[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2435":3,"related-2435":92},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":24,"classification":25,"search_phrases":65,"slug":68,"view_count":69,"doi":70,"paper":71,"created_at":91},2435,"Integrated Geoelectrical and Geospatial Assessment of Groundwater Variation in Selected Location, Port Harcourt, Rivers State, Nigeria","https:\u002F\u002Fdoi.org\u002F10.62292\u002Fnjp.v35(s).2026.693","Groundwater is the major source of potable water in Port Harcourt, Rivers State, Nigeria, owing to increasing degradation of surface water resources resulting from rapid urbanization, industrialization, and population growth. This study integrates Vertical Electrical Sounding (VES), Geographic Information Systems (GIS), Remote Sensing (RS), and the Analytic Hierarchy Process (AHP) to evaluate groundwater potential and aquifer vulnerability in the study area. Ten VES stations were investigated using the Schlumberger electrode configuration with current electrode spacing ranging from 2 to 200 m. The interpreted geoelectrical parameters were used to estimate aquifer hydraulic properties, including hydraulic conductivity, transmissivity, longitudinal conductance, transverse resistance, porosity, and permeability. Four groundwater conditioning factors, rainfall, geology, lineament density, and soil type, were integrated within a GIS environment using AHP to generate the Groundwater Potential Index (GWPI). The AHP weighting identified rainfall (55.8%) as the dominant groundwater conditioning factor, followed by geology (26.3%), lineament density (12.2%), and soil type (5.7%), with a consistency ratio of 0.045, indicating reliable pairwise judgments. Hydraulic conductivity ranged from 0.0839 to 28.7584 m\u002Fday, transmissivity from 6.1 to 853.1 m²\u002Fday, porosity from 0.1764 to 0.6870, and permeability from 100.32 to 34,380 mD, demonstrating considerable spatial heterogeneity in aquifer properties. The GWPI map classified the study area into Very Low, Low, Moderate, High, and Very High groundwater potential zones, with the western, northwestern, and central sectors exhibiting the most favourable groundwater conditions. Validation against VES-derived transmissivity showed 100% agreement, comprising 50% excellent and 50% good correspondence between the GIS-AHP predictions and geoelectrical interpretations. The integrated approach provides a reliable framework for groundwater exploration, borehole siting, aquifer protection, and sustainable groundwater resources management in rapidly urbanizing coastal environments.","地下水是尼日利亚河流州哈科特港的主要饮用水来源，其原因在于快速城市化、工业化和人口增长导致地表水资源日益退化。本研究综合运用垂向电测深（VES）、地理信息系统（GIS）、遥感（RS）和层次分析法（AHP），对研究区地下水潜力和含水层脆弱性进行评价。采用施伦贝格尔电极装置，在10个VES测点开展探测，供电电极距为2至200 m。利用解译的地电参数估算含水层水力特性，包括渗透系数、导水系数、纵向电导、横向电阻、孔隙度和渗透率。将降雨、地质、线性构造密度和土壤类型四个地下水控制因子在GIS环境中通过AHP进行集成，生成地下水潜力指数（GWPI）。AHP权重分析表明，降雨（55.8%）为主导地下水控制因子，其次为地质（26.3%）、线性构造密度（12.2%）和土壤类型（5.7%），一致性比率为0.045，表明两两判断可靠。渗透系数为0.0839至28.7584 m\u002Fd，导水系数为6.1至853.1 m²\u002Fd，孔隙度为0.1764至0.6870，渗透率为100.32至34,380 mD，表明含水层特性具有显著空间非均质性。GWPI图将研究区划分为极低、低、中等、高和极高地下水潜力区，其中西部、西北部和中部的 地下水条件最为有利。以VES解译的导水系数进行验证，结果显示GIS-AHP预测与地电解译之间100%吻合，其中50%为极好对应，50%为良好对应。该综合方法为快速城市化海岸环境中的地下水勘探、井位选址、含水层保护和地下水资源可持续管理提供了可靠框架。",null,"Nigerian Journal of Physics","2026-09-11T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究对象为尼日利亚哈科特港城市地下水潜力评估，与三农、农业信息化及中国数字乡村议题无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23],"遥感监测","地下水评估","GIS应用",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":29},"needs_review","2026-10-22.2","rules",{"candidates":30,"agricultural_evidence":14,"reason":64},[31,41,49,57],{"code":32,"score":33,"supported":34,"title":35,"abstract":36,"tags":40},"I4",9,true,[],[37,38,39],"地理信息","GIS","geographic information",[],{"code":42,"score":43,"supported":34,"title":44,"abstract":45,"tags":48},"I3",7,[],[46,47],"遥感","remote sensing",[21],{"code":50,"score":51,"supported":34,"title":52,"abstract":53,"tags":56},"H2",6,[],[54,55],"土壤","soil",[],{"code":58,"score":59,"supported":34,"title":60,"abstract":61,"tags":63},"H3",3,[],[62],"水资源",[],"农业相关性需要复核",[66,67],"地下水评估 遥感监测 GIS应用","地下水评估 