[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4436":3,"related-4436":105},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":68,"slug":71,"view_count":72,"doi":73,"paper":74,"created_at":104},4436,"Tracing the invisible footprint of mining in Iran: spectral mapping of heavy metal contamination in arid soils with a remote sensing approach","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12518-026-00809-9","This research applies an interdisciplinary framework to investigate the interactions between mining activities, heavy metal contamination, and vegetation response in the Sangan mining areas of Khaf County, Iran. Geochemical analyses of 24 surface soil samples were integrated with remote sensing data from Landsat 8-OLI to develop a robust methodology for monitoring stressed ecosystems. Chemical analysis revealed substantial accumulation of cadmium (Cd, barium (Ba) and arsenic (As). The mean Cd concentration (26.5 mg kg⁻¹) was about 88 times higher than the global shale average (0.3 mg kg⁻¹), which is likely associated with to intensive iron ore mining. This finding underscores the risk of these metals entering the food chain and threatening human health, especially in children, who exhibit higher absorption rates. From a remote-sensing perspective, soil-adjusted vegetation indices such as RVI ( R = 0.82) and MSAVI2 ( R = 0.79) outperformed NDVI ( R = 0.40) in arid environments with sparse vegetation, providing a methodological advance for ecological monitoring. Correlation analyses between heavy metals and vegetation indices revealed a strong negative association between copper (Cu) and most indices, particularly IPVI ( R = − 0.69), indicating copper’s phytotoxic effects on chlorophyll and biomass. In contrast, chromium (Cr) showed a weak positive correlation with NDVI ( R = 0.36). These results highlight the need to select vegetation indices according to local environmental conditions and offer a practical framework for enhancing remote sensing–based monitoring of contaminated ecosystems. The study improves understanding of mining impacts and provides a replicable model for sustainable management of mining-affected landscapes worldwide.","本研究采用跨学科框架，探讨伊朗哈夫县桑甘矿区采矿活动、重金属污染与植被响应之间的相互作用。研究将24个表层土壤样品的地球化学分析与Landsat 8-OLI遥感数据相结合，构建了一套用于监测受胁迫生态系统的稳健方法。化学分析显示，镉（Cd）、钡（Ba）和砷（As）显著累积。镉的平均浓度（26.5 mg kg⁻¹）约为全球页岩平均值（0.3 mg kg⁻¹）的88倍，这很可能与密集的铁矿石开采有关。该发现凸显了这些金属进入食物链并威胁人体健康的风险，尤其是对吸收率更高的儿童而言。从遥感角度看，在植被稀疏的干旱环境中，土壤调节植被指数如RVI（R = 0.82）和MSAVI2（R = 0.79）优于NDVI（R = 0.40），为生态监测提供了方法学进展。重金属与植被指数之间的相关分析显示，铜（Cu）与大多数指数呈强负相关，尤其是与IPVI（R = −0.69），表明铜对叶绿素和生物量具有植物毒性效应。相比之下，铬（Cr）与NDVI呈弱正相关（R = 0.36）。这些结果凸显了根据当地环境条件选择植被指数的必要性，并为加强基于遥感的污染生态系统监测提供了实用框架。该研究增进了对采矿影响的理解，并为全球受采矿影响景观的可持续管理提供了一个可复制的模型。",null,"Applied Geomatics","2026-10-07T00:00:00Z","论文",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,21,17,13,9,1,"方法新颖、数据扎实的矿区土壤重金属遥感监测研究，对农业生态与耕地污染监测有借鉴价值，但属境外案例、影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感监测","矿区生态","植被指数","土壤修复","重金属污染",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":67},[39,48,55,62],{"code":40,"score":41,"supported":42,"title":43,"abstract":45,"tags":47},"I3",16,true,[44],"remote sensing",[46,44],"遥感",[29,27],{"code":49,"score":17,"supported":42,"title":50,"abstract":51,"tags":54},"H2",[],[52,53],"土壤","soil",[30,31],{"code":56,"score":57,"supported":42,"title":58,"abstract":59,"tags":61},"H7",3,[],[60],"干旱",[],{"code":63,"score":22,"supported":14,"title":64,"abstract":65,"tags":66},"H6",[],[],[28],"农业相关性需要复核",[69,70],"伊朗 Sangan 矿区 重金属","Landsat 8 OLI 植被指数","伊朗Sangan矿区重金属-4436",7,"10.1007\u002Fs12518-026-00809-9",{"doi":73,"openalex_id":75,"authors":76,"venue":10,"cited_by_count":95,"oa_url":96,"card":97,"direction":101,"ingested_from":103},"W7220854014",[77,80,83,86,89,92],{"name":78,"orcid":79},"Javad Momeni Damaneh","https:\u002F\u002Forcid.org\u002F0009-0006-9131-6167",{"name":81,"orcid":82},"E Tamassoki","https:\u002F\u002Forcid.org\u002F0000-0002-9873-1957",{"name":84,"orcid":85},"Mohammed Achite","https:\u002F\u002Forcid.org\u002F0000-0001-6084-5759",{"name":87,"orcid":88},"Seyed Mohammad