[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3794":3,"related-3794":76},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":8,"secondary_topics":29,"classification":30,"search_phrases":56,"slug":59,"view_count":60,"doi":61,"paper":62,"created_at":75},3794,"Assessing Climate Change Impacts on Forest Productivity Using Integrated GIS Remote Sensing Climate and Socioeconomic Methods in Itang Special District Gambella Region Ethiopia","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10789368\u002Fv1","Assessing Climate Change Impacts on Forest Productivity Using Integrated GIS Remote Sensing Climate and Socioeconomic Methods in Itang Special District Gambella Region Ethiopia。Research Square",null,"Research Square","2026-09-28T00:00:00Z","论文",10,false,60,{"impact":16,"substance":17,"depth":18,"authority":12,"freshness":19,"relevant":20,"comment":21},8,18,15,9,1,"埃塞俄比亚区域尺度的GIS遥感与气候社会经济综合方法研究，方法有参考价值但地域性强、公共影响有限，时效新。",[23],{"name":9,"url":6},[25,26,27,28],"气候变化","遥感","GIS","森林生产力",[],{"status":31,"locked":13,"version":32,"method":33,"evidence":34},"needs_review","2026-10-22.2","rules",{"candidates":35,"agricultural_evidence":13,"reason":55},[36,44,50],{"code":37,"score":38,"supported":39,"title":40,"abstract":42,"tags":43},"H7",12,true,[41],"climate change",[41],[25],{"code":45,"score":38,"supported":39,"title":46,"abstract":48,"tags":49},"I3",[47],"remote sensing",[47],[26],{"code":51,"score":38,"supported":39,"title":52,"abstract":53,"tags":54},"I4",[27],[27],[27],"农业相关性需要复核",[57,58],"Gambella Itang 森林生产力","GIS 遥感 气候变化","GambellaItang森林生产力-3794",4,"10.21203\u002Frs.3.rs-10789368\u002Fv1",{"doi":61,"openalex_id":63,"authors":64,"venue":9,"cited_by_count":71,"oa_url":72,"card":8,"direction":73,"ingested_from":74},"W7214650564",[65,67,69],{"name":66,"orcid":8},"KOAT MACHAR PUR",{"name":68,"orcid":8},"DAK BANG RUON",{"name":70,"orcid":8},"DUOL NHIAL LUAK",0,"https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10789368\u002Flatest.pdf","农业遥感与作物表型","openalex","2026-09-29T23:30:30.100133Z",{"total":77,"page":20,"page_size":77,"items":78},6,[79,158,228,300,373,434],{"id":80,"title":81,"url":82,"summary":83,"summary_zh":84,"content":8,"source_name":85,"source_url":82,"published_at":86,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":87,"score_detail":88,"sources":91,"tags":93,"primary_topic":97,"secondary_topics":102,"classification":103,"search_phrases":138,"slug":141,"view_count":71,"doi":142,"paper":143,"created_at":157},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":16,"substance":17,"depth":89,"authority":38,"freshness":19,"relevant":20,"comment":90},14,"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。",[92],{"name":85,"url":82},[26,94,95,27,96],"土地利用","土壤侵蚀","水土保持",{"code":51,"slug":98,"name":99,"path":100,"parent_code":101},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":104,"locked":13,"version":32,"method":33,"evidence":105},"classified",{"candidates":106,"agricultural_evidence":39},[107,113,118,125,132],{"code":51,"score":12,"supported":39,"title":108,"abstract":109,"tags":112},[],[110,27,111],"地理信息","geographic information",[27],{"code":45,"score":114,"supported":39,"title":115,"abstract":116,"tags":117},7,[],[26,47],[26],{"code":119,"score":77,"supported":39,"title":120,"abstract":121,"tags":124},"H2",[],[122,123],"土壤","soil",[],{"code":126,"score":127,"supported":39,"title":128,"abstract":129,"tags":131},"H3",3,[],[130],"灌溉",[],{"code":133,"score":134,"supported":13,"title":135,"abstract":136,"tags":137},"H6",2,[],[],[94,96],[139,140],"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":142,"openalex_id":144,"authors":145,"venue":85,"cited_by_count":71,"oa_url":8,"card":151,"direction":73,"ingested_from":74},"W7223117733",[146,148],{"name":147,"orcid":8},"MERİÇ KURT",{"name":149,"orcid":150},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":152,"method":153,"finding":154,"direction":155,"opportunity":156},"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":159,"title":160,"url":161,"summary":162,"summary_zh":163,"content":8,"source_name":164,"source_url":161,"published_at":165,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":166,"score_detail":167,"sources":171,"tags":173,"primary_topic":177,"secondary_topics":181,"classification":182,"search_phrases":207,"slug":210,"view_count":71,"doi":211,"paper":212,"created_at":227},5495,"Artificial Neural Network-Based Prediction and Mapping of Elephant Corridors in West Bengal Using Geospatial and Environmental Variables","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23496876","The fragmentation of forest habitats and increasing anthropogenic pressures have significantly altered elephant movementpatternsinWestBengal,India,resultinginanescalatinghuman-elephantconflict.Accurateidentification and mapping of elephant corridors are essential for landscape-level conservation planning and mitigation.Traditional corridor mapping methods, including expert-based delineation and least-cost path analyses, oftenstruggle to capture the complex and nonlinear relationships between environmental variables influencing elephant movement. This study proposes an Artificial Neural Network (ANN)-based framework to predict potential elephant corridors across West Bengal using remotely sensed and geographic information system (GIS) datasets. The predictor variables included forest cover, normalized difference vegetation index (NDVI), elevation, slope,proximity to water sources, distance from roads, railways, settlements, agricultural land, and historical elephant occurrence records. The ANN model was trained using known elephant movement locations and validated using independent datasets. The predicted suitable surface was integrated into a GIS environment to identify probable movement corridors and prioritize areas for conservation. The proposed approach demonstrates the ability of machine learning to improve corridor prediction compared with conventional GIS-based techniques and provides a decision support tool for forest managers and policymakers.","森林栖息地的破碎化和日益增加的人为压力显著改变了印度西孟加拉邦大象的活动模式，导致人象冲突不断加剧。准确识别和绘制大象廊道对于景观层面的保护规划和冲突缓解至关重要。传统的廊道制图方法，包括基于专家的 delineation（边界划定）和最小成本路径分析，往往难以捕捉影响大象活动的环境变量之间复杂的非线性关系。本研究提出了一种基于人工神经网络（Artificial Neural Network, ANN）的框架，利用遥感数据和地理信息系统（Geographic Information System, GIS）数据集预测西孟加拉邦潜在的大象廊道。预测变量包括森林覆盖、归一化植被指数（Normalized Difference Vegetation Index, NDVI）、海拔、坡度、距水源的邻近度、距道路、铁路、居民点、农田的距离以及历史大象出现记录。ANN模型利用已知的大象活动位置进行训练，并使用独立数据集进行验证。将预测得到的适宜性表面整合到GIS环境中，以识别可能的迁移廊道并确定保护优先区域。所提出的方法展示了机器学习相较于传统基于GIS的技术在改进廊道预测方面的能力，并为森林管理者和政策制定者提供了决策支持工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-20T00:00:00Z",70,{"impact":38,"substance":168,"depth":169,"authority":38,"freshness":19,"relevant":20,"comment":170},20,17,"该研究用人工神经网络结合遥感与GIS预测大象迁徙走廊，方法新颖、数据扎实，对生态保护与乡村人兽冲突治理有参考价值，但属区域性研究，公共影响有限。",[172],{"name":164,"url":161},[174,26,27,175,176],"农业人工智能","野生动物保护","人兽冲突",{"code":101,"slug":178,"name":179,"path":180,"parent_code":8},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":183,"locked":13,"version":32,"method":33,"evidence":184},"partial",{"candidates":185,"agricultural_evidence":39,"reason":206},[186,194,198,202],{"code":187,"score":12,"supported":39,"title":188,"abstract":189,"tags":193},"I2",[],[190,191,192],"机器学习","machine