遥感监测","地下水评估遥感监测GIS应用-2435",1,"10.62292\u002Fnjp.v35(s).2026.693",{"doi":70,"openalex_id":72,"authors":73,"venue":10,"cited_by_count":15,"oa_url":83,"card":84,"direction":88,"ingested_from":90},"W7212370737",[74,76,79,81],{"name":75,"orcid":9},"Sulyman Usamat",{"name":77,"orcid":78},"Godwin Babatunde Egbeyale","https:\u002F\u002Forcid.org\u002F0000-0003-4091-2381",{"name":80,"orcid":9},"Sunday Ajani Adegbenro",{"name":82,"orcid":9},"Salami A. Oladipupo","https:\u002F\u002Fnjp.nipngr.org\u002Findex.php\u002Fnjp\u002Farticle\u002Fdownload\u002F693\u002F479",{"tldr":85,"method":86,"finding":87,"direction":88,"opportunity":89},"综合电测深、GIS、遥感和AHP评估尼日利亚哈科特港地下水潜力与含水层脆弱性。","10个VES测点、Schlumberger装置，结合降雨、地质、线理密度、土壤类","降雨权重最高（55.8%），西部、西北和中部潜力高，GWPI与VES导水系数验证100%吻合。","农业遥感与作物表型","可将该地电-空间耦合框架迁移至农业灌区，结合作物需水与地下水潜力做灌溉适宜性分区。","openalex","2026-09-14T23:30:27.482139Z",{"total":51,"page":69,"page_size":51,"items":93},[94,164,229,308,388,446],{"id":95,"title":96,"url":97,"summary":98,"summary_zh":99,"content":9,"source_name":100,"source_url":97,"published_at":101,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":102,"sources":104,"tags":106,"primary_topic":109,"secondary_topics":114,"classification":125,"search_phrases":146,"slug":149,"view_count":15,"doi":150,"paper":151,"created_at":163},5326,"Flood Hazard and Vulnerability Assessment of Chitrakoot District, Uttar Pradesh: An Integrated Geographical, GIS and Remote Sensing Approach","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.89708","Flood is one of the most important hydro-meteorological hazards affecting human settlements, agriculture, infrastructure and regional economies. Chitrakoot district of Uttar Pradesh presents a distinctive case of hydro-climatic vulnerability because it belongs to the drought-prone Bundelkhand region but simultaneously experiences recurrent riverine and flash-flood conditions during intense monsoon rainfall. The present study assesses the spatial characteristics and determinants of flood hazard and vulnerability in Chitrakoot district using an integrated geographical, GIS and remote sensing approach. The analysis incorporates the Digital Elevation Model (DEM), drainage network, physiography, river proximity, historical flood information, satellite-based flood observations and socio-economic exposure. The district's DEM indicates a marked elevation gradient, approximately from 66 m to 451 m, with relatively higher terrain in the southern part and lower terrain towards the northern Yamuna valley. This topographic configuration facilitates rapid surface runoff from elevated southern terrain towards comparatively low-lying riverine areas. The drainage system is dominated by the Yamuna and Mandakini\u002FPaisuni systems along with Baghain, Gunta, Bardaha and smaller tributaries. The Uttar Pradesh Flood Hazard Atlas identifies 5,114 ha of flood-hazard area in Chitrakoot for 1998–2020, of which 4,129 ha (81%) falls in the very-low hazard category and 985 ha (19%) in the low-hazard category. However, the historical classification does not fully represent the potential consequences of extreme rainfall events. The August 2026 flood provides compelling evidence: the Mandakini at Chitrakoot reached a reported new high flood level of 145.60 m on 29 August 2026, approximately 2.95 m above the previous reported high level, while the water level rose by about 7.5 m within six hours. The study concludes that flood risk in Chitrakoot is spatially concentrated around river corridors, low-elevation areas, floodplains, agricultural land, settlements and tourism infrastructure. A shift from conventional relief-oriented flood management towards integrated floodplain regulation, watershed management, GIS-based risk zonation, real-time monitoring and community-based preparedness is therefore essential.","洪水是影响人类住区、农业、基础设施和区域经济的最重要的水文气象灾害之一。北方邦吉德古德（Chitrakoot）县在水文气候脆弱性方面呈现出独特案例，因为它属于易受干旱影响的本德尔坎德（Bundelkhand）地区，但同时在强季风降雨期间反复遭遇河流洪水和山洪状况。本研究采用地理学、地理信息系统（GIS）和遥感相结合的综合方法，评估吉德古德县洪水灾害与脆弱性的空间特征及其决定因素。分析纳入了数字高程模型（DEM）、河网、地貌、距河流远近、历史洪水信息、基于卫星的洪水观测以及社会经济暴露度。该县的DEM显示出明显的高程梯度，大约从66米到451米，南部地形相对较高，向北部的亚穆纳（Yamuna）河谷地势降低。这种地形格局促使地表径流从较高的南部地形快速流向相对低洼的河流区域。排水系统以亚穆纳河和曼达基尼\u002F派苏尼（Mandakini\u002FPaisuni）水系为主，另有巴盖恩（Baghain）、贡塔（Gunta）、巴尔达哈（Bardaha）及较小支流。北方邦洪水灾害图集确认，1998—2020年吉德古德县洪水灾害面积为5，114公顷，其中4，129公顷（81%）属于极低灾害类别，985公顷（19%）属于低灾害类别。然而，历史分类并不能充分反映极端降雨事件的潜在后果。