[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1893":3,"related-1893":63},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":22,"classification":23,"search_phrases":45,"slug":46,"view_count":40,"doi":47,"paper":48,"created_at":62},1893,"AI-Driven Ecological Economic Evaluation System for Habitat Restoration of Disturbed Estuarine Waterway Environment","https:\u002F\u002Fdoi.org\u002F10.62177\u002Fapemr.v3i7.1713","Estuarine waterways are important coastal ecological corridors that have long been affected by human activities such as shipping, water conservancy construction and reclamation, resulting in varying degrees of damage to their original habitats. To objectively assess the ecological restoration effect of disturbed estuarine waterways and provide scientific decision support for ecological management, this paper constructs an AI-driven ecological economic evaluation system for habitat restoration of disturbed estuarine waterway environment. This system integrates multi-source ecological monitoring data and socioeconomic data, and uses artificial intelligence algorithms to achieve automatic analysis and quantitative evaluation of restoration effects. Compared with the traditional manual evaluation method, the system constructed in this paper significantly improves the evaluation efficiency and result accuracy, and can provide targeted optimization suggestions for the implementation of habitat restoration projects in disturbed estuarine waterways.","河口航道作为重要的海岸生态廊道，长期受到航运、水利建设及围垦等人类活动的影响，导致其原有栖息地遭受不同程度的破坏。为客观评估受扰河口航道生态修复成效，并为生态管理提供科学决策支持，本文构建了一套基于人工智能驱动的受扰河口航道环境栖息地修复生态经济评价系统。该系统整合多源生态监测数据与社会经济数据，运用人工智能算法，实现修复效果的自动化分析与量化评估。与传统人工评估方法相比，本文所构建的系统显著提升了评估效率与结果准确性，并能针对受扰河口航道栖息地修复工程实施提供有针对性的优化建议。",null,"Asia Pacific Economic and Management Review","2026-09-06T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"主题为河口生态修复，不属于三农、农业信息化等范畴，故不入选。",[19],{"name":10,"url":6},[21],"生态修复",[],{"status":24,"locked":14,"version":25,"method":26,"evidence":27},"needs_review","2026-10-22.2","rules",{"candidates":28,"agricultural_evidence":14,"reason":44},[29,38],{"code":30,"score":31,"supported":32,"title":33,"abstract":34,"tags":37},"I2",6,true,[],[35,36],"artificial intelligence","decision support",[],{"code":39,"score":40,"supported":14,"title":41,"abstract":42,"tags":43},"H6",1,[],[],[21],"农业相关性需要复核",[],"AI-DrivenEcologicalEconomicEvaluationSys-1893","10.62177\u002Fapemr.v3i7.1713",{"doi":47,"openalex_id":49,"authors":50,"venue":10,"cited_by_count":15,"oa_url":53,"card":54,"direction":60,"ingested_from":61},"W7210287600",[51],{"name":52,"orcid":9},"Rumeng Bu","https:\u002F\u002Fojs.apspublisher.com\u002Findex.php\u002Fapemr\u002Farticle\u002Fdownload\u002F1713\u002F1334",{"tldr":55,"method":56,"finding":57,"direction":58,"opportunity":59},"构建AI驱动的受扰河口航道生境修复生态经济评价系统，实现自动分析与量化评估。","集成多源生态监测与社会经济数据，利用人工智能算法自动分析。","相比人工评价，系统显著提升评估效率与准确性，并提供优化建议。","其他","可延伸至农业生态系统修复评价，结合遥感与AI，构建农田生境健康与经济协同评估模型。","智慧农业 \u002F 