[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1317":3,"related-1317":94},{"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":25,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":52,"slug":55,"view_count":56,"doi":57,"paper":58,"created_at":93},1317,"Unraveling the dual interaction patterns of land use intensity on carbon balance: spatial heterogeneity and drivers in a transitional region of China","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-69203-0","Abstract Under China’s “Dual Carbon” goals, accurate assessment of regional carbon balance is of critical importance. Land use acts as a core factor governing carbon balance. However, the differentiated effects of land use intensity (LUI)—a comprehensive indicator quantifying human intervention on carbon storage, sequestration, and emissions, as well as their spa-tial driving factors, remain poorly understood. Here we incorporate multi-source remote sensing datasets and the GeoDetector model to systematically explore the spatio-temporal linkages between LUI and carbon balance components in Anhui Province, Chi-na, during the period 2000–2020. Our results reveal that LUI exhibits dual spatial associations on car-bon balance components: LUI shows significant negative spatial correlations with carbon storage (CSt), sequestration (CSeq), and net carbon (NetC), but a significant positive corre-lation with carbon emissions (CE). Notably, in 2020, negative association clusters, covering approximately 22% of the study area, were located in the southern mountains. In contrast, the “High LUI - High CE” agglomerations are centered in the northern urban clusters. GeoDetector further demonstrates that the spatial patterns of CSt and CSeq are primarily associated with interactions between LUI and natural factors (NDVI) (q > 0.4), while the spatial pattern of CE is dominated by interactions between LUI and socio-economic factors (GDP) (q > 0.6). These findings reveal dual interaction patterns through which LUI is associated with carbon balance—a strong joint explanatory effect of LUI and NDVI in the south and a strong joint explanatory effect of LUI and GDP in the north—providing scientific support for regional carbon governance strategies that target these two distinct patterns: protecting existing sinks in the south and curbing emissions in the north.","在中国“双碳”目标下，准确评估区域碳平衡至关重要。土地利用是控制碳平衡的核心因素。然而，土地利用强度（LUI）——一个量化人类干预的综合指标——对碳储存、固碳和碳排放的差异化影响及其空间驱动因素，仍知之甚少。本研究整合多源遥感数据集和地理探测器模型，系统探讨了2000—2020年间安徽省土地利用强度与碳平衡各组分之间的时空关联。结果表明，土地利用强度对碳平衡组分表现出双重空间关联：土地利用强度与碳储存（CSt）、固碳（CSeq）和净碳（NetC）呈显著负空间相关，而与碳排放（CE）呈显著正相关。值得注意的是，2020年，负关联聚集区约占研究区面积的22%，主要位于南部山区。相比之下，“高土地利用强度-高碳排放”聚集区则集中于北部城市群。地理探测器进一步表明，碳储存和固碳的空间格局主要与土地利用强度与自然因子（NDVI）的交互作用相关（q > 0.4），而碳排放的空间格局则由土地利用强度与社会经济因子（GDP）的交互作用主导（q > 0.6）。这些发现揭示了土地利用强度与碳平衡相关联的双重交互模式——南部土地利用强度与NDVI的强联合解释效应，以及北部土地利用强度与GDP的强联合解释效应——为针对这两种不同模式的区域碳治理策略提供了科学支持：南部保护现有碳汇，北部遏制碳排放。",null,"Scientific Reports","2026-08-30T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,3,1,"研究揭示土地利用强度对碳平衡的双重空间关联，为区域差异化碳治理提供科学依据，具有较高学术价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"遥感","土地利用","碳平衡","GeoDetector","区域治理",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":51},[38,46],{"code":39,"score":40,"supported":41,"title":42,"abstract":43,"tags":45},"I3",7,true,[],[26,44],"remote sensing",[26],{"code":47,"score":21,"supported":14,"title":48,"abstract":49,"tags":50},"H6",[],[],[27],"农业相关性需要复核",[53,54],"区域治理 土地利用 碳平衡 遥感","区域治理 土地利用","区域治理土地利用碳平衡遥感-1317",2,"10.1038\u002Fs41598-026-69203-0",{"doi":57,"openalex_id":59,"authors":60,"venue":10,"cited_by_count":83,"oa_url":84,"card":85,"direction":91,"ingested_from":92},"W7204695260",[61,63,65,68,70,72,74,76,79,81],{"name":62,"orcid":9},"Lizao Ye",{"name":64,"orcid":9},"Lijuan Qiao",{"name":66,"orcid":67},"Wei