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

Forests

Cristina Aponte¹  Maurizio Santoro²  Gheorghe Marin³  Bogdan Apostol³  Mihai A. Tanase³  Ignacio Borlaf-Mena³  Ovidiu Badea³ 

[1] Department of Environment and Agronomy, Centro Nacional Instituto de Investigación y Tecnología Agraria y Alimentaria, INIA-CSIC, Ctra. de la Coruña, km 7.5, 28040 Madrid, Spain;Gamma Remote Sensing, Worbstrasse 225, 3073 Gumligen, Switzerland;National Institute for Research and Development in Forestry Marin Dracea, 128 Blvd. Eroilor, 077190 Voluntari, Romania;

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

DOI  :  10.3390/f12070944

来源: DOAJ

【 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.

【 授权许可】

Unknown