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Keywords = vegetation classification

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15 pages, 3267 KiB  
Article
Monitoring and Analyzing Aquatic Vegetation Using Sentinel-2 Imagery Time Series: A Case Study in Chimaditida Shallow Lake in Greece
by Maria Kofidou and Vasilios Ampas
Limnol. Rev. 2025, 25(3), 35; https://doi.org/10.3390/limnolrev25030035 (registering DOI) - 1 Aug 2025
Abstract
Aquatic vegetation plays a crucial role in freshwater ecosystems by providing habitats, regulating water quality, and supporting biodiversity. This study aims to monitor and analyze the dynamics of aquatic vegetation in Chimaditida Shallow Lake, Greece, using Sentinel-2 satellite imagery, with validation from field [...] Read more.
Aquatic vegetation plays a crucial role in freshwater ecosystems by providing habitats, regulating water quality, and supporting biodiversity. This study aims to monitor and analyze the dynamics of aquatic vegetation in Chimaditida Shallow Lake, Greece, using Sentinel-2 satellite imagery, with validation from field measurements. Data processing was performed using Google Earth Engine and QGIS. The study focuses on discriminating and mapping two classes of aquatic surface conditions: areas covered with Floating and Emergent Aquatic Vegetation and open water, covering all seasons from 1 March 2024, to 28 February 2025. Spectral bands such as B04 (red), B08 (near infrared), B03 (green), and B11 (shortwave infrared) were used, along with indices like the Modified Normalized Difference Water Index and Normalized Difference Vegetation Index. The classification was enhanced using Otsu’s thresholding technique to distinguish accurately between Floating and Emergent Aquatic Vegetation and open water. Seasonal fluctuations were observed, with significant peaks in vegetation growth during the summer and autumn months, including a peak coverage of 2.08 km2 on 9 September 2024 and a low of 0.00068 km2 on 28 December 2024. These variations correspond to the seasonal growth patterns of Floating and Emergent Aquatic Vegetation, driven by temperature and nutrient availability. The study achieved a high overall classification accuracy of 89.31%, with producer accuracy for Floating and Emergent Aquatic Vegetation at 97.42% and user accuracy at 95.38%. Validation with Unmanned Aerial Vehicle-based aerial surveys showed a strong correlation (R2 = 0.88) between satellite-derived and field data, underscoring the reliability of Sentinel-2 for aquatic vegetation monitoring. Findings highlight the potential of satellite-based remote sensing to monitor vegetation health and dynamics, offering valuable insights for the management and conservation of freshwater ecosystems. The results are particularly useful for governmental authorities and natural park administrations, enabling near-real-time monitoring to mitigate the impacts of overgrowth on water quality, biodiversity, and ecosystem services. This methodology provides a cost-effective alternative for long-term environmental monitoring, especially in regions where traditional methods are impractical or costly. Full article
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21 pages, 4657 KiB  
Article
A Semi-Automated RGB-Based Method for Wildlife Crop Damage Detection Using QGIS-Integrated UAV Workflow
by Sebastian Banaszek and Michał Szota
Sensors 2025, 25(15), 4734; https://doi.org/10.3390/s25154734 (registering DOI) - 31 Jul 2025
Abstract
Monitoring crop damage caused by wildlife remains a significant challenge in agricultural management, particularly in the case of large-scale monocultures such as maize. The given study presents a semi-automated process for detecting wildlife-induced damage using RGB imagery acquired from unmanned aerial vehicles (UAVs). [...] Read more.
