Satellite estimation of forest carbon using regression models

Abstract

Periodic monitoring of forest carbon is important, since forest cover is changing rapidly in many parts of the world, and becomes a major source of terrestrial carbon emission that may be one of the main drivers of global climate change. Regression is often used to estimate forest variables (including carbon) using satellite sensor data though a low coefficient of determination (R ²) is apparent and this research was designed to investigate both traditional and alternate regression approaches to increase the magnitude of R ². The study area was located in southeastern Bangladesh. Data from Landsat Enhanced Thematic Mapper Plus (ETM+) and ground‐based forest survey were used. This research explored the use of dummy variables in regression models to increase R ², while the dummies were set from the optimal stratification of forestland. The finding will heighten the accuracy of forest attribute estimation and help to understand terrestrial carbon dynamics and global climate change.

§ Present address: Microwave Remote Sensing Laboratory, Center for Environmental Remote Sensing (CEReS), Chiba University, 1‐33 Yayoi, Inage, Chiba 263‐8522, Japan. Permanent address: Bangladesh Space Research and Remote Sensing Organization (SPARRSO), Agargaon, Sher‐E‐Bangla Nagar, Dhaka‐1207, Bangladesh.

Acknowledgment

The first author was granted a scholarship from Gottlieb Daimler and Karl Benz Stiftung for this research. Partial funding was obtained from Gesellschaft von Freunden und Förderern of Dresden University of Technology and Studienbegleitprogramm Sachsen. The Bangladesh Forest Department assisted the ground sampling. The contribution of Michael Köhl, University of Hamburg, is acknowledged for his advice on this investigation.

Notes

§ Present address: Microwave Remote Sensing Laboratory, Center for Environmental Remote Sensing (CEReS), Chiba University, 1‐33 Yayoi, Inage, Chiba 263‐8522, Japan. Permanent address: Bangladesh Space Research and Remote Sensing Organization (SPARRSO), Agargaon, Sher‐E‐Bangla Nagar, Dhaka‐1207, Bangladesh.