[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3671":3,"related-3671":82},{"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":18,"tags":20,"primary_topic":9,"secondary_topics":26,"classification":27,"search_phrases":59,"slug":62,"view_count":63,"doi":64,"paper":65,"created_at":81},3671,"CLOUD-BASED MULTI-TEMPORAL SAR-OPTICAL FUSION USING RANDOM FOREST FOR GEOSPATIAL INTELLIGENCE MONITORING OF KIPP IKN NUSANTARA","https:\u002F\u002Fdoi.org\u002F10.21163\u002Fgt_2027.221.08","The development of Indonesia's new capital (IKN) Nusantara drives dramatic land-cover transformation across a strategic 17,599-hectare area under the persistent cloud cover of Kalimantan, where optical remote sensing alone is insufficient for continuous monitoring.This study proposes a cloud-based multi-temporal SAR-optical fusion approach using Random Forest classification for geospatial-intelligence (GEOINT) monitoring of the Core Government Area (KIPP) of IKN.Using Google Earth Engine, we processed 70 Sentinel-1 GRD and 52 Sentinel-2 images for 2022 and 61 and 81 images for 2024, building a 12-band fusion stack (SAR backscatter VV, VH, VV\u002FVH; optical B2, B3, B4, B8, B11, B12; and NDVI, NDBI, NDWI).Six experiments compared SAR-only, optical-only, and fusion configurations.The Random Forest fusion achieved 88.89% overall accuracy and 0.8609 kappa for 2022 and 85.12% and 0.8127 for 2024 (assessed against ESA WorldCover 2021 class definitions; see Section 2.7), outperforming SAR-only (63.20%) and optical-only (86.44%).Change detection revealed 899.64 ha of direct vegetation-to-built-up conversion, a 60% expansion of built-up area driven by IKN construction.This is the first cloud-based multi-temporal fusion framework applied to KIPP IKN monitoring, with implications for spatial planning, security surveillance, and environmental compliance.","印度尼西亚新首都（IKN）努山塔拉的开发，在加里曼丹持续云覆盖下推动了一个战略性的17,599公顷区域发生剧烈的土地覆盖转变，仅依靠光学遥感不足以实现持续监测。本研究提出了一种基于云的多时相SAR-光学融合方法，采用随机森林分类对IKN核心政府区（KIPP）进行地理空间情报（GEOINT）监测。利用Google Earth Engine，我们处理了2022年的70景Sentinel-1 GRD和52景Sentinel-2影像，以及2024年的61景和81景影像，构建了一个12波段融合数据集（SAR后向散射VV、VH、VV\u002FVH；光学B2、B3、B4、B8、B11、B12；以及NDVI、NDBI、NDWI）。六组实验比较了仅SAR、仅光学和融合配置。随机森林融合在2022年达到88.89%的总体精度和0.8609的kappa系数，2024年为85.12%和0.8127（依据ESA WorldCover 2021类别定义进行评估；见第2.7节），优于仅SAR（63.20%）和仅光学（86.44%）。变化检测揭示了899.64公顷的植被直接转为建成区，IKN建设推动建成区面积扩张60%。这是首个应用于KIPP IKN监测的基于云的多时相融合框架，对空间规划、安全监控和环境合规具有重要意义。",null,"Geographia Technica","2026-09-26T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该研究聚焦印尼新首都建设的遥感监测，属地理空间情报领域，与三农、农业信息化、智慧农业等主题无直接关联，故不进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24,25],"随机森林","遥感监测","土地覆盖变化","SAR光学融合","空间规划",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":31},"needs_review","2026-10-22.2","rules",{"candidates":32,"agricultural_evidence":14,"reason":58},[33,42,48,53],{"code":34,"score":35,"supported":36,"title":37,"abstract":38,"tags":41},"I3",7,true,[],[39,40],"遥感","remote sensing",[22],{"code":43,"score":44,"supported":14,"title":45,"abstract":46,"tags":47},"H6",1,[],[],[23],{"code":49,"score":44,"supported":14,"title":50,"abstract":51,"tags":52},"I2",[],[],[21],{"code":54,"score":44,"supported":14,"title":55,"abstract":56,"tags":57},"I4",[],[],[25],"农业相关性需要复核",[60,61],"Sentinel-1 Sentinel-2 融合","土地覆盖变化 SAR光学融合 空间规划 遥感监测","Sentinel-1Sentinel-2融合-3671",2,"10.21163\u002Fgt_2027.221.08",{"doi":64,"openalex_id":66,"authors":67,"venue":10,"cited_by_count":15,"oa_url":73,"card":74,"direction":78,"ingested_from":80},"W7214434955",[68,70],{"name":69,"orcid":9},"Fajar Sidik Suganda",{"name":71,"orcid":72},"Asep Adang Supriyadi","https:\u002F\u002Forcid.org\u002F0000-0003-1103-6669","https:\u002F\u002Ftechnicalgeography.org\u002Fpdf\u002F1_2027\u002F08_suganda.pdf",{"tldr":75,"method":76,"finding":77,"direction":78,"opportunity":79},"提出云端多时相SAR-光学融合与随机森林方法，监测印尼新首都核心区土地覆盖变化。","Google Earth Engine处理Sentinel-1\u002F2影像，构建12","融合分类精度达88.89%，检测到899.64公顷植被转建设用地，建成区扩张60%。","农业遥感与作物表型","可迁移至多云农业区作物监测，探索融合特征优化与跨区域泛化能力。","openalex","2026-09-28T23:30:27.951741Z",{"total":83,"page":44,"page_size":83,"items":84},6,[85,176,259,319,388,446],{"id":86,"title":87,"url":88,"summary":89,"summary_zh":90,"content":9,"source_name":91,"source_url":88,"published_at":92,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":93,"score_detail":94,"sources":101,"tags":103,"primary_topic":107,"secondary_topics":113,"classification":114,"search_phrases":138,"slug":141,"view_count":15,"doi":142,"paper":143,"created_at":175},5576,"Land