ANALYSIS OF MULTITEMPORAL AERIAL IMAGES FOR FENYŐFŐ FOREST CHANGE DETECTION SHUKHRAT SHOKIROV* 1 – GÉZA KIRÁLY 1 1 Department of Surveying and Remote Sensing, University of West Hungary, Sopron 9400, Hungary *Corresponding author: shukhrat811@gmail.com Received 6 September 2016, accepted in revised form 14 October 2016 Abstract This study evaluated the use of 40 cm spatial resolution aerial images for individual tree crown delinea- tion, forest type 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. Forest type (coniferous/deciduous trees) were distinguished based on the orthomosaic images and seg- ments. Research also investigated the height of individual trees, clear-cut areas and cut crowns between 2012 and 2015 years using Canopy Height Models. Results of the research were examined based on the field measurement data. According to our results, we achieved 75.2% accuracy in individual tree crown delineation. Heights of tree crowns have been calculated with 88.5% 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 type classification was very accurate, but deciduous tree classifi- cation 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. Keywords: Aerial imagery, Canopy Height Model (CHM), Object Based Image Analysis (OBIA) 1. Introduction Nowadays, forests are in a high risk of degradation because of natural and anthropogenic factors. These factors include deforestation, forest fires, pests and disease, climate change causing long dry periods resulting loss of forest areas around the globe year by year. Forest degradation can be generally defined as the reduction of the capacity of a forest to provide goods and services (FAO, 2009). Rapidly increasing forest disturbances give rise to a threat for forest health and substantial economic losses (Nabuurs et al. 2013). Therefore, accurate and cost-efficient detection of stand and tree conditions for timely forest management are needed (Näsi et al. 2015). Remote sensing has been a valuable source of information for mapping and monitoring forests over the course of past few decades. It helps forest managers better understand forest characteristics and also provides opportunities for interpreting forest at an individual tree level. Individual tree delineation techniques enhance the derivation of parameters of interest for forest inventories such as forest stand boundaries, stand density and species composition, tree heights, etc. (Ke – Quackenbush, 2008). Various segmentation methods have been developed to delineate individual tree crowns including local maxima detection (Dralle and Rudemo, 1996), local maxima filtering with fixed or variable window sizes (Wulder et al., 2000), region growing algorithm (Erikson, 2004 a), watershed segmentation (Schardt et al., 2002). These algorithms are mostly based Landscape & Environment 10 (2) 2016. 89-100 DOI: 10.21120/LE/10/2/4