Tajbakhsh","https:\u002F\u002Forcid.org\u002F0000-0002-2097-9944",{"name":90,"orcid":91},"Kusum Pandey","https:\u002F\u002Forcid.org\u002F0009-0005-4888-6650",{"name":93,"orcid":94},"Celso Augusto Guimarães Santos","https:\u002F\u002Forcid.org\u002F0000-0001-7927-9718",0,"https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs12518-026-00809-9.pdf",{"tldr":98,"method":99,"finding":100,"direction":101,"opportunity":102},"结合土壤地球化学与Landsat 8遥感，绘制伊朗干旱矿区重金属污染与植被胁迫图谱。","24个表层土壤样地球化学分析，结合Landsat 8-OLI植被指数与相关性分析","Cd超标88倍；RVI、MSAVI2优于NDVI，Cu与植被指数强负相关。","农业遥感与作物表型","干旱稀疏植被区需筛选适宜植被指数，可发展重金属胁迫遥感反演与食物链风险预警模型。","openalex","2026-10-08T23:30:29.578924Z",{"total":106,"page":22,"page_size":106,"items":107},6,[108,183,258,321,392,448],{"id":109,"title":110,"url":111,"summary":112,"summary_zh":113,"content":9,"source_name":114,"source_url":111,"published_at":115,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":116,"score_detail":117,"sources":121,"tags":123,"primary_topic":127,"secondary_topics":131,"classification":132,"search_phrases":156,"slug":159,"view_count":95,"doi":160,"paper":161,"created_at":182},5490,"Biochar for post-war land remediation in Ukraine: A critical review supported by bibliometric analysis","https:\u002F\u002Fdoi.org\u002F10.1093\u002Finteam\u002Fvjag167","This critical review analyzes the current state of scientific research on biochar in Europe and examines its potential use for post-war restoration and improvement of soil fertility in Ukraine. A bibliometric analysis of scientific publications in the Scopus and Web of Science databases for the period 2014-2023 was conducted using VOSviewer visualization and data processing methods. The analysis identified the main areas of research and gaps in knowledge that are relevant to the future application of this technology in Ukraine. Moreover, remote sensing methodologies were employed to identify potential remediation requirements in the Ukrainian territory. This involved the analysis of changes in the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) of a part of Ukraine's agricultural territory situated along the frontline, prior to and following a period of three years during a full-scale war. The results revealed changes in vegetation and moisture levels, indicating land degradation in the border area under study. However, these observations do not constitute direct evidence of soil contamination or the effectiveness of biochar; rather, they indicate areas where further field studies and an assessment of restoration measures are needed. A strengths, weaknesses, opportunities, and threats (SWOT) analysis was used as a strategic discussion tool to identify the opportunities and challenges associated with the implementation of biochar in Ukraine. This review also identifies the main scientific uncertainties, barriers, economic aspects, and research priorities that should be considered before the large-scale implementation of biochar-based technologies in Ukraine.","这篇批判性综述分析了欧洲生物炭（biochar）科学研究的现状，并探讨其在乌克兰战后恢复与土壤肥力提升中的潜在应用。研究采用VOSviewer可视化与数据处理方法，对Scopus和Web of Science数据库中2014—2023年的科学出版物进行了文献计量分析。分析识别了与该项技术在乌克兰未来应用相关的主要研究领域和知识空白。此外，研究采用遥感方法识别乌克兰境内潜在的修复需求，具体包括对乌克兰沿前线部分农业区域在全面战争前后三年期间的归一化植被指数（NDVI）和归一化水分指数（NDMI）变化进行分析。结果显示植被与水分水平发生变化，表明所研究边境地区存在土地退化。然而，这些观测结果并不构成土壤污染或生物炭有效性的直接证据，而是指示了需要进一步开展田间研究和评估修复措施的区域。研究采用优势、劣势、机会与威胁（SWOT）分析作为战略讨论工具，识别在乌克兰实施生物炭相关的机遇与挑战。本综述还明确了在乌克兰大规模实施基于生物炭的技术之前应考虑的主要科学不确定性、障碍、经济因素及研究优先事项。","Integrated Environmental Assessment and Management","2026-10-19T00:00:00Z",77,{"impact":41,"substance":18,"depth":118,"authority":119,"freshness":17,"relevant":22,"comment":120},18,14,"该综述结合文献计量与遥感NDVI\u002FNDMI分析，评估生物炭用于乌克兰战后农田修复的潜力与不确定性，方法新颖、结论审慎，对农业遥感与土壤治理具参考价值，但属区域性研究，未达重大突破层级。",[122],{"name":114,"url":111},[124,125,27,30,126],"生物炭","土地退化","乌克兰农业",{"code":49,"slug":53,"name":128,"path":129,"parent_code":130},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":133,"locked":14,"version":35,"method":36,"evidence":134},"classified",{"candidates":135,"agricultural_evidence":42},[136,143,147,151],{"code":137,"score":119,"supported":42,"title":138,"abstract":140,"tags":142},"I7",[139],"bibliometric",[141,139],"文献计量",[],{"code":49,"score":17,"supported":42,"title":144,"abstract":145,"tags":146},[],[52,53],[30,124],{"code":40,"score":72,"supported":42,"title":148,"abstract":149,"tags":150},[],[46,44],[27],{"code":152,"score":22,"supported":14,"title":153,"abstract":154,"tags":155},"H1",[],[],[125],[157,158],"生物炭 