Mapping Forest Disturbance Due to Selective Logging in the Congo Basin with RADARSAT-2 Time Series
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 var...
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| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/4/740 |
| _version_ | 1797396269130317824 |
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| author | Oleg Antropov Yrjö Rauste Jaan Praks Frank Martin Seifert Tuomas Häme |
| author_facet | Oleg Antropov Yrjö Rauste Jaan Praks Frank Martin Seifert Tuomas Häme |
| author_sort | Oleg Antropov |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T00:48:54Z |
| format | Article |
| id | doaj.art-b8b6b0e92fb249789583fd227e6e6bf1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T00:48:54Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b8b6b0e92fb249789583fd227e6e6bf12023-12-11T17:23:06ZengMDPI AGRemote Sensing2072-42922021-02-0113474010.3390/rs13040740Mapping Forest Disturbance Due to Selective Logging in the Congo Basin with RADARSAT-2 Time SeriesOleg Antropov0Yrjö Rauste1Jaan Praks2Frank Martin Seifert3Tuomas Häme4VTT Technical Research Centre of Finland, P.O. Box 1000, 00076 Espoo, FinlandVTT Technical Research Centre of Finland, P.O. Box 1000, 00076 Espoo, FinlandDepartment of Electronics and Nanoengineering, Aalto University, P.O. Box 11000, 00076 Aalto, FinlandEuropean Space Agency, ESA-ESRIN, Largo Galileo Galilei 1, 00044 Frascati, ItalyVTT Technical Research Centre of Finland, P.O. Box 1000, 00076 Espoo, FinlandDense 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.https://www.mdpi.com/2072-4292/13/4/740synthetic aperture radartropical forestselective loggingC-bandsatellite image time series |
| spellingShingle | Oleg Antropov Yrjö Rauste Jaan Praks Frank Martin Seifert Tuomas Häme Mapping Forest Disturbance Due to Selective Logging in the Congo Basin with RADARSAT-2 Time Series Remote Sensing synthetic aperture radar tropical forest selective logging C-band satellite image time series |
| title | Mapping Forest Disturbance Due to Selective Logging in the Congo Basin with RADARSAT-2 Time Series |
| title_full | Mapping Forest Disturbance Due to Selective Logging in the Congo Basin with RADARSAT-2 Time Series |
| title_fullStr | Mapping Forest Disturbance Due to Selective Logging in the Congo Basin with RADARSAT-2 Time Series |
| title_full_unstemmed | Mapping Forest Disturbance Due to Selective Logging in the Congo Basin with RADARSAT-2 Time Series |
| title_short | Mapping Forest Disturbance Due to Selective Logging in the Congo Basin with RADARSAT-2 Time Series |
| title_sort | mapping forest disturbance due to selective logging in the congo basin with radarsat 2 time series |
| topic | synthetic aperture radar tropical forest selective logging C-band satellite image time series |
| url | https://www.mdpi.com/2072-4292/13/4/740 |
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