Monitoring crop damage caused by wildlife remains a significant challenge in agricultural management, particularly in the case of large-scale monocultures such as maize. The given study presents a semi-automated process for detecting wildlife-induced damage using RGB imagery acquired from unmanned aerial vehicles (UAVs). The method is designed for non-specialist users and is fully integrated within the QGIS platform. The proposed approach involves calculating three vegetation indices—Excess Green (ExG), Green Leaf Index (GLI), and Modified Green-Red Vegetation Index (MGRVI)—based on a standardized orthomosaic generated from RGB images collected via UAV. Subsequently, an unsupervised k-means clustering algorithm was applied to divide the field into five vegetation vigor classes. Within each class, 25% of the pixels with the lowest average index values were preliminarily classified as damaged. A dedicated QGIS plugin enables drone data analysts (Drone Data Analysts—DDAs) to adjust index thresholds, based on visual interpretation, interactively. The method was validated on a 50-hectare maize field, where 7 hectares of damage (15% of the area) were identified. The results indicate a high level of agreement between the automated and manual classifications, with an overall accuracy of 81%. The highest concentration of damage occurred in the “moderate” and “low” vigor zones. Final products included vigor classification maps, binary damage masks, and summary reports in HTML and DOCX formats with visualizations and statistical data. The results confirm the effectiveness and scalability of the proposed RGB-based procedure for crop damage assessment. The method offers a repeatable, cost-effective, and field-operable alternative to multispectral or AI-based approaches, making it suitable for integration with precision agriculture practices and wildlife population management. Full article
(This article belongs to the Section Remote Sensors)
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31 pages, 12776 KiB  
Article
Multi-Source Data Integration for Sustainable Management Zone Delineation in Precision Agriculture
by Dušan Jovanović, Miro Govedarica, Milan Gavrilović, Ranko Čabilovski and Tamme van der Wal
Sustainability 2025, 17(15), 6931; https://doi.org/10.3390/su17156931 (registering DOI) - 30 Jul 2025
Abstract
Accurate delineation of within-field management zones (MZs) is essential for implementing precision agriculture, particularly in spatially heterogeneous environments. This study evaluates the spatiotemporal consistency and practical value of MZs derived from three complementary data sources: electromagnetic conductivity (EM38-MK2), basic soil chemical properties (pH, [...] Read more.
Accurate delineation of within-field management zones (MZs) is essential for implementing precision agriculture, particularly in spatially heterogeneous environments. This study evaluates the spatiotemporal consistency and practical value of MZs derived from three complementary data sources: electromagnetic conductivity (EM38-MK2), basic soil chemical properties (pH, humus, P2O5, K2O, nitrogen), and vegetation/surface indices (NDVI, SAVI, LCI, BSI) derived from Sentinel-2 imagery. Using kriging, fuzzy k-means clustering, percentile-based classification, and Weighted Overlay Analysis (WOA), MZs were generated for a five-year period (2018–2022), with 2–8 zone classes. Stability and agreement were assessed using the Cohen Kappa, Jaccard, and Dice coefficients on systematic grid samples. Results showed that EM38-MK2 and humus-weighted BSP data produced the most consistent zones (Kappa > 0.90). Sentinel-2 indices demonstrated strong alignment with subsurface data (r > 0.85), offering a low-cost alternative in data-scarce settings. Optimal zoning was achieved with 3–4 classes, balancing spatial coherence and interpretability. These findings underscore the importance of multi-source data integration for robust and scalable MZ delineation and offer actionable guidelines for both data-rich and resource-limited farming systems. This approach promotes sustainable agriculture by improving input efficiency and allowing for targeted, site-specific field management. Full article
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20 pages, 4467 KiB  
Article
Delineation of Dynamic Coastal Boundaries in South Africa from Hyper-Temporal Sentinel-2 Imagery
by Mariel Bessinger, Melanie Lück-Vogel, Andrew Luke Skowno and Ferozah Conrad
Remote Sens. 2025, 17(15), 2633; https://doi.org/10.3390/rs17152633 - 29 Jul 2025
Viewed by 86
Abstract
The mapping and monitoring of coastal regions are critical to ensure their sustainable use and viability in the long term. Delineation of coastlines is becoming increasingly important in the light of climate change and rising sea levels. However, many coastlines are highly dynamic; [...] Read more.
The mapping and monitoring of coastal regions are critical to ensure their sustainable use and viability in the long term. Delineation of coastlines is becoming increasingly important in the light of climate change and rising sea levels. However, many coastlines are highly dynamic; therefore, mono-temporal assessments of coastal ecosystems and coastlines are mere snapshots of limited practical value for space-based planning. Understanding of the spatio-temporal dynamics of coastal ecosystem boundaries is important to inform ecosystem management but also for a meaningful delineation of the high-water mark, which is used as a benchmark for coastal spatial planning in South Africa. This research aimed to use hyper-temporal Sentinel-2 imagery to extract ecological zones on the coast of KwaZulu-Natal, South Africa. A total of 613 images, collected between 2019 and 2023, were classified into four distinct coastal ecological zones—vegetation, bare, surf, and water—using a Random Forest model. Across all classifications, the percentage of each of the four classes’ occurrence per pixel over time was determined. This enabled the identification of ecosystem locations, spatially static ecosystem boundaries, and the occurrence of ecosystem boundaries with a more dynamic location over time, such as the non-permanent vegetation zone of the foredune area as well as the intertidal zone. The overall accuracy of the model was 98.13%, while the Kappa coefficient was 0.975, with user’s and producer’s accuracies ranging between 93.02% and 100%. These results indicate that cloud-based analysis of Sentinel-2 time series holds potential not just for delineating coastal ecosystem boundaries, but also for enhancing the understanding of spatio-temporal dynamics between them, to inform meaningful environmental management, spatial planning, and climate adaptation strategies. Full article
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29 pages, 6179 KiB  
Article
Assessing the Provision of Ecosystem Services Using Forest Site Classification as a Basis for the Forest Bioeconomy in the Czech Republic
by Kateřina Holušová and Otakar Holuša
Forests 2025, 16(8), 1242; https://doi.org/10.3390/f16081242 - 28 Jul 2025
Viewed by 139
Abstract
The ecosystem services (ESs) of forests are the benefits that people derive from forest ecosystems. Their precise recognition is important for differentiating and determining the optimal principles of multifunctional forest management. The aim of this study is to identify some important ESs based [...] Read more.
