[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4592":3,"related-4592":106},{"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":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":81,"slug":84,"view_count":85,"doi":86,"paper":87,"created_at":105},4592,"Desertification Assessment in the Hodna Basin, Algeria using Desertification Degree Index and Geospatial Techniques","https:\u002F\u002Fdoi.org\u002F10.56093\u002Faaz.v65i3.177561","This study assesses desertification in the Hodna Basin, Algeria, using remote sensing and Geographic Information Systems (GIS) through the development of a Desertification Degree Index (DDI) based on feature-space analysis. Landsat 9 imagery was used to derive four spectral indices: the Normalized Difference Vegetation Index (NDVI), surface albedo, the Modified Soil-Adjusted Vegetation Index (MSAVI), and the Topsoil Grain Size Index (TGSI). Five feature-space combinations were evaluated, with the Albedo-NDVI and TGSI-NDVI models exhibiting the strongest statistically significant negative correlations. These two models were subsequently used for DDI generation and comparison. The Albedo-NDVI model achieved the highest performance, with an Overall Accuracy of 91.9% and a Kappa coefficient of 0.89, substantially outperforming the TGSI-NDVI model. The resulting desertification map indicates that approximately 65% of the Hodna Basin is affected by high to severe desertification, particularly in the central and southern regions, where prolonged aridity and intensive human activities have accelerated land degradation. In contrast, the northern part of the basin exhibits relatively low desertification levels owing to denser vegetation cover and more favorable environmental conditions. These findings demonstrate the robustness of the Albedo-NDVI feature-space model for desertification assessment in arid and semi-arid environments and highlight the value of integrating spectral indices with statistical feature-space analysis. The proposed approach provides a reliable and objective framework for large-scale desertification mapping and offers valuable decision support for soil conservation, ecosystem restoration, and sustainable land management in vulnerable dryland regions.","本研究利用遥感与地理信息系统（GIS），通过构建基于特征空间的荒漠化程度指数（DDI），对阿尔及利亚霍德纳盆地的荒漠化进行了评估。研究采用Landsat 9影像提取了四个光谱指数：归一化差异植被指数（NDVI）、地表反照率、修正土壤调整植被指数（MSAVI）和表土粒度指数（TGSI）。对五种特征空间组合进行了评价，其中反照率-NDVI和TGSI-NDVI模型表现出最强的统计显著负相关。随后，这两个模型被用于DDI生成与比较。反照率-NDVI模型取得了最佳性能，总体精度为91.9%，Kappa系数为0.89，显著优于TGSI-NDVI模型。生成的荒漠化图表明，霍德纳盆地约65%的区域受到高度至严重荒漠化的影响，尤其是在中部和南部地区，长期的干旱和密集的人类活动加速了土地退化。相比之下，盆地北部由于植被覆盖较密、环境条件较为有利，荒漠化程度相对较低。这些发现证明了反照率-NDVI特征空间模型在干旱和半干旱环境荒漠化评估中的稳健性，并凸显了将光谱指数与统计特征空间分析相结合的价值。所提出的方法为大规模荒漠化制图提供了可靠且客观的框架，并为脆弱旱地地区的土壤保持、生态系统恢复和可持续土地管理提供了有价值的决策支持。",null,"Annals of Arid Zone","2026-10-08T00:00:00Z","论文",10,false,73,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,13,8,1,"该研究提出基于Landsat 9与特征空间分析的荒漠化程度指数，方法新颖、精度高，对干旱区土地退化监测与农业可持续发展有参考价值，但属区域性案例研究，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感","土地退化","GIS","植被指数","荒漠化监测",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":80},[39,48,54,62,69,75],{"code":40,"score":13,"supported":41,"title":42,"abstract":43,"tags":47},"I4",true,[],[44,45,29,46],"地理信息","空间分析","geographic information",[29],{"code":49,"score":21,"supported":41,"title":50,"abstract":51,"tags":53},"I3",[],[27,52],"remote sensing",[30,27],{"code":55,"score":56,"supported":41,"title":57,"abstract":58,"tags":61},"H2",6,[],[59,60],"土壤","soil",[],{"code":63,"score":64,"supported":41,"title":65,"abstract":66,"tags":68},"H7",3,[],[67],"干旱",[],{"code":70,"score":64,"supported":41,"title":71,"abstract":72,"tags":74},"I2",[],[73],"decision