Search Results (332)

Accurate cropland classification within smallholder farming systems is essential for effective land management, efficient resource allocation, and informed agricultural decision-making. This study evaluates cropland classification performance using Red, Green, Blue (RGB) and multi-spectral (blue, green, red, red-edge, near-infrared) unmanned aerial vehicle (UAV) imagery. Both datasets were derived from imagery acquired using a MicaSense Altum sensor mounted on a DJI Matrice 300 UAV. Cropland classification was performed using machine learning algorithms implemented within the Google Earth Engine (GEE) platform, applying both a non-binary classification of five land cover classes and a binary classification within a probabilistic framework to distinguishing cropland from non-cropland areas. The results indicate that multi-spectral imagery achieved higher classification accuracy than RGB imagery for non-binary classification, with overall accuracies of 75% and 68%, respectively. For binary cropland classification, RGB imagery achieved an area under the receiver operating characteristic curve (AUC–ROC) of 0.75, compared to 0.77 for multi-spectral imagery. These findings suggest that, while multi-spectral data provides improved classification performance, RGB imagery can achieve comparable accuracy for fundamental cropland delineation. This study contributes baseline evidence on the relative performance of RGB and multi-spectral UAV imagery for cropland mapping in heterogeneous smallholder farming landscapes and supports further investigation of RGB-based approaches in resource-constrained agricultural contexts. Full article

Optimizing the “production–living–ecological” space (PLES) is critical for achieving the UN Sustainable Development Goals (SDGs), particularly in ecologically sensitive mountainous border regions. This study investigates the spatial patterns and dynamic evolution of PLES in Lincang City (2010–2020) to reveal the trade-offs between development and conservation. Methodologically, we proposed a coupling-coordination-based grid-level PLES identification framework. This framework integrates the coupling coordination degree model (CCDM) directly into the functional classification process at a 600 m grid scale—a resolution selected to balance the capture of spatial heterogeneity with the maintenance of functional integrity in complex terrains. Spatiotemporal dynamics were further quantified using transition matrices and a dimension-based landscape metric system. The results reveal that (a) ecological space and production–living–ecological space represent the predominant categories in the study area. During the study period, ecological space continued to decrease, while production–living space increased steadily, and other PLES categories showed only marginal variations. (b) Mutual transitions among PLES types primarily occurred among ecological space, production–ecological space, and production–living–ecological space. These transitions intensified markedly between 2015 and 2020 compared to the 2010–2015 period. (c) From 2010 to 2020, the landscape in Lincang evolved towards lower ecological risk yet higher fragmentation. High fragmentation values, often associated with grassland, cropland, and forested areas, were evenly distributed across northeastern and northwestern regions. Likewise, high landscape dominance and isolation appeared in these regions as well as in the southeast. Conversely, landscape disturbance remained relatively uniform throughout the city, with lower values detected in forested land. Full article

Drought is a pervasive and intensifying climate hazard with profound implications for food security, water availability, and socioeconomic stability, particularly in sub-Saharan Africa. In Kenya, where over 80% of the landmass comprises arid and semi-arid lands (ASALs), recurrent droughts have become a critical threat to agricultural productivity and climate resilience. This study presents a comprehensive spatiotemporal analysis of seasonal drought dynamics in Kenya for June–July–August–September (JJAS) from 2000 to 2024, leveraging remote sensing-based drought indices and geospatial analysis for climate risk planning. Using the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Soil Moisture Anomaly (SMA), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) anomaly, a Combined Drought Indicator (CDI) was developed to assess drought severity, persistence, and impact across Kenya’s four climatological seasons. Data were processed using Google Earth Engine and visualized through GIS platforms to produce high-resolution drought maps disaggregated by county and land-use class. The results revealed a marked intensification of drought conditions, with Alert and Warning classifications expanding significantly in ASALs, particularly in Garissa, Kitui, Marsabit, and Tana River. The drought persistence analysis revealed chronic exposure in drought conditions in northeastern and southeastern counties, while cropland exposure increased by over 100% while rangeland vulnerability rose nearly 56-fold. Population exposure to drought also rose sharply, underscoring the socioeconomic risks associated with climate-induced water stress. The study provides an operational framework for integrating remote sensing into early warning systems and policy planning, aligning with global climate adaptation goals and national resilience strategies. The findings advocate for proactive, data-driven drought management and localized adaptation interventions in Kenya’s most vulnerable regions. Full article

