elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Potential of Recurrence Metrics from Sentinel-1 Time Series for Deforestation Mapping

Cremer, Felix and Urbazaev, Mikhail and Cortés, José and Truckenbrodt, John and Schmullius, Christiane C. and Thiel, Christian (2020) Potential of Recurrence Metrics from Sentinel-1 Time Series for Deforestation Mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, pp. 5233-5240. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2020.3019333. ISSN 1939-1404.

[img] PDF - Published version
1MB

Abstract

The REDD+ framework requires accurate estimates of deforestation. These are derived by ground measurements supported by methods based on remote sensing data to automatically detect and delineate deforestations over large areas. In particular, in the tropics, optical data is seldom available due to cloud cover. As synthetic aperture radar (SAR) data overcomes this limitation, we performed a separability analysis of two statistical metrics based on the Sentinel-1 SAR backscatter over forested and deforested areas. We compared the range between the 5th and 95th temporal percentiles (PRange) and the recurrence quantification analysis (RQA) Trend metric. Unlike the PRange, the RQA Trend considers the temporal order of the SAR data acquisitions, thus contrasting between dropping backscatter signals and yearly seasonalities. This enables the estimation of the timing of deforestation events. We assessed the impact of polarization, acquisition orbit, and despeckling on the separability between forested and deforested areas and between different deforestation timings for two test sites in Mexico. We found that the choice of the orbit impacts the detectability of deforestation. In all cases, VH data showed a higher separability between forest and deforestations than VV data. The PRange slightly outperformed RQA Trend in the separation between forest and deforestation. However, the RQA Trend exceeded the PRange in the separation between different deforestation timings. In this study, C-Band backscatter data was used, although it is commonly not considered as the most suitable SAR dataset for forestry applications. Nevertheless, our approach shows the potential of dense C-Band backscatter time series to support the REDD+ framework

Item URL in elib:https://elib.dlr.de/139896/
Document Type:Article
Title:Potential of Recurrence Metrics from Sentinel-1 Time Series for Deforestation Mapping
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Cremer, FelixFelix.Cremer (at) dlr.dehttps://orcid.org/0000-0001-8659-4361
Urbazaev, MikhailFriedrich-Schiller-Universität Jenahttps://orcid.org/0000-0002-0327-6278
Cortés, JoséFriedrich-Schiller-Universität JenaUNSPECIFIED
Truckenbrodt, JohnJohn.Truckenbrodt (at) dlr.dehttps://orcid.org/0000-0002-7259-101X
Schmullius, Christiane C.Friedrich-Schiller-Universität JenaUNSPECIFIED
Thiel, ChristianChristian.Thiel (at) dlr.dehttps://orcid.org/0000-0001-5144-8145
Date:26 September 2020
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1109/JSTARS.2020.3019333
Page Range:pp. 5233-5240
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1939-1404
Status:Published
Keywords:Forestry, radar remote sensing, synthetic aperture radar, time series analysis
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Jena
Institutes and Institutions:Institute of Data Science > Citizen Science
Deposited By: Thiel, Christian
Deposited On:04 Jan 2021 12:36
Last Modified:04 Jan 2021 12:36

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.