GFW Fires ANALYSES
From: 2015-10-3
To: 2015-10-10
ON ISLANDS: Sumatra
*Analysis based on high confidence fires only
During this time period, there were 3920 fire alerts, of which 1378 were high confidence.
Figure 1: DISTRIBUTION OF FIRE ALERTS
High Confidence Fires
Esri, HERE, DeLorme, MapmyIndia, © OpenStreetMap contributors, and the GIS user community |
Figure 2a: DISTRICTS WITH THE GREATEST NUMBER OF FIRE ALERTS
Fire Alerts by Districts
1 - 6
7 - 15
16 - 27
28 - 47
48 - 1086
Esri, HERE, DeLorme, MapmyIndia, © OpenStreetMap contributors, and the GIS user community |
Figure 2b: DISTRICTS WITH THE GREATEST NUMBER OF FIRE ALERTS
DISTRICTISLANDNUMBER OF FIRE ALERTS
OGAN KOMERING ILIRSumatra1086
MUARA ENIMSumatra47
BANYU ASINSumatra36
MUSI BANYU ASINSumatra27
TANJUNG JABUNG TIMURSumatra24
TULANG BAWANGSumatra23
OGAN KOMERING ULUSumatra22
OGAN KOMERING ULU TIMURSumatra21
BATANG HARISumatra15
OGAN KOMERING ULU SELATANSumatra14
Figure 3a: SUBDISTRICTS WITH THE GREATEST NUMBER OF FIRE ALERTS
Fire Alerts by SubDistricts
1 - 10
11 - 31
32 - 163
164 - 359
360 - 505
Esri, HERE, DeLorme, MapmyIndia, © OpenStreetMap contributors, and the GIS user community |
Figure 3b: SUBDISTRICTS WITH THE GREATEST NUMBER OF FIRE ALERTS
SUBDISTRICTISLANDNUMBER OF FIRE ALERTS
TULUNG SELAPANSumatra505
AIR SUGIHANSumatra359
CENGALSumatra163
PEMATANG PANGGANGSumatra31
RAMBANG DANGKUSumatra25
BANYUASIN 2Sumatra25
LAISSumatra18
SADUSumatra16
MADANG SUKU IISumatra16
PAMPANGANSumatra14
Figure 4: FIRE ALERT COUNT JAN 1, 2013 - PRESENT
Created with Highcharts 4.0.1Chart context menuClick and drag in the plot area to zoom in1/8/20133/15/20134/21/20136/2/20136/26/20137/26/20138/26/201310/2/201312/19/20132/4/20142/24/20143/16/20144/21/20146/9/20147/4/20147/26/20148/21/20149/14/201410/9/201411/4/20141/23/20152/24/20153/18/20154/18/20156/14/20157/9/20157/31/20158/25/20159/15/201510/5/201502004006008001000
Figure 5: COMPANY CONCESSIONS WITH FIRE ALERTS
PULPWOOD CONCESSIONS WITH THE HIGHEST SHARE OF FIRE ALERTS
NAMENUMBER OF FIRE ALERTS
PT. BUMI ANDALAS PERMAI 1540
PT. SEBANGUN BUMI ANDALAS WOOD INDUSTRIES231
PT. BUMI MEKAR HIJAU 5146
PT. BUMI ANDALAS PERMAI 237
PT. BUMI MEKAR HIJAU 412
PT. BUMI MEKAR HIJAU 310
PT. RIMBA HUTANI MAS 57
PT. WAHANA LESTARI MAKMUR SUKSES 17
PT. PARAMITRA MULIA LANGGENG 36
PALM OIL CONCESSIONS WITH THE HIGHEST SHARE OF FIRE ALERTS
NAMENUMBER OF FIRE ALERTS
PT. ERAMITRA AGRO LESTARI10
PT. DENDYMARKER INDAH LESTARI 15
PT. PN VI PERSERO /DH. PERKEB IV (DURIAN LUNCUK5
PT. WACHYUNI MANDIRA3
PT. ROEMPOEN ENAM BERSAUDARA2
PT. INDO LAMPUNG PERKASA2
Wilmar - PT Tania Selatan – Burnai Timur POM1
LOGGING CONCESSIONS WITH THE HIGHEST SHARE OF FIRE ALERTS
NAMENUMBER OF FIRE ALERTS
Figure 6: RSPO CERTIFIED CONCESSIONS WITH FIRE ALERTS
CONCESSION TYPENUMBER OF FIRE ALERTS
RSPO CERTIFIED PALM OIL CONCESSIONS1
ALL PALM OIL CONCESSIONS28
Figure 7: FIRE ALERTS BY LAND USE AREA
Created with Highcharts 4.0.1Chart context menu73%2%0%25%Pulpwood PlantationsPalm Oil ConcessionsLogging ConcessionsOutside Concessions
Figure 8: FIRE ALERTS IN PROTECTED AREAS
Created with Highcharts 4.0.1Chart context menu4%96%In Protected AreasOutside Protected Areas
Figure 9: PORTION OF FIRES OCCURRING ON PEATLAND
Created with Highcharts 4.0.1Chart context menu75%25%PeatNon-peat
Figure 10: PORTION OF FIRES OCCURRING IN AN INDICATIVE MORATORIUM AREA
Created with Highcharts 4.0.1Chart context menu9%91%In Indicative Moratorium AreasNot in Indicative Moratorium Areas

WRI used NASA’s Active Fire Data to determine the likely location of fires on the ground. This system uses the NASA MODIS satellites that survey the entire earth every 1-2 days. The sensors on these satellites detect the heat signatures of fires within the infrared spectral band. When the satellite imagery is processed, an algorithm searches for fire-like signatures. When a fire is detected, the system indicates the 1 km2 where the fire occurred with an “alert.” The system will nearly always detect fires of 1,000 m2 in size, but under ideal conditions, can detect flaming fires as small as 50 m2. Since each satellite passes over the equator twice a day, these alerts can be provided in near-real time. Fire alerts are posted on the NASA FIRMS website within 3 hours of detection by the satellite.

The accuracy of fire detection has improved greatly since fire detection systems were first developed for the MODIS satellites. Today, the rate of false positives is 1/10 to 1/1000 what it was under earlier systems first developed in the early 2000s. The algorithm used to detect fires includes steps to eliminate sources of false positives from sun glint, water glint, hot desert environments and others. When the system does not have enough information to detect a fire conclusively, the fire alert is discarded. In general, night observations have higher accuracy than daytime observations. Desert ecosystems have the highest rate of false positives. Many papers have been published to validate the NASA MODIS active fire alerts for use in various applications.

WRI is employing a recommendation for detecting forest clearing fires (described in Morton and Defries, 2008), identifying fires with a Brightness value ≥330 Kelvin and a Confidence value ≥ 30% to indicate fires that have a high confidence for being forest-clearing fires. Low confidence fires are lower intensity fires that could either be from non-forest-clearing fire activity (clearing fields or grass burning), or could be older fires that have decreased in intensity (smoldering rather than flaming fires). The use of this classification establishes a higher standard for fire detection than using all fire alerts equally.

Sources:

NASA FIRMS FAQ Morton, D., R. DeFries, J. T. Randerson, L. Giglio, W. Schroeder, and G. van der Werf. 2008. Agricultural intensification increases deforestation fire activity in Amazonia. Global Change Biology 14:2262-2276.

Data Sources for Figures:

NASA Fire Information for Resource Management (FIRMS) Active Fire Data

Administrative boundaries from GADM and Center for International Forestry Research (CIFOR)