Booklet LAPAN forest fires
UN-SPIDER REGIONAL SUPPORT OFFICES
Efective use of space-based
information to monitor
disasters and its impacts
Lessons Learnt from
Forest and Land Fires
in Indonesia
Prepared by LAPAN, Indonesia
Lessons Learnt from Forest and Land Fires in Indonesia
ISBN 978-602-72335-2-2
Lessons Learnt from Forest and Land Fires in Indonesia
Efective use of space-based information to monitor disasters
and its impacts
Lessons Learnt from Forest and Land Fires
in Indonesia
Suwarsono - LAPAN
Parwai Sofan - LAPAN
Dr. M. Rokhis Khomarudin - LAPAN
Dr. Shirish Ravan - UNOOSA/UN-SPIDER
Contributors (from LAPAN):
Winanto
Tauik Maulana
Yenni Vetrita
Any Zubaidah
M. Priyatna
Kusumaning Ayu DS
Tauik Hidayat
Iskandar Efendi
Ediing and booklet design by Hongmeng Liu and Nicole Ho Yan Lau
Lessons Learnt from Forest and Land Fires in Indonesia
About UN-SPIDER
www.un-spider.org
In its resoluion 61/110 of 14 December 2006 the United Naions General
Assembly agreed to establish the "United Naions Plaform for Spacebased Informaion for Disaster Management and Emergency Response
- UN-SPIDER" as a new United Naions programme, with the following
mission statement: "Ensure that all countries and internaional and
regional organizaions have access to and develop the capacity to use all
types of space-based informaion to support the full disaster management
cycle."
UN-SPIDER aims at providing universal access to all types of space-based
informaion and services relevant to disaster management by being a
gateway to space-based informaion for disaster management support;
serving as a bridge to connect the disaster management and space
communiies; and being a facilitator of capacity building and insituional
strengthening.
Whereas there have been a number of iniiaives in recent years that have
contributed to making space technologies available for humanitarian
and emergency response, UN-SPIDER is the irst to focus on the need
to ensure access to and use of such soluions during all phases of the
disaster management cycle, including the risk reducion phase, which
contributes signiicantly to reducing the loss of lives and property.
Lessons Learnt from Forest and Land Fires in Indonesia
Table of Contents
Foreword................................................................................................ 1
1. Forest /Land Fires in Indonesia and El Niño Southern
Oscillaion…………………………….................................................................2
2. Fire Danger Raing System……………………………….................................5
3. Fire Hotspot Monitoring...........................……….………........................ 11
4. Smoke and Haze Monitoring ……………………………............................... 13
5. Burned Area Mapping …………………………….........……...........................16
6. Informaion Disseminaion ……………………….........…….........................19
7. Technical Assistance Meeing.……………………….…................................21
8. Conclusion………………………………………................................................. 22
Lessons Learnt from Forest and Land Fires in Indonesia
Acronyms
AVHRR
Advanced Very High Resoluion Radiometer
ATSR
Along Track Scanning Radiometer
BAI
Burned Area Index
BNPB
Badan Nasional Penanggulangan Bencana
BUI
Buildup Index
DC
Drought Code
DMC
Duf Moisture Code
DNBR
Diferenced NBR
ENSO
El Nino-Southern Oscillaion
ESA
European Space Agency
FFMC
Fine Fuel Moisture Code
FDRS
Fire Danger Raing System
FWI
Fire Weather Index
GIS
Geographic Informaion System
HANDS
Hotspot and NDVII Diferencing Synergy
ISI
Iniial Spread Index
LAPAN
Indonesian Naional Insitute of Aeronauics and Space
MODIS
Moderate Resoluion Imaging Spectroradiometer
MISR
Muli-angle Imaging SpectroRadiometer
NASA
Naional Aeronauics and Space Administraion
NBR
Normalized Burn Raio
Lessons Learnt from Forest and Land Fires in Indonesia
NDMA
Naional Disaster Management Agency
NOAA
Naional Oceanic and Atmospheric Administraion
NIR
Near-Infrared
NDVI
Normalized Diference Vegetaion Index
RGB
Red, Green, Blue
SWIR
Shortwave-Infrared Relectance
UNOOSA
United Naions Oice for Outer Space Afairs
UN-SPIDER
United Naions Plaform for Space-based Informaion
for Disaster Management and Emergency Response
Lessons Learnt from Forest and Land Fires in Indonesia
Lessons Learnt from Forest and Land Fires in Indonesia
Foreword
The United Naions Plaform for Spacebased Informaion for Disaster Management
and Emergency Response (UN-SPIDER) was
established in 2006 by the United Naions
General Assembly in its resoluion 61/110. It is
a program implemented by the United Naions
Oice for Outer Space Afairs (UNOOSA). UNSPIDER aims to ensure that all countries and
internaional and regional organizaions have
access to and develop the capacity to use all
types of space-based informaion to support
the full disaster management cycle.
