Estimation of forest parameters based on TM imagery and statistical analysis

Abstract

One of the primary forestry research interests lies in estimating forest stand parameters by applying empirical or semi-empirical model to establish the relationship between the forest stand parameters and remote sensing data. Using remote sensing image and the inventory data from 2 compartments in northeast Florida, U.S.A., this paper explored the correlation between forest stand parameters and Landsat TM spectral digital number (DN) value. Results showed that less than 50% of the total variance could be explained by linear regression models with only either a single band or such vegetation indices as vegetation index (VI) or normalized difference vegetation index (NDVI) as predicators. In consequence, multi-linear regression models which synthesized more predicators were introduced to estimate forest parameters. Regression results were tested in terms of the other group of data, and verification showed a better capability of explaining over 75% variance except for forest density. The weakness and further improvement of prediction models were also discussed in the article. This paper is expected to provide a better understanding of the relationship between TM spectral and forest characteristics