[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4312":3,"related-4312":103},{"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":31,"secondary_topics":36,"classification":37,"search_phrases":76,"slug":79,"view_count":21,"doi":80,"paper":81,"created_at":102},4312,"Identification of Cultivable Wastelands in Meghalaya, India using Geospatial Technology for Agricultural Reclamation","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i101131","Cultivable wastelands suitable for cultivation are a significant land resource that can be utilised to improve agricultural productivity and sustainable land management. The present study aimed to estimate the cultivable wastelands of Meghalaya using Remote Sensing and Geographic Information System (GIS) techniques and to assess the possibilities for reclaiming wastelands for agriculture. Multispectral satellite data from the LISS-IV sensor for the period 2025-2026 were processed to create a False Colour Composite (FCC), and Land Use\u002FLand Cover (LULC) classification was performed using visual image interpretation to distinguish the major cultivable wasteland categories: Dense Scrub Land, Sparse Scrub Land, Grasslands\u002FGrazing Lands, and Shifting Cultivation Area. About 340,673.36 ha, representing almost 15.2% of the total geographical area of Meghalaya, were identified as cultivable wastelands. Among the identified categories, Sparse Scrub Land occupied the largest area (206,595.18 ha), followed by Dense Scrub Land (99,488.89 ha), Shifting Cultivation Area (34,331.34 ha), and Grasslands\u002FGrazing Lands (257.94 ha). The distribution of these wastelands suggests considerable potential for reclamation through appropriate land restoration and sustainable management practices. The spatial database and thematic maps generated provide baseline information for land-use planning, resource management, and policy formulation aimed at enhancing agricultural productivity and the sustainable utilisation of wastelands in Meghalaya.","适宜耕作的 cultivable wasteland（可耕荒地）是一项重要的土地资源，可用于提高农业生产力和可持续土地管理。本研究旨在利用遥感（Remote Sensing）和地理信息系统（Geographic Information System, GIS）技术估算梅加拉亚邦的可耕荒地，并评估将荒地开垦用于农业的可能性。对2025—2026年LISS-IV传感器多光谱卫星数据进行处理，生成假彩色合成影像（False Colour Composite, FCC），并通过目视图像解译进行土地利用\u002F土地覆盖（Land Use\u002FLand Cover, LULC）分类，以区分主要可耕荒地类别：密灌丛地、疏灌丛地、草地\u002F放牧地以及轮垦区。约340,673.36公顷土地被识别为可耕荒地，占梅加拉亚邦总地理面积的近15.2%。在已识别类别中，疏灌丛地面积最大（206,595.18公顷），其次为密灌丛地（99,488.89公顷）、轮垦区（34,331.34公顷）和草地\u002F放牧地（257.94公顷）。这些荒地的分布表明，通过适当的土地恢复和可持续管理措施，具有相当大的开垦潜力。所生成的空间数据库和专题图件为土地利用规划、资源管理和政策制定提供了基础信息，旨在提高梅加拉亚邦的农业生产力和实现荒地的可持续利用。",null,"Journal of Geography Environment and Earth Science International","2026-10-05T00:00:00Z","论文",10,false,69,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,20,16,9,1,"基于LISS-IV遥感与GIS的印度梅加拉亚邦可耕荒地清查，数据翔实、方法规范，对农业土地复垦与空间规划有参考价值，但属区域案例，公共影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"可持续农业","耕地保护","遥感监测","GIS","土地整治",{"code":32,"slug":33,"name":34,"path":35,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"partial","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45,"reason":75},[43,52,59,65,70],{"code":44,"score":13,"supported":45,"title":46,"abstract":47,"tags":51},"I3",true,[],[48,49,50],"遥感","remote