Dataset Persistent ID
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doi:10.17528/CIFOR/DATA.00080 |
Publication Date
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2014-08-19 |
Title
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Burned Area and Vegetation Cover Prior Fire
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Alternative URL
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http://worldmap.harvard.edu/data/geonode:idnriau_bu_396
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Author
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Center for International Forestry Research (CIFOR)
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Contact
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Use email button above to contact.
CIFOR-RDM (Center for International Forestry Research (CIFOR))
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Description
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Burned area in Riau province caused by June 2013's fire event and vegetation cover one month prior to burn. (2013)
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Subject
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Climate Change, Energy and low carbon development (CCE); Sustainable Landscapes & Food (SLF); Value Chain, Finance & Investments (VFI)
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Keyword
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imagery (GACS) http://browser.agrisemantics.org/gacs/en/page/C10279
degraded forests (GACS) http://browser.agrisemantics.org/gacs/en/page/C25538
forest fires (GACS) http://browser.agrisemantics.org/gacs/en/page/C4255
peatlands (GACS) http://browser.agrisemantics.org/gacs/en/page/C10734
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Topic Classification
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Biodiversity
Fire and Haze
Forest Management
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Related Publication
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Gaveau, D.L.A.; Salim, M.A.; Hergoualc'h, K.; Locatelli, B.; Sloan, S.; Wooster, M.; Marlier, M.E.; Molidena, E.; Yaen, H.; DeFries, R.; Verchot, L.; Murdiyarso, D.; Nasi, R.; Holmgren, P.; Sheil, D.2014. Major atmospheric emissions from peat fires in Southeast Asia during non-drought years: evidence from the 2013 Sumatran fires. doi: 10.1038/srep06112 https://www.nature.com/articles/srep06112
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Notes
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Pocess Information : We mapped burned areas and the vegetation cover of the same areas one month before the fire using three post-fire LANDSAT 8 images acquired on 25 June, 11 July and 12 August 2013 and two pre-fire images acquired on 22 April and 25 May 2013. We employed multiple pre- and post-fire images to reduce areas contaminated by clouds and haze. In the post-fire LANDSAT imagery displayed in false RGB colour (Short-wave infrared: band 6; Near infrared: band 5; Red: band 4) unburned vegetation appear green. Pink areas reveal unburned areas with exposed soils. Burned areas appear dark red. The most severely burned areas are generally the darkest. Burned, unburned areas and pre-fire vegetation were mapped using a tree-based supervised classification algorithm. Burned areas underneath haze or clouds were digitized using visual interpretation after applying a local contrast enhancement. In the pre-fire LANDSAT imagery, forest, non-forest, Acacia forest industrial plantations, and clouds were mapped. We used a tree-based supervised classification method (See5 module) in the ERDAS Imagine v8.6 remote sensing program extract the burned areas and pre-fire vegetation from the LANDSAT imagery. We collected high-resolution imagery (10-cm) with an Unmanned Aerial Vehicle (UAV) or ‘‘drone’’ (Skywalker Aero model with a camera Canon S100) between 28 July and 02 August 2013 to: (i) evaluate the accuracy of our LANDSAT-based maps (See Supplementary results); and (ii) characterise the vegetation types of the broad LANDSAT-based ‘non-forest’ class. The UAV images were acquired along transects at seven different sites, encompassing a variety of vegetation types and proximity to agriculture, burned and unburned mosaics. The UAV imagery (1,301 ha) was ortho-rectified and registered to our LANDSAT imagery. We characterised the LANDSAT-based ‘non-forest’ class by first interpreting the UAV imagery into five vegetation classes: (i) scrubs and exposed soils, (ii) young oil palm, (iii) mature oil palm, (iv) Acacia, and (v) forest. In Riau, oil palm plantations either belong to small- and medium-scale agriculturalists or to companies. Young and mature oil palm refer to open (<5 years old) and closed (>5 years old) canopy plantations, respectively. Acacia indicate closed-canopy company-owned plantations on peatlands. The pre-fire LANDSAT-based ‘non-forest’ class was then defined by comparing it against the five UAV-based vegetation classes. This comparison was only performed in the portions of UAV imagery identified as ‘unburned’ (567 ha). The error bar is calculated as ±1 Standard Deviation, (n= 7 UAV transects). To evaluate the accuracy of the LANDSAT-based ‘burned area’ map, we randomly sampled 2,088 validation points each being at least 100 m from each other. For each point, a 30 m X 30 m area, approximating a single LANDSAT pixel was visually interpreted as either ‘burned’ or ‘unburned’ in the UAV photos at 1:1000 scale, burned areas being easily discernable. A confusion matrix determined the frequency of class agreement between our reference UAV imagery and our LANDSAT-based burned area map, as determined by overall accuracy (i.e., ‘% correct’), user’s and producer’s accuracy. We also identified the level of correspondence between our LANDSAT-based burned area map and the MODIS fire hotspots data by calculating the percentage of fire hotspots that fell within the burned areas or that were within 500 meters of the burned areas. We repeated this validation procedure using the portions of UAV imagery identified as ‘unburned’ (567 ha) to validate the pre-fire LANDSAT-based vegetation cover (forest, Acacia, non-forest).
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Language
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English
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Grant Information
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CGIAR
Natural Environment Research Council (NERC)
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Distributor
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Center for International Forestry Research (CIFOR) http://www.cifor.org
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Depositor
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Admin, Dataverse
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Deposit Date
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2014-08-19
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Time Period Covered
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Start: 2013 ; End: 2013
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Kind of Data
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spatial data
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Software
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-
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