The animation above first shows an accessibility index calculated as a cost surface with the entrance to the watershed as a starting point. There is an excellent “How To” on calculating cost surfaces in GRASS available here. The cost surface (warm colours accessible, cold colours inaccessible) is followed by an animation of deforestation estimated by supervised classification of a series of landsat images taken in1975, 1987, 1999 and 2003. The area is a focus case study, the Tablon watershed in the Sepaltura biosphere reserve. The set of images in Google Earth format are available here.
Landsat imagery is the most frequently used for tracing patterns of deforestation over time. Landsat has been acquiring coverage of the Earth’s surface since 1972 when Landsat 1 was launched. Since then, four other satellites have been in operation. Landsat 1, 2, and 3 flew in a circular orbit 913 kilometers (570 miles) above the Earth’s surface and circled the Earth every 103 minutes (14 times a day). Landsat 4 and 5 fly about 705 kilometers (440 miles) above the Earth and circle every 98 minutes.
Landsat 4 and 5 are still operating. Landsat 6 was launched in 1993. Thus the sensors used to provide images have changed over time and are not easily comparable. The current Thematic Mapper (TM) class of Landsat sensors began with Landsat-4 (1982). This series continued with the nearly identical sensor on Landsat-5 (1984). Landsat-7 Enhanced Thematic Mapper Plus (ETM+) was carried into orbit in 1999. Currently, both the Landsat-5 TM and the Landsat-7 ETM+ are operational and providing data. Landsat-7 ETM+ experienced a failure of its Scan Line Corrector mechanism in May 2003. Data products have been developed to fill these gaps using other ETM+ scenes.
The images used for this analysis were downloaded from the global land cover facility and processed in GRASS. We are currently engaged in a classifying deforestation over a much larger area using similar imagery. Quantifying patterns of deforestation from these historical satellite images is never straightforward, for many reasons. Differences in the conditions when the images where taken can lead to misclassification of pixels leading to errors when documenting the overall spatial pattern. However even though it can be difficult to tell with certainty if a specific small area has changed, the general regional trends detected are quite reliable.
Recently high resolution images of this particular study region have become available and we are acquiring more. It is encouraging to find that at least the classification into “forest-non-forest” that has been achieved using Landsat imagery provides a very acceptable match with the visual impression provided by high resolution images. This can be seen below. You can check your own visual impression of the match by downloading the KML files and changing the transparency in Google Earth. The following lines load the layers into R.