support",[],{"code":76,"score":22,"supported":14,"title":77,"abstract":78,"tags":79},"H1",[],[],[28],"农业相关性需要复核",[82,83],"Albedo-NDVI 荒漠化","荒漠化监测 土地退化 植被指数 遥感","Albedo-NDVI荒漠化-4592",0,"10.56093\u002Faaz.v65i3.177561",{"doi":86,"openalex_id":88,"authors":89,"venue":10,"cited_by_count":85,"oa_url":97,"card":98,"direction":102,"ingested_from":104},"W7220932821",[90,92,94],{"name":91,"orcid":9},"Sami Mekentichi",{"name":93,"orcid":9},"Salah Abdesselam",{"name":95,"orcid":96},"Razika Sahnouni","https:\u002F\u002Forcid.org\u002F0000-0003-4914-6804","http:\u002F\u002Fepubs.icar.org.in\u002Findex.php\u002FAAZ\u002Farticle\u002Fdownload\u002F177561\u002F68055",{"tldr":99,"method":100,"finding":101,"direction":102,"opportunity":103},"基于Landsat 9与特征空间分析构建荒漠化程度指数，评估阿尔及利亚Hodna盆地荒漠化。","Landsat 9影像提取NDVI、反照率、MSAVI、TGSI，用Albedo","Albedo-NDVI模型精度最高（总体精度91.9%，Kappa 0.89），约65%区域处于高至","农业遥感与作物表型","可将Albedo-NDVI特征空间模型迁移至其他干旱区，并结合时序影像与人类活动因子做驱动力归因。","openalex","2026-10-10T23:30:29.517004Z",{"total":56,"page":22,"page_size":56,"items":107},[108,182,251,324,398,457],{"id":109,"title":110,"url":111,"summary":112,"summary_zh":113,"content":9,"source_name":114,"source_url":111,"published_at":115,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":116,"score_detail":117,"sources":121,"tags":123,"primary_topic":127,"secondary_topics":132,"classification":133,"search_phrases":162,"slug":165,"view_count":85,"doi":166,"paper":167,"created_at":181},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":21,"substance":19,"depth":118,"authority":17,"freshness":119,"relevant":22,"comment":120},14,9,"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。",[122],{"name":114,"url":111},[27,124,125,29,126],"土地利用","土壤侵蚀","水土保持",{"code":40,"slug":128,"name":129,"path":130,"parent_code":131},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":134,"locked":14,"version":35,"method":36,"evidence":135},"classified",{"candidates":136,"agricultural_evidence":41},[137,141,146,150,156],{"code":40,"score":13,"supported":41,"title":138,"abstract":139,"tags":140},[],[44,29,46],[29],{"code":49,"score":142,"supported":41,"title":143,"abstract":144,"tags":145},7,[],[27,52],[27],{"code":55,"score":56,"supported":41,"title":147,"abstract":148,"tags":149},[],[59,60],[],{"code":151,"score":64,"supported":41,"title":152,"abstract":153,"tags":155},"H3",[],[154],"灌溉",[],{"code":157,"score":158,"supported":14,"title":159,"abstract":160,"tags":161},"H6",2,[],[],[124,126],[163,164],"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":166,"openalex_id":168,"authors":169,"venue":114,"cited_by_count":85,"oa_url":9,"card":175,"direction":102,"ingested_from":104},"W7223117733",[170,172],{"name":171,"orcid":9},"MERİÇ KURT",{"name":173,"orcid":174},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":176,"method":177,"finding":178,"direction":179,"opportunity":180},"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":183,"title":184,"url":185,"summary":186,"summary_zh":187,"content":9,"source_name":188,"source_url":185,"published_at":189,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":190,"score_detail":191,"sources":195,"tags":197,"primary_topic":201,"secondary_topics":205,"classification":206,"search_phrases":230,"slug":233,"view_count":85,"doi":234,"paper":235,"created_at":250},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":17,"substance":192,"depth":193,"authority":17,"freshness":119,"relevant":22,"comment":194},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。",[196],{"name":188,"url":185},[198,27,29,199,200],"农业人工智能","野生动物保护","人兽冲突",{"code":131,"slug":202,"name":203,"path":204,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":207,"locked":14,"version":35,"method":36,"evidence":208},"partial",{"candidates":209,"agricultural_evidence":41,"reason":229},[210,216,220,225],{"code":70,"score":13,"supported":41,"title":211,"abstract":212,"tags":215},[],[213,214,73],"机器学习","machine