Shao","https:\u002F\u002Forcid.org\u002F0000-0003-1763-9911",{"name":69,"orcid":9},"Wei Ma",{"name":71,"orcid":9},"Limin Xu",{"name":73,"orcid":9},"Mengxue Jiang",{"name":75,"orcid":9},"Xiyue Hong",{"name":77,"orcid":78},"Donghua Chen","https:\u002F\u002Forcid.org\u002F0000-0003-1786-7551",{"name":80,"orcid":9},"Xiulong Jin",{"name":82,"orcid":9},"Ronghao Yang",0,"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-69203-0_reference.pdf",{"tldr":86,"method":87,"finding":88,"direction":89,"opportunity":90},"利用多源遥感数据和GeoDetector模型，揭示土地利用强度对碳平衡的双重空间关联模式及其驱动因素","多源遥感数据、GeoDetector模型、空间自相关分析","LUI与碳储量、固碳、净碳负相关，与碳排放正相关；南部受LUI和NDVI交互主导，北部受LUI和GD","农业绿色发展与碳","可进一步研究不同尺度下土地利用强度与碳平衡的交互机制，或探索政策干预对双重模式的影响。","农业遥感与作物表型","openalex","2026-09-01T23:30:21.495289Z",{"total":95,"page":21,"page_size":95,"items":96},6,[97,174,257,321,367,420],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":105,"score_detail":106,"sources":111,"tags":113,"primary_topic":117,"secondary_topics":123,"classification":124,"search_phrases":155,"slug":158,"view_count":83,"doi":159,"paper":160,"created_at":173},5572,"Potential erosion risk assessment of Kanak stream basin (Sivas, Sarkisla) with the ICONA model","https:\u002F\u002Fdoi.org\u002F10.51753\u002Fflsrt.1828829","The rapid development of technology following the Industrial Revolution has increased human domination over nature. Erosion, which plays a critical role in the process of natural shaping of the earth, can accelerate over time because of improper utilization of the natural environment by humans. The main objective of this study is to determine the erosion susceptibility of the Kanak Creek Basin, one of the sub-basins in the upper reaches of the Kizilirmak River, using the ICONA model, whose main parameters are slope, lithology, land use and vegetation cover, and Geographic Information Systems (GIS) and Remote Sensing techniques, and to list the measures that can be taken against erosion. According to the results of the ICONA model potential erosion level map, 48% of the study area has low erosion susceptibility. These areas largely correspond to the vicinity of Sama Mountain, which has volcanic rocks resistant to erosion, and the base of Sarkisla Plain where the slope is very low. The plain floor is largely covered with agricultural lands. The expansion of irrigated agriculture in this region is one of the main measures that can be taken against erosion. Erosion susceptibility increases around Turna Mountain on the northern border of the basin and Cal Mountain in the south due to both the increase in slope and the lack of resistance of the rocks in the region to erosion, and corresponds to 30% of the study area. In 22% of the basin, susceptibility to erosion is at a medium level. Most of these areas consist of natural meadows and pastures. It is essential to take measures against overgrazing in these areas to prevent the complete loss of the already sparse vegetation cover. Planning land use in the basin in a sustainable manner, arranging fields and layers perpendicular to the slope, expanding irrigated agriculture, and preventing overgrazing are of vital importance in terms of combating soil erosion.","工业革命以来技术的迅猛发展增强了人类对自然的支配能力。侵蚀在地球自然塑造过程中起着关键作用，而人类对自然环境的不当利用会加速侵蚀进程。本研究的主要目标是利用ICONA模型（其主要参数为坡度、岩性、土地利用和植被覆盖）以及地理信息系统（GIS）和遥感技术，确定克孜勒河上游子流域之一卡纳克溪流域的侵蚀敏感性，并列出可采取的侵蚀防治措施。根据ICONA模型潜在侵蚀等级图的结果，研究区48%的区域侵蚀敏感性较低。这些区域主要对应于具有抗侵蚀火山岩的萨马山附近以及坡度极低的萨尔基什拉平原底部。平原底部大部分为农业用地覆盖。在该地区扩大灌溉农业是防治侵蚀的主要措施之一。在流域北部边界的图尔纳山和南部的恰尔山周围，由于坡度增大以及该地区岩石抗侵蚀能力不足，侵蚀敏感性增加，占研究区面积的30%。流域22%的区域侵蚀敏感性为中等水平。这些区域大部分为天然草甸和牧场。在这些地区采取措施防止过度放牧至关重要，以避免本已稀疏的植被覆盖完全丧失。