[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5010":3,"related-5010":91},{"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":63,"slug":66,"view_count":67,"doi":68,"paper":69,"created_at":90},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。",null,"Journal of Information Technology Software Engineering and Computer Science (ITSECS)","2026-10-13T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},12,20,16,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"遥感监测","矿区生态","土地覆盖变化","支持向量机","镍矿开采",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":62},[38,48,56],{"code":39,"score":40,"supported":41,"title":42,"abstract":43,"tags":47},"I4",9,true,[],[44,45,46],"空间分析","GIS","spatial analysis",[],{"code":49,"score":50,"supported":41,"title":51,"abstract":52,"tags":55},"I3",7,[],[53,54],"遥感","remote sensing",[26],{"code":57,"score":58,"supported":14,"title":59,"abstract":60,"tags":61},"H6",2,[],[],[28,27],"农业相关性需要复核",[64,65],"IWIP 矿区 土地覆盖","Sentinel-2 镍矿 遥感","IWIP矿区土地覆盖-5010",0,"10.58602\u002Fitsecs.v4i4.388",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":67,"oa_url":82,"card":83,"direction":87,"ingested_from":89},"W7221684953",[72,74,76,79],{"name":73,"orcid":9},"Efraim Julian Soma",{"name":75,"orcid":9},"Winsy Christo Deilan Weki",{"name":77,"orcid":78},"Rillya Arundaa","https:\u002F\u002Forcid.org\u002F0009-0008-4979-5525",{"name":80,"orcid":81},"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":84,"method":85,"finding":86,"direction":87,"opportunity":88},"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","农业遥感与作物表型","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。","openalex","2026-10-15T23:30:32.156647Z",{"total":92,"page":21,"page_size":92,"items":93},6,[94,151,239,312,385,456],{"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":67,"score_detail":102,"sources":104,"tags":106,"primary_topic":108,"secondary_topics":113,"classification":114,"search_phrases":126,"slug":129,"view_count":21,"doi":130,"paper":131,"created_at":150},4513,"Spatio-temporal assessment of mining-induced environmental changes using remote sensing: a case study of the Talcher Coalfield, Angul District, India","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74815-7","This study evaluates the spatio-temporal impacts of coal mining on environmental conditions in the Talcher coalfield region, India, using multi-temporal Landsat data (2000, 2014, and 2023) at 30 m resolution. Land use land cover (LULC) was derived using supervised classification (Maximum Likelihood Algorithm), while vegetation dynamics and thermal characteristics were assessed using the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). In addition, atmospheric column densities were incorporated to analyze key atmospheric pollutants SO₂, NO₂, and CO and to assess air quality variations associated with mining activities. The integrated assessment highlights substantial environmental changes associated with mining and land-use transformation. The LULC analysis (2000–2023) reveals a decline in forest cover (41.33% to 37.37%) and agricultural land (45.62% to 45.04%), accompanied by an increase in built-up areas (6.84% to 8.54%) and mining areas (2.12% to 4.94%), indicating intensified anthropogenic pressure. LST and NDVI analyses (2014–2023) show changes in surface thermal conditions and vegetation characteristics. LST results indicate higher maximum and mean temperatures in mining and built-up areas, with changes between 2014 and 2023 varying among land-use classes, with mining areas showing the highest thermal values, where mean summer LST increased from 42.97 °C (2014) to 46.28 °C (2023), and mean winter LST increased from 38.67 °C to 41.81 °C. NDVI analysis indicates a decline in maximum NDVI values over the study period. The results show a spatial association between LULC changes, NDVI, and LST, with areas of mining and built-up expansion generally corresponding to lower NDVI and higher surface temperatures. Analysis of satellite-derived atmospheric column densities indicates higher SO₂, NO₂, and CO values in mining and industrial regions, with higher values observed during winter.","本研究利用30 m分辨率的多时相Landsat数据（2000年、2014年和2023年），评估了印度塔尔切尔煤田地区煤矿开采对环境条件的时空影响。土地利用\u002F土地覆盖（LULC）通过监督分类（最大似然算法）获得，植被动态和热特征则分别采用归一化差异植被指数（NDVI）和地表温度（LST）进行评估。此外，研究还引入大气柱密度以分析主要大气污染物SO₂、NO₂和CO，并评估与采矿活动相关的空气质量变化。综合评估揭示了与采矿和土地利用转型相关的显著环境变化。LULC分析（2000—2023年）显示，森林覆盖面积（从41.33%降至37.37%）和农业用地（从45.62%降至45.04%）减少，而建成区面积（从6.84%增至8.54%）和采矿区域面积（从2.12%增至4.94%）增加，表明人为压力加剧。LST和NDVI分析（2014—2023年）显示地表热条件和植被特征发生变化。LST结果表明，采矿区和建成区的最高温度和平均温度较高，2014年至2023年的变化因土地利用类型而异，其中采矿区的热值最高，夏季平均LST从42.97 °C（2014年）升至46.28 °C（2023年），冬季平均LST从38.67 °C升至41.81 °C。NDVI分析表明，研究期内最大NDVI值下降。结果表明，LULC变化、NDVI和LST之间存在空间关联，采矿和建成区扩张区域通常对应较低的NDVI和较高的地表温度。卫星反演的大气柱密度分析表明，采矿和工业区域的SO₂、NO₂和CO值较高，且冬季观测值更高。","Scientific Reports","2026-10-07T00:00:00Z",{"impact":67,"substance":67,"depth":67,"authority":67,"freshness":67,"relevant":67,"comment":103},"该研究聚焦印度煤矿区环境遥感监测，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},[26,107,27,28],"地表温度",{"code":49,"slug":109,"name":110,"path":111,"parent_code":112},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":115,"locked":14,"version":34,"method":35,"evidence":116},"classified",{"candidates":117,"agricultural_evidence":41},[118,122],{"code":49,"score":13,"supported":41,"title":119,"abstract":120,"tags":121},[54],[],[107,26],{"code":57,"score":58,"supported":14,"title":123,"abstract":124,"tags":125},[],[],[28,27],[127,128],"Talcher 煤田 遥感","Landsat NDVI LST 矿区","Talcher煤田遥感-4513","10.1038\u002Fs41598-026-74815-7",{"doi":130,"openalex_id":132,"authors":133,"venue":100,"cited_by_count":67,"oa_url":144,"card":145,"direction":87,"ingested_from":89},"W7220855962",[134,136,138,141],{"name":135,"orcid":9},"Sk Riyajul Islam",{"name":137,"orcid":9},"Shilpa Pakhira",{"name":139,"orcid":140},"Abhilash Gogineni","https:\u002F\u002Forcid.org\u002F0000-0002-8350-468X",{"name":142,"orcid":143},"Regasa Yadeta Sembeta","https:\u002F\u002Forcid.org\u002F0000-0002-9686-3155","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-74815-7_reference.pdf",{"tldr":146,"method":147,"finding":148,"direction":87,"opportunity":149},"基于多时相Landsat遥感评估印度煤矿区2000-2023年环境变化。","Landsat多时相数据、最大似然监督分类、NDVI、LST及SO₂\u002FNO₂\u002FC","采矿与建设用地扩张导致林地耕地减少，矿区夏季均温升至46.28°C，NDVI下降，污染物冬季偏高。","可借鉴其多指标遥感框架，研究矿区周边农田退化与作物表型响应，构建农业环境风险监测模型。","2026-10-09T23:30:27.643699Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":9,"source_name":157,"source_url":154,"published_at":158,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":159,"score_detail":160,"sources":165,"tags":167,"primary_topic":171,"secondary_topics":177,"classification":178,"search_phrases":201,"slug":204,"view_count":67,"doi":205,"paper":206,"created_at":238},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","2026-10-20T00:00:00Z",79,{"impact":19,"substance":161,"depth":162,"authority":163,"freshness":40,"relevant":21,"comment":164},22,18,14,"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。",[166],{"name":157,"url":154},[26,168,169,28,170],"莫桑比克","土壤盐渍化","盐分反演",{"code":172,"slug":173,"name":174,"path":175,"parent_code":176},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":115,"locked":14,"version":34,"method":35,"evidence":179},{"candidates":180,"agricultural_evidence":41},[181,186,190,197],{"code":172,"score":163,"supported":41,"title":182,"abstract":183,"tags":185},[173],[184,173],"土壤",[],{"code":49,"score":50,"supported":41,"title":187,"abstract":188,"tags":189},[],[53,54],[26],{"code":191,"score":92,"supported":41,"title":192,"abstract":193,"tags":196},"H7",[],[194,195],"气候适应","干旱",[],{"code":57,"score":21,"supported":14,"title":198,"abstract":199,"tags":200},[],[],[28],[202,203],"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":205,"openalex_id":207,"authors":208,"venue":157,"cited_by_count":67,"oa_url":154,"card":233,"direction":87,"ingested_from":89},"W7223069171",[209,211,213,215,218,220,223,226,228,230],{"name":210,"orcid":9},"Yue Li",{"name":212,"orcid":9},"Mengfei Shi",{"name":214,"orcid":9},"Fei Zhang",{"name":216,"orcid":217},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":219,"orcid":9},"Gowhar Meraj",{"name":221,"orcid":222},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":224,"orcid":225},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":227,"orcid":9},"Yuanfang Chai",{"name":229,"orcid":9},"Hongtao Xu",{"name":231,"orcid":232},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":234,"method":235,"finding":236,"direction":87,"opportunity":237},"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":240,"title":241,"url":242,"summary":243,"summary_zh":244,"content":9,"source_name":245,"source_url":242,"published_at":246,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":247,"score_detail":248,"sources":250,"tags":252,"primary_topic":9,"secondary_topics":256,"classification":257,"search_phrases":273,"slug":276,"view_count":67,"doi":277,"paper":278,"created_at":311},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":20,"substance":162,"depth":163,"authority":17,"freshness":20,"relevant":21,"comment":249},"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。",[251],{"name":245,"url":242},[253,26,27,254,255],"生态修复","土壤养分","空间变异",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":258},{"candidates":259,"agricultural_evidence":14,"reason":62},[260,265,269],{"code":172,"score":261,"supported":41,"title":262,"abstract":263,"tags":264},15,[173],[184,173],[254],{"code":57,"score":58,"supported":14,"title":266,"abstract":267,"tags":268},[],[],[253,27],{"code":49,"score":21,"supported":14,"title":270,"abstract":271,"tags":272},[],[],[26],[274,275],"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":277,"openalex_id":279,"authors":280,"venue":245,"cited_by_count":67,"oa_url":305,"card":306,"direction":87,"ingested_from":89},"W7222017864",[281,284,287,290,293,296,299,302],{"name":282,"orcid":283},"Xiyao Feng","https:\u002F\u002Forcid.org\u002F0009-0009-1482-4784",{"name":285,"orcid":286},"Yu Chen","https:\u002F\u002Forcid.org\u002F0009-0007-6975-327X",{"name":288,"orcid":289},"Zhongyou Song","https:\u002F\u002Forcid.org\u002F0009-0001-0791-3457",{"name":291,"orcid":292},"Pan Gao","https:\u002F\u002Forcid.org\u002F0009-0000-0039-4562",{"name":294,"orcid":295},"Pengfei Chen","https:\u002F\u002Forcid.org\u002F0009-0000-8394-5672",{"name":297,"orcid":298},"Fabin Yan","https:\u002F\u002Forcid.org\u002F0009-0000-0496-7532",{"name":300,"orcid":301},"Pei Wang","https:\u002F\u002Forcid.org\u002F0009-0001-8997-421X",{"name":303,"orcid":304},"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":307,"method":308,"finding":309,"direction":87,"opportunity":310},"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":313,"title":314,"url":315,"summary":316,"summary_zh":317,"content":9,"source_name":318,"source_url":315,"published_at":101,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":319,"score_detail":320,"sources":325,"tags":327,"primary_topic":9,"secondary_topics":331,"classification":332,"search_phrases":352,"slug":355,"view_count":92,"doi":356,"paper":357,"created_at":384},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。","Applied