[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5173":3,"related-5173":70},{"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":50,"slug":53,"view_count":54,"doi":55,"paper":56,"created_at":69},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，",null,"Journal of Interdisciplinary Postgraduate Research","2026-10-15T00:00:00Z","论文",10,false,52,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},6,18,14,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"遥感","NDVI","土地利用","植被监测","城郊农业",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":49},[38,44],{"code":39,"score":40,"supported":14,"title":41,"abstract":42,"tags":43},"I3",3,[],[],[27,29,26],{"code":45,"score":21,"supported":14,"title":46,"abstract":47,"tags":48},"H6",[],[],[28],"农业相关性需要复核",[51,52],"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",0,"10.61955\u002Fbplheb",{"doi":55,"openalex_id":57,"authors":58,"venue":10,"cited_by_count":54,"oa_url":61,"card":62,"direction":66,"ingested_from":68},"W7221946991",[59],{"name":60,"orcid":9},"Baba Baba","https:\u002F\u002Fboldscholar.com\u002Fimages\u002FJournal\u002FArticles\u002FI4_MS4_Baba_PeriUrban_Vegetation_NDVI_JIPR.pdf",{"tldr":63,"method":64,"finding":65,"direction":66,"opportunity":67},"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。","农业遥感与作物表型","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。","openalex","2026-10-17T23:30:29.810692Z",{"total":17,"page":21,"page_size":17,"items":71},[72,135,208,277,347,428],{"id":73,"title":74,"url":75,"summary":76,"summary_zh":77,"content":9,"source_name":78,"source_url":75,"published_at":79,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":80,"score_detail":81,"sources":86,"tags":88,"primary_topic":9,"secondary_topics":91,"classification":92,"search_phrases":104,"slug":107,"view_count":21,"doi":108,"paper":109,"created_at":134},5246,"Spatial divergence of climate–vegetation relationships in Florida: A 43-year multi-method analysis (1982–2024)","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2515-7620\u002Faeb5af","Abstract Attributing long-term vegetation change to climate is difficult in subtropical ecosystems, where temperature and precipitation act simultaneously, are confounded by abrupt disturbances, and where statewide averages can conceal offsetting associations. Here we apply a multi-method convergence-of-evidence framework to a sensor-harmonized 43-year record (1982–2024) of NOAA CDR NDVI, temperature, and precipitation across Florida, which spans cool temperate forests to warm subtropical wetlands. Modified Mann–Kendall tests with false-discovery-rate correction detected monotonic trends; pixel-level correlation, spatial-error regression, and lagged cross-correlation evaluated association strength, direction, and timing; Empirical Orthogonal Function decomposition of deseasonalized anomalies separated persistent climatic signals from episodic disturbance, with the leading mode (58.4% of variance) tracking latitudinal warming and a lower-order mode (PC3) tracking hurricane impacts. Florida warmed by +0.033 °C yr⁻¹, significant in 99.6% of land pixels, while precipitation showed no field-significant trend. The temperature–greenness association was positive across essentially the entire state, marginally stronger in the south; localized negative responses were confined to specific southern wetland and urban-fringe pixels rather than forming a systematic north–south reversal. The state-mean precipitation–NDVI correlation was negative at zero lag and reversed from r = −0.263 to r = +0.102 when incoming shortwave radiation was partialed out, consistent with cloud-associated light suppression