以可持续方式规划流域土地利用、沿垂直于坡向布置田块和土层、扩大灌溉农业以及防止过度放牧，对防治土壤侵蚀具有至关重要的意义。","Frontiers in Life Sciences and Related Technologies","2026-10-21T00:00:00Z",61,{"impact":107,"substance":17,"depth":19,"authority":108,"freshness":109,"relevant":21,"comment":110},8,12,9,"该研究以ICONA模型结合GIS与遥感评估土耳其小流域侵蚀风险，方法规范、结论具体，但属区域性案例，公共影响有限，可作为专业参考而非每日精选。",[112],{"name":103,"url":100},[26,27,114,115,116],"土壤侵蚀","GIS","水土保持",{"code":118,"slug":119,"name":120,"path":121,"parent_code":122},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":125,"locked":14,"version":34,"method":35,"evidence":126},"classified",{"candidates":127,"agricultural_evidence":41},[128,134,138,145,151],{"code":118,"score":13,"supported":41,"title":129,"abstract":130,"tags":133},[],[131,115,132],"地理信息","geographic information",[115],{"code":39,"score":40,"supported":41,"title":135,"abstract":136,"tags":137},[],[26,44],[26],{"code":139,"score":95,"supported":41,"title":140,"abstract":141,"tags":144},"H2",[],[142,143],"土壤","soil",[],{"code":146,"score":20,"supported":41,"title":147,"abstract":148,"tags":150},"H3",[],[149],"灌溉",[],{"code":47,"score":56,"supported":14,"title":152,"abstract":153,"tags":154},[],[],[27,116],[156,157],"ICONA 模型 侵蚀风险","Kanak 流域 侵蚀","ICONA模型侵蚀风险-5572","10.51753\u002Fflsrt.1828829",{"doi":159,"openalex_id":161,"authors":162,"venue":103,"cited_by_count":83,"oa_url":9,"card":168,"direction":91,"ingested_from":92},"W7223117733",[163,165],{"name":164,"orcid":9},"MERİÇ KURT",{"name":166,"orcid":167},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":169,"method":170,"finding":171,"direction":89,"opportunity":172},"用ICONA模型评估土耳其Kanak流域潜在侵蚀风险并列出防治措施。","ICONA模型结合GIS与遥感，输入坡度、岩性、土地利用与植被覆盖。","48%区域低侵蚀风险，30%高敏感，22%中等，主因坡度与岩性。","可将ICONA与遥感时序数据结合，动态监测流域侵蚀并评估农业管理措施效果。","2026-10-22T11:20:39.475037Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":9,"source_name":180,"source_url":177,"published_at":181,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":182,"score_detail":183,"sources":188,"tags":190,"primary_topic":191,"secondary_topics":195,"classification":196,"search_phrases":221,"slug":224,"view_count":83,"doi":225,"paper":226,"created_at":256},5487,"Quantification of soil loss by using RUSLE model under different land use and land cover conditions in AKANYARU catchment in Rwanda","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44378-026-00347-1","This study quantified annual soil loss across the Akanyaru Catchment (3,402 km², Rwanda) under four land use and land cover types forest, agriculture, residential, and bare soil using the RUSLE model integrated with GIS. The Rainfall erosivity (R) is derived from 20 years of daily rainfall data obtained from the NASA database. The Soil erodibility (K) used is from global soil texture data; slope length and steepness (LS) from a 30 m DEM; and cover management (C) from Landsat 8 NDVI. The support practice factor (P) was set to 1.0, reflecting the absence of formal conservation structures in the catchment. The Soil loss range from 0 to 51.4391 t\u002Fha\u002Fyear . Near zero soil loss occurred under dense forest cover, while the highest rates were concentrated in bare and intensively farmed areas on steep slopes particularly in the northeastern and southwestern sub-catchments. The three RUSLE factors showing greatest spatial variationwere steep topography (LS: 0–1.678), degraded or absent vegetation cover (C: 0.0004–0.524), and high rainfall erosivity (R: 527.507–888.624 MJ·mm·ha⁻¹·h⁻¹·yr⁻¹). Findings call for prioritizing terracing and contour farming on steep agricultural slopes, expanding agroforestry and cover crops to reduce surface exposure, and improving soil organic matter in erodible sandy-silty soils. The spatially explicit outputs directly support evidence based targeting of conservation resources by Rwanda’s land and water management authorities.","本研究采用RUSLE模型并结合GIS，对阿卡尼亚鲁流域（面积3 402 km²，位于卢旺达）在森林、农业、居民区和裸地四种土地利用与土地覆盖类型下的年土壤流失量进行了定量评估。降雨侵蚀力（R）因子基于从NASA数据库获取的20年日降雨数据推算得出；土壤可蚀性（K）因子采用全球土壤质地数据；坡长与坡度（LS）因子基于30 m数字高程模型（DEM）提取；覆盖管理（C）因子由Landsat 8 NDVI数据获得。