Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon
Selective logging is a major cause of forest degradation in the tropics, but its precise scale, location and timing are not known as wide-area, automated remote sensing methods are not yet available at this scale. This limits the abilities of governments to police illegal logging, or monitor (and th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/17/4233 |
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| author | Harry Carstairs Edward T. A. Mitchard Iain McNicol Chiara Aquino Eric Chezeaux Médard Obiang Ebanega Anaick Modinga Dikongo Mathias Disney |
| author_facet | Harry Carstairs Edward T. A. Mitchard Iain McNicol Chiara Aquino Eric Chezeaux Médard Obiang Ebanega Anaick Modinga Dikongo Mathias Disney |
| author_sort | Harry Carstairs |
| collection | DOAJ |
| description | Selective logging is a major cause of forest degradation in the tropics, but its precise scale, location and timing are not known as wide-area, automated remote sensing methods are not yet available at this scale. This limits the abilities of governments to police illegal logging, or monitor (and thus receive payments for) reductions in degradation. Sentinel-1, a C-band Synthetic Aperture Radar satellite mission with a 12-day repeat time across the tropics, is a promising tool for this due to the known appearance of shadows in images where canopy trees are removed. However, previous work has relied on optical satellite data for calibration and validation, which has inherent uncertainties, leaving unanswered questions about the minimum magnitude and area of canopy loss this method can detect. Here, we use a novel bi-temporal LiDAR dataset in a forest degradation experiment in Gabon to show that canopy gaps as small as 0.02 ha (two 10 m × 10 m pixels) can be detected by Sentinel-1. The accuracy of our algorithm was highest when using a timeseries of 50 images over 20 months and no multilooking. With these parameters, canopy gaps in our study site were detected with a false alarm rate of 6.2%, a missed detection rate of 12.2%, and were assigned disturbance dates that were a good qualitative match to logging records. The presence of geolocation errors and false alarms makes this method unsuitable for confirming individual disturbances. However, we found a linear relationship (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>r</mi><mn>2</mn></msup><mo>=</mo><mn>0.74</mn></mrow></semantics></math></inline-formula>) between the area of detected Sentinel-1 shadow and LiDAR-based canopy loss at a scale of 1 hectare. By applying our method to three years’ worth of imagery over Gabon, we produce the first national scale map of small-magnitude canopy cover loss. We estimate a total gross canopy cover loss of 0.31 Mha, or 1.3% of Gabon’s forested area, which is a far larger area of change than shown in currently available forest loss alert systems using Landsat (0.022 Mha) and Sentinel-1 (0.019 Mha). Our results, which are made accessible through Google Earth Engine, suggest that this approach could be used to quantify the magnitude and timing of degradation more widely across tropical forests. |
| first_indexed | 2024-03-10T01:19:02Z |
| format | Article |
| id | doaj.art-7270e484092446aea899e1833f18967d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T01:19:02Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7270e484092446aea899e1833f18967d2023-11-23T14:03:10ZengMDPI AGRemote Sensing2072-42922022-08-011417423310.3390/rs14174233Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in GabonHarry Carstairs0Edward T. A. Mitchard1Iain McNicol2Chiara Aquino3Eric Chezeaux4Médard Obiang Ebanega5Anaick Modinga Dikongo6Mathias Disney7School of GeoSciences, The University of Edinburgh, Edinburgh EH8 3FF, UKSchool of GeoSciences, The University of Edinburgh, Edinburgh EH8 3FF, UKSchool of GeoSciences, The University of Edinburgh, Edinburgh EH8 3FF, UKSchool of GeoSciences, The University of Edinburgh, Edinburgh EH8 3FF, UKRougier Gabon, Immeuble Le Narval, Libreville, GabonDepartment of Geography, Omar Bongo University, Libreville, GabonAgence Gabonaise d’Etudes et d’Observations Spatiales, Libreville, GabonDepartment of Geography, University College London, London WC1E 6BT, UKSelective logging is a major cause of forest degradation in the tropics, but its precise scale, location and timing are not known as wide-area, automated remote sensing methods are not yet available at this scale. This limits the abilities of governments to police illegal logging, or monitor (and thus receive payments for) reductions in degradation. Sentinel-1, a C-band Synthetic Aperture Radar satellite mission with a 12-day repeat time across the tropics, is a promising tool for this due to the known appearance of shadows in images where canopy trees are removed. However, previous work has relied on optical satellite data for calibration and validation, which has inherent uncertainties, leaving unanswered questions about the minimum magnitude and area of canopy loss this method can detect. Here, we use a novel bi-temporal LiDAR dataset in a forest degradation experiment in Gabon to show that canopy gaps as small as 0.02 ha (two 10 m × 10 m pixels) can be detected by Sentinel-1. The accuracy of our algorithm was highest when using a timeseries of 50 images over 20 months and no multilooking. With these parameters, canopy gaps in our study site were detected with a false alarm rate of 6.2%, a missed detection rate of 12.2%, and were assigned disturbance dates that were a good qualitative match to logging records. The presence of geolocation errors and false alarms makes this method unsuitable for confirming individual disturbances. However, we found a linear relationship (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>r</mi><mn>2</mn></msup><mo>=</mo><mn>0.74</mn></mrow></semantics></math></inline-formula>) between the area of detected Sentinel-1 shadow and LiDAR-based canopy loss at a scale of 1 hectare. By applying our method to three years’ worth of imagery over Gabon, we produce the first national scale map of small-magnitude canopy cover loss. We estimate a total gross canopy cover loss of 0.31 Mha, or 1.3% of Gabon’s forested area, which is a far larger area of change than shown in currently available forest loss alert systems using Landsat (0.022 Mha) and Sentinel-1 (0.019 Mha). Our results, which are made accessible through Google Earth Engine, suggest that this approach could be used to quantify the magnitude and timing of degradation more widely across tropical forests.https://www.mdpi.com/2072-4292/14/17/4233Sentinel-1synthetic aperture radar (SAR)radartropical forestdegradationforest degradation |
| spellingShingle | Harry Carstairs Edward T. A. Mitchard Iain McNicol Chiara Aquino Eric Chezeaux Médard Obiang Ebanega Anaick Modinga Dikongo Mathias Disney Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon Remote Sensing Sentinel-1 synthetic aperture radar (SAR) radar tropical forest degradation forest degradation |
| title | Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon |
| title_full | Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon |
| title_fullStr | Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon |
| title_full_unstemmed | Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon |
| title_short | Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon |
| title_sort | sentinel 1 shadows used to quantify canopy loss from selective logging in gabon |
| topic | Sentinel-1 synthetic aperture radar (SAR) radar tropical forest degradation forest degradation |
| url | https://www.mdpi.com/2072-4292/14/17/4233 |
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