cover change and soil salinization in Mozambique: A counterfactual analysis over two decades (2001−2022)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.catena.2026.110691","Soil salinization is a major issue that constrains the sustainable development of agriculture in arid, semi-arid, and coastal lowland areas. It is of great importance for regional land management and climate adaptation to clarify the influence of land cover change on soil salinization. This study aims to quantitatively assess the contribution of land cover change to soil salinization in Mozambique, where both rapid land cover transformation and salinization issues are evident. Firstly, we integrated soil profile electrical conductivity (EC) data, Landsat time-series remote sensing imagery, TerraClimate meteorological data, and the 30 m resolution land cover data, to construct a remote sensing-based EC inversion model for Mozambique. Secondly, we applied a spatially and environmentally constrained counterfactual matching method to quantitatively identify the contributions of different land cover changes on soil salinization trends. Our results reveal that: (1) the spatial distribution of soil salinization in Mozambique exhibits distinct a clear association with proximity to water bodies, with high EC areas primarily concentrated around Lake Malawi in the north and in the coastal regions of Greater Maputo in the south. (2) 26.98% of Mozambique's territory showed a trend of intensifying soil salinization, while 10.88% exhibited a decreasing trend. Areas with intensifying trends are primarily concentrated in the central alluvial plains, localized agricultural development zones, and coastal lowlands. (3) Urbanization was significantly associated with an increasing salinization trend after false discovery rate correction, suggesting that the expansion of impervious surfaces, changes in drainage patterns, and soil compaction may exacerbate localized salt accumulation. Agricultural expansion showed a negative trend difference, consistent with the potential influence of crop cover, tillage, and wet-season leaching, although this association was not significant after false discovery rate correction. The effects of ecological restoration and other factors were not statistically significant. In addition to elucidating the heterogeneous impacts of land cover changes in Mozambique on soil salinization trends, this study also provides methodological guidance and a scientific basis for monitoring\u002Fmanaging soil salinity risks in other coastal lowlands.","土壤盐渍化是制约干旱、半干旱及沿海低地地区农业可持续发展的主要问题。阐明土地覆盖变化对土壤盐渍化的影响，对区域土地管理和气候适应具有重要意义。本研究旨在定量评估土地覆盖变化对莫桑比克土壤盐渍化的贡献，该国土地覆盖转型迅速且盐渍化问题突出。首先，我们整合了土壤剖面电导率（EC）数据、Landsat时间序列遥感影像、TerraClimate气象数据以及30 m分辨率土地覆盖数据，构建了基于遥感的莫桑比克EC反演模型。其次，我们采用空间与环境约束的反事实匹配方法，定量识别不同土地覆盖变化对土壤盐渍化趋势的贡献。研究结果表明：（1）莫桑比克土壤盐渍化的空间分布与水体邻近性呈现明显的关联，高EC区域主要集中于北部的马拉维湖周边以及南部大马普托沿海地区。（2）莫桑比克26.98%的国土面积呈现土壤盐渍化加剧趋势，而10.88%呈现减轻趋势。加剧趋势区域主要集中于中部冲积平原、局部农业开发区及沿海低地。（3）经错误发现率校正后，城市化与盐渍化加剧趋势显著相关，表明不透水面扩张、排水格局改变及土壤压实可能加剧局部盐分累积。农业扩张呈现负向趋势差异，与作物覆盖、耕作及湿季淋洗的潜在影响一致，但该关联经错误发现率校正后不显著。生态恢复及其他因素的影响在统计上不显著。本研究不仅阐明了莫桑比克土地覆盖变化对土壤盐渍化趋势的异质性影响，还为其他沿海低地土壤盐渍化风险的监测与管理提供了方法学指导和科学依据。","CATENA","2026-10-20T00:00:00Z",79,{"impact":95,"substance":96,"depth":97,"authority":98,"freshness":99,"relevant":44,"comment":100},16,22,18,14,9,"该研究基于遥感与反事实匹配方法，定量评估土地覆盖变化对盐渍化的贡献，方法新颖、结论可靠，对沿海低地盐渍化监测具有参考价值，但属境外区域研究，国内政策与产业影响有限。",[102],{"name":91,"url":88},[22,104,105,23,106],"莫桑比克","土壤盐渍化","盐分反演",{"code":108,"slug":109,"name":110,"path":111,"parent_code":112},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":115,"locked":14,"version":29,"method":30,"evidence":116},"classified",{"candidates":117,"agricultural_evidence":36},[118,123,127,134],{"code":108,"score":98,"supported":36,"title":119,"abstract":120,"tags":122},[109],[121,109],"土壤",[],{"code":34,"score":35,"supported":36,"title":124,"abstract":125,"tags":126},[],[39,40],[22],{"code":128,"score":83,"supported":36,"title":129,"abstract":130,"tags":133},"H7",[],[131,132],"气候适应","干旱",[],{"code":43,"score":44,"supported":14,"title":135,"abstract":136,"tags":137},[],[],[23],[139,140],"莫桑比克 