支持措施因子（P）设为1.0，以反映该流域缺乏正式的水土保持措施。土壤流失量范围为0～51.4391 t\u002Fha\u002F年。茂密森林覆盖下土壤流失量接近于零，而流失率最高的区域集中在陡坡上的裸地和集约耕作区，尤其是东北部和西南部子流域。空间变异最大的三个RUSLE因子分别为陡峭地形（LS：0～1.678）、退化或缺失的植被覆盖（C：0.0004～0.524）以及高降雨侵蚀力（R：527.507～888.624 MJ·mm·ha⁻¹·h⁻¹·yr⁻¹）。研究结果呼吁优先在陡坡农地实施梯田与等高耕作，扩大农林复合经营和覆盖作物以减少地表裸露，并改善易侵蚀砂质粉砂土壤的有机质含量。空间显式输出结果可直接为卢旺达土地与水资源管理部门基于证据的水土保持资源靶向配置提供支持。","Discover Soil.","2026-10-20T00:00:00Z",69,{"impact":108,"substance":184,"depth":185,"authority":186,"freshness":107,"relevant":21,"comment":187},20,16,13,"基于RUSLE与GIS的流域土壤流失量化研究，方法规范、数据扎实，对水土保持与遥感应用有参考价值，但属区域性案例，公共影响有限。",[189],{"name":180,"url":177},[26,27,114,116],{"code":139,"slug":143,"name":192,"path":193,"parent_code":194},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":125,"locked":14,"version":34,"method":35,"evidence":197},{"candidates":198,"agricultural_evidence":41},[199,203,208,213,217],{"code":139,"score":19,"supported":41,"title":200,"abstract":201,"tags":202},[143],[142,143],[],{"code":47,"score":204,"supported":41,"title":205,"abstract":206,"tags":207},5,[],[116],[27,116],{"code":146,"score":20,"supported":41,"title":209,"abstract":210,"tags":212},[],[211],"水资源",[],{"code":118,"score":20,"supported":41,"title":214,"abstract":215,"tags":216},[],[115],[],{"code":39,"score":21,"supported":14,"title":218,"abstract":219,"tags":220},[],[],[26],[222,223],"RUSLE 模型 卢旺达","Akanyaru 流域 土壤流失","RUSLE模型卢旺达-5487","10.1007\u002Fs44378-026-00347-1",{"doi":225,"openalex_id":227,"authors":228,"venue":180,"cited_by_count":83,"oa_url":250,"card":251,"direction":91,"ingested_from":92},"W7223058039",[229,232,235,238,241,244,247],{"name":230,"orcid":231},"Ntirenganya Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0001-5785-2875",{"name":233,"orcid":234},"Eric Derrick Bugenimana","https:\u002F\u002Forcid.org\u002F0000-0002-0363-6915",{"name":236,"orcid":237},"Rose Niyonkuru","https:\u002F\u002Forcid.org\u002F0009-0009-3460-9731",{"name":239,"orcid":240},"Aphrodice Iragena","https:\u002F\u002Forcid.org\u002F0009-0001-7747-244X",{"name":242,"orcid":243},"Pierre Celestin Uwamahoro","https:\u002F\u002Forcid.org\u002F0009-0009-8925-2493",{"name":245,"orcid":246},"Tuyisabe Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0002-7381-3013",{"name":248,"orcid":249},"Judith Uwihirwe","https:\u002F\u002Forcid.org\u002F0000-0002-0351-5726","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44378-026-00347-1.pdf",{"tldr":252,"method":253,"finding":254,"direction":89,"opportunity":255},"用RUSLE与GIS量化卢旺达Akanyaru流域四类土地利用的年土壤流失量。","RUSLE模型结合GIS，采用NASA降雨、全球土壤、DEM与Landsat 8","土壤流失0–51.4 t\u002Fha\u002F年，裸地与陡坡集约农地最高，密林近零。","可结合遥感时序与保护措施情景，评估梯田与农林复合的减蚀效益及碳协同。","2026-10-21T23:30:23.420269Z",{"id":258,"title":259,"url":260,"summary":261,"summary_zh":262,"content":9,"source_name":263,"source_url":260,"published_at":264,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":105,"score_detail":265,"sources":268,"tags":270,"primary_topic":274,"secondary_topics":278,"classification":279,"search_phrases":301,"slug":304,"view_count":83,"doi":305,"paper":306,"created_at":320},5391,"Spatio-temporal analysis and forecasting of land use\u002Fland cover change in East Medinipur district, India using CA-Markov model","https:\u002F\u002Fdoi.org\u002F10.3846\u002Fgac.2026.23669","Land Use\u002FLand Cover (LU\u002FLC) serve as essential indicators of global environmental changes. This study examined the LU\u002FLC change in the East Medinipur district of West Bengal, India, using remote sensing data and the CA-Markov modeling approach. Through GIS tools and satellite imagery, the study maps, monitors, and analyzes LU\u002FLC changes over a 20-year period (2001–2021), utilizing data from Landsat 4–5 and Landsat 8. Analytical tools including ArcGIS 9, IDRISI Terset, and ERDAS 2014 are employed to project LU\u002FLC trends up to 2041. The results highlight significant transformations, notable reductions in agriculture, vegetation, barren land, and water bodies, alongside substantial increases in aquaculture and built-up areas from 2001 to 2021. Projections indicates that these trends will continue through 2041, with further declines in agriculture, vegetation, barren land, and water bodies, accompanied by continued growth in aquaculture and built-up areas.","土地利用\u002F土地覆盖（Land Use\u002FLand Cover，LU\u002FLC）是全球环境变化的重要指标。