[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3080":3,"related-3080":74},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":54,"slug":57,"view_count":58,"doi":59,"paper":60,"created_at":73},3080,"Analysis of Habitat Quality Changes and Their Driving Factors in Rock Desertification Control Areas of Guizhou Province","https:\u002F\u002Fdoi.org\u002F10.22158\u002Fse.v11n4p24","Habitat quality affects the functioning of ecosystems, and analyzing its evolution characteristics and driving factors is important for regional ecological management. This study, based on multi-source remote sensing data and using methods such as the InVEST model and geographic detectors, explores the spatial and temporal evolution of habitat quality in the rocky desertification control area and quantifies the influence of various driving factors. The study found that: (1) from 2000 to 2024, the overall habitat quality in Guizhou's rocky desertification control area showed a slow upward trend, with significant spatial heterogeneity in its distribution.(2) during 2000-2024, the habitat quality level remained generally high, with areas of high-quality habitat gradually expanding and poorly rated habitat areas gradually improving through management.(3) from 2000 to 2024, habitat quality in the study area was driven by both natural and human factors, with human activity gradually becoming the dominant influence. Evaluating the habitat quality in Guizhou's rocky desertification control area allows us to quantify the impact of ecological management on regional habitat quality, providing a theoretical basis and data support for rocky desertification control.","生境质量影响生态系统功能的发挥，分析其演变特征及驱动因素对区域生态管理具有重要意义。本研究基于多源遥感数据，运用InVEST模型和地理探测器等方法，探究石漠化治理区生境质量的时空演变，并量化各驱动因素的影响程度。研究发现：(1)2000—2024年，贵州石漠化治理区生境质量整体呈缓慢上升趋势，其分布具有显著的空间异质性。(2)2000—2024年间，生境质量等级总体较高，高质量生境面积逐步扩大，低等级生境区域通过治理逐步改善。(3)2000—2024年，研究区生境质量受自然因素和人为因素共同驱动，人为活动逐渐成为主导影响因素。评估贵州石漠化治理区生境质量，可以量化生态治理对区域生境质量的影响，为石漠化治理提供理论依据和数据支撑。",null,"Sustainability in Environment","2026-09-18T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,17,13,8,1,"基于多源遥感与InVEST模型量化贵州石漠化治理区生境质量演变及驱动因素，方法规范、数据跨度长，对区域生态治理有参考价值，但属区域案例研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"贵州","生态修复","遥感监测","石漠化治理","生境质量",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":53},[39,48],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":47},"I3",7,true,[],[45,46],"遥感","remote sensing",[29],{"code":49,"score":22,"supported":14,"title":50,"abstract":51,"tags":52},"H6",[],[],[28],"农业相关性需要复核",[55,56],"贵州 石漠化治理 生境质量","InVEST模型 遥感 石漠化","贵州石漠化治理生境质量-3080",0,"10.22158\u002Fse.v11n4p24",{"doi":59,"openalex_id":61,"authors":62,"venue":10,"cited_by_count":58,"oa_url":65,"card":66,"direction":70,"ingested_from":72},"W7213557746",[63],{"name":64,"orcid":9},"Xiaoyue Wang","https:\u002F\u002Fwww.scholink.org\u002Fojs\u002Findex.php\u002Fse\u002Farticle\u002Fdownload\u002F58083\u002F14067",{"tldr":67,"method":68,"finding":69,"direction":70,"opportunity":71},"基于多源遥感与InVEST模型，分析贵州石漠化治理区生境质量时空演变及驱动因素。","多源遥感数据、InVEST模型、地理探测器。","2000-2024年生境质量缓慢上升，空间异质性显著，人为活动渐成主导驱动力。","农业遥感与作物表型","可结合多时相遥感与机器学习，预测不同治理情景下生境质量演变，优化生态修复策略。","openalex","2026-09-21T23:30:27.396495Z",{"total":75,"page":22,"page_size":75,"items":76},6,[77,152,231,324,380,457],{"id":78,"title":79,"url":80,"summary":81,"summary_zh":82,"content":9,"source_name":83,"source_url":80,"published_at":84,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":85,"score_detail":86,"sources":89,"tags":91,"primary_topic":95,"secondary_topics":101,"classification":106,"search_phrases":127,"slug":130,"view_count":58,"doi":131,"paper":132,"created_at":151},5392,"Wetland Loss Associated with Hydro-Agricultural Development in North African Floodplains: Insights from a Historical Cartographic Analysis of the Gharb Plain (Morocco)","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fd18100642","Mediterranean floodplain wetlands are among the most productive