio Machado Guimar?es Ian Dill dos Reis Juliane Rafaele Alves Barros Iug Lopes Marlon Gomes da Costa Denis Pereira Ribeiro Gian Carlo Carvalho Anderson Santos da Silva Carlos Vitor Oliveira Alves

The Cerrado is Brazil’s second largest biome, covering continuous areas in several states. Covering approximately 23% of Brazil’s territory, the Cerrado biome connects with all the main biomes in South America, thus forming a major biological corridor. This biome is one of those that has suffered the most from the incidence of wildfires, leading to a progressive depletion of the region’s natural resources. The aim of this study was to evaluate the use of an Unmanned Aerial Vehicle (UAV) embedded with an RGB sensor to obtain high-resolution digital products that can be used to identify areas of the Brazilian Cerrado affected by wildfires. The study was carried out in a savannah biome area selecting a vegetation corridor with native vegetation free from anthropogenic influence. The following UAV surveys were carried out before and after a burning event. Once the orthomosaics of the area were available, the GLI, VARI, ExG and NGRDI vegetation indices were used to analyze the vegetation. The data indicate that the B band and the GLI and ExG indices are more suitable for environmental impact analysis in Cerrado areas affected by fires, providing a solid basis for environmental monitoring and management in scenarios of fire disturbance.

Geomatics doi: 10.3390/geomatics5010012

Authors: Jeyavanan Karthigesu Toshiaki Owari Satoshi Tsuyuki Takuya Hiroshima

Individual tree parameters are essential for forestry decision-making, supporting economic valuation, harvesting, and silvicultural operations. While extensive research exists on uniform and simply structured forests, studies addressing complex, dense, and mixed forests with highly overlapping, clustered, and multiple tree crowns remain limited. This study bridges this gap by combining structural, textural, and spectral metrics derived from unmanned aerial vehicle (UAV) Red–Green–Blue (RGB) and multispectral (MS) imagery to estimate individual tree parameters using a random forest regression model in a complex mixed conifer–broadleaf forest. Data from 255 individual trees (115 conifers, 67 Japanese oak, and 73 other broadleaf species (OBL)) were analyzed. High-resolution UAV orthomosaic enabled effective tree crown delineation and canopy height models. Combining structural, textural, and spectral metrics improved the accuracy of tree height, diameter at breast height, stem volume, basal area, and carbon stock estimates. Conifers showed high accuracy (R2 = 0.70–0.89) for all individual parameters, with a high estimate of tree height (R2 = 0.89, RMSE = 0.85 m). The accuracy of oak (R2 = 0.11–0.49) and OBL (R2 = 0.38–0.57) was improved, with OBL species achieving relatively high accuracy for basal area (R2 = 0.57, RMSE = 0.08 m2 tree−1) and volume (R2 = 0.51, RMSE = 0.27 m3 tree−1). These findings highlight the potential of UAV metrics in accurately estimating individual tree parameters in a complex mixed conifer–broadleaf forest.

Geomatics doi: 10.3390/geomatics5010011

Authors: Xuanwen Wang Yi Gan Atsuhiro Iio Quan Wang

The leaf area index (LAI) in temperate forests is highly dynamic throughout the season, and lacking such dynamic information has limited our understanding of carbon and water flux patterns in these ecosystems. This study aims to explore the potential of using vegetation indices based on Sentinel-2 data, which includes three additional spectral bands in the red-edge region of its multispectral imager (MSI) sensor compared to previous satellite-borne imagery, to effectively track seasonal variations in LAI within typical cold–temperate deciduous forests originating in rugged terrain in Japan. We evaluated reported vegetation indices and developed an index specific to Sentinel-2 data to effectively monitor the spatiotemporal changes of LAI in mountainous deciduous forests, providing more accurate data for ecological monitoring. Results showed that the developed index (SRB12,B7) was able to track LAI at both seasonal and spatial scales (R2 = 0.576). Further analyses revealed that the index nevertheless performed relatively poorly during the leaf-maturing season when LAI peaks, suggesting that it still suffers from a “saturation” problem. For high-resolution tracking of LAI in temperate deciduous forests at both temporal and spatial scales, future research is needed to incorporate additional information.

Geomatics doi: 10.3390/geomatics5010010

Authors: Constantina Vasilakou Dimitrios E. Tsesmelis Kleomenis Kalogeropoulos Pantelis E. Barouchas Ilias Machairas Elissavet G. Feloni Andreas Tsatsaris Christos A. Karavitis

Drought represents a recurring natural event that holds notable socio-economic and environmental consequences. This research aims to analyze drought patterns in Greece by employing the standardized precipitation index (SPI) and several vegetation indices within a Geographic Information System (GIS) framework. GIS is a potent tool for integrating geospatial data, encompassing climatic, topographic, and hydrological information, enabling a comprehensive assessment of drought conditions. By examining historical precipitation data, the SPI quantifies the severity and duration of drought relative to long-term average precipitation. In addition, the SPI is calculated from precipitation data from a total of 152 meteorological stations. Subsequently, geostatistical techniques are applied to generate drought maps (SPI 6- and 12-timescale) and to examine the secondary effects of drought on different land uses. Satellite data are utilized to calculate indices. This is completed using satellite data by calculating the corresponding indices such as the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). Drought maps extracted using these methods and based on indicators and remote sensing data are useful tools for policymakers, stakeholders, and water experts. The resulting drought maps, based on the indicators and remote sensing data, serve as valuable tools for policymakers and stakeholders.

Geomatics doi: 10.3390/geomatics5010009

Authors: Marc-André Blais Moulay A. Akhloufi

Erosion is a critical geological process that degrades soil and poses significant risks to human settlements and natural habitats. As climate change intensifies, effective coastal erosion management and prevention have become essential for our society and the health of our planet. Given the vast extent of coastal areas, erosion management efforts must prioritize the most vulnerable and critical regions. Identifying and prioritizing these areas is a complex task that requires the accurate monitoring and forecasting of erosion and its potential impacts. Various tools and techniques have been proposed to assess the risks, impacts and rates of coastal erosion. Specialized methods, such as the Coastal Vulnerability Index, have been specifically designed to evaluate the susceptibility of coastal areas to erosion. Coastal boundaries, a critical factor in coastal erosion monitoring, are typically extracted from remote sensing images. Due to the extensive scale of coastal areas and the complexity of the data, manually extracting coastal boundaries is challenging. Recently, artificial intelligence, particularly deep learning, has emerged as a promising and essential tool for this task. This review provides an in-depth analysis of remote sensing and deep learning for extracting coastal boundaries to assist in erosion monitoring. Various remote sensing imaging modalities (optical, thermal, radar), platforms (satellites, drones) and datasets are first presented to provide the context for this field. Artificial intelligence and its associated metrics are then discussed, followed by an exploration of deep learning algorithms for extracting coastal boundaries. The presented algorithms range from basic convolutional networks to encoder–decoder architectures and attention mechanisms. An overview of how these extracted boundaries and other deep learning algorithms can be utilized for monitoring coastal erosion is also provided. Finally, the current gaps, limitations and potential future directions in this field are identified. This review aims to offer critical insights into the future of erosion monitoring and management through deep learning-based boundary extraction.

Geomatics doi: 10.3390/geomatics5010008

Authors: Adis Hamzi? Nedim Kulo Muamer ?idelija Jusuf Topoljak Admir Mulahusi? Nedim Tuno Naida Ademovi?

Terrestrial laser scanners (TLS) are widely employed in structural health monitoring (SHM) of large objects due to their superior capabilities compared to traditional geodetic methods. TLS provides rapid and detailed data on the geometric properties of objects, enabling various types of analyses. In this study, TLS was utilized to examine the minaret of the Bjelave Mosque, located in Sarajevo, Bosnia and Herzegovina. The inclination of the minaret was assessed using principal component analysis (PCA) and linear regression (LR) applied to sampled data from four edges of the minaret’s body. The geodetically determined inclination values were used as input data for subsequent static and pushover analyses conducted in DIANA FEA, where the minaret was modeled. The analyses indicate that the inclination increases stress and strain, leading to larger cracks and reduced structural capacity, as demonstrated by the pushover analysis curves. This study highlights the combined impact of structural inclination, water infiltration, and settlement on the minaret’s integrity and proposes these findings as a basis for future maintenance and strengthening measures.

Geomatics doi: 10.3390/geomatics5010007

Authors: Rizwan Ahmed Ansari Rakesh Malhotra Mohammed Zakariya Ansari

Semantic segmentation of remotely sensed images for building footprint recognition has been extensively researched, and several supervised and unsupervised approaches have been presented and adopted. The capacity to do real-time mapping and precise segmentation on a significant scale while considering the intrinsic diversity of the urban landscape in remotely sensed data has significant consequences. This study presents a novel approach for delineating building footprints by utilizing the compressed sensing and radial basis function technique. At the feature extraction stage, a small set of random features of the built-up areas is extracted from local image windows. The random features are used to train a radial basis neural network to perform building classification; thus, learning and classification are carried out in the compressed sensing domain. By virtue of its ability to represent characteristics in a reduced dimensional space, the scheme shows promise in being robust in the face of variability inherent in urban remotely sensed images. Through a comparison of the proposed method with numerous state-of-the-art approaches utilizing remotely sensed data of different spatial resolutions and building clutter, we establish its robustness and prove its viability. Accuracy assessment is performed for segmented footprints, and comparative analysis is carried out in terms of intersection over union, overall accuracy, precision, recall, and F1 score. The proposed method achieved scores of 93% in overall accuracy, 90.4% in intersection over union, and 91.1% in F1 score, even when dealing with drastically different image features. The results demonstrate that the proposed methodology yields substantial enhancements in classification accuracy and decreases in feature dimensionality.

Geomatics doi: 10.3390/geomatics5010006

Authors: Julee Wardle Zachary Phillips

This study investigates decadal trends in vegetation cover in Djibouti from 1990 to 2020, addressing challenges related to its arid climate and limited resources. Using Digital Earth Africa’s Open Data Cube and thirty years of Landsat imagery, change detection algorithms, and statistical analysis, this research explores vegetation dynamics at various spatial and temporal scales. Studies on change detection have advanced the field through exploring Landsat time series and diverse algorithms, but face limitations in handling data inconsistencies, integrating methods, and addressing practical and socio-environmental challenges. The results, obtained through change detection using NDVI differencing and Welch’s t-test, reveal significant trends in vegetation across Djibouti’s administrative and countrywide levels. Results show significant countrywide vegetative loss from 1990 to 2010, but recovery from 2010 to 2020, as evidenced by Welch’s t-test results. This disproved the Null Hypothesis of no trend and confirmed significant trends across all regions and resolutions analyzed. The findings provide important information for policymakers, land managers, and conservationists, providing awareness into patterns of Djibouti’s vegetation trends in the face of future climate change. The use of Open Data Cube and cloud computing enhances research capacity, allowing for the rapid and repeated analysis of larger time periods and geographical regions.

Geomatics doi: 10.3390/geomatics5010005

Authors: Polina Lemenkova

This article presents the application of novel cartographic methods of vegetation mapping with a case study of the Rif Mountains, northern Morocco. The study area is notable for varied geomorphology and diverse landscapes. The methodology includes ML modules of GRASS GIS ‘r.learn.train’, ‘r.learn.predict’, and ‘r.random’ with algorithms of supervised classification implemented from the Scikit-Learn libraries of Python. This approach provides a platform for processing spatiotemporal data and satellite image analysis. The objective is to determine the robustness of the “DecisionTreeClassifier” and “ExtraTreesClassifier” classification algorithms. The time series of satellite images covering northern Morocco consists of six Landsat scenes for 2023 with a bimonthly time interval. Land cover maps are produced based on the processed, classified, and analyzed images. The results demonstrated seasonal changes in vegetation and land cover types. The validation was performed using a land cover dataset from the Food and Agriculture Organization (FAO). This study contributes to environmental monitoring in North Africa using ML algorithms of satellite image processing. Using RS data combined with the powerful functionality of the GRASS GIS and FAO-derived datasets, the topographic variability, moderate-scale habitat heterogeneity, and bimonthly distribution of land cover types of northern Morocco in 2023 have been assessed for the first time.

