Object-Based Tree Stump Detection Fusing RGB and Multispectral Data

Pranisha Chaturvedi, Maximilian Johenneken, Ahmad Drak, Sebastian Houben, Alexander Asteroth

Abstract: Over the past few years, forests have become increasingly susceptible to various natural stress factors, mainly due to climate change induced events such as drought. Quantifying the objects that lay on the forest floor such as soil, vegetation, wooden debris and tree stumps is a viable method for estimating the extent of the stress factors. Remote sensing is an established modus operandi for collecting data in forest environments. In this work, we focus on Unmanned Aerial Vehicles (UAV) as remote sensing platforms aiming to detect tree stumps in varying stages of decay within a forest calamity area. The platform provides us with both high-resolution images from RGB and multispectral cameras and height data. In addition, a vegetation index for wood is proposed to better highlight the presence of tree stumps using multispectral data. Object detection is performed with feature-level fusion after which Object-Based Image Analysis (OBIA) is carried out. The fused multispectral and RGB data resulted in 53.68% recall and 31.0% precision in detecting tree stumps. In comparison, the fused RGB and height data resulted in 50.0% recall and 20.9% precision.