[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2871":3,"related-2871":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":24,"tags":26,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":62},2871,"Spatial and Temporal characteristics and driving force analysis of vegetation cover change in Shanxi Province, China based on kNDVI and XGBoost-SHAP model","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1948848","Introduction Systematically clarifying the spatiotemporal evolution of vegetation coverage and elucidating its response mechanisms to climatic fluctuations and human activities carries substantial practical implications for advancing the Dual Carbon Strategy and optimizing the pattern of territorial spatial development and conservation across Shanxi Province, China. Methods Based on kernel Normalized Difference Vegetation Index (kNDVI) datasets, multi-source meteorological, topographic and socioeconomic datasets, this study integrated Theil-Sen slope estimation, Mann-Kendall significance test, Hurst exponent, standard deviational ellipse and gravity center migration model to systematically characterize the spatiotemporal patterns of vegetation coverage for the period 2000 to 2024 and predict the potential persistence of its future evolution. Meanwhile, an interpretable XGBoost-SHAP machine learning framework was constructed to quantify the independent contributions, nonlinear marginal responses, and temporal evolutionary characteristics of seven driving factors: elevation, slope, aspect, annual mean temperature, annual mean precipitation, population density and nighttime light intensity. Results (1) Temporally, the multi-year average kNDVI of the whole province reached approximately 0.1699, presenting an extremely significant fluctuating upward trend with an annual growth rate of 0.0041. Annual kNDVI ranges from 0.1132 recorded in 2001 to a peak value of 0.2227 in 2024. (2) Spatially, vegetation coverage exhibited a remarkable differentiation pattern of “low values in the north and high values in the south”. Higher kNDVI values were predominantly distributed within the forest-covered mountainous regions of the Taihang, Lüliang, Zhongtiao, and Taiyue Mountains, whereas relatively low values were distributed across the sandy-hilly regions of northern Shanxi and urban agglomerations along the Fen River Valley. Vegetation restoration was observed across 98.02% of the study area, among which roughly 92.18% exhibited an extremely significant improvement. (3) Over 2000–2024, the major axis of vegetation spatial distribution maintained a stable northeast-southwest orientation, accompanied by moderate outward expansion of the standard deviational ellipse, and a general northwestward shift of the vegetation gravity centre. (4) The Hurst exponent indicated that the vegetation improvement trend in 98.03% of the region would persist, while scattered patches of industrial, mining and urban land (accounting for 1.97%) faced the potential vegetation degradation risk. (5) The regional vegetation driving system experienced three evolutionary stages: a single precipitation-dominated natural driving stage (2000–2005), a climate-human coupled transitional stage (2005–2010), and a multi-factor synergistic balanced stage (2010–2020). Precipitation and temperature acted as core climatic drivers, and elevation largely determined the vertical differentiation baseline of vegetation distribution. Population density and nighttime light intensity exerted persistent suppressive effects on vegetation growth. Empirically derived tentative thresholds are identified for major predictors: elevation ∼1,200 m, slope ∼8°, annual mean temperature ∼7.5 °C, annual mean precipitation ∼500 mm, population density 500 persons\u002Fkm 2 , and nighttime light intensity ∼5. These values can serve as reference boundaries for differentiating