learning",[198],{"code":40,"score":13,"supported":41,"title":217,"abstract":218,"tags":219},[],[44,29,46],[29],{"code":49,"score":221,"supported":41,"title":222,"abstract":223,"tags":224},4,[],[27],[27],{"code":157,"score":22,"supported":14,"title":226,"abstract":227,"tags":228},[],[],[199],"一级领域明确，二级方向需要细分",[231,232],"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":234,"openalex_id":236,"authors":237,"venue":188,"cited_by_count":85,"oa_url":185,"card":244,"direction":249,"ingested_from":104},"W7222999966",[238,240,242],{"name":239,"orcid":9},"Bhutia Dechen Doma",{"name":241,"orcid":9},"Swarup Das",{"name":243,"orcid":9},"Mandal Rakesh Kumar",{"tldr":245,"method":246,"finding":247,"direction":102,"opportunity":248},"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":252,"title":253,"url":254,"summary":255,"summary_zh":256,"content":9,"source_name":257,"source_url":254,"published_at":258,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":259,"score_detail":260,"sources":264,"tags":266,"primary_topic":270,"secondary_topics":271,"classification":272,"search_phrases":307,"slug":310,"view_count":85,"doi":311,"paper":312,"created_at":323},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":21,"substance":261,"depth":262,"authority":64,"freshness":21,"relevant":22,"comment":263},16,15,"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。",[265],{"name":257,"url":254},[267,27,29,268,269],"决策支持系统","水稻种植","土地评价",{"code":131,"slug":202,"name":203,"path":204,"parent_code":9},[],{"status":207,"locked":14,"version":35,"method":36,"evidence":273},{"candidates":274,"agricultural_evidence":41,"reason":229},[275,279,283,292,299,303],{"code":70,"score":17,"supported":41,"title":276,"abstract":277,"tags":278},[73],[73],[267],{"code":40,"score":17,"supported":41,"title":280,"abstract":281,"tags":282},[29],[29],[29],{"code":284,"score":13,"supported":41,"title":285,"abstract":286,"tags":291},"A1",[],[287,288,289,290],"水稻","稻田","rice","paddy",[268],{"code":293,"score":56,"supported":41,"title":294,"abstract":295,"tags":298},"A8",[],[296,297],"粮食安全","food security",[],{"code":55,"score":56,"supported":41,"title":300,"abstract":301,"tags":302},[],[59,60],[],{"code":49,"score":22,"supported":14,"title":304,"abstract":305,"tags":306},[],[],[27],[308,309],"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":311,"openalex_id":313,"authors":314,"venue":257,"cited_by_count":85,"oa_url":254,"card":318,"direction":249,"ingested_from":104},"W7222963628",[315],{"name":316,"orcid":317},"Zeinab Maddahi","https:\u002F\u002Forcid.org\u002F0000-0002-2824-2736",{"tldr":319,"method":320,"finding":321,"direction":102,"opportunity":322},"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":325,"title":326,"url":327,"summary":328,"summary_zh":329,"content":9,"source_name":330,"source_url":327,"published_at":331,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":332,"score_detail":333,"sources":335,"tags":337,"primary_topic":341,"secondary_topics":342,"classification":347,"search_phrases":374,"slug":377,"view_count":85,"doi":378,"paper":379,"created_at":397},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",60,{"impact":21,"substance":19,"depth":262,"authority":13,"freshness":119,"relevant":22,"comment":334},"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。",[336],{"name":330,"url":327},[27,29,338,339,340],"农用地适宜性","土地覆被","