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However, traditional rainwater harvesting frameworks often neglect snow water equivalent (SWE), limiting their applicability in cold, snow-dominated steppe environments. This study develops an integrated remote sensing and GIS-based approach to identify potential sites for spring runoff harvesting. Using Sentinel-2 imagery, snow cover fraction (SCF) was derived and combined with a digital elevation model (DEM) to estimate snow depth through a quadratic regression model, validated against in situ data from 23 meteorological stations. Snow water equivalent was then calculated by incorporating field-measured snow density. Alongside SWE, six additional parameters—slope, drainage density, land use\u002Fland cover (LULC), and soil texture (sand, silt, clay)—were integrated within an Analytical Hierarchy Process (AHP) multi-criteria decision analysis. Results show that SWE, drainage density, and LULC collectively contributed 75% of the weight in site suitability, underscoring their dominant role in snowmelt-driven hydrology. The final suitability map indicates that 28.03% of the region falls into the “Very High” class, concentrated in central and northwestern districts. Developing just 30% of these potential zones could yield approximately 4.2 × 108 m3 of usable meltwater, supporting up to 105,000 ha under full irrigation or 280,000 ha under deficit irrigation. The study demonstrates that integrating high-resolution SWE with MCDA provides a scalable framework for enhancing agricultural resilience, mitigating floods, and strengthening water security in cold, semi-arid landscapes.","季节性积雪是哈萨克斯坦北部的重要水资源，春季融雪主导着地表径流和农业供水。然而，传统的雨水收集框架往往忽视雪水当量（SWE），限制了其在寒冷、以积雪为主的草原环境中的适用性。本研究开发了一种集成遥感和GIS的方法，用于识别春季径流收集的潜在场址。利用Sentinel-2影像提取积雪覆盖率（SCF），并结合数字高程模型（DEM）通过二次回归模型估算雪深，利用23个气象站的实地数据进行验证。随后，结合实地测量的雪密度计算雪水当量。除SWE外，还将坡度、河网密度、土地利用\u002F土地覆盖（LULC）以及土壤质地（砂、粉砂、黏土）等六个附加参数纳入层次分析法（AHP）多准则决策分析。结果表明，SWE、河网密度和LULC在场地适宜性评价中合计贡献了75%的权重，凸显了它们在融雪驱动水文中的主导作用。最终适宜性图显示，该区域28.03%的面积属于“极高”等级，集中在中部和西北部地区。仅开发这些潜在区域的30%，即可产生约4.2 × 10⁸ m³的可用融水，可支持多达105,000公顷的充分灌溉或280,000公顷的亏缺灌溉。本研究表明，将高分辨率SWE与多准则决策分析相结合，为增强农业韧性、减轻洪水和加强寒冷半干旱地区的水安全提供了一个可扩展的框架。","Earth","2026-10-02T00:00:00Z","论文",false,77,{"impact":584,"substance":585,"depth":361,"authority":586,"freshness":587,"relevant":455,"comment":588},16,21,13,9,"该研究将遥感与GIS多准则评估结合，量化了北哈萨克斯坦融雪水资源与灌溉潜力，方法新颖、数据扎实，对寒区农业水资源管理有参考价值，但地域性较强，属细分领域进展。",[590],{"name":578,"url":575},[592,593,594,595,596],"农业水资源","哈萨克斯坦","遥感","GIS","融雪灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[599,600],{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},{"status":602,"locked":581,"version":4,"method":603,"evidence":604},"classified","rules",{"candidates":605,"agricultural_evidence":608},[606,613,617,624,630],{"code":32,"score":607,"supported":608,"title":609,"abstract":611,"tags":612},15,true,[610],"remote sensing",[594,610],[594],{"code":40,"score":416,"supported":608,"title":614,"abstract":615,"tags":616},[595],[595],[595],{"code":152,"score":385,"supported":608,"title":618,"abstract":619,"tags":623},[],[620,621,153,622],"灌溉","水资源","water resource",[592],{"code":144,"score":625,"supported":608,"title":626,"abstract":627,"tags":629},6,[],[628,145],"土壤",[],{"code":184,"score":631,"supported":608,"title":632,"abstract":633,"tags":635},3,[],[634],"干旱",[],[637,638],"北哈萨克斯坦 融雪 灌溉潜力","Sentinel-2 雪水当量 遥感","北哈萨克斯坦融雪灌溉潜力-4085","10.3390\u002Fearth7050163",{"doi":640,"openalex_id":642,"authors":643,"venue":578,"cited_by_count":424,"oa_url":575,"card":665,"direction":669,"ingested_from":671},"W7216430083",[644,647,650,652,654,656,659,662],{"name":645,"orcid":646},"Zhanassyl Teleubay","https:\u002F\u002Forcid.org\u002F0000-0003-4783-5489",{"name":648,"orcid":649},"Erkin Ussalinov","https:\u002F\u002Forcid.org\u002F0000-0003-1907-9532",{"name":651,"orcid":11},"Laura Ryskulbekova",{"name":653,"orcid":11},"Asset Arystanov",{"name":655,"orcid":11},"Ibragim Seitassanov",{"name":657,"orcid":658},"Улжан Онласын","https:\u002F\u002Forcid.org\u002F0000-0001-5789-5464",{"name":660,"orcid":661},"Nuray Kutymova","https:\u002F\u002Forcid.org\u002F0009-0000-7214-1708",{"name":663,"orcid":664},"Farabi Yermekov","https:\u002F\u002Forcid.org\u002F0000-0002-0290-3866",{"tldr":666,"method":667,"finding":668,"direction":669,"opportunity":670},"结合遥感与GIS评估哈萨克斯坦北部季节性雪水资源及春季径流收集潜力。","Sentinel-2与DEM估算雪深和SWE，结合AHP多准则决策分析选址。","SWE、河网密度和土地利用贡献75%权重，28%区域为极高适宜区。","农业遥感与作物表型","可将SWE驱动的选址框架推广至其他寒旱区，并耦合气候情景与灌溉效率优化。","openalex","2026-10-03T23:30:32.427699Z",{"id":674,"title":675,"url":676,"summary":677,"summary_zh":678,"content":11,"source_name":679,"source_url":676,"published_at":680,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":681,"score_detail":682,"sources":684,"tags":686,"primary_topic":692,"secondary_topics":693,"classification":696,"search_phrases":745,"slug":748,"view_count":455,"doi":749,"paper":750,"created_at":768},4038,"Use of artificial intelligence (AI) in vegetable crops: A review","https:\u002F\u002Fdoi.org\u002F10.33545\u002F2664844x.2026.v8.i10a.1995","Artificial Intelligence (AI) is emerging technology in vegetable production, enabling data-driven decision making and precision agriculture practices. AI refers to the intelligence exhibited by machines that can perceive their environment, process large datasets, and make decisions to optimize agricultural productivity. With the integration of technologies such as machine learning, sensors, drones, satellites, Geographic Information Systems (GIS), and Global Positioning Systems (GPS), AI enables efficient monitoring and management of crop production systems In vegetable cultivation, AI technologies assist farmers in several critical operations including soil monitoring, climate assessment, pest and disease diagnosis, irrigation scheduling, and yield prediction. Sensors such as wind vane, cup count anemometer, rain gauge, lux meter, soil moisture sensor, and crop canopy sensors collect real-time environmental and crop data. These data was processed through data loggers and machine learning algorithms to provide accurate recommendations for crop management. Drone-based imaging systems and multispectral cameras further support precision farming by identifying weeds, nutrient deficiencies and disease symptoms at early stages. The use of AI in vegetables also highlights image-based predictive agriculture systems where captured images are processed through stages such as. image cleaning, feature extraction, and model training to detect pests and diseases. Several case studies demonstrate the use of AI in vegetable crops such as tomato, capsicum, and chilli for monitoring canopy growth, irrigation management. Additionally, robotic technologies in harvesting address labor shortages in agriculture. Crop yield prediction of AI in vegetables increases by 40% sensors and algorithms reduces labor cost by 30% moisture sensor increases water use efficiency by 40% spraying done reduces use of pesticide by 60% robotic arms increases harvesting accuracy by 40% artificial neural network data integration saves 25% cost in disease monitoring.","人工智能（AI）正在成为蔬菜生产领域的新兴技术，推动数据驱动决策与精准农业实践。AI是指机器所表现出的智能，能够感知环境、处理大规模数据集并做出决策以优化农业生产力。通过整合机器学习、传感器、无人机、卫星、地理信息系统（GIS）和全球定位系统（GPS）等技术，AI实现了对作物生产系统的高效监测与管理。在蔬菜种植中，AI技术可协助农民完成多项关键作业，包括土壤监测、气候评估、病虫害诊断、灌溉调度和产量预测。风向标、杯式风速计、雨量计、照度计、土壤水分传感器和作物冠层传感器等设备可采集实时的环境与作物数据。这些数据通过数据记录器和机器学习算法进行处理，为作物管理提供精准建议。基于无人机的成像系统与多光谱相机通过早期识别杂草、养分缺乏和病害症状，进一步支持精准农业。AI在蔬菜中的应用还突出了基于图像的预测性农业系统，该系统将采集的图像经过图像清洗、特征提取和模型训练等阶段处理，以检测病虫害。多个案例研究展示了AI在番茄、辣椒和尖椒等蔬菜作物中用于监测冠层生长和灌溉管理的应用。此外，收获机器人技术可缓解农业劳动力短缺问题。AI在蔬菜中的作物产量预测通过传感器和算法提高了40%，劳动力成本降低了30%，土壤水分传感器使水分利用效率提高了40%，喷雾作业使农药使用量减少了60%，机械臂使收获准确率提高了40%，人工神经网络数据集成使病害监测成本节省了25%。","International Journal of Agriculture and Food Science","2026-10-01T00:00:00Z",75,{"impact":361,"substance":508,"depth":584,"authority":416,"freshness":587,"relevant":455,"comment":683},"系统综述AI在蔬菜生产中的应用，含产量、成本、水肥药效等量化数据，对智慧农业主题有聚合价值。",[685],{"name":679,"url":676},[687,688,689,690,691],"智慧农业","农业人工智能","病虫害识别","精准农业","蔬菜种植",{"code":279,"slug":280,"name":281,"path":282,"parent_code":271},[694,695],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},{"status":602,"locked":581,"version":4,"method":603,"evidence":697},{"candidates":698,"agricultural_evidence":608},[699,707,715,721,726,732,736,740],{"code":24,"score":700,"supported":608,"title":701,"abstract":703,"tags":706},19,[702],"artificial intelligence",[704,705,702],"机器学习","machine learning",[688,689],{"code":279,"score":361,"supported":608,"title":708,"abstract":710,"tags":714},[709],"vegetable",[281,711,712,709,713],"番茄","辣椒","tomato",[691],{"code":40,"score":587,"supported":608,"title":716,"abstract":717,"tags":720},[],[718,595,719],"地理信息","geographic information",[],{"code":16,"score":173,"supported":608,"title":722,"abstract":723,"tags":725},[],[690,724],"precision agriculture",[687,690],{"code":104,"score":625,"supported":608,"title":727,"abstract":728,"tags":731},[],[729,730],"农药","pesticide",[],{"code":144,"score":625,"supported":608,"title":733,"abstract":734,"tags":735},[],[628,145],[],{"code":152,"score":625,"supported":608,"title":737,"abstract":738,"tags":739},[],[620,153],[],{"code":32,"score":631,"supported":608,"title":741,"abstract":742,"tags":744},[],[743],"multispectral",[],[746,747],"AI 蔬菜 精准农业","蔬菜 病虫害 图像识别","AI蔬菜精准农业-4038","10.33545\u002F2664844x.2026.v8.i10a.1995",{"doi":749,"openalex_id":751,"authors":752,"venue":679,"cited_by_count":424,"oa_url":11,"card":761,"direction":767,"ingested_from":671},"W7215128283",[753,755,757,759],{"name":754,"orcid":11},"Hrishikesh Nirmala Balkrishna Thube",{"name":756,"orcid":11},"Dr. SK Kshirsagar",{"name":758,"orcid":11},"MG Agale",{"name":760,"orcid":11},"GS Shinde",{"tldr":762,"method":763,"finding":764,"direction":765,"opportunity":766},"综述人工智能在蔬菜作物生产中的应用，涵盖监测、诊断、灌溉、产量预测与采收机器人。","综述机器学习、传感器、无人机、卫星、GIS\u002FGPS及图像处理在蔬菜生产中的应用案","AI使蔬菜产量预测提高40%、节水40%、减农药60%、采收准确率提高40%。","智慧农业 \u002F 农业物联网","可针对小农户蔬菜场景开发低成本多源数据融合模型，并验证AI长期经济与生态效益。","农业人工智能与决策模型","2026-10-02T23:30:40.356457Z",{"id":770,"title":771,"url":772,"summary":773,"summary_zh":774,"content":11,"source_name":775,"source_url":772,"published_at":680,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":776,"score_detail":777,"sources":780,"tags":782,"primary_topic":786,"secondary_topics":787,"classification":789,"search_phrases":816,"slug":819,"view_count":400,"doi":820,"paper":821,"created_at":840},4027,"Groundwater Potential Zonation of Unchehra Watershed, Central India using AHP, Remote Sensing, and GIS Techniques","https:\u002F\u002Fdoi.org\u002F10.17491\u002Fjgsi.2026.174637","ABSTRACT Groundwater is the primary source of domestic and agricultural water in the semi-arid regions of central India. Despite receiving moderate annual rainfall, Unchehra watershed experiences seasonal water shortages, especially during summer, highlighting the need for scientific assessment of groundwater resources. This study delineates groundwater potential zones (GWPZs) in the Unchehra watershed