Geomatics doi: 10.3390/geomatics5010004

Authors: Judith N. Oppong Clement E. Akumu Samuel Dennis Stephanie Anyanwu

Deep learning models offer valuable insights by leveraging large datasets, enabling precise and strategic decision-making essential for modern agriculture. Despite their potential, limited research has focused on the performance of pixel-based deep learning algorithms for detecting and mapping weed canopy cover. This study aims to evaluate the effectiveness of three neural network architectures—U-Net, DeepLabV3 (DLV3), and pyramid scene parsing network (PSPNet)—in mapping weed canopy cover in winter wheat. Drone data collected at the jointing and booting growth stages of winter wheat were used for the analysis. A supervised deep learning pixel classification methodology was adopted, and the models were tested on broadleaved weed species, winter wheat, and other weed species. The results show that PSPNet outperformed both U-Net and DLV3 in classification performance, with PSPNet achieving the highest overall mapping accuracy of 80%, followed by U-Net at 75% and DLV3 at 56.5%. These findings highlight the potential of pixel-based deep learning algorithms to enhance weed canopy mapping, enabling farmers to make more informed, site-specific weed management decisions, ultimately improving production and promoting sustainable agricultural practices.

Geomatics doi: 10.3390/geomatics5010003

Authors: Ericka F. Picado Kerin F. Romero Muditha K. Heenkenda

Various nutrients are needed during the sugarcane growing season for plant development and productivity. However, traditional methods for assessing nutritional status are often costly and time consuming. This study aimed to determine the level of nitrogen (N), phosphorus (P), potassium (K) and chlorophyll of sugarcane plants using remote sensing. Remotely sensed images were obtained using a MicaSense RedEdge-P camera attached to a drone. Leaf chlorophyll content was measured in the field using an N-Tester chlorophyll meter, and leaf samples were collected and analyzed in the laboratory for N, P and K. The highest correlation between field samples and predictor variables (spectral bands, selected vegetation indices, and plant height from Light Detection and Ranging (LiDAR)), were noted.The spatial distribution of chlorophyll, N, P, and K maps achieved 60%, 75%, 96% and 50% accuracies, respectively. The spectral profiles helped to identify areas with visual differences. Spatial variability of nutrient maps confirmed that moisture presence leads to nitrogen and potassium deficiencies, excess phosphorus, and a reduction in vegetation density (93.82%) and height (2.09 m), compared to green, healthy vegetation (97.64% density and 3.11 m in height). This robust method of assessing foliar nutrients is repeatable for the same sugarcane variety at certain conditions and leads to sustainable agricultural practices in Costa Rica.

Geomatics doi: 10.3390/geomatics5010002

Authors: Günther Retscher Jelena Gabela Vassilis Gikas

The LBS2ITS project, titled “Curricula Enrichment Delivered through the Application of Location-Based Services to Intelligent Transport Systems”, is a collaborative initiative funded by the Erasmus+ program of the European Union. The primary objectives of the project were twofold: to develop new curricula and modernize existing programs at four universities in Sri Lanka. This effort was driven by the need to align educational offerings with the rapidly evolving fields of Location-Based Services (LBSs) and Intelligent Transport Systems (ITSs). A key feature of the LBS2ITS project is its interdisciplinary approach, which draws on expertise from a range of academic disciplines. The project has successfully developed curricula that integrate diverse fields such as geomatics, cartography, transport engineering, urban planning, environmental engineering, and computer science. By blending these perspectives, the curricula provide students with a holistic understanding of LBSs and ITSs, preparing them to address complex, real-world challenges that span multiple sectors. In this paper, the curriculum development and modernization process is detailed, with a particular focus on the two key phases: teacher training and curriculum development. The teacher training phase was crucial in equipping educators with the skills and knowledge necessary to deliver the new and updated courses. This phase also provided an opportunity for teachers to familiarize themselves with the latest trends and technologies in LBSs and ITSs, ensuring that they could effectively convey this information to students. The development phase focused on the creation of the curriculum itself, ensuring that it met both academic standards and industry needs. The curriculum was designed to be flexible and responsive to emerging technologies and methodologies, allowing for continuous improvement and adaptation. Additionally, the paper delves into the theoretical frameworks underpinning the methodologies employed in the project. These include Problem-Based Learning (PBL) and Problem-Based e-Learning (PBeL), both of which encourage active student engagement and foster critical thinking by having students tackle real-world problems. The emphasis on PBL ensures that students not only acquire theoretical knowledge but also develop practical problem-solving skills applicable to their future careers in LBSs and ITSs. Furthermore, the project incorporated rigorous quality assurance (QA) mechanisms to ensure that the teaching methods and curriculum content met high standards. This included regular feedback loops, stakeholder involvement, and iterative refinement of course materials based on evaluations from both students and industry experts. These QA measures are essential for maintaining the relevance, effectiveness, and sustainability of the curricula over time. In summary, the LBS2ITS project represents a significant effort to enrich and modernize university curricula in Sri Lanka by integrating cutting-edge technologies and interdisciplinary approaches. Through a combination of innovative teaching methodologies, comprehensive teacher training, and robust quality assurance practices, the project aims to equip students with the skills and knowledge needed to excel in the fields of LBSs and ITSs.

Geomatics doi: 10.3390/geomatics5010001

Authors: Paula Santi Malnis Luis Martin Rothis

Understanding the relationships among watersheds and derived depositional products is critical to developing analog studies with the rock record, especially for continental intermontane basins. Also, it is crucial to study river flood occurrences. Multivariate statistics analysis allows for the comprehension of the relationship among substrate, climate, and depositional products of the watersheds that feed the endorheic Mascasin Saline Basin, San Juan and La Rioja provinces, Argentina. Using a GIS platform, geomorphological, stratigraphic, morphometric, and structural analysis gave a dataset of variables for defining clusters. Under a similar climate, clustering analysis permits defining two main controls on watersheds and depositional products: parent rock composition and geological structures (faults and lineaments). The results underscore the critical role that lithology and structural controls play in basin morphometry and emphasize the need to quantify these variables for landscape evolution models.

Geomatics doi: 10.3390/geomatics4040023

Authors: Giuseppe Masetti Ian Church Anand Hiroji Ove Andersen

Ocean mapping and nautical cartography are foundational to understanding and managing marine environments [...]

Geomatics doi: 10.3390/geomatics4040022

Authors: Grayson R. Morgan Danny Zlotnick Luke North Cade Smith Lane Stevenson

Urban tree canopy (UTC) coverage, or area, is an important metric for monitoring changes in UTC over large areas within a municipality. Several methods have been used to obtain these data, but remote sensing image classification is one of the fastest and most reliable over large areas. However, most studies have tested only one or two classification methods to accomplish this while using costly satellite imagery or LiDAR data. This study seeks to compare three urban tree canopy cover classifiers by testing a deep learning U-Net convolutional neural network (CNN), support vector machine learning classifier (SVM) and a random forests machine learning classifier (RF) on cost-free 2012 aerial imagery over a small southern USA city and midsize, growing southern USA city. The results of the experiment are then used to decide the best classifier and apply it to more recent aerial imagery to determine canopy changes over a 10-year period. The changes are subsequently compared visually and statistically with recent urban heat maps derived from thermal Landsat 9 satellite data to compare the means of temperatures within areas of UTC loss and no change. The U-Net CNN classifier proved to provide the best overall accuracy for both cities (89.8% and 91.4%), while also requiring the most training and classification time. When compared spatially with city heat maps, city periphery regions were most impacted by substantial changes in UTC area as cities grow and the outer regions get warmer. Furthermore, areas of UTC loss had higher temperatures than those areas with no canopy change. The broader impacts of this study reach the urban forestry managers at the local, state/province, and national levels as they seek to provide data-driven decisions for policy makers.

Geomatics doi: 10.3390/geomatics4040021

Authors: Kohinur Aktar Helmut Yabar Takeshi Mizunoya Md. Monirul Islam

Dairy production is one of the most important economic sectors in Bangladesh. However, the traditional management of dairy cow manure and other wastes results in air pollution, eutrophication of surface water, and soil contamination, highlighting the urgent need for more sustainable waste management solutions. To address the environmental problems of dairy waste management, this research explored the potential of community-based biogas production from dairy cow manure in Bangladesh. This study proposed introducing community-based biogas plants using a geographic information system (GIS). The study first applied a restriction analysis to identify sensitive areas, followed by a suitability analysis to determine feasible locations for biogas plants, considering geographical, social, economic, and environmental factors. The final suitable areas were identified by combining the restriction and suitability maps. The spatial distribution of dairy farms was analyzed through a cluster analysis, identifying significant clusters for potential biogas production. A baseline and proposed scenario were designed for five clusters based on the input and output capacities of the biogas plants, estimating the location and capacity for each cluster. The study also calculated electricity generation from the proposed scenario and the net greenhouse gas (GHG) emissions reduction potential of the biogas plants. The findings provide a land-use framework for implementing biogas plants that considers environmental and socio-economic criteria. Five biogas plants were found to be technically and spatially feasible for electricity generation. These plants can collectively produce 31 million m3 of biogas annually, generating approximately 200.60 GWh of energy with a total electricity capacity of 9.8 MW/year in Bangladesh. Implementing these biogas plants is expected to increase renewable energy production by at least 1.25%. Furthermore, the total GHG emission reduction potential is estimated at 104.26 Gg/year CO2eq through the annual treatment of 61.38 thousand tons of dairy manure.

Geomatics doi: 10.3390/geomatics4040020

Authors: Christophe Claramunt

In the past few years since its launch, Geomatics has addressed various areas that form the core of the interdisciplinary field of geomatics [...]

Geomatics doi: 10.3390/geomatics4040019

Authors: Alphonse Maré David Millogo Boalidioa Tankoano Oblé Neya Fousseni Folega Kperkouma Wala Kwame Oppong Hackman Bernadin Namoano Komlan Batawila

The sustainable management of protected areas has increasingly become difficult due to the lack of updated information on land use and land cover transformations caused by anthropogenic pressures. This study investigates the spatiotemporal dynamics of the Dinderesso and Peni classified forests in Burkina Faso from 1986 to 2022. First, a data driven method was adopted to investigate these forests degradation dynamics. Hence, relevant Landsat images data were collected, segmented, and analyzed using QGIS SCP plugin Random Forest algorithm. Ninety percent of the overall adjusted classification accuracies were obtained. The analysis also showed significant degradation and deforestation with high wooded vegetation classes such as clear forest and wooded savannah (i.e., tree savannah) converging to lower vegetation classes like shrub savannah and agroforestry parks. A second investigation carried out through surveys and field trips revealed key anthropogenic drivers including agricultural expansion, demographic pressure, bad management, wood cutting abuse, overexploitation, overgrazing, charcoal production, and bushfires. These findings highlight the critical need for better management to improve these protected areas.

Geomatics doi: 10.3390/geomatics4030018

Authors: Badjaré Bilouktime Folega Fousséni Bawa Demirel Maza-esso Liu Weiguo Huang Hua Guo Wala Kpérkouma Batawila Komlan

Climate variability significantly impacts plant growth, making it crucial to monitor ecosystem performance for optimal carbon sequestration, especially in the context of rising atmospheric CO2 levels. Net Primary Productivity (NPP), which measures the net carbon flux between the atmosphere and plants, serves as a key indicator. This study uses the CASA (Carnegie–Ames–Stanford Approach) model, a radiation use efficiency method, to assess the spatio-temporal dynamics of NPP in Togo from 1987 to 2022 and its climatic drivers. The average annual NPP over 36 years is 4565.31 Kg C ha−1, with notable extremes in 2017 (6312.26 Kg C ha−1) and 1996 (3394.29 Kg C ha−1). Productivity in natural formations increased between 2000 and 2022. While climate change and land use negatively affect Total Production (PT) from 2000 to 2022, they individually enhance NPP variation (58.28% and 188.63%, respectively). NPP shows a strong positive correlation with light use efficiency (r2 = 0.75) and a moderate one with actual evapotranspiration (r2 = 0.43). Precipitation and potential evapotranspiration have weaker correlations (r2 = 0.20; 0.10), and temperature shows almost none (r2 = 0.05). These findings contribute to understanding ecosystem performance, supporting Togo’s climate commitments.