rather than water limitation—a phenomenon we term the Water Paradox. SPI\u002FSPEI–NDVI correlations were near zero, and Everglades water levels were largely decoupled from wetland greenness (r = 0.08, n.s.), indicating that inundation acts as a localized threshold modifier rather than a dominant suppressor. Climate–vegetation models for comparable high-rainfall subtropical systems may therefore need to account for spatially divergent temperature sensitivities and treat precipitation as an energy constraint rather than a uniform benefit.","摘要 在亚热带生态系统中，将长期植被变化归因于气候十分困难，因为温度和降水同时起作用，又受到突发干扰的混杂影响，而且全州尺度的平均值可能掩盖相互抵消的关联。本文采用多方法证据汇聚框架，对佛罗里达州1982—2024年共43年经传感器协调的NOAA CDR NDVI、温度和降水记录进行分析，该区域涵盖从冷温带森林到暖亚热带湿地。采用经错误发现率校正的改进Mann–Kendall检验检测单调趋势；通过像元级相关、空间误差回归和滞后互相关评估关联强度、方向和时间；对去季节化异常进行经验正交函数分解，以区分持续性气候信号与偶发干扰，其中主导模态（占总方差58.4%）追踪纬度变暖，低阶模态（PC3）追踪飓风影响。佛罗里达州以+0.033 °C yr⁻¹的速率变暖，在99.6%的陆地像元上显著，而降水未显示出区域显著趋势。温度—绿度关联在全州几乎均为正，南部略强；局地负响应仅限于特定的南部湿地和城市边缘像元，而非形成系统的南北反转。州平均降水—NDVI相关在零滞后时为负，当偏除入射短波辐射后由r = −0.263反转为r = +0.102，这与云相关的光抑制而非水分限制相一致——我们将这一现象称为“水悖论”。SPI\u002FSPEI—NDVI相关接近零，大沼泽地水位与湿地绿度基本解耦（r = 0.08，不显著），表明淹水作为局地阈值调节因子而非主导抑制因子起作用。因此，针对可比的高降雨亚热带系统的气候—植被模型可能需要考虑空间分异的温度敏感性，并将降水视为能量约束而非均一益处。","Environmental Research Communications","2026-10-16T00:00:00Z",70,{"impact":20,"substance":82,"depth":18,"authority":83,"freshness":84,"relevant":21,"comment":85},22,13,9,"基于43年NDVI遥感记录的多方法气候-植被关系研究，方法扎实、结论新颖，但属区域基础研究，对国内三农信息化仅有方法借鉴价值。",[87],{"name":78,"url":75},[89,26,27,29,90],"气候变化","佛罗里达",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":93},{"candidates":94,"agricultural_evidence":14,"reason":49},[95,99],{"code":39,"score":40,"supported":14,"title":96,"abstract":97,"tags":98},[],[],[27,29,26],{"code":100,"score":21,"supported":14,"title":101,"abstract":102,"tags":103},"H7",[],[],[89],[105,106],"Florida NDVI 气候变化","佛罗里达 植被 遥感","FloridaNDVI气候变化-5246","10.1088\u002F2515-7620\u002Faeb5af",{"doi":108,"openalex_id":110,"authors":111,"venue":78,"cited_by_count":54,"oa_url":128,"card":129,"direction":66,"ingested_from":68},"W7222444517",[112,115,118,121,124,126],{"name":113,"orcid":114},"A. Mandal","https:\u002F\u002Forcid.org\u002F0009-0004-0192-7497",{"name":116,"orcid":117},"Aman Kumar Thapa","https:\u002F\u002Forcid.org\u002F0009-0003-8956-6632",{"name":119,"orcid":120},"Madan Thapa Chhetri","https:\u002F\u002Forcid.org\u002F0009-0002-3567-7591",{"name":122,"orcid":123},"Hongbo Su","https:\u002F\u002Forcid.org\u002F0000-0002-2147-3646",{"name":125,"orcid":9},"Yan Yong",{"name":127,"orcid":9},"Fred Bloetscher","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2515-7620\u002Faeb5af\u002Fpdf",{"tldr":130,"method":131,"finding":132,"direction":66,"opportunity":133},"用多方法分析佛罗里达43年气候与植被关系，揭示空间分异。","NOAA CDR NDVI与气候数据，结合趋势检验、回归、EOF分解。","全州变暖显著，温度与绿度正相关；降水因云量抑制光照呈负相关。","高降雨亚热带区需将降水视为能量约束，并关注温度敏感性的空间分异。","2026-10-18T23:30:31.356484Z",{"id":136,"title":137,"url":138,"summary":139,"summary_zh":140,"content":9,"source_name":141,"source_url":138,"published_at":142,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":143,"score_detail":144,"sources":146,"tags":148,"primary_topic":151,"secondary_topics":156,"classification":157,"search_phrases":184,"slug":187,"view_count":188,"doi":189,"paper":190,"created_at":207},4497,"A SwiGLU enhanced ConvNeXt framework for multispectral Sentinel-2 land use and land cover classification","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-67985-x","Land use and land cover (LCLU) classification from multispectral satellite imagery is important for applications such as urban planning, environmental monitoring, agriculture, and disaster management. Existing deep learning based approaches