ecosystems, yet they are increasingly threatened by agricultural intensification and hydrological modifications. In North Africa, most of these wetlands have been converted to agricultural lands using an extensive ‘drainage–irrigation’ process that has irreversibly degraded them. This study documents this loss through the illustrative example of the Gharb Plain in the northwest of Morocco. The agricultural development process of this plain, initiated in 1927 and only recently completed, is documented by both historical maps and remote sensing data, which we combined to reconstruct the simultaneous wetland decline and agriculture extension. The results reveal a dramatic decline in marshland ecosystems, covering about 88% of the original marshlands in less than one cycle. Several wetlands, including large ones, have entirely disappeared, particularly within the Sebou and Baht river floodplains. To ensure their human occupation, the drainage process and river canalization have been supported by a large program of dam construction upstream of the Gharb Plain. The wetland loss estimate highlights the substantial ecological costs of agricultural intensification and underscores the need for integrated management strategies balancing food production and patrimony conservation. Future research should focus on wetland restoration, ecosystem service assessment, and climate-resilient water management in Mediterranean floodplains.","地中海洪泛平原湿地是最具生产力的生态系统之一，却日益受到农业集约化与水文改造的威胁。在北非，大多数此类湿地已通过大规模的“排水—灌溉”过程被转变为农业用地，并因此遭到不可逆的退化。本研究以摩洛哥西北部加尔卜平原（Gharb Plain）为例，记录了这一丧失过程。该平原的农业开发进程始于1927年，直至近期才告完成，我们结合历史地图与遥感数据，重建了湿地衰退与农业扩张同步发生的过程。结果表明，沼泽生态系统急剧减少，在不到一个周期内约88%的原生沼泽地消失。包括大型湿地在内的多处湿地已完全消失，尤其是在塞布河（Sebou）与巴赫特河（Baht）洪泛平原内。为保障人类占用，排水过程与河流渠化得到了加尔卜平原上游大规模水坝建设计划的支持。湿地丧失的估算凸显了农业集约化所带来的巨大生态代价，并强调需要制定兼顾粮食生产与遗产保护的综合管理策略。未来研究应聚焦于湿地恢复、生态系统服务评估以及地中海洪泛平原的气候韧性水资源管理。","Diversity","2026-10-19T00:00:00Z",76,{"impact":17,"substance":18,"depth":87,"authority":20,"freshness":21,"relevant":22,"comment":88},18,"该研究结合历史地图与遥感数据，量化了北非洪泛平原湿地因农业开发而丧失的情况，方法扎实、结论明确，对农业生态与土地管理有参考价值，但属区域性案例研究，公共影响有限。",[90],{"name":83,"url":80},[28,29,92,93,94],"灌溉农业","土地利用变化","湿地保护",{"code":96,"slug":97,"name":98,"path":99,"parent_code":100},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[102],{"code":49,"slug":103,"name":104,"path":105,"parent_code":100},"biodiversity","生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity",{"status":107,"locked":14,"version":35,"method":36,"evidence":108},"classified",{"candidates":109,"agricultural_evidence":42},[110,116,123],{"code":96,"score":13,"supported":42,"title":111,"abstract":112,"tags":115},[],[113,114,97],"灌溉","水资源",[92],{"code":49,"score":117,"supported":42,"title":118,"abstract":119,"tags":122},9,[],[120,121],"生态系统服务","ecosystem service",[93,94,28],{"code":40,"score":41,"supported":42,"title":124,"abstract":125,"tags":126},[],[45,46],[29],[128,129],"Gharb平原 湿地 遥感","摩洛哥 湿地 灌溉","Gharb平原湿地遥感-5392","10.3390\u002Fd18100642",{"doi":131,"openalex_id":133,"authors":134,"venue":83,"cited_by_count":58,"oa_url":80,"card":146,"direction":70,"ingested_from":72},"W7222966485",[135,138,140,143],{"name":136,"orcid":137},"Mohamed Dakki","https:\u002F\u002Forcid.org\u002F0000-0002-8042-3972",{"name":139,"orcid":9},"Said El Falah",{"name":141,"orcid":142},"Ibrahim Makrane","https:\u002F\u002Forcid.org\u002F0009-0002-9201-206X",{"name":144,"orcid":145},"Ismail