sensing","multispectral",[28],{"code":53,"score":13,"supported":45,"title":54,"abstract":55,"tags":58},"I4",[],[56,29,57],"地理信息","geographic information",[29],{"code":60,"score":61,"supported":14,"title":62,"abstract":63,"tags":64},"H1",2,[],[],[30,27],{"code":66,"score":21,"supported":14,"title":67,"abstract":68,"tags":69},"G2",[],[],[30],{"code":71,"score":21,"supported":14,"title":72,"abstract":73,"tags":74},"H8",[],[],[26],"一级领域明确，二级方向需要细分",[77,78],"Meghalaya 可耕荒地 遥感","LISS-IV 土地利用 分类","Meghalaya可耕荒地遥感-4312","10.9734\u002Fjgeesi\u002F2026\u002Fv30i101131",{"doi":80,"openalex_id":82,"authors":83,"venue":10,"cited_by_count":94,"oa_url":6,"card":95,"direction":99,"ingested_from":101},"W7220436664",[84,86,89,91],{"name":85,"orcid":9},"Ronak Meena",{"name":87,"orcid":88},"Chandan Goswami","https:\u002F\u002Forcid.org\u002F0000-0002-9487-5080",{"name":90,"orcid":9},"C. P. Suresh",{"name":92,"orcid":93},"Akmaul Hoque","https:\u002F\u002Forcid.org\u002F0009-0002-4475-566X",0,{"tldr":96,"method":97,"finding":98,"direction":99,"opportunity":100},"利用遥感与GIS识别印度梅加拉亚邦可耕荒地并评估其农业复垦潜力。","LISS-IV多光谱影像、假彩色合成与目视解译的土地利用分类。","识别出约34万公顷可耕荒地，占全邦15.2%，以稀疏灌木地为主。","农业遥感与作物表型","可结合机器学习自动分类与土壤、气候数据，构建荒地复垦适宜性评价模型。","openalex","2026-10-06T23:30:20.340575Z",{"total":104,"page":21,"page_size":104,"items":105},6,[106,169,245,303,380,454],{"id":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":9,"source_name":112,"source_url":109,"published_at":113,"category":12,"cover_url":9,"hotness":114,"is_selected":14,"score":115,"score_detail":116,"sources":120,"tags":124,"primary_topic":127,"secondary_topics":128,"classification":129,"search_phrases":153,"slug":156,"view_count":94,"doi":157,"paper":158,"created_at":168},4305,"A Comparative Geographical Analysis of Land Use Patterns in Akrani and Nandurbar Tehsils of Nandurbar District","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23155213","Abstract \"A Comparative Geographical Analysis of Land Use Patterns in Akrani and Nandurbar Tehsils in Nandurbar District,\" aims to analyze and contrast the features and spatial distribution of different land-use categories in the two tehsils. An key measure of how human activity and the physical environment interact is land use. Major land-use categories include agricultural land, forest land, fallow land, barren and uncultivable land, grazing land, built-up land, and water bodies are the focus of the study. Akrani Tehsil is distinguished by hilly and rugged terrain, a substantial amount of forest cover, and primarily rain-fed agriculture, whereas Nandurbar Tehsil has relatively favorable terrain, agricultural conditions, irrigation facilities, and a higher concentration of settlements and other non-agricultural activities. In addition to geographical methods like GIS and remote sensing when appropriate, the study makes use of secondary data from government reports, Census publications, statistical records, maps, and other pertinent sources. In order to determine the regional variances, similarities, and differences in land-use patterns, as well as to investigate the impact of socioeconomic and physical factors on land usage, a comparative study is conducted. By improving knowledge of regional differences in land use, the study is expected to support sustainable resource management, land-use planning, and balanced regional development in the Nandurbar District.","《阿克兰尼与南杜尔巴尔塔卢卡土地利用模式的比较地理分析——以楠杜尔巴尔县为例》旨在分析和对比这两个塔卢卡不同土地利用类别的特征与空间分布。