本研究利用遥感数据和CA-Markov建模方法，考察了印度西孟加拉邦东梅迪尼普尔县的土地利用\u002F土地覆盖变化。通过GIS工具和卫星影像，研究利用Landsat 4–5和Landsat 8数据，对2001—2021年20年间的土地利用\u002F土地覆盖变化进行了制图、监测与分析。研究采用ArcGIS 9、IDRISI Terset和ERDAS 2014等分析工具，对至2041年的土地利用\u002F土地覆盖趋势进行预测。结果表明，2001年至2021年间发生了显著转变，农业、植被、裸地和水体明显减少，水产养殖和建成区则大幅增加。预测显示，这些趋势将持续至2041年，农业、植被、裸地和水体进一步减少，水产养殖和建成区持续增长。","Geodesy and Cartography","2026-10-19T00:00:00Z",{"impact":107,"substance":17,"depth":266,"authority":108,"freshness":107,"relevant":21,"comment":267},15,"该研究基于遥感与CA-Markov模型分析印度区域土地利用变化，方法规范、结论明确，但属区域性案例，对国内三农与农业信息化的直接参考价值有限，可作为遥感应用类论文收录，不宜进入每日精选。",[269],{"name":263,"url":260},[271,26,27,272,273],"水产养殖","耕地变化","CA-Markov",{"code":39,"slug":275,"name":276,"path":277,"parent_code":122},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":125,"locked":14,"version":34,"method":35,"evidence":280},{"candidates":281,"agricultural_evidence":41},[282,289,293,297],{"code":283,"score":40,"supported":41,"title":284,"abstract":285,"tags":288},"D",[],[286,287],"水产","aquaculture",[271],{"code":39,"score":40,"supported":41,"title":290,"abstract":291,"tags":292},[],[26,44],[26],{"code":118,"score":20,"supported":41,"title":294,"abstract":295,"tags":296},[],[115],[],{"code":47,"score":21,"supported":14,"title":298,"abstract":299,"tags":300},[],[],[27],[302,303],"Landsat 土地利用 遥感监测","土地利用 水产养殖 耕地变化 遥感","Landsat土地利用遥感监测-5391","10.3846\u002Fgac.2026.23669",{"doi":305,"openalex_id":307,"authors":308,"venue":263,"cited_by_count":83,"oa_url":314,"card":315,"direction":91,"ingested_from":92},"W7222963948",[309,312],{"name":310,"orcid":311},"Parag Jyoti Kashyap","https:\u002F\u002Forcid.org\u002F0000-0003-4273-841X",{"name":313,"orcid":9},"Sahana Bose","https:\u002F\u002Fjournals.vilniustech.lt\u002Findex.php\u002FGAC\u002Farticle\u002Fdownload\u002F23669\u002F13567",{"tldr":316,"method":317,"finding":318,"direction":91,"opportunity":319},"用遥感与CA-Markov模型分析印度东梅迪尼普尔2001-2021年土地利用变化并预测至2041年","Landsat影像、GIS与IDRISI\u002FERDAS，CA-Markov模型做L","农业、植被、裸地和水体持续减少，水产养殖与建设用地显著扩张并将延续至2041年。","可将CA-Markov与作物适宜性\u002F粮食安全模型耦合，评估养殖与建设用地扩张对农业生产的长期威胁。","2026-10-20T23:30:19.057951Z",{"id":322,"title":323,"url":324,"summary":325,"summary_zh":326,"content":9,"source_name":327,"source_url":324,"published_at":328,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":329,"score_detail":330,"sources":332,"tags":334,"primary_topic":9,"secondary_topics":338,"classification":339,"search_phrases":350,"slug":353,"view_count":83,"doi":354,"paper":355,"created_at":366},5173,"Land-use patterns and plant community structure in the peri-urban environs of Lokoja, Kogi state, Nigeria: an integrated field and NDVI assessment, 2015 to 2025","https:\u002F\u002Fdoi.org\u002F10.61955\u002Fbplheb","Rapid urbanisation is transforming natural vegetation on the fringes of medium sized Nigerian cities, yet the flora of these zones is rarely documented. This study assessed plant community structure in relation to land use in the peri-urban fringe of Lokoja, Kogi State, by combining field surveys at three sites, namely Zango, Ganaja and Banda, with a dry season Landsat 8 NDVI record spanning 2015 to 2025. Along one transect of 2 km at each site, trees and shrubs were counted in twenty plots o","快速城市化正在改变尼日利亚中等城市边缘的自然植被，但这些区域的植物区系却鲜有记录。