[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5326":3,"related-5326":91},{"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":18,"tags":20,"primary_topic":25,"secondary_topics":31,"classification":43,"search_phrases":71,"slug":74,"view_count":15,"doi":75,"paper":76,"created_at":90},5326,"Flood Hazard and Vulnerability Assessment of Chitrakoot District, Uttar Pradesh: An Integrated Geographical, GIS and Remote Sensing Approach","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.89708","Flood is one of the most important hydro-meteorological hazards affecting human settlements, agriculture, infrastructure and regional economies. Chitrakoot district of Uttar Pradesh presents a distinctive case of hydro-climatic vulnerability because it belongs to the drought-prone Bundelkhand region but simultaneously experiences recurrent riverine and flash-flood conditions during intense monsoon rainfall. The present study assesses the spatial characteristics and determinants of flood hazard and vulnerability in Chitrakoot district using an integrated geographical, GIS and remote sensing approach. The analysis incorporates the Digital Elevation Model (DEM), drainage network, physiography, river proximity, historical flood information, satellite-based flood observations and socio-economic exposure. The district's DEM indicates a marked elevation gradient, approximately from 66 m to 451 m, with relatively higher terrain in the southern part and lower terrain towards the northern Yamuna valley. This topographic configuration facilitates rapid surface runoff from elevated southern terrain towards comparatively low-lying riverine areas. The drainage system is dominated by the Yamuna and Mandakini\u002FPaisuni systems along with Baghain, Gunta, Bardaha and smaller tributaries. The Uttar Pradesh Flood Hazard Atlas identifies 5,114 ha of flood-hazard area in Chitrakoot for 1998–2020, of which 4,129 ha (81%) falls in the very-low hazard category and 985 ha (19%) in the low-hazard category. However, the historical classification does not fully represent the potential consequences of extreme rainfall events. The August 2026 flood provides compelling evidence: the Mandakini at Chitrakoot reached a reported new high flood level of 145.60 m on 29 August 2026, approximately 2.95 m above the previous reported high level, while the water level rose by about 7.5 m within six hours. The study concludes that flood risk in Chitrakoot is spatially concentrated around river corridors, low-elevation areas, floodplains, agricultural land, settlements and tourism infrastructure. A shift from conventional relief-oriented flood management towards integrated floodplain regulation, watershed management, GIS-based risk zonation, real-time monitoring and community-based preparedness is therefore essential.","洪水是影响人类住区、农业、基础设施和区域经济的最重要的水文气象灾害之一。北方邦吉德古德（Chitrakoot）县在水文气候脆弱性方面呈现出独特案例，因为它属于易受干旱影响的本德尔坎德（Bundelkhand）地区，但同时在强季风降雨期间反复遭遇河流洪水和山洪状况。本研究采用地理学、地理信息系统（GIS）和遥感相结合的综合方法，评估吉德古德县洪水灾害与脆弱性的空间特征及其决定因素。分析纳入了数字高程模型（DEM）、河网、地貌、距河流远近、历史洪水信息、基于卫星的洪水观测以及社会经济暴露度。该县的DEM显示出明显的高程梯度，大约从66米到451米，南部地形相对较高，向北部的亚穆纳（Yamuna）河谷地势降低。这种地形格局促使地表径流从较高的南部地形快速流向相对低洼的河流区域。排水系统以亚穆纳河和曼达基尼\u002F派苏尼（Mandakini\u002FPaisuni）水系为主，另有巴盖恩（Baghain）、贡塔（Gunta）、巴尔达哈（Bardaha）及较小支流。北方邦洪水灾害图集确认，1998—2020年吉德古德县洪水灾害面积为5，114公顷，其中4，129公顷（81%）属于极低灾害类别，985公顷（19%）属于低灾害类别。然而，历史分类并不能充分反映极端降雨事件的潜在后果。2026年8月的洪水提供了有力证据：据报道，2026年8月29日吉德古德的曼达基尼河达到145.60米的新高洪水位，比此前报告的高水位高出约2.95米，而水位在六小时内上升了约7.5米。研究得出结论，吉德古德的洪水风险在空间上集中于河流廊道、低海拔地区、洪泛平原、农业用地、住区和旅游基础设施周围。因此，必须从传统的以救济为导向的洪水管理转向洪泛平原综合调控、流域管理、基于GIS的风险区划、实时监测和社区为基础的备灾。",null,"International Journal For Multidisciplinary Research","2026-10-17T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该研究聚焦印度北方邦的洪水灾害评估，虽涉及GIS与遥感方法，但与三农、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。",[19],{"name":10,"url":6},[21,22,23,24],"防灾减灾","遥感监测","洪水风险评估","GIS应用",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[32,38],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":39,"slug":40,"name":41,"path":42,"parent_code":30},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":44,"locked":14,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,57,64],{"code":26,"score":50,"supported":51,"title":52,"abstract":54,"tags":56},15,true,[53],"remote