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Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping

Pulella, Andrea and Aragao Santos, Rodrigo and Sica, Francescopaolo and Posovszky, Philipp and Rizzoli, Paola (2020) Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping. Remote Sensing, 12 (5), pp. 1-17. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs12050847 ISSN 2072-4292

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Official URL: https://www.mdpi.com/2072-4292/12/5/847

Abstract

This paper reports recent advancements in the field of Synthetic Aperture Radar (SAR) for forest mapping by using interferometric short-time-series. In particular, we first present how the interferometric capabilities of the Sentinel-1 satellites constellation can be exploited for the monthly mapping of the Amazon rainforest. Indeed, the evolution in time of the interferometric coherence can be properly modeled as an exponential decay and the retrieved interferometric parameters can be used, together with the backscatter, as input features to the machine learning Random Forests classifier. Furthermore, we present an analysis on the benefits of the use of textural information, derived from Sentinel-1 backscatter, in order to enhance the classification accuracy. These textures are computed through the Sum And Difference Histograms methodology and the final classification accuracy, resulting by adding them to the aforementioned features, is a thematic map that exceeds an overall agreement of 85%, when validated using the optical external reference Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) map. The experiments presented in the final part of the paper are enriched with a further analysis and discussion on the selected scenes using updated multispectral Sentinel-2 acquisitions.

Item URL in elib:https://elib.dlr.de/134414/
Document Type:Article
Title:Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Pulella, Andreaandrea.pulella (at) dlr.dehttps://orcid.org/0000-0001-6295-617X
Aragao Santos, RodrigoRodrigo.AragaoSantos (at) dlr.deUNSPECIFIED
Sica, FrancescopaoloFrancescopaolo.Sica (at) dlr.dehttps://orcid.org/0000-0003-1593-1492
Posovszky, PhilippPhilipp.Posovszky (at) dlr.dehttps://orcid.org/0000-0003-0656-3691
Rizzoli, PaolaPaola.Rizzoli (at) dlr.dehttps://orcid.org/0000-0001-9118-2732
Date:6 March 2020
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:12
DOI :10.3390/rs12050847
Page Range:pp. 1-17
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Series Name:MDPI Remote Sensing SAR for Forest Mapping
ISSN:2072-4292
Status:Published
Keywords:forest mapping; Sentinel-1; short-time-series; synthetic aperture radar; interferometric coherence; temporal decorrelation; Random Forests; spatial texture
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Projekt TanDEM-X
Location: Oberpfaffenhofen
Institutes and Institutions:Microwaves and Radar Institute > Spaceborne SAR Systems
Deposited By: Pulella, M.Eng. Andrea
Deposited On:12 Mar 2020 18:34
Last Modified:12 Mar 2020 18:34

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