本研究通过将赞戈（Zango）、加纳贾（Ganaja）和班达（Banda）三个样地的野外调查与2015年至2025年旱季Landsat 8 NDVI记录相结合，评估了科吉州洛科贾城郊边缘植物群落结构与土地利用的关系。在每个样地沿一条2 km的样带，于二十个样方中对乔木和灌木进行了计数，","Journal of Interdisciplinary Postgraduate Research","2026-10-15T00:00:00Z",52,{"impact":95,"substance":17,"depth":19,"authority":95,"freshness":107,"relevant":21,"comment":331},"该研究结合实地样方与十年NDVI遥感数据，方法有一定参考价值，但属尼日利亚地方性案例，对国内三农与农业信息化的公共价值有限，不宜进入每日精选。",[333],{"name":327,"url":324},[26,335,27,336,337],"NDVI","植被监测","城郊农业",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":340},{"candidates":341,"agricultural_evidence":14,"reason":51},[342,346],{"code":39,"score":20,"supported":14,"title":343,"abstract":344,"tags":345},[],[],[335,336,26],{"code":47,"score":21,"supported":14,"title":347,"abstract":348,"tags":349},[],[],[27],[351,352],"Lokoja 城郊 NDVI 植被","尼日利亚 土地利用 植物群落","Lokoja城郊NDVI植被-5173","10.61955\u002Fbplheb",{"doi":354,"openalex_id":356,"authors":357,"venue":327,"cited_by_count":83,"oa_url":360,"card":361,"direction":91,"ingested_from":92},"W7221946991",[358],{"name":359,"orcid":9},"Baba Baba","https:\u002F\u002Fboldscholar.com\u002Fimages\u002FJournal\u002FArticles\u002FI4_MS4_Baba_PeriUrban_Vegetation_NDVI_JIPR.pdf",{"tldr":362,"method":363,"finding":364,"direction":91,"opportunity":365},"结合野外样方与2015-2025年Landsat 8 NDVI，评估尼日利亚洛科贾城郊土地利用与植物","三站点各设2km样带20个样方调查乔灌木，结合旱季Landsat 8 NDVI时","城郊快速城市化改变自然植被，但该区域植物区系长期缺乏记录。","可延伸至城郊植被NDVI时序与地面样方耦合的长期监测，填补非洲中小城市城郊生态数据空白。","2026-10-17T23:30:29.810692Z",{"id":368,"title":369,"url":370,"summary":371,"summary_zh":372,"content":9,"source_name":373,"source_url":370,"published_at":374,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":375,"score_detail":376,"sources":378,"tags":380,"primary_topic":384,"secondary_topics":385,"classification":386,"search_phrases":403,"slug":406,"view_count":21,"doi":407,"paper":408,"created_at":419},4677,"Geospatial Analysis of Climate-Driven Landscape Change and Urban Expansion: Assessing Human–Environment Interactions, Resource Pressure, and Sustainable Spatial Planning","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23280111","The increasing demands and changing features of rapidly expanding cities and the impacts on land cover and the surface energy balance are evident. Yet, urban planners need to differentiate between the changes that have occurred and those which have been predicted. This study offers another geospatial analysis of the Delhi National Capital Region. This study complements previously published studies by the use of remote sensing techniques to retrieve land surface temperatures and studies of urban resource pressures. This study also incorporates land use and land cover studies of the years 2003, 2013 and 2023, which are based on the analysis of the Landsat imagery. The primary land use and land cover study analyzed an area of 13,754 km2 that includes the entirety of the Delhi National Capital Region and portions of the districts of Gurugram, Faridabad, Sonipat, Jhajjar, Ghaziabad, Gautam Buddha Nagar and Ghaziabad. This study also carried out a reanalysis of published land use and land cover maps to compute the absolute and relative changes and the annual growth rates. The area of land cover of built-up land increased from 1,893.64 km2 (13.77%) in 2003 to 3,386.76 km2 (24.62%) in the year 2023, which is an increase of 1,493.12 km (78.85%. CAGR 2.95%). Decreases were observed in the land cover of agricultural land, forests, water bodies and open land. The rate of land cover of built-up land slowed from an annual rate of 4.37% during 2003 to 2013, to an annual rate of 1.55% during the 2013 to 2023 period. The peripheral Shannon Entropy increased from a value of 0.24 to 0.36 during the same periods. The average land surface temperature of built-up land in Delhi, as reported independently of this study, increased by 5.23°C from 36.58°C in the year 2001 to 41.81°C in the year 2021. The integrated findings indicate that land that was previously agricultural and ecological cover has been extensively converted into built-up areas, intensifying surface warming and resource pressure across the urban periphery and underscoring the urgent need for sustainable spatial planning.","快速扩张的城市日益增长的需求和不断变化的特征，以及对土地覆盖和地表能量平衡的影响显而易见。