sensing",[55,53],"遥感",[22],{"code":39,"score":58,"supported":51,"title":59,"abstract":61,"tags":63},14,[60],"GIS",[62,60],"地理信息",[],{"code":33,"score":65,"supported":51,"title":66,"abstract":67,"tags":70},8,[],[68,69],"干旱","drought",[23,21],[72,73],"Chitrakoot 洪水风险 GIS","Yamuna 洪水脆弱性 遥感","Chitrakoot洪水风险GIS-5326","10.36948\u002Fijfmr.2026.v08i05.89708",{"doi":75,"openalex_id":77,"authors":78,"venue":10,"cited_by_count":15,"oa_url":82,"card":83,"direction":87,"ingested_from":89},"W7222885988",[79],{"name":80,"orcid":81},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":84,"method":85,"finding":86,"direction":87,"opportunity":88},"用GIS与遥感综合评估Chitrakoot区洪水灾害与脆弱性空间分布。","DEM、河网、历史洪水与卫星观测及社会经济数据叠加分析。","洪水风险集中于河谷低地、农田与居民点，历史分级低估极端降雨风险。","农业遥感与作物表型","可结合时序遥感与水文模型，构建极端降雨情景下的农田洪涝风险动态制图与预警。","openalex","2026-10-19T23:30:24.356743Z",{"total":92,"page":93,"page_size":92,"items":94},6,1,[95,195,281,342,404,459],{"id":96,"title":97,"url":98,"summary":99,"summary_zh":100,"content":9,"source_name":101,"source_url":98,"published_at":102,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":103,"score_detail":104,"sources":109,"tags":111,"primary_topic":115,"secondary_topics":116,"classification":119,"search_phrases":166,"slug":169,"view_count":170,"doi":171,"paper":172,"created_at":194},3495,"Remote Sensing and GIS in Modern Drought Assessment: Bridging Conventional Methods and Emerging Technologies","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123","Drought is a complex and recurring hydroclimatic hazard that affects agricultural production, water resources, ecosystems and socioeconomic development. Effective drought assessment requires approaches capable of capturing its spatial and temporal variability and its multiple dimensions. This review examines the evolution of drought assessment from conventional drought indices to integrated approaches based on remote sensing and Geographic Information Systems (GIS), with an emphasis on their applications, strengths, limitations and emerging developments. Conventional indices, including the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Reconnaissance Drought Index (RDI) and Percent of Normal Precipitation Index (PNPI), remain widely used because of their established methodologies and long-term applicability. However, their dependence on meteorological observations can limit spatial characterisation and the representation of vegetation, soil moisture and other land-surface responses. Remote sensing provides spatially extensive and repeated observations of vegetation condition, land surface temperature, soil moisture, evapotranspiration and water-related conditions, enabling the development of satellite-derived drought indicators and indices. GIS further facilitates the integration, spatial analysis, visualisation, and mapping of drought-related information from multiple sources. The review also discusses hybrid approaches that combine climate-based indices with satellite-derived indicators, as well as drought monitoring platforms and multi-source assessment frameworks. Despite substantial advances, challenges remain regarding cloud contamination, differences in spatial and temporal resolution, data continuity, ground-based validation and uncertainty associated with multi-source datasets. Emerging machine learning, deep learning and artificial intelligence approaches offer opportunities for integrating heterogeneous datasets and improving drought characterisation and early warning. Overall, the integration of conventional observations, remote sensing, GIS and advanced analytical approaches provides a promising framework for more comprehensive drought monitoring and risk assessment under increasing climate variability and change.","干旱是一种复杂且反复出现的水文气候灾害，影响农业生产、水资源、生态系统和社会经济发展。有效的干旱评估需要能够捕捉其时空变异性和多维特征的方法。本文综述了干旱评估从传统干旱指数到基于遥感与地理信息系统（GIS）的综合方法的演变，重点探讨其应用、优势、局限性和新兴发展。传统指数，包括标准化降水指数（SPI）、标准化降水蒸散指数（SPEI）、帕尔默干旱强度指数（PDSI）、侦察干旱指数（RDI）和降水距平百分率指数（PNPI），因其方法成熟且具有长期适用性而仍被广泛使用。然而，这些指数对气象观测的依赖可能限制其空间表征能力以及对植被、土壤水分和其他陆面响应的刻画。遥感提供了对植被状况、地表温度、土壤水分、蒸散量及与水相关状况的大范围重复观测，使得卫星衍生的干旱指标和指数得以发展。GIS进一步促进了多来源干旱相关信息的整合、空间分析、可视化和制图。本文还讨论了将基于气候的指数与卫星衍生指标相结合的混合方法，以及干旱监测平台和多源评估框架。尽管取得了实质性进展，但在云污染、时空分辨率差异、数据连续性、地面验证以及多源数据集相关的不确定性方面仍存在挑战。新兴的机器学习、深度学习和人工智能方法为整合异质数据集、改进干旱表征和预警提供了机遇。