TREEDETECTION: AUTOMATIC TREE DETECTION USING UAV-BASED DATA - Klein Hentz, Ângela Maria; Corte, Ana Paula Dalla; Péllico Netto, Sylvio; Strager, Michael P.; Schoeninger, Emerson Roberto (Floresta, 2018)
In this study it is presented a toolbox built in ArcGIS using ArcPy designed to automatically detect trees in high resolution data obtained from Unmanned Aerial Vehicles (UAV). The toolbox, TreeDetection, contains a tool called TreeDetect, which requires three parameters: a raster input, a conversion factor and an output folder. Three other optional parameters can be changed to improve the detection according to characteristics of the forest and raster source. We tested the TreeDetect tool in three study sites: a young Eucalyptus plantation; adult Eucalyptus and Pinus stands; and a Mixed Hardwood natural forest. We also tested distinct raster inputs, according to the data availability in each site. The tool was considered efficient to detect the trees in the three study areas. The detection accuracy was lower in the natural stand, as expected considering the complex structure of this forest type. All the raster input rested provided satisfactory results, but in the homogeneous stand the Digital Surface Model (DSM) was not as effective as the spectral bands. Furthermore, research can be performed with emphasis in different sensors and band combinations, as well in the parameters’ selection.