[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3189":3,"related-3189":111},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":75,"slug":78,"view_count":79,"doi":80,"paper":81,"created_at":110},3189,"Influence of Drought Events on Vegetation and Land Use Dynamics Utilising Orbital Data: A Case Study in the Pajeú River Basin, Pernambuco","https:\u002F\u002Fdoi.org\u002F10.29150\u002Fjhrs.v16i03.269030","Monitoring drought in the northeastern Semi-Arid region is a challenge compounded by high rainfall variability and a scarcity of in situ monitoring networks, which makes the use of geotechnologies for water resource management essential. This study aimed to analyze land use and occupation dynamics from 2002 to 2022 in the Pajeú River basin in Pernambuco, Brazil, and their relationship with drought events, using indices based on remote sensing. The Standardized Precipitation Index (SPI) was applied to characterize meteorological drought, while the Vegetation Health Index (VHI) and Normalized Vegetation Water Supply Index (NVSWI) were used to assess the ecosystem's response. The results indicated that the period from 2011 to 2016 was the most severe and prolonged drought, which was reinforced by low VHI and NVSWI values, indicating water stress on vegetation during the same period. The correlation analysis revealed that land use classes, such as vegetation and water, responded directly to drought, whereas degraded anthropogenic areas, including pasture and exposed soil, exhibited the opposite behavior. The joint analysis of the three indices and land use and occupation data provided a comprehensive understanding of the evolution of drought in the study area, highlighting the importance of a multiple approach to monitoring complex environments.","监测半干旱东北部地区的干旱是一项挑战，降雨变率大且原位监测网络稀缺使这一挑战更加复杂，因此利用地理技术进行水资源管理至关重要。本研究旨在利用基于遥感的指数，分析2002年至2022年巴西伯南布哥州帕热乌河流域的土地利用与覆盖动态及其与干旱事件的关系。采用标准化降水指数（SPI）表征气象干旱，同时使用植被健康指数（VHI）和归一化植被供水指数（NVSWI）评估生态系统的响应。结果表明，2011年至2016年是最严重且持续时间最长的干旱期，同期较低的VHI和NVSWI值进一步证实了这一点，表明植被在此期间受到水分胁迫。相关性分析显示，植被和水体等土地利用类别对干旱有直接响应，而牧场和裸露土壤等退化的人为区域则表现出相反的行为。三个指数与土地利用及覆盖数据的联合分析为理解研究区域干旱演变提供了全面的认识，凸显了多方法途径在监测复杂环境中的重要性。",null,"Journal of Hyperspectral Remote Sensing","2026-09-19T00:00:00Z","论文",10,false,61,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,18,15,12,1,"该研究利用遥感指数分析巴西半干旱流域干旱与植被动态，方法扎实但属区域性案例，对国内三农信息化参考价值有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"水资源管理","遥感监测","土地利用","干旱监测","植被指数",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":74},[38,46,53,61,69],{"code":39,"score":19,"supported":40,"title":41,"abstract":43,"tags":45},"H7",true,[42],"drought",[44,42],"干旱",[29],{"code":47,"score":17,"supported":40,"title":48,"abstract":49,"tags":52},"I3",[],[50,51],"遥感","remote sensing",[30,27],{"code":54,"score":55,"supported":40,"title":56,"abstract":57,"tags":60},"H3",7,[],[58,59],"水资源","water resource",[26],{"code":62,"score":63,"supported":40,"title":64,"abstract":65,"tags":68},"H2",6,[],[66,67],"土壤","soil",[],{"code":70,"score":21,"supported":14,"title":71,"abstract":72,"tags":73},"H6",[],[],[28],"农业相关性需要复核",[76,77],"Pajeú River