Analysis of ENVISAT ASAR data for forest parameter retrieval and forest type classification—a case study over deciduous forests of central India

Abstract

The present paper gives an account of potential of Environment Satellite‐Advanced Synthetic Aperture Radar (ENVISAT‐ASAR) C‐band data in forest parameter retrieval and forest type classification over deciduous forests of Tadoba Andhari Tiger Reserve (TATR), central India. Ground data on phyto‐sociology and Leaf Area Index (LAI) over the study area was collected in 23 sampling points (20m×20m) over the study area. Phyto‐sociological data collected over the study area was used to compute plot‐wise biometric parameters like basal area, volume, stem density and dominant height. ENVISAT ASAR data covering the study area, pertaining to 24 November 2005, has been geo‐referenced and digital number (DN) values were converted to radar backscatter values. Regression analysis between backscatter and the retrieved biometric variables has been done to explain the relationships between SAR backscatter and forest parameters. Analysis showed a significant correlation between backscatter and biometric parameters and backscatter values typically increased with increase in basal area, volume, stem density and dominant height. The scatter observed between ASAR backscatter and stem density, basal area and dominant height suggested limitation of C‐band data in estimating biometric variables in heterogeneous forest systems. Further, ASAR data was used in conjunction with Indian Remote sensing Satellite (IRS‐P6)—Linear Imaging Self Scanner (LISS) III data of 16 October 2004 to classify the study area into different land use/land cover (LU/LC) classes. Various texture and adaptive filters were applied on ASAR image to reduce speckle noise and enhance image features. An attempt is made to merge ASAR image with LISS‐III to enhance feature discrimination. Training sets corresponding to the ground data have been used to derive confusion matrices for the ASAR and LISS‐III images. Results suggested better discrimination of vegetation types in the merged data suggesting the possible use of ASAR data in forest type discrimination.

Acknowledgements

Authors are grateful to the Director, NRSA and Dy. Director (RS&GIS‐AA), NRSA, Hyderabad for their encouragement and ISRO‐GBP for funding support. The authors thank Mr Chiranjibi Patnaik, Research Scholar, Forestry and Ecology Division and forest officials of Tadoba‐Andhari Tiger Reserve (TATR) for the cooperation and support in field. The authors thank anonymous reviewers for their comments and suggestions.