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

Mihai A Tanase,Ignacio Borlaf-Mena,Gheorghe Marin,Ovidiu Badea,Maurizio Santoro,Cristina Aponte,Bogdan Apostol

doi:10.3390/f12070944

Abstract

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.

Highlights

Forests are among the most biodiverse terrestrial ecosystems and a key element for carbon sequestration as they store large amounts of organic matter
Forest above ground biomass (AGB) estimation is a sensitive research topic, as information on AGB levels and dynamics is needed to estimate greenhouse gases flux and to shape policies development, implementation, and monitoring [1]. This importance is highlighted by the countless forest inventory programs aimed at evaluating, monitoring, and reporting, among others, AGB or Growing Stock Volume (GSV) levels. Such programs use an array of data sources from in situ measurements to information acquired by earth observation (EO) platforms
The results suggested that non-parametric models provide the lowest errors and bias, regardless of polarization or forest species over the selected study area

Summary

Introduction

Forests are among the most biodiverse terrestrial ecosystems and a key element for carbon sequestration as they store large amounts of organic matter (i.e., biomass). Forest above ground biomass (AGB) estimation is a sensitive research topic, as information on AGB levels and dynamics is needed to estimate greenhouse gases flux and to shape policies development, implementation, and monitoring [1]. This importance is highlighted by the countless forest inventory programs aimed at evaluating, monitoring, and reporting, among others, AGB or Growing Stock Volume (GSV) levels. Such programs use an array of data sources from in situ measurements to information acquired by earth observation (EO) platforms. NFI programs are expensive and time consuming while not providing for a synoptic view across the landscape [3]

Objectives

Methods

Results