basin 干旱 遥感","SPI VHI NVSWI 植被","PajeúRiverbasin干旱遥感-3189",3,"10.29150\u002Fjhrs.v16i03.269030",{"doi":80,"openalex_id":82,"authors":83,"venue":10,"cited_by_count":101,"oa_url":102,"card":103,"direction":107,"ingested_from":109},"W7213792298",[84,87,90,93,96,98],{"name":85,"orcid":86},"Juliana Farias Santos de Moraes","https:\u002F\u002Forcid.org\u002F0000-0002-3241-844X",{"name":88,"orcid":89},"Estephania Silva Jovino","https:\u002F\u002Forcid.org\u002F0000-0002-6694-3533",{"name":91,"orcid":92},"Alex Vinícius de Melo Vieira","https:\u002F\u002Forcid.org\u002F0009-0002-5204-3734",{"name":94,"orcid":95},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":97,"orcid":9},"Sylvana Sylvana Melo dos Santos",{"name":99,"orcid":100},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",0,"https:\u002F\u002Fperiodicos.ufpe.br\u002Frevistas\u002Fjhrs\u002Farticle\u002Fdownload\u002F269030\u002F52814",{"tldr":104,"method":105,"finding":106,"direction":107,"opportunity":108},"利用遥感指数分析巴西Pajeú河流域2002-2022年干旱对植被与土地利用的影响。","采用SPI、VHI和NVSWI指数，结合遥感数据与土地利用分类。","2011-2016年干旱最严重，植被和水体响应直接，而牧场和裸地呈相反趋势。","农业遥感与作物表型","可融合多源遥感与机器学习，构建半干旱区干旱-植被-土地利用耦合预警模型。","openalex","2026-09-22T23:30:26.557816Z",{"total":63,"page":21,"page_size":63,"items":112},[113,176,245,320,391,435],{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":9,"source_name":119,"source_url":116,"published_at":120,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":122,"sources":127,"tags":129,"primary_topic":132,"secondary_topics":137,"classification":138,"search_phrases":155,"slug":158,"view_count":79,"doi":159,"paper":160,"created_at":175},3558,"Meteorolojik Kuraklığın Vejetasyon Sağlığı Üzerindeki Mekânsal ve Zamansal Etkilerinin SPI ve NDVI ile Analizi: Gediz Havzası Örneği","https:\u002F\u002Fdoi.org\u002F10.48123\u002Frsgis.1858019","Kuraklık, özellikle yarı kurak iklim koşullarına sahip havzalarda su kaynakları, tarımsal üretim ve ekosistem sürdürülebilirliği üzerinde belirleyici etkiler oluşturan başlıca doğal afetlerden biridir. Bu bağlamda, kuraklığın bitki örtüsü üzerindeki mekânsal ve zamansal etkilerinin bütüncül yaklaşımlarla değerlendirilmesi büyük önem taşımaktadır. Bu çalışmada, meteorolojik kuraklığın vejetasyon sağlığı üzerindeki mekânsal ve zamansal etkileri, Standartlaştırılmış Yağış İndeksi (SPI) ve Normalize Fark Bitki Örtüsü İndeksi (NDVI) kullanılarak Gediz Havzası örneğinde analiz edilmiştir. SPI analizleri 1970–2023 yılları arasındaki uzun dönem yağış verilerine dayanırken, NDVI verileri 2000–2023 dönemine ait MODIS uydu görüntülerinden elde edilmiştir. İki veri seti arasındaki ilişkiler, ortak dönem olan 2000–2023 yılları için incelenmiştir. Elde edilen bulgular, meteorolojik kuraklık ile vejetasyon sağlığı arasındaki ilişkinin mevsimsel olarak değişkenlik gösterdiğini ortaya koymaktadır. En güçlü ilişki yaz mevsiminde gözlenmiş (r ≈ 0.70) ve bu durum vejetasyonun yağış eksikliklerine en duyarlı olduğu dönemin yaz ayları olduğunu göstermiştir. Buna karşılık, kış (r ≈ 0.05) ve ilkbahar (r ≈ 0.02) mevsimlerinde ilişki oldukça zayıf bulunmuştur. Belirlenen kurak yıllarda (2004, 2008 ve 2022) NDVI değerlerinde belirgin düşüşler gözlenmiştir. Sonuç olarak, vejetasyonun kuraklığa verdiği tepkinin yıl boyunca homojen olmadığı, mevsimsel iklim koşulları ve diğer çevresel faktörlere bağlı olarak değiştiği belirlenmiştir.","干旱是主要自然灾害之一，尤其在具有半干旱气候条件的流域中，对水资源、农业生产和生态系统可持续性产生决定性影响。