Mapping Burned Areas in a Mediterranean Environment Using Soft Integration of Spectral Indices from High-Resolution Satellite Images

Abstract

This article presents a new method for burned area mapping using high-resolution satellite images in the Mediterranean ecosystem. In such a complex environment, high-resolution satellite images represent an appropriate data source for identifying fire-affected areas, and single postfire data are often the only available source of information. The method proposed here integrates several spectral indices into a fuzzy synthetic indicator of likelihood of burn. The indices are interpreted through fuzzy membership functions that have been derived with a partially data-driven approach exploiting training data and expert knowledge. The final map of fire-affected areas is produced by applying a region growing algorithm on the basis of seed pixels selected on a conservative threshold of the synthetic fuzzy score. The algorithm has been developed and tested on a set of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes acquired over Southern Italy. Validation showed that the accuracy of the burned area maps is comparable or even better [overall accuracy (OA) . 90%, K . 0.76] than that obtained with approaches based on single index thresholds adapted to each image. The method described here provides an automatic approach for mapping fire-affected areas with very few false alarms (low commission error), whereas omission errors are mainly related to undetected small burned areas and are located in heterogeneous sparse vegetation cover.

Integration of Optical and SAR Data for Burned Area Mapping in Mediterranean Regions

“…Pre-processing of optical and SAR data are described in Sections 3.1 and 3.2, respectively, whereas the details on the implementation of the fuzzy burned area-mapping algorithm are provided in this section. As shown in Figure 3, the ∆σ° and surface reflectance Landsat-5 TM images constitute input data for the fuzzy algorithm [46,47,59].…”

“…This framework is an extension of the automated algorithm originally proposed for Landsat TM/Enhanced TM plus (ETM+) data, based on the use of fuzzy sets theory and a region growing algorithm [46,47]. This paper is mainly focused on the study of Mediterranean-type regions, particularly on Portugal, affected by remarkable fire events during the year 2003.…”

Burned Area Mapping Using Support Vector Machines and the FuzCoC Feature Selection Method on VHR IKONOS Imagery

“…In general, any developed burned area mapping methodology should meet the following criteria in order to be applicable on operational basis: it should be rapid, reliable and automated [9]. Hence, the evaluation of the developed burned area mapping methods of the current study should also take into account the aforementioned criteria.…”

“…Traditionally, medium and coarse resolution satellite data such as Landsat TM (30 m), Landsat MSS (80 m), MODIS (250 m), AVHRR (1km), and SPOT-VGT (1 km) have been used for extraction of fire-related information. In recent years, however, the availability of Very High Resolution (VHR) satellite imagery such as IKONOS, WorldView, and QuickBird has provided new possibilities in burned area mapping at local scales [9]. Since fire plays a crucial role in many ecological processes at the local level (e.g., vegetation composition, biodiversity, soil erosion, and the hydrological cycle), the use of VHR data provide very detailed thematic products and consecutively valuable information.…”

Estimating the area burned by agricultural fires from Landsat 8 Data using the Vegetation Difference Index and Burn Scar Index

“…Most previous studies estimating burned area through remote sensing have classified images using principal component analysis and vegetation indices (Carlson and Ripley 1997;Chuvieco et al 2002;Domenikiotis et al 2002;Hudak and Brockett 2004;Mitri and Gitas 2004;Kučera et al 2005;Loboda et al 2007;Maingi and Henry 2007;Smith et al 2007;Chuvieco et al 2008;Palandjian et al 2009;Stroppiana et al 2009;Boschetti et al 2010;Bastarrika et al 2011;Parker et al 2015). Many studies have relied on the Normalized Difference Vegetation Index (NDVI), including modified versions to reduce the sensitivity of the index to different atmospheric and soil conditions (Chuvieco et al 2002;Domenikiotis et al 2002;Kučera et al 2005;Stroppiana et al 2009;Veraverbeke et al 2011a).…”