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Estimating the Deforestation Rate in the Amazon Rainforest from Sentinel-1 and TanDEM-X Multi-Temporal Stacks

Bueso Bello, Jose Luis and Rizzoli, Paola and Sica, Francescopaolo (2019) Estimating the Deforestation Rate in the Amazon Rainforest from Sentinel-1 and TanDEM-X Multi-Temporal Stacks. In: Proceedings of the ESA Living Planet Symposium. ESA. ESA Living Planet Symposium, 2019-05-13 - 2019-05-17, Milan, Italy.

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Forested areas strongly influence our ecosystem and play an important role in the carbon cycle, biodiversity richness, and climate regulation. Despite the efforts made by the United Nations with the Reducing Emissions from Deforestation and Forest Degradation (REDD) program to reduce deforestation and forest degradation activities, illegal deforestation, especially in the Amazon rainforest, is still increasing at alarming rates. The continuous monitoring of such areas is of particular importance to preserve them and to better understand their influence on the dynamic processes of our planet. Spaceborne synthetic aperture radar (SAR) represents a unique data source for the forest monitoring at a global scale and independently on weather conditions and solar light. The aim of this work is to exploit multi-temporal SAR data stacks, provided by the SAR sensors TanDEM-X and Sentinel-1, to detect changes on the forested areas. In the case of the bistatic TanDEM-X system, where no temporal decorrelation takes place, the volume correlation factor, which quantifies the amount of decorrelation due to scattering from a volume, is a good indicator to distinguish between forest and non-forest areas. Such classification can be improved by applying an image segmentation approach. In the case of Sentinel-1, the combination of the radar backscattering and of the evolution in time of the repeat-pass interferometric coherence can be used to characterize and detect deforested areas. In both multi-temporal SAR data stacks, clear-cuts can be identified and isolated by means of morphological operators and pixel density indicators. A region growing algorithm, as well as different texture analysis operators, will help to evaluate the evolution of the detected clear cuts in time. An ad-hoc consistency analysis between subsequent images plays an important role in reducing false alarms within the multi-temporal change detection algorithm. By implementing a data fusion approach between image stacks provided by both sensors, the deforestation rate for a large time span can be estimated with an increased accuracy. Finally, by exploiting the sensitivity to the tree height of the bistatic TanDEM-X system and the short revisit-time of the Sentinel-1 satellites constellation, the identification of selective logging will also be addressed.

Item URL in elib:https://elib.dlr.de/127046/
Document Type:Conference or Workshop Item (Poster)
Title:Estimating the Deforestation Rate in the Amazon Rainforest from Sentinel-1 and TanDEM-X Multi-Temporal Stacks
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Bueso Bello, Jose LuisUNSPECIFIEDhttps://orcid.org/0000-0003-3464-2186UNSPECIFIED
Rizzoli, PaolaUNSPECIFIEDhttps://orcid.org/0000-0001-9118-2732UNSPECIFIED
Sica, FrancescopaoloUNSPECIFIEDhttps://orcid.org/0000-0003-1593-1492UNSPECIFIED
Date:May 2019
Journal or Publication Title:Proceedings of the ESA Living Planet Symposium
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:Synthetic Aperture Radar (SAR), Interferometric SAR, Digital Elevation Model (DEM), Deforestation, Forest monitoring, Forest Degradation, REDD+
Event Title:ESA Living Planet Symposium
Event Location:Milan, Italy
Event Type:international Conference
Event Dates:2019-05-13 - 2019-05-17
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Projekt TanDEM-X (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Microwaves and Radar Institute > Spaceborne SAR Systems
Deposited By: Bueso Bello, Jose Luis
Deposited On:04 Apr 2019 08:54
Last Modified:15 Nov 2019 13:20

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