Estimation of Linkages between Biometric Indexes of Forests and Pattern of Canopy Spaces on Super-High-Resolution Satellite Images

Abstract

This paper presents the results of studying a promising area related to the remote assessment of canopy spaces in forests by thresholding methods of image segmentation. The study is conducted based on the example of mixed forests in the Losiny Ostrov National Park. The proposed methodological approach to assessing the pattern of forest canopy on super-high (detailed) resolution satellite images is based on an analysis of light and shaded plots of canopy spaces using image-thresholding algorithms. The pixel count for different brightness thresholds give enough information to estimate a range of biometric indexes, including volume density and average age and height of stands from statistical relationships. The accuracy of estimates is assessed for prescribed deviations and verified against the norms of estimation of corresponding taxation data. We have found a statistical relationship of forest-canopy morphology indicators with brightness thresholds for shaded plots of canopy spaces and stemwood phytomass in forest ecosystems. Thus, super-high-resolution images may be considered an information basis for estimating the biometric parameters of stands, morphological indicators of forest canopy, and the productivity of forest ecosystems.