ecological conservation zones from human-disturbed zones. Conclusion This research identified the intertemporal differentiation and nonlinear coupling laws governing vegetation dynamics in Shanxi Province, a temperate transition zone on the Loess Plateau. Differentiated ecological governance strategies targeting climate adaptation, topographic zoning, and anthropogenic-pressure regulation are proposed, which provide observational scientific support for the construction of ecological security barriers on China’s Loess Plateau.","引言 系统厘清植被覆盖的时空演变规律并阐明其对气候波动与人类活动的响应机制，对推进双碳战略、优化山西省国土空间开发保护格局具有重要现实意义。方法 基于核归一化植被指数（kNDVI）数据集及多源气象、地形和社会经济数据，本研究综合运用Theil-Sen斜率估计、Mann-Kendall显著性检验、Hurst指数、标准差椭圆和重心迁移模型，系统刻画了2000—2024年植被覆盖的时空格局，并预测其未来演变的潜在持续性。同时，构建了可解释的XGBoost-SHAP机器学习框架，量化了高程、坡度、坡向、年平均气温、年平均降水量、人口密度和夜间灯光强度7个驱动因子的独立贡献、非线性边际响应及时间演变特征。结果 （1）时间上，全省多年平均kNDVI约为0.1699，呈极显著波动上升趋势，年增长率为0.0041。年kNDVI从2001年的0.1132变化至2024年的峰值0.2227。（2）空间上，植被覆盖呈现“北低南高”的显著分异格局。较高kNDVI值主要分布于太行山、吕梁山、中条山和太岳山等森林覆盖山区，而较低值分布于晋北沙丘丘陵区和汾河谷地城市群。研究区98.02%的区域植被呈恢复态势，其中约92.18%表现为极显著改善。（3）2000—2024年间，植被空间分布的主轴保持稳定的东北—西南走向，标准差椭圆呈中度向外扩张，植被重心总体向西北方向迁移。（4）Hurst指数表明，98.03%区域的植被改善趋势将持续，而零星斑块区域的工业",null,"Frontiers in Environmental Science","2026-09-17T00:00:00Z","论文",10,false,82,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,23,19,13,9,1,"基于kNDVI与XGBoost-SHAP的山西植被时空演变与驱动力研究，方法新颖、数据扎实，对黄土高原生态治理有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"机器学习","生态保护","遥感监测","植被覆盖","黄土高原",[33,34],"山西 kNDVI 植被覆盖","XGBoost-SHAP 植被驱动","山西kNDVI植被覆盖-2871",0,"10.3389\u002Ffenvs.2026.1948848",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":54,"card":55,"direction":59,"ingested_from":61},"W7213469060",[41,43,45,47,49,52],{"name":42,"orcid":9},"Jie Chen",{"name":44,"orcid":9},"Yi Hou",{"name":46,"orcid":9},"Jianhua Xue",{"name":48,"orcid":9},"Jianhua Ni",{"name":50,"orcid":51},"Hao Liu","https:\u002F\u002Forcid.org\u002F0000-0001-8903-0983",{"name":53,"orcid":9},"Pengxiang Gao","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fenvironmental-science\u002Farticles\u002F10.3389\u002Ffenvs.2026.1948848\u002Fpdf",{"tldr":56,"method":57,"finding":58,"direction":59,"opportunity":60},"基于kNDVI与XGBoost-SHAP分析山西2000-2024年植被覆盖时空变化及驱动机制。","kNDVI数据结合Theil-Sen、Mann-Kendall、Hurst指数与","山西植被呈显著上升趋势，98.02%区域改善，驱动因子具非线性与时空差异。","农业遥感与作物表型","可引入多源遥感与作物物候数据，将kNDVI驱动分析拓展至农田尺度精准管理与碳汇评估。","openalex","2026-09-18T23:30:29.212735Z",{"total":64,"page":22,"page_size":64,"items":65},6,[66,122,170,209,256,304],{"id":67,"title":68,"url":69,"summary":70,"summary_zh":71,"content":9,"source_name":72,"source_url":69,"published_at":73,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":74,"score_detail":75,"sources":82,"tags":84,"search_phrases":87,"slug":90,"view_count":36,"doi":91,"paper":92,"created_at":121},2536,"Mapping Native Grass Cover with Random Forest Models: Sentinel-2 Versus Sentinel-2 Combined with Sentinel-1 SAR-Derived GLCM Texture Metrics","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183150","Temperate native grasslands in southeastern Australia have been extensively cleared for agriculture, and the remaining patches are under growing pressure from further land use change, climate variability, and invasive species. Mapping and monitoring their distribution and the cover of native and exotic grasses are critical for their conservation and management. Field-based methods are not always scalable or time-effective, and this study aimed to develop a scalable method to map and monitor the fractional cover-class maps of native C3 and native C4 grass cover as a component of remnant native grasslands on the western outskirts of Melbourne, Victoria, Australia. Field-based reference data for training and validation of random forest machine learning models were collected across multiple sites in 2021. Sentinel-2 optical spectral bands and vegetation indices were used as the primary input data, and