Use of Drone RGB Imagery to Quantify Indicator Variables of Tropical-Forest-Ecosystem Degradation and Restoration

Abstract

Recognizing initial degradation levels is essential to planning effective measures to restore tropical forest ecosystems. However, measuring indicators of forest degradation is labour-intensive, time-consuming, and expensive. This study explored the use of canopy-height models and orthophotos, derived from drone-captured RGB images, above sites at various stages of degradation in northern Thailand to quantify variables related to initial degradation levels and subsequent restoration progression. Stocking density (R2 = 0.71) and relative cover of forest canopy (R2 = 0.83), ground vegetation (R2 = 0.71) and exposed soil + rock (R2 = 0.56) correlated highly with the corresponding ground-survey data. However, mean tree height (R2 = 0.31) and above-ground carbon density (R2 = 0.45) were not well correlated. Differences in correlation strength appeared to be site-specific and related to tree size distribution, canopy openness, and soil exposure. We concluded that drone-based quantification of forest-degradation indicator variables is not yet accurate enough to replace conventional ground surveys when planning forest restoration projects. However, the development of better geo-referencing in parallel with AI systems may improve the accuracy and cost-effectiveness of drone-based techniques in the near future.