learning","decision support",[174],{"code":51,"score":12,"supported":39,"title":195,"abstract":196,"tags":197},[],[110,27,111],[27],{"code":45,"score":60,"supported":39,"title":199,"abstract":200,"tags":201},[],[26],[26],{"code":133,"score":20,"supported":13,"title":203,"abstract":204,"tags":205},[],[],[175],"一级领域明确，二级方向需要细分",[208,209],"西孟加拉邦 大象走廊","人工神经网络 大象走廊","西孟加拉邦大象走廊-5495","10.5281\u002Fzenodo.23496876",{"doi":211,"openalex_id":213,"authors":214,"venue":164,"cited_by_count":71,"oa_url":161,"card":221,"direction":226,"ingested_from":74},"W7222999966",[215,217,219],{"name":216,"orcid":8},"Bhutia Dechen Doma",{"name":218,"orcid":8},"Swarup Das",{"name":220,"orcid":8},"Mandal Rakesh Kumar",{"tldr":222,"method":223,"finding":224,"direction":73,"opportunity":225},"用人工神经网络结合遥感与GIS变量预测并制图西孟加拉邦大象迁徙廊道。","ANN模型，输入遥感与GIS环境变量及历史象群出现记录，GIS集成制图。","ANN能刻画环境变量非线性关系，廊道预测优于传统GIS方法，可作决策支持工具。","可将ANN廊道预测与人类活动、农田冲突热点耦合，探索农业景观中野生动物通道与作物保护的协同规划。","农业人工智能与决策模型","2026-10-21T23:30:43.936453Z",{"id":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":8,"source_name":234,"source_url":231,"published_at":235,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":236,"score_detail":237,"sources":240,"tags":242,"primary_topic":246,"secondary_topics":247,"classification":248,"search_phrases":283,"slug":286,"view_count":71,"doi":287,"paper":288,"created_at":299},5403,"RS-GIS Based Dss (Decision Support System) for Land evaluation and Land resource management – (case study Amol District)","https:\u002F\u002Fdoi.org\u002F10.71500\u002Fthesis.2026.519222","Creating a proper planting pattern according to land talent identification, cannot be achieved without considering socioeconomic conditions and detailed holistic. This issue is considered by development of rice which is one of the main world’s most important agricultural products in rural areas and ensures their food security. In the country rice is considered as a strategic plant. This is among sustainable resources of area and attention to its development and expanding its cultivation causes farmers’ confidence and ensures economic stability of the region. On the other hand, crop insurance is same for all land, because statistical information on agricultural lands are most obtained through traditional methods based on the opinion of experts and individuals and collect this information always accompanied with error. Accordingly annually huge costs are paid for insured loss of agricultural lands Thus land evaluation, identification and resolution of land based on its capabilities by taking the capacity of the soil and environmental risks into account is essential for land management. This study presents a spatial decision support system (SDSS) to land evaluation and identification of suitable areas for rice cultivation in the studied area. After gathering data and field studies, various layers of criteria affecting decisions were created in GIS environment. Development of multi-criteria decision-making techniques (MCDM) is done using Analytical Hierarchy Process AHP, fuzzy logic and artificial neural networks. The comparison and integration of models (model compilation and standardization of fuzzy AHP, FAHP, fuzzy standardization and FAHP) was also carried out. To review and matching between fitness maps obtained from different models and the observed value of kappa coefficient was calculated. Neural network model with kappa coefficient 0.65 has highest fitness coefficient among land suitability class and the harvest amount and this due to the performance value