Sentinel-1 Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture metrics were assessed for their capacity to improve the model. Results show that random forest models trained on Sentinel-2 data without GLCM texture information derived from Sentinel-1 SAR data provided a moderate overall accuracy (C3: 59.1%, C4: 78.1%). Class-specific metrics showed that reliability was highest for better represented lower-cover classes, particularly the 6–25% native C3 class and the 0–5% native C4 class, while higher-cover classes were less reliable because of the limited number of training and validation samples. Grass cover fractions were modelled well for sparse to moderate grass cover, but dense grass cover was not modelled accurately, probably due to limited high-cover samples in the training dataset. Model performance was not improved by the inclusion of Sentinel-1 SAR-derived GLCM texture metrics, indicating that C-band VH-polarised SAR is not sensitive to the fine-scale structural heterogeneity that characterises native grassland ecosystems. Sparse native C3 and C4 grasses could be mapped most reliably in the lower-cover classes as a component of grasslands with optical remote sensing, and the method developed here can now be applied to enable evidence-based management of grasslands, biodiversity conservation and the monitoring of grassland composition in the WGR and elsewhere. Higher-resolution structural datasets and more sophisticated machine learning approaches may be required to accurately predict native C3 and C4 grass cover fractions in denser grasslands.","澳大利亚东南部的温带原生草原已被大面积开垦用于农业，残余斑块正面临土地利用进一步变化、气候变率和入侵物种日益增大的压力。对其分布以及原生和外来草类覆盖度进行制图和监测，对于草原的保护和管理至关重要。基于实地调查的方法并非总是可扩展或省时的，本研究旨在开发一种可扩展的方法，以制图和监测作为残余原生草原组成部分的原生C3和原生C4草类覆盖度的分数覆盖等级图，研究区位于澳大利亚维多利亚州墨尔本西郊。用于训练和验证随机森林机器学习模型的实地参考数据于2021年在多个样点采集。研究以Sentinel-2光学光谱波段和植被指数作为主要输入数据，并评估了Sentinel-1合成孔径雷达（SAR）衍生的灰度共生矩阵（GLCM）纹理指标对提升模型性能的能力。结果表明，仅使用Sentinel-2数据（不含Sentinel-1 SAR衍生的GLCM纹理信息）训练的随机森林模型提供了中等的总体精度（C3：59.1%，C4：78.1%）。分类别指标显示，对于代表性较好的低覆盖度类别，可靠性最高，尤其是6–25%原生C3类别和0–5%原生C4类别，而较高覆盖度类别的可靠性较低，原因是训练和验证样本数量有限。草类覆盖度分数在稀疏至中等草类覆盖条件下建模效果良好，但茂密草类覆盖未能准确建模，可能是由于训练数据集中高覆盖度样本有限。纳入Sentinel-1 SAR衍生的GLCM纹理指标并未改善模型性能，表明C波段VH极化SAR对原生草原生态系统所特有的精细尺度结构异质性不敏感。作为草原的组成部分，稀疏的原生C3和C4草类在低覆盖度类别中利用光学遥感可最可靠地制图，本研究开发的方法现可应用于西维多利亚草原（WGR）及其他地区，以实现基于证据的草原管理、生物多样性保护和草原组成监测。准确预测原生C3和C4草类覆盖度分数可能需要更高分辨率的结构数据集和更先进的机器学习方法。","Remote Sensing","2026-09-13T00:00:00Z",71,{"impact":76,"substance":77,"depth":78,"authority":79,"freshness":80,"relevant":22,"comment":81},12,20,17,14,8,"方法新颖、结论明确（SAR纹理未提升精度），对草地遥感监测有参考价值，但属细分领域研究，影响范围有限。",[83],{"name":72,"url":69},[85,27,29,86,30],"智慧农业","草原生态",[88,89],"智慧农业 机器学习 植被覆盖 草原生态","智慧农业 机器学习","智慧农业机器学习植被覆盖草原生态-2536","10.3390\u002Frs18183150",{"doi":91,"openalex_id":93,"authors":94,"venue":72,"cited_by_count":36,"oa_url":69,"card":116,"direction":59,"ingested_from":61},"W7212561645",[95,98,101,104,106,108,111,113],{"name":96,"orcid":97},"Sabah Sabaghy","https:\u002F\u002Forcid.org\u002F0000-0002-9453-8922",{"name":99,"orcid":100},"M. Abuzar","https:\u002F\u002Forcid.org\u002F0000-0002-6101-1307",{"name":102,"orcid":103},"Steve J. Sinclair","https:\u002F\u002Forcid.org\u002F0000-0002-4282-1021",{"name":105,"orcid":9},"Tony Dugdale",{"name":107,"orcid":9},"Vanessa Hutchins",{"name":109,"orcid":110},"Yogendra K. Karna","https:\u002F\u002Forcid.org\u002F0000-0002-2120-4710",{"name":112,"orcid":9},"Jonathan Wilson",{"name":114,"orcid":115},"Kathryn Sheffield","https:\u002F\u002Forcid.org\u002F0000-0003-2624-9739",{"tldr":117,"method":118,"finding":119,"direction":59,"opportunity":120},"用随机森林结合Sentinel-2与Sentinel-1纹理特征，绘制澳洲原生草地C3\u002FC4草覆盖度","2021年野外样点训练随机森林，Sentinel-2光谱与植被指数为主，Sent","仅用Sentinel-2精度中等（C3 59.1%、C4 