Geomatics doi: 10.3390/geomatics4030017

Authors: Ned Nikolov Karl F. Zeller

Past studies have reported a decreasing planetary albedo and an increasing absorption of solar radiation by Earth since the early 1980s, and especially since 2000. This should have contributed to the observed surface warming. However, the magnitude of such solar contribution is presently unknown, and the question of whether or not an enhanced uptake of shortwave energy by the planet represents positive feedback to an initial warming induced by rising greenhouse-gas concentrations has not conclusively been answered. The IPCC 6th Assessment Report also did not properly assess this issue. Here, we quantify the effect of the observed albedo decrease on Earth’s Global Surface Air Temperature (GSAT) since 2000 using measurements by the Clouds and the Earth’s Radiant Energy System (CERES) project and a novel climate-sensitivity model derived from independent NASA planetary data by employing objective rules of calculus. Our analysis revealed that the observed decrease of planetary albedo along with reported variations of the Total Solar Irradiance (TSI) explain 100% of the global warming trend and 83% of the GSAT interannual variability as documented by six satellite- and ground-based monitoring systems over the past 24 years. Changes in Earth’s cloud albedo emerged as the dominant driver of GSAT, while TSI only played a marginal role. The new climate sensitivity model also helped us analyze the physical nature of the Earth’s Energy Imbalance (EEI) calculated as a difference between absorbed shortwave and outgoing longwave radiation at the top of the atmosphere. Observations and model calculations revealed that EEI results from a quasi-adiabatic attenuation of surface energy fluxes traveling through a field of decreasing air pressure with altitude. In other words, the adiabatic dissipation of thermal kinetic energy in ascending air parcels gives rise to an apparent EEI, which does not represent “heat trapping” by increasing atmospheric greenhouse gases as currently assumed. We provide numerical evidence that the observed EEI has been misinterpreted as a source of energy gain by the Earth system on multidecadal time scales.

Geomatics doi: 10.3390/geomatics4030016

Authors: Florian Huber Volker Steinhage

In the field of geomatics, artificial intelligence (AI) and especially machine learning (ML) are rapidly transforming the field of geomatics with respect to collecting, managing, and analyzing spatial data. Feature selection as a building block in ML is crucial because it directly impacts the performance and predictive power of a model by selecting the most critical variables and eliminating the redundant and irrelevant ones. Random forests have now been used for decades and allow for building models with high accuracy. However, finding the most expressive features from the dataset by selecting the most important features within random forests is still a challenging question. The often-used internal Gini importances of random forests are based on the amount of training examples that are divided by a feature but fail to acknowledge the magnitude of change in the target variable, leading to suboptimal selections. Shapley values are an established and unified framework for feature attribution, i.e., specifying how much each feature in a trained ML model contributes to the predictions for a given instance. Previous studies highlight the effectiveness of Shapley values for feature selection in real-world applications, while other research emphasizes certain theoretical limitations. This study provides an application-driven discussion of Shapley values for feature selection by first proposing four necessary conditions for a successful feature selection with Shapley values that are extracted from a multitude of critical research in the field. Given these valuable conditions, Shapley value feature selection is nevertheless a model averaging procedure by definition, where unimportant features can alter the final selection. Therefore, we additionally present Conditional Feature Selection (CFS) as a novel algorithm for performing feature selection that mitigates this problem and use it to evaluate the impact of model averaging in several real-world examples, covering the use of ML in geomatics. The results of this study show Shapley values as a good measure for feature selection when compared with Gini feature importances on four real-world examples, improving the RMSE by 5% when averaged over selections of all possible subset sizes. An even better selection can be achieved by CFS, improving on the Gini selection by approximately 7.5% in terms of RMSE. For random forests, Shapley value calculation can be performed in polynomial time, offering an advantage over the exponential runtime of CFS, building a trade-off to the lost accuracy in feature selection due to model averaging.

Geomatics doi: 10.3390/geomatics4030015

Authors: Geir-Harald Strand Eva Solbj?rg Flo Heggem Linda Aune-Lundberg Agata Ho?ci?o Adam Wa?niewski

Land cover maps are frequently produced via the classification of satellite imagery. There is a need for a practicable and automated approach for the generalization of these land cover classification results into scalable, digital maps while minimizing information loss. We demonstrate a method where a land cover raster map produced using the classification of Sentinel 2 imagery was generalized to obtain a simpler, more readable land cover map. A replicable procedure following a formal generalization framework was applied. The result of the initial land cover classification was separated into binary layers representing each land cover class. Each binary layer was simplified via structural generalization. The resulting images were merged to create a new, simplified land cover map. This map was enriched by adding statistical information from the original land cover classification result, describing the internal land cover distribution inside each polygon. This enrichment preserved the original statistical information from the classified image and provided an environment for more complex cartography and analysis. The overall accuracy of the generalized map was compared to the accuracy of the original, classified land cover. The accuracy of the land cover classification in the two products was not significantly different, showing that the accuracy did not deteriorate because of the generalization.

Geomatics doi: 10.3390/geomatics4030014

Authors: Herath Mudiyanselage Malhamige Sonali Dinesha Herath Takeshi Fujino Mudalige Don Hiranya Jayasanka Senavirathna

In an era of intense urban development and climate extremes, green infrastructure (GI) has become crucial for creating sustainable, livable, and resilient cities. However, the efficacy of GI is frequently undermined by haphazard implementation and resource misallocation that disregards appropriate spatial scales. This study develops a geographic information system (GIS)-based critical area detection model (CADM) to identify priority areas for the strategic placement of GI, incorporating four main indices—spatial form, green cover, gray cover, and land use change—and utilizing the digital elevation model (DEM), normalized difference vegetation index (NDVI), urban density index (UDI), and up-to-date land use data. By employing the developed method, the study successfully locates priority zones for GI implementation in Saitama City, Japan, effectively pinpointing areas that require immediate attention. This approach not only guarantees efficient resource allocation and maximizes the multifunctional benefits of GI but also highlights the importance of a flexible, all-encompassing GI network to address urbanization and environmental challenges. The findings offer policymakers a powerful tool with which to optimize GI placement, enhancing urban resilience and supporting sustainable development.

Geomatics doi: 10.3390/geomatics4030013

Authors: Guillaume Labbé-Morissette Théau Leclercq Patrick Charron-Morneau Dominic Gonthier Dany Doiron Mohamed-Ali Chouaer Dominic Ndeh Munang

Classification of benthic substrates is a core necessity in many scientific fields like biology, ecology, or geology, with applications branching out to a variety of industries, from fisheries to oil and gas. In the first part, a comparative analysis of supervised learning algorithms has been conducted using geomorphometric features to generate benthic substrate maps of the coastal regions of the North Shore of Quebec in order to establish a quantitative assessment of performance to serve as a benchmark. In the second part, a new method using Gaussian mixture models is showcased on the same dataset. Finally, a side-by-side comparison of both methods is featured to provide a qualitative assessment of the new algorithm’s ability to match human intuition.

Geomatics doi: 10.3390/geomatics4030012

Authors: Colette de Villiers Zinhle Mashaba-Munghemezulu Cilence Munghemezulu George J. Chirima Solomon G. Tesfamichael

Optimizing the prediction of maize (Zea mays L.) yields in smallholder farming systems enhances crop management and thus contributes to reducing hunger and achieving one of the Sustainable Development Goals (SDG 2—zero hunger). This research investigated the capability of unmanned aerial vehicle (UAV)-derived data and machine learning algorithms to estimate maize yield and evaluate its spatiotemporal variability through the phenological cycle of the crop in Bronkhorstspruit, South Africa, where UAV data collection took over four dates (pre-flowering, flowering, grain filling, and maturity). The five spectral bands (red, green, blue, near-infrared, and red-edge) of the UAV data, vegetation indices, and grey-level co-occurrence matrix textural features were computed from the bands. Feature selection relied on the correlation between these features and the measured maize yield to estimate maize yield at each growth period. Crop yield prediction was then conducted using our machine learning (ML) regression models, including Random Forest, Gradient Boosting (GradBoost), Categorical Boosting, and Extreme Gradient Boosting. The GradBoost regression showed the best overall model accuracy with R2 ranging from 0.05 to 0.67 and root mean square error from 1.93 to 2.9 t/ha. The yield variability across the growing season indicated that overall higher yield values were predicted in the grain-filling and mature growth stages for both maize fields. An analysis of variance using Welch’s test indicated statistically significant differences in maize yields from the pre-flowering to mature growing stages of the crop (p-value < 0.01). These findings show the utility of UAV data and advanced modelling in detecting yield variations across space and time within smallholder farming environments. Assessing the spatiotemporal variability of maize yields in such environments accurately and timely improves decision-making, essential for ensuring sustainable crop production.

Geomatics doi: 10.3390/geomatics4020011

Authors: Roy H. Gabrielsen Odleiv Olesen

Application of lineament analysis in structural geology gained renewed interest when remote sensing data and technology became available through dedicated Earth observation satellites like Landsat in 1972. Lineament data have since been widely used in general structural investigations and resource and geohazard studies. The present contribution argues that lineament analysis remains a useful tool in structural geology research both at the regional and local scales. However, the traditional “lineament study” is only one of several methods. It is argued here that structural and lineament remote sensing studies can be separated into four distinct strategies or approaches. The general analyzing approach includes general structural analysis and identification of foliation patterns and composite structural units (mega-units). The general approach is routinely used by most geologists in preparation for field work, and it is argued that at least parts of this should be performed manually by staff who will participate in the field activity. We argue that this approach should be a cyclic process so that the lineament database is continuously revised by the integration of data acquired by field data and supplementary data sets, like geophysical geochronological data. To ensure that general geological (field) knowledge is not neglected, it is our experience that at least a part of this type of analysis should be performed manually. The statistical approach conforms with what most geologists would regard as “lineament analysis” and is based on statistical scrutiny of the available lineament data with the aim of identifying zones of an enhanced (or subdued) lineament density. It would commonly predict the general geometric characteristics and classification of individual lineaments or groups of lineaments. Due to efficiency, capacity, consistency of interpretation methods, interpretation and statistical handling, this interpretative approach may most conveniently be performed through the use of automatized methods, namely by applying algorithms for pattern recognition and machine learning. The focused and dynamic approaches focus on specified lineaments or faults and commonly include a full structural geological analysis and data acquired from field work. It is emphasized that geophysical (potential field) data should be utilized in lineament analysis wherever available in all approaches. Furthermore, great care should be taken in the construction of the database, which should be tailored for this kind of study. The database should have a 3D or even 4D capacity and be object-oriented and designed to absorb different (and even unforeseen) data types on all scales. It should also be designed to interface with shifting modeling tools and other databases. Studies of the Norwegian mainland have utilized most of these strategies in lineament studies on different scales. It is concluded that lineament studies have revealed fracture and fault systems and the geometric relations between them, which would have remained unknown without application of remote sensing data and lineament analysis.

Geomatics doi: 10.3390/geomatics4020010

Authors: Rahul Dev Raju Sudhagar Nagarajan Madasamy Arockiasamy Stephen Castillo

Scour around bridges present a severe threat to the stability of railroad and highway bridges. Scour needs to be monitored to prevent the bridges from becoming damaged. This research studies the feasibility of using green laser for monitoring the scour around candidate railroad and highway bridges. The laboratory experiments that provided the basis for using green laser for underwater mapping are also discussed. The results of the laboratory and field experiments demonstrate the feasibility of using green laser for underwater infrastructure monitoring with limitations on the turbidity of water that affects the penetrability of the laser. This method can be used for scour monitoring around offshore structures in shallow water as well as corrosion monitoring of bridges.

