摘要
稀树草原拥有低密度而总量巨大的地上生物量,是影响全球碳循环的重要地区。但其内部空间差异显著,遥感提取生物量不确定性大。全球生态系统动态调查任务(GEDI)通过激光雷达获取地表植被的三维立体信息,能够在观测足迹上得到地上生物量的高质量估算,但缺乏空间连续的地上生物量数据。选取非洲苏丹草原的稀树草原为研究区,利用Sentinel-2光学和PALSAR-2微波观测,并结合遥感树木覆盖度数据提取了28个特征,与GEDI足迹水平地上生物量数据建立随机森林模型,构建非洲稀树草原高分辨率地上生物量估算方法。结果显示:算法能够生成研究区内空间连续的地上生物量数据,有效提取了以往研究中常被忽略的非森林区域的树木信息。模型平均绝对误差和均方根误差分别为15.798 Mg/hm^(2)和24.626 Mg/hm^(2),使用不同季节光学影像建模的精度接近。当使用雨季光学数据建模时,红、红边和短波红外光谱波段及相关光谱指数重要性高;当使用旱季光学数据时,树木覆盖度和微波数据的重要性显著提升。在进行非洲稀树草原的大尺度生物量估算时,采用多种数据源有助于获得较好的估算精度。本研究为未来低成本监测稀树草原地上生物量提供了方法参考,有助于该区域的植被碳循环研究。
Savannas,characterized by their low vegetation density and substantial total aboveground biomass,represent a critical region for global carbon cycle.Nevertheless,significant spatial heterogeneity exists within these ecosystems,leading to considerable uncertainty in remote sensing biomass estimations.The Global Ecosystem Dynamics Investigation(GEDI)provides high-quality estimates of above-ground biomass within its observed footprint by leveraging three-dimensional surface vegetation information from LiDAR.However,it lacks spatial continuous above-ground biomass data.Sentinel-2,PALSAR-2 and tree cover data were used to extract 28 features,and a random forest model was established with GEDI footprint level above-ground biomass data to build a high-resolution above-ground biomass estimation method for African savanna.The results show that the algorithm can generate spatially continuous above-ground biomass data in study area,and effectively extract tree information in non-forested areas that are often ignored in previous studies.The mean absolute error and root-mean-square error of the model are 15.798 Mg/ha and 24.626 Mg/ha,respectively.The accuracy remains consistent when using optical images from different seasons.When modeling with optical data acquired during rainy season,spectral bands such as red,red edge,and short-wave infrared,along with their relative spectral indices,play crucial roles.In contrast,when using dry season optical data,tree cover and InSAR become significantly more important.When conducting large-scale biomass estimation of African savannas,the use of multiple data sources can help to obtain better estimation accuracy.This study provides a method for low-cost monitoring of aboveground biomass in Savannas in the future,and contributes to the in-depth study of vegetation carbon cycle in this region.
作者
刘颖智
刘洋
刘荣高
陈继龙
魏雪馨
LIU Yingzhi;LIU Yang;LIU Ronggao;CHEN Jilong;WEI Xuexin(Institute of Geographic Sciences and Natural Resources Research,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《遥感技术与应用》
北大核心
2025年第3期681-694,共14页
Remote Sensing Technology and Application
基金
国家重点研发计划项目(2019YFA0606601)
国家自然科学基金国际(地区)合作与交流项目(42161144001)
中国科学院中—非联合研究中心项目(067GJHZ2023033033MI)。
