[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3279":3,"related-3279":56},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":27,"search_phrases":33,"slug":36,"view_count":37,"doi":38,"paper":39,"created_at":55},3279,"ASSESSMENT OF SOIL SALINIZATION IN IRRIGATED LANDS BASED ON REMOTE SENSING AND GIS TECHNOLOGIES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22900049","This thesis analyzes modern approaches to monitoring and assessing soil salinity in irrigated areas of Uzbekistan and Central Asia. Soil salinization is one of the major land degradation processes affecting the reclamation condition of irrigated lands, crop growth and agricultural productivity. Although conventional field and laboratory surveys provide detailed information, they have limitations in terms of regular monitoring of large areas. Therefore, remote sensing and geographic information systems provide promising tools for rapid and spatial assessment of soil salinity. The study considers the application of satellite imagery, particularly Sentinel-2 and Landsat data, for soil salinity assessment, including the use of spectral bands and salinity indices, GIS-based thematic mapping, and validation using field and laboratory observations. Recent studies demonstrate the potential of remote sensing data for spatial assessment and mapping of soil salinity under the conditions of Uzbekistan. Such an integrated approach can support rapid monitoring of the reclamation status of irrigated lands, identification of salt-affected areas and spatial planning of appropriate reclamation measures.","本论文分析了乌兹别克斯坦和中亚灌溉区土壤盐分监测与评估的现代方法。土壤盐渍化是影响灌溉土地改良状况、作物生长和农业生产力的主要土地退化过程之一。尽管传统的野外调查和实验室分析能够提供详细信息，但在大范围定期监测方面存在局限性。因此，遥感与地理信息系统为土壤盐分的快速空间评估提供了有前景的工具。本研究探讨了卫星影像，特别是Sentinel-2和Landsat数据在土壤盐分评估中的应用，包括光谱波段和盐分指数的使用、基于GIS的主题制图，以及利用野外和实验室观测数据进行验证。近期研究表明，遥感数据在乌兹别克斯坦条件下用于土壤盐分空间评估与制图具有潜力。这种综合方法可支持灌溉土地改良状况的快速监测、盐渍化区域的识别以及适当改良措施的空间规划。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-22T00:00:00Z","论文",25,false,66,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,18,15,9,1,"基于遥感与GIS的灌溉区土壤盐渍化监测论文，方法成熟、结论可靠，对盐碱地治理与农业信息化有参考价值，但属区域性研究，影响层级有限。",[24,25],{"name":10,"url":6},{"name":10,"url":26},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22900048",[28,29,30,31,32],"土地退化","遥感监测","GIS","土壤盐渍化","灌溉农业",[34,35],"乌兹别克斯坦 土壤盐渍化 遥感","Sentinel-2 盐渍化 灌溉","乌兹别克斯坦土壤盐渍化遥感-3279",0,"10.5281\u002Fzenodo.22900049",{"doi":38,"openalex_id":40,"authors":41,"venue":10,"cited_by_count":37,"oa_url":6,"card":48,"direction":52,"ingested_from":54},"W7214052773",[42,44,46],{"name":43,"orcid":9},"Mardiyev",{"name":45,"orcid":9},"Ergashaliyev",{"name":47,"orcid":9},"G'aniyev",{"tldr":49,"method":50,"finding":51,"direction":52,"opportunity":53},"综述遥感与GIS评估乌兹别克斯坦及中亚灌溉土壤盐渍化的方法。","Sentinel-2与Landsat影像、光谱盐分指数、GIS制图及野外验证。","遥感与GIS可快速空间评估盐渍化，支持灌溉地改良状态监测与规划。","农业遥感与作物表型","可探索多源遥感与机器学习融合的盐分指数模型，提升干旱区盐渍化反演精度与时效。","openalex","2026-09-23T23:30:19.341486Z",{"total":57,"page":21,"page_size":57,"items":58},6,[59,94,131,170,212,262],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":64,"content":9,"source_name":65,"source_url":62,"published_at":66,"category":12,"cover_url":9,"hotness":67,"is_selected":14,"score":68,"score_detail":69,"sources":73,"tags":75,"search_phrases":78,"slug":81,"view_count":37,"doi":82,"paper":83,"created_at":93},3364,"Geographical