Monitoring forest landscape restoration from unmanned aerial vehicles using Lidar and hyperspectral remote sensing

Abstract

Several countries have submitted ambitious international pledges to restore by 2030 over 350 million hectares of degraded and deforested landscapes into multi-functional landscapes. Planning and monitoring forest and landscape restoration programs requires more comprehensive information at large scale using cost-effective methodologies to distinguish forest typologies and assess the recovery of forest structure and functions. This proposal will advance our understanding of restoration of tropical forests and their functions using several crucial variables derived by an unmanned aerial vehicle (UAV) using 3-D Lidar (Light Detection and Ranging) and HSI (hyperspectral imaging). The information offered by these remote sensors in such low cost platforms will revolutionize measurement and understanding of tropical forests restoration success, bringing detailed information from broad areas such as restoration, management and conservation, to significantly improve decision making in forestry. More specifically, we will investigate the potential of a UAV-based Lidar-HIS fusion approach to (i) distinguish forest types, tree diversity, above-ground biomass, (ii) identify tree species and predict (iii) microclimate, light and water environments and (iv) tree demography. These contributions may be key to advance monitoring of restoration programs implemented in the contexts of the Native Vegetation Protection Law (which replaced the Forest Code), the Brazilian commitments to the Paris Climate Agreement and Bonn Challenge, and national coalitions as such the Atlantic Forest Restoration Pact.