Citation

Brown AR, Petropoulos GP, Ferentinos KP. Appl. Geogr. 2018; 100: 78-89.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.apgeog.2018.10.004

PMID

unavailable

Abstract

The recent launch of Sentinel missions offers a unique opportunity to assess the impacts of wildfires at higher spatial and spectral resolution provided by those Earth Observing (EO) systems. Herein, an assessment of the Sentinel-1 & 2 to map burnt areas has been conducted. Initially the use of Sentinel-2 solely was explored, and then in combination with Sentinel-1 and derived radiometric indices. As a case study, the large wildfire occurred in Pedrógão Grande, Portugal in 2017 was used. Burnt area estimates from the European Forest Fires Information System (EFFIS) were used as reference. Burnt area was delineated using the Maximum Likelihood (ML) and Support Vector Machines (SVMs) classifiers, and two multi-index methods. Following this, burn severity was assessed using SVMs and Artificial Neural Networks (ANNs), again for both standalone Sentinel-2 data and in combination with Sentinel-1 and spectral indices. Soil erosion predictions were evaluated using the Revised Universal Soil Loss Equation (RUSLE) model. The effect of the land cover derived from CORINE operational product was also evaluated across the burnt area and severity maps. SVMs produced the most accurate burnt area map, resulting a 94.8% overall accuracy and a Kappa coefficient of 0.90. SVMs also achieved the highest accuracy in burn severity levels estimation, with an overall accuracy of 77.9% and a kappa of 0.710. From an algorithmic perspective, implementation of the techniques applied herein, is based on EO imagery analysis provided nowadays globally at no cost. It is also robust and adaptable, being potentially integrated with other high EO data available. All in all, our study contributes to the understanding of Mediterranean landscape dynamics and corroborates the usefulness of Sentinels data in wildfire studies.

Language: en

Keywords

Agriculture; Burn severity; Burnt area mapping; Earth observation; Forestry; GIS; Maximum likelihood; RUSLE; Sentinel-1; Sentinel-2; Soil erodibility; Support vector machines