Comparison of satellite images with different spatial resolutions to estimate stand structural diversity in urban forests

Abstract

The structural diversity in urban forests is highly important to protect biodiversity. In particular, fruit trees and bush species, cavity-bearing trees and coarse, woody debris provide habitats for animals to feed, nest and hide. Improper silvicultural practices, intensive recreational use and illegal harvesting lead to a decline in the structural diversity in forests within larger metropolitan cities. It is important to monitor the structural diversity at definite time intervals using effective technologies with a view to instituting the necessary conservation measures. The use of satellite images seems to be appropriate to this end. Here we aimed to identify the associations between the textural features derived from the satellite images with different spatial resolutions and the structural diversity indices in urban forest stands (Shannon–Wiener index, complexity index, dominance index and density of wildlife trees). RapidEye images with a spatial resolution of 5 m × 5 m, ASTER images with a spatial resolution of 15 m × 15 m and Landsat-8 ETM satellite images with a spatial resolution of 30 m × 30 m were used in this study. The first-order (standard deviation of gray levels) and second order (GLCM entropy, GLCM contrast and GLCM correlation) textural features were calculated from the satellite images. When associations between textural features in the images and the structural diversity indices were assessed using the Pearson correlation coefficient, very high associations were found between the image textural features and the diversity indices. The highest association was found between the standard deviation of gray levels (SDGL_RAP) derived from RVI_RAP of RapidEye image and the Shannon–Wiener index (H _h) calculated on the basis of tree height (R ² = 0.64). The findings revealed that RapidEye satellite images with a spatial resolution of 5 m × 5 m are most suitable for estimating the structural diversity in urban forests.

Keywords

Biodiversity / Satellite image / Structural diversity / Texture measures / Urban forests

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