战后土壤修复 乌克兰","NDVI NDMI 遥感 土地退化","生物炭战后土壤修复乌克兰-5490","10.1093\u002Finteam\u002Fvjag167",{"doi":160,"openalex_id":162,"authors":163,"venue":114,"cited_by_count":95,"oa_url":9,"card":176,"direction":101,"ingested_from":103},"W7223053668",[164,167,170,173],{"name":165,"orcid":166},"Olha Shomko","https:\u002F\u002Forcid.org\u002F0000-0003-2418-0985",{"name":168,"orcid":169},"Iryna Davydova","https:\u002F\u002Forcid.org\u002F0000-0001-6535-3948",{"name":171,"orcid":172},"Marco Bartoli","https:\u002F\u002Forcid.org\u002F0000-0002-5022-6206",{"name":174,"orcid":175},"Elena Maestri","https:\u002F\u002Forcid.org\u002F0000-0002-8745-9564",{"tldr":177,"method":178,"finding":179,"direction":180,"opportunity":181},"综述生物炭用于乌克兰战后土地修复，结合文献计量与遥感分析。","Scopus\u002FWoS文献计量、VOSviewer、NDVI\u002FNDMI遥感与SWO","前线农区植被与湿度变化显示土地退化，但需田间验证生物炭效果。","农业绿色发展与碳","可研究战区土地退化的遥感快速诊断与生物炭修复效果的田间验证体系。","2026-10-21T23:30:25.223030Z",{"id":184,"title":185,"url":186,"summary":187,"summary_zh":188,"content":9,"source_name":189,"source_url":186,"published_at":190,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":191,"score_detail":192,"sources":195,"tags":197,"primary_topic":9,"secondary_topics":201,"classification":202,"search_phrases":219,"slug":222,"view_count":95,"doi":223,"paper":224,"created_at":257},5176,"Spatial Variability and Influencing Factors of Soil P and K in a High-Altitude Mining Area: A Case Study in Southwest China","https:\u002F\u002Fdoi.org\u002F10.15244\u002Fpjoes\u002F224833","The spatial variation and influencing factors of various forms of P and K in soil from a high-altitude mining area were assessed using a combination of traditional statistical and geostatistical methods. The primary factors affecting the distributions of P and K were identified using correlation analysis. The mean total phosphorus (TP), available phosphorus (AP), inorganic phosphorus (IP), total potassium (TK), quick-release potassium (AK), and slow-release potassium (SAK) were 1280.78 mg kg-1, 8.99 mg kg-1, 554.04 mg kg-1, 15358.60 mg kg-1, 215.14 mg kg-1, and 1381.70 mg kg-1, respectively. The coefficients of variation ranged from 44.79% to 80.89%, indicating that all parameters exhibited strong variation intensity. The block-to-base ratio of semivariance varied from 0.00% to 24.53%, indicating strong spatial autocorrelation. Moran’s I analysis revealed significant spatial clustering for TP and TK. However, the spatial distributions of AP, IP, and SAK may be random. Soil pH was negatively correlated with TP and AP, positively correlated with IP, and not correlated with TK, AK, or SAK. SOM was positively correlated with TP and AP and significantly negatively correlated with IP and TK, but not with AK or SAK. Cation exchange capacity was significantly positively correlated with TP, but significantly negatively correlated with TK. Except for IP, there was no significant correlation between soil particle composition and the various forms of P and K. Elevation and aspect had no effect on the distribution of various forms of P and K. NDVI was significantly positively correlated with TP and AP and negatively correlated with IP and TK. Mining activities can strongly affect the availability of P and K. These results have practical significance for soil management and ecological restoration in high-altitude mining areas.","采用传统统计学与地统计学相结合的方法，评估了高海拔矿区土壤中各形态磷（P）和钾（K）的空间变异特征及其影响因素。通过相关分析确定了影响磷、钾分布的主要因素。全磷（TP）、速效磷（AP）、无机磷（IP）、全钾（TK）、速效钾（AK）和缓效钾（SAK）的平均含量分别为1280.78 mg kg-1、8.99 mg kg-1、554.04 mg kg-1、15358.60 mg kg-1、215.14 mg kg-1和1381.70 mg kg-1。变异系数范围为44.79%至80.89%，表明所有参数均表现出强变异强度。半方差块基比在0.00%至24.53%之间，表明存在强烈的空间自相关。Moran's I分析显示TP和TK具有显著的空间聚集性，而AP、IP和SAK的空间分布可能呈随机性。土壤pH与TP和AP呈负相关，与IP呈正相关，与TK、AK和SAK无相关性。土壤有机质（SOM）与TP和AP呈正相关，与IP和TK呈显著负相关，与AK和SAK无相关性。阳离子交换量与TP呈显著正相关，与TK呈显著负相关。除IP外，土壤颗粒组成与各形态磷、钾之间无显著相关性。海拔和坡向对各形态磷、钾的分布无影响。归一化植被指数（NDVI）与TP和AP呈显著正相关，与IP和TK呈负相关。