Populus euphratica is a key constructive species in desert ecosystems and plays a vital role in maintaining their stability. However, effective automated methods for accurately delineating its distribution outlines are currently lacking. This study used the mainstream area of the Tarim River as a case study and proposed a technical solution for identifying the distribution outline of Populus euphratica using multi-source thematic classification data. First, cropland thematic data were used to optimize the accuracy of the Populus euphratica classification raster data. Discrete points were removed based on density to reduce their impact on boundary identification. Then, a hierarchical identification scheme was constructed using the alpha-shape algorithm to identify the boundaries of high- and low-density Populus euphratica distribution areas separately. Finally, the outlines of the Populus euphratica distribution polygons were smoothed, and the final distribution outline data were obtained after spatial merging. The results showed the following: (1) Applying a closing operation to the cropland thematic classification data to obtain the distribution range of shelterbelts effectively eliminated misclassified pixels. Using the kd-tree algorithm to remove sparse discrete points based on density, with a removal ratio of 5%, helped suppress the interference of outlier point sets on the Populus euphratica outline identification. (2) Constructing a hierarchical identification scheme based on differences in Populus euphratica density is critical for accurately delineating its distribution contours. Using the alpha-shape algorithm with parameters set to α = 0.02 and α = 0.006, the reconstructed geometries effectively covered both densely and sparsely distributed Populus euphratica areas. (3) In the morphological processing stage, a combination of three methods—Gaussian filtering, equidistant expansion, and gap filling—effectively ensured the accuracy of the Populus euphratica outline. Among the various smoothing algorithms, Gaussian filtering yielded the best results. The equidistant expansion method reduced the impact of elongated cavities, thereby contributing to boundary accuracy. This study enhances the automation of Populus euphratica vector data mapping and holds significant value for the scientific management and research of desert vegetation. Full article

High-precision monitoring of arid wetlands is vital for ecological conservation, yet traditional methods incur prohibitive labeling costs due to complex features. In this study, the wetland of Bosten Lake in Xinjiang is selected as a case area, where Pleiades and PlanetScope-3 multimodal remote sensing data are fused using the Gram–Schmidt method to generate imagery with high spatial and spectral resolution. Based on this dataset, we systematically compare the performance of fully supervised models (FCN, U-Net, DeepLabV3+, and SegFormer) with a weakly supervised learning model, One Model Is Enough (OME), for classifying 19 wetland–cropland mosaic types. Results demonstrate that: (1) SegFormer achieved the best overall performance (98.75% accuracy, 95.33% mIoU), leveraging its attention mechanism to enhance semantic understanding of complex scenes. (2) The weakly supervised OME, using only image-level labels, matched fully supervised performance (98.76% accuracy, 92.82% F1-score) while drastically reducing labeling effort. (3) Multimodal fusion boosted all models’ accuracy, most notably increasing U-Net’s mIoU by 63.39%. (4) Models exhibited complementary strengths: U-Net excelled in wetland vegetation segmentation, DeepLabV3+ in crop classification, and OME in preserving spatial details. This study validates a pathway integrating multimodal fusion with WSL to balance high accuracy and low labeling costs for arid wetland mapping. Full article

