Fraction images derived from Terra Modis data for mapping burnt areas in Brazilian Amazonia

Abstract

The objective of this paper is to present a method for mapping burnt areas in Brazilian Amazonia using Terra MODIS data. The proposed approach is based on image segmentation of the shade fraction images derived from MODIS, using a non-supervised classification algorithm followed by an image editing procedure for minimizing misclassifications. Acre State, the focus of this study, is located in the western region of Brazilian Amazonia and undergoing tropical deforestation. The extended dry season in 2005 affected this region creating conditions for extensive forest fires in addition to fires associated with deforestation and land management. The high temporal resolution of MODIS provides information for studying the resulting burnt areas. Landsat 5 TM images and field observations were also used as ground data for supporting and validating the MODIS results. Multitemporal analysis with MODIS showed that about 6500 km 2 of land surface were burnt in Acre State. Of this, 3700 km 2 corresponded to the previously deforested areas and 2800 km 2 corresponded to areas of standing forests. This type of information and its timely availability are critical for regional and global environmental studies. The results showed that daily MODIS sensor data are useful sources of information for mapping burnt areas, and the proposed method can be used in an operational project in Brazilian Amazonia.

Development of a Point-based Method for Map Validation and Confidence Interval Estimation: A Case Study of Burned Areas in Amazonia

“…Facing the challenge of providing burned area information for the Amazon region a great effort has been made for developing a methodology based on the Linear Spectral Mixture Model (LSMM) that more accurately detects burned area in both productive lands and forests, aiming to support the construction of an operational burned area product for the region [28,30,[32][33][34]. The experiences developed in the above mentioned studies pointed out that the global and regional validation schemes, such as the SAFNet protocol developed in Africa burned areas [14], are not adequate for the complexity and dynamics of the Amazonian burn scar characteristics.…”

“…Moreover, the characteristics of the land cover types require multiple images throughout the dry season to fully capture the burned areas dynamics. In 2008, the new policy adopted by the United States Geological Survey (USGS) have made the geocorrected Landsat archive freely available to any user [34], overcoming the limitation on the restricted number of images suitable for validation purposes. Following the above arguments for establishing a burned area monitoring system to subsidize quantification of carbon emission, to guide local action and mitigation policies on fire prevention, such as law enforcement, it is mandatory that thematic burned area maps are associated with a robust accuracy assessment able to explicitly report uncertainties in estimation.…”

“…The methodology for operationally detecting burned area in Amazonia is well established [28,30,34]. In summary, the method uses daily surface reflectance products MOD09GA and MOD09GQ as well as 8-day surface reflectance product, MOD09Q1 and MOD09A1, collection 5 from MODIS dataset.…”

“…Each resulting fraction image represents the fractional abundance of endmembers in each pixel. The method for detecting burned areas uses a model based on three endmembers: vegetation, soil and shade [30,34]. For detecting burned areas, which present low reflectance, the shade fraction image is used.…”

“…Secondly, we test this method in a study case of burned areas in the Amazon. The methodology for detecting burned areas in the Amazon is built on the previous experiences of the research group [28,30,[32][33][34]. The development of an operational burned area product with accuracy assessment and confidence interval is a joint initiative in Brazil: the National Institute for Space Research, INPE, and the National Center for Monitoring and Early Warning of Natural Disasters -Cemaden.…”