在此背景下，以整体性方法评估干旱对植被的时空影响具有重要意义。本研究以盖迪兹流域为例，利用标准化降水指数（SPI）和归一化植被指数（NDVI）分析了气象干旱对植被健康的时空影响。SPI分析基于1970—2023年的长期降水数据，而NDVI数据则来自2000—2023年期间的MODIS卫星影像。两个数据集之间的关系在共同时段2000—2023年进行了分析。研究结果表明，气象干旱与植被健康之间的关系呈现季节性变化。最显著的关系出现在夏季（r ≈ 0.70），这表明夏季是植被对降水亏缺最为敏感的时期。相比之下，冬季（r ≈ 0.05）和春季（r ≈ 0.02）的关系则非常微弱。在确定的干旱年份（2004年、2008年和2022年），NDVI值出现了明显下降。综上，植被对干旱的响应在全年并非均匀一致，而是随季节性气候条件及其他环境因素而变化。","Turkish Journal of Remote Sensing and GIS","2026-09-24T00:00:00Z",71,{"impact":20,"substance":123,"depth":124,"authority":125,"freshness":17,"relevant":21,"comment":126},21,17,13,"基于SPI与NDVI长时序数据揭示气象干旱对植被健康影响的季节性差异，方法规范、结论可靠，对农业干旱遥感监测有参考价值，但属区域案例研究，公共影响有限。",[128],{"name":119,"url":116},[130,27,29,30,131],"NDVI","SPI",{"code":39,"slug":133,"name":134,"path":135,"parent_code":136},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":139,"locked":14,"version":34,"method":35,"evidence":140},"classified",{"candidates":141,"agricultural_evidence":40},[142,147,151],{"code":39,"score":143,"supported":40,"title":144,"abstract":145,"tags":146},4,[],[44],[29],{"code":54,"score":79,"supported":40,"title":148,"abstract":149,"tags":150},[],[58],[],{"code":47,"score":79,"supported":14,"title":152,"abstract":153,"tags":154},[],[],[130,30,27],[156,157],"Gediz Havzası SPI NDVI","气象干旱 植被健康 遥感","GedizHavzasıSPINDVI-3558","10.48123\u002Frsgis.1858019",{"doi":159,"openalex_id":161,"authors":162,"venue":119,"cited_by_count":101,"oa_url":169,"card":170,"direction":107,"ingested_from":109},"W7214166880",[163,166],{"name":164,"orcid":165},"Kemal Yurddaş","https:\u002F\u002Forcid.org\u002F0000-0003-4691-4038",{"name":167,"orcid":168},"Murat Karabulut","https:\u002F\u002Forcid.org\u002F0000-0002-1456-6908","https:\u002F\u002Fdergipark.org.tr\u002Ftr\u002Fdownload\u002Farticle-file\u002F5578303",{"tldr":171,"method":172,"finding":173,"direction":107,"opportunity":174},"用SPI和NDVI分析土耳其盖迪兹流域气象干旱对植被健康的时空影响。","基于1970-2023年降水SPI与2000-2023年MODIS NDVI，做","干旱与植被关系随季节变化，夏季最强(r≈0.70)，冬春极弱；干旱年NDVI明显下降。","可引入滞后效应与多尺度SPI，结合土壤水分和灌溉数据，提升干旱对植被影响的预测能力。","2026-09-26T23:30:31.357310Z",{"id":177,"title":178,"url":179,"summary":180,"summary_zh":181,"content":9,"source_name":182,"source_url":179,"published_at":183,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":184,"sources":186,"tags":188,"primary_topic":191,"secondary_topics":192,"classification":198,"search_phrases":214,"slug":217,"view_count":218,"doi":219,"paper":220,"created_at":244},2674,"Bi-decadal drought assessment in Northwestern Algeria: Integrating meteorological and remote sensing indices","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00704-026-06526-y","Abstract Drought is an escalating hazard in arid and semi‑arid regions with significant implications for agriculture, ecosystems, and water resources. This study presents a 2003–2023 integrated assessment of meteorological