Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia
Frequent cloud cover in the tropics significantly affects the observation of the surface by satellites. This has enormous implications for current approaches that estimate greenhouse gas (GHG) emissions from fires or map fire scars. These mainly employ data acquired in the visible to middle infrared...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2020-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/ab7765 |
| _version_ | 1797747531077124096 |
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| author | Joao M B Carreiras Shaun Quegan Kevin Tansey Susan Page |
| author_facet | Joao M B Carreiras Shaun Quegan Kevin Tansey Susan Page |
| author_sort | Joao M B Carreiras |
| collection | DOAJ |
| description | Frequent cloud cover in the tropics significantly affects the observation of the surface by satellites. This has enormous implications for current approaches that estimate greenhouse gas (GHG) emissions from fires or map fire scars. These mainly employ data acquired in the visible to middle infrared bands to map fire scars or thermal data to estimate fire radiative power and consequently derive emissions. The analysis here instead explores the use of microwave data from the operational Sentinel-1A (S-1A) in dual-polarisation mode (VV and VH) acquired over Central Kalimantan during the 2015 fire season. Burnt areas were mapped in three consecutive periods between August and October 2015 using the random forests machine learning algorithm. In each mapping period, the omission and commission errors of the unburnt class were always below 3%, while the omission and commission errors of the burnt class were below 20% and 5% respectively. Summing the detections from the three periods gave a total burnt area of ∼1.6 million ha, but this dropped to ∼1.2 million ha if using only a pair of pre- and post-fire season S-1A images. Hence the ability of Sentinel-1 to make frequent observations significantly increases fire scar detection. Comparison with burnt area estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area product at 5 km scale showed poor agreement, with consistently much lower estimates produced by the MODIS data-on average 14%–51% of those obtained in this study. The method presented in this study offers a way to reduce the substantial errors likely to occur in optical-based estimates of GHG emissions from fires in tropical areas affected by substantial cloud cover. |
| first_indexed | 2024-03-12T15:51:54Z |
| format | Article |
| id | doaj.art-7adc2218da144839b2e80c585505c652 |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:51:54Z |
| publishDate | 2020-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-7adc2218da144839b2e80c585505c6522023-08-09T15:05:44ZengIOP PublishingEnvironmental Research Letters1748-93262020-01-0115505400810.1088/1748-9326/ab7765Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE AsiaJoao M B Carreiras0https://orcid.org/0000-0003-2737-9420Shaun Quegan1https://orcid.org/0000-0003-4452-4829Kevin Tansey2https://orcid.org/0000-0002-9116-8081Susan Page3https://orcid.org/0000-0002-3392-9241National Centre for Earth Observation, University of Sheffield , Sheffield, United KingdomNational Centre for Earth Observation, University of Sheffield , Sheffield, United KingdomCentre for Landscape and Climate Research, School of Geography, Geology & the Environment, University of Leicester , Leicester, United KingdomCentre for Landscape and Climate Research, School of Geography, Geology & the Environment, University of Leicester , Leicester, United KingdomFrequent cloud cover in the tropics significantly affects the observation of the surface by satellites. This has enormous implications for current approaches that estimate greenhouse gas (GHG) emissions from fires or map fire scars. These mainly employ data acquired in the visible to middle infrared bands to map fire scars or thermal data to estimate fire radiative power and consequently derive emissions. The analysis here instead explores the use of microwave data from the operational Sentinel-1A (S-1A) in dual-polarisation mode (VV and VH) acquired over Central Kalimantan during the 2015 fire season. Burnt areas were mapped in three consecutive periods between August and October 2015 using the random forests machine learning algorithm. In each mapping period, the omission and commission errors of the unburnt class were always below 3%, while the omission and commission errors of the burnt class were below 20% and 5% respectively. Summing the detections from the three periods gave a total burnt area of ∼1.6 million ha, but this dropped to ∼1.2 million ha if using only a pair of pre- and post-fire season S-1A images. Hence the ability of Sentinel-1 to make frequent observations significantly increases fire scar detection. Comparison with burnt area estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area product at 5 km scale showed poor agreement, with consistently much lower estimates produced by the MODIS data-on average 14%–51% of those obtained in this study. The method presented in this study offers a way to reduce the substantial errors likely to occur in optical-based estimates of GHG emissions from fires in tropical areas affected by substantial cloud cover.https://doi.org/10.1088/1748-9326/ab7765burnt areatropicsSentinel-1radarmachine learningIndonesia |
| spellingShingle | Joao M B Carreiras Shaun Quegan Kevin Tansey Susan Page Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia Environmental Research Letters burnt area tropics Sentinel-1 radar machine learning Indonesia |
| title | Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia |
| title_full | Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia |
| title_fullStr | Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia |
| title_full_unstemmed | Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia |
| title_short | Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia |
| title_sort | sentinel 1 observation frequency significantly increases burnt area detectability in tropical se asia |
| topic | burnt area tropics Sentinel-1 radar machine learning Indonesia |
| url | https://doi.org/10.1088/1748-9326/ab7765 |
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