Analysis of Desertification and Environmental Degradation and the Spatial Variation of Their Manifestations in Parts of the Jifara Plain","https:\u002F\u002Fdoi.org\u002F10.65405\u002Fsjh.2.3.85","This research aims to analyze the spatial variation of desertification and environmental degradation manifestations in the Al-Jfara Plain, identify the degrees, intensity, and geographical distribution of desertification, and determine the main manifestations associated with land degradation. It also seeks to identify the effects of natural and human factors on the spread of desertification and determine the areas most vulnerable to it, thereby contributing to efforts to conserve natural resources and reduce land degradation. The research adopted the analytical approach as the main method for analyzing data related to degraded lands, vegetation cover, rangelands, rainfall, erosion, and agricultural and livestock production. It also employed the descriptive approach to present the natural and human characteristics of the research area, as well as the comparative approach to compare different spatial units and time periods. The research further relied on geographical references, scientific studies, and available data, using percentages and rates of change to compare levels of degradation. The research concluded that there is clear spatial variation in the manifestations of desertification and environmental degradation across the Al-Jfara Plain. Wind erosion, declining vegetation cover, drought, and rainfall variability emerged as the most prominent manifestations of degradation. In addition, overgrazing, excessive exploitation of soil and groundwater resources, and urban expansion contributed to increasing pressure on natural resources. The research recommends reducing overgrazing, rationalizing groundwater use, combating erosion, increasing vegetation cover, regulating land use, and enhancing environmental awareness. It also proposes periodic monitoring of desertification using remote sensing and Geographic Information Systems (GIS), preparing updated maps of land degradation, and implementing projects for the rehabilitation of degraded lands.","本研究旨在分析贾法拉平原荒漠化与环境退化表现的空间差异，识别荒漠化的程度、强度及地理分布，并确定与土地退化相关的主要表现。研究还力求查明自然因素和人为因素对荒漠化蔓延的影响，确定最易受荒漠化影响的区域，从而为保护自然资源和减少土地退化的努力作出贡献。研究采用分析方法作为分析与退化土地、植被覆盖、牧场、降雨、侵蚀以及农牧业生产相关数据的主要方法，同时运用描述性方法呈现研究区域的自然和人文特征，并采用比较方法对不同空间单元和时间段进行比较。研究还依托地理文献、科学研究和现有数据，利用百分比和变化率来比较退化程度。研究得出结论：贾法拉平原各地荒漠化与环境退化表现在明显的空间差异。风蚀、植被覆盖下降、干旱和降雨变率是最突出的退化表现。此外，过度放牧、土壤和地下水资源的过程开发以及城市扩张也加剧了对自然资源的压力。研究建议减少过度放牧、合理利用地下水、防治侵蚀、增加植被覆盖、规范土地利用并增强环境意识。研究还提出利用遥感和地理信息系统（GIS）对荒漠化进行定期监测，编制更新的土地退化地图，并实施退化土地恢复项目。","Shihab Journal of Humanities","2026-09-23T00:00:00Z",10,55,{"impact":70,"substance":18,"depth":71,"authority":57,"freshness":20,"relevant":21,"comment":72},8,14,"区域荒漠化空间分析研究，方法常规、结论有参考价值，但属地方性学术成果，公共影响有限。",[74],{"name":65,"url":62},[28,29,30,76,77],"植被覆盖","荒漠化防治",[79,80],"GIS 荒漠化 遥感监测","荒漠化防治 土地退化 植被覆盖 遥感监测","GIS荒漠化遥感监测-3364","10.65405\u002Fsjh.2.3.85",{"doi":82,"openalex_id":84,"authors":85,"venue":65,"cited_by_count":37,"oa_url":62,"card":88,"direction":52,"ingested_from":54},"W7214127171",[86],{"name":87,"orcid":9},"حسنية السني رجب