have improved LCLU; however, most of them utilize only the red, green, and blue (RGB) bands of multispectral sentinel-2 imagery, thereby underutilizing the complementary spectral information available in the near-infrared (NIR), short-wave infrared (SWIR), and derived spectral indices such as normalized difference built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). In this work, we propose a convolutional neural network (CNN), SwigNeXT, based on a modified ConvNeXt architecture for the classification of multispectral Sentinel-2 imagery using spectral band combinations which are RGB, NIR–Red–Green, SWIR–NIR–Red, and derived index combination NDBI–NDVI–NDWI rather than using only the RGB. The SwigNeXT incorporates SwiGLU-based gated feature modulation to improve spectral spatial feature representation. The final classification is obtained by ensembling the predictions of the three spectral band combinations and the derived index combination. Experimental results show that the proposed ensemble framework achieved an overall accuracy of 99.19% with a Kappa coefficient ( \\(\\kappa \\) ) of 0.9909, outperforming conventional CNNs, transfer learning based CNNs and transformer based approaches. A gradient based SHapley Additive exPlanations framework was further integrated to analyse spatial and spectral feature contributions. The proposed framework was further validated on Sentinel-2 imagery from the Delhi–NCR region, to examine its applicability under geographically distinct real world imaging conditions.","多光谱卫星影像的土地利用与土地覆盖（LCLU）分类对城市规划、环境监测、农业和灾害管理等应用具有重要意义。现有的基于深度学习的方法改进了LCLU分类；然而，大多数方法仅利用多光谱Sentinel-2影像的红、绿、蓝（RGB）波段，从而未能充分利用近红外（NIR）、短波红外（SWIR）以及归一化差异建成区指数（NDBI）、归一化差异植被指数（NDVI）和归一化差异水体指数（NDWI）等衍生光谱指数中可用的互补光谱信息。在本研究中，我们提出了一种基于改进ConvNeXt架构的卷积神经网络（CNN）——SwigNeXT，用于多光谱Sentinel-2影像分类，采用RGB、NIR–Red–Green、SWIR–NIR–Red等光谱波段组合以及NDBI–NDVI–NDWI衍生指数组合，而非仅使用RGB。SwigNeXT引入了基于SwiGLU的门控特征调制，以改进光谱空间特征表示。最终分类通过集成三种光谱波段组合和衍生指数组合的预测结果获得。实验结果表明，所提出的集成框架达到了99.19%的总体精度，Kappa系数（\\(\\kappa\\)）为0.9909，优于传统CNN、基于迁移学习的CNN以及基于Transformer的方法。进一步集成了基于梯度的SHapley加性解释框架，以分析空间和光谱特征贡献。所提出的框架在德里–NCR地区的Sentinel-2影像上进一步验证，以检验其在地理上不同的真实成像条件下的适用性。","Scientific Reports","2026-10-07T00:00:00Z",81,{"impact":18,"substance":82,"depth":18,"authority":19,"freshness":84,"relevant":21,"comment":145},"提出融合SwiGLU门控与多光谱波段组合的ConvNeXt集成框架，在Sentinel-2土地利用分类上达99.19%精度，方法新颖且对农业遥感监测有实用价值。",[147],{"name":141,"url":138},[149,150,26,27,28],"智慧农业","农业人工智能",{"code":39,"slug":152,"name":153,"path":154,"parent_code":155},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":158,"locked":14,"version":34,"method":35,"evidence":159},"classified",{"candidates":160,"agricultural_evidence":162},[161,167,175,179],{"code":39,"score":83,"supported":162,"title":163,"abstract":165,"tags":166},true,[164],"multispectral",[164],[27,26],{"code":168,"score":169,"supported":162,"title":170,"abstract":171,"tags":174},"I2",7,[],[172,173],"深度学习","deep learning",[150],{"code":45,"score":21,"supported":14,"title":176,"abstract":177,"tags":178},[],[],[28],{"code":180,"score":21,"supported":14,"title":181,"abstract":182,"tags":183},"I1",[],[],[149],[185,186],"Sentinel-2 多光谱 土地覆盖分类","ConvNeXt SwiGLU 遥感分类","Sentinel-2多光谱土地覆盖分类-4497",2,"10.1038\u002Fs41598-026-67985-x",{"doi":189,"openalex_id":191,"authors":192,"venue":141,"cited_by_count":54,"oa_url":200,"card":201,"direction":206,"ingested_from":68},"W7220869344",[193,195,197],{"name":194,"orcid":9},"Neeraj Kumar",{"name":196,"orcid":9},"Navin Rajpal",{"name":198,"orcid":199},"Pramod Kumar