ABSTRACT

Background:

The objective of this study was to estimate the wood volume of a Pinus taeda L. plantation using variables extracted from the Sentinel-1 active sensor and the Sentinel-2 passive sensor. To do so, data from a forest inventory with rectangular plots of 550 m² were used to estimate the stand volume. We derived and adapted average vegetation indices per plot from images obtained by Sentinel-1 and Sentinel-2 sensors. The data were then correlated with the volume per plot based on the forest inventory. The Modified Radar Forest Degradation Index (mRDFI) showed the highest correlation for Sentinel-1 data, while the Difference Vegetation-Index (DVI) performed best for Sentinel-2.

Results:

The regression models were built using Stepwise modeling, demonstrating that the models fit with only the Sentinel-2 indices performed better than the others (indices adapted for Sentinel-1 and a combination of Sentinel-1 and Sentinel-2 data), with an R² adjusted between 0.51 to 0.40 and a standard error (Syx%) of 3.66 to 8.97. According to the statistical analyses, we found no significant differences between the volume estimated by the forest inventory (12.56±1.17) and the remote sensing techniques used (Sentinel-2 with 12.56±1.03 and Sentinel-1 with 12.56±0.94). However, further tests should be conducted with other active sensors operating in different spectral bands and polarization modes for other forest species.

Conclusion:

We found no significant differences between the volumetric estimates derived from remote sensing data and forest inventory techniques.

Keywords:
modeling; Sentinel-1; Sentinel-2; wood stock.

HIGHLIGHTS

There was no significant difference between the volumetric estimates derived from remote sensing data and forest inventory techniques, but the regression model built with Sentinel-2 was the most suitable for estimating the volume of Pinus taeda L.