土壤盐渍化 遥感","土地覆盖变化 盐渍化 反事实分析","莫桑比克土壤盐渍化遥感-5576","10.1016\u002Fj.catena.2026.110691",{"doi":142,"openalex_id":144,"authors":145,"venue":91,"cited_by_count":15,"oa_url":88,"card":170,"direction":78,"ingested_from":80},"W7223069171",[146,148,150,152,155,157,160,163,165,167],{"name":147,"orcid":9},"Yue Li",{"name":149,"orcid":9},"Mengfei Shi",{"name":151,"orcid":9},"Fei Zhang",{"name":153,"orcid":154},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":156,"orcid":9},"Gowhar Meraj",{"name":158,"orcid":159},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":161,"orcid":162},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":164,"orcid":9},"Yuanfang Chai",{"name":166,"orcid":9},"Hongtao Xu",{"name":168,"orcid":169},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":171,"method":172,"finding":173,"direction":78,"opportunity":174},"量化莫桑比克2001-2022年土地覆盖变化对土壤盐渍化的贡献。","融合EC剖面、Landsat时序与气候数据反演EC，并用空间环境约束的反事实匹配","26.98%区域盐渍化加剧；城市化显著关联盐渍化上升，农业扩张关联不显著。","可将反事实匹配框架迁移至其他滨海低地，并引入作物类型与灌溉制度细化农业扩张的盐渍化效应。","2026-10-22T11:20:42.713789Z",{"id":177,"title":178,"url":179,"summary":180,"summary_zh":181,"content":9,"source_name":182,"source_url":179,"published_at":183,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":184,"score_detail":185,"sources":189,"tags":191,"primary_topic":195,"secondary_topics":199,"classification":205,"search_phrases":232,"slug":235,"view_count":15,"doi":236,"paper":237,"created_at":258},5096,"Land Cover Change, Ecosystem Service Implications, and Economy–Land Use Decoupling:","https:\u002F\u002Fdoi.org\u002F10.14246\u002Firspsd.14.4_186","Land cover change plays a critical role in shaping watershed ecological conditions and supporting regional economic activities. This study analyzes land cover dynamics in the Tiworo Watershed, Southeast Sulawesi, Indonesia, for 2014, 2019, and 2024, and examines their relationship with sectoral economic growth. Remote sensing and GIS were used to map and quantify land cover changes, while sectoral Gross Regional Domestic Product (GRDP) data for agriculture, forestry, and fisheries represented economic performance. Economy–land use interactions were assessed using the Tapio decoupling elasticity index. Economy–land use interactions were assessed using the Tapio decoupling elasticity index as a retrospective empirical measure based on observed changes in land cover and sectoral GRDP over the study periods. The results show increasing sectoral GRDP accompanied by relatively limited changes in dominant land cover classes. Absolute decoupling was observed during 2014–2019, relative decoupling during 2019–2024, and overall absolute decoupling for 2014–2024, indicating that economic growth was not proportionally associated with large-scale land conversion. Spatial analysis reveals localized landscape transformations, including mangrove degradation, settlement expansion, and plantation growth, which signal emerging pressures on environmentally sensitive areas. Although ecosystem services are not directly valued, land cover change is interpreted as an indicator of ecological pressure with implications for provisioning ecosystem services. These findings highlight the importance of integrating land-use monitoring and economic indicators to support adaptive watershed management, spatial planning, and sustainable development.","土地覆盖变化在塑造流域生态状况和支撑区域经济活动方面发挥着关键作用。本研究分析了印度尼西亚东南苏拉威西省蒂沃罗流域（Tiworo Watershed）2014年、2019年和2024年的土地覆盖动态，并考察其与部门经济增长的关系。研究采用遥感和地理信息系统（GIS）对土地覆盖变化进行制图和量化，同时以农业、林业和渔业的部门地区生产总值（GRDP）数据表征经济表现。经济与土地利用的相互作用采用Tapio脱钩弹性指数进行评估，该指数作为基于研究期内观测到的土地覆盖变化和部门GRDP变化的回顾性经验测度。结果表明，部门GRDP持续增长，而主导土地覆盖类型的变化相对有限。2014—2019年呈现绝对脱钩，2019—2024年呈现相对脱钩，2014—2024年整体呈现绝对脱钩，表明经济增长与大规模土地转换之间不存在比例关联。空间分析揭示了局部景观转型，包括红树林退化、居民点扩张和种植园增长，这些信号表明环境敏感区域正面临新兴压力。尽管生态系统服务未被直接估值，但土地覆盖变化被解释为生态压力的指标，并对供给型生态系统服务具有潜在影响。