TOWARDS OBJECT-BASED EVALUATION OF INDIVIDUAL FIRES AT GLOBAL SCALES

Abstract

Fire is a complex biophysical variable that has shaped the land surface for over 400 million years and continues to play important roles in landscape management, atmospheric emissions, and ecology. Our understanding of global fire patterns has improved dramatically in recent decades, coincident with the rise of systematic acquisition and development of global thematic products based on satellite remote sensing. Currently, there are several operational algorithms which map burned area, relying on coarse spatial resolution sensors with high temporal frequencies to identify fire-affected surfaces. While wildfires have been analyzed over large areas at the pixel level, object-based methods can provide more detailed attributes about individual fires such as fire size, severity, and spread rate. This dissertation evaluates burned area products using object-based methods to quantify errors in burn shapes and to extract individual fires from existing datasets. First, a wall-to-wall intercomparison of four publicly available burned area products highlights differences in the spatial and temporal patterns of burning identified by each product. The results of the intercomparison show that the MODIS Collection 6 MCD64A1 Burned Area product mapped the most burned area out of the four products, and all products except the Copernicus Burnt Area product showed agreement with regard to temporal burning patterns. In order to determine the fitness of the MCD64A1 product for mapping fire shapes, a framework for evaluating the shape accuracy of individual fires was developed using existing object-based metrics and a novel metric, the “edge error”. The object-based accuracy assessment demonstrated that MCD64A1 preserves the fire shape well compared to medium resolution data. Based on this result, an algorithm for extracting individual fires from MCD64A1 data was developed which improves upon existing algorithms through its use of an uncertainty-based approach rather than empirically driven approaches. The individual fires extracted by this algorithm were validated against medium resolution data in Canada and Alaska using object-based metrics, and the results indicate the algorithm provides an improvement over similar datasets. Overall, this dissertation demonstrates the capability of coarse resolution burned area products to accurately identify individual fire shapes and sizes. Recommendations for future work include improving the quality assessment of burned area products and continuing research into identifying spatiotemporal patterns in fire size distributions over large areas.