An assessment of forest restoration after fie using segmentation and cluster analysis of Landsat images

Abstract

For the purposes of monitoring the state of forest ecosystems, it is most effctive to use the capabilities of remote methods. Fragments with an area of 250 km2 (scale 1:5000) were identifid on Landsat multispectral satellite images (time series of summer images for 1995, 2000, 2004, 2008, 2013, 2016) of the Olyokminsky State Nature Reserve. Then polygons were saved at three levels of segmentation: 4, 16, 64 with the scales of 1:2500, 1:1250, 1:625. When deciphering, an unmanaged classifiation of polygons was carried out using the ISODATA method (Iterative Self-Organizing Data Analysis Technigue) into 2, 4, 10 classes. Distribution curves for the values of the forest cover index for polygons of the 3rd level of segmentation were constructed. The results of classifiation into 4 classes were used to calculate the thematic pixel diffrence. According to the classification results for 4.10 classes, statistical processing was carried out with the calculation of the diffrence and similarity indicators of polygons, the dispersion of the general population and the Fisher test (F-test). A method is described for determining the disturbance of ecosystems and their restoration from the distribution curves of the forest cover index. The results of the change in the dispersion of the general population and the F-test at diffrent levels of segmentation and at diffrent stages of forest restoration are considered. The features of the transition between three levels of self-similarity (scaling) of multifractal structures as forests are restored are determined.