Comparison of Algorithms for Tree-top Detection in Drone Image Mosaics of Japanese Mixed Forests

Abstract

Counting trees is a common problem in forest applications often solved by performing field studies that are exceedingly cost-intensive in time and manpower. Consequently, many researchers have used computer vision techniques to automatically detect trees by finding tree tops. The success of these algorithms is highly dependent on the data that they are used on. We present a study using data acquired by ourselves in a natural mixed forest using an Unmanned Aerial Vehicle (UAV). Given the particularly challenging nature of our data, we developed a pre-processing step aimed at preparing the data so that it could be used with six common clustering algorithms to detect tree tops. Extensive experiments using data covering over 40 ha is presented and tree detection accuracy, tree counting metrics and computation and use time considerations are taken into account. Our algorithms detect over 80% with high location accuracy and up to 90% with lower accuracy. Tree counting errors range from 8% to 14% for most methods. Data Acquisition and runtime considerations show how this techniques are ready to have an immediate impact in the processing of real forest data.