土地利用是衡量人类活动与自然环境相互作用的重要指标。研究重点关注的主要土地利用类别包括农业用地、林地、休耕地、荒地和不可耕种地、放牧地、建设用地及水体。阿克兰尼塔卢卡以丘陵崎岖地形、大面积森林覆盖和以雨养农业为主为特征，而南杜尔巴尔塔卢卡则拥有相对有利的地形、农业条件、灌溉设施，以及更集中的居民点和其他非农业活动。本研究在适当情况下运用地理信息系统（GIS）和遥感等地理学方法，同时利用来自政府报告、人口普查出版物、统计记录、地图及其他相关来源的二手数据。为确定土地利用模式的区域差异、相似性与不同之处，并探究社会经济和自然因素对土地利用的影响，本研究进行了比较分析。通过增进对土地利用区域差异的认识，该研究有望为楠杜尔巴尔县的可持续资源管理、土地利用规划和区域均衡发展提供支持。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-31T00:00:00Z",25,45,{"impact":104,"substance":117,"depth":118,"authority":17,"freshness":94,"relevant":21,"comment":119},14,13,"印度地方性土地利用对比研究，方法常规、无全国性价值，且发布日期晚于今日，时效性为零，不建议进入每日精选。",[121,122],{"name":112,"url":109},{"name":112,"url":123},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23155212",[28,125,126,29],"土地利用","区域发展",{"code":32,"slug":33,"name":34,"path":35,"parent_code":9},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":130},{"candidates":131,"agricultural_evidence":45,"reason":75},[132,137,141,148],{"code":44,"score":133,"supported":45,"title":134,"abstract":135,"tags":136},7,[],[48,49],[28],{"code":53,"score":133,"supported":45,"title":138,"abstract":139,"tags":140},[],[56,29],[29],{"code":142,"score":104,"supported":45,"title":143,"abstract":144,"tags":147},"H3",[],[145,146],"灌溉","irrigation",[],{"code":149,"score":21,"supported":14,"title":150,"abstract":151,"tags":152},"H6",[],[],[125],[154,155],"Nandurbar 土地利用 对比","Akrani Nandurbar 遥感","Nandurbar土地利用对比-4305","10.5281\u002Fzenodo.23155213",{"doi":157,"openalex_id":159,"authors":160,"venue":112,"cited_by_count":94,"oa_url":109,"card":163,"direction":99,"ingested_from":101},"W7220403169",[161],{"name":162,"orcid":9},"Amol Rameshwarrao Bhuyar",{"tldr":164,"method":165,"finding":166,"direction":99,"opportunity":167},"对比分析印度Nandurbar县Akrani与Nandurbar两个乡的土地利用格局差异。","基于政府报告、普查等二手数据，结合GIS与遥感进行对比地理分析。","Akrani以林地、雨养农业为主，Nandurbar地形与灌溉条件更优、建设用地更集中。","可引入多时相遥感影像与农户调查，量化地形—灌溉差异对土地利用变化的驱动机制。","2026-10-06T23:30:19.626838Z",{"id":170,"title":171,"url":172,"summary":173,"summary_zh":174,"content":9,"source_name":112,"source_url":172,"published_at":175,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":176,"score_detail":177,"sources":179,"tags":181,"primary_topic":186,"secondary_topics":187,"classification":188,"search_phrases":227,"slug":230,"view_count":94,"doi":231,"paper":232,"created_at":244},5563,"AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531525","Abstract: Crop diseases and pest infestations remain persistent threats to plantation productivity, with pests estimated to destroy up to 40% of global crop production and plant diseases and invasive insects imposing substantial economic losses worldwide (Food and Agriculture Organization of the United Nations [FAO], 2019). Climate change is intensifying these risks by altering the geographic range, seasonality, and population dynamics of pathogens and pests (Gullino et al., 2021; Skendžić et al., 2021). This paper examines how artificial intelligence (AI) — including convolutional neural networks (CNNs), vision transformers (ViTs), unmanned aerial vehicle (UAV)-based multispectral and hyperspectral sensing, and Internet of Things (IoT)-enabled monitoring — can strengthen the early detection of biotic stress in plantation