Mapping forest disturbance due to selective logging in the congo basin with radarsat-2 time series

Oleg Antropov; Yrjö Rauste; Jaan Praks; Frank Martin Seifert; Tuomas Häme

doi:10.3390/rs13040740

Mapping forest disturbance due to selective logging in the congo basin with radarsat-2 time series

Oleg Antropov^*
, Yrjö Rauste
, Jaan Praks
, Frank Martin Seifert
, Tuomas Häme

^*Corresponding author for this work

Aalto University
ESRIN - ESA Centre for Earth Observation

Research output: Contribution to journal › Article › Scientific › peer-review

16 Citations (Scopus)

262 Downloads (Pure)

Abstract

Dense time series of stripmap RADARSAT-2 data acquired in the Multilook Fine mode were used for detecting and mapping the extent of selective logging operations in the tropical forest area in the northern part of the Republic of the Congo. Due to limited radiometric sensitivity to forest biomass variation at C-band, basic multitemporal change detection approach was supplemented by spatial texture analysis to separate disturbed forest from intact. The developed technique primarily uses multi-temporal aggregation of orthorectified synthetic aperture radar (SAR) imagery that are acquired before and after the logging operations. The actual change analysis is based on textural features of the log-ratio image calculated using two SAR temporal composites compiled of SAR scenes acquired before and after the logging operations. Multitemporal aggregation and filtering of SAR scenes decreased speckle and made the extracted textural features more prominent. The overall detection accuracy was around 80%, with some underestimation of the area of forest disturbance compared to reference based on optical data. The user’s accuracy for disturbed forest varied from 76.7% to 94.9% depending on the accuracy assessment approach. We conclude that change detection utilizing RADARSAT-2 time series represents a useful instrument to locate areas of selective logging in tropical forests.

Original language	English
Article number	740
Pages (from-to)	1-16
Number of pages	16
Journal	Remote Sensing
Volume	13
Issue number	4
DOIs	https://doi.org/10.3390/rs13040740
Publication status	Published - 17 Feb 2021
MoE publication type	A1 Journal article-refereed

Funding

The work was performed primarily during GSE Forest Monitoring: REDD extension project funded by European Space Agency. O.A. was also supported by MULTICO project funded by Business Finland, grant agreement 6501/31/2019. All authors were also supported by their institutions. The costs of open access publishing are covered by VTT.RADARSAT-2 data were provided by ESA in the framework of project C1P3810. Authors would like to thank anonymous reviewers for their useful suggestions and comments.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 15 Life on Land

Keywords

C-band
Satellite image time series
Selective logging
Synthetic aperture radar
Tropical forest

Access to Document

10.3390/rs13040740Licence: CC BY

rs13040740Final published version, 18.1 MBLicence: CC BY

Cite this

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title = "Mapping forest disturbance due to selective logging in the congo basin with radarsat-2 time series",

abstract = "Dense time series of stripmap RADARSAT-2 data acquired in the Multilook Fine mode were used for detecting and mapping the extent of selective logging operations in the tropical forest area in the northern part of the Republic of the Congo. Due to limited radiometric sensitivity to forest biomass variation at C-band, basic multitemporal change detection approach was supplemented by spatial texture analysis to separate disturbed forest from intact. The developed technique primarily uses multi-temporal aggregation of orthorectified synthetic aperture radar (SAR) imagery that are acquired before and after the logging operations. The actual change analysis is based on textural features of the log-ratio image calculated using two SAR temporal composites compiled of SAR scenes acquired before and after the logging operations. Multitemporal aggregation and filtering of SAR scenes decreased speckle and made the extracted textural features more prominent. The overall detection accuracy was around 80\%, with some underestimation of the area of forest disturbance compared to reference based on optical data. The user{\textquoteright}s accuracy for disturbed forest varied from 76.7\% to 94.9\% depending on the accuracy assessment approach. We conclude that change detection utilizing RADARSAT-2 time series represents a useful instrument to locate areas of selective logging in tropical forests.",

keywords = "C-band, Satellite image time series, Selective logging, Synthetic aperture radar, Tropical forest",

author = "Oleg Antropov and Yrj{\"o} Rauste and Jaan Praks and Seifert, \{Frank Martin\} and Tuomas H{\"a}me",

note = "Funding Information: The work was performed primarily during GSE Forest Monitoring: REDD extension project funded by European Space Agency. O.A. was also supported by MULTICO project funded by Business Finland, grant agreement 6501/31/2019. All authors were also supported by their institutions. The costs of open access publishing are covered by VTT.RADARSAT-2 data were provided by ESA in the framework of project C1P3810. Authors would like to thank anonymous reviewers for their useful suggestions and comments. Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

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AU - Häme, Tuomas

N1 - Funding Information: The work was performed primarily during GSE Forest Monitoring: REDD extension project funded by European Space Agency. O.A. was also supported by MULTICO project funded by Business Finland, grant agreement 6501/31/2019. All authors were also supported by their institutions. The costs of open access publishing are covered by VTT.RADARSAT-2 data were provided by ESA in the framework of project C1P3810. Authors would like to thank anonymous reviewers for their useful suggestions and comments. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/2/17

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N2 - Dense time series of stripmap RADARSAT-2 data acquired in the Multilook Fine mode were used for detecting and mapping the extent of selective logging operations in the tropical forest area in the northern part of the Republic of the Congo. Due to limited radiometric sensitivity to forest biomass variation at C-band, basic multitemporal change detection approach was supplemented by spatial texture analysis to separate disturbed forest from intact. The developed technique primarily uses multi-temporal aggregation of orthorectified synthetic aperture radar (SAR) imagery that are acquired before and after the logging operations. The actual change analysis is based on textural features of the log-ratio image calculated using two SAR temporal composites compiled of SAR scenes acquired before and after the logging operations. Multitemporal aggregation and filtering of SAR scenes decreased speckle and made the extracted textural features more prominent. The overall detection accuracy was around 80%, with some underestimation of the area of forest disturbance compared to reference based on optical data. The user’s accuracy for disturbed forest varied from 76.7% to 94.9% depending on the accuracy assessment approach. We conclude that change detection utilizing RADARSAT-2 time series represents a useful instrument to locate areas of selective logging in tropical forests.

AB - Dense time series of stripmap RADARSAT-2 data acquired in the Multilook Fine mode were used for detecting and mapping the extent of selective logging operations in the tropical forest area in the northern part of the Republic of the Congo. Due to limited radiometric sensitivity to forest biomass variation at C-band, basic multitemporal change detection approach was supplemented by spatial texture analysis to separate disturbed forest from intact. The developed technique primarily uses multi-temporal aggregation of orthorectified synthetic aperture radar (SAR) imagery that are acquired before and after the logging operations. The actual change analysis is based on textural features of the log-ratio image calculated using two SAR temporal composites compiled of SAR scenes acquired before and after the logging operations. Multitemporal aggregation and filtering of SAR scenes decreased speckle and made the extracted textural features more prominent. The overall detection accuracy was around 80%, with some underestimation of the area of forest disturbance compared to reference based on optical data. The user’s accuracy for disturbed forest varied from 76.7% to 94.9% depending on the accuracy assessment approach. We conclude that change detection utilizing RADARSAT-2 time series represents a useful instrument to locate areas of selective logging in tropical forests.

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KW - Synthetic aperture radar

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Mapping forest disturbance due to selective logging in the congo basin with radarsat-2 time series

Abstract

Funding

UN SDGs

Keywords

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