Improving tropical forest mapping using multi-date Landsat TM data and pre-classification image smoothing

Abstract

The present study explores the possibility of using Landsat imagery for mapping tropical forest types with relevance to forest ecosystem services. The central part in the classification process is the use of multi-date image data and pre-classification image smoothing. The study argues that multi-date imagery contains information on phenological and canopy structural properties, and shows how the use of multi-date imagery has a significant impact on classification accuracy. Furthermore, the study shows the value of applying small kernel smoothing filters to reduce in-class spectral variability and enhance between-class spectral separability. Making use of these approaches and a maximum likelihood algorithm, six tropical forest types were classified with an overall accuracy of 90.94%, and with individual forest classes mapped with accuracies above 75.19% (user's accuracy) and above 74.17% (producer's accuracy).

Acknowledgments

I would like to thank Dr Michael Schultz Rasmussen from the Institute of Geography (University of Copenhagen) for his encouragement, good discussions and valuable advice. Moreover, I am grateful for the support from Dr Tran Duc Vien and his staff at the Centre for Agricultural Research and Ecology Studies (CARES) at Hanoi Agricultural University. In this respect a very special thank you to Dr Pham Tien Dung and Mai Van Thanh from CARES for their assistance during the field trips in Nghe An province. A thank you also to Stephen Leisz and ‘Social forestry and nature conservation in Nghe An province’ for data sharing and helpful advice. The financial support for the research came from the Danish component of the Resource Policy Support Initiative (REPSI) phase II 1999–2001 (administration by NORDECO and funding by DANIDA) and a grant from the World Wildlife Fund, ‘WWF Verdensnaturfonden/Novo Nordisk biodiversitetslegat’. I highly appreciate this support.