crops and thereby support plantation risk management. Drawing on recent peer-reviewed and industry literature, the paper synthesises evidence on detection performance, deployment modalities, and pathways for integration into enterprise risk-management systems. It proposes a conceptual framework in which AI-enabled early-warning systems operate as a continuous, spatially explicit risk-sensing layer linking agronomic observations with operational, financial, and insurance decision-making. AI can improve the timeliness, spatial precision, and consistency of surveillance; however, its risk-management value depends on field validation, representative training data, reliable infrastructure, affordable deployment, and appropriate governance. AI should therefore currently be positioned as decision support that augments, rather than replaces, agronomic expertise and ground-truthing. Keywords: artificial intelligence; plant disease detection; pest infestation; precision agriculture; plantation risk management; machine learning; remote sensing. Title: AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management Author: Dr. Geetha Govindan K Gopalakrishnan International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 14, Issue 2, October 2026 - March 2027 Page No: 54-59 Research Publish Journals Website: www.researchpublish.com Published Date: 21-October-2026 DOI: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526 Paper Download Link (Source) https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-management","摘要：作物病害与虫害侵染始终是种植园生产力的持续威胁，据估计，害虫每年可摧毁全球高达40%的作物产量，植物病害与入侵昆虫在全球范围内造成重大经济损失（联合国粮食及农业组织[FAO]，2019）。气候变化通过改变病原体与害虫的地理分布范围、季节性规律及种群动态，正在加剧这些风险（Gullino等，2021；Skendžić等，2021）。本文探讨人工智能（AI）——包括卷积神经网络（CNN）、视觉变换器（ViT）、基于无人机（UAV）的多光谱与高光谱遥感，以及物联网（IoT）赋能的监测技术——如何强化种植园作物生物胁迫的早期检测，从而支撑种植园风险管理。本文基于近期同行评审文献与行业文献，综合梳理了检测性能、部署模式及其融入企业风险管理系统的路径等方面的证据。文章提出一个概念框架，其中AI赋能的预警系统作为持续运行、空间显性的风险感知层，将农艺观测与运营、财务及保险决策相衔接。AI能够提升监测的及时性、空间精度与一致性；然而，其风险管理价值取决于田间验证、具有代表性的训练数据、可靠的基础设施、可负担的部署成本以及适当的治理。因此，当前应将AI定位为决策支持工具，用以增强而非替代农艺专业知识与实地核验。关键词：人工智能；植物病害检测；虫害侵染；精准农业；种植园风险管理；机器学习；遥感。题目：AI驱动的作物病害与虫害侵染早期检测：种植园风险管理的新范式。作者：Geetha Govindan K Gopalakrishnan博士。《国际管理与商业创新期刊》ISSN 2348-7585（在线）第14卷第2期，2026年10月—2027年3月，第54-59页。研究出版期刊网站：www.researchpublish.com。发布日期：2026年10月21日。DOI：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526。论文下载链接（来源）：https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-ma","2026-10-21T00:00:00Z",56,{"impact":17,"substance":19,"depth":118,"authority":104,"freshness":20,"relevant":21,"comment":178},"综述性论文，系统梳理AI与遥感在作物病虫害早期预警中的应用并提出风险管理框架，有参考价值但方法创新与数据规模有限，信源为普通出版平台。",[180],{"name":112,"url":172},[182,183,28,184,185],"智慧农业","农业人工智能","风险管理","病虫害监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":9},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":189},{"candidates":190,"agricultural_evidence":45,"reason":75},[191,200,205,213,220],{"code":192,"score":13,"supported":45,"title":193,"abstract":194,"tags":199},"I2",[],[195,196,197,198],"机器学习","machine learning","artificial intelligence","decision support",[183],{"code":44,"score":13,"supported":45,"title":201,"abstract":202,"tags":204},[],[48,49,50,203],"hyperspectral",[28],{"code":206,"score":20,"supported":45,"title":207,"abstract":208,"tags":212},"I5",[],[209,210,211],"物联网","internet