البكوري",{"tldr":89,"method":90,"finding":91,"direction":52,"opportunity":92},"分析吉法拉平原荒漠化与环境退化的空间差异及主要表现，并提出防治建议。","采用分析法、描述法与比较法，结合地理文献和统计数据评估退化程度。","风蚀、植被减少、干旱和降水变率是主要退化表现，过度放牧与地下水超采加剧压力。","可结合遥感与GIS开展荒漠化动态监测，构建多因子退化风险评估模型。","2026-09-24T23:30:21.130938Z",{"id":95,"title":96,"url":97,"summary":98,"summary_zh":99,"content":9,"source_name":10,"source_url":97,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":102,"sources":104,"tags":108,"search_phrases":111,"slug":114,"view_count":37,"doi":115,"paper":116,"created_at":130},2321,"ASSESSMENT OF SOIL SALINITY USING THE NDVI VEGETATION INDEX BASED ON REMOTE SENSING DATA","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22702718","This article examines the assessment of soil salinity in the irrigated meadow-sierozem soils of the E. Qahhorov agricultural area in Khovos District, Syrdarya Region, based on remote sensing data. Sentinel-2 satellite data covering the period from 2016 to 2025 were used in the study, and the Normalized Difference Vegetation Index (NDVI) was calculated using geospatial technologies. The long-term and seasonal dynamics of vegetation cover in the study area were analyzed, and the relationship between NDVI values and salinity levels was assessed. Based on an NDVI classification adapted to the natural conditions of the study area, criteria for the indirect assessment of soil salinity levels were proposed. The results demonstrated that the NDVI can be effectively used as an indicator for determining the impact of soil salinity on vegetation cover, assessing salinity spatially, and conducting long-term monitoring of salinity in irrigated agricultural lands.","本文基于遥感数据，对锡尔河州霍沃斯区E. Qahhorov农业区灌溉草甸灰钙土的土壤盐渍化进行了评估。研究使用了2016年至2025年期间的Sentinel-2卫星数据，并利用地理空间技术计算了归一化植被指数（NDVI）。分析了研究区植被覆盖的长期和季节性动态，并评估了NDVI值与盐渍化程度之间的关系。基于适应研究区自然条件的NDVI分类，提出了土壤盐渍化程度间接评估的标准。结果表明，NDVI可有效用作确定土壤盐渍化对植被覆盖影响、空间评估盐渍化以及开展灌溉农田盐渍化长期监测的指标。","2026-09-11T00:00:00Z",65,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":70,"relevant":21,"comment":103},"基于Sentinel-2长时序NDVI数据提出土壤盐渍化间接评估标准，方法可复用但属区域性案例研究，公共影响有限。",[105,106],{"name":10,"url":97},{"name":10,"url":107},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22702719",[109,110,29,31,32],"智慧农业","NDVI",[112,113],"土壤盐渍化 智慧农业 灌溉农业 遥感监测","土壤盐渍化 智慧农业","土壤盐渍化智慧农业灌溉农业遥感监测-2321","10.5281\u002Fzenodo.22702718",{"doi":115,"openalex_id":117,"authors":118,"venue":10,"cited_by_count":37,"oa_url":97,"card":125,"direction":52,"ingested_from":54},"W7212227143",[119,121,123],{"name":120,"orcid":9},"U.K. Umirova",{"name":122,"orcid":9},"D.A. Kodirova",{"name":124,"orcid":9},"O.O. Davronov",{"tldr":126,"method":127,"finding":128,"direction":52,"opportunity":129},"利用Sentinel-2遥感数据计算NDVI，评估灌溉草甸灰钙土的土壤盐渍化程度。","Sentinel-2数据（2016-2025）与NDVI指数，结合地理空间技术分","NDVI可有效指示盐渍化对植被的影响，实现空间评估与长期监测。","可探索多指数融合与机器学习提升盐渍化反演精度，并推广至不同土壤类型区。","2026-09-13T23:30:22.820810Z",{"id":132,"title":133,"url":134,"summary":135,"summary_zh":136,"content":9,"source_name":137,"source_url":134,"published_at":138,"category":12,"cover_url":9,"hotness":67,"is_selected":14,"score":139,"score_detail":140,"sources":144,"tags":146,"search_phrases":150,"slug":153,"view_count":37,"doi":154,"paper":155,"created_at":169},3174,"Remote sensing and geospatial modeling for the detection of war-induced land use abandonment, soil disturbance, and degradation in Sumy region of Ukraine: a systematic