Soni","https:\u002F\u002Forcid.org\u002F0000-0003-0632-5044","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-67985-x_reference.pdf",{"tldr":202,"method":203,"finding":204,"direction":66,"opportunity":205},"提出SwiGLU增强的ConvNeXt框架，融合多光谱波段与指数集成分类Sentinel-2土地利用","改进ConvNeXt加SwiGLU门控，用RGB、NIR、SWIR及NDVI\u002FN","集成框架总体精度99.19%、Kappa 0.9909，优于CNN、迁移学习和Transformer","可探索多光谱指数组合与门控机制在作物精细分类及跨区域迁移中的泛化能力。","智慧农业 \u002F 农业物联网","2026-10-09T23:30:12.234854Z",{"id":209,"title":210,"url":211,"summary":212,"summary_zh":213,"content":9,"source_name":214,"source_url":211,"published_at":215,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":216,"score_detail":217,"sources":220,"tags":222,"primary_topic":224,"secondary_topics":225,"classification":226,"search_phrases":256,"slug":259,"view_count":21,"doi":260,"paper":261,"created_at":276},2514,"Spatiotemporal Assessment of Land Use and Land Cover Change Effects on Eco- Hydroclimatic Conditions in Kiambu County, Kenya","https:\u002F\u002Fdoi.org\u002F10.54536\u002Fajgt.v5i1.8320","Increasing land-use transitions are major drivers of eco-hydroclimatic changes, particularly in rapidly growing urban areas. This study therefore sought to assess the spatiotemporal effects of land use changes on eco-hydroclimatic conditions in Kiambu County, utilizing remote sensing and Geographic Information Systems technologies. Landsat satellite imagery from 2004 and 2024 was used. Rainfall data, Land Surface Temperature (LST), and Normalized Difference Vegetation Index (NDVI) were used to assess variations in vegetation cover, thermal patterns, and precipitation trends. Land use and land cover maps were generated using supervised classification, and accuracy assessments were performed to validate the classified maps. Statistical analyses were conducted to examine the relationships between LULC dynamics and eco-hydroclimatic parameters. The findings showed a significant expansion of built-up areas, accompanied by declines in cultivated land, vegetation cover, and water bodies. These changes were associated with increased LST, reduced vegetation health, and alterations in rainfall patterns. The study further demonstrated that increased urbanization and substantial landscape changes have significantly affected the county’s eco-hydroclimatic conditions. These changes have significant implications for environmental sustainability, water resources, climate change adaptation and resilience, and land use planning at the county level. The findings are expected to provide evidence to support informed policy formulation and sustainable spatial planning aimed at enhancing ecological resilience in Kiambu County.","土地利用转变日益加剧是生态-水文气候发生变化的主要驱动因素，在快速扩张的城市地区尤为显著。因此，本研究旨在利用遥感与地理信息系统技术，评估基安布县土地利用变化对生态-水文气候条件的时空影响。研究使用了2004年和2024年的Landsat卫星影像，并采用降雨数据、地表温度（LST）和归一化植被指数（NDVI）来评估植被覆盖、热力格局和降水趋势的变化。通过监督分类生成土地利用与土地覆盖图，并进行精度评估以验证分类结果。研究还开展了统计分析，以检验土地利用与土地覆盖动态与生态-水文气候参数之间的关系。研究结果表明，建设用地显著扩张，同时耕地、植被覆盖和水体减少。这些变化与地表温度升高、植被健康状况下降以及降雨格局改变相关。研究进一步表明，城市化加剧和景观的显著变化已对该县的生态-水文气候条件产生了重大影响。这些变化对县域层面的环境可持续性、水资源、气候变化适应与韧性以及土地利用规划具有重要意义。研究结果有望为制定知情政策和可持续空间规划提供证据支持，以增强基安布县的生态韧性。","American Journal of Geospatial Technology","2026-09-14T00:00:00Z",62,{"impact":20,"substance":18,"depth":218,"authority":83,"freshness":20,"relevant":21,"comment":219},15,"基于Landsat遥感与GIS的县域土地利用变化对生态水文气候影响研究，方法规范、数据扎实，但属肯尼亚地方案例，对国内三农信息化参考价值有限。",[221],{"name":214,"url":211},[89,26,27,28,223],"水资源",{"code":39,"slug":152,"name":153,"path":154,"parent_code":155},[],{"status":158,"locked":14,"version":34,"method":35,"evidence":227},{"candidates":228,"agricultural_evidence":162},[229,234,239,246,252],{"code":39,"score":20,"supported":162,"title":230,"abstract":231,"tags":233},[],[26,232],"remote sensing",[27,26],{"code":100,"score":169,"supported":162,"title":235,"abstract":236,"tags":238},[],[89,237],"climate