using an integrated Remote Sensing (RS), Geographic Information System (GIS), and Analytical Hierarchy Process (AHP) approach. Seven major factors influencing groundwater occurrence lineament density, geology, land use and land cover (LULC), drainage density, geomorphology, slope and soil type were considered in the analysis. Spatial thematic layers for these parameters were prepared from satellite imagery and Digital Elevation Model (DEM) data using ArcGIS. The relative importance of each factor and its classes was determined through AHP, providing a systematic weighting scheme for groundwater potential assessment. The weighted layers were integrated to generate a groundwater potential zonation map. The resulting map classified the watershed into five groundwater potential zones: very low, low, moderate, high, and very high. The analysis revealed that 7.88% of the watershed falls under very low potential, while significant portions of the area exhibit moderate (28.07%) to very high (22.61%) groundwater potential. To assess the reliability of the generated model, validation was carried out using 41 randomly selected dug wells and Receiver Operating Characteristic (ROC) curve analysis. The near-zero Consistency Ratio (CR) confirms the reliability of the AHP weighting scheme, while the Area Under the Curve (AUC) value of 0.763 indicates good predictive accuracy of the groundwater potential model. The identified high and very high groundwater potential zones are suitable for groundwater development and artificial recharge measures such as check dams, percolation tanks, and recharge wells. The integrated RS-GIS-AHP framework developed in this study provides a reliable decision-support tool for groundwater exploration, recharge planning, and sustainable groundwater management in Unchehra watershed and similar hydrogeological settings.","摘要 地下水是印度中部半干旱地区生活用水和农业用水的主要来源。尽管Unchehra流域年降水量适中，但仍面临季节性缺水问题，尤其是在夏季，这凸显了对地下水资源进行科学评估的必要性。本研究采用遥感（RS）、地理信息系统（GIS）和层次分析法（AHP）相结合的方法，对Unchehra流域的地下水潜力区（GWPZs）进行了划分。分析中考虑了影响地下水赋存的七个主要因素：线性构造密度、地质、土地利用与土地覆盖（LULC）、河网密度、地貌、坡度和土壤类型。利用ArcGIS从卫星影像和数字高程模型（DEM）数据中制备了这些参数的空间专题图层。通过AHP确定各因素及其类别的相对重要性，为地下水潜力评估提供了系统化的权重方案。将加权图层进行叠加整合，生成了地下水潜力分区图。所得图件将流域划分为五个地下水潜力区：极低、低、中等、高和极高。分析表明，流域面积的7.88%属于极低潜力区，而相当部分区域表现出中等（28.07%）至极高（22.61%）的地下水潜力。为评估所生成模型的可靠性，利用41口随机选取的民井和受试者工作特征（ROC）曲线分析进行了验证。接近零的一致性比率（CR）证实了AHP权重方案的可靠性，而曲线下面积（AUC）值为0.763表明地下水潜力模型具有良好的预测精度。所识别的高和极高地下水潜力区适宜进行地下水开发和人工补给措施，如拦水坝、渗滤池和回灌井。本研究开发的RS-GIS-AHP集成框架为Unchehra流域及类似水文地质条件下的地下水勘探、补给规划和可持续地下水管理提供了可靠的决策支持工具。","Journal of the Geological Society of India",66,{"impact":173,"substance":508,"depth":778,"authority":586,"freshness":173,"relevant":455,"comment":779},17,"方法扎实、验证可靠的区域地下水潜力评估论文，对农业水资源管理有参考价值，但属印度区域研究，公共影响有限。",[781],{"name":775,"url":772},[594,783,595,784,785],"水资源管理","印度农业","地下水潜力",{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},[788],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":602,"locked":581,"version":4,"method":603,"evidence":790},{"candidates":791,"agricultural_evidence":608},[792,796,800,804,808,812],{"code":40,"score":361,"supported":608,"title":793,"abstract":794,"tags":795},[595],[718,595,719],[595],{"code":32,"score":607,"supported":608,"title":797,"abstract":798,"tags":799},[610],[594,610],[594],{"code":144,"score":625,"supported":608,"title":801,"abstract":802,"tags":803},[],[628,145],[],{"code":152,"score":408,"supported":608,"title":805,"abstract":806,"tags":807},[],[621],[785,783],{"code":184,"score":631,"supported":608,"title":809,"abstract":810,"tags":811},[],[634],[],{"code":556,"score":455,"supported":581,"title":813,"abstract":814,"tags":815},[],[],[784],[817,818],"地下水潜力 水资源管理 印度农业 遥感","地下水潜力 水资源管理","地下水潜力水资源管理印度农业遥感-4027","10.17491\u002Fjgsi.2026.174637",{"doi":820,"openalex_id":822,"authors":823,"venue":775,"cited_by_count":424,"oa_url":11,"card":835,"direction":669,"ingested_from":671},"W7215068088",[824,826,828,830,832],{"name":825,"orcid":11},"Rabindra Nath Tiwari",{"name":827,"orcid":11},"Brahmanand Sharma",{"name":829,"orcid":11},"Sarvesh Kumar Patel",{"name":831,"orcid":11},"Aditya Singh Baghel",{"name":833,"orcid":834},"Ashish Kumar Mishra","https:\u002F\u002Forcid.org\u002F0000-0002-9953-4576",{"tldr":836,"method":837,"finding":838,"direction":669,"opportunity":839},"用RS、GIS与AHP集成方法划分印度Unchehra流域地下水潜力区并验证精度。","基于遥感与DEM提取7个因子，AHP加权叠加，41口井与ROC验证。","流域分五级潜力区，高与极高潜力占22.61%，模型AUC达0.763，精度良好。","可将该框架与实时灌溉需水、作物布局耦合，支撑半干旱区精准补灌与地下水回补选址。","2026-10-02T23:30:24.566714Z",{"id":842,"title":843,"url":844,"summary":845,"summary_zh":846,"content":11,"source_name":847,"source_url":844,"published_at":848,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":849,"score_detail":850,"sources":852,"tags":854,"primary_topic":859,"secondary_topics":860,"classification":862,"search_phrases":888,"slug":891,"view_count":455,"doi":892,"paper":893,"created_at":906},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":625,"substance":361,"depth":345,"authority":625,"freshness":173,"relevant":455,"comment":851},"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。",[853],{"name":847,"url":844},[855,856,857,858],"遥感监测","土地利用","干旱区农业","GIS应用",{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},[861],{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},{"status":602,"locked":581,"version":4,"method":603,"evidence":863},{"candidates":864,"agricultural_evidence":608},[865,871,875,880,884],{"code":184,"score":385,"supported":608,"title":866,"abstract":867,"tags":870},[],[868,634,869],"气候变化","climate change",[857],{"code":40,"score":587,"supported":608,"title":872,"abstract":873,"tags":874},[],[718,595,719],[],{"code":32,"score":876,"supported":608,"title":877,"abstract":878,"tags":879},7,[],[594,610],[855],{"code":152,"score":631,"supported":608,"title":881,"abstract":882,"tags":883},[],[621],[],{"code":176,"score":455,"supported":581,"title":885,"abstract":886,"tags":887},[],[],[856],[889,890],"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":892,"openalex_id":894,"authors":895,"venue":847,"cited_by_count":424,"oa_url":844,"card":901,"direction":669,"ingested_from":671},"W7214996325",[896,898],{"name":897,"orcid":11},"Deendayal .",{"name":899,"orcid":900},"Om Prakash","https:\u002F\u002Forcid.org\u002F0000-0003-1239-445X",{"tldr":902,"method":903,"finding":904,"direction":669,"opportunity":905},"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":908,"title":909,"url":910,"summary":911,"summary_zh":912,"content":11,"source_name":913,"source_url":910,"published_at":848,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":914,"score_detail":915,"sources":917,"tags":919,"primary_topic":924,"secondary_topics":925,"classification":926,"search_phrases":956,"slug":959,"view_count":400,"doi":960,"paper":961,"created_at":979},3902,"ҚАЗАҚСТАННЫҢ ОҢТҮСТІК-ШЫҒЫСЫНДАҒЫ ТӘЛІМДІ ЖЕРЛЕРДІҢ ЭРОЗИЯЛЫҚ ҚАУІПТІЛІГІН ҚАШЫҚТАН ЗОНДТАУ ДЕРЕКТЕРІ МЕН RUSLE МОДЕЛІ НЕГІЗІНДЕ БАҒАЛАУ","https:\u002F\u002Fdoi.org\u002F10.52578\u002F3134-8556-2026-3-14-23","This study assesses potential soil erosion on arable land in the foothill zone of South-East Kazakhstan using the Revised Universal Soil Loss Equation (RUSLE), remote sensing data, and Geographic Information Systems (GIS). The research focuses on a 17.6-hectare agricultural plot located in the Almaty region. Sentinel-2 satellite imagery and the FABDEM_V1 global digital elevation model were used as the primary data sources for spatial analysis. The methodology involved the calculation and integration of the main RUSLE factors, including rainfall erosivity (R), soil erodibility (K), topographic factor (LS), cover-management factor (C), and support practice factor (P). The LS factor was derived from the FABDEM digital elevation model, while the vegetation cover factor (C) was estimated using the Normalized Difference Vegetation Index (NDVI) for two key agricultural periods: April (pre-growing season) and July (post-harvest period). Based on soil mechanical composition and humus content, the K factor was determined as 0.26. The results show that the average annual soil loss amounted to 10.01 t\u002Fha\u002Fyear in April and increased to 13.67 t\u002Fha\u002Fyear in July. The analysis revealed that erosion risk rises by approximately 36% after harvesting due to reduced vegetation cover. The maximum soil loss reached 55.46 t\u002Fha\u002Fyear in the most erosion-prone areas. The findings highlight that the post-harvest period represents the most critical phase for soil degradation in the study area. The study emphasizes the importance of implementing soil conservation measures and demonstrates the effectiveness of integrating RUSLE modeling with remote sensing and GIS technologies for sustainable land management.","本研究利用修正通用土壤流失方程（RUSLE）、遥感数据和地理信息系统（GIS），评估了哈萨克斯坦东南部山麓地带耕地的潜在土壤侵蚀。研究聚焦于阿拉木图地区一块17.6公顷的农业地块。采用Sentinel-2卫星影像和FABDEM_V1全球数字高程模型作为空间分析的主要数据来源。研究方法包括计算并整合RUSLE的主要因子，包括降雨侵蚀力（R）、土壤可蚀性（K）、地形因子（LS）、植被覆盖与管理因子（C）以及水土保持措施因子（P）。LS因子由FABDEM数字高程模型提取，植被覆盖因子（C）则利用归一化植被指数（NDVI）在两个关键农业时期进行估算：4月（生长季前）和7月（收获后）。根据土壤机械组成和腐殖质含量，确定K因子为0.26。结果表明，4月年均土壤流失量为10.01吨\u002F公顷\u002F年，7月增至13.67吨\u002F公顷\u002F年。分析显示，由于植被覆盖减少，收获后侵蚀风险上升约36%。在侵蚀最严重的区域，最大土壤流失量达到55.46吨\u002F公顷\u002F年。研究结果突出表明，收获后时期是研究区土壤退化最为关键的阶段。本研究强调了实施土壤保持措施的重要性，并证明了将RUSLE模型与遥感和GIS技术相结合用于可持续土地管理的有效性。","Agricultural Systems and Natural Resources",72,{"impact":416,"substance":585,"depth":778,"authority":586,"freshness":587,"relevant":455,"comment":916},"基于RUSLE模型与遥感数据评估哈萨克斯坦东南部耕地侵蚀风险，方法规范、数据具体，对干旱区数字农业与水土保持有参考价值，但属区域案例研究，公共影响有限。",[918],{"name":913,"url":910},[920,594,921,922,923],"数字农业","土壤侵蚀","水土保持","RUSLE",{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},[],{"status":602,"locked":581,"version":4,"method":603,"evidence":927},{"candidates":928,"agricultural_evidence":608},[929,935,939,943,947,952],{"code":40,"score":587,"supported":608,"title":930,"abstract":931,"tags":934},[],[718,932,595,933,719],"空间分析","spatial analysis",[],{"code":32,"score":876,"supported":608,"title":936,"abstract":937,"tags":938},[],[594,610],[594],{"code":144,"score":625,"supported":608,"title":940,"abstract":941,"tags":942},[],[628,145],[],{"code":176,"score":400,"supported":608,"title":944,"abstract":945,"tags":946},[],[922],[922],{"code":64,"score":631,"supported":608,"title":948,"abstract":949,"tags":951},[],[950],"研究方法",[],{"code":16,"score":455,"supported":581,"title":953,"abstract":954,"tags":955},[],[],[920],[957,958],"哈萨克斯坦 阿拉木图 RUSLE 土壤侵蚀","Sentinel-2 NDVI 土壤流失","哈萨克斯坦阿拉木图RUSLE土壤侵蚀-3902","10.52578\u002F3134-8556-2026-3-14-23",{"doi":960,"openalex_id":962,"authors":963,"venue":913,"cited_by_count":424,"oa_url":910,"card":974,"direction":669,"ingested_from":671},"W7214985337",[964,966,968,970,972],{"name":965,"orcid":11},"Д. Кайнушева",{"name":967,"orcid":11},"Р. Жапаев",{"name":969,"orcid":11},"Е. Кенжехан",{"name":971,"orcid":11},"Д. Кыдырбаева",{"name":973,"orcid":11},"Ә. Мақұлбек",{"tldr":975,"method":976,"finding":977,"direction":669,"opportunity":978},"用RUSLE模型和遥感数据评估哈萨克斯坦东南部耕地土壤侵蚀风险。","RUSLE模型、Sentinel-2影像、FABDEM高程数据与GIS空间分析。","年均土壤流失量4月为10.01 