Mansouri","https:\u002F\u002Forcid.org\u002F0000-0002-3679-6050",{"tldr":147,"method":148,"finding":149,"direction":70,"opportunity":150},"用历史地图与遥感重建摩洛哥Gharb平原1927年以来的湿地丧失与农业扩张过程。","历史地图与遥感数据结合，重建湿地退缩与农田扩张的时空过程。","不到一个开发周期内约88%的原生沼泽消失，大型湿地完全丧失。","可延伸至地中海洪泛平原湿地恢复优先区识别与生态系统服务权衡研究。","2026-10-20T23:30:19.135602Z",{"id":153,"title":154,"url":155,"summary":156,"summary_zh":157,"content":9,"source_name":158,"source_url":155,"published_at":159,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":160,"score_detail":161,"sources":165,"tags":167,"primary_topic":9,"secondary_topics":171,"classification":172,"search_phrases":192,"slug":195,"view_count":58,"doi":196,"paper":197,"created_at":230},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":21,"substance":87,"depth":162,"authority":163,"freshness":21,"relevant":22,"comment":164},14,12,"该研究基于实测数据，揭示了高海拔矿区土壤磷钾的空间变异规律及其影响因素，对矿区土壤管理和生态修复有参考价值，但属于区域性案例研究，公共影响有限。",[166],{"name":158,"url":155},[28,29,168,169,170],"矿区生态","土壤养分","空间变异",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":173},{"candidates":174,"agricultural_evidence":14,"reason":53},[175,183,188],{"code":176,"score":177,"supported":42,"title":178,"abstract":180,"tags":182},"H2",15,[179],"soil",[181,179],"土壤",[169],{"code":49,"score":184,"supported":14,"title":185,"abstract":186,"tags":187},2,[],[],[28,168],{"code":40,"score":22,"supported":14,"title":189,"abstract":190,"tags":191},[],[],[29],[193,194],"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":196,"openalex_id":198,"authors":199,"venue":158,"cited_by_count":58,"oa_url":224,"card":225,"direction":70,"ingested_from":72},"W7222017864",[200,203,206,209,212,215,218,221],{"name":201,"orcid":202},"Xiyao Feng","https:\u002F\u002Forcid.org\u002F0009-0009-1482-4784",{"name":204,"orcid":205},"Yu Chen","https:\u002F\u002Forcid.org\u002F0009-0007-6975-327X",{"name":207,"orcid":208},"Zhongyou Song","https:\u002F\u002Forcid.org\u002F0009-0001-0791-3457",{"name":210,"orcid":211},"Pan Gao","https:\u002F\u002Forcid.org\u002F0009-0000-0039-4562",{"name":213,"orcid":214},"Pengfei Chen","https:\u002F\u002Forcid.org\u002F0009-0000-8394-5672",{"name":216,"orcid":217},"Fabin Yan","https:\u002F\u002Forcid.org\u002F0009-0000-0496-7532",{"name":219,"orcid":220},"Pei Wang","https:\u002F\u002Forcid.org\u002F0009-0001-8997-421X",{"name":222,"orcid":223},"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":226,"method":227,"finding":228,"direction":70,"opportunity":229},"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":232,"title":233,"url":234,"summary":235,"summary_zh":236,"content":9,"source_name":237,"source_url":234,"published_at":238,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":239,"score_detail":240,"sources":243,"tags":245,"primary_topic":249,"secondary_topics":252,"classification":264,"search_phrases":291,"slug":294,"view_count":184,"doi":295,"paper":296,"created_at":323},4172,"Machine learning reveals distinct drivers of soil organic carbon across the mountain-oasis-desert system of northwest China","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13021-026-00522-5","The mountain-oasis-desert system (MODS) is a distinctive landscape pattern shaped by interactions between geomorphological configurations and hydrological processes in arid regions. However, most soil organic carbon (SOC) mapping studies treat arid landscapes as homogeneous units, leaving limited quantitative evidence on how SOC patterns and environmental associations differ among MODS subsystems. As a critical ecosystem highly sensitive to climate change and human activities, understanding these responses is essential for regional soil carbon management and ecological restoration. In this study, we developed an ecosystem-based modeling framework in the Hexi Region (HXR) to construct separate SOC prediction models for different subsystems, aiming to investigate the spatiotemporal dynamics of SOC stocks and identify their key driving factors across MODS. The results show that incorporating remote sensing (RS) variables significantly improved model performance, increasing the mean R 2 from 0.51 to 0.58. Among the tested models, XGBoost achieved the best performance, providing an effective tool for SOC mapping in arid regions. Shapley additive explanations (SHAP) analysis of the best-performing XGBoost models identified distinct leading predictors across the MODS, including surface shortwave radiation (SRAD) in mountain and desert ecosystems and the difference vegetation index (DVI) in oasis