Abstract
This work aimed to investigate the potential of image-derived indices derived from Sentinel-2/MSI images in the volumetric modeling of a stand of Pinus taeda L. located in Bom Retiro, State of Santa Catarina. For this purpose, field data derived from a forest inventory were used, by the fixed area method and simple random sampling with an allocation of 18 circular plots of 400 m². The remotely located data comprised an orbital image from the Sentinel-2/MSI sensor. From this image, 14 average vegetation indices per plot were calculated. These indices were correlated with the volume by plot (m³ 0.04 ha-1) derived from the inventory. The indices with the best correlation for volume by plot (m³ 0.04 ha-1) were the Generalized Difference Vegetation Index (GDVI) and Adjusted Soil Vegetation Index (SAVI) with 0.39 and 0.36, respectively. The best regression model completed using these VIs estimated the volume by plot with R² controls of 0.9402 and Syx of 1.44%. The use of spectral indices generated from Sentinel-2/MSI sensor data enabled the volumetric estimate of the Pinus taeda L. stand, not revealing differences between the volume accumulated by forest inventory and by orbital images. However, it is worth pointing out that new tests be carried out on other forest species and with medium to high spatial resolution sensors.
Highlights
Forestry activity is related to plantations formed by high quality trees at the end of the rotation
The use of spectral indices generated from Sentinel-2/MSI sensor data enabled the volumetric estimate of the Pinus taeda L. stand, not revealing differences between the volume accumulated by forest inventory and by orbital images
The IVs demonstrate the antagonistic behavior of the vegetation reflectance in the visible and NIR region (PONZONI & SHIMABUKURO, 2007)
Summary
Forestry activity is related to plantations formed by high quality trees at the end of the rotation. Variables such as wood volume, basal area and height are essential parameters for the management of forest resources (GUNLU et al, 2014) Obtaining dendrometric variables such as volume (v), height (h) and diameter at breast height (dbh) is associated with carrying out forest inventories, which, depending on the area to be analyzed, become costly and expensive (ALVES et al, 2013) providing the study of alternatives and techniques to facilitate the collection of such data (CANAVESI et al, 2010). In this scenario, Remote Sensing (RS) techniques have become a promising alternative to field-based methods for estimating tree volumes at the plot level (TESFAMICHAEL et al, 2010). Several research have already been conducted to explore the potential of multispectral (DUBE & MUTANGA, 2015), hyperspectral (HYCZA et al, 2018) and data from active sensors (CHEN et al, 2021)
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