t\u002Fha，7月增至13.67 t\u002Fha，收获后侵蚀风险上升约36%。","可结合多时相遥感与机器学习，动态预测收获后侵蚀热点，优化保护性耕作措施。","2026-10-01T23:30:29.218572Z",{"id":981,"title":982,"url":983,"summary":984,"summary_zh":985,"content":11,"source_name":986,"source_url":983,"published_at":848,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":987,"score_detail":988,"sources":990,"tags":992,"primary_topic":995,"secondary_topics":996,"classification":999,"search_phrases":1038,"slug":1041,"view_count":455,"doi":1042,"paper":1043,"created_at":1054},3901,"Geospatial analysis of irrigation suitability in Nigeria using remote sensing and GIS","https:\u002F\u002Fdoi.org\u002F10.53030\u002Ftjags.1937471","This research offers a comprehensive geospatial evaluation of land suitability for irrigation across Nigeria. It leverages 2020 Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, processed within the Google Earth Engine (GEE) cloud-based computational environment. Given growing concerns about food security and climate variability, ascertaining areas amenable to irrigation is paramount for fostering sustainable agricultural development within Nigeria. This investigation integrated multi-temporal remote sensing observations, including the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Normalized Difference Water Index - NDWI, to construct an Irrigation Suitability Index (ISI). These datasets underwent analysis at a 500-meter spatial resolution, encompassing the entirety of the nation. Weighted overlay analysis, performed within the GEE environment, generated a nationwide map delineating irrigation potential across five distinct categories: Very Low, Low, Moderate, High, and Very High suitability. The findings reveal suitability scores ranging from a minimum of 0.11 (very low) to a maximum of 1 (very high). Specifically, northern Nigerian states such as Sokoto, Kebbi, Yobe, and Borno demonstrated high to very high suitability. In contrast, southern states, including Lagos, Rivers, Cross River, and Delta, were characterized by very low suitability. The study underscores significant geographical disparities, particularly in arid northern regions which experience greater constraints due to lower precipitation and elevated evapotranspiration. These findings offer actionable directives for agricultural planners, policymakers, and stakeholders seeking to enhance irrigation capacity and strengthen food security resilience amidst evolving climatic conditions.","本研究对尼日利亚全国灌溉土地适宜性进行了综合地理空间评估。研究利用2020年中分辨率成像光谱仪（MODIS）卫星数据，在谷歌地球引擎（GEE）云端计算环境中进行处理。鉴于粮食安全和气候变率日益受到关注，确定适宜灌溉的区域对于促进尼日利亚可持续农业发展至关重要。本研究整合了多时相遥感观测数据，包括归一化植被指数（NDVI）、地表温度（LST）和归一化水体指数（NDWI），以构建灌溉适宜性指数（ISI）。这些数据集以500米空间分辨率进行分析，覆盖全国范围。在GEE环境中进行的加权叠加分析生成了全国灌溉潜力分布图，划分为五个类别：极低、低、中等、高和极高适宜性。研究结果显示，适宜性评分从最低0.11（极低）到最高1（极高）不等。具体而言，尼日利亚北部各州如索科托、凯比、约贝和博尔诺表现出高至极高适宜性。相比之下，南部各州包括拉各斯、河流州、十字河州和三角洲州则以极低适宜性为特征。研究强调了显著的地理差异，特别是干旱的北部地区因降水量较低和蒸散量较高而面临更大限制。这些发现为农业规划者、政策制定者和利益相关者提供了可操作的指导，以在气候条件不断变化的情况下增强灌溉能力并提升粮食安全韧性。","OpenAlex",78,{"impact":361,"substance":321,"depth":778,"authority":586,"freshness":173,"relevant":455,"comment":989},"基于MODIS与GEE的全国尺度灌溉适宜性制图，方法规范、结论具体，对农业规划有参考价值，但属他国案例，国内落地性有限。",[991],{"name":986,"url":983},[920,993,594,595,994],"粮食安全","灌溉适宜性",{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},[997,998],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},{"status":602,"locked":581,"version":4,"method":603,"evidence":1000},{"candidates":1001,"agricultural_evidence":608},[1002,1007,1011,1015,1020,1024,1029,1034],{"code":152,"score":778,"supported":608,"title":1003,"abstract":1004,"tags":1006},[153],[620,153,1005],"evapotranspiration",[],{"code":32,"score":607,"supported":608,"title":1008,"abstract":1009,"tags":1010},[610],[594,610],[594],{"code":40,"score":587,"supported":608,"title":1012,"abstract":1013,"tags":1014},[595],[],[595],{"code":263,"score":876,"supported":608,"title":1016,"abstract":1017,"tags":1019},[],[993,1018],"food security",[993],{"code":184,"score":631,"supported":608,"title":1021,"abstract":1022,"tags":1023},[],[634],[],{"code":192,"score":631,"supported":608,"title":1025,"abstract":1026,"tags":1028},[],[1027],"可持续农业",[],{"code":542,"score":631,"supported":608,"title":1030,"abstract":1031,"tags":1033},[],[1032],"农业规划",[],{"code":16,"score":455,"supported":581,"title":1035,"abstract":1036,"tags":1037},[],[],[920],[1039,1040],"尼日利亚 灌溉适宜性 遥感","Google Earth Engine 灌溉","尼日利亚灌溉适宜性遥感-3901","10.53030\u002Ftjags.1937471",{"doi":1042,"openalex_id":1044,"authors":1045,"venue":11,"cited_by_count":424,"oa_url":11,"card":1049,"direction":669,"ingested_from":671},"W7214955379",[1046],{"name":1047,"orcid":1048},"Emmanuel M. Menegbo","https:\u002F\u002Forcid.org\u002F0000-0002-6694-3226",{"tldr":1050,"method":1051,"finding":1052,"direction":669,"opportunity":1053},"用遥感与GIS评估尼日利亚全国灌溉适宜性并绘制五级分区图。","2020年MODIS数据在GEE中计算NDVI、LST、NDWI并加权叠加。","北部各州适宜性高，南部低，全国适宜性得分0.11至1。","可结合土壤、地下水与作物需水数据，将静态适宜性图升级为动态灌溉决策支持系统。","2026-10-01T23:30:29.159371Z",{"id":1056,"title":1057,"url":1058,"summary":1059,"summary_zh":1060,"content":11,"source_name":1061,"source_url":1058,"published_at":848,"category":580,"cover_url":11,"hotness":447,"is_selected":581,"score":1062,"score_detail":1063,"sources":1065,"tags":1069,"primary_topic":1072,"secondary_topics":1073,"classification":1075,"search_phrases":1090,"slug":1093,"view_count":455,"doi":1094,"paper":1095,"created_at":1111},3895,"Analysis Of Changes In Land Use And Land Cover In Pune City Over A Decade Using Geospatial Techniques.","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053479","Abstract Land use and land cover (LULC) changes play a significant role in reflecting natural and anthropogenic environmental changes. In this study, changes in 'Land Use and Land Cover' (LULC) in Pune city for the period 2015-2025 have been analysed using the techniques of 'Remote Sensing' and 'Geographic Information System' (GIS). The May 2015 and May 2025 30 m resolution Landsat 08 satellite images were used. For LULC classification, the 'Supervised Maximum Likelihood Classification' method was used, resulting in 5 major LULC classes: built-up land, barren land, and agricultural land, vegetation, and water bodies. More than 100 'training samples' were taken for each land cover type to improve classification accuracy. The findings of the study show that there was a considerable shift in the spatial distribution of LULC in this decade. Built-up land increased from 154.48 sq. km (30.48%) in 2015 to 292.26 sq. km (57.86%) in 2025, representing an increase of approximately 137.78 sq. km. Conversely, the area of barren land decreased from 203.94 sq. km (40.24%) to 106.93 sq. km (21.10%). The amount of agricultural land has reduced from 44.39 sq. km (8.75%) to 18.93 sq. km (3.72%), indicating that agricultural land has been converted for urbanisation on a large scale. Regarding vegetation cover, 85.91 sq. km showed a marginal decrease from 16.95% to 15.58%, whereas the area of water bodies decreased from 18.13 sq. km (3.58%) to 10.77 sq. km (2.12%). The results show the rapid growth of Pune city during the study period, which is mainly observed in the loss or transformation of barren land, agricultural land and water bodies. The study shows the usefulness of using Landsat data, ‘supervised maximum likelihood classification’, GIS and ‘change detection' techniques for tracking long-term urban growth and environmental changes. The results of this research can help urban planners and decision makers in formulating policies for sustainable land use and in safeguarding environmentally sensitive areas.","摘要 土地利用与土地覆盖（LULC）变化在反映自然与人为环境变化方面发挥着重要作用。本研究利用遥感（Remote Sensing）和地理信息系统（GIS）技术，分析了浦那市2015—2025年期间土地利用与土地覆盖（LULC）的变化。研究使用了2015年5月和2025年5月分辨率为30米的Landsat 08卫星影像。在LULC分类中，采用了监督最大似然分类（Supervised Maximum Likelihood Classification）方法，最终得到5个主要LULC类别：建设用地、裸地、农用地、植被和水体。为提高分类精度，每种土地覆盖类型均选取了100个以上的训练样本（training samples）。研究结果表明，在这十年间，LULC的空间分布发生了显著变化。建设用地从2015年的154.48平方公里（30.48%）增加到2025年的292.26平方公里（57.86%），增加了约137.78平方公里。相反，裸地面积从203.94平方公里（40.24%）减少到106.93平方公里（21.10%）。农用地面积从44.39平方公里（8.75%）减少到18.93平方公里（3.72%），表明农用地已大规模转化为城市化用地。植被覆盖方面，85.91平方公里略有下降，从16.95%降至15.58%；水体面积则从18.13平方公里（3.58%）减少到10.77平方公里（2.12%）。结果表明，研究期间浦那市快速扩张，主要体现在裸地、农用地和水体的减少或转变上。研究表明，利用Landsat数据、监督最大似然分类、GIS和变化检测（change detection）技术对于追踪长期城市增长与环境变化具有重要价值。本研究结果可帮助城市规划者和决策者制定可持续土地利用政策，并保护环境敏感区域。","Zenodo (CERN European Organization for Nuclear Research)",65,{"impact":173,"substance":508,"depth":584,"authority":416,"freshness":587,"relevant":455,"comment":1064},"基于Landsat遥感与GIS的浦那市十年土地利用变化研究，数据翔实、结论明确，对农业用地流失与城市扩张监测有参考价值，但属区域案例研究，公共影响有限。",[1066,1067],{"name":1061,"url":1058},{"name":1061,"url":1068},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053480",[594,856,1070,595,1071],"Landsat","城市化",{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},[1074],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":602,"locked":581,"version":4,"method":603,"evidence":1076},{"candidates":1077,"agricultural_evidence":608},[1078,1082,1086],{"code":40,"score":385,"supported":608,"title":1079,"abstract":1080,"tags":1081},[],[718,595,719],[595],{"code":32,"score":173,"supported":608,"title":1083,"abstract":1084,"tags":1085},[],[594,610],[1070,594],{"code":176,"score":455,"supported":581,"title":1087,"abstract":1088,"tags":1089},[],[],[856],[1091,1092],"土地利用 城市化 遥感 Landsat","土地利用 城市化","土地利用城市化遥感Landsat-3895","10.5281\u002Fzenodo.23053479",{"doi":1094,"openalex_id":1096,"authors":1097,"venue":1061,"cited_by_count":424,"oa_url":1058,"card":1106,"direction":669,"ingested_from":671},"W7214889813",[1098,1100,1102,1104],{"name":1099,"orcid":11},"Kalyani Machhindra sonawane",{"name":1101,"orcid":11},"Sumitra Nandkumar Nikam",{"name":1103,"orcid":11},"Sunil W. Gaikwad",{"name":1105,"orcid":11},"Yogesh N. Pawar",{"tldr":1107,"method":1108,"finding":1109,"direction":669,"opportunity":1110},"用遥感与GIS分析印度浦那市2015-2025年土地利用变化，发现建设用地大幅扩张、农地水体锐减。","Landsat 8 30米影像，监督最大似然分类，5类LULC与变化检测。","建设用地从30.48%增至57.86%，农地由8.75%降至3.72%，裸地与水体同步减少。","可结合多时相高分辨率影像与CA-Markov模型，预测城市扩张对农地和水体的未来胁迫。","2026-10-01T23:30:28.617704Z",{"id":1113,"title":1114,"url":1115,"summary":1116,"summary_zh":1117,"content":11,"source_name":1118,"source_url":1115,"published_at":1119,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1062,"score_detail":1120,"sources":1122,"tags":1124,"primary_topic":1128,"secondary_topics":1129,"classification":1130,"search_phrases":1155,"slug":1158,"view_count":69,"doi":1159,"paper":1160,"created_at":1175},3849,"Land Use\u002F Land Cover Dynamics and the Decline of Grazing Resources in Dauchi Grazing Reserve, Adamawa State, Nigeria","https:\u002F\u002Fdoi.org\u002F10.33003\u002Ffjs-2026-1018-6083","Land use and land cover (LULC) change is a major driver of rangeland degradation, threatening grazing resources and pastoral livelihoods in the Guinea and Sudan savanna ecosystems of northeastern Nigeria. This study evaluates the impact of LULC change on grazing resources in Dauchi Grazing Reserve, Song LGA, Adamawa State, Nigeria, using multi-temporal satellite imagery covering forty years. Satellite data were processed and classified using supervised classification techniques within a Geographic Information System (GIS) and remote sensing framework. Six LULC classes—grassland, shrubland, cropland, bare surface, built-up areas, and water body—were mapped and analysed to assess spatial and temporal transformations. Change detection analysis was employed to quantify transitions among land cover classes and assess implications for pasture availability. Results revealed a substantial decline in natural grazing resources, particularly grassland, which decreased from 32.77 km² (54.05%) in 1984 to 5.36 km² (8.84%) in 2024, representing a loss of 27.41 km². Conversely, cultivated land expanded considerably, increasing from 21.47 km² in 1984 to 46.35 km² in 2024. These changes reduced the availability and spatial continuity of natural grazing areas, potentially constraining forage availability and grazing capacity. The findings demonstrate that sustained LULC transformation has significantly altered the spatial distribution of grazing resources in Dauchi Grazing Reserve, creating challenges for sustainable livestock production and ecosystem resilience. The study recommends rangeland restoration, controlled grazing, integrated land-use planning, and protection of remaining natural vegetation to conserve grazing resources and support pastoral livelihoods.","土地利用与土地覆盖（LULC）变化是牧场退化的主要驱动因素，威胁着尼日利亚东北部几内亚和苏丹萨瓦纳生态系统的放牧资源和牧民生计。