这些发现凸显了整合土地利用监测与经济指标的重要性，以支持适应性流域管理、空间规划和可持续发展。","International Review for Spatial Planning and Sustainable Development","2026-10-14T00:00:00Z",63,{"impact":186,"substance":97,"depth":95,"authority":187,"freshness":186,"relevant":44,"comment":188},8,13,"基于遥感与GIS的流域土地利用变化与经济增长脱钩研究，方法规范但属区域案例，对国内三农信息化参考价值有限。",[190],{"name":182,"url":179},[22,192,193,194,25],"土地利用变化","流域管理","生态系统服务",{"code":43,"slug":196,"name":197,"path":198,"parent_code":112},"biodiversity","生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity",[200],{"code":54,"slug":201,"name":202,"path":203,"parent_code":204},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",{"status":115,"locked":14,"version":29,"method":30,"evidence":206},{"candidates":207,"agricultural_evidence":36},[208,213,221,225],{"code":43,"score":187,"supported":36,"title":209,"abstract":211,"tags":212},[210],"ecosystem service",[194],[192,194],{"code":54,"score":13,"supported":36,"title":214,"abstract":215,"tags":220},[],[216,217,218,219],"地理信息","空间分析","GIS","spatial analysis",[25],{"code":34,"score":35,"supported":36,"title":222,"abstract":223,"tags":224},[],[39,40],[22],{"code":226,"score":227,"supported":36,"title":228,"abstract":229,"tags":231},"D",3,[],[230],"渔业",[],[233,234],"Tiworo流域 土地利用 遥感","Tapio脱钩 农业经济 土地覆盖","Tiworo流域土地利用遥感-5096","10.14246\u002Firspsd.14.4_186",{"doi":236,"openalex_id":238,"authors":239,"venue":182,"cited_by_count":15,"oa_url":252,"card":253,"direction":78,"ingested_from":80},"W7221789653",[240,243,246,249],{"name":241,"orcid":242},"Safril Kasim","https:\u002F\u002Forcid.org\u002F0009-0009-4679-4233",{"name":244,"orcid":245},"Hasddin Hasddin","https:\u002F\u002Forcid.org\u002F0000-0002-0114-5274",{"name":247,"orcid":248},"La Fariki","https:\u002F\u002Forcid.org\u002F0009-0003-7870-9028",{"name":250,"orcid":251},"Abhishek Agarwal","https:\u002F\u002Forcid.org\u002F0000-0002-0900-6383","https:\u002F\u002Fwww.jstage.jst.go.jp\u002Farticle\u002Firspsd\u002F14\u002F4\u002F14_9\u002F_pdf",{"tldr":254,"method":255,"finding":256,"direction":78,"opportunity":257},"分析印尼Tiworo流域2014-2024年土地覆盖变化与部门经济增长的脱钩关系。","遥感与GIS制图，结合GRDP数据和Tapio脱钩弹性指数。","经济持续增长但土地覆盖变化有限，整体呈绝对脱钩，局部红树林退化等生态压力显现。","可将脱钩指数与生态系统服务价值核算结合，量化流域生态压力并支撑空间规划。","2026-10-16T23:30:28.687070Z",{"id":260,"title":261,"url":262,"summary":263,"summary_zh":264,"content":9,"source_name":265,"source_url":262,"published_at":266,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":267,"score_detail":268,"sources":272,"tags":274,"primary_topic":9,"secondary_topics":278,"classification":279,"search_phrases":294,"slug":297,"view_count":15,"doi":298,"paper":299,"created_at":318},5010,"Analysis of Land Cover Change Using Support Vector Machine Algorithm on Satellite Imagery (Case Study: Mining Area of IWIP).","https:\u002F\u002Fdoi.org\u002F10.58602\u002Fitsecs.v4i4.388","Unsupervised mining activities and the lack of proper management of the environment can bring very serious ecological effects. One of the worst is large-scale land cover change. This paper investigates the land cover changes in the mining area of PT Indonesia Weda Bay Industrial Park (IWIP), Weda Bay Nickel (WBN) area, and measures the performance of the Support Vector Machine technique of land cover classification using satellite imagery. This research is a quantitative study based on a remote sensing technique and spatial analysis in a GIS environment. Multi-temporal satellite images, Landsat-7 (2010), Landsat-8 (2014), and Sentinel-2 (2019 and 2025) were processed to land cover classes (vegetation, open land, cloud) based on the spectral indices NDVI, NDBI, and BSI. Through the post-classification comparison method, land cover changes were detected. The overall change from 2010 to 2025 is quite significant. The vegetation cover decreased from 91.2% to 84.3%, while open land increased from less than 1% to 11.4%. The greatest changes were observed in 2019 and 2025, suggesting that mining activities and related industry development have speed up A lot. The Support Vector Machine algorithm reached excellent classification accuracy over the different periods (97.47%, 96.67%, 89.52%, 95.12% in the cases of 2010 2014 2019 and 2025 respectively), and the results were supported by the high Kappa agreement scores (ranging from 0.84 to 0.96). The low difference between training and testing accuracy indicates a strong generalization without any sign of the model getting overfitted to the training data. The work highlights that Support Vector Machine algorithm, is a very good and reliable method for detecting land cover change in mining areas, where patterns are both complex as well as dynamic.","无监督的采矿活动和缺乏适当的环境管理可能带来非常严重的生态影响。