ارزيابي داده هاي لندست 7 براي تهيه نقشه تراكم جنگل در نواحي خشك و نيمه خشك

An Evaluation of the Potential of Landsat ETM+ for Forest Density Mapping in Arid and Semi Arid Regions

Landsat-ETM+ data from the national park of Khabr in Kerman province, dating May 2000, were analyzed to investigate the potential of this sensor in forest density mapping in arid and semi-arid regions. The quality of the image was initially evaluated. No radiometric error was found. Orthorectification was implemented using ephemeris data, digital elevation model and 14 ground control points. The RMS error was less than half a pixel. The ground truth map allocating 50 percent of the total area was prepared through fieldwork using strip sampling. The best spectral bands were selected based on the divergence between class signatures using sample areas. The supervised, classification utilizing original and synthetic bands (resulted from band arithmetic, principal components analysis and tasseled cap transformation), maximum likelihood (ML), minimum distance to mean (MD), parallelepiped (PPD) and spectral angle mapper (SAM) classifiers, was performed. Within 3 density classes (very thin, thin and semi-dense) MD classifier exhibited the highest overall accuracy and kappa coefficient equal to 47.11% and 0.21 respectively. Signature separability, producer and user accuracies showed that the first and the second classes had the most spectral reflection similarity. By merging these two classes the classification was done. again. In this case also, MD classifier showed the highest overall accuracy. and kappa coefficient equal to 66.15% and 0.30 respectively. Based on these results, in such regions, low forest canopy increases the role of background reflection. High spatial resolution images and improved classification methods will demonstrate the potential of this application.