Evaluation of a Bayesian Algorithm to Detect Burned Areas in the Canary Islands’ Dry Woodlands and Forests Ecoregion Using MODIS Data

Abstract

Burned Area (BA) is deemed as a primary variable to understand the Earth's climate system. Satellite remote sensing data have allowed for the development of various burned area detection algorithms that have been globally applied to and assessed in diverse ecosystems, ranging from tropical to boreal. In this paper, we present a Bayesian algorithm (BY-MODIS) that detects burned areas in a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2002 to 2012 of the Canary Islands' dry woodlands and forests ecoregion (Spain). Based on daily image products MODIS, MOD09GQ (250 m), and MOD11A1 (1 km), the surface spectral reflectance and the land surface temperature, respectively, 10 day composites were built using the maximum temperature criterion. Variables used in BY-MODIS were the Global Environment Monitoring Index (GEMI) and Burn Boreal Forest Index (BBFI), alongside the NIR spectral band, all of which refer to the previous year and the year the fire took place in. Reference polygons for the 14 fires exceeding 100 hectares and identified within the period under analysis were developed using both post-fire LANDSAT images and official information from the forest fires national database by the Ministry of Agriculture and Fisheries, Food and Environment of Spain (MAPAMA). The results obtained by BY-MODIS can be compared to those by official burned area products, MCD45A1 and MCD64A1. Despite that the best overall results correspond to MCD64A1, BY-MODIS proved to be an alternative for burned area mapping in the Canary Islands, a region with a great topographic complexity and diverse types of ecosystems. The total burned area detected by the BY-MODIS classifier was 64.9% of the MAPAMA reference data, and 78.6% according to data obtained from the LANDSAT images, with the lowest average commission error (11%) out of the three products and a correlation (R 2 ) of 0.82. The Bayesian algorithm-originally developed to detect burned areas in North American boreal forests using AVHRR archival data Long-Term Data Record-can be successfully applied to a lower latitude forest ecosystem totally different from the boreal ecosystem and using daily time series of satellite images from MODIS with a 250 m spatial resolution, as long as a set of training areas adequately characterising the dynamics of the forest canopy affected by the fire is defined.

Integration of Multiple Spectral Indices and a Neural Network for Burned Area Mapping Based on MODIS Data

“…In comparison to manual field measurement of a burned area after wildfires, satellite remote sensing is more convenient, safe and applicable with fast response capability. So far, the satellite sensors applied to burned area research mainly consist of the VEGETATION (VEG) [8][9][10], Advanced Very High Resolution Radiometer (AVHRR) [2,[11][12][13][14][15], Moderate Resolution Imaging Spectroradiometer (MODIS) [1,[16][17][18][19], Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) [20][21][22], Visible Infrared Imaging Radiometer Suite (VIIRS) [7], Thematic Mapper (TM) [5,23,24], Enhanced Thematic Mapper plus (ETM+) [24][25][26][27], and Operational Land Imager (OLI) [28]. Besides, the availability of Synthetic Aperture Radar (SAR) for burned area mapping has also been investigated [25,29,30].…”

“…In particular, the complex spectral characteristics of the burned area and unburned types of land cover (hereafter simply "unburned types") can also be obtained from the data. Therefore, the application of remote sensing technology with MODIS data for burned area mapping has been widely investigated in current research [4,19,32].…”

“…Giglio et al [16] proposed an algorithm that applied the dynamic thresholds to the images generated from burn-sensitive vegetation index (bsVI) and a measure of temporal texture, and cumulative active fire maps were also used to select the burned and unburned samples. Guindos-Rojas F. et al [19] developed a Bayesian-based algorithm to detect the burned area using the MODIS 10-day products of surface reflectance and land surface temperature (LST) data for the Canary Islands' dry woodlands and forests ecoregion. In addition, Melchiorre et al [4] utilized the Collection 6 MODIS Global Burned Area Product (MCD64A1) of the global monthly record from 2003 to 2016 and the time series data of normalized burn ratio (NBR) to carry out a global analysis of the burned area persistence time.…”

Assessing Spatio-Temporal Variability of Wildfires and their Impact on Sub-Saharan Ecosystems and Air Quality Using Multisource Remotely Sensed Data and Trend Analysis

“…Fortunately, BA detection and mapping is well established and has been studied since the dawn of satellite imagery [5] with most recent studies focusing on (a) the development and improvement of detection and mapping techniques [6][7][8], (b) enhancement of existing global products both in detection accuracy and spatial detail [9,10] and (c) the inter-comparison and validation in different environmental settings and regions [11]. Additionally, the availability of operational satellite-based products, such as land cover, temperature, rainfall, tree cover, etc., provide prospects for assessing and quantifying the impact of wildfires on the ecosystems and biodiversity.…”

A Comparison of Burned Area Time Series in the Alaskan Boreal Forests from Different Remote Sensing Products

“…NASA built the LTDR (Long Term Data Record) [30] dataset-with a spatial resolution of 0.05 • -from AVHRR-GAC images, making up the largest available time series of satellite daily images of Earth observation. The University of Almería used this dataset to develop an algorithm based in Bayesian networks for BA mapping in boreal regions [31], which was successfully applied by the researchers to other regions, using other types of images [32,33].…”