A Review and an Intercomparison of Remote Sensing Techniques to Map Selective Logging in the Brazilian Amazon
Carlos
Moreira de
Souza Jr., Instituto do Homem e Meio Ambiente da Amazônia - Imazon, Caixa Postal 5101 Belém, PA CEP 66613-397 Brazil, Department of Geography, Univeristy of Califonia, Santa Barbara Ell, carlos@geog.ucsb.edu
(Presenting)
Gregory
Paul
Asner, Carnegie Institution, Dept. of Global Ecology Stanford University 260 Panama St., Stanford CA 94305, USA, gpa@stanford.edu
Dar
A.
Roberts, Department of Geography, Univeristy of Califonia, Santa Barbara Ellison Hall, 3611, Santa Barbara, UC Santa Barbara, CA 93106, US, dar@geog.ucsb.edu
Remote sensing applications to map selective logging in tropical forests are still under development. While methods to map selectively logged area and to estimate biophysical properties of these forests exist, a comparison of these methods is still lacking. Visual interpretation of Landsat TM/ETM provided some of the first remote sensing estimates of the area affected by selective logging in the Brazilian Amazon (INPE, 2000; Matricard et al., 2001). Visual interpretation is possible when logging ‘scars’ are visible on the images. However, the scars only persist for one to two years after logging. Furthermore, visual interpretation is challenging when the logging intensity is low (e.g., < 5 m3 ha-1 for mahogany extraction), is time consuming and can have a human bias.
Digital image processing techniques, such as minimum distance and maximum likelihood classifiers, have also been used to estimate the area affected by selective logging in the Amazon (Stone and Lefebvre 1998) as well as texture and reflectance analyses (Asner, et al. 2002). These techniques failed to map selectively logged areas accurately because of the high spectral ambiguity between intact forest and selectively logged forests.
Another approach tested for mapping selective logging is soil fraction images derived from spectral mixture analysis (SMA). Soil fraction enhances the detection of the log landings and ‘roads’. Based on a site-specific harvesting radius, it is possible to estimate the area affected by selective logging from log landings (Souza Jr. and Barreto, 2000; Monteiro et al., 2003). If field data on forest damage are available, this area mapping technique can be used to estimate forest damage. However, this approach does not provide spatial information about the location of the forest damages.
Asner et al. (2004) have proposed the use of gap fraction data derived from SMA as a mean to estimate forest canopy damage associated with selective logging. The gap fraction map has the potential of being integrated with contextual information, such as log landing and road maps, to distinguish forest gaps caused by selective logging from natural forest gaps. Finally, non-photosynthetic vegetation (NPV) fraction images, also obtained through SMA, have been used to map several levels of forest degradation associated with selective logging (Souza Jr. et al; 2003).
To compare these techniques, we are currently applying them to a 150,000 ha of forests located in Paragominas Municipality – PA. - logged from 1999 to 2002. Reference data include forest inventory and field data on forest damage planned to be acquired in May through June 2004. We expect to integrate the best of each these techniques to develop an unambiguous approach for mapping selective logging that distinguishes it from natural forest disturbances.
Submetido por Carlos Moreira de Souza Jr. em 15-MAR-2004
Tema Científico do LBA: LC (Mudanças dos Usos da Terra e da Vegetação)