本研究利用覆盖四十年的多时相卫星影像，评估了尼日利亚阿达马瓦州松地方政府区道奇放牧保护区内土地利用与土地覆盖变化对放牧资源的影响。卫星数据在地理信息系统（GIS）和遥感框架内采用监督分类技术进行处理和分类。研究绘制并分析了六个土地利用与土地覆盖类别——草地、灌丛地、耕地、裸地、建设用地和水体——以评估其空间和时间变化。采用变化检测分析量化土地覆盖类别之间的转换，并评估其对牧草可利用性的影响。结果表明，天然放牧资源大幅减少，尤其是草地，从1984年的32.77平方公里（54.05%）下降至2024年的5.36平方公里（8.84%），减少了27.41平方公里。相反，耕地显著扩张，从1984年的21.47平方公里增加至2024年的46.35平方公里。这些变化减少了天然放牧区的可利用性和空间连续性，可能制约牧草供应和放牧承载力。研究结果表明，持续的土地利用与土地覆盖变化显著改变了道奇放牧保护区内放牧资源的空间分布，对可持续畜牧生产和生态系统恢复力构成了挑战。研究建议开展牧场恢复、实施控制性放牧、进行综合土地利用规划，并保护剩余天然植被，以保育放牧资源并支持牧民生计。","FUDMA Journal of Sciences","2026-09-28T00:00:00Z",{"impact":173,"substance":508,"depth":584,"authority":416,"freshness":587,"relevant":455,"comment":1121},"基于四十年多时相遥感的牧区草地退化实证研究，数据扎实但属区域案例，对国内农业信息化参考价值有限。",[1123],{"name":1118,"url":1115},[855,856,1125,1126,1127],"尼日利亚","农牧交错带","草地退化",{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},[],{"status":602,"locked":581,"version":4,"method":603,"evidence":1131},{"candidates":1132,"agricultural_evidence":608},[1133,1137,1141,1146,1151],{"code":40,"score":587,"supported":608,"title":1134,"abstract":1135,"tags":1136},[],[718,595,719],[],{"code":32,"score":876,"supported":608,"title":1138,"abstract":1139,"tags":1140},[],[594,610],[855],{"code":332,"score":625,"supported":608,"title":1142,"abstract":1143,"tags":1145},[],[1144,333],"畜牧",[],{"code":364,"score":631,"supported":608,"title":1147,"abstract":1148,"tags":1150},[],[1149],"forage",[],{"code":176,"score":455,"supported":581,"title":1152,"abstract":1153,"tags":1154},[],[],[856],[1156,1157],"Dauchi 放牧保护区 土地利用","Adamawa 草地退化 遥感","Dauchi放牧保护区土地利用-3849","10.33003\u002Ffjs-2026-1018-6083",{"doi":1159,"openalex_id":1161,"authors":1162,"venue":1118,"cited_by_count":424,"oa_url":1169,"card":1170,"direction":669,"ingested_from":671},"W7214627198",[1163,1165,1167],{"name":1164,"orcid":11},"Nagarta Jamala Gailyson",{"name":1166,"orcid":11},"Andrew Egba Ubogu",{"name":1168,"orcid":11},"Yaro Abdu","https:\u002F\u002Ffjs.fudutsinma.edu.ng\u002Findex.php\u002Ffjs\u002Farticle\u002Fdownload\u002F6083\u002F3992",{"tldr":1171,"method":1172,"finding":1173,"direction":669,"opportunity":1174},"用40年遥感影像评估尼日利亚Dauchi放牧保护区土地利用变化对放牧资源的影响。","多时相卫星影像监督分类与GIS变化检测，划分六类地物。","草地从54.05%降至8.84%，耕地大幅扩张，放牧资源显著萎缩。","可结合多源遥感与牧草生产力模型，量化放牧承载力并预警退化风险。","2026-09-30T23:30:31.022343Z",{"id":1177,"title":1178,"url":1179,"summary":1180,"summary_zh":1181,"content":11,"source_name":1061,"source_url":1179,"published_at":848,"category":580,"cover_url":11,"hotness":447,"is_selected":581,"score":424,"score_detail":1182,"sources":1184,"tags":1188,"primary_topic":1191,"secondary_topics":1192,"classification":1193,"search_phrases":1204,"slug":1207,"view_count":631,"doi":1208,"paper":1209,"created_at":1220},3787,"Applied Geography for Sustainable Regional Development in Maharashtra: Potentials, Challenges and Strategic Directions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23013796","Abstract: Applied Geography refers to the practical application of geographical knowledge, concepts, techniques and spatial analysis to contemporary social, economic and environmental problems. Maharashtra exhibits considerable regional variations in physical resources, population distribution, agriculture, urbanisation, infrastructure, transportation, health services and economic development. These spatial variations create both opportunities and challenges for balanced regional development. The present study examines the role of Applied Geography in sustainable regional development in Maharashtra, with particular emphasis on regional planning, agricultural development, urbanisation, natural-resource management, disaster management, transportation, tourism and accessibility to public services. The study is primarily based on secondary data obtained from government reports, Census publications, planning documents, geographical literature and institutional sources. It highlights the potential application of Geographic Information System (GIS), Remote Sensing, GPS, spatial statistics and geospatial modelling in evidence-based regional planning. The study also identifies major challenges, including regional disparities, environmental degradation, unplanned urban growth, climate-related hazards, infrastructure gaps and unequal access to resources and services. It proposes an integrated geographical framework based on spatial planning, sustainable resource utilisation, geospatial technologies, community participation and regional coordination. The study concludes that Applied Geography can make a significant contribution to achieving balanced, inclusive and sustainable regional development in Maharashtra.","摘要：应用地理学是指将地理知识、概念、技术及空间分析实际应用于当代社会、经济与环境问题。马哈拉施特拉邦在自然资源、人口分布、农业、城市化、基础设施、交通运输、卫生服务及经济发展等方面呈现出显著的区域差异。这些空间差异为区域均衡发展既带来了机遇，也构成了挑战。本研究考察应用地理学在马哈拉施特拉邦可持续区域发展中的作用，重点关注区域规划、农业发展、城市化、自然资源管理、灾害管理、交通运输、旅游业及公共服务可及性。本研究主要基于来自政府报告、人口普查出版物、规划文件、地理学文献及机构来源的二手数据。研究强调了地理信息系统（GIS）、遥感、全球定位系统（GPS）、空间统计及地理空间建模在循证区域规划中的潜在应用。研究还识别了主要挑战，包括区域差距、环境退化、城市无序扩张、气候相关灾害、基础设施缺口以及资源与服务获取不均等。研究提出了一个基于空间规划、可持续资源利用、地理空间技术、社区参与和区域协调的综合地理框架。研究结论认为，应用地理学可为马哈拉施特拉邦实现均衡、包容和可持续的区域发展作出重要贡献。",{"impact":424,"substance":424,"depth":424,"authority":424,"freshness":424,"relevant":424,"comment":1183},"研究印度马哈拉施特拉邦的区域地理规划，与三农、农业信息化、数字乡村等主题无直接关联，不建议进入每日精选。",[1185,1186],{"name":1061,"url":1179},{"name":1061,"url":1187},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23013797",[594,595,1189,1190],"应用地理学","区域发展规划",{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},[],{"status":602,"locked":581,"version":4,"method":603,"evidence":1194},{"candidates":1195,"agricultural_evidence":608},[1196,1200],{"code":40,"score":385,"supported":608,"title":1197,"abstract":1198,"tags":1199},[],[718,932,595,933,719],[595],{"code":32,"score":876,"supported":608,"title":1201,"abstract":1202,"tags":1203},[],[594,610],[594],[1205,1206],"马哈拉施特拉邦 区域发展","应用地理学 遥感","马哈拉施特拉邦区域发展-3787","10.5281\u002Fzenodo.23013796",{"doi":1208,"openalex_id":1210,"authors":1211,"venue":1061,"cited_by_count":424,"oa_url":1179,"card":1214,"direction":669,"ingested_from":671},"W7214632446",[1212],{"name":1213,"orcid":11},"M. D. Shaikh",{"tldr":1215,"method":1216,"finding":1217,"direction":1218,"opportunity":1219},"综述应用地理学在马哈拉施特拉邦可持续区域发展中的作用、潜力与挑战。","基于政府报告、普查等二手数据，结合GIS、遥感和空间统计进行评述。","应用地理学与地理空间技术可支撑均衡、包容和可持续的区域发展。","数字乡村与农业信息化","可延伸至农业空间规划与乡村服务可达性的地理空间决策支持研究。","2026-09-29T23:30:28.115123Z",{"id":1222,"title":1223,"url":1224,"summary":1225,"summary_zh":1226,"content":11,"source_name":1227,"source_url":1224,"published_at":1228,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":681,"score_detail":1229,"sources":1231,"tags":1233,"primary_topic":1238,"secondary_topics":1239,"classification":1242,"search_phrases":1274,"slug":1277,"view_count":69,"doi":1278,"paper":1279,"created_at":1293},3617,"Applications and challenges of geospatial technologies in veterinary medicine","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44338-026-00259-y","Geospatial technologies are increasingly recognized as critical assets in veterinary medicine, providing robust frameworks for disease surveillance, risk mapping, and informed decision‑making. The objective of this narrative review is to summarize the role of Geographic Information Systems, Global Positioning System, and Remote Sensing in advancing veterinary medicine by synthesizing current applications, identifying prevailing challenges, and exploring future prospects. Collectively, these tools enable monitoring of disease dynamics, identification of epidemiological hotspots, outbreak prediction, and integration of spatial and epidemiological datasets to guide targeted interventions. For instance, GIS‑based mapping of Rift Valley fever outbreaks in East Africa has enabled rapid identification of epidemiological hotspots and guided vaccination campaigns, while RS‑derived vegetation indices have successfully predicted tick distribution patterns in southern Africa, improving targeted control strategies. Their utility extends beyond livestock health, contributing significantly to wildlife disease monitoring, zoonotic risk assessment, food safety assurance, and broader veterinary decision processes. Despite such successes, adoption remains limited, hindered by technical\u002Finfrastructure barriers, poor data quality, and ethical and policy challenges. In Ethiopia, recent surveys indicate that fewer than 20% of veterinary institutions actively use GIS platforms for routine surveillance, and integration of RS into national veterinary services is still limited to pilot projects. This gap underscores the urgent need for national geospatial data repositories, standardized protocols, and capacity‑building initiatives to ensure equitable access and sustainable utilization. Future opportunities lie in harnessing advances in Artificial Intelligence, Machine Learning, and cloud‑based GIS platforms, alongside global collaboration and training programs, to strengthen veterinary epidemiology and surveillance systems. Addressing these challenges will be essential for the sustainable integration of geospatial technologies into veterinary practice, thereby enhancing disease control, safeguarding public health, and supporting livestock productivity.","地理空间技术在兽医学中日益被视为关键资产，为疾病监测、风险制图和知情决策提供了稳健框架。本叙述性综述旨在总结地理信息系统（Geographic Information Systems, GIS）、全球定位系统（Global Positioning System, GPS）和遥感（Remote Sensing, RS）在推动兽医学发展中的作用，通过综合当前应用、识别主要挑战并探讨未来前景。总体而言，这些工具有助于监测疾病动态、识别流行病学热点、预测疫情，并整合空间与流行病学数据集以指导针对性干预。例如，基于GIS的东非裂谷热疫情制图能够快速识别流行病学热点并指导疫苗接种活动，而遥感衍生的植被指数已成功预测南部非洲蜱虫分布模式，改善了针对性控制策略。其效用不仅限于家畜健康，还对野生动物疾病监测、人兽共患病风险评估、食品安全保障及更广泛的兽医决策过程作出重要贡献。