孟加拉农业",{"code":40,"slug":128,"name":129,"path":130,"parent_code":131},[343],{"code":49,"slug":344,"name":345,"path":346,"parent_code":131},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":134,"locked":14,"version":35,"method":36,"evidence":348},{"candidates":349,"agricultural_evidence":41},[350,354,358,363,370],{"code":40,"score":19,"supported":41,"title":351,"abstract":352,"tags":353},[29],[44,29,46],[29],{"code":49,"score":262,"supported":41,"title":355,"abstract":356,"tags":357},[52],[27,52],[27],{"code":284,"score":56,"supported":41,"title":359,"abstract":360,"tags":362},[],[361,289],"稻米",[],{"code":364,"score":56,"supported":41,"title":365,"abstract":366,"tags":369},"G",[],[367,368],"农村发展","rural development",[],{"code":55,"score":56,"supported":41,"title":371,"abstract":372,"tags":373},[],[59,60],[],[375,376],"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":378,"openalex_id":380,"authors":381,"venue":391,"cited_by_count":85,"oa_url":327,"card":392,"direction":102,"ingested_from":104},"W7222895468",[382,384,386,388],{"name":383,"orcid":9},"Raju Ahmed",{"name":385,"orcid":9},"Shaikh Mehdee Mohammad",{"name":387,"orcid":9},"Ummul Momanin Coalee",{"name":389,"orcid":390},"Khandakar Hasan Mahmud","https:\u002F\u002Forcid.org\u002F0000-0003-1222-0791","The Bangladesh Rural Development Studies",{"tldr":393,"method":394,"finding":395,"direction":102,"opportunity":396},"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":399,"title":400,"url":401,"summary":402,"summary_zh":403,"content":9,"source_name":404,"source_url":401,"published_at":405,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":406,"score_detail":407,"sources":409,"tags":411,"primary_topic":415,"secondary_topics":416,"classification":417,"search_phrases":430,"slug":433,"view_count":85,"doi":434,"paper":435,"created_at":456},5243,"Identificación de índices de vegetación relacionados con componentes de rendimiento de frijol común (Phaseolus vulgaris L.) mediante el uso de sensores multiespectrales","https:\u002F\u002Fdoi.org\u002F10.18845\u002Ftm.v39i4.8378","Bean production in Costa Rica supplies only 20 % of domestic consumption, requiring the importation of the remaining amount; this situation highlights the need to know in advance the actual productive capacity of local farmers to improve planning, agronomic management, and import regulation. Therefore, the objective of this study was to evaluate the use of multispectral data and vegetation indices to analyze their relationship with yield components in bean varieties cultivated in different production areas of Costa Rica and to identify the yield component with the greatest spectral association. The research was conducted at three sites located in Alajuela, Costa Rica. Multispectral reflectance data were collected during the R5 phenological stage using an airborne multispectral sensor. Subsequently, yield components were quantified per meter of crop row, including 100-grain weight, number of pods per meter, number of plants, number of grains, and total grain weight. The number of pods showed the highest association with spectral variables and was therefore identified as the yield component with the greatest potential for analysis through remote sensing. The SR (Simple Ratio) and GCI (Green Chlorophyll Index) indices showed correlations greater than 0.7 with this variable, being identified as the vegetation indices with the highest spectral association during the R5 stage of the crop.","哥斯达黎加的菜豆（bean）产量仅能满足国内消费量的20%，其余部分需要进口；这一状况凸显了提前了解当地农民实际生产能力的必要性，以改进生产规划、农艺管理和进口调控。因此，本研究旨在评估多光谱数据和植被指数（vegetation indices）的应用，分析其与哥斯达黎加不同产区菜豆品种产量构成因素（yield components）之间的关系，并确定与光谱关联性最强的产量构成因素。