Geomatics doi: 10.3390/geomatics4020009

Authors: Guy Blanchard Ikokou Kate Miranda Malale

Image segmentation plays an important role in object-based classification. An optimal image segmentation should result in objects being internally homogeneous and, at the same time, distinct from one another. Strategies that assess the quality of image segmentation through intra- and inter-segment homogeneity metrics cannot always predict possible under- and over-segmentations of the image. Although the segmentation scale parameter determines the size of the image segments, it cannot synchronously guarantee that the produced image segments are internally homogeneous and spatially distinct from their neighbors. The majority of image segmentation assessment methods largely rely on a spatial autocorrelation measure that makes the global objective function fluctuate irregularly, resulting in the image variance increasing drastically toward the end of the segmentation. This paper relied on a series of image segmentations to test a more stable image variance measure based on the standard deviation model as well as a more robust hybrid spatial autocorrelation measure based on the current Moran’s index and the spatial autocorrelation coefficient models. The results show that there is a positive and inversely proportional correlation between the inter-segment heterogeneity and the intra-segment homogeneity since the global heterogeneity measure increases with a decrease in the image variance measure. It was also found that medium-scale parameters produced better quality image segments when used with small color weights, while large-scale parameters produced good quality segments when used with large color factor weights. Moreover, with optimal segmentation parameters, the image autocorrelation measure stabilizes and follows a near horizontal fluctuation while the image variance drops to values very close to zero, preventing the heterogeneity function from fluctuating irregularly towards the end of the image segmentation process.

Geomatics doi: 10.3390/geomatics4020008

Authors: Thomas H. Meyer

This paper recasts four geodetic curves—the great ellipse, the normal section, the loxodrome, and the curve of alignment—into a parametric form of vector-algebra formula. These formulas allow these curves to be drawn using simple, efficient, and robust algorithms. The curve of alignment, which seems to be quite obscure, ought not to be. Like the great ellipse and the loxodrome, and unlike the normal section, the curve of alignment from point A to point B (both on the same ellipsoid) is the same as the curve of alignment from point B to point A. The algorithm used to draw the curve of alignment is much simpler than any of the others and its shape is quite similar to that of the geodesic, which suggests it would be a practical surrogate when drawing these curves.

Geomatics doi: 10.3390/geomatics4020007

Authors: Giuseppe Nunnari Sonia Calvari

This paper addresses the classification of images depicting the eruptive activity of Mount Etna, captured by a network of ground-based thermal cameras. The proposed approach utilizes Convolutional Neural Networks (CNNs), focusing on pretrained models. Eight popular pretrained neural networks underwent systematic evaluation, revealing their effectiveness in addressing the classification problem. The experimental results demonstrated that, following a retraining phase with a limited dataset, specific networks such as VGG-16 and AlexNet, achieved an impressive total accuracy of approximately 90%. Notably, VGG-16 and AlexNet emerged as practical choices, exhibiting individual class accuracies exceeding 90%. The case study emphasized the pivotal role of transfer learning, as attempts to solve the classification problem without pretrained networks resulted in unsatisfactory outcomes.

Geomatics doi: 10.3390/geomatics4020006

Authors: Suryakant Bajirao Tarate N. R. Patel Abhishek Danodia Shweta Pokhariyal Bikash Ranjan Parida

Effective management of water resources is crucial for sustainable development in any region. When considering computer-aided analysis for resource management, geospatial technology, i.e., the use of remote sensing (RS) combined with Geographic Information Systems (GIS) proves to be highly valuable. Geospatial technology is more cost-effective and requires less labor compared to ground-based surveys, making it highly suitable for a wide range of agricultural applications. Effectively utilizing the timely, accurate, and objective data provided by RS technologies presents a crucial challenge in the field of water resource management. Satellite-based RS measurements offer consistent information on agricultural and hydrological conditions across extensive land areas. In this study, we carried out a detailed analysis focused on addressing agricultural water management issues in India through the application of RS and GIS technologies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we systematically reviewed published research articles, providing a comprehensive and detailed analysis. This study aims to explore the use of RS and GIS technologies in crucial agricultural water management practices with the goal of enhancing their effectiveness and efficiency. This study primarily examines the current use of geospatial technology in Indian agricultural water management and sustainability. We revealed that considerable research has primarily used multispectral Landsat series data. Cutting-edge technologies like Sentinel, Unmanned Aerial Vehicles (UAVs), and hyperspectral technology have not been fully investigated for the assessment and monitoring of water resources. Integrating RS and GIS allows for consistent agricultural monitoring, offering valuable recommendations for effective management.

Geomatics doi: 10.3390/geomatics4010005

Authors: Giles M. Foody

The ground reference dataset used in the assessment of classification accuracy is typically assumed implicitly to be perfect (i.e., 100% correct and representing ground truth). Rarely is this assumption valid, and errors in the ground dataset can cause the apparent accuracy of a classification to differ greatly from reality. The effect of variations in the quality in the ground dataset and of class abundance on accuracy assessment is explored. Using simulations of realistic scenarios encountered in remote sensing, it is shown that substantial bias can be introduced into a study through the use of an imperfect ground dataset. Specifically, estimates of accuracy on a per-class and overall basis, as well as of a derived variable, class areal extent, can be biased as a result of ground data error. The specific impacts of ground data error vary with the magnitude and nature of the errors, as well as the relative abundance of the classes. The community is urged to be wary of direct interpretation of accuracy assessments and to seek to address the problems that arise from the use of imperfect ground data.

Geomatics doi: 10.3390/geomatics4010004

Authors: Markus Schaffert Dominik Warch Hartmut Müller

Many single-family homes built in Germany in the first decades following the Second World War are now occupied by elderly residents. If local conditions are unfavorable, a large number of these buildings may enter the real estate market in a short period of time and put pressure on the local housing market. Planners and decision-makers therefore need detailed spatiotemporal information about these neighborhoods to effectively address and counteract such developments. We present the design and implementation of a planning support system that can generate the required information. The architecture of this newly developed software consists of a composite, multitier framework to perform the complex tasks of data importation, data processing, and visualization. Legally mandated municipal population registers provide the key data for the calculation of indicators as a base for spatiotemporal analyses and visualizations. These registers offer high data quality in terms of completeness, logical consistency, spatial, and temporal and thematic accuracy. We demonstrate the implemented method using population data from a local government in a rural area in southwestern Germany. The results show that the new tool, which relies on open software components, is capable to identify and prioritize areas with particularly high levels of problem pressure. The tool can be used not only for analyses in a local context, but also at a regional level.

Geomatics doi: 10.3390/geomatics4010003

Authors: Laura Muscas Roberto Demontis Eva B. Lorrai Zeno Heilmann Guido Satta Gian Piero Deidda Antonio Trogu

The Italian territory of Sardinia Island has an enormous cultural and identity heritage from the Pre-Nuragic and Nuragic periods, with archaeological evidence of more than 7000 sites. However, many other undiscovered remnants of these ancient times are believed to be present. In this context, it can be helpful to analyze data from different types of sensors on a single information technology platform, to better identify and perimeter hidden archaeological structures. The main objective of the study is to define a methodology that through the processing, analysis, and comparison of data obtained using different non-invasive survey techniques could help to identify and document archaeological sites not yet or only partially investigated. The non-invasive techniques include satellite, unmanned aerial vehicle, and geophysical surveys that have been applied at the nuraghe Nanni Arrù, one of the most important finds in recent times. The complexity of this ancient megalithic edifice and its surroundings represents an ideal use case. The surveys showed some anomalies in the areas south–east and north–east of the excavated portion of the Nanni Arrù site. The comparison between data obtained with the different survey techniques used in the study suggests that in areas where anomalies have been confirmed by multiple data types, buried structures may be present. To confirm this hypothesis, further studies are believed necessary, for example, additional geophysical surveys in the excavated part of the site.

Geomatics doi: 10.3390/geomatics4010002

Authors: Derek C. Sowers Larry A. Mayer Giuseppe Masetti Erik Cordes Ryan Gasbarro Elizabeth Lobecker Kasey Cantwell Samuel Candio Shannon Hoy Mashkoor Malik Michael White Matthew Dornback

A coordinated multi-year ocean exploration campaign on the Blake Plateau offshore of the southeastern U.S. has mapped what appears to be the most expansive cold-water coral (CWC) mound province thus far discovered. Nearly continuous CWC mound features span an area up to 500 km long and 110 km wide, with a core area of high-density mounds up to 254 km long by 42 km wide. This study synthesized bathymetric data from 31 multibeam sonar mapping surveys and generated a standardized geomorphic classification of the region in order to delineate and quantify CWC mound habitats and compare mound morphologies among subregions of the coral province. Based on the multibeam bathymetry, a total of 83,908 individual peak features were delineated, providing the first estimate of the overall number of potential CWC mounds mapped in the region to date. Five geomorphic landform classes were mapped and quantified: peaks (411 km2), valleys (3598 km2), ridges (3642 km2), slopes (23,082 km2), and flats (102,848 km2). The complex geomorphology of eight subregions was described qualitatively with geomorphic “fingerprints” (spatial patterns) and quantitatively by measurements of mound density and vertical relief. This study demonstrated the value of applying an objective automated terrain segmentation and classification approach to geomorphic characterization of a highly complex CWC mound province. Manual delineation of these features in a consistent repeatable way with a comparable level of detail would not have been possible.

Geomatics doi: 10.3390/geomatics4010001

Authors: Alanna D. Shapiro Weibo Liu

Physical and climatic variables such as Tree Canopy coverage, Normalized Difference Vegetation Index (NDVI), Distance to Roads, Distance to the Coast, Impervious Surface, and Precipitation can affect land surface temperature (LST). This paper examines the relationships using linear regression models and explores LST trends in the Miami Statistical Area (MSA) between 2002 and 2021. This study evaluates the effect of dry and wet seasons as well as day and night data on LST. A multiscale investigation is used to examine LST trends at the MSA scale, the individual county level, and at the pixel level to provide a detailed local perspective. The multiscale results are needed to understand spatiotemporal LST distributions to plan mitigation measures such as planting trees or greenery to regulate temperature and reduce the impacts of surface urban heat islands. The results indicate that LST values are rising in the MSA with a positive trend throughout the 20-year study period. The rate of change (RoC) for the wet season is smaller than for the dry season. The pixel-level analysis suggests that the RoC is primarily in rural areas and less apparent in urban areas. New development in rural areas may trigger increased RoC. This RoC relates to LST in the MSA and is different from global or regional RoC using air temperature. Results also suggest that climatic explanatory variables have different impacts during the night than they do in the daytime. For instance, the Tree Canopy variable has a positive coefficient, while during the day, the Tree Canopy variable has a negative relationship with LST. The Distance to the Coast variable changes from day to night as well. The increased granularity achieved with the multiscale analysis provides critical information needed to improve the effectiveness of potential mitigation efforts.

Geomatics doi: 10.3390/geomatics3040031

Authors: Francesco Balducci Agneta St?hl Ola Svensson Benny Jonsson Yngve Westerlund Jacopo Dolcini Carlos Chiatti

Previous research showed that living closer to bus stops could be a factor in promoting a healthy and active lifestyle. However, most of the studies relied on self-reported measures of distance, which might be affected by several confounders. In this study, self-reported distances among study participants were compared to actual ones, computed by the use of GIS (Geographic Information System) technology and routing algorithms. We tested whether distance to the bus stop is associated with health and socioeconomic conditions of the respondents, using data among 2398 older people (75–90 years) in three cities in Sweden. We found that several variables including older age, female gender, living alone, and worse health status are associated with an over-estimation of bus stop distance. People who use public transport daily or several times a week and are satisfied with the walking environment in the neighbourhood tend to underestimate bus stop distances. Evidence based on self-reported measures only should be treated cautiously. Considering the limitations still present in open-data-based routing algorithms, the best indication is to combine the subjective with the objective measure of distance. Having the possibility to combine the two measures appears as a sound strategy to overcome the limitations associated with each single measure.

Geomatics doi: 10.3390/geomatics3040030

Authors: Cigdem Askar Harald Sternberg

Laser scanning technology has long been the preferred method for capturing interior scenes in various industries. With a growing market, smaller and more affordable scanners have emerged, offering end products with sufficient accuracy. While not on par with professional scanners, Apple has made laser scanning technology accessible to users with the introduction of the new iPhone Pro models, democratizing 3D scanning. Thus, this study aimed to assess the performance of the iPhone’s lidar technology as a low-cost solution for building documentation. Four scanning applications were evaluated to determine the accuracy, precision, and user experience of the generated point clouds compared with a terrestrial laser scanner. The results reveal varying performances on the same device, highlighting the influence of software. Notably, there is room for improvement, particularly in tracking the device’s position through software solutions. As it stands, the technology is well suited for applications such as indoor navigation and the generation of quick floor plans in the context of building documentation.