review","https:\u002F\u002Fdoi.org\u002F10.31548\u002Fzemleustriy2026.03.015","The full-scale military invasion of Ukraine has caused multidimensional transformations of agricultural landscapes, land cover, and ecosystems. The Sumy region, located along the northeastern border with the Russian Federation, is a critical region that has suffered from intense fighting, prolonged artillery shelling, and ongoing border clashes during the study period from 2022 to May 2026. This paper systematically evaluates the application of remote sensing and geospatial modeling techniques to detect, quantify, and monitor war-induced land abandonment, as well as physical land cover disturbances, chemical contamination, and broader environmental degradation in this theater of war. Combining recent scientific publications on the war in Ukraine and comparative global examples (Syria, Iraq, and Sudan), the effectiveness of integrating multi-sensor data, including optical (Sentinel-2, Landsat), synthetic aperture radar (SAR; Sentinel-1), thermal, and very high resolution (VHR; Maxar, WorldView) platforms, as well as advanced machine and deep learning algorithms (Random Forest, Deep Learning) and time series analysis (FANTA, two-period curve approximation), is assessed. The results of the study show that while geospatial modeling is an indispensable tool for rapid, large-scale, and non-contact damage assessment in active conflict settings, significant critical research gaps remain, namely separating short-term cessation of agricultural use from actual land abandonment caused by war, verifying such facts in the face of a severe shortage of reliable ground data, and managing sensitive geospatial information. This systematic review highlights existing methodological gaps and outlines future perspectives needed to build a harmonized spatial monitoring system capable of guiding post-conflict recovery and environmental remediation based on actual remote sensing data at the regional scale.Received: 01.07.2026;Accepted:26.08.2026;","乌克兰遭受的大规模军事入侵已导致农业景观、土地覆盖和生态系统发生多维转变。苏梅州位于乌克兰与俄罗斯联邦东北部接壤的边境沿线，是一个关键地区，在研究期间（2022年至2026年5月）遭受了激烈战斗、长期炮击以及持续边境冲突的影响。本文系统评估了遥感与地理空间建模技术在探测、量化和监测该战区战争导致的土地撂荒、土地覆盖物理扰动、化学污染及更广泛环境退化方面的应用。结合近期关于乌克兰战争的科学出版物及全球比较案例（叙利亚、伊拉克和苏丹），本文评估了多传感器数据整合的有效性，包括光学（Sentinel-2、Landsat）、合成孔径雷达（SAR；Sentinel-1）、热红外及甚高分辨率（VHR；Maxar、WorldView）平台，以及先进的机器学习和深度学习算法（随机森林、深度学习）和时间序列分析（FANTA、两期曲线近似）。研究结果表明，尽管地理空间建模是在活跃冲突环境中进行快速、大规模、非接触式损害评估不可或缺的工具，但仍存在重要的关键研究空白，即如何区分农业利用的短期中止与战争导致的实际土地撂荒，如何在严重缺乏可靠地面数据的情况下核实此类事实，以及如何管理敏感的地理空间信息。本系统综述指出了现有的方法论空白，并勾勒了未来前景，以构建一个协调的空间监测系统，从而基于区域尺度上的实际遥感数据指导冲突后恢复和环境修复。收稿日期：2026年7月1日；接受日期：2026年8月26日；","Zemleustrìj kadastr ì monìtorìng zemelʹ","2026-09-21T00:00:00Z",75,{"impact":18,"substance":141,"depth":142,"authority":17,"freshness":70,"relevant":21,"comment":143},20,17,"系统综述遥感与地理空间建模在战区耕地撂荒与土壤退化识别中的应用，方法体系与数据源梳理扎实，对农业遥感监测有参考价值，但属境外冲突场景研究，国内落地关联度有限。",[145],{"name":137,"url":134},[147,28,29,148,149],"农业人工智能","耕地撂荒","冲突农业",[151,152],"乌克兰 苏梅州 遥感 耕地撂荒","Sentinel-1 合成孔径雷达 