ecosystems. Furthermore, the partial least squares path model (PLS-PM) revealed contrasting influences of climate change and human activities across ecological subsystems. Climatic factors exerted the strongest influence in the mountainous subsystem, whereas in the oasis subsystem intensive human management became the dominant driver of soil carbon cycling. From 2001 to 2023, SOC stocks in oasis regions increased significantly at a rate of 0.17 t C ha⁻ 1 year⁻ 1 , potentially associated with land-use change and agricultural management. Approximately 19.3% of the study area experienced significant SOC stocks declines. By resolving subsystem-specific SOC patterns and environmental associations, this study advances ecosystem-aware SOC assessment in arid regions. The resulting framework also provides a basis for differentiated carbon management across MODS.","山地-绿洲-荒漠系统（MODS）是干旱区地貌格局与水文过程相互作用形成的独特景观格局。然而，大多数土壤有机碳（SOC）制图研究将干旱区景观视为均质单元，关于MODS各子系统间SOC格局及其环境关联差异的定量证据仍然有限。作为对气候变化和人类活动高度敏感的关键生态系统，理解这些响应对于区域土壤碳管理和生态恢复至关重要。本研究在河西地区（HXR）构建了基于生态系统的建模框架，针对不同子系统分别建立SOC预测模型，旨在探究MODS中SOC储量的时空动态并识别其关键驱动因子。结果表明，引入遥感（RS）变量显著提升了模型性能，平均R²从0.51提高至0.58。在测试的模型中，XGBoost表现最佳，为干旱区SOC制图提供了有效工具。对最优XGBoost模型的SHAP（Shapley加法解释）分析识别出MODS各子系统的不同主导预测因子，其中山地和荒漠生态系统为地表短波辐射（SRAD），绿洲生态系统为差值植被指数（DVI）。此外，偏最小二乘路径模型（PLS-PM）揭示了气候变化和人类活动在各生态子系统中的差异化影响。气候因子在山地子系统中影响最强，而在绿洲子系统中，集约化人类管理成为土壤碳循环的主导驱动因素。2001年至2023年，绿洲区域SOC储量以0.17 t C ha⁻¹ year⁻¹的速率显著增加，这可能与土地利用变化和农业管理有关。约19.3%的研究区域经历了SOC储量的显著下降。通过解析子系统特定的SOC格局及其环境关联，本研究推进了干旱区生态系统感知型SOC评估，所构建的框架也为MODS差异化碳管理提供了依据。","Carbon Balance and Management","2026-10-03T00:00:00Z",79,{"impact":17,"substance":241,"depth":87,"authority":162,"freshness":117,"relevant":22,"comment":242},22,"基于XGBoost与SHAP的干旱区SOC分异驱动研究，方法新颖、数据翔实，对区域碳管理与生态修复有参考价值。",[244],{"name":237,"url":234},[246,247,28,29,248],"机器学习","土壤有机碳","干旱区农业",{"code":176,"slug":179,"name":250,"path":251,"parent_code":100},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[253,258],{"code":254,"slug":255,"name":256,"path":257,"parent_code":100},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":259,"slug":260,"name":261,"path":262,"parent_code":263},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":107,"locked":14,"version":35,"method":36,"evidence":265},{"candidates":266,"agricultural_evidence":42},[267,271,278,283,287],{"code":176,"score":177,"supported":42,"title":268,"abstract":269,"tags":270},[179],[181,179],[247],{"code":254,"score":13,"supported":42,"title":272,"abstract":273,"tags":277},[],[274,275,276],"气候变化","干旱","climate change",[248],{"code":259,"score":117,"supported":42,"title":279,"abstract":281,"tags":282},[280],"machine learning",[],[246],{"code":40,"score":41,"supported":42,"title":284,"abstract":285,"tags":286},[],[45,46],[29],{"code":49,"score":22,"supported":14,"title":288,"abstract":289,"tags":290},[],[],[28],[292,293],"河西地区 土壤有机碳 机器学习","山地-绿洲-荒漠 遥感 碳储量","河西地区土壤有机碳机器学习-4172","10.1186\u002Fs13021-026-00522-5",{"doi":295,"openalex_id":297,"authors":298,"venue":237,"cited_by_count":58,"oa_url":234,"card":318,"direction":70,"ingested_from":72},"W7219075005",[299,301,304,306,309,311,314,316],{"name":300,"orcid":9},"Y. Chen",{"name":302,"orcid":303},"Haiyang Xi","https:\u002F\u002Forcid.org\u002F0000-0003-3158-8161",{"name":305,"orcid":9},"Meng Zhu",{"name":307,"orcid":308},"Wenju Cheng","https:\u002F\u002Forcid.org\u002F0000-0002-1822-5668",{"name":310,"orcid":9},"Yulian Hao",{"name":312,"orcid":313},"Linbo Qu","https:\u002F\u002Forcid.org\u002F0009-0007-4669-8407",{"name":315,"orcid":9},"Zhehui Wang",{"name":317,"orcid":9},"Bin Wang",{"tldr":319,"method":320,"finding":321,"direction":70,"opportunity":322},"构建基于生态系统的机器学习框架，预测西北荒漠-绿洲-山地系统土壤有机碳并识别各子系统驱动因子。","XGBoost、SHAP、PLS-PM，结合遥感变量与2001-2023年多源数","遥感变量提升模型R²至0.58；山地和沙漠由辐射主导，绿洲由人类管理主导，绿洲碳储量年增0.17 