Geomatics doi: 10.3390/geomatics3040029

Authors: Milad Moradi Stéphane Roche Mir Abolfazl Mostafavi

OpenStreetMap (OSM) is among the most prominent Volunteered Geographic Information (VGI) initiatives, aiming to create a freely accessible world map. Despite its success, the data quality of OSM remains variable. This study begins by identifying the quality metrics proposed by earlier research to assess the quality of OSM building footprints. It then evaluates the quality of OSM building data from 2018 and 2023 for five cities within Québec, Canada. The analysis reveals a significant quality improvement over time. In 2018, the completeness of OSM building footprints in the examined cities averaged around 5%, while by 2023, it had increased to approximately 35%. However, this improvement was not evenly distributed. For example, Shawinigan saw its completeness surge from 2% to 99%. The study also finds that OSM contributors were more likely to digitize larger buildings before smaller ones. Positional accuracy saw enhancement, with the average error shrinking from 3.7 m in 2018 to 2.3 m in 2023. The average distance measure suggests a modest increase in shape accuracy over the same period. Overall, while the quality of OSM building footprints has indeed improved, this study shows that the extent of the improvement varied significantly across different cities. Shawinigan experienced a substantial increase in data quality compared to its counterparts.

Geomatics doi: 10.3390/geomatics3040028

Authors: Levente Juhász Jinwen Xu Randall W. Parkinson

Sea-level rise (SLR) is a critical consequence of climate change, posing significant threats to coastal regions worldwide. Accurate and efficient assessment of potential inundation areas is crucial for effective coastal planning and adaptation strategies. This study aimed to explore the utility of free and open-source software for geospatial (FOSS4G) tools for mapping SLR inundation, providing cost-effective solutions that are accessible to researchers and policymakers. We employed a combination of geospatial data, including high-resolution elevation models, tidal data, and projected SLR scenarios. Utilizing widely available FOSS4G tools, like QGIS, GDAL/OGR, and GRASS GIS, we developed an integrated workflow to map inundation extents, using a passive bathtub approach for various SLR scenarios. We demonstrate the approach through a case study in Virginia Key, Florida, however, the methodology can be replicated in any area where the input datasets are available. This paper demonstrates that FOSS4G tools offer a reliable and accessible means to map SLR inundation, empowering stakeholders to assess coastal vulnerabilities and to devise sustainable adaptation measures. The open-source approach facilitates collaboration and reproducibility, fostering a comprehensive understanding of the potential impacts of SLR on coastal ecosystems and communities.

Geomatics doi: 10.3390/geomatics3040027

Authors: Simone Ott Benjamin Burkhard Corinna Harmening Jens-André Paffenholz Bastian Steinhoff-Knopp

Detecting changes in soil micro-relief in farmland helps to understand degradation processes like sheet erosion. Using the high-resolution technique of terrestrial laser scanning (TLS), we generated point clouds of three 2 × 3 m plots on a weekly basis from May to mid-June in 2022 on cultivated farmland in Germany. Three well-known applications for eliminating vegetation points in the generated point cloud were tested: Cloth Simulation Filter (CSF) as a filtering method, three variants of CANUPO as a machine learning method, and ArcGIS PointCNN as a deep learning method, a sub-category of machine learning using deep neural networks. We assessed the methods with hard criteria such as F1 score, balanced accuracy, height differences, and their standard deviations to the reference surface, resulting in data gaps and robustness, and with soft criteria such as time-saving capacity, accessibility, and user knowledge. All algorithms showed a low performance at the initial measurement epoch, increasing with later epochs. While most of the results demonstrate a better performance of ArcGIS PointCNN, this algorithm revealed an exceptionally low performance in plot 1, which is describable by the generalization gap. Although CANUPO variants created the highest amount of data gaps, we recommend that CANUPO include colour values in combination with CSF.

Geomatics doi: 10.3390/geomatics3040026

Authors: Reneilwe Maake Onisimo Mutanga George Chirima Mbulisi Sibanda

Recently, the move from cost-tied to open-access data has led to the mushrooming of research in pursuit of algorithms for estimating the aboveground grass biomass (AGGB). Nevertheless, a comprehensive synthesis or direction on the milestones achieved or an overview of how these models perform is lacking. This study synthesises the research from decades of experiments in order to point researchers in the direction of what was achieved, the challenges faced, as well as how the models perform. A pool of findings from 108 remote sensing-based AGGB studies published from 1972 to 2020 show that about 19% of the remote sensing-based algorithms were tested in the savannah grasslands. An uneven annual publication yield was observed with approximately 36% of the research output from Asia, whereas countries in the global south yielded few publications (<10%). Optical sensors, particularly MODIS, remain a major source of satellite data for AGGB studies, whilst studies in the global south rarely use active sensors such as Sentinel-1. Optical data tend to produce low regression accuracies that are highly inconsistent across the studies compared to radar. The vegetation indices, particularly the Normalised Difference Vegetation Index (NDVI), remain as the most frequently used predictor variable. The predictor variables such as the sward height, red edge position and backscatter coefficients produced consistent accuracies. Deciding on the optimal algorithm for estimating the AGGB is daunting due to the lack of overlap in the grassland type, location, sensor types, and predictor variables, signalling the need for standardised remote sensing techniques, including data collection methods to ensure the transferability of remote sensing-based AGGB models across multiple locations.

Geomatics doi: 10.3390/geomatics3040025

Authors: Mauro De Donatis Giulio Fabrizio Pappafico

Open-source software applications, especially those useful for GIS, have been used in the field of geology both in research and teaching at the University of Urbino for decades. The experiences described in this article range from land-surveying cases to cartographic processing and 3D printing of geological models. History of their use and development is punctuated by trials, failures, and slowdowns, but the idea of using digital tools in areas where they are traditionally frowned upon, such as in soil geology, is now rooted in and validated by applications in projects of various types. Although the current situation is not definitive, given that the evolution of information technology provides increasingly faster tools that are performance-oriented and easier to use, this article aims to contribute to the development of methodologies through an exchange of information and experiences.

Geomatics doi: 10.3390/geomatics3030024

Authors: Hayatullah Hekmat Tauseef Ahmad Suraj Kumar Singh Shruti Kanga Gowhar Meraj Pankaj Kumar

This study delves into the patterns of urban expansion in Kabul, using Landsat and Sentinel satellite imagery as primary tools for analysis. We classified land use and land cover (LULC) into five distinct categories: water bodies, vegetation, barren land, barren rocky terrain, and buildings. The necessary data processing and analysis was conducted using ERDAS Imagine v.2015 and ArcGIS 10.8 software. Our main objective was to scrutinize changes in LULC across five discrete decades. Additionally, we traced the long-term evolution of built-up areas in Kabul from 1973 to 2020. The classified satellite images revealed significant changes across all categories. For instance, the area of built-up land reduced from 29.91% in 2013 to 23.84% in 2020, while barren land saw a decrease from 33.3% to 28.4% over the same period. Conversely, the proportion of barren rocky terrain exhibited an increase from 22.89% in 2013 to 29.97% in 2020. Minor yet notable shifts were observed in the categories of water bodies and vegetated land use. The percentage of water bodies shrank from 2.51% in 2003 to 1.30% in 2013, and the extent of vegetated land use showed a decline from 13.61% in 2003 to 12.6% in 2013. Our study unveiled evolving land use patterns over time, with specific periods recording an increase in barren land and a slight rise in vegetated areas. These findings underscored the dynamic transformation of Kabul’s urban landscape over the years, with significant implications for urban planning and sustainability.

Geomatics doi: 10.3390/geomatics3030023

Authors: James Kapp Jaco Kemp

Urban areas are rapidly expanding globally. The detection of settlement expansion can, however, be challenging due to the rapid rate of expansion, especially for informal settlements. This paper presents a solution in the form of an unsupervised autocorrelation-based approach. Temporal autocorrelation function (ACF) values derived from hyper-temporal Sentinel-1 imagery were calculated for all time lags using VV backscatter values. Various thresholds were applied to these ACF values in order to create urban change maps. Two different orbital combinations were tested over four informal settlement areas in South Africa. Promising results were achieved in the two of the study areas with mean normalized Matthews Correlation Coefficients (MCCn) of 0.79 and 0.78. A lower performance was obtained in the remaining two areas (mean MCCn of 0.61 and 0.65) due to unfavorable building orientations and low building densities. The first results also indicate that the most stable and optimal ACF-based threshold of 95 was achieved when using images from both relative orbits, thereby incorporating more incidence angles. The results demonstrate the capacity of ACF-based methods for detecting settlement expansion. Practically, this ACF-based method could be used to reduce the time and labor costs of detecting and mapping newly built settlements in developing regions.

Geomatics doi: 10.3390/geomatics3030022

Authors: Polina Lemenkova

The volcanic Kerguelen Islands are formed on one of the world’s largest submarine plateaus. Located in the remote segment of the southern Indian Ocean close to Antarctica, the Kerguelen Plateau is notable for a complex tectonic origin and geologic formation related to the Cretaceous history of the continents. This is reflected in the varying age of the oceanic crust adjacent to the plateau and the highly heterogeneous bathymetry of the Kerguelen Plateau, with seafloor structure differing for the southern and northern segments. Remote sensing data derived from marine gravity and satellite radar altimetry surveys serve as an important source of information for mapping complex seafloor features. This study incorporates geospatial information from NOAA, EMAG2, WDMAM, ETOPO1, and EGM96 datasets to refine the extent and distribution of the extracted seafloor features. The cartographic joint analysis of topography, magnetic anomalies, tectonic and gravity grids is based on the integrated mapping performed using the Generic Mapping Tools (GMT) programming suite. Mapping of the submerged features (Broken Ridge, Crozet Islands, seafloor fabric, orientation, and frequency of magnetic anomalies) enables analysis of their correspondence with free-air gravity and magnetic anomalies, geodynamic setting, and seabed structure in the southwest Indian Ocean. The results show that integrating the datasets using advanced cartographic scripting language improves identification and visualization of the seabed objects. The results include 11 new maps of the region covering the Kerguelen Plateau and southwest Indian Ocean. This study contributes to increasing the knowledge of the seafloor structure in the French Southern and Antarctic Lands.

Geomatics doi: 10.3390/geomatics3030021

Authors: Aggeliki Kyriou Vassiliki Mpelogianni Konstantinos Nikolakopoulos Peter P. Groumpos

During the past few decades, remote sensing has been established as an innovative, effective and cost-efficient option for the provision of high-quality information concerning infrastructure to governments or decision makers in order to update their plans and/or take actions towards the mitigation of the infrastructure risk. Meanwhile, climate change has emerged as a serious global challenge and hence there is an urgent need to develop reliable and cost-efficient infrastructure monitoring solutions. In this framework, the current study conducts a comprehensive review concerning the use of different remote-sensing sensors for the monitoring of multiple types of infrastructure including roads and railways, dams, bridges, archaeological sites and buildings. The aim of this contribution is to identify the best practices and processing methodologies for the comprehensive monitoring of critical national infrastructure falling under the research project named “PROION”. In light of this, the review summarizes the wide variety of approaches that have been utilized for the monitoring of infrastructure and are based on the collection of remote-sensing data, acquired using the global navigation satellite system (GNSS), synthetic aperture radar (SAR), light detection and ranging (LiDAR) and unmanned aerial vehicles (UAV) sensors. Moreover, great emphasis is given to the contribution of the state-of-the-art soft computing methods throughout infrastructure monitoring aiming to increase the automation of the procedure. The statistical analysis of the reviewing publications revealed that SARs and LiDARs are the prevalent remote-sensing sensors used in infrastructure monitoring concepts, while regarding the type of infrastructure, research is orientated onto transportation networks (road and railway) and bridges. Added to this, deep learning-, fuzzy logic- and expert-based approaches have gained ground in the field of infrastructure monitoring over the past few years.

Geomatics doi: 10.3390/geomatics3020020

Authors: Christophe Claramunt Maryam Lotfian

Geomatics has long been recognized as an information-technology-oriented discipline whose objective is to integrate and deliver multiple sources of geolocated data to a wide range of environmental and urban sciences [...]