总体而言，在气候变异性和变化日益加剧的背景下，传统观测、遥感、GIS和先进分析方法的整合为更全面的干旱监测和风险评估提供了一个有前景的框架。","Journal of Geography Environment and Earth Science International","2026-09-24T00:00:00Z",66,{"impact":105,"substance":106,"depth":107,"authority":105,"freshness":65,"relevant":93,"comment":108},12,18,16,"综述系统梳理遥感与GIS在干旱评估中的应用演进，方法学价值明确，但属综述类论文、非国内落地事件，影响力有限。",[110],{"name":101,"url":98},[112,113,55,60,114],"智慧农业","农业人工智能","干旱监测",{"code":39,"slug":40,"name":41,"path":42,"parent_code":30},[117,118],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},{"status":44,"locked":14,"version":45,"method":46,"evidence":120},{"candidates":121,"agricultural_evidence":51},[122,129,133,137,147,154,161],{"code":39,"score":106,"supported":51,"title":123,"abstract":124,"tags":128},[60],[62,125,60,126,127],"空间分析","spatial analysis","geographic information",[60],{"code":33,"score":50,"supported":51,"title":130,"abstract":131,"tags":132},[69],[68,69],[114],{"code":26,"score":50,"supported":51,"title":134,"abstract":135,"tags":136},[53],[55,53],[55],{"code":138,"score":13,"supported":51,"title":139,"abstract":140,"tags":146},"I2",[],[141,142,143,144,145],"深度学习","机器学习","machine learning","deep learning","artificial intelligence",[113],{"code":148,"score":92,"supported":51,"title":149,"abstract":150,"tags":153},"H2",[],[151,152],"土壤","soil",[],{"code":155,"score":92,"supported":51,"title":156,"abstract":157,"tags":160},"H3",[],[158,159],"水资源","evapotranspiration",[],{"code":162,"score":93,"supported":14,"title":163,"abstract":164,"tags":165},"I1",[],[],[112],[167,168],"遥感 GIS 干旱评估","卫星遥感 干旱指数","遥感GIS干旱评估-3495",5,"10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123",{"doi":171,"openalex_id":173,"authors":174,"venue":101,"cited_by_count":15,"oa_url":98,"card":189,"direction":87,"ingested_from":89},"W7214156857",[175,177,179,181,183,185,187],{"name":176,"orcid":9},"V. Dhanalakshmi",{"name":178,"orcid":9},"N. Manikandan",{"name":180,"orcid":9},"V. S. Jinsy",{"name":182,"orcid":9},"K. V. Sumesh",{"name":184,"orcid":9},"P. Nideesh",{"name":186,"orcid":9},"P. S. Manju",{"name":188,"orcid":9},"N. Gopika",{"tldr":190,"method":191,"finding":192,"direction":87,"opportunity":193},"综述了从传统干旱指数到遥感、GIS及AI集成的现代干旱评估方法演进。","文献综述，对比SPI、SPEI等传统指数与遥感、GIS及混合方法。","遥感与GIS弥补传统指数空间局限，但云污染、分辨率差异和验证仍是挑战。","可探索多源遥感与机器学习融合的干旱早期预警，重点解决数据不确定性与地面验证。","2026-09-25T23:30:30.576065Z",{"id":196,"title":197,"url":198,"summary":199,"summary_zh":200,"content":9,"source_name":201,"source_url":198,"published_at":202,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":203,"score_detail":204,"sources":210,"tags":212,"primary_topic":216,"secondary_topics":217,"classification":220,"search_phrases":258,"slug":261,"view_count":93,"doi":262,"paper":263,"created_at":280},2945,"Assessing spatial suitability and climate resilience of agroforestry systems in the Indian Himalaya of Uttarakhand using remote sensing and artificial neural networks","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44415-026-00113-9","Agroforestry systems are widely recognized for their capacity to enhance ecological sustainability, livelihood security, and climate resilience in mountain environments. However, their spatial suitability and long-term resilience under changing climatic conditions remain poorly quantified in the Indian Himalaya. This study assesses the spatial suitability and climate resilience of agroforestry systems in Uttarakhand using an integrated framework of remote sensing, geographic information systems (GIS), and artificial neural networks (ANNs). Landsat 8 OLI imagery (30 m resolution) was classified using a supervised Gaussian Maximum Likelihood Classifier (MLC), while land-use categories were defined based on a modified Anderson Level I\u002FII classification scheme to represent the heterogeneous Himalayan landscape. Field surveys conducted across representative agroecological zones documented the composition of tree and crop species within prevailing agroforestry systems. A multi-criteria land suitability analysis, guided by FAO (Food and Agriculture Organization of the United Nations) principles, was implemented using key biophysical variables including altitude, slope, aspect, Normalized Difference Vegetation Index (NDVI), soil properties, temperature, and precipitation. Future agroforestry patterns were simulated using an ANN model under RCP 4.5 climate scenarios. The model demonstrated strong predictive performance, indicating reliable simulation of agroforestry distribution. Results show that mid-altitudinal zones offer the highest suitability for agroforestry expansion, whereas small and fragmented systems exhibit reduced climate resilience under future projections. The findings provide a spatially explicit framework to support climate-resilient agroforestry planning and policy formulation in the Himalayan region.","农林复合系统因其在山区环境中提升生态可持续性、生计安全及气候适应能力方面的作用而受到广泛认可。