t","可延伸研究绿洲农业管理措施对土壤碳的长期影响，并开发子系统自适应的碳监测与决策模型。","2026-10-04T23:30:24.631460Z",{"id":325,"title":326,"url":327,"summary":328,"summary_zh":329,"content":9,"source_name":330,"source_url":327,"published_at":331,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":85,"score_detail":332,"sources":334,"tags":336,"primary_topic":9,"secondary_topics":339,"classification":340,"search_phrases":357,"slug":360,"view_count":184,"doi":361,"paper":362,"created_at":379},3791,"Topographic heterogeneity regulates nonlinear vegetation responses to climate and human activities in China’s ecological transition zone","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fffgc.2026.1948232","Ecological transition zones, identified as climate-sensitive areas and biodiversity hotspots, are essential for maintaining regional ecological balance through their vegetation dynamics. However, the impact of topographic heterogeneity on the nonlinear responses of vegetation to climate change and human activities is not well understood. This gap limits our comprehension of ecosystem resilience. This study focuses on the ecological transition zone of the Qinling–Daba Mountains in China. We utilized 30-m resolution Landsat NDVI data from 2000 to 2022, along with climate, human activity, and topographic data. To characterize vegetation dynamics, we employed Theil–Sen trend analysis, the Mann–Kendall test, and the Hurst index. A Bayesian-optimized XGBoost model, combined with SHAP values, was employed to evaluate the relative contributions and response patterns of potential drivers across various vegetation trend zones and topographic gradients. The analysis revealed a significant greening trend, with the normalized difference vegetation index (NDVI) increasing at a rate of 0.0051 yr. −1 ( p \u003C 0.05). Approximately 90.13% of the region displayed an improving trend, while 89.83% indicated a propensity for continued enhancement. The most pronounced vegetation improvements were observed primarily at moderate elevations (1,750–2,250 m), on moderate slopes (15–20°), and on north-facing slopes. Land use and land cover (LULC) (42.5%), elevation (13.2%), population density (8.2%), and precipitation (7.0%) emerged as the most significant contributors to NDVI predictions across the study area. LULC consistently served as the most influential predictor in both regions experiencing significant increases and those facing declines. In contrast, the relative importance and nonlinear response patterns of other factors varied across different trend zones. These findings provide a scientific foundation for targeted ecological restoration and adaptive vegetation management in ecological transition zones.","生态过渡带被认为是气候敏感区和生物多样性热点区，其植被动态对维持区域生态平衡至关重要。然而，地形异质性如何影响植被对气候变化和人类活动的非线性响应，目前尚不明确。这一空白限制了我们对生态系统恢复力的理解。本研究以中国秦岭—大巴山生态过渡带为对象，利用2000—2022年30 m分辨率的Landsat NDVI数据，并结合气候、人类活动和地形数据。为刻画植被动态，我们采用了Theil–Sen趋势分析、Mann–Kendall检验和Hurst指数。采用贝叶斯优化的XGBoost模型，并结合SHAP值，评估了不同植被趋势区和地形梯度上潜在驱动因子的相对贡献及响应模式。分析揭示出显著的绿化趋势，归一化植被指数（NDVI）以0.0051 yr⁻¹的速率增加（p \u003C 0.05）。约90.13%的区域呈改善趋势，89.83%的区域表现出持续增强的倾向。植被改善最显著的区域主要出现在中等海拔（1,750–2,250 m）、中等坡度（15–20°）和北向坡。土地利用与土地覆盖（LULC）（42.5%）、海拔（13.2%）、人口密度（8.2%）和降水（7.0%）是整个研究区NDVI预测中最重要的贡献因子。在显著增加区和下降区，LULC始终是最具影响力的预测因子。相比之下，其他因素的相对重要性和非线性响应模式在不同趋势区存在差异。这些发现为生态过渡带的针对性生态恢复和适应性植被管理提供了科学依据。","Frontiers in Forests and Global Change","2026-09-28T00:00:00Z",{"impact":162,"substance":241,"depth":87,"authority":20,"freshness":117,"relevant":22,"comment":333},"基于30米Landsat NDVI与XGBoost-SHAP量化地形对植被非线性响应的研究，方法新颖、数据扎实，对生态过渡带精准修复有参考价值，但属学术论文，公共传播价值有限。",[335],{"name":330,"url":327},[274,28,337,29,338],"NDVI","植被动态",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":341},{"candidates":342,"agricultural_evidence":14,"reason":53},[343,348,352],{"code":49,"score":41,"supported":42,"title":344,"abstract":345,"tags":347},[],[346,103],"生物多样性",[28],{"code":254,"score":41,"supported":42,"title":349,"abstract":350,"tags":351},[],[274,276],[274],{"code":40,"score":353,"supported":14,"title":354,"abstract":355,"tags":356},3,[],[],[337,338,29],[358,359],"秦岭大巴山 生态过渡带 植被","NDVI 地形异质性 