Geomatics doi: 10.3390/geomatics3020019

Authors: Junshan Liu Robert A. Bugg Cort W. Fisher

Sediment Barriers (SBs) are crucial for effective erosion control, and understanding their capacities and limitations is essential for environmental protection. This study compares the accuracy and effectiveness of Terrestrial Laser Scanning (TLS) and Robotic Total Station (RTS) techniques for quantifying sediment retention in SBs. To achieve this, erosion tests were conducted in a full-scale testing apparatus with TLS and RTS methods to collect morphological data of sediment retention surfaces before and after each experiment. The acquired datasets were processed and integrated into a Building Information Modeling (BIM) platform to create Digital Elevation Models (DEMs). These were then used to calculate the volume of accumulated sediment upstream of the SB system. The results indicated that TLS and RTS techniques could effectively measure sediment retention in a full-scale testing environment. However, TLS proved to be more accurate, exhibiting a standard deviation of 0.41 ft3 in contrast to 1.94 ft3 for RTS and more efficient, requiring approximately 15% to 50% less time per test than RTS. The main conclusions of this study highlight the benefits of using TLS over RTS for sediment retention measurement and provide valuable insights for improving erosion control strategies and sediment barrier design.

Geomatics doi: 10.3390/geomatics3020018

Authors: Patricia Duncan Erika Podest Karen J. Esler Sjirk Geerts Candice Lyons

Invasive alien plants (IAPs) pose a serious threat to biodiversity, agriculture, health, and economies globally. Accurate mapping of IAPs is crucial for their management, to mitigate their impacts and prevent further spread where possible. Remote sensing has become a valuable tool in detecting IAPs, especially with freely available data such as Sentinel-2 satellite imagery. Yet, remote sensing methods to map herbaceous IAPs, which tend to be more difficult to detect, particularly in shrubland Mediterranean-type ecosystems, are still limited. There is a growing need to detect herbaceous IAPs at a large scale for monitoring and management; however, for countries or organizations with limited budgets, this is often not feasible. To address this, we aimed to develop a classification methodology based on optical satellite data to map herbaceous IAP’s using Echium plantagineum as a case study in the Fynbos Biome of South Africa. We investigate the use of freely available Sentinel-2 data, use the robust non-parametric classifier Random Forest, and identify the most important variables in the classification, all within the cloud-based platform, Google Earth Engine. Findings reveal the importance of the shortwave infrared and red-edge parts of the spectrum and the importance of including vegetation indices in the classification for discriminating E. plantagineum. Here, we demonstrate the potential of Sentinel-2 data, the Random Forest classifier, and Google Earth Engine for mapping herbaceous IAPs in Mediterranean ecosystems.

Geomatics doi: 10.3390/geomatics3020017

Authors: Bhogendra Mishra Rupesh Bhandari Krishna Prasad Bhandari Dinesh Mani Bhandari Nirajan Luintel Ashok Dahal Shobha Poudel

Sustainable agricultural management requires knowledge of where and when crops are grown, what they are, and for how long. However, such information is not yet available in Nepal. Remote sensing coupled with farmers’ knowledge offers a solution to fill this gap. In this study, we created a high-resolution (10 m) seasonal crop map and cropping pattern in a mountainous area of Nepal through a semi-automatic workflow using Sentinel-2 A/B time-series images coupled with farmer knowledge. We identified agricultural areas through iterative self-organizing data clustering of Sentinel imagery and topographic information using a digital elevation model automatically. This agricultural area was analyzed to develop crop calendars and to track seasonal crop dynamics using rule-based methods. Finally, we computed a pixel-level crop-intensity map. In the end our results were compared to ground-truth data collected in the field and published crop calendars, with an overall accuracy of 88% and kappa coefficient of 0.83. We found variations in crop intensity and seasonal crop extension across the study area, with higher intensity in plain areas with irrigation facilities and longer fallow cycles in dry and hilly regions. The semi-automatic workflow was successfully implemented in the heterogeneous topography and is applicable to the diverse topography of the entire country, providing crucial information for mapping and monitoring crops that is very useful for the formulation of strategic agricultural plans and food security in Nepal.

Geomatics doi: 10.3390/geomatics3010016

Authors: Paul Ryu David Brown Kevin Arsenault Byunggu Cho Andrew March Wael H. Ali Aaron Charous Pierre F. J. Lermusiaux

Mapping the seafloor in the deep ocean is currently performed using sonar systems on surface vessels (low-resolution maps) or undersea vessels (high-resolution maps). Surface-based mapping can cover a much wider search area and is not burdened by the complex logistics required for deploying undersea vessels. However, practical size constraints for a towbody or hull-mounted sonar array result in limits in beamforming and imaging resolution. For cost-effective high-resolution mapping of the deep ocean floor from the surface, a mobile wide-aperture sparse array with subarrays distributed across multiple autonomous surface vessels (ASVs) has been designed. Such a system could enable a surface-based sensor to cover a wide area while achieving high-resolution bathymetry, with resolution cells on the order of 1 m2 at a 6 km depth. For coherent 3D imaging, such a system must dynamically track the precise relative position of each boat’s sonar subarray through ocean-induced motions, estimate water column and bottom reflection properties, and mitigate interference from the array sidelobes. Sea testing of this core sparse acoustic array technology has been conducted, and planning is underway for relative navigation testing with ASVs capable of hosting an acoustic subarray.

Geomatics doi: 10.3390/geomatics3010015

Authors: Paul T. Schrum Carter D. Jameson Laura G. Tateosian Gary B. Blank Karl W. Wegmann Stacy A. C. Nelson

Increased availability of QL1/QL2 Lidar terrain data has resulted in large datasets, often including large quantities of redundant points. Because of these large memory requirements, practitioners often use decimation to reduce the number of points used to create models. This paper introduces a novel approach to improve decimation, thereby reducing the total count of ground points in a Lidar dataset while retaining more accuracy than Random Decimation. This reduction improves efficiency of downstream processes while maintaining output quality nearer to the undecimated dataset. Points are selected for retention based on their discrete curvature values computed from the mesh geometry of the TIN model of the points. Points with higher curvature values are preferred for retention in the resulting point cloud. We call this technique Curvature Weighted Decimation (CWD). We implement CWD in a new free, open-source software tool, CogoDN, which is also introduced in this paper. We evaluate the effectiveness of CWD against Random Decimation by comparing the resulting introduced error values for the two kinds of decimation over multiple decimation percentages, multiple statistical types, and multiple terrain types. The results show that CWD reduces introduced error values over Random Decimation when 15 to 50% of the points are retained.

Geomatics doi: 10.3390/geomatics3010014

Authors: Colbert M. Jackson Elhadi Adam Iqra Atif Muhammad A. Mahboob

Accurate mapping of selective logging (SL) serves as the foundation for additional research on forest restoration and regeneration, species diversification and distribution, and ecosystem dynamics, among other applications. This study aimed to model canopy gaps created by illegal logging of Ocotea usambarensis in Mt. Kenya Forest Reserve (MKFR). A texture-spectral analysis approach was applied to exploit the potential of WorldView-3 (WV-3) multispectral imagery. First, texture properties were explored in the sub-band images using fused grey-level co-occurrence matrix (GLCM)- and local binary pattern (LBP)-based texture feature extraction. Second, the texture features were fused with colour using the multivariate local binary pattern (MLBP) model. The G-statistic and Euclidean distance similarity measures were applied to increase accuracy. The random forest (RF) and support vector machine (SVM) were used to identify and classify distinctive features in the texture and spectral domains of the WV-3 dataset. The variable importance measurement in RF ranked the relative influence of sets of variables in the classification models. Overall accuracy (OA) scores for the respective MLBP models were in the range of 80–95.1%. The respective user’s accuracy (UA) and producer’s accuracy (PA) for the univariate LBP and MLBP models were in the range of 67–75% and 77–100%, respectively.

Geomatics doi: 10.3390/geomatics3010013

Authors: Julian Le Deunf Thierry Schmitt Yann Keramoal Ronan Jarno Morvan Fally

With more than 300 years of existence, Shom is the oldest active hydrographic service in the world. Compiling and deconflicting this much history automatically is a real challenge. This article will present the types of data Shom has to manipulate and the different steps of the workflow that allows Shom to compile over 300 years of bathymetric knowledge. The Téthys project for Shom will be presented in detail. The implementation of this type of process is a scientific, algorithmic, and infrastructure challenge.

Geomatics doi: 10.3390/geomatics3010012

Authors: Tommaso Orusa Annalisa Viani Duke Cammareri Enrico Borgogno Mondino

Google Earth Engine has deeply changed the way in which Earth observation data are processed, allowing the analysis of wide areas in a faster and more efficient way than ever before. Since its inception, many functions have been implemented by a rapidly expanding community, but none so far has focused on the computation of phenological metrics in mountain areas with high-resolution data. This work aimed to fill this gap by developing an open-source Google Earth Engine algorithm to map phenological metrics (PMs) such as the Start of Season, End of Season, and Length of Season and detect the Peak of Season in mountain areas worldwide using high-resolution free satellite data from the Landsat collection and Sentinel-2. The script was tested considering the entire Alpine chain. The validation was performed by the cross-computation of PMs using the R package greenbrown, which permits land surface phenology and trend analysis, and the Moderate-Resolution Imaging Spectroradiometer (MODIS) in homogeneous quote and land cover alpine landscapes. MAE and RMSE were computed. Therefore, this algorithm permits one to compute with a certain robustness PMs retrieved from higher-resolution free EO data from GEE in mountain areas worldwide.

Geomatics doi: 10.3390/geomatics3010011

Authors: Altayeb Obaid Elhadi Adam K. Adem Ali

Understanding long-term land use/land cover (LULC) change patterns is vital to implementing policies for effective environmental management practices and sustainable land use. This study assessed patterns of change in LULC in the Vaal Dam Catchment area, one of the most critically important areas in South Africa, since it contributes a vast portion of water to the Vaal Dam Reservoir. The reservoir has been used to supply water to about 13 million inhabitants in Gauteng province and its surrounding areas. Multi-temporal Landsat imagery series were used to map LULC changes between 1986 and 2021. The LULC classification was performed by applying the random forest (RF) algorithm to the Landsat data. The change-detection analysis showed grassland being the dominant land cover type (ranging from 52% to 57% of the study area) during the entire period. The second most dominant land cover type was agricultural land, which included cleared fields, while cultivated land covered around 41% of the study area. Other land use types covering small portions of the study area included settlements, mining activities, water bodies and woody vegetation. Time series analysis showed patterns of increasing and decreasing changes for all land cover types, except in the settlement class, which showed continuous increase owing to population growth. From the study results, the settlement class increased considerably for 1986–1993, 1993–2000, 2000–2007, 2007–2014 and 2014–2021 by 712.64 ha (0.02%), 10245.94 ha (0.26%), 3736.62 ha (0.1%), 1872.09 ha (0.05%) and 3801.06 ha (0.1%), respectively. This study highlights the importance of using remote sensing techniques in detecting LULC changes in this vitally important catchment.

Geomatics doi: 10.3390/geomatics3010010

Authors: Lars Erikstad Trond Simensen Vegar Bakkestuen Rune Halvorsen

To monitor the changes in the landscape, and to relate these to ecological processes, we need robust and reproducible methods for quantifying the changes in landscape patterns. The main aim of this study is to present, exemplify and discuss a gradient-based index of land use intensity. This index can easily be calculated from spatial data that are available for most areas and may therefore have a wide applicability. Further, the index is adapted for use based on official data sets and can thus be used directly in decision-making at different levels. The index in its basic form consists of two parts where the first is based on the data of buildings and roads and the second of infrastructure land cover. We compared the index with two frequently used ‘wilderness indices’ in Norway called INON and the Human Footprint Index. Our index captures important elements of infrastructure in more detailed scales than the other indices. A particularly attractive feature of the index is that it is based on map databases that are updated regularly. The index has the potential to serve as an important tool in land use planning as well as a basis for monitoring, the assessment of ecological state and ecological integrity and for ecological accounting as well as strategic environmental assessments.