of things","IoT",[],{"code":214,"score":133,"supported":45,"title":215,"abstract":216,"tags":219},"I1",[],[217,218],"精准农业","precision agriculture",[182],{"code":221,"score":104,"supported":45,"title":222,"abstract":223,"tags":226},"H7",[],[224,225],"气候变化","climate change",[],[228,229],"AI 作物病害 早期检测","无人机 多光谱 病虫害监测","AI作物病害早期检测-5563","10.5281\u002Fzenodo.23531525",{"doi":231,"openalex_id":233,"authors":234,"venue":112,"cited_by_count":94,"oa_url":172,"card":237,"direction":243,"ingested_from":101},"W7223085400",[235],{"name":236,"orcid":9},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":238,"method":239,"finding":240,"direction":241,"opportunity":242},"综述AI（CNN、ViT、无人机遥感、物联网）用于作物病虫害早期检测并融入种植园风险管理。","文献综述，整合CNN、ViT、无人机多\u002F高光谱与IoT监测的检测性能与部署方式。","AI可提升监测及时性与空间精度，但价值取决于田间验证、代表性数据与治理，应作决策支持而非替代专家。","农业人工智能与决策模型","可将AI预警层与保险、金融风控系统对接，研究跨域数据融合与可解释风险定价的空白。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.428080Z",{"id":246,"title":247,"url":248,"summary":249,"summary_zh":250,"content":9,"source_name":112,"source_url":248,"published_at":251,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":252,"score_detail":253,"sources":256,"tags":258,"primary_topic":261,"secondary_topics":262,"classification":263,"search_phrases":283,"slug":286,"view_count":94,"doi":287,"paper":288,"created_at":302},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。","2026-10-20T00:00:00Z",70,{"impact":17,"substance":18,"depth":254,"authority":17,"freshness":20,"relevant":21,"comment":255},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。",[257],{"name":112,"url":248},[183,48,29,259,260],"野生动物保护","人兽冲突",{"code":32,"slug":33,"name":34,"path":35,"parent_code":9},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":264},{"candidates":265,"agricultural_evidence":45,"reason":75},[266,270,274,279],{"code":192,"score":13,"supported":45,"title":267,"abstract":268,"tags":269},[],[195,196,198],[183],{"code":53,"score":13,"supported":45,"title":271,"abstract":272,"tags":273},[],[56,29,57],[29],{"code":44,"score":275,"supported":45,"title":276,"abstract":277,"tags":278},4,[],[48],[48],{"code":149,"score":21,"supported":14,"title":280,"abstract":281,"tags":282},[],[],[259],[284,285],"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":287,"openalex_id":289,"authors":290,"venue":112,"cited_by_count":94,"oa_url":248,"card":297,"direction":241,"ingested_from":101},"W7222999966",[291,293,295],{"name":292,"orcid":9},"Bhutia Dechen Doma",{"name":294,"orcid":9},"Swarup Das",{"name":296,"orcid":9},"Mandal Rakesh Kumar",{"tldr":298,"method":299,"finding":300,"direction":99,"opportunity":301},"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。","2026-10-21T23:30:43.936453Z",{"id":304,"title":305,"url":306,"summary":307,"summary_zh":308,"content":9,"source_name":309,"source_url":306,"published_at":310,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":311,"score_detail":312,"sources":317,"tags":319,"primary_topic":323,"secondary_topics":324,"classification":325,"search_phrases":363,"slug":366,"view_count":94,"doi":367,"paper":368,"created_at":379},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":313,"substance":19,"depth":314,"authority":315,"freshness":313,"relevant":21,"comment":316},8,15,3,"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