Farmland abandonment is becoming a growing land use challenge in the Brazilian Cerrado, yet its extent, spatial distribution, and underlying drivers remain poorly understood. This study addresses the following question: Can deep learning methods reliably identify abandoned farmlands in tropical savanna environments using multispectral satellite images? To answer this question, we used a Fully Connected Neural Network (FCNN) classifier to map abandoned farmlands in the municipality of Buritizeiro, Minas Gerais State, Brazil, using Sentinel-2 images acquired in 2018 and 2022. Seven land use and land cover (LULC) classes were mapped using visible and near-infrared bands, spectral indices, spectral mixture components, and principal components as input parameters for the CNN. The LULC map for 2022 achieved high classification performance (overall accuracy = 94.7%; Kappa coefficient = 0.93). Agricultural areas classified in 2018 as annual croplands, cultivated pastures, eucalyptus plantations, or harvested eucalyptus that transitioned to grasslands or shrublands in 2022 were considered abandoned. Based on this definition, we identified 13,147 hectares of abandoned land in 2022, representing 4.7% of the municipality’s agricultural area in 2018. Most abandoned areas corresponded to eucalyptus plantations established for charcoal production. This study provides the first deep learning-based assessment of farmland abandonment in the Cerrado. Our findings demonstrated the potential of FCNN classifiers for detecting abandoned farmlands in this biome and provide important contribution for public policies focused on ecological restoration, carbon sequestration, and sustainable agricultural planning. Full article

Accurate identification of cultivated land in plateau and mountainous regions remains challenging due to complex terrain and the fragmented, small-scale distribution of farmland. This study develops a high-precision cropland identification model tailored to such environments, aiming to advance precision agriculture and support the scientific planning and refined management of agricultural resources. Taking Xundian County, Yunnan Province, as a case study, multispectral, synthetic aperture radar (SAR), topographic, texture, and time-series features were integrated to construct a comprehensive multi-source feature space. A baseline land use map was generated by fusing datasets from the European Space Agency (ESA), the Environmental Systems Research Institute (ESRI), and the China Resource and Environment Data Cloud (CRLC). Using 4000 randomly selected sample points, five machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), Tabular Multiple Prediction (TABM), XGBoost, and the NSGA-II optimized XGBoost (NSGA-II-XGBoost)—were compared for cropland identification. Results show that the NSGA-II-XGBoost model consistently achieved superior performance in classification accuracy, stability, and adaptability, reaching an overall accuracy of 95.75%, a Kappa coefficient of 0.91, a recall of 0.96, and an F1-score of 0.96. These findings demonstrate the strong capability of the NSGA-II-XGBoost model for cropland mapping under complex topographic conditions, providing a robust technical framework and methodological reference for farmland protection and natural resource classification in other mountainous regions. Full article

Understanding how land tenure and socio-economic pressures shape landscape transformation is critical for sustainable management in biodiversity-rich regions. This study examines three decades (1987–2017) of land use and land cover (LU&LC) change in the Ngerenyi area of the Taita Hills, Kenya, by integrating multispectral Landsat analysis with household survey data. Harmonized pre-processing and supervised classification of four LU&LC classes, agriculture, built-up areas, high-canopy vegetation, and low-canopy vegetation, achieved overall accuracies above 80% and Kappa values exceeding 0.75. Transition modeling using the Minimum Information Loss Transition Estimation (MILTE) approach, combined with net-versus-swap metrics, revealed persistent decline and fragmentation of high-canopy vegetation, cyclical transitions between agriculture and low-canopy vegetation, and the near-irreversible expansion of built-up areas. Low-canopy vegetation exhibited the highest dynamism, reflecting both degradation from canopy loss and natural regeneration from fallowed cropland. Household surveys (n = 141) identified agricultural expansion, charcoal production, fuelwood extraction, and population growth as the dominant perceived drivers, with significant variation across tenure categories. The population in Taita Taveta County increased from 205,334 in 2009 to 340,671 in 2019, reinforcing documented pressures on land resources and woody biomass. As part of the Eastern Arc biodiversity hotspot, the landscape’s diminishing high-canopy patches underscore the importance of conserving undisturbed vegetation remnants as ecological baselines and biodiversity refuges. The findings highlight the need for tenure-sensitive, landscape-scale planning that integrates private landowners, regulates subdivision, promotes agroforestry and alternative energy options, and safeguards remaining high-canopy vegetation to enhance ecological resilience while supporting local livelihoods. Full article