Geomatics",68,{"impact":20,"substance":321,"depth":322,"authority":323,"freshness":40,"relevant":21,"comment":324},21,17,13,"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。",[326],{"name":318,"url":315},[26,27,328,329,330],"植被指数","土壤修复","重金属污染",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":333},{"candidates":334,"agricultural_evidence":14,"reason":62},[335,339,343,348],{"code":49,"score":19,"supported":41,"title":336,"abstract":337,"tags":338},[54],[53,54],[328,26],{"code":172,"score":20,"supported":41,"title":340,"abstract":341,"tags":342},[],[184,173],[329,330],{"code":191,"score":344,"supported":41,"title":345,"abstract":346,"tags":347},3,[],[195],[],{"code":57,"score":21,"supported":14,"title":349,"abstract":350,"tags":351},[],[],[27],[353,354],"伊朗 Sangan 矿区 重金属","Landsat 8 OLI 植被指数","伊朗Sangan矿区重金属-4436","10.1007\u002Fs12518-026-00809-9",{"doi":356,"openalex_id":358,"authors":359,"venue":318,"cited_by_count":67,"oa_url":378,"card":379,"direction":87,"ingested_from":89},"W7220854014",[360,363,366,369,372,375],{"name":361,"orcid":362},"Javad Momeni Damaneh","https:\u002F\u002Forcid.org\u002F0009-0006-9131-6167",{"name":364,"orcid":365},"E Tamassoki","https:\u002F\u002Forcid.org\u002F0000-0002-9873-1957",{"name":367,"orcid":368},"Mohammed Achite","https:\u002F\u002Forcid.org\u002F0000-0001-6084-5759",{"name":370,"orcid":371},"Seyed Mohammad Tajbakhsh","https:\u002F\u002Forcid.org\u002F0000-0002-2097-9944",{"name":373,"orcid":374},"Kusum Pandey","https:\u002F\u002Forcid.org\u002F0009-0005-4888-6650",{"name":376,"orcid":377},"Celso Augusto Guimarães Santos","https:\u002F\u002Forcid.org\u002F0000-0001-7927-9718","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs12518-026-00809-9.pdf",{"tldr":380,"method":381,"finding":382,"direction":87,"opportunity":383},"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。","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。","2026-10-08T23:30:29.578924Z",{"id":386,"title":387,"url":388,"summary":389,"summary_zh":390,"content":9,"source_name":391,"source_url":388,"published_at":392,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":247,"score_detail":393,"sources":395,"tags":397,"primary_topic":400,"secondary_topics":401,"classification":402,"search_phrases":438,"slug":441,"view_count":344,"doi":442,"paper":443,"created_at":455},3846,"Integrating Earth Observation Data for Assessing the Impact of Mining Ponds on Land Use and Land Cover Change","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijgem.vol.11.no10.2025.pg136.153","This study examines the relationship between mining ponds and land use and land cover (LULC) dynamics, emphasizing both the environmental challenges and the socio-economic opportunities they present. Earth observation data from Landsat and questionnaire administration were used to evaluate the spatial and socio-environmental impacts of mining activities in Jos South Local Government Area (LGA). Supervised Maximum Likelihood Classification was applied to multitemporal satellite imagery to determine LULC changes, while nearest neighborhood analysis assessed the spatial distribution of mining ponds. A stratified random sampling method was used to collect responses from five districts Du, Zawan, Kuru, Vwang, and Gyel. Results revealed that mining ponds accounted for approximately 2.03% (10.20 km²) of the total land area