in determining the classes is predictable in this model. After that, Fuzzy standardization compilation models and FAHP and Fuzzy standardization and AHP with Kappa coefficient of 0.54 and 0.51 respectively showed highest agreement between the land suitability classes and performance. The majority of lands located on non-prone class have high slope and if cultivation increase risk of soil erosion. Investigating area under rice plantation (paddy fields) with very prone and prone lands in different styles showed that almost the majority of this area is currently devoted to rice cultivation. Although neural network model showed higher accuracy than other models, but this assessment model requires data on rice yield and collecting performance rate required accuracy, cost and much time and sometimes land management and environmental hazards affects it.","根据土地适宜性识别来制定合理的种植模式，离不开对社会经济条件和详细整体性因素的考量。水稻作为世界最重要的农产品之一，其发展在农村地区受到重视，并保障了当地的粮食安全。在我国，水稻被视为战略作物。水稻是区域可持续资源之一，关注其发展并扩大种植面积能够增强农民信心，保障区域经济稳定。另一方面，作物保险对所有土地采取统一标准，因为农业用地的统计信息大多基于专家和个人意见通过传统方法获取，且此类信息的采集始终伴随着误差。因此，每年为农业用地保险损失支付巨额费用。有鉴于此，土地评价、识别及基于土地能力的土地问题解决，必须将土壤容量和环境风险纳入考量，这对土地管理至关重要。本研究提出了一种空间决策支持系统（SDSS），用于研究区域的土地评价和水稻适宜种植区识别。在收集数据和实地调研之后，于GIS环境中创建了影响决策的各准则图层。多准则决策技术（MCDM）的开发采用了层次分析法（AHP）、模糊逻辑和人工神经网络。同时进行了模型的比较与集成（模型编制与模糊AHP标准化、FAHP、模糊标准化与FAHP）。为审查和匹配不同模型获得的适宜性图件，计算了观测值与kappa系数。神经网络模型的kappa系数为0.65，在土地适宜性等级与产量之间具有最高的拟合系数，这归因于该模型在确定等级方面的性能值具有可预测性。其后，模糊标准化编制模型与FAHP、模糊标准化与AHP的kappa系数分别为0.54和0.51，在土地适宜性等级与产量之间表现出最高的一致性。位于非适宜等级的大多数土地具有高坡度，若扩大种植将增加土壤侵蚀风险。对不同适宜性等级下水稻种植区（稻田）的调查……","Test","2026-10-19T00:00:00Z",50,{"impact":16,"substance":238,"depth":18,"authority":127,"freshness":16,"relevant":20,"comment":239},16,"案例研究有一定方法参考价值，但来源不明、地域局限，不足以进入每日精选。",[241],{"name":234,"url":231},[243,26,27,244,245],"决策支持系统","水稻种植","土地评价",{"code":101,"slug":178,"name":179,"path":180,"parent_code":8},[],{"status":183,"locked":13,"version":32,"method":33,"evidence":249},{"candidates":250,"agricultural_evidence":39,"reason":206},[251,255,259,268,275,279],{"code":187,"score":38,"supported":39,"title":252,"abstract":253,"tags":254},[192],[192],[243],{"code":51,"score":38,"supported":39,"title":256,"abstract":257,"tags":258},[27],[27],[27],{"code":260,"score":12,"supported":39,"title":261,"abstract":262,"tags":267},"A1",[],[263,264,265,266],"水稻","稻田","rice","paddy",[244],{"code":269,"score":77,"supported":39,"title":270,"abstract":271,"tags":274},"A8",[],[272,273],"粮食安全","food security",[],{"code":119,"score":77,"supported":39,"title":276,"abstract":277,"tags":278},[],[122,123],[],{"code":45,"score":20,"supported":13,"title":280,"abstract":281,"tags":282},[],[],[26],[284,285],"Amol District 水稻 土地适宜性","RS-GIS 决策支持系统 土地评价","AmolDistrict水稻土地适宜性-5403","10.71500\u002Fthesis.2026.519222",{"doi":287,"openalex_id":289,"authors":290,"venue":234,"cited_by_count":71,"oa_url":231,"card":294,"direction":226,"ingested_from":74},"W7222963628",[291],{"name":292,"orcid":293},"Zeinab Maddahi","https:\u002F\u002Forcid.org\u002F0000-0002-2824-2736",{"tldr":295,"method":296,"finding":297,"direction":73,"opportunity":298},"构建基于RS-GIS的空间决策支持系统，评估伊朗Amol地区水稻种植适宜性。","GIS叠加多准则决策、AHP、模糊逻辑与人工神经网络，用Kappa系数验证。","神经网络模型Kappa系数0.65最高，土地适宜性与产量吻合最好。","可将该SDSS与实时遥感、作物保险定损结合，发展动态土地适宜性监测。","2026-10-20T23:30:40.596410Z",{"id":301,"title":302,"url":303,"summary":304,"summary_zh":305,"content":8,"source_name":306,"source_url":303,"published_at":307,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":308,"sources":310,"tags":312,"primary_topic":316,"secondary_topics":317,"classification":322,"search_phrases":349,"slug":352,"view_count":71,"doi":353,"paper":354,"created_at":372},5321,"GIS