2026年8月的洪水提供了有力证据：据报道，2026年8月29日吉德古德的曼达基尼河达到145.60米的新高洪水位，比此前报告的高水位高出约2.95米，而水位在六小时内上升了约7.5米。研究得出结论，吉德古德的洪水风险在空间上集中于河流廊道、低海拔地区、洪泛平原、农业用地、住区和旅游基础设施周围。因此，必须从传统的以救济为导向的洪水管理转向洪泛平原综合调控、流域管理、基于GIS的风险区划、实时监测和社区为基础的备灾。","International Journal For Multidisciplinary Research","2026-10-17T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":103},"该研究聚焦印度北方邦的洪水灾害评估，虽涉及GIS与遥感方法，但与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[105],{"name":100,"url":97},[107,21,108,23],"防灾减灾","洪水风险评估",{"code":42,"slug":110,"name":111,"path":112,"parent_code":113},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[115,119],{"code":32,"slug":116,"name":117,"path":118,"parent_code":113},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":120,"slug":121,"name":122,"path":123,"parent_code":124},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":126,"locked":14,"version":27,"method":28,"evidence":127},"classified",{"candidates":128,"agricultural_evidence":34},[129,134,139],{"code":42,"score":130,"supported":34,"title":131,"abstract":132,"tags":133},15,[47],[46,47],[21],{"code":32,"score":135,"supported":34,"title":136,"abstract":137,"tags":138},14,[38],[37,38],[],{"code":120,"score":140,"supported":34,"title":141,"abstract":142,"tags":145},8,[],[143,144],"干旱","drought",[108,107],[147,148],"Chitrakoot 洪水风险 GIS","Yamuna 洪水脆弱性 遥感","Chitrakoot洪水风险GIS-5326","10.36948\u002Fijfmr.2026.v08i05.89708",{"doi":150,"openalex_id":152,"authors":153,"venue":100,"cited_by_count":15,"oa_url":157,"card":158,"direction":88,"ingested_from":90},"W7222885988",[154],{"name":155,"orcid":156},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":159,"method":160,"finding":161,"direction":88,"opportunity":162},"用GIS与遥感综合评估Chitrakoot区洪水灾害与脆弱性空间分布。","DEM、河网、历史洪水与卫星观测及社会经济数据叠加分析。","洪水风险集中于河谷低地、农田与居民点，历史分级低估极端降雨风险。","可结合时序遥感与水文模型，构建极端降雨情景下的农田洪涝风险动态制图与预警。","2026-10-19T23:30:24.356743Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":9,"source_name":170,"source_url":167,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":172,"score_detail":173,"sources":176,"tags":178,"primary_topic":181,"secondary_topics":182,"classification":184,"search_phrases":210,"slug":213,"view_count":69,"doi":214,"paper":215,"created_at":228},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":51,"substance":174,"depth":135,"authority":51,"freshness":140,"relevant":69,"comment":175},18,"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。",[177],{"name":170,"url":167},[21,179,180,23],"土地利用","干旱区农业",{"code":120,"slug":121,"name":122,"path":123,"parent_code":124},[183],{"code":32,"slug":116,"name":117,"path":118,"parent_code":113},{"status":126,"locked":14,"version":27,"method":28,"evidence":185},{"candidates":186,"agricultural_evidence":34},[187,193,197,201,205],{"code":120,"score":13,"supported":34,"title":188,"abstract":189,"tags":192},[],[190,143,191],"气候变化","climate change",[180],{"code":32,"score":33,"supported":34,"title":194,"abstract":195,"tags":196},[],[37,38,39],[],{"code":42,"score":43,"supported":34,"title":198,"abstract":199,"tags":200},[],[46,47],[21],{"code":58,"score":59,"supported":34,"title":202,"abstract":203,"tags":204},[],[62],[],{"code":206,"score":69,"supported":14,"title":207,"abstract":208,"tags":209},"H6",[],[],[179],[211,212],"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":214,"openalex_id":216,"authors":217,"venue":170,"cited_by_count":15,"oa_url":167,"card":223,"direction":88,"ingested_from":90},"W7214996325",[218,220],{"name":219,"orcid":9},"Deendayal .",{"name":221,"orcid":222},"Om Prakash","https:\u002F\u002Forcid.org\u002F0000-0003-1239-445X",{"tldr":224,"method":225,"finding":226,"direction":88,"opportunity":227},"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":230,"title":231,"url":232,"summary":233,"summary_zh":234,"content":9,"source_name":235,"source_url":232,"published_at":236,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":237,"score_detail":238,"sources":243,"tags":245,"primary_topic":9,"secondary_topics":249,"classification":250,"search_phrases":278,"slug":281,"view_count":15,"doi":282,"paper":283,"created_at":307},5577,"Assessing Land Use and Land Cover Change in the Jamanxim River Region (Brazilian Amazon) from 2015 to 2025 Using CBERS-4\u002FPAN