研究在哥斯达黎加阿拉胡埃拉（Alajuela）的三个地点开展。在R5物候期使用机载多光谱传感器采集多光谱反射率数据。随后，按每米作物行量化产量构成因素，包括百粒重、每米豆荚数、植株数、籽粒数和籽粒总重。豆荚数与光谱变量的关联性最高，因此被确定为最具遥感分析潜力的产量构成因素。SR（简单比值，Simple Ratio）和GCI（绿色叶绿素指数，Green Chlorophyll Index）与该变量的相关系数大于0.7，被确定为作物R5期光谱关联性最高的植被指数。","Revista Tecnología en Marcha","2026-10-16T00:00:00Z",62,{"impact":21,"substance":19,"depth":262,"authority":17,"freshness":119,"relevant":22,"comment":408},"该研究利用机载多光谱传感器识别与菜豆产量组分相关的植被指数，方法清晰、结论具体，但属于哥斯达黎加的小区域研究，公共影响有限，可作为遥感作物监测领域的技术性资讯收录。",[410],{"name":404,"url":401},[412,413,27,30,414],"智慧农业","产量预测","菜豆",{"code":49,"slug":344,"name":345,"path":346,"parent_code":131},[],{"status":134,"locked":14,"version":35,"method":36,"evidence":418},{"candidates":419,"agricultural_evidence":41},[420,425],{"code":49,"score":17,"supported":41,"title":421,"abstract":422,"tags":424},[],[27,52,423],"multispectral",[413,30,27],{"code":426,"score":22,"supported":14,"title":427,"abstract":428,"tags":429},"I1",[],[],[412],[431,432],"哥斯达黎加 菜豆 多光谱 遥感","Phaseolus vulgaris 植被指数 产量","哥斯达黎加菜豆多光谱遥感-5243","10.18845\u002Ftm.v39i4.8378",{"doi":434,"openalex_id":436,"authors":437,"venue":404,"cited_by_count":85,"oa_url":450,"card":451,"direction":102,"ingested_from":104},"W7222720635",[438,441,444,447],{"name":439,"orcid":440},"Guillermo Vargas-Hernández","https:\u002F\u002Forcid.org\u002F0000-0003-1410-3898",{"name":442,"orcid":443},"Eddison José Araya-Morales","https:\u002F\u002Forcid.org\u002F0009-0001-0550-414X",{"name":445,"orcid":446},"Juan Carlos Hernández","https:\u002F\u002Forcid.org\u002F0000-0002-1915-3213",{"name":448,"orcid":449},"Ignacio Mora-Cordero","https:\u002F\u002Forcid.org\u002F0009-0007-0925-0099","https:\u002F\u002Frevistas.tec.ac.cr\u002Findex.php\u002Ftec_marcha\u002Farticle\u002Fdownload\u002F8378\u002F8641",{"tldr":452,"method":453,"finding":454,"direction":102,"opportunity":455},"用机载多光谱传感器评估菜豆产量构成与植被指数的关系。","机载多光谱遥感获取R5期反射率，结合地面产量构成测定与相关分析。","豆荚数与光谱变量关联最强，SR和GCI指数与其相关系数超过0.7。","可探索将SR\u002FGCI用于菜豆产量早期预测，并扩展到多品种、多生育期与无人机高光谱验证。","2026-10-18T23:30:20.259545Z",{"id":458,"title":459,"url":460,"summary":461,"summary_zh":462,"content":9,"source_name":463,"source_url":460,"published_at":464,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":85,"score_detail":465,"sources":467,"tags":469,"primary_topic":9,"secondary_topics":470,"classification":471,"search_phrases":486,"slug":489,"view_count":85,"doi":490,"paper":491,"created_at":507},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":85,"substance":85,"depth":85,"authority":85,"freshness":85,"relevant":85,"comment":466},"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。",[468],{"name":463,"url":460},[27,125,29],[],{"status":34,"locked":14,"version":35,"method":36,"evidence":472},{"candidates":473,"agricultural_evidence":14,"reason":80},[474,478,482],{"code":40,"score":262,"supported":41,"title":475,"abstract":476,"tags":477},[29],[44,29],[29],{"code":55,"score":56,"supported":41,"title":479,"abstract":480,"tags":481},[],[59,60],[],{"code":49,"score":22,"supported":14,"title":483,"abstract":484,"tags":485},[],[],[27],[487,488],"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":490,"openalex_id":492,"authors":493,"venue":463,"cited_by_count":85,"oa_url":500,"card":501,"direction":506,"ingested_from":104},"W7222052256",[494,496,498],{"name":495,"orcid":9},"GIORGI CHITIASHVILI",{"name":497,"orcid":9},"IRMA INASHVILI",{"name":499,"orcid":9},"IRINA KLIMIASHVILI","https:\u002F\u002Fwww.everant.org\u002Findex.php\u002Fetj\u002Farticle\u002Fdownload\u002F2054\u002F2236",{"tldr":502,"method":503,"finding":504,"direction":102,"opportunity":505},"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"]