Geomatics doi: 10.3390/geomatics3010009

Authors: Konstantinos Karachristos Vassilis Anastassopoulos

In ship detection by means of Polarimetric SAR imagery, a very promising feature is the characterization of the pixels of the ship based on the elementary scattering mechanisms that can be extracted using different decomposition algorithms. Elementary scattering mechanisms provide information regarding the physical, electrical and geometrical properties of the scatterers in each Polarimetric SAR pixel. In this work, the newly established algorithm of the Double Scatterer Model is applied to interpret each pixel of the Polarimetric SAR image with the contributions of two elementary scattering mechanisms, namely, primary and secondary. The main idea is to construct a binary image while preserving the rich information content in order to proceed in simple and fast image processing for target detection. The present algorithm is applied to datasets with different inherent characteristics acquired by Radarsat-2 and ALOS-PALSAR. The results presented by this new perspective on ship monitoring are remarkable.

Geomatics doi: 10.3390/geomatics3010008

Authors: Taohan Lin Nataliya Rybnikova

Night-time light (NTL) data have been widely used as a remote proxy for the economic performance of regions. The use of these data is more advantageous than the traditional census approach is due to its timeliness, low cost, and comparability between regions and countries. Several recent studies have explored monthly NTL composites produced by the Visible Infrared Imaging Radiometer Suite (VIIRS) and revealed a dimming of the light in some countries during the national lockdowns due to the COVID-19 pandemic. Here, we explicitly tested the extent to which the observed decrease in the amount of NTL is associated with the economic recession at the subnational level. Specifically, we explore how the association between Gross Domestic Product (GDP) and the amount of NTL is modulated by the pandemic and whether NTL data can still serve as a sufficiently reliable proxy for the economic performance of regions even during stressful pandemic periods. For this reason, we use the states of the US and quarterly periods within 2014–2021 as a case study. We start with building a linear mixed effects model linking the state-level quarterly GDPs with the corresponding pre-processed NTL data, additionally controlling only for a long-term trends and seasonal fluctuations. We intentionally do not include other socio-economic predictors, such as population density and structure, in the model, aiming to observe the ‘pure’ explanatory potential of NTL. As it is built only for the pre-COVID-19 period, this model demonstrates a rather good performance, with R2 = 0.60, while its extension across the whole period (2014–2021) leads to a considerable worsening of this (R2 = 0.42), suggesting that not accounting for the COVID-19 phenomenon substantially weakens the ‘natural’ GDP–NTL association. At the same time, the model’s enrichment with COVID-19 dummies restores the model fit to R2 = 0.62. As a plausible application, we estimated the state-level economic losses by comparing actual GDPs in the pandemic period with the corresponding predictions generated by the pre-COVID-19 model. The states’ vulnerability to the crisis varied from ~8 to ~18% (measured as a fraction of the pre-pandemic GDP level in the 4th quarter of 2019), with the largest losses being observed in states with a relatively low pre-pandemic GDP per capita, a low number of remote jobs, and a higher minority ratio.

Geomatics doi: 10.3390/geomatics3010007

Authors: Ronald Tombe Serestina Viriri

Deep learning approaches are gaining popularity in image feature analysis and in attaining state-of-the-art performances in scene classification of remote sensing imagery. This article presents a comprehensive review of the developments of various computer vision methods in remote sensing. There is currently an increase of remote sensing datasets with diverse scene semantics; this renders computer vision methods challenging to characterize the scene images for accurate scene classification effectively. This paper presents technology breakthroughs in deep learning and discusses their artificial intelligence open-source software implementation framework capabilities. Further, this paper discusses the open gaps/opportunities that need to be addressed by remote sensing communities.

Geomatics doi: 10.3390/geomatics3010006

Authors: Lwandile Nduku Cilence Munghemezulu Zinhle Mashaba-Munghemezulu Ahmed Mukalazi Kalumba George Johannes Chirima Wonga Masiza Colette De Villiers

Wheat is an important staple crop in the global food chain. The production of wheat in many regions is constrained by the lack of use of advanced technologies for wheat monitoring. Unmanned Aerial Vehicles (UAVs) is an important platform in remote sensing for providing near real-time farm-scale information. This information aids in making recommendations for monitoring and improving crop management to ensure food security. This study appraised global scientific research trends on wheat and UAV studies between 2005 and 2021, using a bibliometric method. The 398 published documents were mined from Web of Science, Scopus, and Dimensions. Results showed that an annual growth rate of 23.94% indicates an increase of global research based on wheat and UAVs for the surveyed period. The results revealed that China and USA were ranked as the top most productive countries, and thus their dominance in UAVs extensive usage and research developments for wheat monitoring during the study period. Additionally, results showed a low countries research collaboration prevalent trend, with only China and Australia managing multiple country publications. Thus, most of the wheat- and UAV-related studies were based on intra-country publications. Moreover, the results showed top publishing journals, top cited documents, Zipf’s law authors keywords co-occurrence network, thematic evolution, and spatial distribution map with the lack of research outputs from Southern Hemisphere. The findings also show that “UAV” is fundamental in all keywords with the largest significant appearance in the field. This connotes that UAV efficiency was important for most studies that were monitoring wheat and provided vital information on spatiotemporal changes and variability for crop management. Findings from this study may be useful in policy-making decisions related to the adoption and subsidizing of UAV operations for different crop management strategies designed to enhance crop yield and the direction of future studies.

Geomatics doi: 10.3390/geomatics3010005

Authors: Zama Eric Mashimbye Kyle Loggenberg

Landform classification is crucial for a host of applications that include geomorphological, soil mapping, radiative and gravity-controlled processes. Due to the complexity and rapid developments in the field of landform delineation, this study provides a scoping review to identify trends in the field. The review is premised on the PRISMA standard and is aimed to respond to the research questions pertaining to the global distribution of landform studies, methods used, datasets, analysis units and validation techniques. The articles were screened based on relevance and subject matter of which a total of 59 articles were selected for a full review. The parameters relating to where studies were conducted, datasets, methods of analysis, units of analysis, scale and validation approaches were collated and summarized. The study found that studies were predominantly conducted in Europe, South and East Asia and North America. Not many studies were found that were conducted in South America and the African region. The review revealed that locally sourced, very high-resolution digital elevation model ( DEM) products were becoming more readily available and employed for landform classification research. Of the globally available DEM sources, the SRTM still remains the most commonly used dataset in the field. Most landform delineation studies are based on expert knowledge. While object-based analysis is gaining momentum recently, pixel-based analysis is common and is also growing. Whereas validation techniques appeared to be mainly based on expert knowledge, most studies did not report on validation techniques. These results suggest that a systematic review of landform delineation may be necessary. Other aspects that may require investigation include a comparison of different DEMs for landform delineation, exploring more object-based studies, probing the value of quantitative validation approaches and data-driven analysis methods.

Geomatics doi: 10.3390/geomatics3010004

Authors: Yingisani Chabalala Elhadi Adam Khalid Adem Ali

Fruit-tree crops generate food and income for local households and contribute to South Africa’s gross domestic product. Timely and accurate phenotyping of fruit-tree crops is essential for innovating and achieving precision agriculture in the horticulture industry. Traditional methods for fruit-tree crop classification are time-consuming, costly, and often impossible to use for mapping heterogeneous horticulture systems. The application of remote sensing in smallholder agricultural landscapes is more promising. However, intercropping systems coupled with the presence of dispersed small agricultural fields that are characterized by common and uncommon crop types result in imbalanced samples, which may limit conventionally applied classification methods for phenotyping. This study assessed the influence of balanced and imbalanced multi-class distribution and data-sampling techniques on fruit-tree crop detection accuracy. Seven data samples were used as input to adaptive boosting (AdaBoost), gradient boosting (GB), random forest (RF), support vector machine (SVM), and eXtreme gradient boost (XGBoost) machine learning algorithms. A pixel-based approach was applied using Sentinel-2 (S2). The SVM algorithm produced the highest classification accuracy of 71%, compared with AdaBoost (67%), RF (65%), XGBoost (63%), and GB (62%), respectively. Individually, the majority of the crop types were classified with an F1 score of between 60% and 100%. In addition, the study assessed the effect of size and ratio of class imbalance in the training datasets on algorithms’ sensitiveness and stability. The results show that the highest classification accuracy of 71% could be achieved from an imbalanced training dataset containing only 60% of the original dataset. The results also showed that S2 data could be successfully used to map fruit-tree crops and provide valuable information for subtropical crop management and precision agriculture in heterogeneous horticultural landscapes.

Geomatics doi: 10.3390/geomatics3010003

Authors: Geomatics Editorial Office Geomatics Editorial Office

High-quality academic publishing is built on rigorous peer review [...]

Geomatics doi: 10.3390/geomatics3010002

Authors: Klaudia Kamati Julian Smit Simon Hull

We demonstrate the application of geomatics tools (remote sensing and geographic information systems) for spatial data analysis to determine potential locations for wind and solar photovoltaic (PV) energy plants in the Central North region of Namibia. In accordance with sustainable development goal 7 (affordable and clean energy) and goal 13 (climate action), the Namibian government has committed to reducing reliance on fossil fuels. In support of this, suitable locations for renewable energy plants need to be identified. Using multi-criteria decision-making and the analytical hierarchy process, sites were selected considering topographical, economic, climatic, and environmental factors. It was found that the highest potential for solar PV energy plants is in the northwest, southwest, and southern regions of the study area, whereas only the northwest region is highly suitable for wind power plants. These results were substantiated by comparison with global suitability maps, with some differences due to the datasets used. The findings can be used as a guide by governments, commercial investors, and other stakeholders to determine prospective sites for the development of renewable energy in Central North Namibia.

Geomatics doi: 10.3390/geomatics3010001

Authors: Günther Retscher

A variety of positioning systems have emerged for indoor localization which are based on several system strategies, location methods, and technologies while using different signals, such as radio frequency (RF) signals. Demands regarding positioning in terms of performance, robustness, availability and positioning accuracies are increasing. The overall goal of indoor positioning is to provide GNSS-like functionality in places where GNSS signals are not available. Analysis of the state-of-the-art indicates that although a lot of work is being done to combine both the outdoor and indoor positioning systems, there are still many problems and challenges to be solved. Most people moving on the city streets and interiors of public facilities have a smartphone, and most professionals working in public facilities or construction sites are equipped with tablets or smartphone devices. If users already have the necessary equipment, they should be provided with further functionalities that will help them in day-to-day life and work. In this review study, user requirements and the state-of-the-art in system development for smartphone localization are discussed. In particular, localization with current and upcoming ‘signals-of-opportunity’ (SoP) for use in mobile devices is the main focus of this paper.

Geomatics doi: 10.3390/geomatics2040029

Authors: Eric Younkin S. Harper Umfress

In the world of seafloor mapping, the ability to explore and experiment with a dataset in its raw and processed forms is critical. Kluster is an open-source multibeam data processing software package written in Python that enables this exploration. Kluster provides a suite of multibeam processing features, including analysis, visualization, gridding, and data cleaning. We demonstrated these features using a recently acquired dataset from a Kongsberg EM712 multibeam echosounder aboard NOAA Ship Fairweather. This test dataset served to illustrate the fundamental analysis abilities of the software, as well as its utility as a troubleshooting tool both in the field and during post-processing. Kluster has the capability to perform the Sonar Acceptance Test in full, including common experiments like the patch test, extinction test, and accuracy test, which are generally performed on new systems. When questions arise regarding the integration or parameter settings of a system, this software allows the user to quickly and clearly visualize much of the raw data and its associated metadata, which is a vital step in any investigative effort. With its emphasis on accessibility and ease of use, Kluster is an excellent tool for users who are inexperienced with multibeam sonar data processing.