农业物联网","openalex","2026-09-08T23:30:11.855970Z",{"total":31,"page":40,"page_size":31,"items":64},[65,150,229,321,365,420],{"id":66,"title":67,"url":68,"summary":69,"summary_zh":70,"content":9,"source_name":71,"source_url":68,"published_at":72,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":73,"score_detail":74,"sources":81,"tags":83,"primary_topic":88,"secondary_topics":94,"classification":99,"search_phrases":124,"slug":127,"view_count":15,"doi":128,"paper":129,"created_at":149},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":75,"substance":76,"depth":77,"authority":78,"freshness":79,"relevant":40,"comment":80},16,21,18,13,8,"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。",[82],{"name":71,"url":68},[21,84,85,86,87],"遥感监测","灌溉农业","土地利用变化","湿地保护",{"code":89,"slug":90,"name":91,"path":92,"parent_code":93},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[95],{"code":39,"slug":96,"name":97,"path":98,"parent_code":93},"biodiversity","生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity",{"status":100,"locked":14,"version":25,"method":26,"evidence":101},"classified",{"candidates":102,"agricultural_evidence":32},[103,109,116],{"code":89,"score":13,"supported":32,"title":104,"abstract":105,"tags":108},[],[106,107,90],"灌溉","水资源",[85],{"code":39,"score":110,"supported":32,"title":111,"abstract":112,"tags":115},9,[],[113,114],"生态系统服务","ecosystem service",[86,87,21],{"code":117,"score":118,"supported":32,"title":119,"abstract":120,"tags":123},"I3",7,[],[121,122],"遥感","remote sensing",[84],[125,126],"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":128,"openalex_id":130,"authors":131,"venue":71,"cited_by_count":15,"oa_url":68,"card":143,"direction":147,"ingested_from":61},"W7222966485",[132,135,137,140],{"name":133,"orcid":134},"Mohamed Dakki","https:\u002F\u002Forcid.org\u002F0000-0002-8042-3972",{"name":136,"orcid":9},"Said El Falah",{"name":138,"orcid":139},"Ibrahim Makrane","https:\u002F\u002Forcid.org\u002F0009-0002-9201-206X",{"name":141,"orcid":142},"Ismail Mansouri","https:\u002F\u002Forcid.org\u002F0000-0002-3679-6050",{"tldr":144,"method":145,"finding":146,"direction":147,"opportunity":148},"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。","农业遥感与作物表型","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":151,"title":152,"url":153,"summary":154,"summary_zh":155,"content":9,"source_name":156,"source_url":153,"published_at":157,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":158,"score_detail":159,"sources":163,"tags":165,"primary_topic":9,"secondary_topics":169,"classification":170,"search_phrases":190,"slug":193,"view_count":15,"doi":194,"paper":195,"created_at":228},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":79,"substance":77,"depth":160,"authority":161,"freshness":79,"relevant":40,"comment":162},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。",[164],{"name":156,"url":153},[21,84,166,167,168],"矿区生态","土壤养分","空间变异",[],{"status":24,"locked":14,"version":25,"method":26,"evidence":171},{"candidates":172,"agricultural_evidence":14,"reason":44},[173,181,186],{"code":174,"score":175,"supported":32,"title":176,"abstract":178,"tags":180},"H2",15,[177],"soil",[179,177],"土壤",[167],{"code":39,"score":182,"supported":14,"title":183,"abstract":184,"tags":185},2,[],[],[21,166],{"code":117,"score":40,"supported":14,"title":187,"abstract":188,"tags":189},[],[],[84],[191,192],"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":194,"openalex_id":196,"authors":197,"venue":156,"cited_by_count":15,"oa_url":222,"card":223,"direction":147,"ingested_from":61},"W7222017864",[198,201,204,207,210,213,216,219],{"name":199,"orcid":200},"Xiyao Feng","https:\u002F\u002Forcid.org\u002F0009-0009-1482-4784",{"name":202,"orcid":203},"Yu Chen","https:\u002F\u002Forcid.org\u002F0009-0007-6975-327X",{"name":205,"orcid":206},"Zhongyou Song","https:\u002F\u002Forcid.org\u002F0009-0001-0791-3457",{"name":208,"orcid":209},"Pan Gao","https:\u002F\u002Forcid.org\u002F0009-0000-0039-4562",{"name":211,"orcid":212},"Pengfei