土地退化监测","乌克兰苏梅州遥感耕地撂荒-3174","10.31548\u002Fzemleustriy2026.03.015",{"doi":154,"openalex_id":156,"authors":157,"venue":137,"cited_by_count":37,"oa_url":134,"card":164,"direction":52,"ingested_from":54},"W7213890485",[158,160,162],{"name":159,"orcid":9},"V. Bogdanets",{"name":161,"orcid":9},"Ye. Berezhniak",{"name":163,"orcid":9},"D. Brovko",{"tldr":165,"method":166,"finding":167,"direction":52,"opportunity":168},"系统综述遥感与地理空间建模在乌克兰苏梅地区检测战争导致的土地弃耕、土壤扰动与退化的应用。","综述多源遥感（Sentinel-2\u002F1、Landsat、VHR）与机器学习、时间","遥感是冲突区快速非接触损害评估的关键工具，但区分短期停耕与真正弃耕、地面数据匮乏及敏感信息管理仍是重","可研究多源时序遥感与弱监督学习结合，构建冲突区弃耕识别与地面验证缺失下的不确定性量化框架。","2026-09-22T23:30:23.289241Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":175,"content":9,"source_name":176,"source_url":173,"published_at":177,"category":12,"cover_url":9,"hotness":67,"is_selected":14,"score":178,"score_detail":179,"sources":181,"tags":183,"search_phrases":187,"slug":190,"view_count":37,"doi":191,"paper":192,"created_at":211},2943,"SPATIO-TEMPORAL DYNAMICS OF LAND USE AND LAND COVER IN A GRANITE QUARRYING AREA IN MANÉAH, GUINEA: A REMOTE SENSING AND GIS-BASED ANALYSIS FROM 1980 TO 2024","https:\u002F\u002Fdoi.org\u002F10.30574\u002Fgscarr.2026.28.3.0222","Quarrying is a major driver of transformation in peri-urban areas, particularly in spaces experiencing both rapid urbanization and increasing pressure on natural resources. This study analyzes the spatio-temporal dynamics of land cover in the urban commune of Manéah, Guinea, between 1980, 2020, and 2024, in order to characterize the territorial changes associated with the development of extractive activities and urban expansion. The methodological approach relies on the use of multi-temporal satellite imagery, its processing in a GIS environment, supervised classification of the main land cover classes, diachronic analysis of changes, construction of a transition matrix, and evaluation of the quality of the classifications using the Kappa index. The results highlight a profound transformation of the landscape. Between 1980 and 2024, the proportion of built-up areas increased from 7.22% to 32.67%, a rise of 25.45 percentage points, while quarries and construction sites, absent in 1980, represented 2.05% of the territory in 2024, after reaching 7.50% in 2020. Conversely, cultivated land and fallow land increased overall by 18.58 percentage points. In contrast, natural formations experienced a sharp decline: shrub savannas decreased from 40.50% to 1.73%, bodies of water from 31.46% to 11.30%, and mangroves from 8.85% to 0.84%. The Kappa values of 0.76, 0.79, and 0.82 indicate a satisfactory agreement between the classifications. These changes reflect the increasing human impact on the territory, resulting from the combined effects of urbanization, agriculture, and extractive activities. The change matrix also confirms a structural recomposition of the territory, with built-up areas increasing from 963.406 ha to 4,357.55 ha, while shrub savannas decreased from 5,401.819 ha to only 230.389 ha over the entire study period. The study thus highlights the need to further integrate remote sensing data and GIS tools into land-use planning, environmental monitoring, and the spatial management of quarries in Manéah.","采石活动是城郊地区转型的主要驱动力，尤其是在同时经历快速城市化和自然资源压力加剧的区域。本研究分析了几内亚马内阿（Manéah）城市公社在1980年、2020年和2024年的土地覆盖时空动态，以刻画与采掘活动发展和城市扩张相关的领土变化。研究方法依赖于多时相卫星影像的使用、在GIS环境中的处理、主要土地覆盖类别的监督分类、变化的历史对比分析、转移矩阵的构建，以及利用Kappa指数评估分类质量。结果揭示了景观的深刻转变。1980年至2024年间，建成区比例从7.22%增至32.67%，上升了25.45个百分点；而采石场和建筑工地1980年尚不存在，2020年达到7.50%，2024年占领土的2.05%。相反，耕地和休耕地总体增加了18.58个百分点。