然而，在印度喜马拉雅地区，其空间适宜性及气候变化条件下的长期适应能力仍缺乏充分的量化评估。本研究采用遥感、地理信息系统（GIS）与人工神经网络（ANN）相结合的综合框架，评估了北阿坎德邦农林复合系统的空间适宜性与气候适应能力。利用监督式高斯最大似然分类器（MLC）对Landsat 8 OLI影像（30 m分辨率）进行分类，同时基于改进的Anderson一级\u002F二级分类方案界定土地利用类别，以表征喜马拉雅地区异质性景观。在代表性农业生态区开展实地调查，记录了现有农林复合系统中树种和作物物种的组成。依据联合国粮食及农业组织（FAO）原则，采用多准则土地适宜性分析，纳入海拔、坡度、坡向、归一化植被指数（NDVI）、土壤属性、温度和降水等关键生物物理变量。利用ANN模型在RCP 4.5气候情景下模拟未来农林复合系统格局。模型表现出较强的预测性能，表明对农林复合系统分布的模拟可靠。结果表明，中海拔区域为农林复合系统扩展提供了最高的适宜性，而小型和破碎化系统在未来预测情景下表现出较低的气候适应能力。研究结果为喜马拉雅地区气候适应型农林复合系统规划与政策制定提供了空间明确的框架支持。","Discover Forests","2026-09-18T00:00:00Z",72,{"impact":105,"substance":205,"depth":206,"authority":207,"freshness":208,"relevant":93,"comment":209},21,17,13,9,"遥感与人工神经网络结合评估喜马拉雅地区农林复合系统空间适宜性与气候韧性，方法新颖、结论有区域政策参考价值，但属境外区域研究，公共影响有限。",[211],{"name":201,"url":198},[113,55,213,214,215],"气候韧性","土地适宜性","农林复合经营",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[218,219],{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},{"code":39,"slug":40,"name":41,"path":42,"parent_code":30},{"status":44,"locked":14,"version":45,"method":46,"evidence":221},{"candidates":222,"agricultural_evidence":51},[223,230,234,238,242,249,254],{"code":33,"score":106,"supported":51,"title":224,"abstract":226,"tags":229},[225],"climate resilience",[227,228,225],"气候变化","气候适应",[213],{"code":26,"score":50,"supported":51,"title":231,"abstract":232,"tags":233},[53],[55,53],[55],{"code":39,"score":208,"supported":51,"title":235,"abstract":236,"tags":237},[],[62,60,127],[],{"code":148,"score":92,"supported":51,"title":239,"abstract":240,"tags":241},[],[151,152],[],{"code":243,"score":244,"supported":51,"title":245,"abstract":246,"tags":248},"H6",3,[],[247],"农业生态",[],{"code":250,"score":93,"supported":14,"title":251,"abstract":252,"tags":253},"H1",[],[],[214],{"code":138,"score":93,"supported":14,"title":255,"abstract":256,"tags":257},[],[],[113],[259,260],"Uttarakhand 农林复合系统 遥感","印度喜马拉雅 人工神经网络 土地适宜性","Uttarakhand农林复合系统遥感-2945","10.1007\u002Fs44415-026-00113-9",{"doi":262,"openalex_id":264,"authors":265,"venue":201,"cited_by_count":15,"oa_url":198,"card":275,"direction":87,"ingested_from":89},"W7213553408",[266,268,270,273],{"name":267,"orcid":9},"Deepak Kumar Mishra",{"name":269,"orcid":9},"Ujjwal Kumar",{"name":271,"orcid":272},"A. Arunachalam","https:\u002F\u002Forcid.org\u002F0000-0001-6590-4113",{"name":274,"orcid":9},"Ayyanadar Arunachalam",{"tldr":276,"method":277,"finding":278,"direction":87,"opportunity":279},"结合遥感、GIS与人工神经网络评估印度喜马拉雅地区农林复合系统的空间适宜性与气候韧性。","Landsat 8影像监督分类、FAO多准则适宜性分析、ANN在RCP4.5情景","中海拔区最适宜农林复合扩展，小而破碎系统在未来气候下韧性较低。","可将该遥感-ANN框架迁移至中国山区，耦合多情景气候与农户数据优化农林复合布局。","2026-09-19T23:30:33.082917Z",{"id":282,"title":283,"url":284,"summary":285,"summary_zh":286,"content":9,"source_name":287,"source_url":284,"published_at":288,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":289,"score_detail":290,"sources":292,"tags":294,"primary_topic":298,"secondary_topics":299,"classification":301,"search_phrases":323,"slug":326,"view_count":15,"doi":327,"paper":328,"created_at":341},4747,"Spatiotemporal mapping of agricultural drought using geospatial approach in the East Bale Zone, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fagg2.70475","Abstract Agricultural drought significantly threatens food security, livelihoods, and ecosystem stability, especially in regions prone to drought, such as the East Bale zone. This study investigates the spatial and temporal patterns of agricultural drought over 30 years (1993–2024) by combining meteorological station data with satellite‐derived vegetation and thermal indices. To assess meteorological drought, the standardized precipitation index (SPI) was calculated at 3‐, 6‐, and 12‐month intervals. For agricultural and thermal stress analysis, remote sensing‐based indices including the vegetation condition index (VCI), temperature condition