Eric Van Bogaert

A New, Global, Multi-annual (2000-2007) Burnt Area Product at 1 Km Resolution

This paper reports on the development and validation of a new, global, burnt area product. Burnt areas are reported at a resolution of 1 km for seven fire years (2000 to 2007). A modified version of a Global Burnt Area (GBA) 2000 algorithm is used to compute global burnt area. The total area burnt each year (2000-2007) is estimated to be between 3.5 million km 2 and 4.5 million km(2). The total amount of vegetation burnt by cover type according to the Global Land Cover (GLC) 2000 product is reported. Validation was undertaken using 72 Landsat TM scenes was undertaken. Correlation statistics between estimated burnt areas are reported for major vegetation types. The accuracy of this new global data set depends on vegetation type.

Automated Training Sample Extraction for Global Land Cover Mapping

Land cover is one of the essential climate variables of the ESA Climate Change Initiative (CCI). In this context, the Land Cover CCI (LC CCI) project aims at building global land cover maps suitable for climate modeling based on Earth observation by satellite sensors. The challenge is to generate a set of successive maps that are both accurate and consistent over time. To do so, operational methods for the automated classification of optical images are investigated. The proposed approach consists of a locally trained classification using an automated selection of training samples from existing, but outdated land cover information. Combinations of local extraction (based on spatial criteria) and self-cleaning of training samples (based on spectral criteria) are quantitatively assessed. Two large study areas, one in Eurasia and the other in South America, are considered. The proposed morphological cleaning of the training samples leads to higher accuracies than the statistical outlier removal in the spectral domain. An optimal neighborhood has been identified for the local sample extraction. The results are coherent for the two test areas, showing an improvement of the overall accuracy compared with the original reference datasets and a significant reduction of macroscopic errors. More importantly, the proposed method partly controls the reliability of existing land cover maps as sources of training samples for supervised classification.