Growing Stock Volume Retrieval from Single and Multi-Frequency Radar Backscatt

编号 030030203

推送时间 20210802

研究领域 森林经理 

年份 2021 

类型 期刊 

语种 英语

标题 Growing Stock Volume Retrieval from Single and Multi-Frequency Radar Backscatt

来源期刊 Forests

期 第302期

发表时间 20210717

关键词 forest growing stock volume;  synthetic aperture radar;  ALOS PALSAR-2;  Sentinel-1;  national forest inventory;  machine learning; 

摘要 While products generated at global levels provide easy access to information on forest growing stock volume (GSV), their use at regional to national levels is limited by temporal frequency, spatial resolution, or unknown local errors that may be overcome through locally calibrated products. This study assessed the need, and utility, of developing locally calibrated GSV products for the Romanian forests. To this end, we used national forest inventory (NFI) permanent sampling plots with largely concurrent SAR datasets acquired at C- and L-bands to train and validate a machine learning algorithm. Different configurations of independent variables were evaluated to assess potential synergies between C- and L-band. The results show that GSV estimation errors at C- and L-band were rather similar, relative root mean squared errors (RelRMSE) around 55% for forests averaging over 450 m3?ha?1, while synergies between the two wavelengths were limited. Locally calibrated models improved GSV estimation by 14% when compared to values obtained from global datasets. However, even the locally calibrated models showed particularly large errors over low GSV intervals. Aggregating the results over larger areas considerably reduced (down to 25%) the relative estimation errors.

服务人员 付贺龙

服务院士 唐守正

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