Characterization of Shrub Fuel Structure and Spatial Distribution Using Multispectral and 3D Multitemporal UAV Data

Shrubland vegetation plays a crucial role in ecological processes, but its conservation is facing threats due to climate change, wildfires, and human activities. Unmanned Aerial Vehicles (UAVs), or ‘drones’, have become valuable tools for detailed vegetation mapping, providing high-resolution imagery and 3D models despite challenges such as legal restrictions and limited coverage. We developed a methodology for estimating vegetation height, map vegetation classes, and fuel models by using multitemporal UAV data (imagery and point clouds from the imagery) and other ancillary data to provide insights into habitat condition and fuel characteristics. Two different random forest classification methods (an object- and a pixel-based approach) for discriminating between vegetation classes and fuel models were developed and compared. The method showed promise for characterizing vegetation structure (shrub height), with an RMSE of less than 0.3 m and slight overestimation of taller heights. For discriminating between vegetation classes and fuel models, the best results were obtained with the object-based random forest approach, with overall accuracies of 0.96 and 0.93, respectively. Although some difficulties were encountered in distinguishing low shrubs and brackens and in distinguishing low-height fuel models due to the spatial mixture, accurate results were obtained for most classes. Future improvements include refining terrain models by including data acquired with UAV aerial scanners and exploring different phenological stages and machine learning approaches for classification.