Analysis of Correlation between Canopy Cover and Vegetation Indices

Abstract

A canopy cover is geospatial information attributes that are commonly used in forestry and observation equipment allocation. However, it is extremely difficult to measure canopy cover ratio of all tree regions in the field, and tree’s leaves are continuously changed by season and climate. Hence, a remote sensing technique that makes it possible to obtain forestry information on broad areas within a short period of time has been considered. This study has attempted to investigate the reliability of the results obtained from canopy cover based on the vegetation index of mid-resolution images by performing regression analysis between Landsat Normalized Difference Vegetation Index (NDVI), which has a relatively broad image acquisition area, LAI and DMT and reference data for canopy cover extracted from high-resolution images. It shows that the relationship of canopy cover and NDVI is higher than that of canopy cover and LAI, DMT. The DMT is needed improving the accuracy and updating more frequently. It is also found that the pixel-based canopy cover extraction approach provides better correlation results than the segment-based approach.