78.1%），加入SAR纹理未提升，高覆盖","高覆盖度草地样本不足且C波段SAR不敏感，可探索高分辨率结构数据与深度模型提升密草覆盖反演。","2026-09-15T23:30:21.287053Z",{"id":123,"title":124,"url":125,"summary":126,"summary_zh":127,"content":9,"source_name":128,"source_url":125,"published_at":129,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":130,"score_detail":131,"sources":135,"tags":137,"search_phrases":140,"slug":143,"view_count":36,"doi":144,"paper":145,"created_at":169},2430,"Pollution-driven surface water quality improvement and expanding population benefits in karst regions of China","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs43247-026-04006-9","Karst regions in China have high geological permeability, fragmented river networks, and strong surface–groundwater interactions, making water quality management challenging. Despite their ecological and societal importance, long-term patterns of surface water quality remain poorly understood. Here we provide a nationwide, multi-decadal evaluation of surface water quality in karst regions using remote sensing and machine learning. The results show that surface water quality improved markedly, with the proportion of water bodies classified as the highest quality increasing from 22.7% to 35.9%. Attribution analysis indicates that pollution-related factors account for the dominant contribution across basins (> 65%), whereas climate effects are secondary and land-use contributions are generally small (\u003C 5%). The number of people benefiting from high-quality water rose to 4.86 million by 2020, although 1.36 million people remained exposed to polluted water near expanding settlements. Future scenario projections indicate that these improvements may be reversed without sustained pollution control. These findings highlight the importance of protecting surface water in karst regions for ecosystem and human well-being. Surface water quality in China’s karst regions improved substantially, with top-quality waters increasing by up to 36 percent and 4.86 million people benefiting, but gains may reverse without continued controls, according to remote sensing and machine learning.","中国喀斯特地区地质渗透性强、河网破碎、地表水与地下水相互作用强烈，水质管理面临较大挑战。尽管这些区域具有重要的生态和社会意义，但其地表水水质的长期变化规律仍缺乏系统认识。本研究基于遥感和机器学习，对中国喀斯特地区地表水水质开展了全国尺度、跨数十年的评估。结果表明，地表水水质显著改善，水质类别为最高等级的水体比例从22.7%上升至35.9%。归因分析显示，污染相关因素在各流域中占主导贡献（>65%），气候影响次之，土地利用贡献总体较小（\u003C5%）。到2020年，受益于优质水的人口增至486万，但仍有136万人暴露于不断扩张的居民点附近的污染水体中。未来情景预测表明，若缺乏持续的污染控制，上述改善可能发生逆转。这些发现凸显了保护喀斯特地区地表水对生态系统和人类福祉的重要性。基于遥感和机器学习的研究表明，中国喀斯特地区地表水水质大幅改善，最高等级水体比例增加达36%，486万人因此受益，但若缺乏持续管控，这一改善可能逆转。","Communications Earth & Environment","2026-09-11T00:00:00Z",84,{"impact":132,"substance":18,"depth":17,"authority":79,"freshness":133,"relevant":22,"comment":134},22,7,"基于遥感与机器学习的全国性喀斯特地区地表水质多年代评估，数据规模大、结论有新意，对农业用水与乡村生态保护有参考价值，但主题偏生态环境而非农业信息化核心，故未达每日精选顶级门槛。",[136],{"name":128,"url":125},[27,28,29,138,139],"水质评价","喀斯特地区",[141,142],"喀斯特地区 机器学习 水质评价 生态保护","喀斯特地区 机器学习","喀斯特地区机器学习水质评价生态保护-2430","10.1038\u002Fs43247-026-04006-9",{"doi":144,"openalex_id":146,"authors":147,"venue":128,"cited_by_count":36,"oa_url":162,"card":163,"direction":59,"ingested_from":61},"W7212291140",[148,150,152,155,157,159],{"name":149,"orcid":9},"Mingxia He",{"name":151,"orcid":9},"Jie Niu",{"name":153,"orcid":154},"Chuanhao Wu","https:\u002F\u002Forcid.org\u002F0000-0003-4855-7716",{"name":156,"orcid":9},"Dongdong Liu",{"name":158,"orcid":9},"Pan Wu",{"name":160,"orcid":161},"Bill X. Hu","https:\u002F\u002Forcid.org\u002F0000-0003-4490-5250","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs43247-026-04006-9_reference.pdf",{"tldr":164,"method":165,"finding":166,"direction":167,"opportunity":168},"用遥感与机器学习评估中国喀斯特地区地表水水质长期变化及受益人口。","遥感与机器学习，多年代全国尺度水质分类与归因分析。","水质显著改善，污染治理主导，486万人受益，但控制放松可能逆转。","农业绿色发展与碳","可探究农业面源污染在喀斯特水质改善中的具体贡献及持续控制策略。","2026-09-14T23:30:27.091671Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":175,"content":9,"source_name":176,"source_url":173,"published_at":177,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":178,"score_detail":179,"sources":181,"tags":183,"search_phrases":186,"slug":189,"view_count":36,"doi":190,"paper":191,"created_at":208},2062,"Multi-scale