摘要
【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。
【Objective】Using open source active and passive remote sensing data(Sentinel-1&-2)combined with forest canopy height can improve the estimation accuracy of forest net primary productivity(NPP),to provide a scientific basis for the formulation of forest precision management stragtegies and a“carbon peaking and carbon neutrality”strategy.【Method】Zixing City,which is a key forest area in the south,was used as the research area.Based on Sentinel-1 and Sentinel-2 active and passive remote sensing data,four models,namely multiple stepwise regression,artificial neural network,K-nearest neighbor,and random forest,were used to estimate NPP.On this basis,the canopy height obtained by Sentinel-1 through the difference between InSAR and SRTM DEM was added to analyze its effect on the accuracy of NPP estimation.【Result】(1)The mean value of forest NPP in the study area in 2019 was 7.79 t/hm^(2),showing the spatial distribution characteristics of high in the central southwest and low in the northwest.(2)Among the four models,the accuracy of NPP estimation by active and passive remote sensing was higher than that by single remote sensing;the accuracy of random forest estimation of regional forest NPP was the highest,and the model performed the best.(3)Adding canopy height improved the estimation accuracy of forest NPP to a certain extent,with R2 increased from 0.70 to 0.75.【Conclusion】The accuracy of NPP estimation can be improved based on Sentinel active and passive remote sensing data and canopy height factor,which is obtained by DEM difference method.
作者
田春红
李明阳
李陶
李登攀
田雷
TIAN Chunhong;LI Mingyang;LI Tao;LI Dengpan;TIAN Lei(College of Forestry and Grassland,Nanjing Forestry University,Nanjing 210037,China)
出处
《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
Journal of Nanjing Forestry University：Natural Sciences Edition
基金
国家自然科学基金项目(31770679)。