decades, especially related to the aspects
of environments and natural resources
degradaion. In Indonesia region, especially
Sumatera (Sumatra) and Kalimantan, almost
every year, thousands of hectares of forest and
lands are consumed by wildire resuling in loss
of property with signiicant economic costs
and environmental impacts. The phenomena
is related to land uilizaion paterns and
deforestaion. Land and forest ires can be
detected, ideniied, monitored and mapped by
using satellite images.
One of the main tasks of UN-SPIDER is knowledge
management and transfer. This publicaion is
prepared as one of the main forms of support
for the exchange of knowledge about the
applicaion of remote sensing for forest and
land ire monitoring. This publicaion will be
incorporated into the UN-SPIDER Knowledge
Portal (www.un-spider.org), a web-based
plaform for informaion, communicaion and
process support and fosters the exchange of
informaion for sharing experiences, capacitybuilding and Technical Advisory Support.
This booklet contains some applicaions
of remote sensing for forest and land ires
monitoring which includes ire hotspot
monitoring, smoke and haze monitoring, burned
area mapping and ire danger raing systems.
Several scieniic publicaions relaing to the
use of remote sensing for monitoring forest
and land ires are referred while preparing this
booklet. The images used in the booklet are the
results of a work carried out in LAPAN.
Forest and land ire has become a serious
threat to global communiies for the last two
Special acknowledgement to all contributors in
the preparaion of this publicaion.
Dr. M. Rokhis Khomarudin
Director, Remote Sensing Applicaion Center,
Indonesian Naional Insitute of Aeronauics
and Space (LAPAN)
Forest ire in peatlands, Central Kalimantan,
September 18, 2014
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Lessons Learnt from Forest and Land Fires in Indonesia
1. Forest/Land Fires in Indonesia and
El Niño Southern Oscillation
Forest/land ires is a major source of concern for
both environmental, public health and safety
reasons in many parts of the world. This is due
to the fact that they cause casualies as well as
important economic, social and environmental
losses. These events play an important role in
global climate change as they are responsible
for a signiicant amount of greenhouse gases,
pariculates and aerosol emissions into the
atmosphere (Merino et al., 2008). Throughout
the world, forest/land ires are also a cause
of deforestaion, land clearing, grassland
management, pest control and other agricultural
applicaions. As a natural disturbance agent,
forest/land ires not only work destrucively
but also as construcive power in terms of
maintaining the health of ecosystems. Through
the process of consumpion of biomass,
recycling nutrients and carbon, forest/land
ires have strong impacts on the structure and
funcions of ecosystems and determine the
secondary succession and restoraion over ime,
especially for ire-prone ecosystems (Chen,
2006). For both purposes of ire management
and facilitaing post-ire restoraion, it is
essenial to well understand forest/land ire
characterisics and other relevant factors such
as ire regime, meteorology, vegetaion type,
topography and the temporal and spaial scale
of ire combusion. Space-based technologies
can serve as an eicient and efecive tool in
this process.
Current esimates suggest that globally over 85%
of ires occur in the equatorial and subtropical
regions, primarily in Africa, South America
and Southeast Asia. Much of this acivity is
concentrated in developing countries where
extent of burning is not always adequately
documented (Prins et al., 2002).