The cover-management factor (C-factor) plays a pivotal role in soil erosion control and is the most easily influenced by policymakers. Despite the availability of numerous C-factor estimation methods, systematic comparisons of their applicability and associated uncertainties remain limited, particularly for future projections under climate change scenarios. This study systematically evaluates multiple widely used C-factor estimation models and projects potential C-factor changes under future scenarios up to 2070, using 2015 as a baseline. Results reveal substantial spatial variability among models, with the land use/land cover-based model (CLu) showing the strongest correlation with the reference model (r = 0.960) and the lowest error (RMSE = 0.048). Using the CLu model, global average C-factor values are projected to increase across all Shared Socioeconomic Pathways–Representative Concentration Pathways (SSP-RCP) scenarios, rising from 0.077 to 0.079–0.082 by 2070. Statistically significant trends were observed in 28.0% (SSP1-RCP2.6) and 26.6% (SSP5-RCP8.5) of global land areas, identified as hotspot regions (HRs). In these HRs, mean C-factor values are expected to increase by 16.1% and 33.4%, respectively, relative to the 2015 baseline. Economic development analysis revealed distinct trajectories across income categories. Low-income countries (LICs, World Bank classification) exhibited a pronounced dependency on development pathways, with C-factor values decreasing by −50.3% under SSP1-RCP2.6 but increasing by +95.8% under SSP5-RCP8.5 compared to 2015. In contrast, lower-middle-income, upper-middle-income, and high-income countries exhibited consistent C-factor increases across all scenarios. These variations were closely linked to cropland dynamics, with cropland areas in LICs decreasing by 64.6% under SSP1-RCP2.6 but expanding under other scenarios and income categories between 2015 and 2070. These findings highlight the critical importance of sustainable land-use policies, particularly in LICs, which demonstrate the highest magnitude of both improvement and degradation under varying scenarios. This research provides a scientific foundation basis for optimizing soil conservation strategies and land-use planning under future climate and socioeconomic scenarios. Full article

The increasing rate of land use change, particularly deforestation and agricultural expansion, has intensified soil degradation, leading to reduced sediment retention and accelerated soil erosion. This study aims to analyze soil erosion and sediment retention in the Lam Phra Phloeng (LPP) watershed, Thailand, using a coupled modelling approach integrating the Revised Universal Soil Loss Equation (RUSLE) and the Sediment Delivery Ratio (SDR) model from the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) suite. Six land use classes (forest, cropland, rangeland, flooded vegetation, built-up areas, and water bodies) were identified using Sentinel-2 MSI satellite data, with a Random Forest (RF) classification algorithm achieving an overall accuracy of 91.3% (Kappa coefficient = 0.89). The results indicate that forested areas exhibit the highest sediment retention, whereas croplands and rangelands experience the most significant soil loss due to erosion. The RUSLE model estimated an average annual soil loss ranging between 50 and 90 tons/ha/year, with the highest erosion rates observed in agricultural lands with steep slopes and minimal vegetation cover. The InVEST SDR model further corroborates these findings, showing that sediment retention is predominantly concentrated in densely vegetated areas, reinforcing the crucial role of natural forests in preventing soil displacement. This complementary modelling approach identifies priority areas for soil conservation practices. This study is the first study to integrate the RUSLE and InVEST models for the Lam Phra Phloeng watershed, providing a coupled assessment of erosion risk and sediment retention capacity and offering a novel and transferable framework for watershed-scale conservation planning and soil management in tropical monsoonal environments. Full article