Imagery","https:\u002F\u002Fdoi.org\u002F10.29150\u002Fjhrs.v16i4.269068","Land use and land cover (LULC) changes in the Amazon are among the main drivers of biodiversity loss, ecosystem degradation, and the intensification of climate change. This study aimed to analyze the multitemporal dynamics of LULC around the Jamanxim River, Pará, Brazilian Amazon, between 2015 and 2025, using high-resolution imagery from the CBERS-4\u002FPAN satellite. The study area covers 492.74 km², including part of the Jamanxim National Forest, a conservation unit under strong pressure from agricultural expansion and logging activities. The methodology included geometric correction of the images, Gram–Schmidt pansharpening, and supervised classification with the Random Forest algorithm, validated with historical imagery from Google Earth Pro. Thematic maps of land use and cover were generated, and performance metrics such as overall accuracy, omission and commission errors, and F1-scores were assessed. The results indicated a significant reduction in forest cover, from 412.28 km² in 2015 to 355.47 km² in 2025 (–13.78%). Non-vegetated areas increased by 132.47%, while agricultural areas expanded by 54.97%. The forest and water body classes showed high accuracy (F1-score > 0.95), while confusion persisted between agricultural and bare soil classes. Overall accuracy remained above 90% across all years, with the highest performance recorded in 2020 (96.31%). These results highlight the potential of CBERS-4\u002FPAN imagery for monitoring multitemporal changes in tropical forest landscapes. At the same time, they reveal the intensification of deforestation and the growing threats to conservation in the Amazon, reinforcing the importance of remote sensing for environmental management and policy support.","亚马逊地区的土地利用与土地覆盖（LULC）变化是生物多样性丧失、生态系统退化以及气候变化加剧的主要驱动因素之一。本研究旨在利用CBERS-4\u002FPAN卫星的高分辨率影像，分析2015年至2025年间巴西亚马孙州雅曼辛河（Jamanxim River）周边土地利用与土地覆盖的多时相动态。研究区面积为492.74 km²，包括雅曼辛国家森林（Jamanxim National Forest）的一部分，该保护区正面临农业扩张和采伐活动的强烈压力。研究方法包括影像几何校正、Gram–Schmidt全色锐化以及基于随机森林（Random Forest）算法的监督分类，并利用Google Earth Pro的历史影像进行验证。研究生成了土地利用与覆盖专题图，并评估了总体精度、漏分误差与错分误差以及F1分数等性能指标。结果表明，森林覆盖显著减少，从2015年的412.28 km²降至2025年的355.47 km²（–13.78%）。无植被区域增加了132.47%，农业区域扩张了54.97%。森林和水体类别表现出较高精度（F1分数 > 0.95），而农业类别与裸土类别之间仍存在混淆。各年份总体精度均保持在90%以上，其中2020年性能最高（96.31%）。这些结果凸显了CBERS-4\u002FPAN影像在监测热带森林景观多时相变化方面的潜力，同时揭示了亚马逊地区森林砍伐的加剧以及保护面临的日益增长的威胁，进一步强调了遥感在环境管理和政策支持中的重要性。","Journal of Hyperspectral Remote Sensing","2026-10-20T00:00:00Z",78,{"impact":174,"substance":239,"depth":240,"authority":241,"freshness":33,"relevant":69,"comment":242},22,17,12,"该研究利用CBERS-4\u002FPAN影像量化亚马逊保护区十年间森林减少13.78%，方法规范、结论可靠，对遥感监测与生态保护有参考价值，但地域性强、与国内三农直接关联有限。",[244],{"name":235,"url":232},[21,179,246,247,248],"森林保护","亚马逊","CBERS-4",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":251},{"candidates":252,"agricultural_evidence":34,"reason":277},[253,259,263,267,271],{"code":206,"score":43,"supported":34,"title":254,"abstract":255,"tags":258},[],[256,257],"生物多样性","biodiversity",[179],{"code":42,"score":43,"supported":34,"title":260,"abstract":261,"tags":262},[],[46,47],[21],{"code":120,"score":51,"supported":34,"title":264,"abstract":265,"tags":266},[],[190,191],[],{"code":50,"score":59,"supported":34,"title":268,"abstract":269,"tags":270},[],[55],[],{"code":272,"score":59,"supported":34,"title":273,"abstract":274,"tags":276},"I7",[],[275],"研究方法",[],"多个产业证据相当，需要确认主归属",[279,280],"CBERS-4 亚马逊 土地利用","Jamanxim 森林覆盖 遥感","CBERS-4亚马逊土地利用-5577","10.29150\u002Fjhrs.v16i4.269068",{"doi":282,"openalex_id":284,"authors":285,"venue":235,"cited_by_count":15,"oa_url":232,"card":302,"direction":88,"ingested_from":90},"W7223141286",[286,289,291,294,297,300],{"name":287,"orcid":288},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":290,"orcid":9},"Gabriel Henrique Santos Silva",{"name":292,"orcid":293},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":295,"orcid":296},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":298,"orcid":299},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":301,"orcid":9},"Sylvana Melo dos Santos",{"tldr":303,"method":304,"finding":305,"direction":88,"opportunity":306},"用CBERS-4\u002FPAN影像分析亚马逊Jamanxim河流域2015-2025年土地利用变化。","几何校正、Gram-Schmidt融合、随机森林监督分类，Google