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Potential of Sentinel-1 time series for deforestation and forest degradation mapping in temperate and tropical forests

Urbazaev, Mikhail and Cremer, Felix and Schmullius, Christiane and Thiel, Christian (2018) Potential of Sentinel-1 time series for deforestation and forest degradation mapping in temperate and tropical forests. International Workshop on Retrieval of Bio- & Geo-physical Parameters from SAR Data for Land Applications, 12.-15. November 2018, Oberpfaffenhofen, Germany.

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Abstract

In this study we investigated the potential of dense synthetic aperture radar (SAR) time series collected by the ESA’s Sentinel-1 satellites to detect deforestation and forest degradation areas. Since SAR data are affected by speckle, it is crucial to filter speckle before the time series analysis. Accordingly, we explored the potential of empirical mode decomposition (EMD), a data-driven approach to decompose the temporal signal into components of different frequencies. Based on the assumption that the high frequency components are corresponding to speckle, these effects can be isolated and removed. Since the EMD approach operates in the time domain only, it fully preserves the geometric resolution, which is required to detect small scale changes (e.g., forest degradation). We assessed the speckle filtering performance of the EMD approach. The results over forested areas showed similar statistics compared to the multi-temporal Quegan speckle filter in terms of speckle suppression (based on Equivalent Number of Looks) and an improved edge preservation. In the next step, we analyzed EMD filtered Sentinel-1 data for detection of deforestation and forest degradation areas. For this, we first selected forested, deforested and degraded areas based on visual interpretation of multi-temporal very high resolution (1 m) optical imagery over temperate and tropical forests of Mexico. Further, we plotted EMD filtered Sentinel-1 time series for the three reference classes and were able to determine the time frame of deforestation and forest degradation. The initial analyses showed promising results regarding the separation of forest and forest-change classes with EMD-filtered Sentinel-1 data in contrast to original SAR backscatter images. Furthermore, we present preliminary deforestation maps for study sites in Mexico and South Africa based on Bayesian probability approach and EMD-filtered Sentinel-1 time series backscatter. This study is supported by DLR in the Sentinel4REDD project (FKZ:50EE1540) to develop new remote sensing based methods using Sentinel-1 and Sentinel-2 data to support UNFCC (United Nations Framework Convention on Climate Change) REDD+ MRV (Measurement, Reporting and Verification) Systems.

Item URL in elib:https://elib.dlr.de/125413/
Document Type:Conference or Workshop Item (Speech)
Title:Potential of Sentinel-1 time series for deforestation and forest degradation mapping in temperate and tropical forests
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Urbazaev, MikhailFriedrich-Schiller-Universität Jenahttps://orcid.org/0000-0002-0327-6278
Cremer, FelixFriedrich-Schiller-Universität JenaUNSPECIFIED
Schmullius, ChristianeFriedrich-Schiller-Universität JenaUNSPECIFIED
Thiel, ChristianChristian.Thiel (at) dlr.dehttps://orcid.org/0000-0001-5144-8145
Date:2018
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Sentinel-1, time series, REDD+, empirical mode decomposition
Event Title:International Workshop on Retrieval of Bio- & Geo-physical Parameters from SAR Data for Land Applications
Event Location:Oberpfaffenhofen, Germany
Event Type:international Conference
Event Dates:12.-15. November 2018
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:25 Nov 2019 08:52
Last Modified:25 Nov 2019 08:52

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