Geomatics doi: 10.3390/geomatics2040028

Authors: Blessing Kavhu Zama Eric Mashimbye Linda Luvuno

Understanding the spatial spread pathways and connectivity of Land Use/Cover (LULC) change within basins is critical to natural resources management. However, existing studies approach LULC change as distinct patches but ignore the connectivity between them. It is crucial to investigate approaches that can detect the spread pathways of LULC change to aid natural resource management and decision-making. This study aims to evaluate the utility of the Circuit Theory to detect the spread and connectivity of LULC change within the Okavango basin. Patches of LULC change sites that were derived from change detection of LULC based on the Deep Neural Network (DNN) for the period between 2004 and 2020 were used. The changed sites were categorized based on the nature of the change of the classes, namely Category A (natural classes to artificial classes), Category B (artificial classes to natural classes), and Category C (natural classes to natural classes). In order to generate the resistance layer; an ensemble of machine learning algorithms was first calibrated with social-ecological drivers of LULC change and centroids of LULC change patches to determine the susceptibility of the landscape to LULC change. An inverse function was then applied to the susceptibility layer to derive the resistance layer. In order to analyze the connectivity and potential spread pathways of LULC change, the Circuit Theory (CT) model was built for each LULC change category. The CT model was calibrated using the resistance layer and patches of LULC change in Circuitscape 4.0. The corridor validation index was used to evaluate the performance of CT modeling. The use of the CT model calibrated with a resistance layer (derived from susceptibility modeling) successfully established the spread pathways and connectivity of LULC change for all the categories (validation index > 0.60). Novel maps of LULC change spread pathways in the Okavango basin were generated. The spread pathways were found to be concentrated in the northwestern, central, and southern parts of the basin for Category A transitions. As for category B transitions, the spread pathways were mainly concentrated in the northeastern and southern parts of the basin and along the major rivers. While for Category C transitions were found to be spreading from the central towards the southern parts, mainly in areas associated with semi-arid climatic conditions. A total of 186 pinch points (Category A: 57, Category B: 71, Category C: 58) were detected. The pinch points can guide targeted management LULC change through the setting up of conservation areas, forest restoration projects, drought monitoring, and invasive species control programs. This study provides a new decision-making method for targeted LULC change management in transboundary basins. The findings of this study provide insights into underlying processes driving the spread of LULC change and enhanced indicators for the evaluation of LULC spread in complex environments. Such information is crucial to inform land use planning, monitoring, and sustainable natural resource management, particularly water resources.

Geomatics doi: 10.3390/geomatics2040027

Authors: Andrew Ayangeaor Ugese Jesugbemi Olaoye Ajiboye Esther Shupel Ibrahim Efron Nduke Gajere Atang Itse Halilu Ahmad Shaba

Soil loss caused by erosion has destroyed landscapes, as well as depositing sterile material on fertile lands and rivers, clogged waterways and accelerated flash floods, declined the populations of fish and other species, and diminish soil fertility. In some places, erosion has also destroyed buildings, caused mudflow, create new landforms, displaced people, and slowed down the economy of the affected community by destroying roads and homes. Erosion is aggravated by climate change and anthropogenic factors such as deforestation, overgrazing, inappropriate methods of tillage, and unsustainable agricultural practices. In this study, remote sensing (RS) and geographic information (GIS) data and tools were used to model erosion and estimate soil loss in the catchment area of Koromi-Federe in Jos East, Plateau State Nigeria which is our study area. Soil loss estimation was performed using the revised universal soil loss equation (RUSLE) model and was computed by substituting the corresponding values of each factor inherent in the equation (rainfall erosivity, soil erodibility, slope steepness and slope length, cover management, and conservation practices) using RS and GIS tools. Soil data was obtained from the study area and analyzed in the laboratory, rainfall data, land cover, digital elevation model (DEM), as well as the management practice of the study area were the parameters computed in spatial analyst tool using map algebra based on RUSLE. The soil loss generated was classified into four classes and the results revealed 95.27% of the catchment with a tolerable loss of less than 10 t/h−1/y−1. At 3.6%, a low or minimal loss of 10–20 t/h−1/y−1, at 1.03% there exists a moderate loss of 20–50 t/h−1/y−1, while there was and critical or high loss of >50 t/h−1/y−1 at 0.12% of the catchment. The result showed that critical soil loss in the catchment area is exacerbated by the influence of the slope length and steepness, and the amount of rainfall received. This poses great concern with annual rainfall projected to increase up to 12% in West Africa. However, our sensitivity analysis revealed that it can be reduced with the effect of vegetated cover and management practices. This is an important finding as it can guide sustainability practices to control erosion and the loss of valuable lands in the region, especially now under climate change.

Geomatics doi: 10.3390/geomatics2040026

Authors: Giuseppe Masetti Ove Andersen Nicki R. Andreasen Philip S. Christiansen Marcus A. Cole James P. Harris Kasper Langdahl Lasse M. Schwenger Ian B. Sonne

Denmark’s Depth Model (DDM) is a Digital Bathymetric Model based on hundreds of bathymetric survey datasets and historical sources within the Danish Exclusive Economic Zone. The DDM represents the first publicly released model covering the Danish waters with a grid resolution of 50 m. When modern datasets are not available for a given area, historical sources are used, or, as the last resort, interpolation is applied. The model is generated by averaging depths values from validated sources, thus, not targeted for safety of navigation. The model is available by download from the Danish Geodata Agency website. DDM is also made available by means of Open Geospatial Consortium web services (i.e., Web Map Service). The original datasets—not distributed with the model—are described in the auxiliary layers to provide information about the bathymetric sources used during the compilation.

Geomatics doi: 10.3390/geomatics2040025

Authors: Abderrazzaq Kharroubi Florent Poux Zouhair Ballouch Rafika Hajji Roland Billen

Change detection is an important step for the characterization of object dynamics at the earth’s surface. In multi-temporal point clouds, the main challenge is to detect true changes at different granularities in a scene subject to significant noise and occlusion. To better understand new research perspectives in this field, a deep review of recent advances in 3D change detection methods is needed. To this end, we present a comprehensive review of the state of the art of 3D change detection approaches, mainly those using 3D point clouds. We review standard methods and recent advances in the use of machine and deep learning for change detection. In addition, the paper presents a summary of 3D point cloud benchmark datasets from different sensors (aerial, mobile, and static), together with associated information. We also investigate representative evaluation metrics for this task. To finish, we present open questions and research perspectives. By reviewing the relevant papers in the field, we highlight the potential of bi- and multi-temporal point clouds for better monitoring analysis for various applications.

Geomatics doi: 10.3390/geomatics2040024

Authors: Waytehad Rose Moskola? Wahabou Abdou Albert Dipanda Kolyang

The satellite image time series are used for several applications such as predictive analysis. New techniques such as deep learning (DL) algorithms generally require long sequences of data to perform well; however, the complexity of satellite image preprocessing tasks leads to a lack of preprocessed datasets. Moreover, using conventional collection and preprocessing methods is time- and storage-consuming. In this paper, a workflow for collecting, preprocessing, and preparing Sentinel-1 images to use with DL algorithms is proposed. The process mainly consists of using scripts for collecting and preprocessing operations. The goal of this work is not only to provide the community with easily modifiable programs for image collection and batch preprocessing but also to publish a database with prepared images. The experimental results allowed the researchers to build three time series of Sentinel-1 images corresponding to three study areas, namely the Bouba Ndjida National Park, the Dja Biosphere Reserve, and the Wildlife Reserve of Togodo. A total of 628 images were processed using scripts based on the SNAP graph processing tool (GPT). In order to test the effectiveness of the proposed methodology, three DL models were trained with the Bouba Ndjida and Togodo images for the prediction of the next occurrence in a sequence.

Geomatics doi: 10.3390/geomatics2040023

Authors: Mohammed Abdul Athick AS Shih-Yu Lee

The effect of climate prevails on a diverse time scale from days to seasons and decades. Between 1993 and 2013, global warming appeared to have paused even though there was an increase in atmospheric greenhouse gases. The variations in oceanographic variables, like current speed and sea surface temperature (SST), under the influence of the global warming hiatus (1993–2013), have drawn the attention of the global research community. However, the magnitude of ocean current and SST characteristics oscillates and varies with their geographic locations. Consequently, investigating the spatio-temporal changing aspects of oceanographic parameters in the backdrop of climate change is essential, specifically in coastal regions along Kuroshio current (KC), where fisheries are predominant. This study analyzes the trend of ocean current and SST induced mainly during the global warming hiatus, before and till the recent time based on the daily ocean current data from 1993 to 2020 and SST between 1982 and 2020. The Kuroshio extent is delineated from its surrounding water masses using an aggregation of raster classification, stretching, equalization, and spatial filters such as edge detection, convolution, and Laplacian. Finally, on the extracted Kuroshio extent, analyses such as time series decomposition (additive) and statistical trend computation methods (Yue and Wang trend test and Theil–Sen’s slope estimator) were applied to dissect and investigate the situations. An interesting downward trend is observed in the KC between the East coast of Taiwan and Tokara Strait (Tau = −0.05, S = −2430, Sen’s slope = −5.19 × 10−5, and Z = −2.61), whereas an upward trend from Tokara Strait to Nagoya (Tau = 0.89, S = 4344, Sen’s slope = 8.4 × 10−5, and Z = 2.56). In contrast, a consistent increasing SST in trend is visualized in the southern and mid-KC sections but with varying magnitude.

Geomatics doi: 10.3390/geomatics2040022

Authors: Elissa Penfound Christopher Wellen Eric Vaz

There are only two species of native vascular plants found on the Antarctic Peninsula and the surrounding islands, Deschampsia Antarctica, and Colobanthus quitensis. Poa annua, a successful invasive species, poses a threat to D. antarctica and C. quitensis. This region may experience extreme changes in biodiversity due to climate change over the next 100 years. This study explores the relationship between vascular vegetation and changing temperature on the Antarctic Peninsula and uses a systems modelling approach to account for three climate change scenarios over a 100-year period. The results of this study indicate that (1) D. antarctica, C. quitensis, and P. annua will likely be impacted by temperature increases, and greater temperature increases will facilitate more rapid species expansion, (2) in all scenarios D. antarctica species occurrences increase to higher values compared to C. quitensis and P. annua, suggesting that D. antarctica populations may be more successful at expanding into newly forming ice-free areas, (3) C. quitensis may be more vulnerable to the spread of P. annua than D. antarctica if less extreme warming occurs, and (4) C. quitensis relative growth rate is capable of reaching higher values than D. antarctica and P. annua, but only under extreme warming conditions.

Geomatics doi: 10.3390/geomatics2030021

Authors: Salem Morsy Ahmed Shaker Ahmed El-Rabbany

Classification of airborne light detection and ranging (LiDAR) point cloud is still challenging due to the irregular point cloud distribution, relatively low point density, and the complex urban scenes being observed. The availability of multispectral LiDAR systems allows for acquiring data at different wavelengths with a variety of spectral information from land objects. In this research, a rule-based point classification method of three levels for multispectral airborne LiDAR data covering urban areas is presented. The first level includes ground filtering, which attempts to distinguish aboveground from ground points. The second level aims to divide the aboveground and ground points into buildings, trees, roads, or grass using three spectral indices, namely normalized difference feature indices (NDFIs). A multivariate Gaussian decomposition is then used to divide the NDFIs’ histograms into the aforementioned four classes. The third level aims to label more classes based on their spectral information such as power lines, types of trees, and swimming pools. Two data subsets were tested, which represent different complexity of urban scenes in Oshawa, Ontario, Canada. It is shown that the proposed method achieved an overall accuracy up to 93%, which is increased to over 98% by considering the spatial coherence of the point cloud.

Geomatics doi: 10.3390/geomatics2030020

Authors: Rahul Dev Raju Sudhagar Nagarajan Madasamy Arockiasamy Stephen Castillo

Scour around bridge piers is considered as one of the major factors which causes failure of bridges in the United States. An undetected scour can affect the stability of the bridge, eventually leading to the collapse of the bridge. The experimental investigation of scour around a pier using a non-contact measuring method is carried out in this research. A green laser-based non-contact ranging technique is performed on a prefabricated scour hole to study the factors influencing the ability to reconstruct the shape of a scour hole. The experiment was conducted in a 10 ft diameter pool and Leica scan station II was used for the scanning of the scour hole. The turbidity of the water was changed by adding Kaolinite powder to the water. The turbidity was varied from 1.2 NTU to 20.8 NTU by adding Kaolinite. The lab experiments involved changing the turbidity of water to simulate real world conditions. The results from the experimental study show that the turbidity of the water has a direct dependence on the efficiency of the green laser to map the underwater scour profile. The ability of the green laser to capture the fabricated scour hole and pool bed topography were decreased as the turbidity was increased even when the water depth of the pool was reduced. The results from the study show that the green laser is effective in underwater scanning and can be also used for bathymetry profiling and the detection of underwater objects. The method of underwater scanning using a green laser for detecting scour around bridge pier is safe, efficient, and economical.