尽管取得了这些成功，其应用仍然有限，受到技术\u002F基础设施障碍、数据质量不佳以及伦理和政策挑战的制约。在埃塞俄比亚，近期调查表明，不到20%的兽医机构在常规监测中积极使用GIS平台，而遥感纳入国家兽医服务仍局限于试点项目。这一差距凸显了建立国家地理空间数据存储库、标准化方案和能力建设举措的迫切需求，以确保公平获取和可持续利用。未来的机遇在于利用人工智能、机器学习和基于云的GIS平台的进步，以及全球合作和培训项目，来加强兽医流行病学和监测系统。应对这些挑战对于将地理空间技术可持续地整合到兽医实践中至关重要，从而增强疾病控制、保障公共卫生并支持家畜生产力。","Discover Animals","2026-09-24T00:00:00Z",{"impact":361,"substance":508,"depth":778,"authority":416,"freshness":173,"relevant":455,"comment":1230},"综述系统梳理GIS、GPS与遥感在兽医领域的应用与瓶颈，含东非裂谷热、南部非洲蜱虫分布及埃塞俄比亚使用率不足20%等实证数据，对农业信息化与动物疫病防控有参考价值。",[1232],{"name":1227,"url":1224},[1234,855,1235,1236,1237],"动物疫病防控","人兽共患病","兽医信息化","地理信息系统",{"code":372,"slug":373,"name":374,"path":375,"parent_code":332},[1240,1241],{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":602,"locked":581,"version":4,"method":603,"evidence":1243},{"candidates":1244,"agricultural_evidence":608},[1245,1252,1256,1260,1264,1270],{"code":372,"score":700,"supported":608,"title":1246,"abstract":1248,"tags":1251},[1247],"veterinary",[1249,1247,1250],"人兽共患","zoonotic",[1235,1234],{"code":40,"score":385,"supported":608,"title":1253,"abstract":1254,"tags":1255},[],[718,595,719],[1237],{"code":24,"score":587,"supported":608,"title":1257,"abstract":1258,"tags":1259},[],[704,705,702],[],{"code":32,"score":876,"supported":608,"title":1261,"abstract":1262,"tags":1263},[],[594,610],[855],{"code":465,"score":625,"supported":608,"title":1265,"abstract":1266,"tags":1269},[],[1267,1268],"食品安全","food safety",[],{"code":332,"score":631,"supported":608,"title":1271,"abstract":1272,"tags":1273},[],[333],[],[1275,1276],"GIS 兽医 疾病监测","遥感 蜱虫 分布 预测","GIS兽医疾病监测-3617","10.1007\u002Fs44338-026-00259-y",{"doi":1278,"openalex_id":1280,"authors":1281,"venue":1227,"cited_by_count":424,"oa_url":1287,"card":1288,"direction":765,"ingested_from":671},"W7214225608",[1282,1285],{"name":1283,"orcid":1284},"Assaye Wollelie Fentie","https:\u002F\u002Forcid.org\u002F0009-0005-9099-9819",{"name":1286,"orcid":11},"Daniel Haile Asefa","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44338-026-00259-y.pdf",{"tldr":1289,"method":1290,"finding":1291,"direction":669,"opportunity":1292},"综述地理空间技术在兽医领域的应用、挑战与前景，聚焦疾病监测与风险制图。","叙述性综述，整合GIS、GPS、遥感在兽医流行病学中的应用案例与调查数据。","GIS\u002FRS能有效预测疫情和病媒分布，但应用受限于基础设施、数据质量及政策，如埃塞俄比亚使用率不足2","可探索AI\u002F机器学习与云GIS结合，构建兽医领域标准化地理空间数据平台与预警系统。","2026-09-27T23:30:26.015992Z",{"id":1295,"title":1296,"url":1297,"summary":1298,"summary_zh":1299,"content":11,"source_name":1300,"source_url":1297,"published_at":1228,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":776,"score_detail":1301,"sources":1303,"tags":1305,"primary_topic":1307,"secondary_topics":1308,"classification":1311,"search_phrases":1345,"slug":1348,"view_count":408,"doi":1349,"paper":1350,"created_at":1372},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",{"impact":416,"substance":361,"depth":584,"authority":416,"freshness":173,"relevant":455,"comment":1302},"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。",[1304],{"name":1300,"url":1297},[687,688,594,595,1306],"干旱监测",{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},[1309,1310],{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":602,"locked":581,"version":4,"method":603,"evidence":1312},{"candidates":1313,"agricultural_evidence":608},[1314,1318,1323,1327,1333,1337,1341],{"code":40,"score":361,"supported":608,"title":1315,"abstract":1316,"tags":1317},[595],[718,932,595,933,719],[595],{"code":184,"score":607,"supported":608,"title":1319,"abstract":1321,"tags":1322},[1320],"drought",[634,1320],[1306],{"code":32,"score":607,"supported":608,"title":1324,"abstract":1325,"tags":1326},[610],[594,610],[594],{"code":24,"score":385,"supported":608,"title":1328,"abstract":1329,"tags":1332},[],[1330,704,705,1331,702],"深度学习","deep learning",[688],{"code":144,"score":625,"supported":608,"title":1334,"abstract":1335,"tags":1336},[],[628,145],[],{"code":152,"score":625,"supported":608,"title":1338,"abstract":1339,"tags":1340},[],[621,1005],[],{"code":16,"score":455,"supported":581,"title":1342,"abstract":1343,"tags":1344},[],[],[687],[1346,1347],"遥感 GIS 干旱评估","卫星遥感 干旱指数","遥感GIS干旱评估-3495","10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123",{"doi":1349,"openalex_id":1351,"authors":1352,"venue":1300,"cited_by_count":424,"oa_url":1297,"card":1367,"direction":669,"ingested_from":671},"W7214156857",[1353,1355,1357,1359,1361,1363,1365],{"name":1354,"orcid":11},"V. Dhanalakshmi",{"name":1356,"orcid":11},"N. Manikandan",{"name":1358,"orcid":11},"V. S. Jinsy",{"name":1360,"orcid":11},"K. V. Sumesh",{"name":1362,"orcid":11},"P. Nideesh",{"name":1364,"orcid":11},"P. S. Manju",{"name":1366,"orcid":11},"N. Gopika",{"tldr":1368,"method":1369,"finding":1370,"direction":669,"opportunity":1371},"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":1374,"title":1375,"url":1376,"summary":1377,"summary_zh":1378,"content":11,"source_name":1379,"source_url":1376,"published_at":1380,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":329,"score_detail":1381,"sources":1383,"tags":1385,"primary_topic":1388,"secondary_topics":1389,"classification":1390,"search_phrases":1416,"slug":1419,"view_count":400,"doi":1420,"paper":1421,"created_at":1436},3367,"Digital Technology Adoption Conditioning Analysis Model in Agriculture","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.1880.v1","Technological advancements have been responsible for a significant part of the growth in agricultural productivity in recent years. Digital technologies have a high potential to enable the development of the agricultural sector, reshape value chains, and significantly contribute to more productive, resilient, and transparent food systems; however, their adoption in Brazil remains uneven due to regional disparities and structural bottlenecks. The research investigated this problem to build and validate the Digital Technology Adoption Conditioning Analysis Model (MAC-AgriTech), through a case study with Brazilian agricultural data, encompassing the identification of conditioning factors, their territorial evaluation, and the proposition of actions, while providing structured data collection and analysis instruments. The spatial analysis revealed deep territorial asymmetries, concentrating resources and infrastructure in the South and Southeast regions. Econometric modeling demonstrated that digital adoption is primarily driven by the producer’s digital familiarity, connectivity quality, and property scale, with 77% of producers identifying acquisition and maintenance costs as the primary barrier. The transition to digital agriculture in Brazil requires targeted, multidimensional public policies—such as expanded rural connectivity, technical training, and subsidized credit—to overcome regional gaps, and to increase agricultural competitiveness, efficiency, and sustainability.","近年来，技术进步对农业生产力增长贡献显著。数字技术具有巨大潜力，能够推动农业部门发展、重塑价值链，并为构建更高产、更具韧性且更透明的粮食体系作出重要贡献；然而，由于区域差异和结构性瓶颈，其在巴西的采用仍不均衡。本研究针对这一问题，通过一项基于巴西农业数据的案例研究，构建并验证了数字技术采用条件分析模型（MAC-AgriTech），涵盖条件因素的识别、其区域性评估以及行动建议的提出，同时提供了结构化的数据收集与分析工具。空间分析揭示了深刻的区域不对称性，资源和基础设施集中在南部和东南部地区。计量经济建模表明，数字采用主要受生产者数字熟悉度、连接质量和财产规模的驱动，其中77%的生产者将购置和维护成本视为主要障碍。巴西向数字农业的转型需要有针对性的、多维度的公共政策——如扩大农村连接、技术培训和补贴信贷——以克服区域差距，并提高农业竞争力、效率和可持续性。","Preprints.org","2026-09-22T00:00:00Z",{"impact":584,"substance":508,"depth":778,"authority":625,"freshness":173,"relevant":455,"comment":1382},"基于巴西农业数据的数字技术采纳条件分析模型研究，方法系统、结论有实证支撑，但属预印本且聚焦巴西，对国内参考价值有限。",[1384],{"name":1379,"url":1376},[920,687,688,1386,1387],"巴西农业","农村数字化",{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},[],{"status":602,"locked":581,"version":4,"method":603,"evidence":1391},{"candidates":1392,"agricultural_evidence":608},[1393,1398,1402,1407,1412],{"code":16,"score":173,"supported":608,"title":1394,"abstract":1395,"tags":1397},[],[920,1396],"digital agriculture",[920,687],{"code":40,"score":625,"supported":608,"title":1399,"abstract":1400,"tags":1401},[],[932,933],[],{"code":488,"score":631,"supported":608,"title":1403,"abstract":1404,"tags":1406},[],[1405],"技术培训",[],{"code":56,"score":631,"supported":608,"title":1408,"abstract":1409,"tags":1411},[],[1410],"agricultural data",[],{"code":24,"score":455,"supported":581,"title":1413,"abstract":1414,"tags":1415},[],[],[688],[1417,1418],"巴西 数字农业 技术采纳","MAC-AgriTech 模型","巴西数字农业技术采纳-3367","10.20944\u002Fpreprints202609.1880.v1",{"doi":1420,"openalex_id":1422,"authors":1423,"venue":1379,"cited_by_count":424,"oa_url":1376,"card":1431,"direction":1218,"ingested_from":671},"W7214109425",[1424,1427,1429],{"name":1425,"orcid":1426},"Isabela Santos","https:\u002F\u002Forcid.org\u002F0009-0002-3659-2020",{"name":1428,"orcid":11},"Eduardo Dias",{"name":1430,"orcid":11},"Lidia Scoton",{"tldr":1432,"method":1433,"finding":1434,"direction":1218,"opportunity":1435},"构建并验证MAC-AgriTech模型，分析巴西农业数字技术采纳的条件因素与区域差异。","巴西农业数据案例研究，空间分析与计量经济建模。","采纳主要由数字熟悉度、连接质量和农场规模驱动，77%生产者视成本为首要障碍。","可延伸至中国等发展中国家，探究数字素养、基础设施与政策组合对技术采纳的因果效应。","2026-09-24T23:30:27.046035Z",{"id":1438,"title":1439,"url":1440,"summary":1441,"summary_zh":1442,"content":11,"source_name":1443,"source_url":1440,"published_at":1444,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1445,"score_detail":1446,"sources":1448,"tags":1450,"primary_topic":1454,"secondary_topics":1455,"classification":1457,"search_phrases":1492,"slug":1495,"view_count":455,"doi":1496,"paper":1497,"created_at":1507},3364,"Geographical Analysis of Desertification and Environmental Degradation and the Spatial Variation of Their Manifestations in Parts of the Jifara Plain","https:\u002F\u002Fdoi.org\u002F10.65405\u002Fsjh.2.3.85","This research aims to analyze the spatial variation of desertification and environmental degradation manifestations in the Al-Jfara Plain, identify the degrees, intensity, and geographical distribution of desertification, and determine the main manifestations associated with land degradation. It also seeks to identify the effects of natural and human factors on the spread of desertification and determine the areas most vulnerable to it, thereby contributing to efforts to conserve natural resources and reduce land degradation. The research adopted the analytical approach as the main method for analyzing data related to degraded lands, vegetation cover, rangelands, rainfall, erosion, and agricultural and livestock production. It also employed the descriptive approach to present the natural and human characteristics of the research area, as well as the comparative approach to compare different spatial units and time periods. The research further relied on geographical references, scientific studies, and available data, using percentages and rates of change to compare levels of degradation. The research concluded that there is clear spatial variation in the manifestations of desertification and environmental degradation across the Al-Jfara Plain. Wind erosion, declining vegetation cover, drought, and rainfall variability emerged as the most prominent manifestations of degradation. In addition, overgrazing, excessive exploitation of soil and groundwater resources, and urban expansion contributed to increasing pressure on natural resources. The research recommends reducing overgrazing, rationalizing groundwater use, combating erosion, increasing vegetation cover, regulating land use, and enhancing environmental awareness. It also proposes periodic monitoring of desertification using remote sensing and Geographic Information Systems (GIS), preparing updated maps of land degradation, and implementing projects for the rehabilitation of degraded lands.","本研究旨在分析贾法拉平原荒漠化与环境退化表现的空间差异，识别荒漠化的程度、强度及地理分布，并确定与土地退化相关的主要表现。