The ecosystem services (ESs) of forests are the benefits that people derive from forest ecosystems. Their precise recognition is important for differentiating and determining the optimal principles of multifunctional forest management. The aim of this study is to identify some important ESs based on a site classification system at the lowest level—i.e., forest stands, at the forest owner level—as a tool for differentiated management. ESs were assessed within the Czech Republic and are expressed in units in accordance with the very sophisticated Forest Site Classification System. (1) Biomass production: The vertical differentiation of ecological conditions given by vegetation tiers, which reflect the influence of altitude, exposure, and climate, provides a basic overview of biomass production; the highest value is in the fourth vegetation tier, i.e., the Fageta abietis community. Forest stands are able to reach a stock of up to 900–1200 m3·ha−1. The lowest production is found in the eighth vegetation tier, i.e., the Piceeta community, with a wood volume of 150–280 m3·ha−1. (2) Soil conservation function: Geological bedrock, soil characteristics, and the geomorphological shape of the terrain determine which habitats serve a soil conservation function according to forest type sets. (3) The hydricity of the site, depending on the soil type, determines the hydric-water protection function of forest stands. Currently, protective forests occupy 53,629 ha in the Czech Republic; however, two subcategories of protective forests—exceptionally unfavorable locations and natural alpine spruce communities below the forest line—potentially account for 87,578 ha and 15,277 ha, respectively. Forests with an increased soil protection function—a subcategory of special-purpose forests—occupy 133,699 ha. The potential area of soil protection forests could be up to 188,997 ha. Water resource protection zones of the first degree—another subcategory of special-purpose forests—occupy 8092 ha, and there is potentially 289,973 ha of forests serving a water protection function (specifically, a water management function) in the Czech Republic. A separate subcategory of water protection with a bank protection function accounts for 80,529 ha. A completely new approach is presented for practical use by forest owners: based on the characteristics of the habitat, they can obtain information about the fulfillment of the habitat’s ecosystem services and, thus, have basic information for the determination of forest categories and the principles of differentiated management. Full article
(This article belongs to the Section Forest Economics, Policy, and Social Science)
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15 pages, 465 KiB  
Article
Ultra-Processed Food Intake as an Effect Modifier in the Association Between Depression and Diabetes in Brazil: A Cross-Sectional Study
by Yunxiang Sun, Poliana E. Correia, Paula P. Teixeira, Bernardo F. Spiazzi, Elisa Brietzke, Mariana P. Socal and Fernando Gerchman
Nutrients 2025, 17(15), 2454; https://doi.org/10.3390/nu17152454 - 28 Jul 2025
Viewed by 366
Abstract
Background/Objectives: Recent studies linked a diet rich in ultra-processed foods (UPFs) with depression and diabetes. Although common risk factors, such as aging, are defined for both diseases, how UPFs are associated with the bidirectional relationship between them is not known. This study aimed [...] Read more.