Chen","https:\u002F\u002Forcid.org\u002F0009-0000-8394-5672",{"name":214,"orcid":215},"Fabin Yan","https:\u002F\u002Forcid.org\u002F0009-0000-0496-7532",{"name":217,"orcid":218},"Pei Wang","https:\u002F\u002Forcid.org\u002F0009-0001-8997-421X",{"name":220,"orcid":221},"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":224,"method":225,"finding":226,"direction":147,"opportunity":227},"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":230,"title":231,"url":232,"summary":233,"summary_zh":234,"content":9,"source_name":235,"source_url":232,"published_at":236,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":237,"score_detail":238,"sources":241,"tags":243,"primary_topic":247,"secondary_topics":250,"classification":261,"search_phrases":288,"slug":291,"view_count":182,"doi":292,"paper":293,"created_at":320},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":75,"substance":239,"depth":77,"authority":160,"freshness":110,"relevant":40,"comment":240},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。",[242],{"name":235,"url":232},[244,245,21,84,246],"机器学习","土壤有机碳","干旱区农业",{"code":174,"slug":177,"name":248,"path":249,"parent_code":93},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[251,256],{"code":252,"slug":253,"name":254,"path":255,"parent_code":93},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":30,"slug":257,"name":258,"path":259,"parent_code":260},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":100,"locked":14,"version":25,"method":26,"evidence":262},{"candidates":263,"agricultural_evidence":32},[264,268,275,280,284],{"code":174,"score":175,"supported":32,"title":265,"abstract":266,"tags":267},[177],[179,177],[245],{"code":252,"score":13,"supported":32,"title":269,"abstract":270,"tags":274},[],[271,272,273],"气候变化","干旱","climate change",[246],{"code":30,"score":110,"supported":32,"title":276,"abstract":278,"tags":279},[277],"machine learning",[],[244],{"code":117,"score":118,"supported":32,"title":281,"abstract":282,"tags":283},[],[121,122],[84],{"code":39,"score":40,"supported":14,"title":285,"abstract":286,"tags":287},[],[],[21],[289,290],"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":292,"openalex_id":294,"authors":295,"venue":235,"cited_by_count":15,"oa_url":232,"card":315,"direction":147,"ingested_from":61},"W7219075005",[296,298,301,303,306,308,311,313],{"name":297,"orcid":9},"Y. Chen",{"name":299,"orcid":300},"Haiyang Xi","https:\u002F\u002Forcid.org\u002F0000-0003-3158-8161",{"name":302,"orcid":9},"Meng Zhu",{"name":304,"orcid":305},"Wenju Cheng","https:\u002F\u002Forcid.org\u002F0000-0002-1822-5668",{"name":307,"orcid":9},"Yulian Hao",{"name":309,"orcid":310},"Linbo Qu","https:\u002F\u002Forcid.org\u002F0009-0007-4669-8407",{"name":312,"orcid":9},"Zhehui Wang",{"name":314,"orcid":9},"Bin Wang",{"tldr":316,"method":317,"finding":318,"direction":147,"opportunity":319},"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":322,"title":323,"url":324,"summary":325,"summary_zh":9,"content":9,"source_name":326,"source_url":324,"published_at":327,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":328,"score_detail":329,"sources":331,"tags":333,"primary_topic":9,"secondary_topics":336,"classification":337,"search_phrases":353,"slug":356,"view_count":40,"doi":357,"paper":358,"created_at":364},4095,"Identification of priority areas using GIS and remote sensing for ecological restoration: contributions to the conservation