Eart","森林覆盖减少13.78%，农业扩张54.97%，裸地增132.47%，总体精度超90%。","可探索中巴地球资源卫星在热带森林退化监测中的业务化应用与多源数据融合。","2026-10-22T11:20:42.901761Z",{"id":309,"title":310,"url":311,"summary":312,"summary_zh":313,"content":9,"source_name":314,"source_url":311,"published_at":236,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":315,"score_detail":316,"sources":319,"tags":321,"primary_topic":326,"secondary_topics":329,"classification":330,"search_phrases":350,"slug":353,"view_count":15,"doi":354,"paper":355,"created_at":387},5576,"Land cover change and soil salinization in Mozambique: A counterfactual analysis over two decades (2001−2022)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.catena.2026.110691","Soil salinization is a major issue that constrains the sustainable development of agriculture in arid, semi-arid, and coastal lowland areas. It is of great importance for regional land management and climate adaptation to clarify the influence of land cover change on soil salinization. This study aims to quantitatively assess the contribution of land cover change to soil salinization in Mozambique, where both rapid land cover transformation and salinization issues are evident. Firstly, we integrated soil profile electrical conductivity (EC) data, Landsat time-series remote sensing imagery, TerraClimate meteorological data, and the 30 m resolution land cover data, to construct a remote sensing-based EC inversion model for Mozambique. Secondly, we applied a spatially and environmentally constrained counterfactual matching method to quantitatively identify the contributions of different land cover changes on soil salinization trends. Our results reveal that: (1) the spatial distribution of soil salinization in Mozambique exhibits distinct a clear association with proximity to water bodies, with high EC areas primarily concentrated around Lake Malawi in the north and in the coastal regions of Greater Maputo in the south. (2) 26.98% of Mozambique's territory showed a trend of intensifying soil salinization, while 10.88% exhibited a decreasing trend. Areas with intensifying trends are primarily concentrated in the central alluvial plains, localized agricultural development zones, and coastal lowlands. (3) Urbanization was significantly associated with an increasing salinization trend after false discovery rate correction, suggesting that the expansion of impervious surfaces, changes in drainage patterns, and soil compaction may exacerbate localized salt accumulation. Agricultural expansion showed a negative trend difference, consistent with the potential influence of crop cover, tillage, and wet-season leaching, although this association was not significant after false discovery rate correction. The effects of ecological restoration and other factors were not statistically significant. In addition to elucidating the heterogeneous impacts of land cover changes in Mozambique on soil salinization trends, this study also provides methodological guidance and a scientific basis for monitoring\u002Fmanaging soil salinity risks in other coastal lowlands.","土壤盐渍化是制约干旱、半干旱及沿海低地地区农业可持续发展的主要问题。阐明土地覆盖变化对土壤盐渍化的影响，对区域土地管理和气候适应具有重要意义。本研究旨在定量评估土地覆盖变化对莫桑比克土壤盐渍化的贡献，该国土地覆盖转型迅速且盐渍化问题突出。首先，我们整合了土壤剖面电导率（EC）数据、Landsat时间序列遥感影像、TerraClimate气象数据以及30 m分辨率土地覆盖数据，构建了基于遥感的莫桑比克EC反演模型。其次，我们采用空间与环境约束的反事实匹配方法，定量识别不同土地覆盖变化对土壤盐渍化趋势的贡献。研究结果表明：（1）莫桑比克土壤盐渍化的空间分布与水体邻近性呈现明显的关联，高EC区域主要集中于北部的马拉维湖周边以及南部大马普托沿海地区。（2）莫桑比克26.98%的国土面积呈现土壤盐渍化加剧趋势，而10.88%呈现减轻趋势。加剧趋势区域主要集中于中部冲积平原、局部农业开发区及沿海低地。（3）经错误发现率校正后，城市化与盐渍化加剧趋势显著相关，表明不透水面扩张、排水格局改变及土壤压实可能加剧局部盐分累积。农业扩张呈现负向趋势差异，与作物覆盖、耕作及湿季淋洗的潜在影响一致，但该关联经错误发现率校正后不显著。生态恢复及其他因素的影响在统计上不显著。本研究不仅阐明了莫桑比克土地覆盖变化对土壤盐渍化趋势的异质性影响，还为其他沿海低地土壤盐渍化风险的监测与管理提供了方法学指导和科学依据。","CATENA",79,{"impact":317,"substance":239,"depth":174,"authority":135,"freshness":33,"relevant":69,"comment":318},16,"该研究基于遥感与反事实匹配方法，定量评估土地覆盖变化对盐渍化的贡献，方法新颖、结论可靠，对沿海低地盐渍化监测具有参考价值，但属境外区域研究，国内政策与产业影响有限。",[320],{"name":314,"url":311},[21,322,323,324,325],"莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":50,"slug":55,"name":327,"path":328,"parent_code":124},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[],{"status":126,"locked":14,"version":27,"method":28,"evidence":331},{"candidates":332,"agricultural_evidence":34},[333,337,341,346],{"code":50,"score":135,"supported":34,"title":334,"abstract":335,"tags":336},[55],[54,55],[],{"code":42,"score":43,"supported":34,"title":338,"abstract":339,"tags":340},[],[46,47],[21],{"code":120,"score":51,"supported":34,"title":342,"abstract":343,"tags":345},[],[344,143],"气候适应",[],{"code":206,"score":69,"supported":14,"title":347,"abstract":348,"tags":349},[],[],[324],[351,352],"莫桑比克 