Background/Objectives: Recent studies linked a diet rich in ultra-processed foods (UPFs) with depression and diabetes. Although common risk factors, such as aging, are defined for both diseases, how UPFs are associated with the bidirectional relationship between them is not known. This study aimed to investigate whether UPF intake modifies the association between depression and diabetes within the Brazilian adult population. Methods: This cross-sectional analysis utilized data from the 2019 Brazilian National Health Survey, involving over 87,000 adults (aged 18–92 years). Participants provided self-reported data on diabetes and depression diagnoses, dietary habits (assessed by qualitative FFQ), as well as demographic, and socioeconomic variables. Multivariate logistic regression models were used to evaluate the associations, employing two classification methods—UPF1 and UPF2—based on different thresholds of weekly consumption, for high/low UPF intake. Analyses were stratified by age groups to identify variations in associations. Results: There was a significant association between depression and diabetes, especially among participants with high UPF consumption. Models adjusted by demographic characteristics, as well as meat and vegetable consumptions, demonstrated elevated odds ratios (ORs) for diabetes among individuals with depression consuming high levels of UPF, compared to those with a low UPF intake (OR: 1.258; 95% CI: 1.064–1.489 for UPF1 and OR: 1.251; 95% CI: 1.059–1.478 for UPF2). Stratified analysis by age further amplified these findings, with younger individuals showing notably stronger associations (non-old adult group OR: 1.596; 95% CI: 1.127–2.260 for UPF1, and OR: 6.726; 95% CI: 2.625–17.233 for UPF2). Conclusions: These findings suggest that high UPF intake may influence the relationship between depression and diabetes, especially in younger adults. Future longitudinal studies are warranted to establish causality, investigate underlying biological mechanisms, and examine whether improving overall nutrient intake through dietary interventions can reduce the co-occurrence of depression and diabetes. Full article
(This article belongs to the Special Issue Ultra-Processed Foods and Chronic Diseases Nutrients)
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25 pages, 8105 KiB  
Article
Monitoring Critical Mountain Vertical Zonation in the Surkhan River Basin Based on a Comparative Analysis of Multi-Source Remote Sensing Features
by Wenhao Liu, Hong Wan, Peng Guo and Xinyuan Wang
Remote Sens. 2025, 17(15), 2612; https://doi.org/10.3390/rs17152612 - 27 Jul 2025
Viewed by 271
Abstract
Amidst the intensification of global climate change and the increasing impacts of human activities, ecosystem patterns and processes have undergone substantial transformations. The distribution and evolutionary dynamics of mountain ecosystems have become a focal point in ecological research. The Surkhan River Basin is [...] Read more.
Amidst the intensification of global climate change and the increasing impacts of human activities, ecosystem patterns and processes have undergone substantial transformations. The distribution and evolutionary dynamics of mountain ecosystems have become a focal point in ecological research. The Surkhan River Basin is located in the transitional zone between the arid inland regions of Central Asia and the mountain systems, where its unique physical and geographical conditions have shaped distinct patterns of vertical zonation. Utilizing Landsat imagery, this study applies a hierarchical classification approach to derive land cover classifications within the Surkhan River Basin. By integrating the NDVI (normalized difference vegetation index) and DEM (digital elevation model (30 m SRTM)), an “NDVI-DEM-Land Cover” scatterplot is constructed to analyze zonation characteristics from 1980 to 2020. The 2020 results indicate that the elevation boundary between the temperate desert and mountain grassland zones is 1100 m, while the boundary between the alpine cushion vegetation zone and the ice/snow zone is 3770 m. Furthermore, leveraging DEM and LST (land surface temperature) data, a potential energy analysis model is employed to quantify potential energy differentials between adjacent zones, enabling the identification of ecological transition areas. The potential energy analysis further refines the transition zone characteristics, indicating that the transition zone between the temperate desert and mountain grassland zones spans 1078–1139 m with a boundary at 1110 m, while the transition between the alpine cushion vegetation and ice/snow zones spans 3729–3824 m with a boundary at 3768 m. Cross-validation with scatterplot results confirms that the scatterplot analysis effectively delineates stable zonation boundaries with strong spatiotemporal consistency. Moreover, the potential energy analysis offers deeper insights into ecological transition zones, providing refined boundary identification. The integration of these two approaches addresses the dimensional limitations of traditional vertical zonation studies, offering a transferable methodological framework for mountain ecosystem research. Full article
(This article belongs to the Special Issue Temporal and Spatial Analysis of Multi-Source Remote Sensing Images)
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22 pages, 5097 KiB  
Article
Application of Landsat High Spatial Resolution Phenological Synthesized Data in Mountainous Land Cover Classification
by Zhengzheng Hu, Fei Xiao, Yun Du, Zhou Wang, Jiahuan Luo, Qi Feng and Miaomiao Chen
Remote Sens. 2025, 17(15), 2603; https://doi.org/10.3390/rs17152603 - 27 Jul 2025
Viewed by 222
Abstract
Classifying land cover in mountainous areas has always been challenging due to the high diversity of ecosystems and the complexity of the spectral–temporal–spatial relationships caused by the rugged terrain. This paper introduces multi-year synthesized phenology data to improve land cover classification in these [...] Read more.