Fires in Indonesia occur especially in Sumatera
and Kalimantan during the dry season months
of June-September and are more frequent and
severe during El Niño years due to a pronounced
rainfall deicit (Chandra et al., 1998; Wooster et
al., 1998; Kitaet al., 2000; Siegert and Hofmann,
2000; Roswiniari and Raman, 2003).
In 1982 - 1983, parts of the region were afected
by large scale vegetaion ires. This occurred
due to interacions between land clearance
aciviies and the abnormally dry weather
condiions associated with the ongoing El NiñoSouthern Oscillaion (ENSO) event. During this
earlier period, the largest uncontrolled forest/
land ires currently documented reportedly
damaged around 5 million hectares of primary
and secondary rain forest on Borneo (Wooster
& Strub, 2002).
A ireighter from the
Ministry of Forestry of
Indonesia, together with
army, spray water into
peat land forests, Riau
September 9, 2015.
Image: REUTERS
2
Lessons Learnt from Forest and Land Fires in Indonesia
Historical sea surface temperature anomaly in Niño 3.4 (red colour
refer to El Niño; blue colour refer to La Nina). Source: IRI
The Niño3.4 region in the east-central tropical Paciic Ocean for sea
surface temperature (red dashed line). Source: NOAA Climate.gov
3
Lessons Learnt from Forest and Land Fires in Indonesia
The 1997-1998 ires were apparently of a similar
magnitude. The 1997-1998 ires generated
an unprecedented of-site efect in terms of
the thick smog that blanketed Sumatera and
Borneo and neighbouring regions of Malaysia
and Singapore. The total ire-afected land area
during the 1997/1998 ENSO was about 9.75
million ha (BAPPENAS-ADB, 1999).The economic
losses due to the 1997/98 ires that resulted
in forest degradaion and deforestaion were
esimated between $1.6 and 2.7 billion. While
the smoke haze polluion costs were $674 - 799 President Joko Widodo checking the forest/land
million, and the carbon emissions costs were ires and haze in South Sumatera on September
around $2.8 billion (Taconi, 2003).
7, 2015. Image: ANTARA
The strong El Niño has reoccurred in 2015. The
sea surface temperature in Niño 3.4 region has
increased during April to November 2015.The
sea surface temperature in Indonesia region
was lower than the normal temperature.
There was almost no rainfall during September
2015. Severe ires occurred in the peat lands
of Sumatera and Kalimantan. Haze problems
existed around 2 months during September unil
October over Sumatera and Kalimantan regions
of Indonesia and also other neighbouring
countries. The social and economic aciviies Students walking in front of school buildings
were hampered over 2 months. The World Bank shrouded in smog in Palangkaraya, Central
esimated the economic cost to be around $15 Kalimantanon October 3, 2015. Image: ANTARA
billion.
The sea surface temperature anomaly on September 9, 2015. Source: NOAA NESDIS
4
Lessons Learnt from Forest and Land Fires in Indonesia
2. Fire Danger Rating System
Fire Danger Raing System (FDRS)
is a system for evaluaing the ire
environment (such as ease of
igniion, rate of spread, diiculty
of control and impact of ire) on
regular intervals and in an objecive
way (de Groot et al., 2006).
FDRS was developed from the
Canadian Forest Fire Weather Index
(FWI) System, and was adapted
to other global regions, e.g.,
New Zealand, Florida, Fiji, Spain,
Indonesia, Malaysia and South
Africa.
FWI system evaluates fuel moisture
content and relaive ire behaviour The Moisture Codes are an accouning method to keep
by using the past and present efect track of moisture content through the weing and drying
phases in the fuel layers (Groot et al., 2006)
of weather on forest loor fuels (de
Groot et al., 2006).
