An assessment of seasonal changes in forests using the segmentation and cluster analysis of Landsat space images

Abstract

For the purposes of monitoring the state of forest ecosystems, it is most effective to use the capabilities of remote methods. On multispectral Landsat satellite images (a time series of summer and autumn images for 2000, 2001, 2013, 2014) of the territory of the Olekminsky State Nature Reserve, fragments of forests dominated by Gmelin larch (Larix gmelinii Rupr.) with an area of 250 km2 (scale 1:5000). Then the polygons were saved at three levels of segmentation (scaling) – 4, 16, 64 with scales of 1:2500, 1:1250, 1:625. During decryption, uncontrolled classification of polygons using the ISODATA (Iterative Self-Organizing Data Analysis Technigue) method into 2,4,10 classes was carried out. Classification into two classes was used to calculate the forest cover index. Seasonal changes were determined by the difference in the forest cover index values of the polygons in summer and autumn. It has been shown that the greater the difference, the greater the proportion of larch in mixed forest stands. Distribution curves of forest cover index values were constructed for polygons of the 3rd scaling level. Based on the results of classification into 4, 10 classes, statistical processing was carried out with the calculation of indicators of difference and similarity of polygons - dispersion of the general population and Fisher's test (F-test). The results of changes in population dispersion and F-test at different levels of segmentation and in different years are considered.