in 1990, showing a slight decline to 2.00% (10.04 km²) by 2021. Despite their negative impacts, such as land degradation, erosion, and pollution, mining ponds also contribute positively to local livelihoods. An all-year-round supply of water by these ponds makes activities such as irrigation farming, fish farming, block making, recreation and livestock husbandry possible at all times. These benefits generate a wide range of opportunities, which attract investments from private companies, all channeled towards the socio-economic development of Jos-south. We recommended prioritizing the adoption of modern technologies such as GIS and remote sensing by the government for consistent supervision of the expansion of these mining ponds, towards sustainable environmental development of the Jos-South local government area.","本研究考察了矿坑与土地利用和土地覆盖（LULC）动态之间的关系，重点关注其带来的环境挑战与社会经济机遇。研究利用Landsat地球观测数据和问卷调查，评估了乔斯南部地方政府辖区（LGA）采矿活动的空间及社会环境的影响。研究对多时相卫星影像采用监督最大似然分类法以确定土地利用和土地覆盖变化，同时运用最近邻分析评估矿坑的空间分布。采用分层随机抽样方法，从Du、Zawan、Kuru、Vwang和Gyel五个区收集了调查问卷。结果显示，1990年矿坑约占土地总面积的2.03%（10.20 km²），到2021年略微下降至2.00%（10.04 km²）。尽管矿坑带来了土地退化、侵蚀和污染等负面影响，但也对当地生计作出了积极贡献。这些矿坑全年供水，使得灌溉农业、养鱼、制砖、休闲娱乐和畜牧等活动得以常年开展。这些效益产生了广泛的机遇，吸引了私营企业的投资，全部用于乔斯南部的社会经济发展。我们建议政府优先采用地理信息系统（GIS）和遥感等现代技术，对这些矿坑的扩张进行持续监管，以促进乔斯南部地方政府辖区的环境可持续发展。","IIARD INTERNATIONAL JOURNAL OF GEOGRAPHY AND ENVIRONMENTAL MANAGEMENT","2026-09-28T00:00:00Z",{"impact":20,"substance":162,"depth":261,"authority":13,"freshness":40,"relevant":21,"comment":394},"基于Landsat遥感与问卷评估尼日利亚矿区池塘对土地利用变化的影响，方法规范但属区域案例，对国内三农信息化参考价值有限。",[396],{"name":391,"url":388},[398,26,399,27],"农业遥感","土地利用",{"code":49,"slug":109,"name":110,"path":111,"parent_code":112},[],{"status":115,"locked":14,"version":34,"method":35,"evidence":403},{"candidates":404,"agricultural_evidence":41},[405,409,416,423,428,434],{"code":49,"score":20,"supported":41,"title":406,"abstract":407,"tags":408},[],[53,54],[398,26],{"code":410,"score":92,"supported":41,"title":411,"abstract":412,"tags":415},"C",[],[413,414],"畜牧","livestock",[],{"code":417,"score":92,"supported":41,"title":418,"abstract":419,"tags":422},"H3",[],[420,421],"灌溉","irrigation",[],{"code":39,"score":92,"supported":41,"title":424,"abstract":425,"tags":427},[],[426,45],"地理信息",[],{"code":429,"score":344,"supported":41,"title":430,"abstract":431,"tags":433},"D",[],[432],"fish farming",[],{"code":57,"score":58,"supported":14,"title":435,"abstract":436,"tags":437},[],[],[399,27],[439,440],"Jos South 矿区 土地利用","Landsat 遥感 土地覆盖","JosSouth矿区土地利用-3846","10.56201\u002Fijgem.vol.11.no10.2025.pg136.153",{"doi":442,"openalex_id":444,"authors":445,"venue":391,"cited_by_count":67,"oa_url":449,"card":450,"direction":87,"ingested_from":89},"W7214571622",[446],{"name":447,"orcid":448},"Momoh Khalifa Adeiza","https:\u002F\u002Forcid.org\u002F0009-0000-8594-7372","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJGEM\u002FVOL. 11 NO. 10 2025\u002FIntegrating Earth Observation 136-153.pdf",{"tldr":451,"method":452,"finding":453,"direction":87,"opportunity":454},"用Landsat影像与问卷评估矿区池塘对土地利用变化及社会经济的影响。","Landsat多时相影像、最大似然分类、最近邻分析与分层随机抽样问卷。","矿塘面积占比由1990年2.03%微降至2021年2.00%，但带来退化与生计双重效应。","可结合时序遥感与农户调查，量化矿塘对灌溉农业与生计的长期耦合影响。","2026-09-30T23:30:29.520262Z",{"id":457,"title":458,"url":459,"summary":460,"summary_zh":461,"content":9,"source_name":462,"source_url":459,"published_at":463,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":67,"score_detail":464,"sources":466,"tags":468,"primary_topic":9,"secondary_topics":472,"classification":473,"search_phrases":493,"slug":496,"view_count":58,"doi":497,"paper":498,"created_at":512},3671,"CLOUD-BASED MULTI-TEMPORAL SAR-OPTICAL