drivers of wildfire burn severity in Central Zagros: insights from remote sensing and machine learning","https:\u002F\u002Fdoi.org\u002F10.3832\u002Fifor5027-019","Over the past few decades, wildfire activity has increased globally. In the Central Zagros Mountains of Iran, widespread oak decline has significantly altered fuel structures and raised ecological concerns. However, the specific contribution of long-term vegetation degradation to wildfire burn severity remains under-quantified. This study addresses this gap by investigating whether decadal declines in the Normalized Difference Vegetation Index (NDVI) are associated with higher burn severity, while assessing their relative importance against short-term pre-fire environmental conditions, fuel types, topography, and human access gradients. Focusing on a major wildfire in June 2024 in the Kashkan Watershed, we mapped burn severity using the differenced Normalized Burn Ratio (dNBR) derived from Sentinel-2 imagery. A comprehensive suite of predictors was evaluated, including the long-term NDVI trend (May- September 2014-2023), pre-fire Normalized Difference Moisture Index (NDMI), Land Surface Temperature (LST), fuel categories based on ESA WorldCover, slope, aspect, spatial texture contrast, and distance to roads and settlements. A stratified sampling design supported statistical inference using ordinary least squares regression and predictive modeling via Random Forest and XGBoost algorithms with five-fold cross-validation. XGBoost demonstrated the highest predictive performance (cross-validated R2 = 0.70; RMSE = 0.11), outperforming Random Forest (R2 = 0.64) and linear regression (R2 = 0.34). Fuel moisture (NDMI) and thermal stress (LST) emerged as the primary drivers of severity, followed by land cover, topography, and spatial texture. While the long-term NDVI decline showed a small but consistent positive association with dNBR at the landscape scale, the results highlight a distinct multi-scale mechanism. In this dynamic, pre-fire moisture and temperature establish the broad watershed-scale environmental susceptibility, whereas specific fuel types and their spatial configurations drive the local-level amplification or damping of burn severity. These findings provide a framework for proactive vulnerability mapping in Zagros oak woodlands, suggesting that integrating dynamic fire-weather data and field-based fuel measurements could further refine future predictability.","过去几十年间，全球野火活动日益加剧。在伊朗中扎格罗斯山脉，大范围的橡树衰退显著改变了可燃物结构，并引发了生态担忧。然而，长期植被退化对野火烧毁严重程度的具体贡献仍缺乏充分的量化研究。本研究通过探讨归一化植被指数（NDVI）的十年尺度下降是否与更高的烧毁严重程度相关，填补了这一空白，同时评估了其相对于短期火前环境条件、可燃物类型、地形和人类可达性梯度的相对重要性。以2024年6月卡什坎流域的一场重大野火为研究对象，我们利用Sentinel-2影像衍生的差分归一化燃烧比（dNBR）绘制了烧毁严重程度图。研究评估了一套综合预测因子，包括长期NDVI趋势（2014—2023年5—9月）、火前归一化差异水分指数（NDMI）、地表温度（LST）、基于ESA WorldCover的可燃物类别、坡度、坡向、空间纹理对比度以及与道路和居民点的距离。分层抽样设计支持了普通最小二乘回归的统计推断，并通过随机森林和XGBoost算法结合五折交叉验证进行预测建模。XGBoost表现出最高的预测性能（交叉验证R² = 0.70；RMSE = 0.11），优于随机森林（R² = 0.64）和线性回归（R² = 0.34）。可燃物湿度（NDMI）和热胁迫（LST）是烧毁严重程度的主要驱动因素，其次是土地覆盖、地形和空间纹理。尽管长期NDVI下降在景观尺度上与dNBR呈现出微小但一致的正相关关系，结果仍揭示了一种独特的多尺度机制。在这一机制中，火前湿度和温度奠定了流域尺度的广泛环境脆弱性，而特定可燃物类型及其空间配置则驱动了局部尺度烧毁严重程度的放大或抑制。这些发现为扎格罗斯橡树林地的主动脆弱性制图提供了框架，并表明整合动态火险天气数据和实地可燃物测量可进一步提升未来的可预测性。","iForest - Biogeosciences and Forestry","2026-09-08T00:00:00Z",66,{"impact":80,"substance":77,"depth":78,"authority":20,"freshness":80,"relevant":22,"comment":180},"伊朗扎格罗斯山区野火烧伤严重度多尺度驱动研究，方法扎实但属境外区域案例，对国内农业信息化仅有方法借鉴价值。",[182],{"name":176,"url":173},[27,28,29,184,185],"森林防火","植被退化",[187,188],"机器学习 森林防火 植被退化 生态保护","机器学习 森林防火","机器学习森林防火植被退化生态保护-2062","10.3832\u002Fifor5027-019",{"doi":190,"openalex_id":192,"authors":193,"venue":176,"cited_by_count":36,"oa_url":202,"card":203,"direction":59,"ingested_from":61},"W7211926672",[194,196,198,200],{"name":195,"orcid":9},"F Shahidinejad",{"name":197,"orcid":9},"M