index, and vegetation health index (VHI) were utilized. The results indicate that the years 2003, 2008, and 2018 experienced the most severe droughts, as shown consistently across all indices. Spatial analysis revealed that the central, southern, and eastern lowland areas faced the highest frequency and severity of drought, while the northwestern highlands remained comparatively resilient. Cross‐index correlation revealed strong agreement between SPI and VCI ( R 2 = 0.77), and between VHI and VCI ( R 2 = 0.83), supporting the effectiveness of integrated drought monitoring approaches. This research advances the field of drought assessment by enhancing spatial and temporal resolution and validating cross‐index relationships at the subregional level. The results provide critical insights for localized drought early warning systems, climate adaptation strategies, and future research to improve agricultural resilience under changing climatic conditions. Hence, agricultural drought risk mapping serves as a valuable tool for guiding decision‐making in drought monitoring and for mitigating its impacts on agricultural productivity.","摘要 农业干旱严重威胁粮食安全、生计和生态系统稳定性，尤其是在东巴莱区等易旱地区。本研究结合气象站数据与卫星反演的植被和热力指数，探讨了30年（1993—2024年）间农业干旱的时空格局。为评估气象干旱，计算了3个月、6个月和12个月尺度的标准化降水指数（SPI）。在农业与热胁迫分析方面，采用了基于遥感的植被状态指数（VCI）、温度状态指数和植被健康指数（VHI）。结果表明，2003年、2008年和2018年经历了最严重的干旱，各指数均一致反映了这一情况。空间分析显示，中部、南部和东部低地地区干旱发生频率最高、程度最严重，而西北部高地则相对具有较强恢复力。跨指数相关性分析表明，SPI与VCI之间（R²=0.77）以及VHI与VCI之间（R²=0.83）具有高度一致性，支持了综合干旱监测方法的有效性。本研究通过提高时空分辨率并在次区域尺度上验证跨指数关系，推进了干旱评估领域的发展。研究结果为地方性干旱预警系统、气候适应策略以及未来研究提供了重要见解，有助于在气候变化条件下提升农业韧性。因此，农业干旱风险制图为干旱监测决策及其对农业生产影响的减缓提供了有价值的工具。","Agrosystems Geosciences & Environment","2026-10-11T00:00:00Z",68,{"impact":65,"substance":205,"depth":206,"authority":207,"freshness":208,"relevant":93,"comment":291},"基于30年气象与遥感指数交叉验证的农业干旱时空制图研究，方法扎实、结论可靠，但属区域性案例，对国内三农信息化仅有方法借鉴价值。",[293],{"name":287,"url":284},[295,21,22,296,297],"粮食安全","植被指数","农业干旱",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[300],{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},{"status":44,"locked":14,"version":45,"method":46,"evidence":302},{"candidates":303,"agricultural_evidence":51},[304,308,312,319],{"code":33,"score":106,"supported":51,"title":305,"abstract":306,"tags":307},[69],[227,228,68,69],[21],{"code":26,"score":65,"supported":51,"title":309,"abstract":310,"tags":311},[],[55,53],[296,22],{"code":313,"score":314,"supported":51,"title":315,"abstract":316,"tags":318},"A8",7,[],[295,317],"food security",[295],{"code":39,"score":92,"supported":51,"title":320,"abstract":321,"tags":322},[],[125,126],[],[324,325],"埃塞俄比亚 East Bale 农业干旱","SPI VCI 干旱监测","埃塞俄比亚EastBale农业干旱-4747","10.1002\u002Fagg2.70475",{"doi":327,"openalex_id":329,"authors":330,"venue":287,"cited_by_count":15,"oa_url":284,"card":336,"direction":87,"ingested_from":89},"W7221455809",[331,333],{"name":332,"orcid":9},"Murad Hedo Haji",{"name":334,"orcid":335},"Karuturi Venkata Suryabhagavan","https:\u002F\u002Forcid.org\u002F0000-0003-2528-9106",{"tldr":337,"method":338,"finding":339,"direction":87,"opportunity":340},"结合气象站与卫星植被热指数，绘制埃塞俄比亚East Bale区30年农业干旱时空图。","用SPI（3\u002F6\u002F12月）与遥感VCI、TCI、VHI指数做交叉相关分析。","2003、2008、2018年干旱最重，中南部和东部低地最脆弱，SPI与VCI相关性R²=0.77。","可在小区域尺度融合多源指数构建干旱预警模型，并验证指数在不同作物区的适用性。","2026-10-12T23:30:24.342375Z",{"id":343,"title":344,"url":345,"summary":346,"summary_zh":347,"content":9,"source_name":348,"source_url":345,"published_at":349,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":350,"score_detail":351,"sources":353,"tags":355,"primary_topic":358,"secondary_topics":359,"classification":361,"search_phrases":385,"slug":388,"view_count":93,"doi":389,"paper":390,"created_at":403},3903,"An Analysis of Land Use and Land Cover Change Detection Using Geospatial Technology-A Case Study of Phalsoond Tehsil in Jaisalmer District, Rajasthan, India","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84983","Analysis of Land Use and Land Cover (LULC) change is significant indicators of environmental changes, resource utilization and sustainable development, especially in arid regions where land and water resources are very fragile. The present study has investigated the spatio-temporal analysis of LULC changes in Phascoond Tehsil of Jaisalmer District of Rajasthan, India from the year 2017 to 