相比之下，自然 formations 急剧减少：灌木草原从40.50%降至1.73%，水体从31.46%降至11.30%，红树林从8.85%降至0.84%。Kappa值分别为0.76、0.79和0.82，表明分类之间具有令人满意的一致性。这些变化反映了人类对领土日益加剧的影响，这是城市化、农业和采掘活动共同作用的结果。变化矩阵也证实了领土的结构性重组，建成区从963.406公顷增至4，357.55公顷，而灌木草原在整个研究期内从5，401.819公顷降至仅230.389公顷。因此，本研究强调了进一步将遥感数据和GIS工具纳入马内阿的土地利用规划、环境监测和采石场空间管理的必要性。","GSC Advanced Research and Reviews","2026-09-18T00:00:00Z",60,{"impact":70,"substance":18,"depth":19,"authority":67,"freshness":20,"relevant":21,"comment":180},"基于多时相遥感与GIS的矿区土地利用变化研究，方法规范、数据翔实，但地域性强、与国内农业信息化关联有限，可作为遥感应用案例参考。",[182],{"name":176,"url":173},[184,29,185,30,186],"城镇化","土地利用","矿区生态",[188,189],"Manéah 几内亚 土地利用","遥感 GIS 采石场 土地覆盖","Manéah几内亚土地利用-2943","10.30574\u002Fgscarr.2026.28.3.0222",{"doi":191,"openalex_id":193,"authors":194,"venue":176,"cited_by_count":37,"oa_url":173,"card":206,"direction":52,"ingested_from":54},"W7213550535",[195,197,199,202,204],{"name":196,"orcid":9},"Sogbè KEITA",{"name":198,"orcid":9},"Mamady Minata CONDÉ",{"name":200,"orcid":201},"Ibrahima Thiam","https:\u002F\u002Forcid.org\u002F0000-0001-8553-1043",{"name":203,"orcid":9},"Lucien SOLIE",{"name":205,"orcid":9},"Alpha Issaga Pallé Diallo",{"tldr":207,"method":208,"finding":209,"direction":52,"opportunity":210},"基于遥感与GIS分析几内亚Manéah采石区1980-2024年土地利用与覆盖的时空变化。","多时相卫星影像、GIS监督分类、变化矩阵与Kappa精度评价。","建设用地由7.22%升至32.67%，灌丛草原由40.50%降至1.73%，采石场2024年占2.0","可延伸至采石与城市化对周边农地、红树林的耦合影响及生态修复监测研究。","2026-09-19T23:30:32.836654Z",{"id":213,"title":214,"url":215,"summary":216,"summary_zh":217,"content":9,"source_name":218,"source_url":215,"published_at":219,"category":12,"cover_url":9,"hotness":67,"is_selected":14,"score":220,"score_detail":221,"sources":225,"tags":227,"search_phrases":231,"slug":234,"view_count":37,"doi":235,"paper":236,"created_at":261},2791,"Integrating multi-source data and support vector machine to assess the spatio-temporal pattern of land degradation in the Eastern Cape of South Africa","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.indic.2026.101524","Land degradation remains a major environmental challenge, particularly in semi-arid and heterogeneous landscapes, where interactions between vegetation loss and soil exposure are complex and spatially dynamic. This study, therefore, seeks to evaluate the spatial extent of land degradation and drivers over time (2005 - 2025) using Landsat data series and support vector machine (SVM) in the Keiskamma Catchment of South Africa. Degraded land followed a non-monotonic trajectory: it declined from ∼197 km 2 in 2005 to ∼157 km 2 in 2015 (a temporary contraction of 20.3%, consistent with short-term restoration and land-use shifts), before rising sharply and unsustainably to ∼328 km 2 by 2025 (a 108.9% increase relative to 2015, and a net increase of 66.5% over the full two-decade period), largely at the expense of grassland and agricultural land. Furthermore, the findings show that soil-sensitive indicators, particularly BSI and SWIR spectral bands, play a crucial role in determining degraded land. In contrast, vegetation indices such as NDVI contribute less under degraded conditions because degraded areas were severely dominated by exposed soil rather than vegetation. Correlation matrix analysis further reveals a temporal shift from mixed soil–vegetation spectral relationships toward strong soil-dominated reflectance patterns by 2025, indicating advanced degradation stages. Overall, the integration of SVM classification with VIF and SHAP provides a transparent, reliable, and spatially explicit framework for monitoring land degradation. The findings support land degradation neutrality monitoring and provide critical insights for sustainable land-management planning in support of Sustainable Development Goal (SDG) 15.3.","土地退化仍然是一项重大环境挑战，尤其是在半干旱和异质性景观中，植被丧失与土壤裸露之间的相互作用复杂且具有空间动态性。