土壤盐渍化 遥感","土地覆盖变化 盐渍化 反事实分析","莫桑比克土壤盐渍化遥感-5576","10.1016\u002Fj.catena.2026.110691",{"doi":354,"openalex_id":356,"authors":357,"venue":314,"cited_by_count":15,"oa_url":311,"card":382,"direction":88,"ingested_from":90},"W7223069171",[358,360,362,364,367,369,372,375,377,379],{"name":359,"orcid":9},"Yue Li",{"name":361,"orcid":9},"Mengfei Shi",{"name":363,"orcid":9},"Fei Zhang",{"name":365,"orcid":366},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":368,"orcid":9},"Gowhar Meraj",{"name":370,"orcid":371},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":373,"orcid":374},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":376,"orcid":9},"Yuanfang Chai",{"name":378,"orcid":9},"Hongtao Xu",{"name":380,"orcid":381},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":383,"method":384,"finding":385,"direction":88,"opportunity":386},"量化莫桑比克2001-2022年土地覆盖变化对土壤盐渍化的贡献。","融合EC剖面、Landsat时序与气候数据反演EC，并用空间环境约束的反事实匹配","26.98%区域盐渍化加剧；城市化显著关联盐渍化上升，农业扩张关联不显著。","可将反事实匹配框架迁移至其他滨海低地，并引入作物类型与灌溉制度细化农业扩张的盐渍化效应。","2026-10-22T11:20:42.713789Z",{"id":389,"title":390,"url":391,"summary":392,"summary_zh":393,"content":9,"source_name":394,"source_url":391,"published_at":395,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":315,"score_detail":396,"sources":398,"tags":400,"primary_topic":405,"secondary_topics":411,"classification":412,"search_phrases":429,"slug":432,"view_count":15,"doi":433,"paper":434,"created_at":445},5565,"A Unified Survival-Based Energy Framework Linking Plant Color, Stress, and Productivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23540961","Plant coloration, expressed through leaf greenness, floral pigmentation, and fruit color intensity, reflects the integrated outcome of pigment biosynthesis, optical absorption, metabolic regulation, and energy–mass exchange with the environment. While the biochemical pathways governing the synthesis and degradation of major pigment classes—including chlorophylls, carotenoids, and anthocyanins—are well established, existing models largely interpret color expression as a localized biochemical phenomenon. Such approaches do not explicitly account for whole-plant energy balance, transpiration-driven losses, or thermodynamic constraints, limiting their ability to explain widely observed patterns of color saturation, stress-induced fading, and species-specific stability across environmental gradients. Here we develop a unified framework for plant color generation and stability that treats pigmentation as a survival-limited energetic process embedded within the broader dynamics of absorption, transport, metabolism, and entropy production. Central to the framework is a dimensionless energy survival factor, Ψ=AE\u002FTE+ε where AE represents absorbed and retained energetic and elemental resources relevant to pigment formation, TE denotes transpiration-linked, metabolic, and environmental dissipation losses, and ε captures irreducible entropy-producing degradation inherent to non-equilibrium biological systems. By construction, 0\u003CΨ\u003C1, ensuring full compliance with the second law of thermodynamics. When combined with pigment-specific optical absorption coefficients and physiological limitation functions, the framework yields a physically interpretable description of both pigment concentration and color stability. Empirical ranges for absorbed photosynthetically active radiation (≈45–70%), excitation survival (≈85–95%), and respiratory and transpiration losses (≈30–60% of assimilated resources) naturally constrain Ψ, predicting observed limits on sustained pigment accumulation without invoking biochemical inefficiency or ad hoc correction factors. The analysis demonstrates that increased light availability or enhanced biosynthetic capacity alone does not intensify coloration unless absorbed resources survive transport and loss processes. Stress-induced