Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Data

Abstract

The objective of this study was to evaluate the use of the MSI Sentinel-2 and SRTM data to estimate the volume of wood in a Semidecidual Seasonal Forest. Regression equations were fitted based on the remote sensing data, taking into consideration the individual bands and vegetation index of the MSI, elevation values and their derivatives obtained from the SRTM mission and the combination of the data drawn from the MSI and SRTM. RMSE and graphic analysis of residues were used to assess the accuracy of the fitted equations. The best model revealed values of 0.6508 and RMSE of 20.41% in the fit, and of 0.5680 and RMSE of 26.61% in the validation, using the combined MSI and SRTM data as predictors. The volume estimation using spectral data showed satisfactory results, highlighting the importance of topography in the prediction of the volume of wood for the area under investigation.

Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

“…The simplest approaches are based on multispectral analysis of freely-available VNIR imagery having a spatial resolution of the order of 10 m or coarser [11][12][13][14][15][16][17]. Useful enrichment of the available feature space has been demonstrated using multitemporal datasets [18][19][20][21], incorporating texture measures [14,22] and field-derived or satellite-derived three-dimensional information [23][24][25][26][27][28][29][30]. Other approaches are based on the use of ultra-high-resolution VNIR imagery (usually not free of cost) [31,32], radar imagery [1,[33][34][35][36][37][38][39][40][41][42][43][44][45][46], or combinations of VNIR and radar imagery [47][48][49][50][51][52][53].…”

“…Figure 1 attempts to give a simple overview of the current situation regarding remote sensing estimation of GSV. It has been compiled from quantitative data abstracted from many publications [13,14,16,[21][22][23]25,27,32,33,37,40,47,48,50,[56][57][58][59][60]. As Figure 1 shows, typical accuracies for spaceborne methods are approximately 20 to 40% RMSE, becoming somewhat poorer at lower values of GSV. )…”