采矿活动可强烈影响磷和钾的有效性。上述结果对高海拔矿区的土壤管理和生态恢复具有实际意义。","Polish Journal of Environmental Studies","2026-10-15T00:00:00Z",60,{"impact":17,"substance":118,"depth":119,"authority":193,"freshness":17,"relevant":22,"comment":194},12,"该研究基于实测数据，揭示了高海拔矿区土壤磷钾的空间变异规律及其影响因素，对矿区土壤管理和生态修复有参考价值，但属于区域性案例研究，公共影响有限。",[196],{"name":189,"url":186},[198,27,28,199,200],"生态修复","土壤养分","空间变异",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":203},{"candidates":204,"agricultural_evidence":14,"reason":67},[205,210,215],{"code":49,"score":206,"supported":42,"title":207,"abstract":208,"tags":209},15,[53],[52,53],[199],{"code":63,"score":211,"supported":14,"title":212,"abstract":213,"tags":214},2,[],[],[198,28],{"code":40,"score":22,"supported":14,"title":216,"abstract":217,"tags":218},[],[],[27],[220,221],"西南高海拔矿区 土壤磷钾","矿区土壤 空间变异","西南高海拔矿区土壤磷钾-5176","10.15244\u002Fpjoes\u002F224833",{"doi":223,"openalex_id":225,"authors":226,"venue":189,"cited_by_count":95,"oa_url":251,"card":252,"direction":101,"ingested_from":103},"W7222017864",[227,230,233,236,239,242,245,248],{"name":228,"orcid":229},"Xiyao Feng","https:\u002F\u002Forcid.org\u002F0009-0009-1482-4784",{"name":231,"orcid":232},"Yu Chen","https:\u002F\u002Forcid.org\u002F0009-0007-6975-327X",{"name":234,"orcid":235},"Zhongyou Song","https:\u002F\u002Forcid.org\u002F0009-0001-0791-3457",{"name":237,"orcid":238},"Pan Gao","https:\u002F\u002Forcid.org\u002F0009-0000-0039-4562",{"name":240,"orcid":241},"Pengfei Chen","https:\u002F\u002Forcid.org\u002F0009-0000-8394-5672",{"name":243,"orcid":244},"Fabin Yan","https:\u002F\u002Forcid.org\u002F0009-0000-0496-7532",{"name":246,"orcid":247},"Pei Wang","https:\u002F\u002Forcid.org\u002F0009-0001-8997-421X",{"name":249,"orcid":250},"Pan Zhou","https:\u002F\u002Forcid.org\u002F0009-0003-8001-6626","https:\u002F\u002Fwww.pjoes.com\u002Fpdf-224833-149266?filename=Spatial-Variability-and-I.pdf",{"tldr":253,"method":254,"finding":255,"direction":101,"opportunity":256},"评估高海拔矿区土壤磷钾各形态的空间变异及其影响因素。","传统统计与地统计结合，辅以相关分析和Moran's I。","磷钾各形态变异强、空间自相关显著，pH、有机质、NDVI和采矿活动是主要影响因素。","可结合多时相遥感与机器学习，量化采矿扰动下土壤养分空间格局的动态演变与生态修复响应。","2026-10-17T23:30:30.161837Z",{"id":259,"title":260,"url":261,"summary":262,"summary_zh":263,"content":9,"source_name":264,"source_url":261,"published_at":265,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":266,"score_detail":267,"sources":270,"tags":272,"primary_topic":9,"secondary_topics":276,"classification":277,"search_phrases":296,"slug":299,"view_count":95,"doi":300,"paper":301,"created_at":320},5010,"Analysis of Land Cover Change Using Support Vector Machine Algorithm on Satellite Imagery (Case Study: Mining Area of IWIP).","https:\u002F\u002Fdoi.org\u002F10.58602\u002Fitsecs.v4i4.388","Unsupervised mining activities and the lack of proper management of the environment can bring very serious ecological effects. One of the worst is large-scale land cover change. This paper investigates the land cover changes in the mining area of PT Indonesia Weda Bay Industrial Park (IWIP), Weda Bay Nickel (WBN) area, and measures the performance of the Support Vector Machine technique of land cover classification using satellite imagery. This research is a quantitative study based on a remote sensing technique and spatial analysis in a GIS environment. Multi-temporal satellite images, Landsat-7 (2010), Landsat-8 (2014), and Sentinel-2 (2019 and 2025) were processed to land cover classes (vegetation, open land, cloud) based on the spectral indices NDVI, NDBI, and BSI. Through the post-classification comparison method, land cover changes were detected. The overall change from 2010 to 2025 is quite significant. The vegetation cover decreased from 91.2% to 84.3%, while open land increased from less than 1% to 11.4%. The greatest changes were observed in 2019 and 2025, suggesting that mining activities and related industry development have speed up A lot. The Support Vector Machine algorithm reached excellent classification accuracy over the different periods (97.47%, 96.67%, 89.52%, 95.12% in the cases of 2010 2014 2019 and 2025 respectively), and the results were supported by the high Kappa agreement scores (ranging from 0.84 to 0.96). The low difference between training and testing accuracy indicates a strong generalization without any sign of the model getting overfitted to the training data. The work highlights that Support Vector Machine algorithm, is a very good and reliable method for detecting land cover change in mining areas, where patterns are both complex as well as dynamic.","无监督的采矿活动和缺乏适当的环境管理可能带来非常严重的生态影响。