因此，本研究旨在利用Landsat数据序列和支持向量机（SVM），评估南非Keiskamma集水区2005—2025年间土地退化的空间范围及其驱动因素。退化土地呈非单调变化轨迹：从2005年的约197 km²下降至2015年的约157 km²（暂时收缩20.3%，与短期恢复和土地利用变化相一致），随后急剧且不可持续地上升至2025年的约328 km²（较2015年增加108.9%，在整个二十年期间净增加66.5%），且主要以草地和农用地为代价。此外，研究结果表明，土壤敏感指标，尤其是BSI和SWIR光谱波段，在判定退化土地方面发挥着关键作用。相比之下，NDVI等植被指数在退化条件下贡献较小，因为退化区域严重以裸露土壤为主，而非植被。相关矩阵分析进一步揭示，到2025年，光谱关系由土壤—植被混合关系向强烈的土壤主导反射模式发生时间转变，表明退化已进入后期阶段。总体而言，将SVM分类与VIF和SHAP相结合，为监测土地退化提供了一个透明、可靠且具有空间显式性的框架。研究结果支持土地退化零增长监测，并为支持可持续发展目标（SDG）15.3的可持续土地管理规划提供了关键见解。","Environmental and Sustainability Indicators","2026-09-16T00:00:00Z",77,{"impact":19,"substance":222,"depth":18,"authority":223,"freshness":20,"relevant":21,"comment":224},22,13,"基于Landsat时序与SVM\u002FSHAP的南非土地退化监测研究，方法透明可复现，对农业遥感与土地退化中性监测有参考价值，但属区域案例、非国内三农直接政策信息。",[226],{"name":218,"url":215},[228,229,230,28,29],"农业遥感","机器学习","可持续发展",[232,233],"可持续发展 农业遥感 土地退化 机器学习","可持续发展 农业遥感","可持续发展农业遥感土地退化机器学习-2791","10.1016\u002Fj.indic.2026.101524",{"doi":235,"openalex_id":237,"authors":238,"venue":218,"cited_by_count":37,"oa_url":255,"card":256,"direction":52,"ingested_from":54},"W7213298151",[239,241,244,247,249,252],{"name":240,"orcid":9},"Mandisa Zameko",{"name":242,"orcid":243},"Kgabo Humphrey Thamaga","https:\u002F\u002Forcid.org\u002F0000-0002-2305-9975",{"name":245,"orcid":246},"Mthunzi Mndela","https:\u002F\u002Forcid.org\u002F0000-0002-2384-6856",{"name":248,"orcid":9},"Matthieu Tshanga",{"name":250,"orcid":251},"Nobert Tafadzwa Mukomberanwa","https:\u002F\u002Forcid.org\u002F0009-0003-1896-9813",{"name":253,"orcid":254},"Mohamed Zhran","https:\u002F\u002Forcid.org\u002F0000-0002-1112-387X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2665972726004137\u002Fpdf",{"tldr":257,"method":258,"finding":259,"direction":52,"opportunity":260},"用Landsat与SVM评估南非Keiskamma流域2005-2025年土地退化时空格局。","Landsat时序数据、SVM分类，结合VIF与SHAP做特征解释。","退化面积先降后升，2025年达328km²，土壤光谱指标比NDVI更关键。","可将该SVM-SHAP框架迁移到其他半干旱区，并耦合气候与土地利用驱动做退化预警。","2026-09-17T23:30:36.054539Z",{"id":263,"title":264,"url":265,"summary":266,"summary_zh":267,"content":9,"source_name":268,"source_url":265,"published_at":269,"category":12,"cover_url":9,"hotness":67,"is_selected":14,"score":68,"score_detail":270,"sources":272,"tags":274,"search_phrases":277,"slug":280,"view_count":37,"doi":281,"paper":282,"created_at":300},2784,"Monitoring & Evaluating of Urban Expansion and Its Impact on Land use Land Cover Change of Sargodha, Pakistan (2000-2025)","https:\u002F\u002Fdoi.org\u002F10.71317\u002Fjgst.2.9(s).2026.624","Urban growth is a significant land-use transition that is taking place in many rapidly growing cities, especially where the expansion is taking place on productive agricultural land. The purpose of this research was to explore the Spatio-temporal pattern of urban expansion and changes in land use\u002Fland cover (LULC) in Sargodha Tehsil, Pakistan, for the period 