and remote sensing based multicriteria analysis for identifying potential agricultural land: A case study on Savar Upazila, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbrds.v26i1.94265","Despite of gradually decreasing its contribution to gross domestic product (GDP), agriculture, however, is the backbone of the Bangladeshi economy. Farming is the primary mean of rural livelihoods in Bangladesh but agricultural land is slowly declining day by day. Although, at present, Bangladesh is self-sufficient in terms of producing food, especially rice and some other major agricultural products, it is predicted that, in near future, the country is going to face immense challenges due to climate related threats. The research aims to identify the potential lands for suitable farming in Savar Upazila (sub-district) under Dhaka district applying a geographic information system (GIS) and remote sensing (RS) based multicriteria analysis. Relevant biophysical characteristics of soil, terrain, and landuse and land cover (LULC) were considered. Soil and evelvation data was collected from Soil Resource Development Institute (SRDI)and Shuttle Radar Topography Mission (SRTM), respectively, whereas satellite images were collected from Landsat-8. The maximum likelihood supervised classification technique was used for LULC classification, and the multicriteria evaluation (MCE) and analytical hierarchy process (AHP) approach was employed to identify suitable areas for farming. The research finds the LULC classes are agricultural land, built-up area, fallow land, vegetation cover, waterbodies, and wetland are 12.42%, 31.01%, 25.69%, 25.98%, 4.81%, and 0.09%, respectively. This research also finds that agricultural land suitability was classified as highly suitable, moderately suitable, marginally suitable, and not suitable are 24.28%, 29.53%, 24.66%, and 21.53%, respectively. In contrast, the current agricultural land usage accounts for just 12.42% of the total land area in this research area. The Kappa accuracy has been measured at a value of 0.88 and an overall accuracy value of 0.90 which indicates that the LULC classification is done almost perfectly. This study offered information at the local level that farmers may utilize to decide on cropping patterns and appropriateness. Bangladesh Rural Development Studies, 26(1), 2023 (13-26)","尽管农业在国内生产总值（GDP）中的贡献逐渐下降，但它仍是孟加拉国经济的支柱。农耕是孟加拉国农村生计的主要方式，但农业用地正日益减少。虽然目前孟加拉国在粮食生产方面，尤其是稻米和其他一些主要农产品方面已实现自给自足，但据预测，在不久的将来，该国将因气候相关威胁而面临巨大挑战。本研究旨在运用地理信息系统（GIS）和遥感（RS）基础上的多准则分析，确定达卡县萨瓦尔乡（分区）适宜耕作的土地潜力。研究考虑了土壤、地形以及土地利用与土地覆盖（LULC）等相关生物物理特征。土壤数据来自土壤资源开发研究所（SRDI），高程数据来自航天飞机雷达地形测绘任务（SRTM），卫星影像则来自Landsat-8。土地利用与土地覆盖分类采用最大似然监督分类技术，适宜耕作区的识别则运用多准则评价（MCE）和层次分析法（AHP）。研究发现，土地利用与土地覆盖类别中，农业用地、建成区、休耕地、植被覆盖、水体及湿地分别占12.42%、31.01%、25.69%、25.98%、4.81%和0.09%。研究还发现，农业用地适宜性分为高度适宜、中度适宜、勉强适宜和不适宜，分别占24.28%、29.53%、24.66%和21.53%。相比之下，当前农业用地仅占本研究区土地总面积的12.42%。Kappa精度值为0.88，总体精度值为0.90，表明土地利用与土地覆盖分类近乎完美。本研究提供了地方层面的信息，可供农民在决定种植模式和适宜性时参考。《孟加拉国农村发展研究》，第26卷第1期，2023年（第13-26页）","OpenAlex","2026-10-18T00:00:00Z",{"impact":16,"substance":17,"depth":18,"authority":12,"freshness":19,"relevant":20,"comment":309},"方法规范、数据可靠的区域农用地适宜性案例研究，但属孟加拉地方尺度，对国内三农信息化仅有方法借鉴价值，不宜进入每日精选。",[311],{"name":306,"url":303},[26,27,313,314,315],"农用地适宜性","土地覆被","孟加拉农业",{"code":51,"slug":98,"name":99,"path":100,"parent_code":101},[318],{"code":45,"slug":319,"name":320,"path":321,"parent_code":101},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":104,"locked":13,"version":32,"method":33,"evidence":323},{"candidates":324,"agricultural_evidence":39},[325,329,333,338,345],{"code":51,"score":17,"supported":39,"title":326,"abstract":327,"tags":328},[27],[110,27,111],[27],{"code":45,"score":18,"supported":39,"title":330,"abstract":331,"tags":332},[47],[26,47],[26],{"code":260,"score":77,"supported":39,"title":334,"abstract":335,"tags":337},[],[336,265],"稻米",[],{"code":339,"score":77,"supported":39,"title":340,"abstract":341,"tags":344},"G",[],[342,343],"农村发展","rural