植被响应","秦岭大巴山生态过渡带植被-3791","10.3389\u002Fffgc.2026.1948232",{"doi":361,"openalex_id":363,"authors":364,"venue":330,"cited_by_count":58,"oa_url":373,"card":374,"direction":70,"ingested_from":72},"W7214562314",[365,367,369,371],{"name":366,"orcid":9},"Xinwen Su",{"name":368,"orcid":9},"Di Wen",{"name":370,"orcid":9},"Jun Li",{"name":372,"orcid":9},"Peng Dailiang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fforests-and-global-change\u002Farticles\u002F10.3389\u002Fffgc.2026.1948232\u002Fpdf",{"tldr":375,"method":376,"finding":377,"direction":70,"opportunity":378},"研究秦岭-大巴山生态过渡带植被非线性响应气候与人类活动，并揭示地形异质性的调控作用。","用2000-2022年30米Landsat NDVI、气候、人类活动和地形数据，","区域显著绿化，NDVI增速0.0051\u002F年；土地利用、海拔、人口密度和降水是主要驱动因子，地形梯度调","可探究其他生态过渡带中地形异质性对植被非线性响应的普适性，并耦合多源遥感与可解释机器学习优化生态修复。","2026-09-29T23:30:28.608664Z",{"id":381,"title":382,"url":383,"summary":384,"summary_zh":385,"content":9,"source_name":386,"source_url":383,"published_at":387,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":388,"score_detail":389,"sources":392,"tags":394,"primary_topic":398,"secondary_topics":402,"classification":403,"search_phrases":424,"slug":427,"view_count":22,"doi":428,"paper":429,"created_at":456},3172,"Demining and revitalising war-affected landscapes in Ukraine: multi-actor governance responses","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11625-026-01892-y","Abstract Ukraine faces one of the world’s largest and most complex explosive-ordnance contamination problems, affecting agricultural land, settlements, forests, and other natural ecosystems. This paper examines how global demining strategies are adapted within Ukraine’s multi-actor governance system and what this means for linking clearance with land recovery, soil restoration, and long-term environmental resilience. It addresses a gap in understanding how mine-action governance supports integrated landscape recovery in war-affected settings. We developed a seven-strategy analytical typology of mine-action and land-recovery approaches, comprising manual clearance, mechanical demining, remote sensing and mapping, community-based approaches, ecological remediation, legal and institutional frameworks, and post-conflict land-use zoning. Applying this typology to Ukraine and drawing on stakeholder analysis, policy review, and expert interviews, we found that manual clearance and mechanical land-release methods remain central, yet supported by new legal, institutional, and governance arrangements. Digital innovation in Ukraine’s mine-action sector is advancing rapidly, with UAV mapping, AI-supported detection, and integrated prioritization platforms increasingly incorporated into survey and planning processes. Uptake remains uneven, however, and interoperability and data-sharing gaps continue to limit implementation. Ukraine’s mine-action governance system is multi-actor and polycentric, but coordination remains challenging because of overlapping mandates, fragmented information flows, and differing donor priorities. These conditions reveal both opportunities and coordination challenges in linking mine action to environmental recovery and long-term land-use planning. Environmental concerns, including soil contamination in and around explosion craters, damage to forest areas, and degradation of shelterbelts, are receiving increasing attention, yet implementation remains limited and often confined to small or time-bound pilot projects. Community participation in prioritization of mine-action activities remains uneven, and interviewees reported that smallholders risk being sidelined by larger agricultural actors with greater influence over clearance demands. Scale remains a major challenge, requiring coordination across national, regional, and local levels while reconciling different humanitarian, economic, environmental, and recovery priorities. Our findings highlight that sustainable recovery depends on integrating mine action governance, environmental assessment and remediation, land-use planning, and long-term soil and ecosystem monitoring into a coherent recovery process. Ukraine’s evolving multi-actor approach offers lessons for other conflict-affected regions seeking to address explosive-ordnance contamination, environmental degradation, and landscape and livelihood recovery during and after armed conflict.","摘要：乌克兰面临着全球规模最大、最复杂的爆炸物污染问题之一，污染波及农田、居民点、森林及其他自然生态系统。