Bangladesh is highly susceptible to land use land cover (LULC) changes due to its geographical location and dense population. These changes have significant effects on food security, urban development, and natural resource management. Policy planning and resource management largely depend on accurate and detailed LULC maps. However, Bangladesh does not have its own national scale detailed high-resolution LULC maps. This study aims to develop high-resolution land use land cover (HRLULC) maps for Bangladesh for the years 2020 and 2023 using a deep learning method based on convolutional neural network (CNN), and to analyze LULC changes between these years. We used an advanced LULC classification algorithm, namely SACLASS2, that was developed by JAXA to work on multi-temporal satellite data from different sensors. Our HRLULC maps with 14 categories achieved an overall accuracy of 94.55 ± 0.41% with Kappa coefficient 0.93 for 2020 and 94.32 ± 0.42% with Kappa coefficient 0.93 for 2023, which is higher than the commonly accepted standard of around 87 overall accuracy for 14 category LULC map. Between 2020 and 2023, the most notable LULC increase were observed in single cropland (17 ± 4%), aquaculture (20 ± 5%), and brickfield (56 ± 25%). Conversely, decrease occurred for salt pans (47 ± 16%), bare land (24 ± 3%), and built-up (13 ± 3%). These findings offer valuable insights into the spatio-temporal patterns of LULC in Bangladesh, which can support policymakers in making informed decisions and developing effective conservation strategies aimed at promoting sustainable land management and urban planning. Full article

The Qinling Mountains ecosystem serves as a vital ecological barrier and geographic demarcation line in China. Monitoring long-term land cover changes in the Qinling Mountains is essential for ecosystem pattern evaluation, environmental protection, and sustainable development. Focusing on the Qinling Mountains in Shaanxi Province, this study aimed to quantify the land cover changes from 1986 to 2020 using remote sensing and GIS technologies. An optimized Support Vector Machine (SVM) classification method was developed using Landsat satellite images and historical field samples. The method was employed to conduct land cover classification across eight discrete time periods: 1986, 1990, 1995, 2000, 2005, 2010, 2015, and 2020. The average overall accuracy (OA) of the classification results for the eight time periods was 96.42%, with a Kappa coefficient (K) of 0.9230, thus confirming the reliability of the mapping results. We subsequently developed a spatiotemporal Geo-information Tupu that facilitated a detailed analysis of land cover changes in the study area across different periods. The results show the following: (1) Forest was the dominant land cover type, followed by cropland. From 1986 to 2020, the forest, impervious surface, and water body areas showed overall increasing trends, although fluctuations were observed over time, and the increase was estimated at 6677.30 km², 557.57 km², and 135.71 km², respectively. In contrast, the areas of cropland, grassland, and bare soil showed a fluctuating decreasing trend, with a decrease in areal coverage of 2790.57 km², 1528.76 km², and 3042.66 km², respectively. During the study period, the forest area experienced the greatest increase but maintained the lowest dynamic degree. In contrast, bare soil showed the largest decrease and the highest dynamic degree. (2) A total of 30.74% of the area underwent dynamic changes during the study period, with the most active transformation occurring after 2010; these changes were mainly manifested in the outflow of cropland (4997.27 km²), the transfer of forest (8557.43 km²), and the expansion of impervious surfaces (771.33 km²). In conclusion, the overall ecological environment is improving. The results demonstrate a land cover reconstruction process that enables the management department to rationally utilize natural resources in the Qinling Mountains. Full article

Fine-grained identification of crop planting structures provides key data for precision agriculture, thereby supporting scientific production and evidence-based policy making. This study selected a representative experimental farmland in Qingyang, Gansu Province, and acquired Unmanned Aerial Vehicle (UAV) multi-temporal data (six epochs) from multiple sensors (multispectral [visible–NIR], thermal infrared, and LiDAR). By fusing 59 feature indices, we achieved high-accuracy extraction of cropland and planting structures and identified the key feature combinations that discriminate among crops. The results show that (1) multi-source UAV data from April + June can effectively delineate cropland and enable accurate plot segmentation; (2) July is the optimal time window for fine-scale extraction of all planting-structure types in the area (legumes, millet, maize, buckwheat, wheat, sorghum, maize–legume intercropping, and vegetables), with a cumulative importance of 72.26% for the top ten features, while the April + June combination retains most of the separability (67.36%), enabling earlier but slightly less precise mapping; and (3) under July imagery, the SAM (Segment Anything Model) segmentation + RF (Random Forest) classification approach—using the RF-selected top 10 of the 59 features—achieved an overall accuracy of 92.66% with a Kappa of 0.9163, representing a 7.57% improvement over the contemporaneous SAM + CNN (Convolutional Neural Network) method. This work establishes a basis for UAV-based recognition of typical crops in the Qingyang sector of the Loess Plateau and, by deriving optimal recognition timelines and feature combinations from multi-epoch data, offers useful guidance for satellite-based mapping of planting structures across the Loess Plateau following multi-scale data fusion. Full article