and vegetation-based drought across northwest Algeria, using the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset and Moderate Resolution Imaging Spectroradiometer (MODIS) remotely‑sensed products. Meteorological drought was quantified with the Standardized Precipitation Index (SPI) approximated using standardized precipitation anomalies, at 3‑, 6‑, and 12‑month timescales, whereas vegetation and thermal stress were assessed with MODIS‑derived Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). Temporal trends were evaluated using the Mann–Kendall test and Sen’s slope estimator, and relationships between precipitation and vegetation were examined with Pearson correlation. We identified recurrent drought episodes in 2007–2009, 2011–2012, and a pronounced dry phase from 2020–2023. Mann–Kendall results indicated widespread drying across all SPI timescales, with 56% of the study area showing significant negative trends at SPI‑3 (mean Sen’s slope = − 0.10 yr⁻ 1 ). Vegetation indices mirrored these changes, with VHI showing substantially more degraded area than improvement (4.89% vs 0.51% of the domain), while VCI and TCI responses were spatially heterogeneous. The correlation analysis showed moderate but statistically significant relationships between SPI and VHI at the short-term scale (SPI-3 vs. VHI, r = 0.567, p = 0.007), followed by SPI-6 ( r = 0.509, p = 0.019), indicating that vegetation response is more strongly associated with short- to medium-term precipitation variability. These results demonstrate the value of combining meteorological and satellite vegetation indices for regional drought monitoring and early warning, and underscore an ongoing shift toward increased aridity with implications for water management and agricultural adaptation.","摘要 干旱是干旱和半干旱地区日益加剧的灾害，对农业、生态系统和水资源具有重大影响。本研究利用气候灾害组红外降水与站点数据（CHIRPS）数据集和中分辨率成像光谱仪（MODIS）遥感产品，对阿尔及利亚西北部2003—2023年气象干旱和基于植被的干旱进行了综合评估。气象干旱采用标准化降水指数（SPI）量化，该指数通过标准化降水距平在3个月、6个月和12个月时间尺度上近似计算；植被和热胁迫则通过MODIS衍生的植被状态指数（VCI）、温度状态指数（TCI）和植被健康指数（VHI）进行评估。采用Mann–Kendall检验和Sen斜率估计器评估时间趋势，并使用Pearson相关分析检验降水与植被之间的关系。我们识别出2007—2009年、2011—2012年的反复干旱事件，以及2020—2023年显著的干旱阶段。Mann–Kendall结果表明，所有SPI时间尺度上均呈现大范围干旱化趋势，研究区56%的面积在SPI-3上表现出显著负趋势（Sen斜率均值 = −0.10 yr⁻¹）。植被指数反映了这些变化，VHI显示退化面积远大于改善面积（分别占研究区的4.89%和0.51%），而VCI和TCI的响应在空间上具有异质性。相关分析表明，SPI与VHI在短期尺度上存在中等但统计显著的关系（SPI-3与VHI，r = 0.567，p = 0.007），其次为SPI-6（r = 0.509，p = 0.019），表明植被响应与短期至中期降水变率的关系更为密切。这些结果证明了结合气象指标和卫星植被指数进行区域干旱监测与预警的价值，并凸显了干旱化持续加剧的趋势及其对水资源管理和农业适应的影响。","Theoretical and Applied Climatology","2026-09-15T00:00:00Z",{"impact":20,"substance":123,"depth":124,"authority":125,"freshness":17,"relevant":21,"comment":185},"基于CHIRPS与MODIS的阿尔及利亚西北部20年干旱综合评估，方法整合气象与遥感指标，结论对区域干旱监测与农业适应有参考价值，但属境外区域研究，国内产业影响有限。",[187],{"name":182,"url":179},[189,26,27,30,190],"农业气象","干旱预警",{"code":39,"slug":133,"name":134,"path":135,"parent_code":136},[193],{"code":47,"slug":194,"name":195,"path":196,"parent_code":197},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":139,"locked":14,"version":34,"method":35,"evidence":199},{"candidates":200,"agricultural_evidence":40},[201,206,210],{"code":39,"score":202,"supported":40,"title":203,"abstract":204,"tags":205},16,[42],[44,42],[189,190],{"code":47,"score":125,"supported":40,"title":207,"abstract":208,"tags":209},[51],[50],[30,27],{"code":54,"score":143,"supported":40,"title":211,"abstract":212,"tags":213},[],[58],[26],[215,216],"水资源管理 