Unsupervised tree species classification with UAV ultra-high resolution multispectral imaging

Abstract. This paper aims to evaluate the performance of ISODATA clustering for tree species classification using ultra-high-resolution multispectral data collected with Unmanned Aerial Vehicle. The study focuses on two sites in Żednia forest district near the city of Bialystok, northeastern Poland. The input data consist of 10-band multispectral orthomosaics with a resolution of 10 cm, acquired from an UAV platform equipped with a MicaSense RedEdge-MX dual camera and image-based Canopy Height Model. The classifications were conducted at two levels of forest detail: forest types, including two classes (broadleaf and conifer), and tree species, comprising four classes in Study Area 1 and ten species in Study Area 2. Multiple classifications were generated, testing different input parameters such as the number of clusters and various combinations of input data. For the first level of classification (forest type), overall accuracies range from 84,09% to 97,57% in Study Area 1 and from 82,31% to 92,74% in Study Area 2. At the second level of classification (tree species), overall accuracies vary from 70.73% to 91.77% in Study Area 1 and from 36,51% to 72,33% in Study Area 2. Overall, ISODATA demonstrates robust performance in classifying forest types in both study areas. However, performance in classifying tree species varies across different classes, with relatively high accuracies observed for certain species such as spruce, pine, oak, larch, and birch. The results underscore the potential of multispectral UAV data and unsupervised classification methods for accurately classifying tree species.