然而，城市规划者需要区分已经发生的变化和预测的变化。本研究提供了对德里国家首都辖区的另一项地理空间分析。本研究通过利用遥感技术反演地表温度以及研究城市资源压力，对先前发表的研究进行了补充。本研究还纳入了基于Landsat影像分析的2003年、2013年和2023年的土地利用与土地覆盖研究。主要的土地利用与土地覆盖研究分析了13,754 km²的区域，涵盖德里国家首都辖区全境以及古尔冈、法里达巴德、索尼帕特、贾贾尔、加济阿巴德、乔达摩菩提那加尔和加济阿巴德等地区的部分区域。本研究还对已发表的土地利用与土地覆盖图进行了再分析，以计算绝对变化量、相对变化量及年增长率。建设用地土地覆盖面积从2003年的1,893.64 km²（13.77%）增加至2023年的3,386.76 km²（24.62%），增加了1,493.12 km²（78.85%，复合年增长率2.95%）。农业用地、森林、水体和裸地的土地覆盖均有所减少。建设用地土地覆盖的增长速率从2003年至2013年的年均4.37%放缓至2013年至2023年的年均1.55%。同期，边缘香农熵从0.24增加至0.36。据独立于本研究的报道，德里建设用地平均地表温度从2001年的36.58°C上升至2021年的41.81°C，升高了5.23°C。综合研究结果表明，原有的农业和生态用地已被大量转化为建设用地，加剧了城市边缘的地表增温和资源压力，凸显了可持续空间规划的迫切需求。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z",79,{"impact":17,"substance":18,"depth":17,"authority":186,"freshness":107,"relevant":21,"comment":377},"基于Landsat遥感数据量化德里首都圈2003-2023年建设用地扩张与地表升温，方法扎实、数据具体，对农业信息化中的遥感监测与城乡空间规划有参考价值。",[379],{"name":373,"url":370},[26,27,381,382,383],"地表温度","城市扩张","空间规划",{"code":39,"slug":275,"name":276,"path":277,"parent_code":122},[],{"status":125,"locked":14,"version":34,"method":35,"evidence":387},{"candidates":388,"agricultural_evidence":41},[389,393,399],{"code":39,"score":107,"supported":41,"title":390,"abstract":391,"tags":392},[],[26,44],[381,26],{"code":118,"score":394,"supported":41,"title":395,"abstract":396,"tags":398},4,[],[397],"空间分析",[383],{"code":47,"score":21,"supported":14,"title":400,"abstract":401,"tags":402},[],[],[27],[404,405],"城市扩张 地表温度 遥感","土地利用 地表温度 城市扩张 空间规划","城市扩张地表温度遥感-4677","10.5281\u002Fzenodo.23280111",{"doi":407,"openalex_id":409,"authors":410,"venue":373,"cited_by_count":83,"oa_url":370,"card":414,"direction":91,"ingested_from":92},"W7221308370",[411],{"name":412,"orcid":413},"Amit Kumar Singh","https:\u002F\u002Forcid.org\u002F0009-0008-5573-0525",{"tldr":415,"method":416,"finding":417,"direction":91,"opportunity":418},"基于Landsat遥感分析德里首都区2003-2023年土地利用变化、地表温度与城市扩张，评估资源压","Landsat影像解译、土地利用\u002F覆盖分类、地表温度反演、Shannon熵与增长","建设用地增78.85%，农林业与水体减少，地表升温5.23°C，城市外围资源压力加剧。","可结合多源遥感与作物模型，量化城市扩张对周边农地生产力与粮食安全的时空影响。","2026-10-11T23:30:30.090512Z",{"id":421,"title":422,"url":423,"summary":424,"summary_zh":425,"content":9,"source_name":426,"source_url":423,"published_at":427,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":428,"score_detail":429,"sources":431,"tags":433,"primary_topic":436,"secondary_topics":437,"classification":438,"search_phrases":457,"slug":460,"view_count":21,"doi":461,"paper":462,"created_at":487},4585,"Integrated multi-criteria decision analysis and bivariate statistical modeling for landslide susceptibility zonation in the northwestern Ethiopian highlands","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44475-026-00073-w","Landslides represent a critical geo-environmental hazard in the Ethiopian highlands, specifically within the Telemt, Janamora, and Beyeda districts, where they frequently result in fatalities and the destruction of vital infrastructure and agricultural land. This study integrated Geographic Information Systems (GIS) and remote sensing data to perform landslide susceptibility mapping (LSM) using two distinct modeling approaches: the Frequency Ratio (FR) and the Analytical Hierarchy Process (AHP). A total of 140 data points (98 for training (70%) and 42 for testing (30%)) were considered for modeling and validation. Eleven geo-environmental conditioning factors: