Growing Stock Volume Retrieval from Single and Multi-Frequency Radar Backscatter
نویسندگان
چکیده
While products generated at global levels provide easy access to information on forest growing stock volume (GSV), their use regional national 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 GSV for Romanian forests. To this end, we used inventory (NFI) permanent sampling plots with largely concurrent SAR datasets acquired C- L-bands train validate a machine learning algorithm. Different configurations independent variables were evaluated assess potential synergies between L-band. The results show estimation L-band rather similar, relative root mean squared (RelRMSE) around 55% forests averaging over 450 m3 ha−1, while two wavelengths limited. Locally models improved 14% when compared values obtained from datasets. However, even showed particularly large low intervals. Aggregating larger areas considerably reduced (down 25%) errors.
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ژورنال
عنوان ژورنال: Forests
سال: 2021
ISSN: ['1999-4907']
DOI: https://doi.org/10.3390/f12070944