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基于Sentinel 1&2主被动遥感数据和冠层高度的森林净初级生产力估测
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作者 田春红 李明阳 +2 位作者 李陶 李登攀 田雷 《南京林业大学学报(自然科学版)》 CAS CSCD 北大核心 2024年第4期132-140,共9页
【目的】利用Sentinel-1&2主被动遥感数据结合森林冠层高度,对区域森林净初级生产力(NPP)进行高效、准确的估测,为森林精准经营措施以及“双碳”目标的制定提供科学依据。【方法】以南方重点林区资兴市为研究区,基于Sentinel-1和Sen... 【目的】利用Sentinel-1&2主被动遥感数据结合森林冠层高度,对区域森林净初级生产力(NPP)进行高效、准确的估测,为森林精准经营措施以及“双碳”目标的制定提供科学依据。【方法】以南方重点林区资兴市为研究区,基于Sentinel-1和Sentinel-2主被动遥感数据,采用了多元逐步回归、人工神经网络、K最邻近、随机森林4种模型估算NPP。在此基础上加入了Sentinel-1通过InSAR与SRTM DEM差分得到的冠层高度,分析其对NPP估测精度的影响。【结果】(1)研究区2019年的森林NPP均值为7.79 t/hm^(2),呈中南部高、西北低的空间分布特征。(2)4种模型中,主被动遥感结合估测NPP的精度均高于单源遥感方式;随机森林估测区域森林NPP的精度最高,模型表现最好。(3)加入冠层高度可一定程度提高森林NPP估测精度,R2从0.70提高到了0.75。【结论】基于Sentinel 1&2主被动遥感数据并结合DEM差分法获取的冠层高度因子,可有效提高NPP估测精度。 展开更多
关键词 森林净初级生产力 Sentinel-1 Sentinel-2 ICESat-2 森林冠层高度 资兴市 智慧林业 森林精准经营
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结合冠层密度的森林净初级生产力遥感估测 被引量:4
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作者 李陶 李明阳 钱春花 《南京林业大学学报(自然科学版)》 CAS CSCD 北大核心 2021年第5期153-160,共8页
【目的】森林冠层密度与林分年龄、植被生长状况有关,在区域森林净初级生产力遥感估测中,结合森林冠层密度以期提高估测精度。【方法】以广东省韶关市为研究对象,选用2017年Landsat-8 OLI影像、2017年357块森林资源连续清查固定样地数... 【目的】森林冠层密度与林分年龄、植被生长状况有关,在区域森林净初级生产力遥感估测中,结合森林冠层密度以期提高估测精度。【方法】以广东省韶关市为研究对象,选用2017年Landsat-8 OLI影像、2017年357块森林资源连续清查固定样地数据为主要信息源,分别采用随机森林、多元线性回归、人工神经网络和K最近邻分类法等4种模型,结合森林冠层密度制图器(FCD)进行区域森林净初级生产力特征变量的选取、参数建模、模型精度评价和森林净初级生产力空间制图。【结果】特征变量中,红光波段(B4)、归一化植被指数(NDVI)、比值植被指数(RVI)、叶面积指数(LAI)、缨帽变换土壤植被因子、纹理特征和地形特征在森林净初级生产力反演中有重要作用。将森林冠层密度因子加入反演模型后,4种遥感估测模型精度均有大幅度提高。对4种遥感估测模型进行性能比较,随机森林模型精度最高,其次是多元线性回归模型、人工神经网络模型,K-最近邻分类模型精度最低。研究区内森林净初级生产力平均值为10.689 t/(hm^(2)·a),高森林净初级生产力[≥18 t/(hm^(2)·a)]林分面积仅占研究区的19.61%,主要分布在海拔较高的西北部。【结论】结合冠层密度进行森林净初级生产力的建模,可有效提高模型估测精度。 展开更多
关键词 森林净初级生产力 冠层密度 遥感反演 广东韶关市
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Examining Forest Net Primary Productivity Dynamics and Driving Forces in Northeastern China During 1982–2010 被引量:16
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作者 MAO Dehua WANG Zongming +2 位作者 WU Changshan SONG Kaishan REN Chunying 《Chinese Geographical Science》 SCIE CSCD 2014年第6期631-646,共16页
Forest net primary productivity (NPP) is a key parameter for forest monitoring and management. In this study, monthly and annual forest NPP in the northeastern China from 1982 to 2010 were simulated by using Carnegi... Forest net primary productivity (NPP) is a key parameter for forest monitoring and management. In this study, monthly and annual forest NPP in the northeastern China from 1982 to 2010 were simulated by using Carnegie-Ames-Stanford Approach (CASA) model with normalized difference vegetation index (NDVI) sequences derived from Advanced Very High Resolution Radiometer (AVHRR) Global Invento y Modeling and Mapping Studies (GIMMS) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) products. To address the problem of data inconsistency between AVHRR and MODIS data, a per-pixel unary linear regres- sion model based on least ~;quares method was developed to derive the monthly NDVI sequences. Results suggest that estimated forest NPP has mean relative error of 18.97% compared to observed NPP from forest inventory. Forest NPP in the northeastern China in- creased significantly during the twenty-nine years. The results of seasonal dynamic show that more clear increasing trend of forest NPP occurred in spring and awmnn. This study also examined the relationship between forest NPP and its driving forces including the climatic and anthropogenic factors. In spring and winter, temperature played the most pivotal role in forest NPR In autumn, precipitation acted as the most importanl factor affecting forest NPP, while solar radiation played the most important role in the summer. Evaportran- spiration had a close correlation with NPP for coniferous forest, mixed coniferous broadleaved forest, and broadleaved deciduous forest. Spatially, forest NPP in the Da Hinggan Mountains was more sensitive to climatic changes than in the other ecological functional re- gions. In addition to climalie change, the degradation and improvement of forests had important effects on forest NPP. Results in this study are helpful for understanding the regional carbon sequestration and can enrich the cases for the monitoring of vegetation during long time series. 展开更多
关键词 FOREST net primary productivity (NPP) Carnegie-Ames-Stanford Approach (CASA) model normalized difference vegeta-tion index (NDVI) northeastern China
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