研究还力求查明自然因素和人为因素对荒漠化蔓延的影响，确定最易受荒漠化影响的区域，从而为保护自然资源和减少土地退化的努力作出贡献。研究采用分析方法作为分析与退化土地、植被覆盖、牧场、降雨、侵蚀以及农牧业生产相关数据的主要方法，同时运用描述性方法呈现研究区域的自然和人文特征，并采用比较方法对不同空间单元和时间段进行比较。研究还依托地理文献、科学研究和现有数据，利用百分比和变化率来比较退化程度。研究得出结论：贾法拉平原各地荒漠化与环境退化表现在明显的空间差异。风蚀、植被覆盖下降、干旱和降雨变率是最突出的退化表现。此外，过度放牧、土壤和地下水资源的过程开发以及城市扩张也加剧了对自然资源的压力。研究建议减少过度放牧、合理利用地下水、防治侵蚀、增加植被覆盖、规范土地利用并增强环境意识。研究还提出利用遥感和地理信息系统（GIS）对荒漠化进行定期监测，编制更新的土地退化地图，并实施退化土地恢复项目。","Shihab Journal of Humanities","2026-09-23T00:00:00Z",55,{"impact":173,"substance":361,"depth":345,"authority":625,"freshness":587,"relevant":455,"comment":1447},"区域荒漠化空间分析研究，方法常规、结论有参考价值，但属地方性学术成果，公共影响有限。",[1449],{"name":1443,"url":1440},[1451,855,595,1452,1453],"土地退化","植被覆盖","荒漠化防治",{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},[1456],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":602,"locked":581,"version":4,"method":603,"evidence":1458},{"candidates":1459,"agricultural_evidence":608},[1460,1464,1468,1472,1476,1480,1484,1488],{"code":40,"score":385,"supported":608,"title":1461,"abstract":1462,"tags":1463},[],[718,595,719],[595],{"code":32,"score":173,"supported":608,"title":1465,"abstract":1466,"tags":1467},[],[594,610],[1452,855],{"code":144,"score":625,"supported":608,"title":1469,"abstract":1470,"tags":1471},[],[628,145],[],{"code":184,"score":625,"supported":608,"title":1473,"abstract":1474,"tags":1475},[],[634,1320],[],{"code":332,"score":631,"supported":608,"title":1477,"abstract":1478,"tags":1479},[],[333],[],{"code":152,"score":631,"supported":608,"title":1481,"abstract":1482,"tags":1483},[],[621],[],{"code":136,"score":455,"supported":581,"title":1485,"abstract":1486,"tags":1487},[],[],[1451],{"code":176,"score":455,"supported":581,"title":1489,"abstract":1490,"tags":1491},[],[],[1453],[1493,1494],"GIS 荒漠化 遥感监测","荒漠化防治 土地退化 植被覆盖 遥感监测","GIS荒漠化遥感监测-3364","10.65405\u002Fsjh.2.3.85",{"doi":1496,"openalex_id":1498,"authors":1499,"venue":1443,"cited_by_count":424,"oa_url":1440,"card":1502,"direction":669,"ingested_from":671},"W7214127171",[1500],{"name":1501,"orcid":11},"حسنية السني رجب البكوري",{"tldr":1503,"method":1504,"finding":1505,"direction":669,"opportunity":1506},"分析吉法拉平原荒漠化与环境退化的空间差异及主要表现，并提出防治建议。","采用分析法、描述法与比较法，结合地理文献和统计数据评估退化程度。","风蚀、植被减少、干旱和降水变率是主要退化表现，过度放牧与地下水超采加剧压力。","可结合遥感与GIS开展荒漠化动态监测，构建多因子退化风险评估模型。","2026-09-24T23:30:21.130938Z",{"id":1509,"title":1510,"url":1511,"summary":1512,"summary_zh":1513,"content":11,"source_name":1514,"source_url":1511,"published_at":1444,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1515,"score_detail":1516,"sources":1518,"tags":1520,"primary_topic":1526,"secondary_topics":1527,"classification":1528,"search_phrases":1556,"slug":1559,"view_count":400,"doi":1560,"paper":1561,"created_at":1586},3342,"Impact of agricultural greening on food production resilience-empirical evidence from China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1938875","The Food Production System is generally confronted with external uncertain shocks and rigid constraints from ecological factors. How to strengthen the system’s adaptability and resilience through green transition pathways is a crucial research topic in the field of modern agricultural governance. With the provincial panel data from China, the entropy method is adopted in this paper to measure agricultural greening and food production resilience, and then the two-way fixed-effect model, the moderating-effect model, the threshold-effect model and spatial econometric models are applied for empirical testing. According to the results, the degree of agricultural greening has promoted food production resilience; at the same time, regions show heterogeneity across geographical regions and grain functional zones. Green finance and rural scientific and technological input will also be effective driving forces. There is a single threshold for economic development level; after exceeding it, the marginal promoting effect on resilience weakens, but remains significantly positive. Food production resilience also exhibits significant spatial dependence, and agricultural greening generates positive spillover effects on neighboring provinces under the adjacency-based spatial specification. This paper will provide new ideas for the theory of resilience enhancement and offer some references to help optimize the distribution of agricultural green factors and foster long-term stable grain production.","粮食生产系统普遍面临外部不确定冲击与生态因素的刚性约束。如何通过绿色转型路径增强系统的适应性与韧性，是现代农业农村治理领域的重要研究课题。本文基于中国省级面板数据，采用熵值法测度农业绿色化与粮食生产韧性，进而运用双向固定效应模型、调节效应模型、门槛效应模型及空间计量模型进行实证检验。结果表明，农业绿色化程度提升了粮食生产韧性；同时，不同地理区域与粮食功能区呈现出异质性。绿色金融与农村科技投入也是有效的驱动力量。经济发展水平存在单一门槛，跨越门槛后对韧性的边际促进效应减弱，但仍显著为正。粮食生产韧性还表现出显著的空间依赖性，在邻接空间设定下，农业绿色化对邻近省份产生正向溢出效应。本文将为韧性提升理论提供新思路，并为优化农业绿色要素配置、促进粮食长期稳定生产提供一定参考。","Frontiers in Sustainable Food Systems",79,{"impact":361,"substance":321,"depth":361,"authority":586,"freshness":173,"relevant":455,"comment":1517},"基于中国省级面板数据的实证研究，方法扎实、结论有政策参考价值，但属学术论文而非产业级事件，适合进入主题聚合而非头条精选。",[1519],{"name":1514,"url":1511},[1521,1522,1523,1524,1525],"农业绿色发展","粮食生产韧性","农业科技投入","绿色金融","空间溢出效应",{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},[],{"status":602,"locked":581,"version":4,"method":603,"evidence":1529},{"candidates":1530,"agricultural_evidence":608},[1531,1536,1540,1544,1548,1552],{"code":40,"score":400,"supported":608,"title":1532,"abstract":1533,"tags":1535},[],[1534],"空间计量",[1525],{"code":263,"score":455,"supported":581,"title":1537,"abstract":1538,"tags":1539},[],[],[1522],{"code":503,"score":455,"supported":581,"title":1541,"abstract":1542,"tags":1543},[],[],[1524],{"code":160,"score":455,"supported":581,"title":1545,"abstract":1546,"tags":1547},[],[],[1521],{"code":192,"score":455,"supported":581,"title":1549,"abstract":1550,"tags":1551},[],[],[1521],{"code":549,"score":455,"supported":581,"title":1553,"abstract":1554,"tags":1555},[],[],[1523],[1557,1558],"农业绿色化 粮食生产韧性","绿色金融 农村科技投入","农业绿色化粮食生产韧性-3342","10.3389\u002Ffsufs.2026.1938875",{"doi":1560,"openalex_id":1562,"authors":1563,"venue":1514,"cited_by_count":424,"oa_url":1511,"card":1580,"direction":1584,"ingested_from":671},"W7214113879",[1564,1566,1569,1571,1573,1575,1578],{"name":1565,"orcid":11},"Huimin Shao",{"name":1567,"orcid":1568},"Wenjuan Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-5062-1292",{"name":1570,"orcid":11},"Qiong Peng",{"name":1572,"orcid":11},"Haichuan Liu",{"name":1574,"orcid":11},"Liangyan Lu",{"name":1576,"orcid":1577},"Mcxin Tee","https:\u002F\u002Forcid.org\u002F0000-0001-7990-8377",{"name":1579,"orcid":11},"Qing Jin",{"tldr":1581,"method":1582,"finding":1583,"direction":1584,"opportunity":1585},"基于中国省级面板数据，实证检验农业绿色化对粮食生产韧性的提升作用。","熵值法测度指标，双向固定效应、调节效应、门槛效应与空间计量模型。","农业绿色化显著提升粮食生产韧性，存在区域异质性、经济门槛及正向空间溢出。","农业绿色发展与碳","可探究绿色金融与科技投入的协同机制，及跨区域溢出路径的微观落地。","2026-09-24T23:30:07.857044Z",{"id":1588,"title":1589,"url":1590,"summary":1591,"summary_zh":11,"content":11,"source_name":1592,"source_url":11,"published_at":1593,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1594,"score_detail":1595,"sources":1598,"tags":1600,"primary_topic":1604,"secondary_topics":1605,"classification":1606,"search_phrases":1633,"slug":1636,"view_count":408,"doi":11,"paper":1637,"created_at":1645},3320,"人工智能对农业新质生产力的驱动机制与效应——基于278个地级市面板数据（2010—2024）","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F165477.html","《农业经济与管理》2026年第03期。钟文、郑林泽、严芝清（江西理工大学）研究表明：人工智能技术创新对农业新质生产力具有显著直接提升效应。机制检验表明，人工智能主要通过强化农业科技创新能力、激发农村地区创业活力、深化农业生产数字化水平三条核心路径驱动农业新质生产力发展。异质性分析表明，人工智能的促进作用具有普遍性但存在显著梯度差异，受区域资源禀赋、发展阶段及农业生产模式多样性深刻影响。空间计量检验进一步发现，人工智能技术创新存在显著正向空间溢出效应，但农村人力资本水平构成关键调节变量，其不足将显著制约溢出效能的有效释放。","《农业经济与管理》2026年第03期","2026-09-17T00:00:00Z",86,{"impact":321,"substance":1596,"depth":700,"authority":345,"freshness":173,"relevant":455,"comment":1597},23,"基于278个地级市面板数据的实证研究，方法规范、结论有新意，对农业AI政策制定具参考价值，值得进入每日精选。",[1599],{"name":1592,"url":1590},[688,1601,1602,1525,1603],"农业新质生产力","农业数字化","农村创业",{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},[],{"status":602,"locked":581,"version":4,"method":603,"evidence":1607},{"candidates":1608,"agricultural_evidence":608},[1609,1613,1617,1621,1625,1629],{"code":40,"score":400,"supported":608,"title":1610,"abstract":1611,"tags":1612},[],[1534],[1525],{"code":511,"score":455,"supported":581,"title":1614,"abstract":1615,"tags":1616},[],[],[1603],{"code":7,"score":455,"supported":581,"title":1618,"abstract":1619,"tags":1620},[],[],[1602],{"code":16,"score":455,"supported":581,"title":1622,"abstract":1623,"tags":1624},[],[],[1602],{"code":24,"score":455,"supported":581,"title":1626,"abstract":1627,"tags":1628},[],[],[688],{"code":549,"score":455,"supported":581,"title":1630,"abstract":1631,"tags":1632},[],[],[1601],[1634,1635],"人工智能 农业新质生产力 地级市面板","农业新质生产力 农业人工智能 空间溢出效应 农业数字化","人工智能农业新质生产力地级市面板-3320",{"doi":11,"openalex_id":11,"authors":1638,"venue":11,"cited_by_count":424,"oa_url":11,"card":1639,"direction":1218,"ingested_from":1644},[],{"tldr":1640,"method":1641,"finding":1642,"direction":1218,"opportunity":1643},"基于278个地级市面板数据，实证检验人工智能对农业新质生产力的驱动机制与空间溢出效应。","278个地级市2010—2024年面板数据，机制检验、异质性分析与空间计量模型。","人工智能显著直接提升农业新质生产力，经三条路径驱动，并具正向空间溢出，但受农村人力资本调节制约。","可深入探究农村人力资本门槛下AI空间溢出的非线性衰减机制及跨区域协同提升路径。","agent","2026-09-24T00:04:02.275354Z",{"id":1647,"title":1648,"url":1649,"summary":1650,"summary_zh":1651,"content":11,"source_name":1061,"source_url":1649,"published_at":1380,"category":580,"cover_url":11,"hotness":447,"is_selected":581,"score":776,"score_detail":1652,"sources":1654,"tags":1658,"primary_topic":1661,"secondary_topics":1662,"classification":1665,"search_phrases":1688,"slug":1691,"view_count":400,"doi":1692,"paper":1693,"created_at":1707},3279,"ASSESSMENT OF SOIL SALINIZATION IN IRRIGATED LANDS BASED ON REMOTE SENSING AND GIS TECHNOLOGIES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22900049","This thesis analyzes modern approaches to monitoring and assessing soil salinity in irrigated areas of Uzbekistan and Central Asia. Soil salinization is one of the major land degradation processes affecting the reclamation condition of irrigated lands, crop growth and agricultural productivity. Although conventional field and laboratory surveys provide detailed information, they have limitations in terms of regular monitoring of large areas. Therefore, remote sensing and geographic information systems provide promising tools for rapid and spatial assessment of soil salinity. The study considers the application of satellite imagery, particularly Sentinel-2 and Landsat data, for soil salinity assessment, including the use of spectral bands and salinity indices, GIS-based thematic mapping, and validation using field and laboratory observations. Recent studies demonstrate the potential of remote sensing data for spatial assessment and mapping of soil salinity under the conditions of Uzbekistan. Such an integrated approach can support rapid monitoring of the reclamation status of irrigated lands, identification of salt-affected areas and spatial planning of appropriate reclamation measures.","本论文分析了乌兹别克斯坦和中亚灌溉区土壤盐分监测与评估的现代方法。