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Online Map of June's 2013 Fire Affected Area in Riau (Before and After)

Center for International Forestry Research (CIFOR) Dataverse OAI Archive

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Title Online Map of June's 2013 Fire Affected Area in Riau (Before and After)
 
Identifier https://doi.org/10.17528/CIFOR/DATA.00082
 
Creator Center for International Forestry Research (CIFOR)
 
Publisher Center for International Forestry Research (CIFOR)
 
Description This interactive online map
shows area affected by June 2013's fire that created trans-boundary haze events in South East Asia. The trans-boundary haze events are associated with large forest and peatland fires in Indonesia. These episodes of extreme air pollution usually occur during drought years induced by climate anomalies from the Pacific (El Niño Southern Oscillation) and Indian Oceans (Indian Ocean Dipole). However, in June 2013 – a non-drought year – Singapore’s 24-hr Pollutants Standards Index reached an all-time record 246 (rated ‘‘very unhealthy’’). Here, we show using remote sensing, rainfall records and other data, that the Indonesian fires behind the 2013 haze followed a two-month dry spell in a wetter-than-average year. These fires were short-lived (one week) and limited to a localized area in Central Sumatra (1.6% of Indonesia): burning an estimated 163,336 ha, including 137,044 ha (84%) on peat. Most burning was confined to deforested lands (82%; 133,216 ha). The greenhouse gas (GHG) emissions during this brief, localized event were considerable: 172 ± 59 Tg CO2-eq (or 31 ± 12 Tg C), representing 5–10% of Indonesia’s mean annual GHG emissions for 2000–2005. Our observations show that extreme air pollution episodes in Southeast Asia are no longer restricted to drought years. We expect major haze events to be increasingly frequent because of ongoing deforestation of Indonesian peatlands.
 
Subject Climate Change, Energy and low carbon development (CCE)
Value Chain, Finance & Investments (VFI)
imagery
degraded forests
peatlands
forest fires
 
Language English
 
Contributor Admin, Dataverse
 
Type spatial data