2024 using Remote sensing and Geographic Information System (GIS) techniques. To classify the study area, multi-temporal Sentinel-2 data with 10 m spatial resolution were provided by the Esri Sentinel-2 Land Cover Explorer, which was used to separate the area into five categories i.e. water bodies, agriculture land, built-up areas, bare ground and wasteland. LULC changes were quantified in terms of magnitude and direction using post-classification comparison and GIS-based change detection techniques. The findings show significant change in landscape over the year 2017 to 2024. Wasteland remained the dominant land-cover category but declined from 779.07 km² (78.28%) in 2017 to 726.86 km² (71.41%) in 2024. Agricultural area grew by 86.62 km² (+19.48% to 280.52 km²), adding 94.76 km² of wasteland. Gradual settlement and infrastructure development also resulted in an increase in built-up area from 3.89 km2 to 7.73 km2 . The water bodies were small in spatial extent and slight shifts happened between bare ground and other land-cover classes. The results clearly show that the Sentinel-2 data and GIS-based analysis is useful in monitoring LULC dynamics in data-sparse arid regions. The present extent of agriculture and development has occurred at a time when there is a need to manage the land and groundwater resources sustainably, monitor environment, and plan land use with climate change in Phalsoond tehsil of Jaisalmer District in western Rajasthan.","土地利用与土地覆盖（LULC）变化分析是环境变化、资源利用和可持续发展的重要指标，在土地与水资源极为脆弱的干旱地区尤为如此。本研究利用遥感与地理信息系统（GIS）技术，对印度拉贾斯坦邦杰伊瑟尔梅尔县Phascoond乡2017年至2024年间的LULC变化进行了时空分析。为对研究区进行分类，采用了Esri Sentinel-2土地覆盖浏览器提供的10 m空间分辨率多时相Sentinel-2数据，将区域划分为五个类别，即水体、农用地、建设用地、裸地和荒地。通过分类后比较和基于GIS的变化检测技术，从幅度和方向两方面对LULC变化进行了量化。研究结果表明，2017年至2024年间景观发生了显著变化。荒地仍为优势土地覆盖类型，但从2017年的779.07 km²（78.28%）下降至2024年的726.86 km²（71.41%）。农业面积增加了86.62 km²（增幅19.48%，达到280.52 km²），占用了94.76 km²的荒地。逐步的聚落与基础设施建设也导致建设用地从3.89 km²增加至7.73 km²。水体空间范围较小，裸地与其他土地覆盖类别之间发生了轻微转换。结果清楚表明，Sentinel-2数据与基于GIS的分析对于监测数据稀缺干旱地区的LULC动态具有实用价值。当前农业与开发的程度出现在这样一个时期：拉贾斯坦邦西部杰伊瑟尔梅尔县Phalsoond乡亟需可持续管理土地与地下水资源、监测环境，并在气候变化背景下进行土地利用规划。","International Journal for Research in Applied Science and Engineering Technology","2026-09-30T00:00:00Z",52,{"impact":92,"substance":106,"depth":58,"authority":92,"freshness":65,"relevant":93,"comment":352},"基于Sentinel-2与GIS的印度干旱区土地利用变化研究，方法规范但属区域性案例，对国内三农信息化参考价值有限。",[354],{"name":348,"url":345},[22,356,357,24],"土地利用","干旱区农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[360],{"code":39,"slug":40,"name":41,"path":42,"parent_code":30},{"status":44,"locked":14,"version":45,"method":46,"evidence":362},{"candidates":363,"agricultural_evidence":51},[364,369,373,377,381],{"code":33,"score":13,"supported":51,"title":365,"abstract":366,"tags":368},[],[227,68,367],"climate change",[357],{"code":39,"score":208,"supported":51,"title":370,"abstract":371,"tags":372},[],[62,60,127],[],{"code":26,"score":314,"supported":51,"title":374,"abstract":375,"tags":376},[],[55,53],[22],{"code":155,"score":244,"supported":51,"title":378,"abstract":379,"tags":380},[],[158],[],{"code":243,"score":93,"supported":14,"title":382,"abstract":383,"tags":384},[],[],[356],[386,387],"Jaisalmer 土地利用变化","Sentinel-2 遥感 干旱区","Jaisalmer土地利用变化-3903","10.22214\u002Fijraset.2026.84983",{"doi":389,"openalex_id":391,"authors":392,"venue":348,"cited_by_count":15,"oa_url":345,"card":398,"direction":87,"ingested_from":89},"W7214996325",[393,395],{"name":394,"orcid":9},"Deendayal .",{"name":396,"orcid":397},"Om Prakash","https:\u002F\u002Forcid.org\u002F0000-0003-1239-445X",{"tldr":399,"method":400,"finding":401,"direction":87,"opportunity":402},"基于Sentinel-2和GIS分析印度拉贾斯坦邦干旱区2017-2024年土地利用\u002F覆盖变化。","Sentinel-2 10m影像与GIS分类，采用分类后比较法检测LULC变化。","荒地减少52.21 km²，农业增加86.62 km²，建设用地翻倍，水资源面积小。","可结合多源遥感与机器学习，在数据稀疏干旱区开展高精度LULC动态监测与驱动机制研究。","2026-10-01T23:30:29.277138Z",{"id":405,"title":406,"url":407,"summary":408,"summary_zh":409,"content":9,"source_name":410,"source_url":407,"published_at":411,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":412,"score_detail":413,"sources":415,"tags":417,"primary_topic":419,"secondary_topics":420,"classification":422,"search_phrases":442,"slug":445,"view_count":434,"doi":446,"paper":447,"created_at":458},2529,"Flood Risk Assessment and Environmental Change Analysis Using