。",[318],{"name":309,"url":306},[320,48,29,321,322],"决策支持系统","水稻种植","土地评价",{"code":32,"slug":33,"name":34,"path":35,"parent_code":9},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":326},{"candidates":327,"agricultural_evidence":45,"reason":75},[328,332,336,345,352,359],{"code":192,"score":17,"supported":45,"title":329,"abstract":330,"tags":331},[198],[198],[320],{"code":53,"score":17,"supported":45,"title":333,"abstract":334,"tags":335},[29],[29],[29],{"code":337,"score":13,"supported":45,"title":338,"abstract":339,"tags":344},"A1",[],[340,341,342,343],"水稻","稻田","rice","paddy",[321],{"code":346,"score":104,"supported":45,"title":347,"abstract":348,"tags":351},"A8",[],[349,350],"粮食安全","food security",[],{"code":353,"score":104,"supported":45,"title":354,"abstract":355,"tags":358},"H2",[],[356,357],"土壤","soil",[],{"code":44,"score":21,"supported":14,"title":360,"abstract":361,"tags":362},[],[],[48],[364,365],"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":367,"openalex_id":369,"authors":370,"venue":309,"cited_by_count":94,"oa_url":306,"card":374,"direction":241,"ingested_from":101},"W7222963628",[371],{"name":372,"orcid":373},"Zeinab Maddahi","https:\u002F\u002Forcid.org\u002F0000-0002-2824-2736",{"tldr":375,"method":376,"finding":377,"direction":99,"opportunity":378},"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":381,"title":382,"url":383,"summary":384,"summary_zh":385,"content":9,"source_name":386,"source_url":383,"published_at":387,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":388,"score_detail":389,"sources":392,"tags":394,"primary_topic":398,"secondary_topics":399,"classification":400,"search_phrases":428,"slug":431,"view_count":94,"doi":432,"paper":433,"created_at":453},5166,"Application of the SCS–CN Method Integrated with GIS and Remote Sensing for Watershed Runoff Estimation: A Review","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijecc\u002F2026\u002Fv16i105735","Accurate estimation of runoff is crucial for watershed planning, soil and water conservation, flood mitigation, and the management of ungauged watersheds. The Soil Conservation Service Curve Number (SCS-CN) method has been extensively used because of its minimal input requirements and its ability to model the spatial influence of land use, soil properties, and hydrologic soil groups. This review compiles applications of the GIS-based SCS-CN method reported for watersheds in semi-arid and monsoon-affected countries such as India, Saudi Arabia, Afghanistan, China, Iraq, and Ethiopia. The reviewed literature employed rainfall data, land-use\u002Fland-cover data, soil maps, hydrologic soil groups, digital elevation models, and watershed morphometry to calculate curve numbers and direct runoff. The reported CN values varied substantially, from about 40 under dry conditions to more than 90 under urban, rocky, agricultural, or poorly infiltrating conditions. Runoff estimates ranged from relatively low values in arid catchments to more than 1,300 mm per year in high-rainfall watersheds. Several studies reported high R2 correlations between observed and estimated runoff, typically ranging from 0.73 to 0.98. Important limitations identified in the review include the sensitivity of results to the choice of curve number, antecedent moisture condition, initial abstraction assumption, land-use classification, soil-group assignment, rainfall representation, and the lack of observed discharge data. The overall design of the SCS-CN method and its integration with RS and GIS offer a practical approach to assessing spatial runoff and prioritising watersheds, but the method requires local calibration, uncertainty analysis, and validation against observed rainfall–runoff data.","径流的准确估算对于流域规划、水土保持、洪水缓解以及无资料流域的管理至关重要。