Jourgholami",{"name":199,"orcid":9},"MM Pourhanifeh",{"name":201,"orcid":9},"A Esfandyar","https:\u002F\u002Fiforest.sisef.org\u002Fpdf\u002F?id=ifor5027-019",{"tldr":204,"method":205,"finding":206,"direction":59,"opportunity":207},"结合遥感与机器学习，量化伊朗扎格罗斯中部野火烧毁严重度的多尺度驱动因素。","Sentinel-2 dNBR、长期NDVI趋势、随机森林与XGBoost建模。","燃料湿度和热胁迫主导烧毁严重度，长期NDVI下降有微弱正相关，呈现多尺度机制。","可引入动态火险天气与地面燃料实测，提升多尺度火险制图与预测精度。","2026-09-10T23:30:28.899368Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":215,"source_url":212,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":216,"score_detail":217,"sources":220,"tags":222,"search_phrases":226,"slug":229,"view_count":36,"doi":230,"paper":231,"created_at":255},2804,"A Novel Data-Driven Framework for Stubble Burning Detection Using NASA FIRMS and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02584-x","A Novel Data-Driven Framework for Stubble Burning Detection Using NASA FIRMS and Machine Learning。Journal of the Indian Society of Remote Sensing","一种基于NASA FIRMS和机器学习的新型数据驱动秸秆焚烧检测框架。《印度遥感学会杂志》","Journal of the Indian Society of Remote Sensing",76,{"impact":218,"substance":77,"depth":17,"authority":20,"freshness":13,"relevant":22,"comment":219},15,"该论文提出基于NASA FIRMS卫星数据与机器学习的秸秆焚烧检测新框架，方法新颖、数据规模明确，对农业遥感监测有实质参考价值，但属细分领域学术进展，影响力限于专业圈层。",[221],{"name":215,"url":212},[223,27,29,224,225],"农业人工智能","秸秆焚烧","卫星数据",[227,228],"农业人工智能 卫星数据 机器学习 秸秆焚烧","农业人工智能 卫星数据","农业人工智能卫星数据机器学习秸秆焚烧-2804","10.1007\u002Fs12524-026-02584-x",{"doi":230,"openalex_id":232,"authors":233,"venue":215,"cited_by_count":36,"oa_url":9,"card":249,"direction":254,"ingested_from":61},"W7213455429",[234,237,239,242,244,247],{"name":235,"orcid":236},"Mohit Dua","https:\u002F\u002Forcid.org\u002F0000-0001-7071-8323",{"name":238,"orcid":9},"Oshin Rastogi",{"name":240,"orcid":241},"Ashish Saini","https:\u002F\u002Forcid.org\u002F0000-0003-3061-2342",{"name":243,"orcid":9},"Raviya",{"name":245,"orcid":246},"Nidhi Chakravarty","https:\u002F\u002Forcid.org\u002F0000-0002-5454-1561",{"name":248,"orcid":9},"Shelza Dua",{"tldr":250,"method":251,"finding":252,"direction":59,"opportunity":253},"提出基于NASA FIRMS与机器学习的数据驱动框架，用于检测秸秆焚烧。","使用NASA FIRMS火点数据结合机器学习分类算法。","该框架能有效识别秸秆焚烧事件，提升检测精度。","可结合多源遥感与深度学习，提升小尺度焚烧检测与实时预警能力。","农业人工智能与决策模型","2026-09-17T23:30:59.313408Z",{"id":257,"title":258,"url":259,"summary":260,"summary_zh":261,"content":9,"source_name":262,"source_url":259,"published_at":263,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":264,"score_detail":265,"sources":267,"tags":269,"search_phrases":273,"slug":276,"view_count":36,"doi":277,"paper":278,"created_at":303},2791,"Integrating multi-source data and support vector machine to assess the spatio-temporal pattern of land degradation in the Eastern Cape of South Africa","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.indic.2026.101524","Land degradation remains a major environmental challenge, particularly in semi-arid and heterogeneous landscapes, where interactions between vegetation loss and soil exposure are complex and spatially dynamic. This study, therefore, seeks to evaluate the spatial extent of land degradation and drivers over time (2005 - 2025) using Landsat data series and support vector machine (SVM) in the Keiskamma Catchment of South Africa. Degraded land followed a non-monotonic trajectory: it declined from ∼197 km 2 in 2005 to ∼157 km 2 in 2015 (a temporary contraction of 20.3%, consistent with short-term restoration and land-use shifts), before rising sharply and unsustainably to ∼328 km 2 by 2025 (a 108.9% increase relative to 2015, and a net increase of 66.5% over the full two-decade period), largely at the expense of grassland and agricultural land. Furthermore, the findings show that soil-sensitive indicators, particularly BSI and SWIR spectral bands, play a crucial role in determining degraded land. In contrast, vegetation indices such as NDVI contribute less under degraded