其中最严重的影响之一是大规模的土地覆盖变化。本文研究了PT Indonesia Weda Bay Industrial Park（IWIP）矿区、Weda Bay Nickel（WBN）地区的土地覆盖变化，并评估了利用卫星影像进行土地覆盖分类的支持向量机（Support Vector Machine）技术的性能。本研究是一项基于遥感技术和GIS环境下空间分析的定量研究。多时相卫星影像Landsat-7（2010年）、Landsat-8（2014年）和Sentinel-2（2019年和2025年）基于光谱指数NDVI、NDBI和BSI被处理为土地覆盖类别（植被、裸地、云）。通过分类后比较法，检测了土地覆盖变化。2010年至2025年的总体变化相当显著。植被覆盖从91.2%下降至84.3%，而裸地从不足1%增加至11.4%。最大的变化出现在2019年和2025年，表明采矿活动及相关产业发展已大幅加速。支持向量机算法在不同时期均达到了优异的分类精度（2010年、2014年、2019年和2025年分别为97.47%、96.67%、89.52%、95.12%），且结果得到了高Kappa一致性系数的支持（范围为0.84至0.96）。训练精度与测试精度之间的低差异表明模型具有较强的泛化能力，没有出现过拟合训练数据的迹象。该研究强调，支持向量机算法是一种非常好且可靠的方法，可用于检测采矿区域的土地覆盖变化，因为这些区域的模式既复杂又动态。","Journal of Information Technology Software Engineering and Computer Science (ITSECS)","2026-10-13T00:00:00Z",66,{"impact":269,"substance":270,"depth":95,"authority":13,"freshness":186,"relevant":44,"comment":271},12,20,"以SVM结合多时相卫星影像量化IWIP镍矿区2010—2025年植被与裸地变化，方法可靠、数据扎实，对矿区生态遥感监测有参考价值，但属区域案例研究，公共影响有限。",[273],{"name":265,"url":262},[22,275,23,276,277],"矿区生态","支持向量机","镍矿开采",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":280},{"candidates":281,"agricultural_evidence":14,"reason":58},[282,286,290],{"code":54,"score":99,"supported":36,"title":283,"abstract":284,"tags":285},[],[217,218,219],[],{"code":34,"score":35,"supported":36,"title":287,"abstract":288,"tags":289},[],[39,40],[22],{"code":43,"score":63,"supported":14,"title":291,"abstract":292,"tags":293},[],[],[23,275],[295,296],"IWIP 矿区 土地覆盖","Sentinel-2 镍矿 遥感","IWIP矿区土地覆盖-5010","10.58602\u002Fitsecs.v4i4.388",{"doi":298,"openalex_id":300,"authors":301,"venue":265,"cited_by_count":15,"oa_url":312,"card":313,"direction":78,"ingested_from":80},"W7221684953",[302,304,306,309],{"name":303,"orcid":9},"Efraim Julian Soma",{"name":305,"orcid":9},"Winsy Christo Deilan Weki",{"name":307,"orcid":308},"Rillya Arundaa","https:\u002F\u002Forcid.org\u002F0009-0008-4979-5525",{"name":310,"orcid":311},"Mahardika Inra Takaendengan","https:\u002F\u002Forcid.org\u002F0000-0001-8622-7537","https:\u002F\u002Fejournal.techcart-press.com\u002Findex.php\u002Fitsecs\u002Farticle\u002Fdownload\u002F388\u002F338",{"tldr":314,"method":315,"finding":316,"direction":78,"opportunity":317},"用SVM分类多时相卫星影像，分析印尼IWIP矿区2010-2025年土地覆盖变化。","Landsat-7\u002F8与Sentinel-2影像，NDVI\u002FNDBI\u002FBSI指数","植被从91.2%降至84.3%，裸地升至11.4%，SVM精度89.5%-97.5%，Kappa 0","可将SVM与深度学习结合，提升多云热带矿区影像分类鲁棒性并做变化驱动力归因。","2026-10-15T23:30:32.156647Z",{"id":320,"title":321,"url":322,"summary":323,"summary_zh":324,"content":9,"source_name":325,"source_url":322,"published_at":183,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":326,"score_detail":327,"sources":329,"tags":331,"primary_topic":9,"secondary_topics":335,"classification":336,"search_phrases":365,"slug":368,"view_count":44,"doi":369,"paper":370,"created_at":387},5001,"Mapping groundwater-dependent vegetation in temperate climates on the example of Central Germany","https:\u002F\u002Fdoi.org\u002F10.1098\u002Frsos.260538","Abstract Groundwater-dependent ecosystems are biodiversity hotspots that provide habitat for specialized species. The European Union (EU) Water Framework Directive (WFD) stresses the importance of identifying and protecting these ecosystems. So far, most studies focus on (semi-) arid regions, where groundwater use by ecosystems is both more prevalent and easier to detect. In this study, we adapt groundwater-dependent vegetation (GDV) mapping approaches developed for dry climates to temperate regions by replacing seasonal drought-based indicators with contrasts between dry and wet years. We implement this concept by integrating for the first time to our knowledge, ECOSTRESS evapotranspiration data with high-resolution remote sensing, regional geospatial data and botanical field observations. To test our framework, we trained and validated random forest models with eight predictor variables using 166 vegetation plots to identify GDV in Central Germany. Ground-truth GDV plots prefer cooler, wetter, nutrient-richer and more acidic to alkaline environments compared to non-GDV. The final model using a phreatophyte cover threshold of 25% representing GDV plots achieved a testing accuracy of 0.97, identifying 2067 km² of GDV. Currently, only 19% is protected under the EU WFD. The proposed mapping framework offers a solution for identifying GDV in temperate regions. The new GDV maps can contribute to managing groundwater resources and preserving biodiversity hotspots in regions facing increasing droughts, ultimately supporting the implementation of the EU WFD.","摘要 地下水依赖型生态系统是生物多样性热点区域，为特化物种提供栖息地。