Estimating regional forest cover in East Texas using Advanced Very High Resolution Radiometer (AVHRR) data |
| |
| Institution: | 1. Department of Forest Science, Texas A&M University, College Station, TX 77843, USA;2. Faculty of Forestry, University of Toronto, Ontario, Canada M5S 3B3;3. Department of Rangeland Ecology and Management, Texas A&M University, College Station, TX 77843, USA;1. Institute of Pathology, Medical University of Graz, Graz, Austria;2. Department of Gynecology, General Hospital Leoben, Leoben, Austria;3. Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA;4. Yale Comprehensive Cancer Center, Yale School of Medicine, New Haven, CT, USA;5. Department of Pathology, Huntsman Cancer Hospital, University of Utah, Salt Lake City, UT, USA;6. MLD Pathology, Houston, TX, USA;1. Miami University, Oxford, OH 45056, United States;2. University of Iowa, Iowa City, IA 52242, United States;3. Indiana University, Bloomington, IN 47405, United States;1. Proteome and Metabolome Research, Center for Biotechnology (CeBiTec), Faculty of Biology, Bielefeld University, Bielefeld, Germany;2. Research Institute for Cognition and Robotics (CoR-Lab), Bielefeld University, Bielefeld, Germany;3. Department of Urology, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany;4. Institute of Pathology, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany;5. Department of Urology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany;6. Institut für Pathologie, Johannes Wesling Klinikum Minden, Minden, Germany;7. Department of Urology, Semmelweis University Budapest, Budapest, Hungary;8. Medical School OWL, Bielefeld University, Bielefeld, Germany;9. Institute of Pathology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany |
| |
| Abstract: | This study tested the degree to which single date, near-nadir AVHRR image could provide forest cover estimates comparable to the phase I estimates obtained from the traditional photo-based techniques of the Forest Inventory and Analysis (FIA) program. FIA program is part of the United States Department of Agriculture-Forest Service (USFS). A six-county region in east Texas was selected for this study. Manual identification of ground control points (GCPs) was necessary for geo-referencing this image with higher precision. Through digital image classification techniques forest classes were separated from other non-forest classes in the study area. Classified AVHRR imagery was compared to two verification datasets: photo-center points and the USFS FIA plots. The overall accuracy values obtained were 67 and 71%, respectively. Analyses of the error matrices indicated that the AVHRR image correctly classified more forested areas than non-forested areas; however, most of the errors could be attributed to certain land cover and land use classes. Several pastures with tree cover, which were field-identified as non-forest, were misclassified as forest in the AVHRR image using the image classification system developed in this study. Recently harvested and young pine forests were misclassified as non-forest in the imagery. County-level forest cover estimates obtained from the AVHRR imagery were within the 95% confidence interval of the corresponding estimates from traditional photo-based methods. These results indicate that AVHRR imagery could be used to estimate county-level forest cover; however, the precision associated with these estimates was lower than that obtained through traditional photo-based techniques. |
| |
| Keywords: | AVHRR Forest cover USDA-Forest Service FIA Remote sensing Texas |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