conditions because degraded areas were severely dominated by exposed soil rather than vegetation. Correlation matrix analysis further reveals a temporal shift from mixed soil–vegetation spectral relationships toward strong soil-dominated reflectance patterns by 2025, indicating advanced degradation stages. Overall, the integration of SVM classification with VIF and SHAP provides a transparent, reliable, and spatially explicit framework for monitoring land degradation. The findings support land degradation neutrality monitoring and provide critical insights for sustainable land-management planning in support of Sustainable Development Goal (SDG) 15.3.","土地退化仍然是一项重大环境挑战，尤其是在半干旱和异质性景观中，植被丧失与土壤裸露之间的相互作用复杂且具有空间动态性。因此，本研究旨在利用Landsat数据序列和支持向量机（SVM），评估南非Keiskamma集水区2005—2025年间土地退化的空间范围及其驱动因素。退化土地呈非单调变化轨迹：从2005年的约197 km²下降至2015年的约157 km²（暂时收缩20.3%，与短期恢复和土地利用变化相一致），随后急剧且不可持续地上升至2025年的约328 km²（较2015年增加108.9%，在整个二十年期间净增加66.5%），且主要以草地和农用地为代价。此外，研究结果表明，土壤敏感指标，尤其是BSI和SWIR光谱波段，在判定退化土地方面发挥着关键作用。相比之下，NDVI等植被指数在退化条件下贡献较小，因为退化区域严重以裸露土壤为主，而非植被。相关矩阵分析进一步揭示，到2025年，光谱关系由土壤—植被混合关系向强烈的土壤主导反射模式发生时间转变，表明退化已进入后期阶段。总体而言，将SVM分类与VIF和SHAP相结合，为监测土地退化提供了一个透明、可靠且具有空间显式性的框架。研究结果支持土地退化零增长监测，并为支持可持续发展目标（SDG）15.3的可持续土地管理规划提供了关键见解。","Environmental and Sustainability Indicators","2026-09-16T00:00:00Z",77,{"impact":218,"substance":132,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":266},"基于Landsat时序与SVM\u002FSHAP的南非土地退化监测研究，方法透明可复现，对农业遥感与土地退化中性监测有参考价值，但属区域案例、非国内三农直接政策信息。",[268],{"name":262,"url":259},[270,27,271,272,29],"农业遥感","可持续发展","土地退化",[274,275],"可持续发展 农业遥感 土地退化 机器学习","可持续发展 农业遥感","可持续发展农业遥感土地退化机器学习-2791","10.1016\u002Fj.indic.2026.101524",{"doi":277,"openalex_id":279,"authors":280,"venue":262,"cited_by_count":36,"oa_url":297,"card":298,"direction":59,"ingested_from":61},"W7213298151",[281,283,286,289,291,294],{"name":282,"orcid":9},"Mandisa Zameko",{"name":284,"orcid":285},"Kgabo Humphrey Thamaga","https:\u002F\u002Forcid.org\u002F0000-0002-2305-9975",{"name":287,"orcid":288},"Mthunzi Mndela","https:\u002F\u002Forcid.org\u002F0000-0002-2384-6856",{"name":290,"orcid":9},"Matthieu Tshanga",{"name":292,"orcid":293},"Nobert Tafadzwa Mukomberanwa","https:\u002F\u002Forcid.org\u002F0009-0003-1896-9813",{"name":295,"orcid":296},"Mohamed Zhran","https:\u002F\u002Forcid.org\u002F0000-0002-1112-387X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2665972726004137\u002Fpdf",{"tldr":299,"method":300,"finding":301,"direction":59,"opportunity":302},"用Landsat与SVM评估南非Keiskamma流域2005-2025年土地退化时空格局。","Landsat时序数据、SVM分类，结合VIF与SHAP做特征解释。","退化面积先降后升，2025年达328km²，土壤光谱指标比NDVI更关键。","可将该SVM-SHAP框架迁移到其他半干旱区，并耦合气候与土地利用驱动做退化预警。","2026-09-17T23:30:36.054539Z",{"id":305,"title":306,"url":307,"summary":308,"summary_zh":309,"content":9,"source_name":72,"source_url":307,"published_at":263,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":310,"sources":312,"tags":314,"search_phrases":318,"slug":321,"view_count":36,"doi":322,"paper":323,"created_at":357},2657,"Retrieval of Optically Active and Inactive Water Quality Parameters Using Remote Sensing and Machine Learning: Evidence from Water Hyacinth-Infested Lake Tana, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183185","Monitoring water quality is critical for protecting freshwater ecosystems and supporting sustainable water resource management. Lake Tana, Ethiopia’s largest freshwater lake, faces increasing agricultural and urban pressures, while conventional monitoring remains costly and spatially constrained. This study developed an integrated Sentinel-2 remote sensing and machine learning framework to estimate chlorophyll-a (Chl-a), turbidity (TU), total nitrogen (TN), and total phosphorus (TP) using 858 in situ observations and Google Earth Engine. Random Forest (RF), Extreme Gradient Boosting (XGB), Artificial Neural Networks (ANN), and Support Vector Regression (SVR) were evaluated using spectral bands, band