Forest growing stock volume estimation using optical remote sensing over snow-covered ground: a case study for Sentinel-2 data and the Russian Southern Taiga region

Abstract

ABSTRACT This paper describes an approach to forest growing stock volume (GSV) estimation based on remotely sensed optical data in red and near-infrared (NIR) bands collected during the period of persistent snow cover. The approach was applied to Sentinel-2 reflectance measurements over forest with snow-covered understory in the north-eastern part of Russian Kostroma region. An in-house dataset with a forest stand-level GSV data was used to approximate GSV-reflectance relationship based on a power function for spruce-dominated, pine-dominated and birch-dominated forests. Highest coefficient of determination (R 2) = 0.84 was obtained for spruce-dominated forest and red band. A cross-validation was performed to estimate the accuracy of a stand-level GSV estimation based on the obtained GSV-reflectance relationship model and Sentinel-2 data. Best results were achieved for pine-dominated forest and NIR band: R 2 = 0.66; root-mean-square error (RMSE) = 58 m3/ha. This GSV estimation approach was validated with an independent dataset of field survey-based GSV measurements at the sample plot level. Validation showed R 2 values comparable to cross-validation results but higher RMSE. Overall Sentinel-2 data tested was found to be informative for GSV estimation; however performance of the described approach varied significantly depending on forest type, spectral band, GSV values range and spatial aggregation level.