摘要
由于光学遥感穿透性差,不能穿透林冠层识别林下植被,基于单一光学遥感提取的植被覆盖度,难以反映林下植被信息,从而无法为土壤侵蚀评价提供有效植被覆盖因子。针对此问题,本文以白洋淀–大清河流域为研究对象,结合实测数据,探究不同光子点分类下光子计数ICESat-2/ATLAS植被覆盖度采样的能力,并实现了研究区内星地协同植被覆盖度采样。在此基础上,联合Sentinel-2和Sentinel-1以及DEM等多源数据,基于随机森林回归模型方法实现植被覆盖度反演,并与传统常用的NDVI像元二分法提取结果进行对比。结果表明:相比于传统的NDVI像元二分法提取的反演结果,利用本研究中构建的随机森林回归模型估算的植被覆盖度精度更高,一定程度上可以对茂密森林的林下植被进行监测,避免了光学遥感存在的林下植被信号缺失的问题。在0.05、0.1和0.15不同的植被覆盖度误差容忍范围内,精度分别提升–4.1%、5.3%和9.4%,分别达到55.6%、71.1%和94.3%。
Due to poor penetrability,optical remote sensing can not identify understory vegetation beneath the canopy.Thus,the vegetation coverage extracted by optical remote sensing alone could not sufficiently capture understory vegetation information to formulate the vegetation coverage factor for soil erosion evaluation.To address this issue,the authors took the Baiyangdian-Daqing River Basin as the research object and considered the photon counting ICESat-2/ATLAS vegetation coverage sampling under different photon point classifications.Based on the measured data,satellite-ground collaborative vegetation coverage sampling was achieved in the study area.The results showed that compared with the inversion results extracted by the traditional NDVI pixel dichotomy,the vegetation coverage estimated by the random forest regression model constructed in this study was more accurate.To a certain extent,the proposed model can monitor the understory vegetation of dense forests and complement the lack of understory vegetation signal in optical remote sensing.In the three error tolerance 0.05,0.1,and 0.15 ranges,the inversion accuracy of vegetation coverage was increased by–4.1%,5.3%,and 9.4%,reaching the accuracy of 55.6%,71.1%,and 94.3%,respectively.
作者
杨瑾
史明昌
杨建英
程复
于红凤
YANG Jin;SHI Mingchang;YANG Jianying;CHENG Fu;YU Hongfeng(College of Soil and Water Conservation,Beijing Forestry University,Beijing 100083,China;Key Laboratory of Soil and Water Conservation and Desertification Combating,Ministry of Education,Beijing Forestry University,Beijing 100083,China;Monitor Center of Soil and Water Conservation,Ministry of Water Resources,Beijing 100055,China)
基金
The National Science and Technology Major Project of the Ministry of Science and Technology of China(2018ZX07110001).
作者简介
YANG Jin,E-mail:yj3190604@bjfu.edu.cn;Corresponding author:SHI Mingchang,E-mail:shimc@dtgis.com。