of the Doradito Tucumano (Tucumán – Argentina)","https:\u002F\u002Fdoi.org\u002F10.19137\u002Fhuellas-2026-3015","Tucumano (Tucumán -Argentina)","Huellas","2026-10-01T00:00:00Z",57,{"impact":79,"substance":77,"depth":75,"authority":31,"freshness":110,"relevant":40,"comment":330},"GIS与遥感用于生态修复优先区识别，方法有参考价值，但属区域鸟类保护研究，与农业信息化关联偏弱，公共影响有限。",[332],{"name":326,"url":324},[121,21,334,335],"GIS","生物多样性保护",[],{"status":24,"locked":14,"version":25,"method":26,"evidence":338},{"candidates":339,"agricultural_evidence":14,"reason":44},[340,344,349],{"code":117,"score":110,"supported":32,"title":341,"abstract":342,"tags":343},[122],[],[121],{"code":345,"score":110,"supported":32,"title":346,"abstract":347,"tags":348},"I4",[334],[],[334],{"code":39,"score":40,"supported":14,"title":350,"abstract":351,"tags":352},[],[],[21],[354,355],"阿根廷 Tucumán Doradito Tucumano","GIS 遥感 生态修复","阿根廷TucumánDoraditoTucumano-4095","10.19137\u002Fhuellas-2026-3015",{"doi":357,"openalex_id":359,"authors":360,"venue":326,"cited_by_count":15,"oa_url":324,"card":9,"direction":147,"ingested_from":61},"W7216242382",[361],{"name":362,"orcid":363},"Andrea Margarita Díaz","https:\u002F\u002Forcid.org\u002F0009-0000-0126-0400","2026-10-03T23:30:35.510515Z",{"id":366,"title":367,"url":368,"summary":369,"summary_zh":370,"content":9,"source_name":326,"source_url":371,"published_at":327,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":372,"score_detail":373,"sources":377,"tags":379,"primary_topic":382,"secondary_topics":386,"classification":388,"search_phrases":405,"slug":408,"view_count":182,"doi":409,"paper":410,"created_at":419},4093,"Identificação de áreas prioritárias utilizando SIG e sensoriamento remoto para restauração ecológica: contribuições para a conservação do Doradito Tucumano (Tucumán – Argentina)","https:\u002F\u002Fdoi.org\u002F10.19137\u002F\u002Fhuellas-2026-3015","Landscape transformation driven by agricultural frontier expansion has intensified the fragmentation of habitats used by the Tucumán Doradito (Pseudocolopteryx dinelliana), a Near Threatened (NT) species, reducing ecological connectivity and the availability of suitable habitats in Tucumán Province, Argentina. This study aimed to identify priority areas for ecological restoration through the integration of Geographic Information Systems (GIS), remote sensing, and multicriteria analysis. Species occurrence records from GBIF, EcoRegistros, and the National Biodiversity Information System were combined with the IUCN potential distribution, Landsat 8\u002F9 imagery, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and a Digital Elevation Model provided by the National Geographic Institute. Map algebra and suitability and exclusion criteria were applied to identify 50,481 ha of high-priority restoration areas, representing 2.2% of the provincial territory. The results indicate that the departments of Graneros, Leales and Simoca contain 55.9% of the priority area, constituting the main core for restoring landscape connectivity. The proposed methodology provides an objective framework to support territorial planning and prioritize ecological restoration actions aimed at strengthening the conservation of the Tucumán Doradito in highly transformed landscapes.","农业前沿扩张驱动的景观转变加剧了图库曼多拉迪托鸟（Pseudocolopteryx dinelliana）所利用生境的破碎化，该物种为近危（NT）物种，导致阿根廷图库曼省的生态连通性和适宜生境可用性降低。本研究旨在通过整合地理信息系统（GIS）、遥感和多准则分析，确定生态恢复的优先区域。将来自GBIF、EcoRegistros和国家生物多样性信息系统的物种分布记录与IUCN潜在分布区、Landsat 8\u002F9影像、归一化植被指数（NDVI）、归一化水体指数（NDWI）以及国家地理研究所提供的数字高程模型相结合。