本文考察了全球排雷策略如何在乌克兰的多主体治理体系中加以调适，以及这对于将排雷与土地恢复、土壤修复和长期环境韧性相衔接意味着什么。本文填补了关于排雷行动治理如何支持受战争影响地区综合景观恢复这一认知空白。我们构建了一个包含七种策略的排雷行动与土地恢复分析方法类型学，包括人工排雷、机械排雷、遥感与测绘、社区参与式方法、生态修复、法律与制度框架，以及冲突后土地利用分区。将该类型学应用于乌克兰，并结合利益相关者分析、政策审查和专家访谈，我们发现人工排雷和机械土地释放方法仍居核心地位，但得到了新的法律、制度和治理安排的支撑。乌克兰排雷行动领域的数字创新正在快速发展，无人机测绘、人工智能辅助探测和综合优先级排序平台日益融入调查与规划流程。然而，应用程度仍不均衡，互操作性和数据共享方面的差距继续制约着实施。乌克兰的排雷行动治理体系是多主体和多中心的，但由于职责重叠、信息流动碎片化以及捐助方优先事项各异，协调仍面临挑战。这些条件既揭示了将排雷行动与环境恢复和长期土地利用规划相衔接的机遇，也暴露了其中的协调难题。环境关切——包括爆炸坑内及周边的土壤污染、林区破坏和林带退化——正受到越来越多的关注，但实施仍然有限，且往往局限于小规模或有时限的试点项目。社区在排雷行动活动优先级排序中的参与仍不均衡，受访者反映，小农户有可能被对排雷需求拥有更大影响力的大型农业主体边缘化。规模仍是一大挑战，需要在国家、区域和地方各级之间进行协调，同时兼顾人道主义、经济、环境和恢复等不同优先事项。我们的发现凸显了……","Sustainability Science","2026-09-21T00:00:00Z",82,{"impact":87,"substance":241,"depth":390,"authority":162,"freshness":117,"relevant":22,"comment":391},19,"核心期刊论文，系统剖析乌克兰排雷治理与土地生态恢复的多主体协同机制，对冲突地区农业用地修复与遥感数字化应用有借鉴价值。",[393],{"name":386,"url":383},[28,29,395,396,397],"排雷治理","土地复垦","无人机测绘",{"code":40,"slug":399,"name":400,"path":401,"parent_code":263},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":107,"locked":14,"version":35,"method":36,"evidence":404},{"candidates":405,"agricultural_evidence":42},[406,410,414,420],{"code":40,"score":21,"supported":42,"title":407,"abstract":408,"tags":409},[],[45,46],[397,29],{"code":176,"score":75,"supported":42,"title":411,"abstract":412,"tags":413},[],[181,179],[],{"code":415,"score":353,"supported":42,"title":416,"abstract":417,"tags":419},"G2",[],[418],"小农户",[],{"code":49,"score":22,"supported":14,"title":421,"abstract":422,"tags":423},[],[],[28],[425,426],"乌克兰 排雷 土地恢复","地雷污染 农业用地 遥感","乌克兰排雷土地恢复-3172","10.1007\u002Fs11625-026-01892-y",{"doi":428,"openalex_id":430,"authors":431,"venue":386,"cited_by_count":58,"oa_url":383,"card":450,"direction":70,"ingested_from":72},"W7213868215",[432,435,438,441,444,447],{"name":433,"orcid":434},"Mariana Melnykovych","https:\u002F\u002Forcid.org\u002F0000-0003-0481-2842",{"name":436,"orcid":437},"Olena Melnyk","https:\u002F\u002Forcid.org\u002F0000-0001-5763-0431",{"name":439,"orcid":440},"Oleksandr Soshenskyi","https:\u002F\u002Forcid.org\u002F0000-0002-3028-0723",{"name":442,"orcid":443},"Anna McKean","https:\u002F\u002Forcid.org\u002F0000-0002-1107-2881",{"name":445,"orcid":446},"Владислав Дудар","https:\u002F\u002Forcid.org\u002F0009-0006-9677-9531",{"name":448,"orcid":449},"Patrick O. Waeber","https:\u002F\u002Forcid.org\u002F0000-0002-3229-0124",{"tldr":451,"method":452,"finding":453,"direction":454,"opportunity":455},"分析乌克兰排雷与土地恢复的多主体治理，提出七类策略并评估数字技术应用。","七策略类型学、利益相关者分析、政策审查与专家访谈。","排雷治理呈多中心但协调难，数字技术应用不均，环境恢复多为小规模试点。","数字乡村与农业信息化","可研究排雷后土地恢复的数字化协同平台与数据共享机制，关注小农参与。","2026-09-22T23:30:23.129381Z",{"id":458,"title":459,"url":460,"summary":461,"summary_zh":462,"content":9,"source_name":463,"source_url":460,"published_at":464,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":465,"score_detail":466,"sources":468,"tags":470,"primary_topic":474,"secondary_topics":475,"classification":476,"search_phrases":492,"slug":495,"view_count":353,"doi":496,"paper":497,"created_at":536},3016,"Afforestation enhances ecosystem stability in the farming-pastoral ecotone of northern China, but its effects are constrained by water availability","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjpe\u002Frtag232","Abstract The farming-pastoral ecotone of northern China (FPENC), a typical semi-agricultural and semi-pastoral ecologically fragile zone, has long faced controversy over afforestation due to its water-limited environment, raising concerns about how afforestation affects this fragile