The FWI System components are
calculated using daily temperature,
relaive humidity, wind speed and
24-hour rainfall data collected at
1200 Local Standard Time.
Refer to de Groot et al. (2006),
the FWI System provides relaive
indicators of general ire danger
across the landscape based on
weather. It comprises three fuel
moisture
codes
represening
diferent layers in the forest loor
and three ire behaviour indices:
•
FWI System
Fine Fuel Moisture Code
(FFMC), a numerical raing of
the moisture content of surface
liter and other cured ine fuels
on the forest loor;
5
Lessons Learnt from Forest and Land Fires in Indonesia
•
•
Duf Moisture Code (DMC), a numerical
raing of the average moisture content
of loosely compacted organic layers of
moderate depth in the forest loor;
Drought Code (DC),
a numerical raing
of the average
moisture content
of deep, compact
organic layers in
the forest loor;
providing comprehensive and muli-temporal
coverage of large areas in real-ime at frequent
intervals. Remote sensing data of various
resoluion can be used to map and monitor
forest ire in a cost efecive way. Remote sensing
based FDRS does not consider meteorological
parameters directly, but the data processing
is more complex and clouds oten cover land
observaion.
Khomarudin et al. (2005) tried to esimate the
elements of meteorological for supporing the
Indonesian FDRS using Moderate Resoluion
Imaging Spectroradiometer (MODIS) data and
Noviar et al. (2005) atempted to operate the
Indonesian FDRS using the Naional Oceanic and
Atmospheric Administraion (NOAA) Advanced
Very High Resoluion Radiometer (AVHRR).
In order to implement the FWI system in
local area, Indonesian Naional Insitute of
Aeronauics and Space (LAPAN) has built the
FWI Calculator tool that can be easily run by
Source of ield images: Canadian Forest Services
anyone to calculate the value of the FDRS codes.
FWI Training Document
Through inpuing the coordinates of locaion
• Iniial Spread Index (ISI), a numerical raing and the value of meteorological parameters,
of the expected rate of ire spread;
one can obtain inal FDRS values. For warning
• Buildup Index (BUI), a numerical raing tool in the ield, the FDRS dashboard can be
of the total amount of fuel available for placed in ire-prone areas such as peat lands,
plantaions and forests.
combusion; and
•
FWI, a numerical raing of ire intensity that
is used as a general indicator of ire danger.
The development of remotely-sensed FDRS for
Western Indonesia was moivated by:
•
The limited number of weather staions in
Indonesia
•
The sparse/uneven distribuions of these
weather staions
•
The required Fire Danger Raing informaion
on local scales (provincial/district scales) by
the local government.
Thus, the use of satellite remote sensing data
becomes the best alternaive.
Remote sensing data has the advantage of
6
Lessons Learnt from Forest and Land Fires in Indonesia
FWI Calculator for local area implementaion and its dashboard in the ield. Source: LAPAN
7
Lessons Learnt from Forest and Land Fires in Indonesia
8
Lessons Learnt from Forest and Land Fires in Indonesia
FDRS Codes generated from remote sensing data. Source: LAPAN
9
Lessons Learnt from Forest and Land Fires in Indonesia
The comparison analysis between remotesensed FDRS evaluaion and FDRS generated
from
ground
climatological
staion
measurements by Indonesian Agency for
Meteorological, Climatological and Geophysics
(BMKG) showed a good relaionship.
DC and FFMC codes based on remote sensing data compared with measurement
climatological based.
10
Lessons Learnt from Forest and Land Fires in Indonesia
3. Fire Hotspot Monitoring
A variety of space-borne sensors are used to
map ires on a global scale. Fire hotspots can
be detected by using several remote sensing
data, i.e., NOAA-AVHRR, European Space
Agency (ESA) Along Track Scanning Radiometer
(ATSR) and the Naional Aeronauics and Space
Administraion (NASA) MODIS.
those are more likely to cause false alarms. This
relectance is denoted by ρ2.
Fire detecion from satellite-based sensors
is determined by sensor characterisics such
as spectral bands, data processing chains,
detecion algorithms and revisit frequency.
Fire detecion also depends on ire regimes as
these results in diferent spaial and temporal
paterns of burning (Stolle et al., 2004).
Global acive ire detecion is performed
using a hotspot algorithm based on the band
informaion of mid and thermal infrared
sensors from NOAA-AVHRR, ESA-ATSR and
NASA-MODIS.