change",[89],{"code":240,"score":17,"supported":162,"title":241,"abstract":242,"tags":245},"I4",[],[243,244],"地理信息","geographic information",[],{"code":247,"score":248,"supported":162,"title":249,"abstract":250,"tags":251},"H3",4,[],[223],[223],{"code":45,"score":21,"supported":14,"title":253,"abstract":254,"tags":255},[],[],[28],[257,258],"土地利用 气候变化 水资源 遥感","土地利用 气候变化","土地利用气候变化水资源遥感-2514","10.54536\u002Fajgt.v5i1.8320",{"doi":260,"openalex_id":262,"authors":263,"venue":214,"cited_by_count":54,"oa_url":270,"card":271,"direction":206,"ingested_from":68},"W7212965144",[264,266,268],{"name":265,"orcid":9},"Pauline Angerlyline Wamalwa",{"name":267,"orcid":9},"Samson Mabwoga",{"name":269,"orcid":9},"Charity Konana","https:\u002F\u002Fjournals.e-palli.com\u002Fhome\u002Findex.php\u002Fajgt\u002Farticle\u002Fdownload\u002F8320\u002F4014",{"tldr":272,"method":273,"finding":274,"direction":66,"opportunity":275},"基于Landsat影像评估肯尼亚Kiambu县2004-2024年土地利用变化对生态水文气候的影响。","Landsat影像监督分类、NDVI、地表温度与降雨数据统计分析。","建设用地扩张导致耕地、植被和水体减少，地表温度升高、降雨格局改变。","可结合多源遥感与水文模型，量化城市化对农业区水热平衡的长期影响机制。","2026-09-15T23:30:08.755171Z",{"id":278,"title":279,"url":280,"summary":281,"summary_zh":282,"content":9,"source_name":283,"source_url":280,"published_at":284,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":285,"score_detail":286,"sources":289,"tags":291,"primary_topic":295,"secondary_topics":299,"classification":300,"search_phrases":327,"slug":330,"view_count":54,"doi":331,"paper":332,"created_at":346},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":20,"substance":18,"depth":19,"authority":287,"freshness":84,"relevant":21,"comment":288},12,"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。",[290],{"name":283,"url":280},[26,28,292,293,294],"土壤侵蚀","GIS","水土保持",{"code":240,"slug":296,"name":297,"path":298,"parent_code":155},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[],{"status":158,"locked":14,"version":34,"method":35,"evidence":301},{"candidates":302,"agricultural_evidence":162},[303,307,311,318,323],{"code":240,"score":13,"supported":162,"title":304,"abstract":305,"tags":306},[],[243,293,244],[293],{"code":39,"score":169,"supported":162,"title":308,"abstract":309,"tags":310},[],[26,232],[26],{"code":312,"score":17,"supported":162,"title":313,"abstract":314,"tags":317},"H2",[],[315,316],"土壤","soil",[],{"code":247,"score":40,"supported":162,"title":319,"abstract":320,"tags":322},[],[321],"灌溉",[],{"code":45,"score":188,"supported":14,"title":324,"abstract":325,"tags":326},[],[],[28,294],[328,329],"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":331,"openalex_id":333,"authors":334,"venue":283,"cited_by_count":54,"oa_url":9,"card":340,"direction":66,"ingested_from":68},"W7223117733",[335,337],{"name":336,"orcid":9},"MERİÇ KURT",{"name":338,"orcid":339},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":341,"method":342,"finding":343,"direction":344,"opportunity":345},"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。","农业绿色发展与碳","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":348,"title":349,"url":350,"summary":351,"summary_zh":352,"content":9,"source_name":353,"source_url":350,"published_at":354,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":355,"score_detail":356,"sources":360,"tags":362,"primary_topic":363,"secondary_topics":367,"classification":368,"search_phrases":392,"slug":395,"view_count":54,"doi":396,"paper":397,"created_at":427},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":287,"substance":357,"depth":358,"authority":83,"freshness":20,"relevant":21,"comment":359},20,16,"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。",[361