Slope, rainfall, lithology, density of lineaments, curvature, aspect, normalized difference vegetation index, land use\u002Fland cover, distance to drainage, drainage density, and soil were analyzed to evaluate their influence on landslide occurrence. According to the AHP model, 13.69% and 26.91% of the study area were classified as very high and high susceptibility zones, respectively. In comparison, the FR model identified 12.09% as very high and 23.78% as high susceptibility zones. Both models underscored that steep slopes, porphyritic basalt unit, high lineament density, proximity to drainage systems, and concave terrain curvature are the primary drivers of slope instability. Conversely, gentle slopes, sandstone unit, and dense vegetation cover were strongly associated with low-susceptibility regions. Model performance was validated using the Receiver Operating Characteristic (ROC) curve, yielding an Area Under the Curve (AUC) value of 0.812 for the AHP model and 0.793 for the FR model, alongside a prediction rate AUC value of 77.4% (0.774) for the AHP model and 70.4% for the FR model, confirming the predictive reliability of both methods. These results provide essential geospatial intelligence for policy-makers and stakeholders to guide disaster risk management, sustainable land-use planning, and targeted rehabilitation efforts in the region.","滑坡是埃塞俄比亚高地，特别是Telemt、Janamora和Beyeda地区面临的严重地质环境灾害，频繁造成人员伤亡以及重要基础设施和农田的破坏。本研究综合地理信息系统（GIS）和遥感数据，采用频率比（FR）和层次分析法（AHP）两种不同的建模方法进行滑坡易发性制图（LSM）。建模与验证共考虑了140个数据点（98个用于训练（70%），42个用于测试（30%））。分析了十一个地质环境条件因子：坡度、降雨量、岩性、线理密度、曲率、坡向、归一化植被指数、土地利用\u002F土地覆被、距水系距离、水系密度和土壤，以评估它们对滑坡发生的影响。根据AHP模型，研究区分别有13.69%和26.91%的区域被划分为极高和高易发区。相比之下，FR模型将12.09%的区域划为极高易发区，23.78%划为高易发区。两种模型均强调，陡坡、斑状玄武岩单元、高线理密度、邻近水系以及凹形地形曲率是斜坡失稳的主要驱动因素。相反，缓坡、砂岩单元和茂密植被覆盖与低易发区密切相关。采用受试者工作特征（ROC）曲线对模型性能进行验证，AHP模型的曲线下面积（AUC）值为0.812，FR模型为0.793，同时AHP模型的预测率AUC值为77.4%（0.774），FR模型为70.4%，证实了两种方法的预测可靠性。这些结果为政策制定者和利益相关者提供了重要的地理空间情报，可指导该地区的灾害风险管理、可持续土地利用规划和有针对性的修复工作。","Discover Hazards","2026-10-09T00:00:00Z",64,{"impact":107,"substance":184,"depth":185,"authority":108,"freshness":107,"relevant":21,"comment":430},"GIS与遥感结合的滑坡易发性制图研究，方法规范、数据扎实，对农业灾害风险管理和土地规划有参考价值，但属区域性案例，公共影响有限。",[432],{"name":426,"url":423},[26,27,115,434,435],"灾害风险","滑坡易发性",{"code":118,"slug":119,"name":120,"path":121,"parent_code":122},[],{"status":125,"locked":14,"version":34,"method":35,"evidence":439},{"candidates":440,"agricultural_evidence":41},[441,445,449,453],{"code":118,"score":13,"supported":41,"title":442,"abstract":443,"tags":444},[],[131,115,132],[115],{"code":39,"score":40,"supported":41,"title":446,"abstract":447,"tags":448},[],[26,44],[26],{"code":139,"score":95,"supported":41,"title":450,"abstract":451,"tags":452},[],[142,143],[],{"code":47,"score":21,"supported":14,"title":454,"abstract":455,"tags":456},[],[],[27],[458,459],"埃塞俄比亚高原 滑坡易发性","GIS 遥感 滑坡风险制图","埃塞俄比亚高原滑坡易发性-4585","10.1007\u002Fs44475-026-00073-w",{"doi":461,"openalex_id":463,"authors":464,"venue":426,"cited_by_count":83,"oa_url":423,"card":482,"direction":91,"ingested_from":92},"W7221122595",[465,468,470,472,474,476,478,480],{"name":466,"orcid":467},"Engdaw Gulbet Tebege","https:\u002F\u002Forcid.org\u002F0009-0008-4252-1249",{"name":469,"orcid":9},"Belete Getahun",{"name":471,"orcid":9},"Yohannes Dessalegn Girma",{"name":473,"orcid":9},"Yibeltal Awoke Temeche",{"name":475,"orcid":9},"Worku Birhan Fenklew",{"name":477,"orcid":9},"Ayalnesh Mihrete Tagele",{"name":479,"orcid":9},"Gashaw Tesfaw Chekole",{"name":481,"orcid":9},"Muluken Mintesinot",{"tldr":483,"method":484,"finding":485,"direction":91,"opportunity":486},"用GIS与遥感结合FR和AHP模型，对埃塞俄比亚高原滑坡易发性进行分区制图与验证。","GIS与遥感数据，11个孕灾因子，频率比与层次分析法，ROC曲线验证。","AHP精度略高（AUC 0.812），陡坡、玄武岩、高线密度与近水系为主要致灾因子。","可将滑坡易发性与农田地块、作物分布叠加，评估农业用地风险并支撑山区土地整治决策。","2026-10-10T23:30:27.880607Z"]