应用地图代数以及适宜性和排除标准，确定了50,481公顷的高优先级恢复区域，占全省面积的2.2%。结果表明，Graneros、Leales和Simoca等省辖区包含优先区域的55.9%，构成了恢复景观连通性的核心区域。所提出的方法为支持国土空间规划、优先开展旨在加强高度转变景观中图库曼多拉迪托鸟保护的生态恢复行动提供了客观框架。","https:\u002F\u002Fcerac.unlpam.edu.ar\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F9861",59,{"impact":79,"substance":374,"depth":375,"authority":31,"freshness":79,"relevant":40,"comment":376},20,17,"阿根廷图库曼省利用GIS与遥感识别生态修复优先区的论文，方法可借鉴但属区域案例，公共价值有限。",[378],{"name":326,"url":371},[121,21,380,334,381],"生物多样性","景观连通性",{"code":345,"slug":383,"name":384,"path":385,"parent_code":260},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[387],{"code":39,"slug":96,"name":97,"path":98,"parent_code":93},{"status":100,"locked":14,"version":25,"method":26,"evidence":389},{"candidates":390,"agricultural_evidence":32},[391,397,401],{"code":345,"score":13,"supported":32,"title":392,"abstract":393,"tags":396},[],[394,334,395],"地理信息","geographic information",[334],{"code":39,"score":79,"supported":32,"title":398,"abstract":399,"tags":400},[],[380,96],[21,380],{"code":117,"score":118,"supported":32,"title":402,"abstract":403,"tags":404},[],[121,122],[121],[406,407],"Tucumán Doradito 生态修复","Landsat NDVI 优先区识别","TucumánDoradito生态修复-4093","10.19137\u002F\u002Fhuellas-2026-3015",{"doi":409,"openalex_id":411,"authors":412,"venue":326,"cited_by_count":15,"oa_url":371,"card":414,"direction":147,"ingested_from":61},"W7216132579",[413],{"name":362,"orcid":363},{"tldr":415,"method":416,"finding":417,"direction":147,"opportunity":418},"结合GIS、遥感和多准则分析，识别阿根廷图库曼省优先生态恢复区以保护近危鸟类。","使用GBIF等物种记录、Landsat影像、NDVI、NDWI和DEM进行地图代","识别出50,481公顷高优先级恢复区，占省域2.2%，集中在Graneros、Leales和Simo","可延伸至农业景观中多物种连通性保护规划，或结合时序遥感动态评估恢复成效。","2026-10-03T23:30:35.383506Z",{"id":421,"title":422,"url":423,"summary":424,"summary_zh":425,"content":9,"source_name":426,"source_url":423,"published_at":427,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":73,"score_detail":428,"sources":430,"tags":432,"primary_topic":9,"secondary_topics":435,"classification":436,"search_phrases":452,"slug":455,"view_count":182,"doi":456,"paper":457,"created_at":474},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":160,"substance":239,"depth":77,"authority":78,"freshness":110,"relevant":40,"comment":429},"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。",[431],{"name":426,"url":423},[271,21,433,84,434],"NDVI","植被动态",[],{"status":24,"locked":14,"version":25,"method":26,"evidence":437},{"candidates":438,"agricultural_evidence":14,"reason":44},[439,443,447],{"code":39,"score":118,"supported":32,"title":440,"abstract":441,"tags":442},[],[380,96],[21],{"code":252,"score":118,"supported":32,"title":444,"abstract":445,"tags":446},[],[271,273],[271],{"code":117,"score":448,"supported":14,"title":449,"abstract":450,"tags":451},3,[],[],[433,434,84],[453,454],"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":456,"openalex_id":458,"authors":459,"venue":426,"cited_by_count":15,"oa_url":468,"card":469,"direction":147,"ingested_from":61},"W7214562314",[460,462,464,466],{"name":461,"orcid":9},"Xinwen Su",{"name":463,"orcid":9},"Di Wen",{"name":465,"orcid":9},"Jun Li",{"name":467,"orcid":9},"Peng Dailiang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fforests-and-global-change\u002Farticles\u002F10.3389\u002Fffgc.2026.1948232\u002Fpdf",{"tldr":470,"method":471,"finding":472,"direction":147,"opportunity":473},"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"]