chlorosis, transient anthocyanin accumulation, and saturation of greenness under high irradiance emerge as direct consequences of reduced energy survival rather than impaired pigment synthesis. By explicitly separating survival from synthesis, the framework reconciles molecular-scale pigment biochemistry with whole-plant and ecosystem-scale color dynamics. This survival-based formulation provides a unified physical basis for interpreting plant color variation across species and environments and offers a scalable foundation for crop stress diagnostics, remote-sensing interpretation, and comparative plant phenotyping. More broadly, it extends survival-limited energy principles to visible plant traits, advancing plant science beyond isolated biochemical descriptions toward a systems-level understanding of how energy governs phenotypic expression.","植物色泽通过叶片绿色度、花色苷着色和果实颜色强度体现，反映了色素生物合成、光学吸收、代谢调控以及与环境的能量-物质交换的综合结果。尽管主要色素类别——包括叶绿素、类胡萝卜素和花青素——的合成与降解生化途径已较为明确，但现有模型大多将颜色表达解释为局部生化现象。此类方法未明确考虑整株植物能量平衡、蒸腾驱动的损耗或热力学约束，限制了其解释广泛观察到的颜色饱和度、胁迫诱导褪色以及跨环境梯度的物种特异性稳定性等模式的能力。本文构建了一个统一的植物颜色生成与稳定性框架，将色素沉着视为一种受生存限制的能量过程，并将其嵌入吸收、运输、代谢和熵产生的更广泛动力学之中。该框架的核心是一个无量纲能量生存因子Ψ=AE\u002FTE+ε，其中AE代表与色素形成相关的被吸收并保留的能量和元素资源，TE表示与蒸腾相关的、代谢的和环境的耗散损失，ε则捕捉非平衡生物系统固有的不可约熵产生降解。根据构建方式，0\u003CΨ\u003C1，确保完全符合热力学第二定律。当与色素特异性光学吸收系数和生理限制函数相结合时，该框架可对色素浓度和颜色稳定性给出具有物理可解释性的描述。吸收性光合有效辐射（约45–70%）、激发能存活率（约85–95%）以及呼吸和蒸腾损失（约占同化资源的30–60%）的经验范围自然约束了Ψ，从而预测出所观察到的持续色素积累限度，而无需引入生化低效性或特设校正因子。分析表明，仅增加光照可用性或增强生物合成能力并不会加深颜色，除非被吸收的资源能够在运输和损失过程中存活下来。胁迫诱导的黄化、瞬时花青素积累以及高辐照下绿色度饱和，均表现为能量生存率降低而非色素合成受损的直接后果。通过明确分离生存","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",{"impact":174,"substance":239,"depth":174,"authority":241,"freshness":33,"relevant":69,"comment":397},"提出以能量生存因子统一解释植物颜色与胁迫的物理框架，方法新颖且对遥感解译和作物诊断有潜在价值，但属预印本理论模型，尚缺田间验证。",[399],{"name":394,"url":391},[401,21,402,403,404],"智慧农业","植物表型","作物胁迫诊断","能量平衡",{"code":406,"slug":407,"name":408,"path":409,"parent_code":410},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":126,"locked":14,"version":27,"method":28,"evidence":413},{"candidates":414,"agricultural_evidence":34},[415,420,425],{"code":406,"score":59,"supported":34,"title":416,"abstract":417,"tags":419},[],[418],"萝卜",[],{"code":421,"score":69,"supported":14,"title":422,"abstract":423,"tags":424},"I1",[],[],[401],{"code":42,"score":69,"supported":14,"title":426,"abstract":427,"tags":428},[],[],[21],[430,431],"植物颜色 能量生存因子","作物胁迫 遥感诊断","植物颜色能量生存因子-5565","10.5281\u002Fzenodo.23540961",{"doi":433,"openalex_id":435,"authors":436,"venue":394,"cited_by_count":15,"oa_url":391,"card":440,"direction":88,"ingested_from":90},"W7223098474",[437],{"name":438,"orcid":439},"Mokhdum Azam Mashrafi","https:\u002F\u002Forcid.org\u002F0009-0002-1801-1130",{"tldr":441,"method":442,"finding":443,"direction":88,"opportunity":444},"提出基于生存的能量框架，用能量存活因子解释植物颜色生成与稳定性。","构建无量纲能量存活因子Ψ=AE\u002F(TE+ε)，结合色素吸收系数与生理限制函数。","颜色深浅取决于吸收资源经运输损耗后的存活率，而非单纯合成能力。","可将Ψ因子与冠层光谱\u002F表型遥感结合，发展胁迫早期诊断与生产力预测模型。","2026-10-22T11:20:31.195819Z",{"id":447,"title":448,"url":449,"summary":450,"summary_zh":451,"content":9,"source_name":394,"source_url":449,"published_at":395,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":452,"score_detail":453,"sources":456,"tags":458,"primary_topic":462,"secondary_topics":466,"classification":467,"search_phrases":505,"slug":508,"view_count":15,"doi":509,"paper":510,"created_at":522},5563,"AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531525","Abstract: Crop diseases and pest infestations remain persistent threats to plantation productivity, with pests estimated to destroy up to 40% of global crop production and plant diseases and invasive insects imposing substantial economic losses worldwide (Food and Agriculture Organization of the United Nations [FAO], 2019). Climate change is intensifying these risks by altering the geographic range, seasonality, and population dynamics of pathogens and pests (Gullino et al., 2021; Skendžić et al., 2021). This paper examines how artificial intelligence (AI) — including convolutional neural networks (CNNs), vision transformers (ViTs), unmanned aerial vehicle (UAV)-based multispectral and hyperspectral sensing, and Internet of Things (IoT)-enabled monitoring — can strengthen the early detection of biotic stress in plantation