Improving the detection of wildfire disturbances in space and time based on indicators extracted from MODIS data: a case study in northern Portugal

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Marcos, B (Author) Other; Goncalves, J (Author) Other; Alcaraz Segura, D (Author) Other; Mario Cunha (Author) FCUP; Joao Honrado (Author) FCUP

Journal

Title: International Journal of Applied Earth Observation and Geoinformation

Vol. 78

Pages: 77-85

ISSN: 0303-2434

Publisher: International Institute for Aerial Survey and Earth Sciences

Indexing

ISI Web of Knowledge - 17 Citations

Scopus - 20 Citations

Other information

Authenticus ID: P-00Q-EDM

DOI: 10.1016/j.jag.2018.12.003

Abstract (EN): Wildfires constitute an important threat to human lives and livelihoods worldwide, as well as a major ecological disturbance. However, available wildfire databases often provide incomplete or inaccurate information, namely regarding the timing and extension of fire events. In this study, we described a generic framework to compare, rank and combine multiple remotely-sensed indicators of wildfire disturbances, in order to not only select the best indicators for each specific case, as well as to provide multi-indicator consensus approaches that can be used to detect wildfire disturbances in space and time. For this end, we compared the performance of different remotely-sensed variables to discriminate burned areas, by applying a simple change-point analysis procedure on time-series of MODIS imagery for the northern half of Portugal, without external information (e.g. active fire maps). Overall, our results highlight the importance of adopting a multi-indicator consensus approach for mapping and detecting wildfire disturbances at a regional scale, that allows to profit from spectral indices capturing different aspects of the Earth's surface, and derived from distinct regions of the electromagnetic spectrum. Finally, we argue that the framework here described can be used: (i) in a wide variety of geographical and environmental contexts; (ii) to support the identification of the best possible remotely-sensed functional indicators of wildfire disturbance; and (iii) for improving and complementing incomplete wildfire databases.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9