欧盟（EU）水框架指令（WFD）强调了识别和保护这些生态系统的重要性。迄今为止，大多数研究集中于（半）干旱地区，因为这些地区生态系统对地下水的利用更为普遍且更易检测。在本研究中，我们将针对干旱气候开发的地下水依赖型植被（GDV）制图方法推广至温带地区，以干湿年份之间的对比替代基于季节性干旱的指标。据我们所知，我们首次将ECOSTRESS蒸散发数据与高分辨率遥感、区域地理空间数据及植物学野外观测相结合来实现这一理念。为检验我们的框架，我们利用166个植被样地训练并验证了包含八个预测变量的随机森林模型，以识别德国中部的GDV。与未标记为GDV的样地相比，地面验证的GDV样地偏好更凉爽、更湿润、养分更丰富以及酸性至碱性的环境。最终模型采用25%的深根植物覆盖度阈值来判定GDV样地，达到了0.97的测试精度，识别出2067 km²的GDV。目前，其中仅有19%受到欧盟水框架指令的保护。所提出的制图框架为温带地区GDV的识别提供了解决方案。新的GDV地图可有助于管理地下水资源并保护面临日益加剧干旱区域的生物多样性热点，最终支持欧盟水框架指令的实施。","Royal Society Open Science",81,{"impact":97,"substance":96,"depth":97,"authority":98,"freshness":99,"relevant":44,"comment":328},"首次将ECOSTRESS蒸散发数据用于温带地区地下水依赖植被制图，方法新颖、精度高，对干旱加剧背景下的生态保护与水资源管理有参考价值。",[330],{"name":325,"url":322},[21,332,22,333,334],"生态保护","地下水依赖植被","欧盟水框架指令",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":337},{"candidates":338,"agricultural_evidence":14,"reason":58},[339,347,352,356,361],{"code":340,"score":99,"supported":36,"title":341,"abstract":342,"tags":346},"H3",[],[343,344,345],"水资源","蒸散发","evapotranspiration",[],{"code":43,"score":35,"supported":36,"title":348,"abstract":349,"tags":351},[],[350,196],"生物多样性",[332],{"code":34,"score":35,"supported":36,"title":353,"abstract":354,"tags":355},[],[39,40],[22],{"code":128,"score":83,"supported":36,"title":357,"abstract":358,"tags":360},[],[132,359],"drought",[],{"code":49,"score":44,"supported":14,"title":362,"abstract":363,"tags":364},[],[],[21],[366,367],"ECOSTRESS 蒸散发 遥感","德国中部 地下水依赖植被","ECOSTRESS蒸散发遥感-5001","10.1098\u002Frsos.260538",{"doi":369,"openalex_id":371,"authors":372,"venue":325,"cited_by_count":15,"oa_url":381,"card":382,"direction":78,"ingested_from":80},"W7221647026",[373,376,378],{"name":374,"orcid":375},"Léonard El-Hokayem","https:\u002F\u002Forcid.org\u002F0000-0002-0552-3949",{"name":377,"orcid":9},"David Emanuel Schulz",{"name":379,"orcid":380},"Christopher Conrad","https:\u002F\u002Forcid.org\u002F0000-0003-1077-7086","https:\u002F\u002Froyalsocietypublishing.org\u002Frsos\u002Farticle-pdf\u002Fdoi\u002F10.1098\u002Frsos.260538\u002F6170405\u002Frsos.260538.pdf",{"tldr":383,"method":384,"finding":385,"direction":78,"opportunity":386},"将干旱区地下水依赖植被制图方法迁移到温带，以德国中部为例绘制GDV分布图。","融合ECOSTRESS蒸散、高分辨率遥感、地理数据与166个植被样地，训练随机森","模型精度0.97，识别出2067 km² GDV，但仅19%受欧盟水框架指令保护。","温带GDV制图框架可推广至其他区域，并探索GDV与农业用水、干旱风险的耦合机制。","2026-10-15T23:30:29.070183Z",{"id":389,"title":390,"url":391,"summary":392,"summary_zh":393,"content":9,"source_name":394,"source_url":391,"published_at":395,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":396,"score_detail":397,"sources":400,"tags":402,"primary_topic":9,"secondary_topics":405,"classification":406,"search_phrases":421,"slug":424,"view_count":15,"doi":425,"paper":426,"created_at":445},4689,"Landscape historic trough spatial changes of mangroves in Benin Coastal area (West-Africa) using random forest algorithm applied to Landsat images","https:\u002F\u002Fdoi.org\u002F10.52638\u002Frfpt.2026.736","Mangrove forests, recognized as the main ecosystem of coastal zones and providing various goods and services, are facing are facing increasing anthropogenic pressures and require continuous investigation. Ensuring the sustainable management of these ecosystems implies permanent monitoring of their dynamics. In line with this, the present study carried out on the mangrove forests of Benin (West-Africa) aimed to evaluate the effect of anthropic perturbation on their cover dynamics over the past four decades (including both Ramsar sites 1017 and 1018). The adopted method focused on remote sensing techniques using Landsat images acquired between 1986 and 2023. The Random Forest