Classifying land cover in mountainous areas has always been challenging due to the high diversity of ecosystems and the complexity of the spectral–temporal–spatial relationships caused by the rugged terrain. This paper introduces multi-year synthesized phenology data to improve land cover classification in these regions. Using the Shennongjia Forestry District in Hubei Province, China, as a case study, we investigate how incorporating multi-year synthesized phenology data enhances the accuracy of land cover classification with single-temporal and multi-temporal remote sensing imagery, as well as how it aids in identifying different vegetation types in shaded areas of the mountains. The research results indicate that incorporating multi-year synthesized phenology data significantly improves the accuracy of land cover classification for single summer imagery, single autumn imagery, multi-temporal summer–autumn imagery, and mountain shadow areas. The Kappa coefficient (Kappa) increased by 1.57% to 9.93%, while overall accuracy (OA) improved by 1.4% to 8.75%. Notably, the improvement in classification accuracy was most pronounced for single summer imagery. Furthermore, the results demonstrate that, in the absence of terrain data, multi-year synthesized phenology data provide even greater enhancements in land cover classification accuracy using remote sensing imagery. Full article
(This article belongs to the Special Issue Remote Sensing for Vegetation Phenology in a Changing Environment)
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26 pages, 8709 KiB  
Article
Minding Spatial Allocation Entropy: Sentinel-2 Dense Time Series Spectral Features Outperform Vegetation Indices to Map Desert Plant Assemblages
by Frederick N. Numbisi
Remote Sens. 2025, 17(15), 2553; https://doi.org/10.3390/rs17152553 - 23 Jul 2025
Viewed by 257
Abstract
The spatial distribution of ephemeral and perennial dryland plant species is increasingly modified and restricted by ever-changing climates and development expansion. At the interface of biodiversity conservation and developmental planning in desert landscapes is the growing need for adaptable tools in identifying and [...] Read more.
The spatial distribution of ephemeral and perennial dryland plant species is increasingly modified and restricted by ever-changing climates and development expansion. At the interface of biodiversity conservation and developmental planning in desert landscapes is the growing need for adaptable tools in identifying and monitoring these ecologically fragile plant assemblages, habitats, and, often, heritage sites. This study evaluates usage of Sentinel-2 time series composite imagery to discriminate vegetation assemblages in a hyper-arid landscape. Spatial predictor spaces were compared to classify different vegetation communities: spectral components (PCs), vegetation indices (VIs), and their combination. Further, the uncertainty in discriminating field-verified vegetation assemblages is assessed using Shannon entropy and intensity analysis. Lastly, the intensity analysis helped to decipher and quantify class transitions between maps from different spatial predictors. We mapped plant assemblages in 2022 from combined PCs and VIs at an overall accuracy of 82.71% (95% CI: 81.08, 84.28). A high overall accuracy did not directly translate to high class prediction probabilities. Prediction by spectral components, with comparably lower accuracy (80.32, 95% CI: 78.60, 81.96), showed lower class uncertainty. Class disagreement or transition between classification models was mainly contributed by class exchange (a component of spatial allocation) and less so from quantity disagreement. Different artefacts of vegetation classes are associated with the predictor space—spectral components versus vegetation indices. This study contributes insights into using feature extraction (VIs) versus feature selection (PCs) for pixel-based classification of plant assemblages. Emphasising the ecologically sensitive vegetation in desert landscapes, the study contributes uncertainty considerations in translating optical satellite imagery to vegetation maps of arid landscapes. These are perceived to inform and support vegetation map creation and interpretation for operational management and conservation of plant biodiversity and habitats in such landscapes. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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20 pages, 12036 KiB  
Article
Spatiotemporal Mapping of Grazing Livestock Behaviours Using Machine Learning Algorithms
by Guo Ye and Rui Yu
Sensors 2025, 25(15), 4561; https://doi.org/10.3390/s25154561 - 23 Jul 2025
Viewed by 280
Abstract
Grassland ecosystems are fundamentally shaped by the complex behaviours of livestock. While most previous studies have monitored grassland health using vegetation indices, such as NDVI and LAI, fewer have investigated livestock behaviours as direct drivers of grassland degradation. In particular, the spatial clustering [...] Read more.