农业气象 干旱预警 植被指数","水资源管理 农业气象","水资源管理农业气象干旱预警植被指数-2674",2,"10.1007\u002Fs00704-026-06526-y",{"doi":219,"openalex_id":221,"authors":222,"venue":182,"cited_by_count":101,"oa_url":179,"card":239,"direction":107,"ingested_from":109},"W7213346236",[223,226,229,232,234,237],{"name":224,"orcid":225},"Ramzi Benhizia","https:\u002F\u002Forcid.org\u002F0000-0002-8967-2051",{"name":227,"orcid":228},"Brahim Abdelkebir","https:\u002F\u002Forcid.org\u002F0000-0002-8761-8537",{"name":230,"orcid":231},"Behnam Ata","https:\u002F\u002Forcid.org\u002F0000-0002-9690-7269",{"name":233,"orcid":9},"Mukovhe Vele Singo",{"name":235,"orcid":236},"Kwanele Phinzi","https:\u002F\u002Forcid.org\u002F0000-0003-1865-7011",{"name":238,"orcid":9},"György Szabó",{"tldr":240,"method":241,"finding":242,"direction":107,"opportunity":243},"整合气象与遥感指数评估阿尔及利亚西北部2003–2023年干旱演变。","CHIRPS降水与MODIS植被指数，SPI、VCI、TCI、VHI，Mann-","全区普遍变干，SPI-3显著负趋势占56%，VHI退化面积远大于改善，短期降水与植被相关性最强。","可引入机器学习融合多源遥感与气象数据，构建区域干旱预警与作物适应性决策模型。","2026-09-16T23:30:30.795285Z",{"id":246,"title":247,"url":248,"summary":249,"summary_zh":250,"content":9,"source_name":10,"source_url":248,"published_at":251,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":252,"score_detail":253,"sources":257,"tags":259,"primary_topic":9,"secondary_topics":263,"classification":264,"search_phrases":294,"slug":297,"view_count":101,"doi":298,"paper":299,"created_at":319},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。","2026-10-20T00:00:00Z",78,{"impact":18,"substance":254,"depth":124,"authority":20,"freshness":255,"relevant":21,"comment":256},22,9,"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。",[258],{"name":10,"url":248},[27,28,260,261,262],"森林保护","亚马逊","CBERS-4",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":265},{"candidates":266,"agricultural_evidence":40,"reason":293},[267,273,277,283,287],{"code":70,"score":55,"supported":40,"title":268,"abstract":269,"tags":272},[],[270,271],"生物多样性","biodiversity",[28],{"code":47,"score":55,"supported":40,"title":274,"abstract":275,"tags":276},[],[50,51],[27],{"code":39,"score":63,"supported":40,"title":278,"abstract":279,"tags":282},[],[280,281],"气候变化","climate change",[],{"code":62,"score":79,"supported":40,"title":284,"abstract":285,"tags":286},[],[67],[],{"code":288,"score":79,"supported":40,"title":289,"abstract":290,"tags":292},"I7",[],[291],"研究方法",[],"多个产业证据相当，需要确认主归属",[295,296],"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":298,"openalex_id":300,"authors":301,"venue":10,"cited_by_count":101,"oa_url":248,"card":314,"direction":107,"ingested_from":109},"W7223141286",[302,305,307,310,311,312],{"name":303,"orcid":304},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":306,"orcid":9},"Gabriel Henrique Santos