combinations, and indices. RF provided the best predictions for Chl-a (R2 = 0.94 ± 0.01; RMSE = 2.11 ± 0.18 µg L−1; MARE = 5%) and TP (R2 = 0.91 ± 0.01; RMSE = 0.26 ± 0.01 mg L−1; MARE = 8.7%), whereas XGB performed best for TU (R2 = 0.93 ± 0.01; RMSE = 5.17 ± 0.43 NTU; MARE = 7%) and TN (R2 = 0.94 ± 0.02; RMSE = 0.18 ± 0.02 mg L−1; MARE = 9.9%). The strong predictive performance of RF and XGB across both optically active and inactive parameters demonstrates the capability of the framework to capture complex spectral water quality relationships and support spatially continuous assessment. Significant seasonal differences (p \u003C 0.001) showed higher dry season Chl-a (137.1%) and higher wet season TP (21.7%), TU (7.5%), and TN (3.9%). Long-term paired observations further indicated increases in Chl-a (73.7%), TN (30%), and TP (14.3%) from December 2016 to December 2025 (p \u003C 0.001). Spatial hotspot analysis revealed strong clustering of TU, TN, and TP, particularly around tributary mouths and nearshore areas, highlighting priority zones for monitoring and intervention. Overall, integrating field observations, Sentinel-2 imagery, and machine learning provides an accurate, scalable, and cost-effective approach for monitoring diverse water quality parameters. The framework offers a transferable solution for strengthening freshwater monitoring in data-scarce regions and supporting sustainable management of lakes under increasing water quality pressures.","监测水质对于保护淡水生态系统和支撑可持续水资源管理至关重要。埃塞俄比亚最大的淡水湖——塔纳湖（Lake Tana）面临着日益加剧的农业和城市压力，而传统监测手段仍然成本高昂且受空间限制。本研究开发了一套集成Sentinel-2遥感与机器学习的框架，利用858个原位观测数据和Google Earth Engine估算叶绿素a（Chl-a）、浊度（TU）、总氮（TN）和总磷（TP）。采用光谱波段、波段组合和指数，评估了随机森林（RF）、极端梯度提升（XGB）、人工神经网络（ANN）和支持向量回归（SVR）的性能。RF对Chl-a（R2 = 0.94 ± 0.01；RMSE = 2.11 ± 0.18 µg L−1；MARE = 5%）和TP（R2 = 0.91 ± 0.01；RMSE = 0.26 ± 0.01 mg L−1；MARE = 8.7%）的预测效果最佳，而XGB对TU（R2 = 0.93 ± 0.01；RMSE = 5.17 ± 0.43 NTU；MARE = 7%）和TN（R2 = 0.94 ± 0.02；RMSE = 0.18 ± 0.02 mg L−1；MARE = 9.9%）的预测效果最佳。RF和XGB在光学活性和非光学活性参数上均表现出强大的预测性能，表明该框架能够捕捉复杂的光谱水质关系并支持空间连续评估。显著的季节性差异（p \u003C 0.001）显示旱季Chl-a较高（137.1%），雨季TP（21.7%）、TU（7.5%）和TN（3.9%）较高。长期配对观测进一步表明，从2016年12月至2025年12月，Chl-a（73.7%）、TN（30%）和TP（14.3%）均有所增加（p \u003C 0.001）。空间热点分析揭示了TU、TN和TP的强烈聚集性，尤其是在支流河口和近岸区域，凸显了优先监测和干预区域。总体而言，整合实地观测、Sentinel-2影像和机器学习为监测多种水质参数提供了一种准确、可扩展且具有成本效益的方法。该框架为加强数据稀缺地区的淡水监测以及支持在水质压力日益增大背景下湖泊的可持续管理提供了一种可迁移的解决方案。",{"impact":17,"substance":132,"depth":17,"authority":79,"freshness":13,"relevant":22,"comment":311},"基于Sentinel-2与机器学习实现湖泊多参数水质反演，方法可迁移至国内农业面源污染与渔业水域监测，数据规模与精度均具参考价值。",[313],{"name":72,"url":307},[27,315,316,29,317],"农业面源污染","智慧渔业","水质监测",[319,320],"农业面源污染 智慧渔业 机器学习 水质监测","农业面源污染 智慧渔业","农业面源污染智慧渔业机器学习水质监测-2657","10.3390\u002Frs18183185",{"doi":322,"openalex_id":324,"authors":325,"venue":72,"cited_by_count":36,"oa_url":307,"card":352,"direction":59,"ingested_from":61},"W7213230686",[326,329,331,334,336,339,341,343,345,347,349],{"name":327,"orcid":328},"Lakachew Y. Alemneh","https:\u002F\u002Forcid.org\u002F0009-0004-3471-3778",{"name":330,"orcid":9},"Daganchew Aklog",{"name":332,"orcid":333},"Ann van Griensven","https:\u002F\u002Forcid.org\u002F0000-0002-2105-6287",{"name":335,"orcid":9},"Minychl G. Dersseh",{"name":337,"orcid":338},"Goraw Goshu","https:\u002F\u002Forcid.org\u002F0000-0001-9629-0126",{"name":340,"orcid":9},"Seleshi Yalew",{"name":342,"orcid":9},"Demesew A. Mhiret",{"name":344,"orcid":9},"Sisay B. Asress",{"name":346,"orcid":9},"Tigistu Wassie Agegnehu",{"name":348,"orcid":9},"Shawl Abebe Desta",{"name":350,"orcid":351},"Samuel Berihun Kassa","https:\u002F\u002Forcid.org\u002F0009-0004-5618-9743",{"tldr":353,"method":354,"finding":355,"direction":59,"opportunity":356},"用Sentinel-2与机器学习反演埃塞俄比亚塔纳湖四类水质参数并分析时空变化。","Sentinel-2影像、Google Earth Engine、858个实测点","RF与XGB精度最高（R²达0.91-0.94），水质参数呈显著季节与年际上升趋势。","可迁移至其他数据稀缺湖泊，探索水葫芦覆盖下水体光谱混合与多源遥感协同反演。","2026-09-16T23:30:28.415573Z"]