Grassland ecosystems are fundamentally shaped by the complex behaviours of livestock. While most previous studies have monitored grassland health using vegetation indices, such as NDVI and LAI, fewer have investigated livestock behaviours as direct drivers of grassland degradation. In particular, the spatial clustering and temporal concentration patterns of livestock behaviours are critical yet underexplored factors that significantly influence grassland ecosystems. This study investigated the spatiotemporal patterns of livestock behaviours under different grazing management systems and grazing-intensity gradients (GIGs) in Wenchang, China, using high-resolution GPS tracking data and machine learning classification. the K-Nearest Neighbours (KNN) model combined with SMOTE-ENN resampling achieved the highest accuracy, with F1-scores of 0.960 and 0.956 for continuous and rotational grazing datasets. The results showed that the continuous grazing system failed to mitigate grazing pressure when grazing intensity was reduced, as the spatial clustering of livestock behaviours did not decrease accordingly, and the frequency of temporal peaks in grazing behaviour even showed an increasing trend. Conversely, the rotational grazing system responded more effectively, as reduced GIGs led to more evenly distributed temporal activity patterns and lower spatial clustering. These findings highlight the importance of incorporating livestock behavioural patterns into grassland monitoring and offer data-driven insights for sustainable grazing management. Full article
(This article belongs to the Section Smart Agriculture)
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31 pages, 4937 KiB  
Article
Proximal LiDAR Sensing for Monitoring of Vegetative Growth in Rice at Different Growing Stages
by Md Rejaul Karim, Md Nasim Reza, Shahriar Ahmed, Kyu-Ho Lee, Joonjea Sung and Sun-Ok Chung
Agriculture 2025, 15(15), 1579; https://doi.org/10.3390/agriculture15151579 - 23 Jul 2025
Viewed by 249
Abstract
Precise monitoring of vegetative growth is essential for assessing crop responses to environmental changes. Conventional methods of geometric characterization of plants such as RGB imaging, multispectral sensing, and manual measurements often lack precision or scalability for growth monitoring of rice. LiDAR offers high-resolution, [...] Read more.
Precise monitoring of vegetative growth is essential for assessing crop responses to environmental changes. Conventional methods of geometric characterization of plants such as RGB imaging, multispectral sensing, and manual measurements often lack precision or scalability for growth monitoring of rice. LiDAR offers high-resolution, non-destructive 3D canopy characterization, yet applications in rice cultivation across different growth stages remain underexplored, while LiDAR has shown success in other crops such as vineyards. This study addresses that gap by using LiDAR for geometric characterization of rice plants at early, middle, and late growth stages. The objective of this study was to characterize rice plant geometry such as plant height, canopy volume, row distance, and plant spacing using the proximal LiDAR sensing technique at three different growth stages. A commercial LiDAR sensor (model: VPL−16, Velodyne Lidar, San Jose, CA, USA) mounted on a wheeled aluminum frame for data collection, preprocessing, visualization, and geometric feature characterization using a commercial software solution, Python (version 3.11.5), and a custom algorithm. Manual measurements compared with the LiDAR 3D point cloud data measurements, demonstrating high precision in estimating plant geometric characteristics. LiDAR-estimated plant height, canopy volume, row distance, and spacing were 0.5 ± 0.1 m, 0.7 ± 0.05 m3, 0.3 ± 0.00 m, and 0.2 ± 0.001 m at the early stage; 0.93 ± 0.13 m, 1.30 ± 0.12 m3, 0.32 ± 0.01 m, and 0.19 ± 0.01 m at the middle stage; and 0.99 ± 0.06 m, 1.25 ± 0.13 m3, 0.38 ± 0.03 m, and 0.10 ± 0.01 m at the late growth stage. These measurements closely matched manual observations across three stages. RMSE values ranged from 0.01 to 0.06 m and r2 values ranged from 0.86 to 0.98 across parameters, confirming the high accuracy and reliability of proximal LiDAR sensing under field conditions. Although precision was achieved across growth stages, complex canopy structures under field conditions posed segmentation challenges. Further advances in point cloud filtering and classification are required to reliably capture such variability. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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26 pages, 11237 KiB  
Article
Reclassification Scheme for Image Analysis in GRASS GIS Using Gradient Boosting Algorithm: A Case of Djibouti, East Africa
by Polina Lemenkova
J. Imaging 2025, 11(8), 249; https://doi.org/10.3390/jimaging11080249 - 23 Jul 2025
Viewed by 423
Abstract
Image analysis is a valuable approach in a wide array of environmental applications. Mapping land cover categories depicted from satellite images enables the monitoring of landscape dynamics. Such a technique plays a key role for land management and predictive ecosystem modelling. Satellite-based mapping [...] Read more.