GIS and Remote Sensing","https:\u002F\u002Fdoi.org\u002F10.34248\u002Fbsengineering.1994338","Floods can be very destructive natural hazard; it causes significant environmental, social, and economic impacts. Havza is a district in Northern part of Türkiye, experienced major flood events in May 1998 and May 2026, which emphasize the need of identify flood-prone areas and analyze the factors that influence flood vulnerability. The aim of this study is to asses flood risk and analyzes land use\u002Fland cover (LULC) monitoring derived from remote sensing data in Havza. Furthermore, topographic characteristics were also obtained from Shuttle Radar Topography Mission (SRTM) and Digital Elevation Model (DEM) using Geographic Information Systems (GIS). Hydrological analyses include slope and flow accumulation models, and these analyses were used to identify susceptible areas. It has been observed that the mean NDVI value increased from 0.4239 in 1998 to 0.7447 in 2026, indicating a substantial increase in vegetation cover. LULC classification results also showed that urban areas increased from 16% to 20% while agricultural land decreased 54% to 50% in approximately last 3 decades. 3-Dimensional (3D) terrain visualization and river corridor-based flood risk mapping revealed that low elevation valley floors and settlements located very near to stream channels, which is the most vulnerable areas of floods. The findings of this study demonstrate the effectiveness of integration of GIS and remote sensing, topographic analysis, and LULC change detection within a single framework for flood risk assessment and sustainable land management. The results provide valuable support for disaster risk reduction, urban planning, and future development strategies in Havza and similar watershed environments.","洪水是一种极具破坏性的自然灾害，会对环境、社会和经济造成显著影响。哈夫扎（Havza）是土耳其北部的一个区，1998年5月和2026年5月经历了重大洪水事件，这凸显了识别洪水易发区域并分析影响洪水脆弱性因素的必要性。本研究旨在评估哈夫扎的洪水风险，并分析基于遥感数据获得的土地利用\u002F土地覆盖（LULC）监测结果。此外，还利用地理信息系统（GIS）从航天飞机雷达地形测绘任务（SRTM）和数字高程模型（DEM）中获取了地形特征。水文分析包括坡度和汇流累积模型，这些分析用于识别易受洪水影响的区域。研究发现，NDVI均值从1998年的0.4239上升至2026年的0.7447，表明植被覆盖显著增加。LULC分类结果还显示，在近三十年间，城市面积从16%增加至20%，而农业用地从54%减少至50%。三维（3D）地形可视化和基于河流廊道的洪水风险制图表明，低海拔河谷底部和紧邻河道的居民点是洪水最脆弱的区域。本研究结果表明，将GIS与遥感、地形分析以及LULC变化检测整合于同一框架内，对于洪水风险评估和可持续土地管理具有有效性。研究结果为哈夫扎及类似流域环境的减灾、城市规划和未来发展战略提供了有价值的支持。","Black Sea Journal of Engineering and Science","2026-09-14T00:00:00Z",61,{"impact":65,"substance":106,"depth":50,"authority":105,"freshness":65,"relevant":93,"comment":414},"以GIS与遥感融合评估土耳其流域洪水风险与土地利用变化，方法框架对农业防灾与乡村空间规划有参考价值，但属区域案例研究，公共影响有限。",[416],{"name":410,"url":407},[21,55,23,60,418],"土地利用变化",{"code":39,"slug":40,"name":41,"path":42,"parent_code":30},[421],{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},{"status":44,"locked":14,"version":45,"method":46,"evidence":423},{"candidates":424,"agricultural_evidence":51},[425,429,433,438],{"code":39,"score":106,"supported":51,"title":426,"abstract":427,"tags":428},[60],[62,60,127],[60],{"code":26,"score":50,"supported":51,"title":430,"abstract":431,"tags":432},[53],[55,53],[55],{"code":33,"score":434,"supported":14,"title":435,"abstract":436,"tags":437},2,[],[],[23,21],{"code":243,"score":93,"supported":14,"title":439,"abstract":440,"tags":441},[],[],[418],[443,444],"土地利用变化 洪水风险评估 防灾减灾 遥感","土地利用变化 洪水风险评估","土地利用变化洪水风险评估防灾减灾遥感-2529","10.34248\u002Fbsengineering.1994338",{"doi":446,"openalex_id":448,"authors":449,"venue":410,"cited_by_count":15,"oa_url":407,"card":453,"direction":87,"ingested_from":89},"W7212642487",[450],{"name":451,"orcid":452},"Sibel Canaz","https:\u002F\u002Forcid.org\u002F0000-0001-5552-6067",{"tldr":454,"method":455,"finding":456,"direction":87,"opportunity":457},"结合GIS与遥感评估土耳其Havza地区洪水风险，并分析近30年土地利用与植被变化。","使用SRTM\u002FDEM地形分析、坡度与汇流模型、NDVI及LULC分类。","低海拔河谷与近河道居民点最易受灾；城区扩张、农地减少、植被覆盖显著增加。","可引入时序遥感与机器学习，构建流域尺度洪水风险动态预警与土地利用协同优化模型。","2026-09-15T23:30:20.567355Z",{"id":460,"title":461,"url":462,"summary":463,"summary_zh":464,"content":9,"source_name":465,"source_url":462,"published_at":466,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":467,"score_detail":468,"sources":471,"tags":473,"primary_topic":477,"secondary_topics":478,"classification":480,"search_phrases":491,"slug":494,"view_count":15,"doi":495,"paper":496,"created_at":519},4826,"Multi-Index Remote Sensing