土壤盐渍化是影响灌溉土地改良状况、作物生长和农业生产力的主要土地退化过程之一。尽管传统的野外调查和实验室分析能够提供详细信息，但在大范围定期监测方面存在局限性。因此，遥感与地理信息系统为土壤盐分的快速空间评估提供了有前景的工具。本研究探讨了卫星影像，特别是Sentinel-2和Landsat数据在土壤盐分评估中的应用，包括光谱波段和盐分指数的使用、基于GIS的主题制图，以及利用野外和实验室观测数据进行验证。近期研究表明，遥感数据在乌兹别克斯坦条件下用于土壤盐分空间评估与制图具有潜力。这种综合方法可支持灌溉土地改良状况的快速监测、盐渍化区域的识别以及适当改良措施的空间规划。",{"impact":416,"substance":361,"depth":607,"authority":416,"freshness":587,"relevant":455,"comment":1653},"基于遥感与GIS的灌溉区土壤盐渍化监测论文，方法成熟、结论可靠，对盐碱地治理与农业信息化有参考价值，但属区域性研究，影响层级有限。",[1655,1656],{"name":1061,"url":1649},{"name":1061,"url":1657},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22900048",[1451,855,595,1659,1660],"土壤盐渍化","灌溉农业",{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},[1663,1664],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},{"status":602,"locked":581,"version":4,"method":603,"evidence":1666},{"candidates":1667,"agricultural_evidence":608},[1668,1672,1676,1680,1684],{"code":40,"score":361,"supported":608,"title":1669,"abstract":1670,"tags":1671},[595],[718,595,719],[595],{"code":32,"score":607,"supported":608,"title":1673,"abstract":1674,"tags":1675},[610],[594,610],[855],{"code":144,"score":345,"supported":608,"title":1677,"abstract":1678,"tags":1679},[145],[628,145],[],{"code":152,"score":400,"supported":608,"title":1681,"abstract":1682,"tags":1683},[],[620],[1660],{"code":136,"score":455,"supported":581,"title":1685,"abstract":1686,"tags":1687},[],[],[1451],[1689,1690],"乌兹别克斯坦 土壤盐渍化 遥感","Sentinel-2 盐渍化 灌溉","乌兹别克斯坦土壤盐渍化遥感-3279","10.5281\u002Fzenodo.22900049",{"doi":1692,"openalex_id":1694,"authors":1695,"venue":1061,"cited_by_count":424,"oa_url":1649,"card":1702,"direction":669,"ingested_from":671},"W7214052773",[1696,1698,1700],{"name":1697,"orcid":11},"Mardiyev",{"name":1699,"orcid":11},"Ergashaliyev",{"name":1701,"orcid":11},"G'aniyev",{"tldr":1703,"method":1704,"finding":1705,"direction":669,"opportunity":1706},"综述遥感与GIS评估乌兹别克斯坦及中亚灌溉土壤盐渍化的方法。","Sentinel-2与Landsat影像、光谱盐分指数、GIS制图及野外验证。","遥感与GIS可快速空间评估盐渍化，支持灌溉地改良状态监测与规划。","可探索多源遥感与机器学习融合的盐分指数模型，提升干旱区盐渍化反演精度与时效。","2026-09-23T23:30:19.341486Z",{"id":1709,"title":1710,"url":1711,"summary":1712,"summary_zh":1713,"content":11,"source_name":1714,"source_url":1715,"published_at":1716,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1717,"score_detail":1718,"sources":1720,"tags":1722,"primary_topic":1724,"secondary_topics":1725,"classification":1727,"search_phrases":1750,"slug":1753,"view_count":408,"doi":1754,"paper":1755,"created_at":1768},3187,"Spatio-temporal analysis of Land Use and Land Cover (LULC) change using high resolution satellite data: A case study of Nagaon, Assam","https:\u002F\u002Fdoi.org\u002F10.31018\u002Fjans.v18i3.7774","Geographic information systems (GIS) coupled with satellite remote sensing (RS) have had a significant impact on the assessment and mapping of land surface dynamics, specifically the analysis of land-use land-cover (LULC) change. This study uses high-resolution multispectral LISS-IV (Linear Imaging Self-Scanning Sensor-IV) imagery with a 5.8-meter resolution to evaluate LULC trends in the Nagaon district of Assam between 2015 and 2024. Land use classes in a variety of categories, including vegetation, water bodies, agricultural land, built-up areas, scrubland, sandbars, tea plantations, and trees outside forests (TOF), were identified applying the maximum likelihood classification algorithm. Overall, The accuracy was 85.7% in 2015 and 90.3% in 2024, with respective Kappa Coefficients of 0.836 and 0.889, respectively. The results showed that between 2015 and 2024, the areas of tea plantations, natural vegetation, scrubland, and agricultural land fell by 0.26%, 6.14%, 0.26%, and 9.83%, respectively. Conversely, the waterbody, sandbar, built-up, and TOF have all increased by 0.29%, 0.91%, 0.82%, and 14.46%, respectively. This noticeable shift from conventional agricultural and natural vegetation landscapes toward tree-based systems and urban expansion on the study area, emphasize on matters concerning climate stress, declining soil fertility, economic benefits, policy support, and the need for resilient livelihoods.","地理信息系统（GIS）与卫星遥感（RS）相结合，对地表动态的评估与制图产生了显著影响，尤其是土地利用与土地覆盖（LULC）变化分析。本研究利用分辨率为5.8米的高分辨率多光谱LISS-IV（线性成像自扫描传感器-IV）影像，评估了阿萨姆邦纳冈县2015年至2024年间的LULC变化趋势。采用最大似然分类算法，识别了植被、水体、农地、建设用地、灌丛地、沙洲、茶园及林外树木（TOF）等多种土地利用类别。总体而言，2015年分类精度为85.7%，2024年为90.3%，Kappa系数分别为0.836和0.889。结果表明，2015年至2024年间，茶园、自然植被、灌丛地和农地面积分别减少了0.26%、6.14%、0.26%和9.83%。相反，水体、沙洲、建设用地和TOF分别增加了0.29%、0.91%、0.82%和14.46%。研究区从传统农业和自然植被景观向林基系统和城市扩张的显著转变，凸显了气候胁迫、土壤肥力下降、经济效益、政策支持以及韧性生计需求等相关问题。","Journal of Applied and Natural Science","https:\u002F\u002Fjournals.ansfoundation.org\u002Findex.php\u002Fjans\u002Farticle\u002Fview\u002F7774","2026-09-20T00:00:00Z",62,{"impact":173,"substance":361,"depth":607,"authority":586,"freshness":173,"relevant":455,"comment":1719},"基于高分辨率遥感的区域土地利用变化实证研究，方法规范、数据翔实，对农业遥感监测有参考价值，但属地方性案例，公共影响有限。",[1721],{"name":1714,"url":1715},[1723,855,856,784,1452],"农业遥感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[1726],{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},{"status":602,"locked":581,"version":4,"method":603,"evidence":1728},{"candidates":1729,"agricultural_evidence":608},[1730,1734,1738,1742,1746],{"code":32,"score":416,"supported":608,"title":1731,"abstract":1732,"tags":1733},[],[594,610,743],[1723,1452,855],{"code":40,"score":587,"supported":608,"title":1735,"abstract":1736,"tags":1737},[],[718,595,719],[],{"code":144,"score":625,"supported":608,"title":1739,"abstract":1740,"tags":1741},[],[628,145],[],{"code":176,"score":455,"supported":581,"title":1743,"abstract":1744,"tags":1745},[],[],[856],{"code":556,"score":455,"supported":581,"title":1747,"abstract":1748,"tags":1749},[],[],[784],[1751,1752],"Nagaon Assam LULC 遥感","LISS-IV 土地利用变化","NagaonAssamLULC遥感-3187","10.31018\u002Fjans.v18i3.7774",{"doi":1754,"openalex_id":1756,"authors":1757,"venue":1714,"cited_by_count":424,"oa_url":1715,"card":1763,"direction":669,"ingested_from":671},"W7213942371",[1758,1760],{"name":1759,"orcid":11},"J. C. Das",{"name":1761,"orcid":1762},"Prodyut Bhattacharya","https:\u002F\u002Forcid.org\u002F0000-0002-4294-5585",{"tldr":1764,"method":1765,"finding":1766,"direction":669,"opportunity":1767},"利用高分辨率卫星影像分析印度阿萨姆邦纳冈地区2015-2024年土地利用\u002F覆盖变化。","LISS-IV 5.8米多光谱影像，最大似然分类，精度与Kappa系数评估。","农业用地和自然植被分别减少9.83%和6.14%，而林外树木和建设用地分别增加14.46%和0.82","可结合时序高分辨率影像与农户调查，探究林外树木扩张对农业韧性和碳汇的驱动机制。","2026-09-22T23:30:25.539323Z",{"id":1770,"title":1771,"url":1772,"summary":1773,"summary_zh":1774,"content":11,"source_name":1775,"source_url":1772,"published_at":1776,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":424,"score_detail":1777,"sources":1779,"tags":1781,"primary_topic":1784,"secondary_topics":1785,"classification":1787,"search_phrases":1811,"slug":1814,"view_count":587,"doi":1815,"paper":1816,"created_at":1832},3071,"Urban Influence on Landscape Transformation and Land Cover Interactions During Urban Expansion (1972–2022): A Remote Sensing and GIS-Based Analysis","https:\u002F\u002Fdoi.org\u002F10.2478\u002Fjlecol-2026-0038","Abstract This study investigates how urban expansion influences land cover dynamics. An interactive analytical approach was adopted to examine the transitions among built-up areas, cropland, and bare land throughout the urbanization process. Remote Sensing and GIS techniques, in conjunction with the Land Transfer Matrix (LTM), Landscape Metrics (LM), Multi-buffer Ring (MBR) and complementary spatial tools, were employed to analyze the city of Tiaret in Algeria over the past half century (1972-2022). The results reveal a marked intensification of urbanization, with an Overall Average Annual Urban Expansion Rate (OA-AUER) of 28.3 ha and a concurrent rise in bare land driven by cropland conversion under urban pressure. Notably, 89.4 % of cropland loss initially transitions into bare land, while subsequent urban development originates from cropland (52 %) and bare land (47 %). The observed density patterns in peripheral areas confirm the significant influence of the urban core on surrounding land-cover transformations. These findings elucidate the urbanization process while considering the transitional role of bare land and further explain diffusion coalescence process. Further empirical research is recommended to refine theoretical frameworks on landscape transformation dynamics and to inform policies aimed at mitigating bare-land expansion in peri-urban areas, controlling cropland urbanization, and planning sustainable urban growth.","摘要 本研究探讨城市扩张如何影响土地覆盖动态。采用交互式分析方法，考察城市化进程中建成区、耕地和裸地之间的转换。运用遥感与地理信息系统技术，结合土地转移矩阵（LTM）、景观格局指数（LM）、多缓冲环（MBR）及辅助空间工具，对阿尔及利亚提亚雷特市过去半个世纪（1972—2022年）的变化进行了分析。结果表明，城市化显著加剧，年均城市扩张总面积率（OA-AUER）为28.3公顷，同时在城市压力下耕地转换导致裸地同步增加。值得注意的是，89.4%的耕地损失最初转变为裸地，而随后的城市发展来源于耕地（52%）和裸地（47%）。外围区域观测到的密度格局证实了城市核心对周边土地覆盖变化的显著影响。这些发现阐明了城市化过程，同时考虑了裸地的过渡性作用，并进一步解释了扩散聚合过程。