crops and thereby support plantation risk management. Drawing on recent peer-reviewed and industry literature, the paper synthesises evidence on detection performance, deployment modalities, and pathways for integration into enterprise risk-management systems. It proposes a conceptual framework in which AI-enabled early-warning systems operate as a continuous, spatially explicit risk-sensing layer linking agronomic observations with operational, financial, and insurance decision-making. AI can improve the timeliness, spatial precision, and consistency of surveillance; however, its risk-management value depends on field validation, representative training data, reliable infrastructure, affordable deployment, and appropriate governance. AI should therefore currently be positioned as decision support that augments, rather than replaces, agronomic expertise and ground-truthing. Keywords: artificial intelligence; plant disease detection; pest infestation; precision agriculture; plantation risk management; machine learning; remote sensing. Title: AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management Author: Dr. Geetha Govindan K Gopalakrishnan International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 14, Issue 2, October 2026 - March 2027 Page No: 54-59 Research Publish Journals Website: www.researchpublish.com Published Date: 21-October-2026 DOI: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526 Paper Download Link (Source) https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-management","摘要：作物病害与虫害侵染始终是种植园生产力的持续威胁，据估计，害虫每年可摧毁全球高达40%的作物产量，植物病害与入侵昆虫在全球范围内造成重大经济损失（联合国粮食及农业组织[FAO]，2019）。气候变化通过改变病原体与害虫的地理分布范围、季节性规律及种群动态，正在加剧这些风险（Gullino等，2021；Skendžić等，2021）。本文探讨人工智能（AI）——包括卷积神经网络（CNN）、视觉变换器（ViT）、基于无人机（UAV）的多光谱与高光谱遥感，以及物联网（IoT）赋能的监测技术——如何强化种植园作物生物胁迫的早期检测，从而支撑种植园风险管理。本文基于近期同行评审文献与行业文献，综合梳理了检测性能、部署模式及其融入企业风险管理系统的路径等方面的证据。文章提出一个概念框架，其中AI赋能的预警系统作为持续运行、空间显性的风险感知层，将农艺观测与运营、财务及保险决策相衔接。AI能够提升监测的及时性、空间精度与一致性；然而，其风险管理价值取决于田间验证、具有代表性的训练数据、可靠的基础设施、可负担的部署成本以及适当的治理。因此，当前应将AI定位为决策支持工具，用以增强而非替代农艺专业知识与实地核验。关键词：人工智能；植物病害检测；虫害侵染；精准农业；种植园风险管理；机器学习；遥感。题目：AI驱动的作物病害与虫害侵染早期检测：种植园风险管理的新范式。作者：Geetha Govindan K Gopalakrishnan博士。《国际管理与商业创新期刊》ISSN 2348-7585（在线）第14卷第2期，2026年10月—2027年3月，第54-59页。研究出版期刊网站：www.researchpublish.com。发布日期：2026年10月21日。DOI：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526。论文下载链接（来源）：https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-ma",56,{"impact":241,"substance":317,"depth":454,"authority":51,"freshness":33,"relevant":69,"comment":455},13,"综述性论文，系统梳理AI与遥感在作物病虫害早期预警中的应用并提出风险管理框架，有参考价值但方法创新与数据规模有限，信源为普通出版平台。",[457],{"name":394,"url":449},[401,459,21,460,461],"农业人工智能","风险管理","病虫害监测",{"code":113,"slug":463,"name":464,"path":465,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":468,"locked":14,"version":27,"method":28,"evidence":469},"partial",{"candidates":470,"agricultural_evidence":34,"reason":504},[471,480,486,494,500],{"code":472,"score":13,"supported":34,"title":473,"abstract":474,"tags":479},"I2",[],[475,476,477,478],"机器学习","machine learning","artificial intelligence","decision support",[459],{"code":42,"score":13,"supported":34,"title":481,"abstract":482,"tags":485},[],[46,47,483,484],"multispectral","hyperspectral",[21],{"code":487,"score":33,"supported":34,"title":488,"abstract":489,"tags":493},"I5",[],[490,491,492],"物联网","internet of things","IoT",[],{"code":421,"score":43,"supported":34,"title":495,"abstract":496,"tags":499},[],[497,498],"精准农业","precision agriculture",[401],{"code":120,"score":51,"supported":34,"title":501,"abstract":502,"tags":503},[],[190,191],[],"一级领域明确，二级方向需要细分",[506,507],"AI 作物病害 早期检测","无人机 多光谱 病虫害监测","AI作物病害早期检测-5563","10.5281\u002Fzenodo.23531525",{"doi":509,"openalex_id":511,"authors":512,"venue":394,"cited_by_count":15,"oa_url":449,"card":515,"direction":521,"ingested_from":90},"W7223085400",[513],{"name":514,"orcid":9},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":516,"method":517,"finding":518,"direction":519,"opportunity":520},"综述AI（CNN、ViT、无人机遥感、物联网）用于作物病虫害早期检测并融入种植园风险管理。","文献综述，整合CNN、ViT、无人机多\u002F高光谱与IoT监测的检测性能与部署方式。","AI可提升监测及时性与空间精度，但价值取决于田间验证、代表性数据与治理，应作决策支持而非替代专家。","农业人工智能与决策模型","可将AI预警层与保险、金融风控系统对接，研究跨域数据融合与可解释风险定价的空白。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.428080Z"]