Silva",{"name":308,"orcid":309},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":94,"orcid":95},{"name":99,"orcid":100},{"name":313,"orcid":9},"Sylvana Melo dos Santos",{"tldr":315,"method":316,"finding":317,"direction":107,"opportunity":318},"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":321,"title":322,"url":323,"summary":324,"summary_zh":325,"content":9,"source_name":326,"source_url":323,"published_at":251,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":327,"sources":329,"tags":331,"primary_topic":334,"secondary_topics":339,"classification":345,"search_phrases":368,"slug":371,"view_count":101,"doi":372,"paper":373,"created_at":390},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE",{"impact":101,"substance":101,"depth":101,"authority":101,"freshness":101,"relevant":101,"comment":328},"研究聚焦埃塞俄比亚城市扩张与土地利用变化，属城市地理与遥感应用，与三农、农业信息化主题无直接关联，不建议进入每日精选。",[330],{"name":326,"url":323},[332,27,28,333],"机器学习","城市扩张",{"code":335,"slug":336,"name":337,"path":338,"parent_code":197},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[340],{"code":341,"slug":342,"name":343,"path":344,"parent_code":197},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":139,"locked":14,"version":34,"method":35,"evidence":346},{"candidates":347,"agricultural_evidence":40},[348,353,360,364],{"code":335,"score":19,"supported":40,"title":349,"abstract":351,"tags":352},[350],"machine learning",[332,350],[332],{"code":341,"score":255,"supported":40,"title":354,"abstract":355,"tags":359},[],[356,357,358],"空间分析","GIS","spatial analysis",[],{"code":47,"score":55,"supported":40,"title":361,"abstract":362,"tags":363},[],[50,51],[27],{"code":70,"score":21,"supported":14,"title":365,"abstract":366,"tags":367},[],[],[28],[369,370],"Gambella Town 城市扩张","Random Forest 土地利用分类","GambellaTown城市扩张-5485","10.1371\u002Fjournal.pone.0360419",{"doi":372,"openalex_id":374,"authors":375,"venue":326,"cited_by_count":101,"oa_url":323,"card":385,"direction":107,"ingested_from":109},"W7223022155",[376,379,381,383],{"name":377,"orcid":378},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":380,"orcid":9},"Goanar Jal Dup",{"name":382,"orcid":9},"Weyesa Merga Chemeda",{"name":384,"orcid":9},"Shankar Karuppannan",{"tldr":386,"method":387,"finding":388,"direction":107,"opportunity":389},"用机器学习与遥感分析埃塞俄比亚甘贝拉镇2005-2025年城市扩张与土地利用变化。","Landsat多时相影像、随机森林分类与GIS空间分析，结合277份问卷。","建成区从93公顷增至1956公顷，灌草地大幅转为建成区和农地。","可将随机森林时序分类与驱动力调查结合，扩展到更多非洲小城镇并耦合城市增长模拟。","2026-10-21T23:30:23.271293Z",{"id":392,"title":393,"url":394,"summary":395,"summary_zh":9,"content":9,"source_name":396,"source_url":394,"published_at":251,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":397,"sources":399,"tags":401,"primary_topic":9,"secondary_topics":403,"classification":404,"search_phrases":415,"slug":418,"view_count":101,"doi":419,"paper":420,"created_at":434},5484,"Spatiotemporal Dynamics and Assess of Ecological Quality in Sylhet Metropolitan Area amid Rapid Urbanization: A Remote Sensing Approach","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11422676\u002Fv1","Spatiotemporal Dynamics and Assess of Ecological Quality in Sylhet Metropolitan Area amid Rapid Urbanization: A Remote Sensing Approach。