Image analysis is a valuable approach in a wide array of environmental applications. Mapping land cover categories depicted from satellite images enables the monitoring of landscape dynamics. Such a technique plays a key role for land management and predictive ecosystem modelling. Satellite-based mapping of environmental dynamics enables us to define factors that trigger these processes and are crucial for our understanding of Earth system processes. In this study, a reclassification scheme of image analysis was developed for mapping the adjusted categorisation of land cover types using multispectral remote sensing datasets and Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The data included four Landsat 8–9 satellite images on 2015, 2019, 2021 and 2023. The sequence of time series was used to determine land cover dynamics. The classification scheme consisting of 17 initial land cover classes was employed by logical workflow to extract 10 key land cover types of the coastal areas of Bab-el-Mandeb Strait, southern Red Sea. Special attention is placed to identify changes in the land categories regarding the thermal saline lake, Lake Assal, with fluctuating salinity and water levels. The methodology included the use of machine learning (ML) image analysis GRASS GIS modules ‘r.reclass’ for the reclassification of a raster map based on category values. Other modules included ‘r.random’, ‘r.learn.train’ and ‘r.learn.predict’ for gradient boosting ML classifier and ‘i.cluster’ and ‘i.maxlik’ for clustering and maximum-likelihood discriminant analysis. To reveal changes in the land cover categories around the Lake of Assal, this study uses ML and reclassification methods for image analysis. Auxiliary modules included ‘i.group’, ‘r.import’ and other GRASS GIS scripting techniques applied to Landsat image processing and for the identification of land cover variables. The results of image processing demonstrated annual fluctuations in the landscapes around the saline lake and changes in semi-arid and desert land cover types over Djibouti. The increase in the extent of semi-desert areas and the decrease in natural vegetation proved the processes of desertification of the arid environment in Djibouti caused by climate effects. The developed land cover maps provided information for assessing spatial–temporal changes in Djibouti. The proposed ML-based methodology using GRASS GIS can be employed for integrating techniques of image analysis for land management in other arid regions of Africa. Full article
(This article belongs to the Special Issue Self-Supervised Learning for Image Processing and Analysis)
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20 pages, 3263 KiB  
Article
Land Cover Transformations and Thermal Responses in Representative North African Oases from 2000 to 2023
by Tallal Abdel Karim Bouzir, Djihed Berkouk, Safieddine Ounis, Sami Melik, Noradila Rusli and Mohammed M. Gomaa
Urban Sci. 2025, 9(7), 282; https://doi.org/10.3390/urbansci9070282 - 18 Jul 2025
Viewed by 280
Abstract
Oases in arid regions are critical ecosystems, providing essential ecological, agricultural, and socio-economic functions. However, urbanization and climate change increasingly threaten their sustainability. This study examines land cover (LULC) and land surface temperature (LST) dynamics in four representative North African oases: Tolga (Algeria), [...] Read more.
Oases in arid regions are critical ecosystems, providing essential ecological, agricultural, and socio-economic functions. However, urbanization and climate change increasingly threaten their sustainability. This study examines land cover (LULC) and land surface temperature (LST) dynamics in four representative North African oases: Tolga (Algeria), Nefta (Tunisia), Ghadames (Libya), and Siwa (Egypt) over the period 2000–2023, using Landsat satellite imagery. A three-step analysis was employed: calculation of NDVI (Normalized Difference Vegetation Index), NDBI (Normalized Difference Built-up Index), and LST, followed by supervised land cover classification and statistical tests to examine the relationships between the studied variables. The results reveal substantial reductions in bare soil (e.g., 48.10% in Siwa) and notable urban expansion (e.g., 136.01% in Siwa and 48.46% in Ghadames). Vegetation exhibited varied trends, with a slight decline in Tolga (0.26%) and a significant increase in Siwa (+27.17%). LST trends strongly correlated with land cover changes, demonstrating increased temperatures in urbanized areas and moderated temperatures in vegetated zones. Notably, this study highlights that traditional urban designs integrated with dense palm groves significantly mitigate thermal stress, achieving lower LST compared to modern urban expansions characterized by sparse, heat-absorbing surfaces. In contrast, areas dominated by fragmented vegetation or seasonal crops exhibited reduced cooling capacity, underscoring the critical role of vegetation type, spatial arrangement, and urban morphology in regulating oasis microclimates. Preserving palm groves, which are increasingly vulnerable to heat-driven pests, diseases and the introduction of exotic species grown for profit, together with a revival of the traditional compact urban fabric that provides shade and has been empirically confirmed by other oasis studies to moderate the microclimate more effectively than recent low-density extensions, will maintain the crucial synergy between buildings and vegetation, enhance the cooling capacity of these settlements, and safeguard their tangible and intangible cultural heritage. Full article
(This article belongs to the Special Issue Geotechnology in Urban Landscape Studies)
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17 pages, 11610 KiB  
Article
Exploring the Impact of Species Participation Levels on the Performance of Dominant Plant Identification Models in the Sericite–Artemisia Desert Grassland by Using Deep Learning
by Wenhao Liu, Guili Jin, Wanqiang Han, Mengtian Chen, Wenxiong Li, Chao Li and Wenlin Du
Agriculture 2025, 15(14), 1547; https://doi.org/10.3390/agriculture15141547 - 18 Jul 2025
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Abstract
Accurate plant species identification in desert grasslands using hyperspectral data is a critical prerequisite for large-scale, high-precision grassland monitoring and management. However, due to prolonged overgrazing and the inherent ecological vulnerability of the environment, sericite–Artemisia desert grassland has experienced significant ecological degradation. [...] Read more.