其中最严重的影响之一是大规模的土地覆盖变化。本文研究了PT Indonesia Weda Bay Industrial Park（IWIP）矿区、Weda Bay Nickel（WBN）地区的土地覆盖变化，并评估了利用卫星影像进行土地覆盖分类的支持向量机（Support Vector Machine）技术的性能。本研究是一项基于遥感技术和GIS环境下空间分析的定量研究。多时相卫星影像Landsat-7（2010年）、Landsat-8（2014年）和Sentinel-2（2019年和2025年）基于光谱指数NDVI、NDBI和BSI被处理为土地覆盖类别（植被、裸地、云）。通过分类后比较法，检测了土地覆盖变化。2010年至2025年的总体变化相当显著。植被覆盖从91.2%下降至84.3%，而裸地从不足1%增加至11.4%。最大的变化出现在2019年和2025年，表明采矿活动及相关产业发展已大幅加速。支持向量机算法在不同时期均达到了优异的分类精度（2010年、2014年、2019年和2025年分别为97.47%、96.67%、89.52%、95.12%），且结果得到了高Kappa一致性系数的支持（范围为0.84至0.96）。训练精度与测试精度之间的低差异表明模型具有较强的泛化能力，没有出现过拟合训练数据的迹象。该研究强调，支持向量机算法是一种非常好且可靠的方法，可用于检测采矿区域的土地覆盖变化，因为这些区域的模式既复杂又动态。","Journal of Information Technology Software Engineering and Computer Science (ITSECS)","2026-10-13T00:00:00Z",66,{"impact":193,"substance":268,"depth":41,"authority":13,"freshness":17,"relevant":22,"comment":269},20,"以SVM结合多时相卫星影像量化IWIP镍矿区2010—2025年植被与裸地变化，方法可靠、数据扎实，对矿区生态遥感监测有参考价值，但属区域案例研究，公共影响有限。",[271],{"name":264,"url":261},[27,28,273,274,275],"土地覆盖变化","支持向量机","镍矿开采",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":278},{"candidates":279,"agricultural_evidence":14,"reason":67},[280,288,292],{"code":281,"score":21,"supported":42,"title":282,"abstract":283,"tags":287},"I4",[],[284,285,286],"空间分析","GIS","spatial analysis",[],{"code":40,"score":72,"supported":42,"title":289,"abstract":290,"tags":291},[],[46,44],[27],{"code":63,"score":211,"supported":14,"title":293,"abstract":294,"tags":295},[],[],[273,28],[297,298],"IWIP 矿区 土地覆盖","Sentinel-2 镍矿 遥感","IWIP矿区土地覆盖-5010","10.58602\u002Fitsecs.v4i4.388",{"doi":300,"openalex_id":302,"authors":303,"venue":264,"cited_by_count":95,"oa_url":314,"card":315,"direction":101,"ingested_from":103},"W7221684953",[304,306,308,311],{"name":305,"orcid":9},"Efraim Julian Soma",{"name":307,"orcid":9},"Winsy Christo Deilan Weki",{"name":309,"orcid":310},"Rillya Arundaa","https:\u002F\u002Forcid.org\u002F0009-0008-4979-5525",{"name":312,"orcid":313},"Mahardika Inra Takaendengan","https:\u002F\u002Forcid.org\u002F0000-0001-8622-7537","https:\u002F\u002Fejournal.techcart-press.com\u002Findex.php\u002Fitsecs\u002Farticle\u002Fdownload\u002F388\u002F338",{"tldr":316,"method":317,"finding":318,"direction":101,"opportunity":319},"用SVM分类多时相卫星影像，分析印尼IWIP矿区2010-2025年土地覆盖变化。","Landsat-7\u002F8与Sentinel-2影像，NDVI\u002FNDBI\u002FBSI指数","植被从91.2%降至84.3%，裸地升至11.4%，SVM精度89.5%-97.5%，Kappa 0","可将SVM与深度学习结合，提升多云热带矿区影像分类鲁棒性并做变化驱动力归因。","2026-10-15T23:30:32.156647Z",{"id":322,"title":323,"url":324,"summary":325,"summary_zh":326,"content":9,"source_name":327,"source_url":324,"published_at":328,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":329,"score_detail":330,"sources":333,"tags":335,"primary_topic":339,"secondary_topics":344,"classification":349,"search_phrases":363,"slug":366,"view_count":95,"doi":367,"paper":368,"created_at":391},4826,"Multi-Index Remote Sensing Assessment of Agro-Climatic Change and Agricultural Responses in Temperate and Semi-Arid Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18203496","Diverse climatic regions exhibit unique agro-climatic vulnerabilities that are significantly shaped by climate stress. Understanding these distinct vulnerabilities is essential for developing effective policies to mitigate climate change impacts and ensuring sustainable environmental management. This study examines agro-climatic vulnerabilities in Nottinghamshire County (United Kingdom) and Udaipur District (India) from 2000 to 2024, comparing climate change impacts across temperate and semi-arid agricultural systems that represent fundamentally different system behaviours under contrasting climatic conditions. Using multi-temporal Landsat imagery and WorldClim climate data, nine environmental