FUSION USING RANDOM FOREST FOR GEOSPATIAL INTELLIGENCE MONITORING OF KIPP IKN NUSANTARA","https:\u002F\u002Fdoi.org\u002F10.21163\u002Fgt_2027.221.08","The development of Indonesia's new capital (IKN) Nusantara drives dramatic land-cover transformation across a strategic 17,599-hectare area under the persistent cloud cover of Kalimantan, where optical remote sensing alone is insufficient for continuous monitoring.This study proposes a cloud-based multi-temporal SAR-optical fusion approach using Random Forest classification for geospatial-intelligence (GEOINT) monitoring of the Core Government Area (KIPP) of IKN.Using Google Earth Engine, we processed 70 Sentinel-1 GRD and 52 Sentinel-2 images for 2022 and 61 and 81 images for 2024, building a 12-band fusion stack (SAR backscatter VV, VH, VV\u002FVH; optical B2, B3, B4, B8, B11, B12; and NDVI, NDBI, NDWI).Six experiments compared SAR-only, optical-only, and fusion configurations.The Random Forest fusion achieved 88.89% overall accuracy and 0.8609 kappa for 2022 and 85.12% and 0.8127 for 2024 (assessed against ESA WorldCover 2021 class definitions; see Section 2.7), outperforming SAR-only (63.20%) and optical-only (86.44%).Change detection revealed 899.64 ha of direct vegetation-to-built-up conversion, a 60% expansion of built-up area driven by IKN construction.This is the first cloud-based multi-temporal fusion framework applied to KIPP IKN monitoring, with implications for spatial planning, security surveillance, and environmental compliance.","印度尼西亚新首都（IKN）努山塔拉的开发，在加里曼丹持续云覆盖下推动了一个战略性的17,599公顷区域发生剧烈的土地覆盖转变，仅依靠光学遥感不足以实现持续监测。本研究提出了一种基于云的多时相SAR-光学融合方法，采用随机森林分类对IKN核心政府区（KIPP）进行地理空间情报（GEOINT）监测。利用Google Earth Engine，我们处理了2022年的70景Sentinel-1 GRD和52景Sentinel-2影像，以及2024年的61景和81景影像，构建了一个12波段融合数据集（SAR后向散射VV、VH、VV\u002FVH；光学B2、B3、B4、B8、B11、B12；以及NDVI、NDBI、NDWI）。六组实验比较了仅SAR、仅光学和融合配置。随机森林融合在2022年达到88.89%的总体精度和0.8609的kappa系数，2024年为85.12%和0.8127（依据ESA WorldCover 2021类别定义进行评估；见第2.7节），优于仅SAR（63.20%）和仅光学（86.44%）。变化检测揭示了899.64公顷的植被直接转为建成区，IKN建设推动建成区面积扩张60%。这是首个应用于KIPP IKN监测的基于云的多时相融合框架，对空间规划、安全监控和环境合规具有重要意义。","Geographia Technica","2026-09-26T00:00:00Z",{"impact":67,"substance":67,"depth":67,"authority":67,"freshness":67,"relevant":67,"comment":465},"该研究聚焦印尼新首都建设的遥感监测，属地理空间情报领域，与三农、农业信息化、智慧农业等主题无直接关联，故不进入每日精选。",[467],{"name":462,"url":459},[469,26,28,470,471],"随机森林","SAR光学融合","空间规划",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":474},{"candidates":475,"agricultural_evidence":14,"reason":62},[476,480,484,489],{"code":49,"score":50,"supported":41,"title":477,"abstract":478,"tags":479},[],[53,54],[26],{"code":57,"score":21,"supported":14,"title":481,"abstract":482,"tags":483},[],[],[28],{"code":485,"score":21,"supported":14,"title":486,"abstract":487,"tags":488},"I2",[],[],[469],{"code":39,"score":21,"supported":14,"title":490,"abstract":491,"tags":492},[],[],[471],[494,495],"Sentinel-1 Sentinel-2 融合","土地覆盖变化 SAR光学融合 空间规划 遥感监测","Sentinel-1Sentinel-2融合-3671","10.21163\u002Fgt_2027.221.08",{"doi":497,"openalex_id":499,"authors":500,"venue":462,"cited_by_count":67,"oa_url":506,"card":507,"direction":87,"ingested_from":89},"W7214434955",[501,503],{"name":502,"orcid":9},"Fajar Sidik Suganda",{"name":504,"orcid":505},"Asep Adang Supriyadi","https:\u002F\u002Forcid.org\u002F0000-0003-1103-6669","https:\u002F\u002Ftechnicalgeography.org\u002Fpdf\u002F1_2027\u002F08_suganda.pdf",{"tldr":508,"method":509,"finding":510,"direction":87,"opportunity":511},"提出云端多时相SAR-光学融合与随机森林方法，监测印尼新首都核心区土地覆盖变化。","Google Earth Engine处理Sentinel-1\u002F2影像，构建12","融合分类精度达88.89%，检测到899.64公顷植被转建设用地，建成区扩张60%。","可迁移至多云农业区作物监测，探索融合特征优化与跨区域泛化能力。","2026-09-28T23:30:27.951741Z"]