ecosystem. In this study, based on Google Earth Engine and a Random Forest model, we derive afforestation distribution (1985-2020), and further investigated ecosystem stability under afforestation from the perspectives of short-term responses and long-term dynamic changes. The results showed: (1) By 2020, 67,700 km2 had been afforested in FPENC (6.20% of its area). Afforestation survival strongly depended on precipitation, with relatively low retention rates (40%-70%) in the zones with \u003C 400 mm. (2) At short-term timescales, afforestation mitigated ecosystem state deviations caused by extreme drought events and improved surrounding ecosystems within a 150 m buffer. (3) Over long-term timescales, NDVI across the FPENC exhibited a significant increasing trend over the past four decades, accompanied by an overall enhancement in ecosystem recovery capacity. In zones with > 400 mm annual precipitation and with forest cover exceeding 40%, ecosystem recovery capacity increased significantly, showing there might be a potential linkage among afforestation, precipitation, and ecosystem recovery processes. This study revealed despite water scarcity and low tree survival, afforestation under suitable precipitation can effectively enhance regional ecosystem stability.","中国北方农牧交错带（FPENC）是典型的半农半牧生态脆弱区，由于环境水分受限，长期以来在造林问题上存在争议，引发了人们对造林如何影响这一脆弱生态系统的担忧。本研究基于Google Earth Engine和随机森林模型，反演了1985—2020年造林分布，并进一步从短期响应和长期动态变化两个角度研究了造林条件下的生态系统稳定性。结果表明：（1）截至2020年，FPENC已造林67,700 km²，占其面积的6.20%。造林存活强烈依赖降水，在降水量小于400 mm的区域，保存率相对较低（40%—70%）。（2）在短期时间尺度上，造林缓解了极端干旱事件引起的生态系统状态偏离，并改善了150 m缓冲区内周边生态系统。（3）在长期时间尺度上，过去四十年FPENC的NDVI呈显著增加趋势，同时生态系统恢复能力总体增强。在年降水量大于400 mm且森林覆盖率超过40%的区域，生态系统恢复能力显著提高，表明造林、降水与生态系统恢复过程之间可能存在潜在联系。本研究揭示，尽管存在水分短缺和树木存活率低的问题，但在适宜降水条件下，造林能够有效增强区域生态系统稳定性。","Journal of Plant Ecology","2026-09-17T00:00:00Z",80,{"impact":87,"substance":241,"depth":87,"authority":162,"freshness":21,"relevant":22,"comment":467},"基于遥感与随机森林量化北方农牧交错带造林成效及水分约束，数据规模大、结论有新意，对生态修复与农业信息化有参考价值。",[469],{"name":463,"url":460},[28,29,471,472,473],"造林","农牧交错带","水资源约束",{"code":254,"slug":255,"name":256,"path":257,"parent_code":100},[],{"status":107,"locked":14,"version":35,"method":36,"evidence":477},{"candidates":478,"agricultural_evidence":42},[479,484,488],{"code":254,"score":75,"supported":42,"title":480,"abstract":481,"tags":483},[],[275,482],"drought",[],{"code":49,"score":22,"supported":14,"title":485,"abstract":486,"tags":487},[],[],[28],{"code":40,"score":22,"supported":14,"title":489,"abstract":490,"tags":491},[],[],[29],[493,494],"北方农牧交错带 造林 水资源","Google Earth Engine 造林 遥感","北方农牧交错带造林水资源-3016","10.1093\u002Fjpe\u002Frtag232",{"doi":496,"openalex_id":498,"authors":499,"venue":463,"cited_by_count":58,"oa_url":530,"card":531,"direction":70,"ingested_from":72},"W7213445880",[500,503,506,508,510,512,514,516,518,520,523,525,528],{"name":501,"orcid":502},"Yuchao Luo","https:\u002F\u002Forcid.org\u002F0000-0003-0063-3505",{"name":504,"orcid":505},"Haile Zhao","https:\u002F\u002Forcid.org\u002F0000-0002-5870-062X",{"name":507,"orcid":9},"Qianhe Wang",{"name":509,"orcid":9},"Yi Zhou",{"name":511,"orcid":9},"Xin Chen",{"name":513,"orcid":9},"Yuling Jin",{"name":515,"orcid":9},"Xingjie Yin",{"name":517,"orcid":9},"Guoliang Zhang",{"name":519,"orcid":9},"Haorui Sun",{"name":521,"orcid":522},"Jun Bai","https:\u002F\u002Forcid.org\u002F0000-0002-1408-4271",{"name":524,"orcid":9},"Huiyao Shi",{"name":526,"orcid":527},"Zhihua Pan","https:\u002F\u002Forcid.org\u002F0000-0002-8187-1574",{"name":529,"orcid":9},"Pingli An","https:\u002F\u002Facademic.oup.com\u002Fjpe\u002Fadvance-article-pdf\u002Fdoi\u002F10.1093\u002Fjpe\u002Frtag232\u002F71179175\u002Frtag232.pdf",{"tldr":532,"method":533,"finding":534,"direction":70,"opportunity":535},"基于GEE和随机森林反演1985-2020年北方农牧交错带造林分布，评估其对生态系统稳定性的影响。","Google Earth Engine遥感数据与随机森林模型，分析造林分布及ND","造林可缓解干旱影响并提升恢复力，但存活率与效果受降水制约，400mm为关键阈值。","可探究不同降水梯度下造林-水分-恢复力耦合机制，优化生态修复的水资源约束阈值。","2026-09-20T23:30:22.592517Z"]