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 (R2) 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.
Highlights
As a recurring natural process, fire has been a basic element in preserving the health and biodiversity of numerous terrestrial ecosystems for thousands of years [1,2,3]
This paper presents the application of the above methodology [23,57,58] to a lower latitude forest ecosystem, totally different from the boreal ecosystem and using daily time series of satellite images corresponding to the study region using the methodology based on the Bayesian classifier and the same variables, modifying the values of its parameters by training with reference data extracted from official fire records and post-fire Landsat images; and (iii) to assess the spatial and temporal accuracy of the detected Burned Area with reference data, and to compare the results with those of the official Moderate Resolution Imaging Spectroradiometer (MODIS) Burned Area products with analogous spatial resolution (MCD45A1 and RMemCoDte 6S4enAs.12)0.18, 10, 789
Three consecutive months without rainfall and with hot temperatures, alongside intense and long-lasting southerly winds, led to a relative humidity lower than 20%, which made it easier for the fire to spread in just six hours. This situation makes it harder for BY-MODIS or MCD45A1 products, which are based on algorithms seeking to sense drastic changes in post-fire reflectances, to be effective for this fire
Summary
As a recurring natural process, fire has been a basic element in preserving the health and biodiversity of numerous terrestrial ecosystems for thousands of years [1,2,3]. Prominent amongst said disturbances are the loss of biodiversity due to the decimation of species and biomass [2]; soil deterioration and erosion which, alongside the change in land cover (terrestrial albedo) [4,5], alter water and energy flows; and the emission of large amounts of aerosols and greenhouse gases affecting the carbon cycle and global warming [6,7,8,9]. Burned Area maps are necessary in other applications of vital interest to our society as an early warning fire alert system; the fire hazard assessment for ecosystems management; the study of changes in atmospheric chemistry; and in biogeochemical modelling and that of the vegetation dynamics at a global scale [12]
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