Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1088/1748-9326/ab7765 |
Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia | |
Carreiras, Joao M. B.1; Quegan, Shaun1; Tansey, Kevin2; Page, Susan2 | |
2020-05-01 | |
发表期刊 | ENVIRONMENTAL RESEARCH LETTERS |
ISSN | 1748-9326 |
出版年 | 2020 |
卷号 | 15期号:5 |
文章类型 | Article |
语种 | 英语 |
国家 | England |
英文摘要 | 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 similar to 1.6 million ha, but this dropped to similar 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. |
英文关键词 | burnt area tropics Sentinel-1 radar machine learning Indonesia |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000531268100001 |
WOS关键词 | GLOBAL FIRE EMISSIONS ; FOREST-FIRES ; CARBON EMISSIONS ; SPOT-VEGETATION ; HIGH-RESOLUTION ; INDONESIA ; DEFORESTATION ; PERFORMANCE ; ALGORITHM ; DATABASE |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/279308 |
专题 | 气候变化 |
作者单位 | 1.Univ Sheffield, Natl Ctr Earth Observat, Sheffield, S Yorkshire, England; 2.Univ Leicester, Ctr Landscape & Climate Res, Sch Geog Geol & Environm, Leicester, Leics, England |
推荐引用方式 GB/T 7714 | Carreiras, Joao M. B.,Quegan, Shaun,Tansey, Kevin,et al. Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia[J]. ENVIRONMENTAL RESEARCH LETTERS,2020,15(5). |
APA | Carreiras, Joao M. B.,Quegan, Shaun,Tansey, Kevin,&Page, Susan.(2020).Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia.ENVIRONMENTAL RESEARCH LETTERS,15(5). |
MLA | Carreiras, Joao M. B.,et al."Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia".ENVIRONMENTAL RESEARCH LETTERS 15.5(2020). |
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