Research Square","Research Square",{"impact":101,"substance":101,"depth":101,"authority":101,"freshness":101,"relevant":101,"comment":398},"研究孟加拉国锡尔赫特市生态质量，与三农及农业信息化无关，不予精选。",[400],{"name":396,"url":394},[27,28,402],"城市生态",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":405},{"candidates":406,"agricultural_evidence":14,"reason":74},[407,411],{"code":47,"score":20,"supported":40,"title":408,"abstract":409,"tags":410},[51],[51],[27],{"code":70,"score":218,"supported":14,"title":412,"abstract":413,"tags":414},[],[],[28,402],[416,417],"Sylhet 城市扩张 遥感","土地利用 城市生态 遥感监测","Sylhet城市扩张遥感-5484","10.21203\u002Frs.3.rs-11422676\u002Fv1",{"doi":419,"openalex_id":421,"authors":422,"venue":396,"cited_by_count":101,"oa_url":433,"card":9,"direction":107,"ingested_from":109},"W7223013355",[423,425,427,430],{"name":424,"orcid":9},"Md Saikoth Jahan",{"name":426,"orcid":9},"Mst Arjumoni Anu",{"name":428,"orcid":429},"Md. Inzamul Haque","https:\u002F\u002Forcid.org\u002F0000-0003-1756-5561",{"name":431,"orcid":432},"Most. Atia Parvin","https:\u002F\u002Forcid.org\u002F0000-0002-2718-9259","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-11422676\u002Flatest.pdf","2026-10-21T23:30:23.223087Z",{"id":436,"title":437,"url":438,"summary":439,"summary_zh":440,"content":9,"source_name":441,"source_url":438,"published_at":442,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":443,"sources":445,"tags":447,"primary_topic":9,"secondary_topics":450,"classification":451,"search_phrases":470,"slug":473,"view_count":101,"doi":474,"paper":475,"created_at":491},5481,"Comparison of Satellite-Based Drought Monitoring Indicators (NDVI, NDMI, and LST): The Case of the Northern Aegean Drainage Basin","https:\u002F\u002Fdoi.org\u002F10.30910\u002Fturkjans.2010067","Thanks to advancements in technology, satellite remote sensing provides a valuable means of monitoring the spatial and temporal responses of vegetation, surface moisture conditions, and land surface temperature to drought. Its capacity to monitor large areas with high temporal continuity significantly eliminates the spatial limitations of ground observations. This allows for the simultaneous and comparable analysis of key variables such as vegetation dynamics, soil moisture, and land surface temperature. Indicators such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST) provide complementary information on vegetation condition, surface moisture, and thermal stress, thereby supporting drought monitoring. This integrated monitoring framework supports the sustainable management of water resources while strengthening the scientific basis of decision-making processes, significantly contributing to disaster risk reduction. This study comparatively evaluates NDVI, NDMI, and LST values derived from Landsat 8 data obtained in March, April, and May between 2019 and 2022, and examines the relationships between these variables across the Northern Aegean Drainage Basin (NADB). The results show increasingly strong negative relationships between LST and the spectral indices from March to May, with the strongest