],{"name":353,"url":350},[26,28,292,294],{"code":312,"slug":316,"name":364,"path":365,"parent_code":366},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":158,"locked":14,"version":34,"method":35,"evidence":369},{"candidates":370,"agricultural_evidence":162},[371,375,380,384,388],{"code":312,"score":19,"supported":162,"title":372,"abstract":373,"tags":374},[316],[315,316],[],{"code":45,"score":376,"supported":162,"title":377,"abstract":378,"tags":379},5,[],[294],[28,294],{"code":247,"score":40,"supported":162,"title":381,"abstract":382,"tags":383},[],[223],[],{"code":240,"score":40,"supported":162,"title":385,"abstract":386,"tags":387},[],[293],[],{"code":39,"score":21,"supported":14,"title":389,"abstract":390,"tags":391},[],[],[26],[393,394],"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":396,"openalex_id":398,"authors":399,"venue":353,"cited_by_count":54,"oa_url":421,"card":422,"direction":66,"ingested_from":68},"W7223058039",[400,403,406,409,412,415,418],{"name":401,"orcid":402},"Ntirenganya Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0001-5785-2875",{"name":404,"orcid":405},"Eric Derrick Bugenimana","https:\u002F\u002Forcid.org\u002F0000-0002-0363-6915",{"name":407,"orcid":408},"Rose Niyonkuru","https:\u002F\u002Forcid.org\u002F0009-0009-3460-9731",{"name":410,"orcid":411},"Aphrodice Iragena","https:\u002F\u002Forcid.org\u002F0009-0001-7747-244X",{"name":413,"orcid":414},"Pierre Celestin Uwamahoro","https:\u002F\u002Forcid.org\u002F0009-0009-8925-2493",{"name":416,"orcid":417},"Tuyisabe Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0002-7381-3013",{"name":419,"orcid":420},"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":423,"method":424,"finding":425,"direction":344,"opportunity":426},"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":429,"title":430,"url":431,"summary":432,"summary_zh":433,"content":9,"source_name":434,"source_url":431,"published_at":435,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":285,"score_detail":436,"sources":438,"tags":440,"primary_topic":444,"secondary_topics":445,"classification":446,"search_phrases":468,"slug":471,"view_count":54,"doi":472,"paper":473,"created_at":487},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":20,"substance":18,"depth":218,"authority":287,"freshness":20,"relevant":21,"comment":437},"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。",[439],{"name":434,"url":431},[441,26,28,442,443],"水产养殖","耕地变化","CA-Markov",{"code":39,"slug":152,"name":153,"path":154,"parent_code":155},[],{"status":158,"locked":14,"version":34,"method":35,"evidence":447},{"candidates":448,"agricultural_evidence":162},[449,456,460,464],{"code":450,"score":169,"supported":162,"title":451,"abstract":452,"tags":455},"D",[],[453,454],"水产","aquaculture",[441],{"code":39,"score":169,"supported":162,"title":457,"abstract":458,"tags":459},[],[26,232],[26],{"code":240,"score":40,"supported":162,"title":461,"abstract":462,"tags":463},[],[293],[],{"code":45,"score":21,"supported":14,"title":465,"abstract":466,"tags":467},[],[],[28],[469,470],"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":472,"openalex_id":474,"authors":475,"venue":434,"cited_by_count":54,"oa_url":481,"card":482,"direction":66,"ingested_from":68},"W7222963948",[476,479],{"name":477,"orcid":478},"Parag Jyoti Kashyap","https:\u002F\u002Forcid.org\u002F0000-0003-4273-841X",{"name":480,"orcid":9},"Sahana Bose","https:\u002F\u002Fjournals.vilniustech.lt\u002Findex.php\u002FGAC\u002Farticle\u002Fdownload\u002F23669\u002F13567",{"tldr":483,"method":484,"finding":485,"direction":66,"opportunity":486},"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"]