indices were analysed: the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Normalized Difference Water Index (NDWI), Moisture Stress Index (MSI), Land Surface Temperature (LST), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Drought Risk Index (DRI), and Crop Health Index (CHI). The results reveal contrasting regional trajectories that challenge conventional assumptions about climate vulnerability. Udaipur exhibited improved vegetation and moisture conditions across the observation period, whereas Nottinghamshire showed intensifying moisture stress and declining precipitation. Warming was more pronounced in Nottinghamshire, while strong inter-index correlations validated the monitoring approach. These findings indicate that Udaipur benefits from intensified monsoon patterns, requiring flood mitigation and water-storage infrastructure, whereas Nottinghamshire’s rising moisture stress underscores the need for sustained monitoring and adaptive management. This comparative framework provides evidence-based insights for region-specific adaptation strategies essential to sustainable agricultural development across diverse agro-climatic zones.","不同气候区域表现出独特的农业气候脆弱性，而这些脆弱性在很大程度上受到气候胁迫的影响。理解这些不同的脆弱性对于制定有效政策以减缓气候变化影响、确保可持续环境管理至关重要。本研究考察了2000年至2024年间英国诺丁汉郡与印度乌代布尔县的农业气候脆弱性，比较了温带与半干旱农业系统在气候变化影响下的差异，这两类系统在截然不同的气候条件下代表了根本不同的系统行为。研究利用多时相Landsat影像和WorldClim气候数据，分析了九个环境指数：归一化植被指数（NDVI）、植被状态指数（VCI）、归一化水体指数（NDWI）、水分胁迫指数（MSI）、地表温度（LST）、温度状态指数（TCI）、植被健康指数（VHI）、干旱风险指数（DRI）和作物健康指数（CHI）。结果揭示了截然不同的区域变化轨迹，挑战了关于气候脆弱性的传统假设。乌代布尔在观测期内表现出植被和水分条件的改善，而诺丁汉郡则显示出加剧的水分胁迫和降水减少。诺丁汉郡的增温更为显著，而指数间较强的相关性验证了该监测方法。这些发现表明，乌代布尔受益于季风模式的增强，需要防洪和水储存基础设施，而诺丁汉郡不断上升的水分胁迫则凸显了持续监测和适应性管理的必要性。这一比较框架为因地制宜的适应策略提供了循证依据，对跨不同农业气候区的可持续发展至关重要。","Remote Sensing","2026-10-12T00:00:00Z",75,{"impact":193,"substance":331,"depth":118,"authority":119,"freshness":21,"relevant":22,"comment":332},22,"基于多时相遥感与九项环境指数的跨区域对比研究，方法扎实、结论具政策参考价值，但属学术论文，产业影响有限。",[334],{"name":327,"url":324},[27,29,336,337,338],"干旱风险","适应策略","农业气候",{"code":40,"slug":340,"name":341,"path":342,"parent_code":343},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[345],{"code":56,"slug":346,"name":347,"path":348,"parent_code":130},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":133,"locked":14,"version":35,"method":36,"evidence":350},{"candidates":351,"agricultural_evidence":42},[352,356],{"code":40,"score":13,"supported":42,"title":353,"abstract":354,"tags":355},[44],[],[29,27],{"code":56,"score":21,"supported":42,"title":357,"abstract":358,"tags":362},[],[359,60,360,361],"气候变化","climate change","drought",[],[364,365],"Landsat 遥感 农业气候","诺丁汉郡 乌代布尔","Landsat遥感农业气候-4826","10.3390\u002Frs18203496",{"doi":367,"openalex_id":369,"authors":370,"venue":327,"cited_by_count":95,"oa_url":324,"card":386,"direction":101,"ingested_from":103},"W7221570035",[371,374,377,380,383],{"name":372,"orcid":373},"Bhanwar Vishvendra Raj Singh","https:\u002F\u002Forcid.org\u002F0000-0001-7750-5240",{"name":375,"orcid":376},"Vivek Agarwal","https:\u002F\u002Forcid.org\u002F0000-0003-1185-3802",{"name":378,"orcid":379},"Stuart Marsh","https:\u002F\u002Forcid.org\u002F0000-0002-1168-6760",{"name":381,"orcid":382},"Gajendra Singh Sisodiya","https:\u002F\u002Forcid.org\u002F0009-0006-4118-7491",{"name":384,"orcid":385},"Ajay Sharma","https:\u002F\u002Forcid.org\u002F0009-0001-6077-9875",{"tldr":387,"method":388,"finding":389,"direction":101,"opportunity":390},"用多时相遥感和九项指数对比英国诺丁汉郡与印度乌代浦2000—2024年农业气候脆弱性。","Landsat多时相影像与WorldClim气候数据，计算NDVI、VCI、ND","乌代浦植被与水分条件改善、季风增强，诺丁汉郡水分胁迫加剧、降水下降且升温更明显。","可拓展多区域长时序遥感指数耦合机器学习，构建跨气候带农业气候脆弱性预警与适应决策模型。","2026-10-13T23:30:24.062929Z",{"id":393,"title":394,"url":395,"summary":396,"summary_zh":397,"content":9,"source_name":398,"source_url":395,"published_at":328,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":399,"score_detail":400,"sources":402,"tags":404,"primary_topic":9,"secondary_topics":408,"classification":409,"search_phrases":424,"slug":427,"view_count":95,"doi":428,"paper":429,"created_at":447},4823,"Comparing Sentinel-2 Spectral Indices for Mapping Forest Fire Damage in Pollino National