correlations observed in May for NDMI–LST (r = −0.78) and NDVI–LST (r = −0.74). The NDMI-LST relationship can be said to be slightly better than the NDVI-LST relationship. The correlation coefficient between NDMI and NDVI was calculated to be lowest in March at r=0.54 and highest in May at r=0.90. It was determined that areas with high LST values had lower NDVI and NDMI values. Drought monitoring using remote sensing methods is important in terms of time and accuracy, but it is not sufficient on its own. In addition to problems arising from satellite imagery, land surfaces are affected by short-term climatic factors. Therefore, the indices obtained from satellite images must be evaluated together with meteorological and terrestrial data. However, it is thought that satellite-based drought monitoring will produce more reliable results under current technological and methodological conditions.","得益于技术进步，卫星遥感为监测植被、地表水分状况和地表温度对干旱的时空响应提供了重要手段。其大范围、高时间连续性的监测能力显著消除了地面观测的空间局限性，使得植被动态、土壤水分和地表温度等关键变量能够被同步、可比地分析。归一化差异植被指数（NDVI）、归一化差异水分指数（NDMI）和地表温度（LST）等指标提供了关于植被状况、地表水分和热胁迫的互补信息，从而支持干旱监测。这一综合监测框架既支持水资源的可持续管理，又增强了决策过程的科学基础，对减少灾害风险具有重要贡献。本研究对2019年至2022年3月、4月和5月获取的Landsat 8数据所衍生的NDVI、NDMI和LST值进行了比较评估，并考察了北爱琴海流域（NADB）内这些变量之间的关系。结果表明，从3月到5月，LST与光谱指数之间的负相关关系日益增强，5月观测到最强相关性，NDMI–LST（r = −0.78）和NDVI–LST（r = −0.74）。可以认为NDMI-LST关系略优于NDVI-LST关系。NDMI与NDVI之间的相关系数在3月最低（r=0.54），5月最高（r=0.90）。研究确定，LST值较高的区域其NDVI和NDMI值较低。利用遥感方法进行干旱监测在时间和精度方面具有重要意义，但仅靠其自身并不充分。除卫星影像带来的问题外，地表还受到短期气候因素的影响。因此，从卫星影像获得的指数必须与气象和地面数据结合评估。然而，可以认为在当前技术和方法条件下，基于卫星的干旱监测将产生更可靠的结果。","Türk Tarım ve Doğa Bilimleri Dergisi","2026-10-19T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":444},"方法对比扎实、结论明确，但属区域案例研究，公共影响有限，适合作为遥感干旱监测主题的补充素材而非头条精选。",[446],{"name":441,"url":438},[130,27,29,448,449],"NDMI","LST",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":452},{"candidates":453,"agricultural_evidence":14,"reason":74},[454,458,462,466],{"code":39,"score":19,"supported":40,"title":455,"abstract":456,"tags":457},[42],[44,42],[29],{"code":47,"score":17,"supported":40,"title":459,"abstract":460,"tags":461},[],[50,51],[130,27],{"code":62,"score":63,"supported":40,"title":463,"abstract":464,"tags":465},[],[66,67],[],{"code":54,"score":79,"supported":40,"title":467,"abstract":468,"tags":469},[],[58],[],[471,472],"北爱琴海流域 干旱监测","干旱监测 遥感监测 NDMI NDVI","北爱琴海流域干旱监测-5481","10.30910\u002Fturkjans.2010067",{"doi":474,"openalex_id":476,"authors":477,"venue":441,"cited_by_count":101,"oa_url":484,"card":485,"direction":490,"ingested_from":109},"W7222954725",[478,481],{"name":479,"orcid":480},"Arman Monshizadeh Karimi","https:\u002F\u002Forcid.org\u002F0000-0002-2945-5346",{"name":482,"orcid":483},"İ̇smail Taş","https:\u002F\u002Forcid.org\u002F0000-0003-0872-2529","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F6296754",{"tldr":486,"method":487,"finding":488,"direction":107,"opportunity":489},"比较Landsat 8的NDVI、NDMI与LST三种遥感干旱指标在北爱琴海流域的相关性。","用2019-2022年3-5月Landsat 8数据计算NDVI、NDMI、LS","LST与NDMI、NDVI负相关随春季增强，5月最强（r=-0.78、-0.74），NDMI略优。","可融合气象与地面数据构建多源干旱指数，并验证NDMI-LST关系在其他气候区的普适性。","智慧农业 \u002F 农业物联网","2026-10-21T23:30:10.758669Z"]