Assessment of Agro-Climatic Change and Agricultural Responses in Temperate and Semi-Arid Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18203496","Diverse climatic regions exhibit unique agro-climatic vulnerabilities that are significantly shaped by climate stress. Understanding these distinct vulnerabilities is essential for developing effective policies to mitigate climate change impacts and ensuring sustainable environmental management. This study examines agro-climatic vulnerabilities in Nottinghamshire County (United Kingdom) and Udaipur District (India) from 2000 to 2024, comparing climate change impacts across temperate and semi-arid agricultural systems that represent fundamentally different system behaviours under contrasting climatic conditions. Using multi-temporal Landsat imagery and WorldClim climate data, nine environmental indices were analysed: the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Normalized Difference Water Index (NDWI), Moisture Stress Index (MSI), Land Surface Temperature (LST), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Drought Risk Index (DRI), and Crop Health Index (CHI). The results reveal contrasting regional trajectories that challenge conventional assumptions about climate vulnerability. Udaipur exhibited improved vegetation and moisture conditions across the observation period, whereas Nottinghamshire showed intensifying moisture stress and declining precipitation. Warming was more pronounced in Nottinghamshire, while strong inter-index correlations validated the monitoring approach. These findings indicate that Udaipur benefits from intensified monsoon patterns, requiring flood mitigation and water-storage infrastructure, whereas Nottinghamshire’s rising moisture stress underscores the need for sustained monitoring and adaptive management. This comparative framework provides evidence-based insights for region-specific adaptation strategies essential to sustainable agricultural development across diverse agro-climatic zones.","不同气候区域表现出独特的农业气候脆弱性，而这些脆弱性在很大程度上受到气候胁迫的影响。理解这些不同的脆弱性对于制定有效政策以减缓气候变化影响、确保可持续环境管理至关重要。本研究考察了2000年至2024年间英国诺丁汉郡与印度乌代布尔县的农业气候脆弱性，比较了温带与半干旱农业系统在气候变化影响下的差异，这两类系统在截然不同的气候条件下代表了根本不同的系统行为。研究利用多时相Landsat影像和WorldClim气候数据，分析了九个环境指数：归一化植被指数（NDVI）、植被状态指数（VCI）、归一化水体指数（NDWI）、水分胁迫指数（MSI）、地表温度（LST）、温度状态指数（TCI）、植被健康指数（VHI）、干旱风险指数（DRI）和作物健康指数（CHI）。结果揭示了截然不同的区域变化轨迹，挑战了关于气候脆弱性的传统假设。乌代布尔在观测期内表现出植被和水分条件的改善，而诺丁汉郡则显示出加剧的水分胁迫和降水减少。诺丁汉郡的增温更为显著，而指数间较强的相关性验证了该监测方法。这些发现表明，乌代布尔受益于季风模式的增强，需要防洪和水储存基础设施，而诺丁汉郡不断上升的水分胁迫则凸显了持续监测和适应性管理的必要性。这一比较框架为因地制宜的适应策略提供了循证依据，对跨不同农业气候区的可持续发展至关重要。","Remote Sensing","2026-10-12T00:00:00Z",75,{"impact":105,"substance":469,"depth":106,"authority":58,"freshness":208,"relevant":93,"comment":470},22,"基于多时相遥感与九项环境指数的跨区域对比研究，方法扎实、结论具政策参考价值，但属学术论文，产业影响有限。",[472],{"name":465,"url":462},[22,296,474,475,476],"干旱风险","适应策略","农业气候",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[479],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":44,"locked":14,"version":45,"method":46,"evidence":481},{"candidates":482,"agricultural_evidence":51},[483,487],{"code":26,"score":13,"supported":51,"title":484,"abstract":485,"tags":486},[53],[],[296,22],{"code":33,"score":208,"supported":51,"title":488,"abstract":489,"tags":490},[],[227,68,367,69],[],[492,493],"Landsat 遥感 农业气候","诺丁汉郡 乌代布尔","Landsat遥感农业气候-4826","10.3390\u002Frs18203496",{"doi":495,"openalex_id":497,"authors":498,"venue":465,"cited_by_count":15,"oa_url":462,"card":514,"direction":87,"ingested_from":89},"W7221570035",[499,502,505,508,511],{"name":500,"orcid":501},"Bhanwar Vishvendra Raj Singh","https:\u002F\u002Forcid.org\u002F0000-0001-7750-5240",{"name":503,"orcid":504},"Vivek Agarwal","https:\u002F\u002Forcid.org\u002F0000-0003-1185-3802",{"name":506,"orcid":507},"Stuart Marsh","https:\u002F\u002Forcid.org\u002F0000-0002-1168-6760",{"name":509,"orcid":510},"Gajendra Singh Sisodiya","https:\u002F\u002Forcid.org\u002F0009-0006-4118-7491",{"name":512,"orcid":513},"Ajay Sharma","https:\u002F\u002Forcid.org\u002F0009-0001-6077-9875",{"tldr":515,"method":516,"finding":517,"direction":87,"opportunity":518},"用多时相遥感和九项指数对比英国诺丁汉郡与印度乌代浦2000—2024年农业气候脆弱性。","Landsat多时相影像与WorldClim气候数据，计算NDVI、VCI、ND","乌代浦植被与水分条件改善、季风增强，诺丁汉郡水分胁迫加剧、降水下降且升温更明显。","可拓展多区域长时序遥感指数耦合机器学习，构建跨气候带农业气候脆弱性预警与适应决策模型。","2026-10-13T23:30:24.062929Z"]