development",[],{"code":119,"score":77,"supported":39,"title":346,"abstract":347,"tags":348},[],[122,123],[],[350,351],"Savar Upazila 农用地适宜性","Landsat-8 土地覆被分类","SavarUpazila农用地适宜性-5321","10.3329\u002Fbrds.v26i1.94265",{"doi":353,"openalex_id":355,"authors":356,"venue":366,"cited_by_count":71,"oa_url":303,"card":367,"direction":73,"ingested_from":74},"W7222895468",[357,359,361,363],{"name":358,"orcid":8},"Raju Ahmed",{"name":360,"orcid":8},"Shaikh Mehdee Mohammad",{"name":362,"orcid":8},"Ummul Momanin Coalee",{"name":364,"orcid":365},"Khandakar Hasan Mahmud","https:\u002F\u002Forcid.org\u002F0000-0003-1222-0791","The Bangladesh Rural Development Studies",{"tldr":368,"method":369,"finding":370,"direction":73,"opportunity":371},"用GIS与遥感多准则分析评估孟加拉Savar地区潜在农业用地适宜性。","Landsat-8监督分类提取土地利用，结合土壤、地形数据用AHP与MCE做适宜","仅12.42%为农地，高、中、边缘、不适宜分别占24.28%、29.53%、24.66%、21.53","可引入时序遥感与气候情景，动态评估适宜性变化并耦合作物种植决策支持。","2026-10-19T23:30:22.906704Z",{"id":374,"title":375,"url":376,"summary":377,"summary_zh":378,"content":8,"source_name":379,"source_url":376,"published_at":380,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":166,"score_detail":381,"sources":385,"tags":387,"primary_topic":8,"secondary_topics":391,"classification":392,"search_phrases":403,"slug":406,"view_count":20,"doi":407,"paper":408,"created_at":433},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",{"impact":16,"substance":382,"depth":17,"authority":383,"freshness":19,"relevant":20,"comment":384},22,13,"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。",[386],{"name":379,"url":376},[25,26,388,389,390],"NDVI","植被监测","佛罗里达",[],{"status":31,"locked":13,"version":32,"method":33,"evidence":393},{"candidates":394,"agricultural_evidence":13,"reason":55},[395,399],{"code":45,"score":127,"supported":13,"title":396,"abstract":397,"tags":398},[],[],[388,389,26],{"code":37,"score":20,"supported":13,"title":400,"abstract":401,"tags":402},[],[],[25],[404,405],"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":407,"openalex_id":409,"authors":410,"venue":379,"cited_by_count":71,"oa_url":427,"card":428,"direction":73,"ingested_from":74},"W7222444517",[411,414,417,420,423,425],{"name":412,"orcid":413},"A. Mandal","https:\u002F\u002Forcid.org\u002F0009-0004-0192-7497",{"name":415,"orcid":416},"Aman Kumar Thapa","https:\u002F\u002Forcid.org\u002F0009-0003-8956-6632",{"name":418,"orcid":419},"Madan Thapa Chhetri","https:\u002F\u002Forcid.org\u002F0009-0002-3567-7591",{"name":421,"orcid":422},"Hongbo Su","https:\u002F\u002Forcid.org\u002F0000-0002-2147-3646",{"name":424,"orcid":8},"Yan Yong",{"name":426,"orcid":8},"Fred Bloetscher","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2515-7620\u002Faeb5af\u002Fpdf",{"tldr":429,"method":430,"finding":431,"direction":73,"opportunity":432},"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":435,"title":436,"url":437,"summary":438,"summary_zh":439,"content":8,"source_name":440,"source_url":437,"published_at":441,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":71,"score_detail":442,"sources":444,"tags":446,"primary_topic":8,"secondary_topics":447,"classification":448,"search_phrases":463,"slug":466,"view_count":71,"doi":467,"paper":468,"created_at":484},5187,"Determination