土壤保持服务曲线数（SCS-CN）方法因其输入需求少、能够模拟土地利用、土壤性质和水文土壤组的空间影响而被广泛使用。本综述汇编了基于GIS的SCS-CN方法在半干旱和受季风影响国家（如印度、沙特阿拉伯、阿富汗、中国、伊拉克和埃塞俄比亚）流域的应用报道。所综述的文献利用降雨数据、土地利用\u002F土地覆盖数据、土壤图、水文土壤组、数字高程模型和流域形态计量来计算曲线数和直接径流。报道的CN值差异很大，从干旱条件下的约40到城市、岩石、农业或入渗不良条件下的90以上不等。径流估算值从干旱集水区的相对较低值到高降雨流域的年径流量超过1300 mm不等。多项研究报告了观测径流与估算径流之间的高R²相关性，通常在0.73至0.98之间。本综述识别出的重要局限性包括：结果对曲线数选择、前期土壤湿润条件、初始损失假设、土地利用分类、土壤组赋值、降雨代表性以及缺乏实测流量数据的敏感性。SCS-CN方法的总体设计及其与遥感和GIS的集成，为评估空间径流和流域优先排序提供了一种实用方法，但该方法需要本地校准、不确定性分析，并依据实测降雨—径流数据进行验证。","International Journal of Environment and Climate Change","2026-10-16T00:00:00Z",63,{"impact":313,"substance":390,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":391},18,"综述系统梳理了GIS与遥感集成的SCS-CN径流估算方法及其局限，对农业信息化中的流域水资源与水土保持管理有参考价值，但属方法综述、影响面有限，未达每日精选门槛。",[393],{"name":386,"url":383},[48,29,395,396,397],"水土保持","流域管理","水文模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":9},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":401},{"candidates":402,"agricultural_evidence":45,"reason":75},[403,407,411,415,419,424],{"code":44,"score":17,"supported":45,"title":404,"abstract":405,"tags":406},[49],[48],[48],{"code":53,"score":17,"supported":45,"title":408,"abstract":409,"tags":410},[29],[29],[29],{"code":353,"score":104,"supported":45,"title":412,"abstract":413,"tags":414},[],[356,357],[],{"code":149,"score":275,"supported":45,"title":416,"abstract":417,"tags":418},[],[395],[395],{"code":221,"score":315,"supported":45,"title":420,"abstract":421,"tags":423},[],[422],"干旱",[],{"code":142,"score":21,"supported":14,"title":425,"abstract":426,"tags":427},[],[],[397],[429,430],"SCS-CN GIS 遥感 径流估算","水土保持 水文模型 流域管理 遥感","SCS-CNGIS遥感径流估算-5166","10.9734\u002Fijecc\u002F2026\u002Fv16i105735",{"doi":432,"openalex_id":434,"authors":435,"venue":386,"cited_by_count":94,"oa_url":383,"card":448,"direction":99,"ingested_from":101},"W7222632567",[436,438,440,442,444,446],{"name":437,"orcid":9},"Sudhir Lad",{"name":439,"orcid":9},"R. D. Bansod",{"name":441,"orcid":9},"Bhaupatil Gavit",{"name":443,"orcid":9},"Sachin Nandgude",{"name":445,"orcid":9},"Shailendra Gadge",{"name":447,"orcid":9},"Mahesh Patil",{"tldr":449,"method":450,"finding":451,"direction":99,"opportunity":452},"综述GIS与遥感集成的SCS-CN法在半干旱与季风区流域径流估算的应用。","汇总印度、中国等国文献，用降雨、土地利用、土壤图、DEM与流域形态计算CN和径流","CN约40至90以上，年径流差异大，R²多在0.73至0.98，但结果对CN与前期湿度等敏感。","可研究CN本地化校准与不确定性量化，并结合实测径流验证，提升无资料流域径流估算可靠性。","2026-10-17T23:30:28.373754Z",{"id":455,"title":456,"url":457,"summary":458,"summary_zh":459,"content":9,"source_name":460,"source_url":457,"published_at":461,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":462,"score_detail":463,"sources":466,"tags":468,"primary_topic":472,"secondary_topics":473,"classification":474,"search_phrases":485,"slug":488,"view_count":21,"doi":489,"paper":490,"created_at":511},4586,"Advanced Assessment of the