[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3904":3,"related-3904":63},{"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":21,"classification":22,"search_phrases":38,"slug":39,"view_count":40,"doi":41,"paper":42,"created_at":62},3904,"Unraveling spatio-temporal dynamics of eco-environmental quality in Brahmani-Dwarka interfluve using an integrated remote sensing ecological index and optimal parameter-based GeoDetector approach","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01056-w","Stone crushing and mining activities in the Brahmani-Dwarka interfluve have significantly contributed to environmental degradation, emphasising the need to assess their impact on environmental quality. This study aims to fill this research gap by evaluating environmental quality using the Eco-Environmental Quality index (EEQ) and employing a remote sensing-based technique. The study further employed spatial autocorrelation analysis to identify evolving hotspots of EEQ and utilised optimal parameter-based Geodetector (OPGD) methods to assess its spatial heterogeneity. This research is significant for its distinctive approach to evaluating EEQ in a stone-crushing and mining context, and for applying OPGD, a method rarely used to assess EEQ heterogeneity. The findings revealed that poor and very poor ecological zones expanded from 19.17 km 2 in 1993 to 57.81 km 2 in 2023, mainly along industrial clusters. Between 1993 and 2023, 11.41 km 2 of the region shifted into highly degraded classes and nearly 80 km 2 into degraded categories. Spatial autocorrelation remained strong across all years (Moran’s I = 0.728–0.850), and LISA maps consistently identified stable high–high hotspots around major mining and stone-crushing clusters. Geodetector results indicated that elevation, proximity to stone-crushing sites, proximity to mining sites, and their interactions were the key factors linked to EEQ heterogeneity. Validation of the EEQ maps demonstrated good predictive performance and robustness, as evidenced by consistently high AUC values of 0.865, 0.831, 0.731, and 0.695 for the years 2023, 2013, 2003, and 1993, respectively. The findings will provide policymakers with valuable insights for developing strategies to promote environmental resilience in the region.","布拉赫马尼-德瓦尔卡河间地区的碎石和采矿活动显著加剧了环境退化，因此有必要评估其对环境质量的影响。本研究旨在填补这一研究空白，采用生态环境质量指数（EEQ）并运用基于遥感的技术来评估环境质量。研究进一步采用空间自相关分析来识别EEQ的动态热点区域，并利用基于最优参数的地理探测器（OPGD）方法评估其空间异质性。本研究的意义在于其独特的碎石和采矿背景下EEQ评估方法，以及应用了OPGD这一鲜少用于评估EEQ异质性的方法。研究结果表明，生态较差和极差区域从1993年的19.17 km²扩展至2023年的57.81 km²，主要集中在工业集群沿线。1993年至2023年间，该区域有11.41 km²转变为高度退化类别，近80 km²转变为退化类别。各年份空间自相关均保持较强水平（Moran's I = 0.728–0.850），LISA图始终识别出主要采矿和碎石集群周围稳定的高-高热点区。地理探测器结果表明，海拔、距碎石场距离、距采矿场距离及其交互作用是影响EEQ空间异质性的关键因素。EEQ图的验证表现出良好的预测性能和稳健性，2023年、2013年、2003年和1993年的AUC值分别 consistently 高达0.865、0.831、0.731和0.695。研究结果将为政策制定者提供有价值的参考，以制定促进该地区环境韧性的策略。",null,"Discover Environment","2026-09-29T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦印度矿区生态环境质量遥感评估，与三农、农业信息化、智慧农业等主题无关，不建议进入每日精选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":37},[28],{"code":29,"score":30,"supported":31,"title":32,"abstract":34,"tags":36},"I3",14,true,[33],"remote sensing",[35,33],"遥感",[],"农业相关性需要复核",[],"Unravelingspatio-temporaldynamicsofeco-e-3904",2,"10.1007\u002Fs44274-026-01056-w",{"doi":41,"openalex_id":43,"authors":44,"venue":10,"cited_by_count":15,"oa_url":54,"card":55,"direction":59,"ingested_from":61},"W7214756608",[45,48,51],{"name":46,"orcid":47},"Bhaskar Mandal","https:\u002F\u002Forcid.org\u002F0009-0003-2151-5775",{"name":49,"orcid":50},"Kaushalendra Prakash Goswami","https:\u002F\u002Forcid.org\u002F0000-0002-5105-8152",{"name":52,"orcid":53},"Sharmistha Mondal","https:\u002F\u002Forcid.org\u002F0009-0001-5333-515X","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01056-w.pdf",{"tldr":56,"method":57,"finding":58,"direction":59,"opportunity":60},"用遥感生态指数与OPGD评估采石采矿区生态质量时空演变。","遥感生态指数EEQ、空间自相关、最优参数地理探测器OPGD。","劣质生态区从19.17扩至57.81平方公里，高程与采矿采石距离为主控因子。","农业遥感与作物表型","可将EEQ与OPGD框架迁移至农业区，量化采矿扰动对耕地生态与作物生产的影响。","openalex","2026-10-01T23:30:30.440501Z",{"total":15,"page":64,"page_size":64,"items":65},1,[]]