Accurate plant species identification in desert grasslands using hyperspectral data is a critical prerequisite for large-scale, high-precision grassland monitoring and management. However, due to prolonged overgrazing and the inherent ecological vulnerability of the environment, sericite–Artemisia desert grassland has experienced significant ecological degradation. Therefore, in this study, we obtained spectral images of the grassland in April 2022 using a Soc710 VP imaging spectrometer (Surface Optics Corporation, San Diego, CA, USA), which were classified into three levels (low, medium, and high) based on the level of participation of Seriphidium transiliense (Poljakov) Poljakov and Ceratocarpus arenarius L. in the community. The optimal index factor (OIF) was employed to synthesize feature band images, which were subsequently used as input for the DeepLabv3p, PSPNet, and UNet deep learning models in order to assess the influence of species participation on classification accuracy. The results indicated that species participation significantly impacted spectral information extraction and model classification performance. Higher participation enhanced the scattering of reflectivity in the canopy structure of S. transiliense, while the light saturation effect of C. arenarius was induced by its short stature. Band combinations—such as Blue, Red Edge, and NIR (BREN) and Red, Red Edge, and NIR (RREN)—exhibited strong capabilities in capturing structural vegetation information. The identification model performances were optimal, with a high level of S. transiliense participation and with DeepLabv3p, PSPNet, and UNet achieving an overall accuracy (OA) of 97.86%, 96.51%, and 98.20%. Among the tested models, UNet exhibited the highest classification accuracy and robustness with small sample datasets, effectively differentiating between S. transiliense, C. arenarius, and bare ground. However, when C. arenarius was the primary target species, the model’s performance declined as its participation levels increased, exhibiting significant omission errors for S. transiliense, whose producer’s accuracy (PA) decreased by 45.91%. The findings of this study provide effective technical means and theoretical support for the identification of plant species and ecological monitoring in sericite–Artemisia desert grasslands. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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Article
Assessment of Environmental Dynamics and Ecosystem Services of Guadua amplexifolia J. Presl in San Jorge River Basin, Colombia
by Yiniva Camargo-Caicedo, Jorge Augusto Montoya Arango and Fredy Tovar-Bernal
Resources 2025, 14(7), 115; https://doi.org/10.3390/resources14070115 - 18 Jul 2025
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Abstract
Guadua amplexifolia J. Presl is a Neotropical bamboo native to southern Mexico through Central America to Colombia, where it thrives in riparian zones of the San Jorge River basin. Despite its ecological and socio-economic importance, its environmental dynamics and provision of ecosystem services [...] Read more.
Guadua amplexifolia J. Presl is a Neotropical bamboo native to southern Mexico through Central America to Colombia, where it thrives in riparian zones of the San Jorge River basin. Despite its ecological and socio-economic importance, its environmental dynamics and provision of ecosystem services remain poorly understood. This study (1) quantifies spatial and temporal land use/cover changes in the municipality of Montelíbano between 2002 and 2022 and (2) evaluates the ecosystem services that local communities derive from in 2002, 2012, and 2022, and they were classified in QGIS using G. amplexifolia. We applied a supervised classification of Landsat imagery (2002, 2012, 2022) in QGIS, achieving 85% overall accuracy and a Cohen’s Kappa of 0.82 (n = 45 reference points). For the social assessment, we held participatory workshops and conducted semi-structured interviews with artisans, fishers, authorities, and NGO representatives; responses were manually coded to extract key themes. The results show a 12% decline in total vegetated area from 2002 to 2012, followed by an 8% recovery by 2022, with bamboo-dominated stands following a similar pattern. Communities identified raw material provision (87% of mentions), climate regulation (82%), and cultural–recreational benefits (58%) as the most important services provided by G. amplexifolia. This is the first integrated assessment of G. amplexifolia’s landscape dynamics and community-valued services in the San Jorge basin, highlighting its dual function as a renewable resource and a natural safeguard against environmental risks. Our findings offer targeted recommendations for management practices and land use policies to support the species’ conservation and sustainable utilization. Full article
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