algorithm was used for image processing. Results revealed that whatever the period, the overall accuracy values obtained after post-classification analysis ranged from 84.56% to 96.52% indicating a correct image classification. From these results, a decline in mangrove cover was observed between 1986 and 2013 followed by an increase from 2013 to 2023. In Ramsar site 1017, mangroves lost about 61.90% (3261.06 ha) of their initial area from 1986 to 2000. Between 2000 and 2014, mangrove cover loss was negligible and estimated at 2.29% (59.04 ha). In contrast, from 2014 to 2023, mangroves experienced an increase estimated at about 16.66% (324.63 ha). The same trend was observed in Ramsar site 1018, with a loss of 31.12% (459.63 ha) and 27.44% (279.18 ha) respectively from 1986 to 2001 and from 2001 to 2013 followed by a gain estimated at 39.89% (279.18 ha) between 2013 and 2023. Landscape changes analysis revealed that regardless of the site or period, mangroves were mainly converted into meadow, other plant and water body (e.g. in Ramsar site 1017, from 1986 to 2001, about 568.26 ha of mangroves were converted into meadows, 33.66 ha into other plant, and 35.46 ha into water body). The principal changes observed in this study focused on Benin coastal zones confirm the vulnerability of mangrove forests facing anthropogenic pressure. Keywords: Mangrove, Dynamics, Remote sensing, Benin.","红树林被公认为海岸带的主要生态系统，提供多种商品和服务，正面临日益增加的人为压力，需要持续开展调查研究。确保这些生态系统的可持续管理意味着对其动态进行长期监测。据此，本研究针对贝宁（西非）的红树林开展，旨在评估人为干扰对其过去四十年（包括拉姆萨尔湿地1017和1018）覆盖动态的影响。所采用的方法侧重于利用1986年至2023年间获取的Landsat影像的遥感技术。采用随机森林（Random Forest）算法进行图像处理。结果表明，无论哪个时期，分类后分析获得的总体精度值介于84.56%至96.52%之间，表明图像分类正确。从这些结果来看，1986年至2013年间红树林覆盖面积下降，随后2013年至2023年间有所增加。在拉姆萨尔湿地1017中，1986年至2000年间红树林丧失了其初始面积的约61.90%（3261.06公顷）。2000年至2014年间，红树林覆盖损失可忽略不计，估计为2.29%（59.04公顷）。相比之下，2014年至2023年间，红树林增加了约16.66%（324.63公顷）。拉姆萨尔湿地1018也观察到相同趋势，1986年至2001年和2001年至2013年分别损失31.12%（459.63公顷）和27.44%（279.18公顷），随后2013年至2023年间增加了约39.89%（279.18公顷）。景观变化分析显示，无论哪个地点或时期，红树林主要转化为草地、其他植物和水体（例如在拉姆萨尔湿地1017中，1986年至2001年间，约568.26公顷红树林转化为草地，33.66公顷转化为其他植物，35.46公顷转化为水体）。本研究中观察到的贝宁海岸带主要变化证实了红树林面对人为压力的脆弱性。关键词：红树林，动态，遥感，贝宁。","Revue Française de Photogrammétrie et de Télédétection","2026-10-09T00:00:00Z",70,{"impact":269,"substance":270,"depth":398,"authority":187,"freshness":186,"relevant":44,"comment":399},17,"基于Landsat与随机森林的四十年红树林动态监测研究，方法成熟、数据翔实，对滨海湿地遥感监测有参考价值，但属区域案例研究，公共影响有限。",[401],{"name":394,"url":391},[21,22,192,403,404],"红树林","滨海湿地",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":407},{"candidates":408,"agricultural_evidence":14,"reason":58},[409,413,417],{"code":34,"score":35,"supported":36,"title":410,"abstract":411,"tags":412},[],[39,40],[22],{"code":43,"score":63,"supported":14,"title":414,"abstract":415,"tags":416},[],[],[192,403],{"code":49,"score":44,"supported":14,"title":418,"abstract":419,"tags":420},[],[],[21],[422,423],"贝宁 红树林 遥感","Landsat 红树林 动态监测","贝宁红树林遥感-4689","10.52638\u002Frfpt.2026.736",{"doi":425,"openalex_id":427,"authors":428,"venue":394,"cited_by_count":15,"oa_url":439,"card":440,"direction":78,"ingested_from":80},"W7221179267",[429,431,433,435,437],{"name":430,"orcid":9},"Mahoutin Gildas Serge Zanvo",{"name":432,"orcid":9},"Nicolas DELBART",{"name":434,"orcid":9},"Paul PASSY",{"name":436,"orcid":9},"Kolawolé Valère Salako",{"name":438,"orcid":9},"Romain Glele-Kakai","https:\u002F\u002Frfpt.sfpt.fr\u002Findex.php\u002FRFPT\u002Farticle\u002Fdownload\u002F736\u002F528",{"tldr":441,"method":442,"finding":443,"direction":78,"opportunity":444},"利用随机森林与Landsat影像分析贝宁沿海红树林1986-2023年时空动态。","Landsat影像（1986-2023）结合随机森林分类与景观变化分析。","红树林先减后增，主要转为草甸、其他植被和水体，人为压力显著。","可结合多源遥感与驱动因子模型，探究红树林恢复的生态与社会机制。","2026-10-11T23:30:33.091927Z",{"id":447,"title":448,"url":449,"summary":450,"summary_zh":451,"content":9,"source_name":452,"source_url":449,"published_at":453,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":454,"sources":456,"tags":458,"primary_topic":460,"secondary_topics":464,"classification":465,"search_phrases":476,"slug":479,"view_count":44,"doi":480,"paper":481,"created_at":500},4513,"Spatio-temporal assessment of mining-induced environmental changes using remote sensing: a case study of the Talcher Coalfield, Angul