2000-2025. This study used the remote sensing and GIS techniques to analyze the satellite imageries of 6 years. These years include 2000, 2005, 2010, 2015, 2020 and 2025. The study area covered an expanse of 1536 km² and comprised four land use\u002Fland cover (LULC) classes, including agricultural land, built-up area, barren land, and water bodies. The study used supervised classification Maximum likelihood classifier (MLC) to identify the dominant land use and land cover types and determine the percentage of the region that was covered by each class. The results show that there is a growing trend in the built-up area throughout the years. It increased from 115 km² in 2000 to 180 km² in 2005, 233 km² in 2010, 297 km² in 2015, 361 km² in 2020, and 440 km² in 2025. Thus, it can be stated that there was an increase in the built-up area by 325 km² or 282.6% over the studied period. In contrast, the amount of farmland decreased from 1,014 km² to 815 km² or 199 km². The area of barren land decreased as well from 280 km² to 153 km². At the same time, there was no significant change in the amount of water bodies from 127 km² to 128 km². Findings reveal a significant change from a primarily agricultural region to a region with a considerable presence of developed areas and emphasize the increasing pressure on productive land. The research underlines the significance of planned urbanization, protection of productive agricultural lands, regular geospatial monitoring, and appropriate management of the periphery belt.","城市增长是许多快速发展的城市正在经历的重要土地利用转变，尤其是在生产力较高的农业用地上发生扩张的地区。本研究旨在探讨巴基斯坦萨戈达县（Sargodha Tehsil）2000—2025年期间城市扩张的时空格局及土地利用\u002F土地覆盖（LULC）变化。本研究采用遥感与GIS技术，对6个年份的卫星影像进行了分析，包括2000年、2005年、2010年、2015年、2020年和2025年。研究区面积为1536 km²，涵盖四种土地利用\u002F土地覆盖（LULC）类型，包括农业用地、建设用地、裸地和水体。研究采用监督分类中的最大似然分类器（MLC）识别主要土地利用与土地覆盖类型，并确定各类型所占区域百分比。结果表明，建设用地面积在各年份间呈持续增长趋势，从2000年的115 km²增至2005年的180 km²、2010年的233 km²、2015年的297 km²、2020年的361 km²，以及2025年的440 km²。因此可以认为，在研究期间建设用地面积增加了325 km²，增幅达282.6%。相比之下，农田面积从1014 km²减少至815 km²，减少了199 km²。裸地面积也从280 km²减少至153 km²。与此同时，水体面积从127 km²变化至128 km²，没有显著变化。研究结果揭示了该区域从以农业为主向建设用地显著增加的区域发生了重大转变，并凸显了生产性土地日益增大的压力。本研究强调了有计划城市化、保护生产性农业用地、定期地理空间监测以及对外围地带进行适当管理的重要性。","Journal of Global Social Transformation","2026-09-17T00:00:00Z",{"impact":70,"substance":18,"depth":71,"authority":57,"freshness":20,"relevant":21,"comment":271},"基于遥感与GIS的巴基斯坦城市扩张对农用地影响研究，数据翔实但属区域案例，对国内三农信息化参考价值有限。",[273],{"name":268,"url":265},[275,29,185,30,276],"耕地保护","城市扩张",[278,279],"土地利用 城市扩张 耕地保护 遥感监测","土地利用 城市扩张","土地利用城市扩张耕地保护遥感监测-2784","10.71317\u002Fjgst.2.9(s).2026.624",{"doi":281,"openalex_id":283,"authors":284,"venue":268,"cited_by_count":37,"oa_url":265,"card":295,"direction":52,"ingested_from":54},"W7213443079",[285,287,290,292],{"name":286,"orcid":9},"Muhammad Israr",{"name":288,"orcid":289},"Omar Riaz","https:\u002F\u002Forcid.org\u002F0000-0002-5391-109X",{"name":291,"orcid":9},"Muhammad Safeer Hussain",{"name":293,"orcid":294},"Muhammad Imran","https:\u002F\u002Forcid.org\u002F0000-0003-4072-4997",{"tldr":296,"method":297,"finding":298,"direction":52,"opportunity":299},"用遥感与GIS分析巴基斯坦萨戈达2000-2025年城市扩张及土地利用变化。","6期Landsat影像，最大似然监督分类，提取四类LULC并统计面积。","建设用地增325km²（282.6%），耕地减199km²，城市扩张大量侵占优质农田。","可结合耕地质量与作物生产力数据，量化城市扩张对农业产能的具体损失并构建预警模型。","2026-09-17T23:30:34.285631Z"]