As a critical ecological barrier and key dryland agricultural zone in China, the Loess Plateau is faced with acute tensions between food security risks arising from cropland abandonment (CA) and the imperatives of ecological conservation. Yet, existing research has failed to adequately capture the long-term, high-spatiotemporal-resolution dynamics of abandonment in this region or to quantitatively couple its driving mechanisms with implications for food security. To address these gaps, this study establishes a high-precision identification system for CA tailored to the Plateau’s complex topographic conditions, distinguishing among interannual abandonment, multiyear abandonment, conversion to forest/grassland, and reclamation. Leveraging long-term data from 2000 to 2023 and integrating the Mann–Kendall test with the random forest algorithm, we examine the spatiotemporal trajectories, driving forces, and food security consequences of CA. Guided by a “type differentiation–grade classification–temporal tracking” framework, the analysis reveals a marked transition in dominant drivers from “socioeconomic factors” to “topographic–climatic factors.” It further identifies an “increasing loss–slowing growth” effect of abandonment on grain production, alongside a “pressure alleviation” trend in per capita carrying capacity. The results showed that: (1) Between 2000 and 2023, the area of CA on the Loess Plateau expanded from 2.72 million ha to 6.96 million ha, with high-grade abandonment (≥8 years) accounting for 58.9% of the total and being spatially concentrated in the hilly–gully regions of northern Shaanxi and eastern Gansu; (2) The Grain for Green Project (GFGP) peaked at approximately 340,000 hectares in 2018, followed by a slight decline, but has generally remained at around 300,000 hectares since then; (3) The reclamation rate of CA remained between 5% and 12% during 2003–2015, with minimal overall fluctuations, but after 2016, it gradually increased and peaked at 23.4% in 2022; (4) In terms of driving forces, population density (14.99%) was the primary determinant in 2005, whereas by 2020, slope (15.43%) and mean annual precipitation (15.63%) emerged as core factors; and (5) Grain yield losses attributable to abandonment increased from less than 100 t to nearly 450 t, though the growth rate slowed after 2016, accompanied by gradual alleviation of pressure on per capita carrying capacity. Overall, the study offers robust empirical evidence to inform cropland protection, food security strategies, and sustainable agricultural development policies on the Loess Plateau. Full article

The lack of reliable methods for cropland and forest monitoring remains a challenge in the Betsiboka basin and Ankarafantsika National Park (ANP), Madagascar. A key novelty of our study is the comparative analysis of multiple high-resolution datasets for 2017 and 2021 and future projections under five Shared Socioeconomic Pathways (SSPs) from 2020 to 2100 using Google Earth Engine and Python. Results indicate that forest cover has remained below ~9% in the Betsiboka basin and above ~35% in ANP, while cropland stays under 7% in both areas. Inter-dataset agreement showed high overall accuracy (OA = 0.87–0.95), with stronger agreement in ANP (Kappa = 0.68–0.90). FROM-GLC10 and ESA performed best for cropland classification in Betsiboka, while Dynamic World and ESRI were most accurate for forest, particularly in ANP. Projections suggest that by 2100, forest area in Betsiboka may increase by +230% under SSP3 and +300% under SSP5, whereas ANP could see declines up to 39% under SSP1, −2.2% SSP5, and −1.4% SSP3. The predicted minor cropland increase across both regions suggests that forest expansion is unlikely to significantly constrain agricultural land, illustrating the potential for sustainable intensification and agroforestry to address food security challenges. Full article