District, India","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74815-7","This study evaluates the spatio-temporal impacts of coal mining on environmental conditions in the Talcher coalfield region, India, using multi-temporal Landsat data (2000, 2014, and 2023) at 30 m resolution. Land use land cover (LULC) was derived using supervised classification (Maximum Likelihood Algorithm), while vegetation dynamics and thermal characteristics were assessed using the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). In addition, atmospheric column densities were incorporated to analyze key atmospheric pollutants SO₂, NO₂, and CO and to assess air quality variations associated with mining activities. The integrated assessment highlights substantial environmental changes associated with mining and land-use transformation. The LULC analysis (2000–2023) reveals a decline in forest cover (41.33% to 37.37%) and agricultural land (45.62% to 45.04%), accompanied by an increase in built-up areas (6.84% to 8.54%) and mining areas (2.12% to 4.94%), indicating intensified anthropogenic pressure. LST and NDVI analyses (2014–2023) show changes in surface thermal conditions and vegetation characteristics. LST results indicate higher maximum and mean temperatures in mining and built-up areas, with changes between 2014 and 2023 varying among land-use classes, with mining areas showing the highest thermal values, where mean summer LST increased from 42.97 °C (2014) to 46.28 °C (2023), and mean winter LST increased from 38.67 °C to 41.81 °C. NDVI analysis indicates a decline in maximum NDVI values over the study period. The results show a spatial association between LULC changes, NDVI, and LST, with areas of mining and built-up expansion generally corresponding to lower NDVI and higher surface temperatures. Analysis of satellite-derived atmospheric column densities indicates higher SO₂, NO₂, and CO values in mining and industrial regions, with higher values observed during winter.","本研究利用30 m分辨率的多时相Landsat数据（2000年、2014年和2023年），评估了印度塔尔切尔煤田地区煤矿开采对环境条件的时空影响。土地利用\u002F土地覆盖（LULC）通过监督分类（最大似然算法）获得，植被动态和热特征则分别采用归一化差异植被指数（NDVI）和地表温度（LST）进行评估。此外，研究还引入大气柱密度以分析主要大气污染物SO₂、NO₂和CO，并评估与采矿活动相关的空气质量变化。综合评估揭示了与采矿和土地利用转型相关的显著环境变化。LULC分析（2000—2023年）显示，森林覆盖面积（从41.33%降至37.37%）和农业用地（从45.62%降至45.04%）减少，而建成区面积（从6.84%增至8.54%）和采矿区域面积（从2.12%增至4.94%）增加，表明人为压力加剧。LST和NDVI分析（2014—2023年）显示地表热条件和植被特征发生变化。LST结果表明，采矿区和建成区的最高温度和平均温度较高，2014年至2023年的变化因土地利用类型而异，其中采矿区的热值最高，夏季平均LST从42.97 °C（2014年）升至46.28 °C（2023年），冬季平均LST从38.67 °C升至41.81 °C。NDVI分析表明，研究期内最大NDVI值下降。结果表明，LULC变化、NDVI和LST之间存在空间关联，采矿和建成区扩张区域通常对应较低的NDVI和较高的地表温度。卫星反演的大气柱密度分析表明，采矿和工业区域的SO₂、NO₂和CO值较高，且冬季观测值更高。","Scientific Reports","2026-10-07T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":455},"该研究聚焦印度煤矿区环境遥感监测，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[457],{"name":452,"url":449},[22,459,275,23],"地表温度",{"code":34,"slug":461,"name":462,"path":463,"parent_code":204},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":115,"locked":14,"version":29,"method":30,"evidence":466},{"candidates":467,"agricultural_evidence":36},[468,472],{"code":34,"score":13,"supported":36,"title":469,"abstract":470,"tags":471},[40],[],[459,22],{"code":43,"score":63,"supported":14,"title":473,"abstract":474,"tags":475},[],[],[23,275],[477,478],"Talcher 煤田 遥感","Landsat NDVI LST 矿区","Talcher煤田遥感-4513","10.1038\u002Fs41598-026-74815-7",{"doi":480,"openalex_id":482,"authors":483,"venue":452,"cited_by_count":15,"oa_url":494,"card":495,"direction":78,"ingested_from":80},"W7220855962",[484,486,488,491],{"name":485,"orcid":9},"Sk Riyajul Islam",{"name":487,"orcid":9},"Shilpa Pakhira",{"name":489,"orcid":490},"Abhilash Gogineni","https:\u002F\u002Forcid.org\u002F0000-0002-8350-468X",{"name":492,"orcid":493},"Regasa Yadeta Sembeta","https:\u002F\u002Forcid.org\u002F0000-0002-9686-3155","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-74815-7_reference.pdf",{"tldr":496,"method":497,"finding":498,"direction":78,"opportunity":499},"基于多时相Landsat遥感评估印度煤矿区2000-2023年环境变化。","Landsat多时相数据、最大似然监督分类、NDVI、LST及SO₂\u002FNO₂\u002FC","采矿与建设用地扩张导致林地耕地减少，矿区夏季均温升至46.28°C，NDVI下降，污染物冬季偏高。","可借鉴其多指标遥感框架，研究矿区周边农田退化与作物表型响应，构建农业环境风险监测模型。","2026-10-09T23:30:27.643699Z"]