Change detection in Fenyőfő forest reserve based on multitemporal aerial images - Shokirov Shukhrat (2016)
ABSTRACT Development of science and technology has gradually increased the potential of remote sensing for forest management. Nowadays, remote sensing is used to perform many tasks of forestry, including inventory data collection and health assessment. This study evaluated the use of high resolution aerial images for individual tree crown delineation, species classification, health estimation and clear-cut area detection in Fenyőfő forest reserves in 2012 and 2015 years. Region growing algorithm was used for segmentation of individual tree crowns. Initially, tree tops were detected and used as the starting point of the region to grow the segment to delineate individual tree crowns. Further, coniferous and deciduous tree species were classified based on orthomosaic images and already created segments. Research also investigated the clear-cut areas and cut crowns between 2012 and 2015 years using Canopy Height Models and current health status of the forest was estimated by analyzing orthomosaic. Results of the research were examined based on the field trip data. According to our results, we achieved 75.2% accuracy in individual tree crown delineation. Heights of tree crowns calculated with high accuracy. This study had promising result in clear cut area and individual cut crown detection. Overall accuracy of classification was 77.2%, analysis showed that coniferous tree species classification was very accurate, but deciduous tree classification had a lot of omission errors. Based on the results and analysis, general information about forest health conditions has been presented. Finally, strengths and limitations of the research were discussed and recommendations were given for further research.