Park, Italy","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fisprs-archives-xlviii-m-13-2026-1-2026","In recent years, climate change has significantly increased the severity and frequency of wildfires across Europe, with the 2025 season marking an unprecedented peak of one million hectares burned. Rapid and cost-effective mapping of these damaged areas is crucial for environmental recovery, making satellite remote sensing an indispensable tool. This study evaluates and compares the performance of four Sentinel-2 spectral burn indices for mapping forest fire damage within the Pollino National Park (Italy) occurred in September 2025, an area severely affected by the devastating summer fires of 2025. The analyzed indices include the Normalized Burn Ratio (NBR), Normalized Burn Ratio Plus (NBR+), Normalized Burn Ratio Short Wave Infrared (NBR-SWIR), and Normalized Difference Short Wave Infrared (ND-SWIR). An image differencing technique was applied to pre-fire (June 7, 2025) and post-fire (November 14, 2025) datasets, followed by a supervised Maximum Likelihood classification to map burned and unburned classes. Accuracy assessment via confusion matrices showed that NBR and NBR+ delivered the highest performance, achieving an Overall Accuracy (OA) of 0.976 and 0.974, respectively. ND-SWIR performed closely at 0.966, whereas NBR-SWIR yielded a lower OA of 0.873 due to the misclassification of bare soil transitions as burned areas. These findings highlight the critical role of specific SWIR-band combinations in optimizing automated wildfire monitoring systems.","近年来，气候变化显著加剧了欧洲野火的严重程度和发生频率，2025年火灾季烧毁面积达到一百万公顷，创下历史新高。快速且经济高效地绘制这些受损区域对于环境恢复至关重要，使卫星遥感成为不可或缺的工具。本研究评估并比较了四种哨兵2号（Sentinel-2）光谱燃烧指数在绘制2025年9月发生于意大利波利诺国家公园（Pollino National Park）的森林火灾损害中的表现，该地区受2025年夏季毁灭性火灾的严重影响。所分析的指数包括归一化燃烧比率（NBR）、归一化燃烧比率增强版（NBR+）、归一化燃烧比率短波红外（NBR-SWIR）以及归一化差值短波红外（ND-SWIR）。研究对火灾前（2025年6月7日）和火灾后（2025年11月14日）数据集应用了图像差值技术，随后采用监督最大似然分类法绘制燃烧与未燃烧类别。通过混淆矩阵进行的精度评估表明，NBR和NBR+表现最佳，总体精度（OA）分别达到0.976和0.974。ND-SWIR紧随其后，为0.966，而NBR-SWIR由于将裸土转变误分类为燃烧区域，总体精度较低，为0.873。这些发现凸显了特定短波红外波段组合在优化自动化野火监测系统中的关键作用。","˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences\u002FInternational archives of the photogrammetry, remote sensing and spatial information sciences",74,{"impact":193,"substance":331,"depth":118,"authority":20,"freshness":21,"relevant":22,"comment":401},"方法对比扎实、结论明确，但属区域案例研究，对农业信息化有间接参考价值，可入精选但非必选。",[403],{"name":398,"url":395},[405,27,406,407,29],"Sentinel-2","灾害评估","森林火灾",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":410},{"candidates":411,"agricultural_evidence":14,"reason":67},[412,416,420],{"code":40,"score":21,"supported":42,"title":413,"abstract":414,"tags":415},[],[46,44],[405,29,27],{"code":56,"score":17,"supported":42,"title":417,"abstract":418,"tags":419},[],[359,360],[407,406],{"code":49,"score":57,"supported":42,"title":421,"abstract":422,"tags":423},[],[53],[],[425,426],"Sentinel-2 森林火灾 遥感","Pollino 国家公园 火烧迹地","Sentinel-2森林火灾遥感-4823","10.5194\u002Fisprs-archives-xlviii-m-13-2026-1-2026",{"doi":428,"openalex_id":430,"authors":431,"venue":398,"cited_by_count":95,"oa_url":441,"card":442,"direction":101,"ingested_from":103},"W7221528348",[432,434,436,439],{"name":433,"orcid":9},"Giuseppina Costanzo",{"name":435,"orcid":9},"Gianluigi Ferrara",{"name":437,"orcid":438},"Claudio Parente","https:\u002F\u002Forcid.org\u002F0000-0002-8328-9764",{"name":440,"orcid":9},"Vincenzo Verde","https:\u002F\u002Fisprs-archives.copernicus.org\u002Farticles\u002FXLVIII-M-13-2026\u002F1\u002F2026\u002Fisprs-archives-XLVIII-M-13-2026-1-2026.pdf",{"tldr":443,"method":444,"finding":445,"direction":101,"opportunity":446},"比较四种 Sentinel-2 光谱指数在意大利 Pollino 国家公园 2025 年林火损伤制图","Sentinel-2 火前火后影像差值 + 最大似然监督分类，用混淆矩阵评估四种","NBR 与 NBR+ 精度最高（OA 0.976、0.974），NBR-SWIR 因裸土误判仅 0.","可探索多指数融合与机器学习分类器，提升复杂地表下火烧迹地自动制图鲁棒性。","2026-10-13T23:30:23.838067Z",{"id":449,"title":450,"url":451,"summary":452,"summary_zh":453,"content":9,"source_name":454,"source_url":451,"published_at":455,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":456,"sources":458,"tags":460,"primary_topic":464,"secondary_topics":465,"classification":467,"search_phrases":489,"slug":492,"view_count":95,"doi":493,"paper":494,"created_at":507},4747,"Spatiotemporal