Agro-Silvo-Pastoral Coherence in Transboundary Transitional Water Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101916","Transitional waters play a vital role in the ecological health and sustainability of coastal areas. According to European Union legislation, transitional waters are coastal water bodies influenced by freshwater flows but are partly saline due to their proximity to the sea. This study focuses on agricultural and forestry regions adjacent to transboundary transitional waters—i.e., areas where two or more countries share an estuary, tidal inlet, delta, or lagoon and have a contiguous border in their territorial waters. The objective of the study is to determine the patterns of riparian agroforestry around the transboundary transitional waters in Europe and the dependence of the sustainability of these agro-silvo-pastoral systems under stress on the overall cross-border coherence of the countries sharing these waters. The innovative methodology uses GIS and geostatistical interpretation to quantitatively interpret multi-layered spatial data from remote sensing and other sources. The article’s main finding is that in the direct catchment areas of 12 transboundary transitional waters in three European bioregions—the Baltic Sea, the Adriatic and Black Seas, and the Northeast Atlantic—the Dissimilarity Index values vary from 1.5% to 8.2% in different agroforestry systems.","过渡水域对沿海地区的生态健康与可持续性具有至关重要的作用。根据欧盟立法，过渡水域是受淡水径流影响但因靠近海洋而具有一定盐度的沿海水体。本研究聚焦于毗邻跨界过渡水域的农业与林业区域，即两个或多个国家共享同一河口、潮汐入口、三角洲或潟湖，且其领海边界相连的区域。本研究旨在确定欧洲跨界过渡水域周边河岸农林复合系统的格局，以及这些处于压力下的农林牧系统可持续性对共享这些水域的国家之间整体跨境协调性的依赖程度。该创新性方法采用地理信息系统（GIS）和地统计解译技术，对来自遥感及其他来源的多层空间数据进行定量解译。本文的主要发现是：在欧洲三个生物区——波罗的海、亚得里亚海与黑海以及东北大西洋——的12处跨界过渡水域的直接集水区内，不同农林复合系统的相异指数值在1.5%至8.2%之间变化。","Land","2026-10-09T00:00:00Z",72,{"impact":17,"substance":464,"depth":254,"authority":118,"freshness":20,"relevant":21,"comment":465},21,"方法新颖、数据扎实的欧洲跨境滨海农林牧系统遥感评估论文，对农业空间信息化有参考价值，但属区域案例研究，公共影响有限。",[467],{"name":460,"url":457},[28,469,470,29,471],"跨境流域","农林牧复合系统","滨海湿地",{"code":32,"slug":33,"name":34,"path":35,"parent_code":9},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":475},{"candidates":476,"agricultural_evidence":45,"reason":75},[477,481],{"code":44,"score":133,"supported":45,"title":478,"abstract":479,"tags":480},[],[48,49],[28],{"code":53,"score":133,"supported":45,"title":482,"abstract":483,"tags":484},[],[56,29],[29],[486,487],"欧洲 跨境过渡水域 农林复合","GIS 遥感 农林牧系统 一致性","欧洲跨境过渡水域农林复合-4586","10.3390\u002Fland15101916",{"doi":489,"openalex_id":491,"authors":492,"venue":460,"cited_by_count":94,"oa_url":457,"card":506,"direction":99,"ingested_from":101},"W7221179136",[493,495,497,500,503],{"name":494,"orcid":9},"Aistė Jurkienė",{"name":496,"orcid":9},"Egidijus Jurkus",{"name":498,"orcid":499},"Ramūnas Povilanskas","https:\u002F\u002Forcid.org\u002F0000-0003-0895-3917",{"name":501,"orcid":502},"Eduardas Spiriajevas","https:\u002F\u002Forcid.org\u002F0000-0002-4684-5979",{"name":504,"orcid":505},"Arvydas Urbis","https:\u002F\u002Forcid.org\u002F0000-0002-9641-9214",{"tldr":507,"method":508,"finding":509,"direction":99,"opportunity":510},"用GIS与地统计量化欧洲12处跨境过渡水域周边农林牧系统的一致性差异。","GIS与地统计解读遥感等多源多层空间数据，计算相异指数。","三大生物区12处水域集水区农林系统相异指数为1.5%至8.2%。","可拓展跨境流域农林牧系统一致性对生态可持续性的驱动机制与动态监测研究。","2026-10-10T23:30:27.974100Z"]