of Erosion Potential of Rioni River Basin Using GIS and RUSLE Model","https:\u002F\u002Fdoi.org\u002F10.47191\u002Fetj\u002Fv11i10.07","This study focuses on determining the soil erosion potential within the Rioni River basin using the Revised Universal Soil Loss Equation (RUSLE) and Geographical Information System (GIS) technologies. A 90-meter resolution Digital Elevation Model (DEM) from the Shuttle Radar Topographic Mission (SRTM) was utilized to delineate the basin boundaries and hydrological network. The RUSLE model, an empirical approach, was applied to estimate the long-term average annual soil loss. Individual RUSLE factors were computed using specific methodologies: the rainfall-runoff factor (R) was derived from a linear empirical relationship based on average atmospheric precipitation (1989-2021); the soil erodibility factor (K) was calculated using the Sharpley–Williams equation, yielding a value of 0.0184 for the prevalent soil type; the topographic factor (LS) was determined using a RUSLE-adapted approach; the cover-management factor (C) was calculated using the Durigon equation from NDVI values; and the support practice factor (P) was assigned based on slope and cultivation practices. Results indicate that the R-factor ranges from 313.65 to 473.89 MJ×mm\u002F(ha×hours), with an average of 391.02 MJ×mm\u002F(ha×hours). The maximum LS-factor reached 7137.810. The C-factor values (0.104 to 0.769) suggest a significant risk of water erosion across the basin. Overall, the study concludes that riverbed erosion predominantly occurs in the Rioni River basin, primarily influenced by flow velocity and slope.","本研究聚焦于利用修正通用土壤流失方程（RUSLE）与地理信息系统（GIS）技术，确定里奥尼河流域的土壤侵蚀潜力。研究采用航天飞机雷达地形测绘任务（SRTM）提供的90米分辨率数字高程模型（DEM）来划定流域边界与水文网络。RUSLE模型作为一种经验方法，被用于估算长期年均土壤流失量。各RUSLE因子采用特定方法计算：降雨-径流因子（R）基于1989—2021年平均大气降水量，通过线性经验关系推导得出；土壤可蚀性因子（K）采用Sharpley–Williams方程计算，针对主要土壤类型所得值为0.0184；地形因子（LS）采用RUSLE适配方法确定；覆盖管理因子（C）利用NDVI值，通过Durigon方程计算；支持措施因子（P）则依据坡度与耕作方式赋值。结果表明，R因子范围为313.65至473.89 MJ×mm\u002F（ha×小时），平均值为391.02 MJ×mm\u002F（ha×小时）。LS因子最大值达到7137.810。C因子值（0.104至0.769）表明全流域存在显著的水蚀风险。总体而言，研究认为里奥尼河流域的侵蚀主要发生在河床，主要受流速与坡度影响。","Engineering and Technology Journal","2026-10-15T00:00:00Z",{"impact":71,"substance":71,"depth":71,"authority":71,"freshness":71,"relevant":71,"comment":443},"该研究以格鲁吉亚里奥尼河流域为对象，属于水土保持与遥感应用类论文，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。",[445],{"name":440,"url":437},[26,95,27],[],{"status":31,"locked":13,"version":32,"method":33,"evidence":449},{"candidates":450,"agricultural_evidence":13,"reason":55},[451,455,459],{"code":51,"score":18,"supported":39,"title":452,"abstract":453,"tags":454},[27],[110,27],[27],{"code":119,"score":77,"supported":39,"title":456,"abstract":457,"tags":458},[],[122,123],[],{"code":45,"score":20,"supported":13,"title":460,"abstract":461,"tags":462},[],[],[26],[464,465],"RUSLE GIS 土壤侵蚀","土壤侵蚀 遥感 GIS","RUSLEGIS土壤侵蚀-5187","10.47191\u002Fetj\u002Fv11i10.07",{"doi":467,"openalex_id":469,"authors":470,"venue":440,"cited_by_count":71,"oa_url":477,"card":478,"direction":483,"ingested_from":74},"W7222052256",[471,473,475],{"name":472,"orcid":8},"GIORGI CHITIASHVILI",{"name":474,"orcid":8},"IRMA INASHVILI",{"name":476,"orcid":8},"IRINA KLIMIASHVILI","https:\u002F\u002Fwww.everant.org\u002Findex.php\u002Fetj\u002Farticle\u002Fdownload\u002F2054\u002F2236",{"tldr":479,"method":480,"finding":481,"direction":73,"opportunity":482},"用GIS和RUSLE模型评估里奥尼河流域土壤侵蚀潜力。","RUSLE模型结合SRTM DEM、NDVI与降水数据计算各侵蚀因子。","流域以河床侵蚀为主，主要受流速和坡度影响，存在显著水蚀风险。","可引入高分辨率遥感与时序NDVI改进C因子，并耦合水文模型动态评估侵蚀。","数字乡村与农业信息化","2026-10-17T23:30:38.427888Z"]