mapping of agricultural drought using geospatial approach in the East Bale Zone, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fagg2.70475","Abstract Agricultural drought significantly threatens food security, livelihoods, and ecosystem stability, especially in regions prone to drought, such as the East Bale zone. This study investigates the spatial and temporal patterns of agricultural drought over 30 years (1993–2024) by combining meteorological station data with satellite‐derived vegetation and thermal indices. To assess meteorological drought, the standardized precipitation index (SPI) was calculated at 3‐, 6‐, and 12‐month intervals. For agricultural and thermal stress analysis, remote sensing‐based indices including the vegetation condition index (VCI), temperature condition index, and vegetation health index (VHI) were utilized. The results indicate that the years 2003, 2008, and 2018 experienced the most severe droughts, as shown consistently across all indices. Spatial analysis revealed that the central, southern, and eastern lowland areas faced the highest frequency and severity of drought, while the northwestern highlands remained comparatively resilient. Cross‐index correlation revealed strong agreement between SPI and VCI ( R 2 = 0.77), and between VHI and VCI ( R 2 = 0.83), supporting the effectiveness of integrated drought monitoring approaches. This research advances the field of drought assessment by enhancing spatial and temporal resolution and validating cross‐index relationships at the subregional level. The results provide critical insights for localized drought early warning systems, climate adaptation strategies, and future research to improve agricultural resilience under changing climatic conditions. Hence, agricultural drought risk mapping serves as a valuable tool for guiding decision‐making in drought monitoring and for mitigating its impacts on agricultural productivity.","摘要 农业干旱严重威胁粮食安全、生计和生态系统稳定性，尤其是在东巴莱区等易旱地区。本研究结合气象站数据与卫星反演的植被和热力指数，探讨了30年（1993—2024年）间农业干旱的时空格局。为评估气象干旱，计算了3个月、6个月和12个月尺度的标准化降水指数（SPI）。在农业与热胁迫分析方面，采用了基于遥感的植被状态指数（VCI）、温度状态指数和植被健康指数（VHI）。结果表明，2003年、2008年和2018年经历了最严重的干旱，各指数均一致反映了这一情况。空间分析显示，中部、南部和东部低地地区干旱发生频率最高、程度最严重，而西北部高地则相对具有较强恢复力。跨指数相关性分析表明，SPI与VCI之间（R²=0.77）以及VHI与VCI之间（R²=0.83）具有高度一致性，支持了综合干旱监测方法的有效性。本研究通过提高时空分辨率并在次区域尺度上验证跨指数关系，推进了干旱评估领域的发展。研究结果为地方性干旱预警系统、气候适应策略以及未来研究提供了重要见解，有助于在气候变化条件下提升农业韧性。因此，农业干旱风险制图为干旱监测决策及其对农业生产影响的减缓提供了有价值的工具。","Agrosystems Geosciences & Environment","2026-10-11T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":457},"基于30年气象与遥感指数交叉验证的农业干旱时空制图研究，方法扎实、结论可靠，但属区域性案例，对国内三农信息化仅有方法借鉴价值。",[459],{"name":454,"url":451},[461,462,27,29,463],"粮食安全","防灾减灾","农业干旱",{"code":56,"slug":346,"name":347,"path":348,"parent_code":130},[466],{"code":40,"slug":340,"name":341,"path":342,"parent_code":343},{"status":133,"locked":14,"version":35,"method":36,"evidence":468},{"candidates":469,"agricultural_evidence":42},[470,475,479,485],{"code":56,"score":118,"supported":42,"title":471,"abstract":472,"tags":474},[361],[359,473,60,361],"气候适应",[462],{"code":40,"score":17,"supported":42,"title":476,"abstract":477,"tags":478},[],[46,44],[29,27],{"code":480,"score":72,"supported":42,"title":481,"abstract":482,"tags":484},"A8",[],[461,483],"food security",[461],{"code":281,"score":106,"supported":42,"title":486,"abstract":487,"tags":488},[],[284,286],[],[490,491],"埃塞俄比亚 East Bale 农业干旱","SPI VCI 干旱监测","埃塞俄比亚EastBale农业干旱-4747","10.1002\u002Fagg2.70475",{"doi":493,"openalex_id":495,"authors":496,"venue":454,"cited_by_count":95,"oa_url":451,"card":502,"direction":101,"ingested_from":103},"W7221455809",[497,499],{"name":498,"orcid":9},"Murad Hedo Haji",{"name":500,"orcid":501},"Karuturi Venkata Suryabhagavan","https:\u002F\u002Forcid.org\u002F0000-0003-2528-9106",{"tldr":503,"method":504,"finding":505,"direction":101,"opportunity":506},"结合气象站与卫星植被热指数，绘制埃塞俄比亚East Bale区30年农业干旱时空图。","用SPI（3\u002F6\u002F12月）与遥感VCI、TCI、VHI指数做交叉相关分析。","2003、2008、2018年干旱最重，中南部和东部低地最脆弱，SPI与VCI相关性R²=0.77。","可在小区域尺度融合多源指数构建干旱预警模型，并验证指数在不同作物区的适用性。","2026-10-12T23:30:24.342375Z"]