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Using Sentinel 1-SAR for Monitoring Long Term Variation in Burnt Forest Areas

Ruiz-Ramos, Javier; Marino, Armando and Boardman, Carl P. (2018). Using Sentinel 1-SAR for Monitoring Long Term Variation in Burnt Forest Areas. In: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 22-27 Jul 2018, Valencia, Spain, pp. 4901–4904.

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Over the past decades, the great technological advances made in airborne and space sensors have led to a significant improvement in remote sensing methods and techniques used for studying worldwide natural ecosystem disturbances. More commonly, optical sensors are chosen for detecting landscapes transformation, however, in addition to the problematics associated to this technology due the requirement of certain technical and environmental conditions (sunlight, no cloud-coverage), these systems may offer a significant lower performance when studying the post-disturbance evolution. In response to this challenge, this research aims to highlight the capabilities of Synthetic Aperture Radar - SAR satellite sensors for investigating the environmental evolution of forest areas affected by Mediterranean fire events. The use of a multitemporal analysis of ESA-Sentinel 1 SAR and Landsat 7 & Sentinel 2 optical satellite image series allowed us to explore the post-fire natural evolution of the areas affected by the Doñana national park forest fire occurred in June-July 2017 in two different ways: 1. Monitoring radar image intensity changes to evaluate environmental disturbances within the affected areas; 2. Comparing the response of both optical and radar satellite systems to the extraction of long-term environmental information. The results obtained from both the backscatter signal analysis and the systems comparison illustrate the SAR technology's efficiency in detecting and studying temporal changes in the natural conditions of the areas affected by fire events.

Item Type: Conference or Workshop Item
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Engineering and Innovation
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 58507
Depositing User: ORO Import
Date Deposited: 04 Jan 2019 09:15
Last Modified: 29 Mar 2019 10:41
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