建议开展进一步实证研究，以完善景观转型动态的理论框架，并为旨在减缓城郊地区裸地扩张、控制耕地城市化以及规划可持续城市增长的政策提供依据。","Journal of Landscape Ecology","2026-09-19T00:00:00Z",{"impact":424,"substance":424,"depth":424,"authority":424,"freshness":424,"relevant":424,"comment":1778},"该论文聚焦阿尔及利亚Tiaret市城市扩张与土地覆盖变化，属城市地理与遥感应用研究，与三农、农业信息化、智慧农业主题无直接关联，不建议进入每日精选。",[1780],{"name":1775,"url":1772},[1782,855,856,1783],"耕地保护","城市扩张",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[1786],{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},{"status":602,"locked":581,"version":4,"method":603,"evidence":1788},{"candidates":1789,"agricultural_evidence":608},[1790,1794,1798,1803,1807],{"code":32,"score":607,"supported":608,"title":1791,"abstract":1792,"tags":1793},[610],[594,610],[855],{"code":40,"score":345,"supported":608,"title":1795,"abstract":1796,"tags":1797},[595],[718,595],[],{"code":496,"score":631,"supported":608,"title":1799,"abstract":1800,"tags":1802},[],[1801],"land transfer",[],{"code":136,"score":455,"supported":581,"title":1804,"abstract":1805,"tags":1806},[],[],[1782],{"code":176,"score":455,"supported":581,"title":1808,"abstract":1809,"tags":1810},[],[],[856],[1812,1813],"Tiaret 城市扩张 遥感","耕地 建设用地 转移矩阵","Tiaret城市扩张遥感-3071","10.2478\u002Fjlecol-2026-0038",{"doi":1815,"openalex_id":1817,"authors":1818,"venue":1775,"cited_by_count":424,"oa_url":1826,"card":1827,"direction":669,"ingested_from":671},"W7213652939",[1819,1821,1823],{"name":1820,"orcid":11},"Amina Kalbaza",{"name":1822,"orcid":11},"Belkacem Marir",{"name":1824,"orcid":1825},"Farida Naceur","https:\u002F\u002Forcid.org\u002F0009-0004-1962-4937","https:\u002F\u002Freference-global.com\u002Fdownload\u002Farticle\u002F10.2478\u002Fjlecol-2026-0038.pdf",{"tldr":1828,"method":1829,"finding":1830,"direction":669,"opportunity":1831},"基于遥感与GIS分析阿尔及利亚提亚雷特1972—2022年城市扩张对景观与土地覆盖的影响。","遥感、GIS、土地转移矩阵、景观格局指数与多缓冲环分析。","耕地先转为裸地再被城市占用，城市核心显著驱动外围土地覆盖转变。","可延伸研究裸地作为耕地—城市转换中间态的机制，并构建城郊耕地流失预警模型。","2026-09-21T23:30:24.332177Z",{"id":1834,"title":1835,"url":1836,"summary":1837,"summary_zh":1838,"content":11,"source_name":1839,"source_url":1836,"published_at":1716,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1840,"score_detail":1841,"sources":1843,"tags":1845,"primary_topic":1847,"secondary_topics":1848,"classification":1850,"search_phrases":1870,"slug":1873,"view_count":69,"doi":1874,"paper":1875,"created_at":1897},3069,"Impact of Urbanization on Agriculture and Wetland Management in Makurdi LGA of Benue State, Nigeria","https:\u002F\u002Fdoi.org\u002F10.65757\u002Fhjev1n22","Wetlands are among the most productive ecosystems, providing essential ecological, hydrological, and socio-economic services, including flood regulation, groundwater recharge, water purification, biodiversity conservation, and agricultural support. However, rapid urbanization has accelerated the conversion of wetlands to built-up areas and agricultural land, threatening their ecological integrity. This study assessed the impact of urbanization on agriculture and wetland management in Makurdi Local Government Area, Benue State, over a twenty-year period (2004–2024). The specific objectives were to examine the spatial distribution and extent of wetlands, evaluate the relationship between urban growth and wetland dynamics, and determine the extent of wetland encroachment resulting from urban development. The study employed Geographic Information Systems (GIS) and Remote Sensing techniques using Landsat 7 ETM+, Landsat 8 OLI\u002FTIRS, and Landsat 9 OLI-2\u002FTIRS-2 satellite imagery acquired for 2004, 2014, and 2024. Image preprocessing, supervised classification, change detection, cross-tabulation matrix analysis, and accuracy assessment were performed in ArcGIS and ERDAS Imagine, while field observations and GPS data were used for ground-truthing and validation. Seven land-use\u002Fland-cover classes were identified: wetlands, farmland, shrubland, forest, water bodies, sandbars, and settlements. The findings revealed significant land-use and land-cover changes over the study period. Wetland coverage declined from 4,478.76 ha (5.37%) in 2004 to 2,664.36 ha (2.39%) in 2024, while settlements expanded from 1,182.60 ha (1.42%) to 5,119.22 ha (4.61%) during the same period. Farmland increased from 39.23% to 41.09%, indicating continued conversion of natural ecosystems to agricultural and urban uses. Cross-tabulation analysis further demonstrated substantial transitions of wetlands and farmlands into settlement areas, confirming that urban expansion has become a major driver of wetland degradation in Makurdi. Classification accuracy assessments yielded overall accuracies ranging from 72.15% to 75.93%, with Kappa coefficients between 0.60 and 0.68, indicating that the classified maps were sufficiently reliable for land-use change analysis. The study concludes that rapid urbanization has significantly contributed to wetland loss and fragmentation and has increasingly encroached on agricultural lands, posing serious threats to ecosystem sustainability, biodiversity conservation, flood regulation, and long-term food security. To address these challenges, the study recommends strengthening wetland protection policies, implementing continuous GIS-based monitoring, promoting sustainable urban development, and establishing institutional frameworks to conserve and effectively manage wetlands and agricultural lands in Makurdi","湿地是最具生产力的生态系统之一，提供重要的生态、水文和社会经济服务，包括洪水调节、地下水补给、水质净化、生物多样性保护和农业支持。然而，快速城市化加速了湿地向建成区和农业用地的转化，威胁其生态完整性。本研究评估了贝努埃州马库尔迪地方政府区二十年期间（2004—2024年）城市化对农业和湿地管理的影响。具体目标包括：考察湿地的空间分布与范围，评估城市增长与湿地动态之间的关系，并确定城市发展导致的湿地侵占程度。研究采用地理信息系统（GIS）和遥感技术，使用2004年、2014年和2024年的Landsat 7 ETM+、Landsat 8 OLI\u002FTIRS和Landsat 9 OLI-2\u002FTIRS-2卫星影像。在ArcGIS和ERDAS Imagine中进行了影像预处理、监督分类、变化检测、交叉列联表矩阵分析和精度评估，同时利用实地观测和GPS数据进行地面验证与确认。研究识别出七类土地利用\u002F土地覆盖类型：湿地、农田、灌丛、森林、水体、沙洲和居民点。研究结果显示，研究期间土地利用和土地覆盖发生了显著变化。湿地覆盖面积从2004年的4,478.76公顷（5.37%）下降至2024年的2,664.36公顷（2.39%），而居民点面积同期从1,182.60公顷（1.42%）扩张至5,119.22公顷（4.61%）。农田面积从39.23%增至41.09%，表明自然生态系统持续向农业和城市用途转化。交叉列联表分析进一步表明，湿地和农田大量转变为居民点用地，证实城市扩张已成为马库尔迪湿地退化的主要驱动因素。分类精度评估的总体精度为72.15%至75.93%，Kappa系数在0.60至0.68之间，表明分类图对于土地利用变化分析具有足够的可靠性。研究认为，快速城市化显著加剧了湿地丧失和破碎化，并日益侵占农业用地，对生态系统可持续性、生物多样性保护","Hensard Journal of Environment",56,{"impact":173,"substance":361,"depth":607,"authority":625,"freshness":587,"relevant":455,"comment":1842},"基于Landsat遥感与GIS的尼日利亚湿地退化实证研究，数据扎实但属区域案例，公共影响有限。",[1844],{"name":1839,"url":1836},[993,855,856,1071,1846],"湿地保护",{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},[1849],{"code":176,"slug":177,"name":178,"path":179,"parent_code":128},{"status":602,"locked":581,"version":4,"method":603,"evidence":1851},{"candidates":1852,"agricultural_evidence":608},[1853,1857,1862,1866],{"code":40,"score":587,"supported":608,"title":1854,"abstract":1855,"tags":1856},[],[718,595,719],[],{"code":176,"score":173,"supported":608,"title":1858,"abstract":1859,"tags":1861},[],[1860,177],"生物多样性",[856,1846],{"code":32,"score":876,"supported":608,"title":1863,"abstract":1864,"tags":1865},[],[594,610],[855],{"code":263,"score":400,"supported":608,"title":1867,"abstract":1868,"tags":1869},[],[1018],[993],[1871,1872],"Makurdi 湿地 城市化","Benue State 遥感","Makurdi湿地城市化-3069","10.65757\u002Fhjev1n22",{"doi":1874,"openalex_id":1876,"authors":1877,"venue":1839,"cited_by_count":424,"oa_url":1836,"card":1892,"direction":669,"ingested_from":671},"W7213745048",[1878,1880,1882,1884,1886,1888,1890],{"name":1879,"orcid":11},"Joy Iganya Agene",{"name":1881,"orcid":11},"Onoja Sunday",{"name":1883,"orcid":11},"Akintunde Elijah",{"name":1885,"orcid":11},"Ugese Ayangeaor Andrew",{"name":1887,"orcid":11},"Kebiru Umoru",{"name":1889,"orcid":11},"Aku Umaku Esther",{"name":1891,"orcid":11},"Ombugu Gideon",{"tldr":1893,"method":1894,"finding":1895,"direction":669,"opportunity":1896},"用遥感与GIS评估尼日利亚马库尔迪20年间城市化对湿地和农业用地的侵占影响。","Landsat 7\u002F8\u002F9影像，监督分类、变化检测与交叉表分析，GPS实地验证。","湿地占比由5.37%降至2.39%，建设用地由1.42%升至4.61%，城市化是湿地退化主因。","可结合多源高分辨率遥感与CA-Markov模型，预测湿地-农田-城市冲突并支撑国土空间优化。","2026-09-21T23:30:24.084401Z",{"id":1899,"title":1900,"url":1901,"summary":1902,"summary_zh":1903,"content":11,"source_name":1061,"source_url":1901,"published_at":1904,"category":580,"cover_url":11,"hotness":447,"is_selected":581,"score":1840,"score_detail":1905,"sources":1907,"tags":1911,"primary_topic":1914,"secondary_topics":1915,"classification":1916,"search_phrases":1931,"slug":1934,"view_count":455,"doi":1935,"paper":1936,"created_at":1954},3068,"MAPPING LAND USE OF BENUE STATE UNIVERSITY, MAKURDI MAIN CAMPUS, BENUE STATE, NIGERIA USING GEOSPATIAL TECHNIQUES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22865603","This study examined the spatial distribution and extent of land use types within Benue State University (BSU), Makurdi Main Campus, using Geographic Information System (GIS) and remote sensing techniques. Satellite imagery obtained from the National Space Research and Development Agency (NASRDA) was processed and analyzed in ArcGIS 10.8. Land use classification was achieved through on-screen digitization supported by intensive field survey and ground-truthing. The results reveal a heterogeneous land use structure with a total mapped area of 166.69 hectares. Agricultural land use dominates with 20.35%, followed by residential (15.24%) and forest cover (12.29%). Environmentally sensitive land uses, including forestry, green areas, and marshlands, collectively account for 29.18% of the total mapped area, indicating significant ecological value. Institutional land uses (administrative, educational, and religious) account for 15.50%, while commercial and mixed uses constitute 9.29%. The findings highlight a semi-urban institutional landscape undergoing gradual transformation, with increasing development pressures. The study demonstrates the effectiveness of GIS in land use planning and recommends sustainable land management strategies to balance development with environmental conservation.","本研究利用地理信息系统（GIS）和遥感技术，考察了马库尔迪贝努埃州立大学（BSU）主校区内土地利用类型的空间分布与范围。研究对来自国家空间研究与发展局（NASRDA）的卫星影像在ArcGIS 10.8中进行了处理与分析。土地利用分类通过屏幕数字化完成，并辅以密集的实地调查与地面验证。结果显示，该区域土地利用结构具有异质性，总制图面积为166.69公顷。农业用地占主导地位，为20.35%，其次为住宅用地（15.24%）和林地覆盖（12.29%）。环境敏感型土地利用类型，包括林业用地、绿地和沼泽地，合计占总制图面积的29.18%，表明其具有显著的生态价值。机构用地（行政、教育和宗教）占15.50%，而商业和混合用途占9.29%。研究结果凸显出一个正处于逐步转型中的半城市机构景观，其面临日益增大的开发压力。本研究证明了GIS在土地利用规划中的有效性，并建议采取可持续土地管理策略，以平衡开发与环境保护。","2026-09-21T00:00:00Z",{"impact":625,"substance":584,"depth":586,"authority":416,"freshness":587,"relevant":455,"comment":1906},"基于GIS与遥感的校园土地利用分类研究，方法规范、数据具体，但属尼日利亚个案，对国内三农与农业信息化参考价值有限。",[1908,1909],{"name":1061,"url":1901},{"name":1061,"url":1910},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22865602",[594,856,595,1912,1913],"土地分类","校园规划",{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},[],{"status":602,"locked":581,"version":4,"method":603,"evidence":1917},{"candidates":1918,"agricultural_evidence":608},[1919,1923,1927],{"code":40,"score":385,"supported":608,"title":1920,"abstract":1921,"tags":1922},[],[718,595,719],[595],{"code":32,"score":876,"supported":608,"title":1924,"abstract":1925,"tags":1926},[],[594,610],[594],{"code":176,"score":455,"supported":581,"title":1928,"abstract":1929,"tags":1930},[],[],[856],[1932,1933],"土地分类 土地利用 校园规划 遥感","土地分类 土地利用","土地分类土地利用校园规划遥感-3068","10.5281\u002Fzenodo.22865603",{"doi":1935,"openalex_id":1937,"authors":1938,"venue":1061,"cited_by_count":424,"oa_url":1901,"card":1949,"direction":669,"ingested_from":671},"W7213752705",[1939,1941,1943,1945,1947],{"name":1940,"orcid":11},"Alex S. Ortese",{"name":1942,"orcid":11},"Innocent E. Bello",{"name":1944,"orcid":11},"I. K. SAMAILA",{"name":1946,"orcid":11},"M. ALKALI",{"name":1948,"orcid":11},"A. Mahmud",{"tldr":1950,"method":1951,"finding":1952,"direction":669,"opportunity":1953},"用GIS和遥感技术绘制尼日利亚贝努埃州立大学主校区土地利用分布图。","NASRDA卫星影像，ArcGIS 10.8屏幕数字化，结合实地调查与地面验证。","农业用地占20.35%居首，环境敏感用地共占29.18%，校园呈半城市化转型压力。","可延伸至校园及城郊农业用地动态监测与生态敏感区保护规划研究。","2026-09-21T23:30:23.914355Z",{"technologies":1956,"keywords":1969},[1957,1959,1961,1962,1963,1964,1965,1966,1967],{"name":594,"count":1958},34,{"name":595,"count":1960},27,{"name":855,"count":585},{"name":688,"count":524},{"name":690,"count":408},{"name":1723,"count":400},{"name":1237,"count":69},{"name":704,"count":69},{"name":1968,"count":69},"物联网",[1970,1971,1972,1974,1975,1976,1977,1978,1979,1981,1982,1983],{"name":856,"count":778},{"name":687,"count":587},{"name":1973,"count":400},"数字乡村",{"name":920,"count":400},{"name":922,"count":400},{"name":783,"count":400},{"name":1451,"count":631},{"name":1452,"count":631},{"name":1980,"count":631},"洪水风险评估",{"name":1660,"count":631},{"name":1525,"count":631},{"name":993,"count":631}]