Illustraion of how ire hotspots represent ire
locaion in the ield. Source: FIRMS
The ire hotspot algorithm of MODIS from
Jusice et al. (2002) is developed from heritage
algorithms which are based on AVHRR and
Tropical Rainfall Measuring Mission (TRMM)
Visible and Infrared Scanner (VIRS). The
algorithm uses brightness temperatures
derived from the MODIS 4 and 11 µm channels,
denoted by T4 and T11, respecively.
Stolle et al. (2004) atempted to verify the ire
hotspot by using mulisource satellite data.
The AVHRR, ATSR, Defense Meteorological
Satellite Program-Operaional Linescan System
(DMSP-OLS) and MODIS ire datasets for
Indonesia were compared with each other
and with burnscar maps derived from high
spaial resoluion data. Results show that each
The MODIS instrument has two 4-µm channels, dataset detects diferent ires. More than twonumbered 21 and 22, both of which are used by thirds of the ires detected by one dataset are
the detecion algorithm. Channel 21 saturates not detected by any other datasets. None of
at nearly 500 K; channel 22 saturates at 331 K. the datasets detect all ires in test areas. Fire
Since the low-saturaion channel is less noisy and datasets were not complemening each other
has a smaller quanizaion error, T4 is derived as they all had commission as well as omission
from this channel whenever possible. However, errors. The detecion of ires is determined by
when channel 22 saturates, or has missing data, ire regime, sensor characterisics and the ire
it is replaced with the high saturaion channel detecion algorithm used.
to derive T4. T11 is computed from the 11- Several organisaions provide ire hotspot
µm channel (channel 31), which saturates at informaion on their websites for free. The
approximately 400 K. The 250-m near-infrared following are some web addresses that provide
band (0.86 µm), averaged to 1-km resoluion, hotspot data:
is also used to idenify highly relecive surfaces
11
Lessons Learnt from Forest and Land Fires in Indonesia
Avalibility of Fire Hotspot data on market
Insituion
LAPAN
FIRMS NASA
GIC - AIT
ASMC
Data Source
MODIS, VIIRS
MODIS
MODIS
NOAA/AVHRR
Web Address
htp://modis-catalog.lapan.go.id/
htps://earthdata.nasa.gov/data/near-real-ime-data/irms
htp://www.geoinfo.ait.ac.th/modis/
htp://asmc.asean.org/home/
Daily ire hotspot informaion derived from MODIS and VIIRS in Indonesia on
September 22-23, 2015. Image: LAPAN
12
Lessons Learnt from Forest and Land Fires in Indonesia
Sentinel (online hotspot detection system), Australia
Seninel is a naional bushire monitoring system that provides imely informaion
about hotspots to emergency service managers across Australia. The mapping
system allows users to idenify ire locaions with a potenial risk to communiies
and property.This system was iniially developed during the bushires in New South
Wales and the Australian Capital Territory in early 2002.
It uilizes satellites with thermal infra-red sensors to detect hotspots which indicate
bushires. The satellites pass over Australia each morning and aternoon, observing
the land surface and beaming that informaion to Geoscience Australia's ground.
The informaion is analysed automaically by a computer to detect hotspots. Within
an hour of the satellite overpass the hotspots appear on the internet site. The
system is funcioning as a powerful online tool for bushire mapping for Australian
Government Geoscience Australia.
Sources:
htp://www.ga.gov.au/ausgeonews/ausgeonews200906/ire.jsp
htp://seninel.ga.gov.au/#/announcement
4. Smoke and Haze Monitoring
Atmospheric aerosols play a potenially
important role in the Earth’s climate system.
They have direct interacion with solar and
terrestrial radiaion through scatering and
absorpion thereby modifying the Earth’s
radiaion budget. Biomass burning is recognized
as an important source of atmospheric polluion
giving rise to the release of large quaniies
of gaseous emissions and pariculate mater
(Crutzen and Andreae, 1990). Each year, more
than 100 million tons of smoke aerosols are
released into the atmosphere from biomass
burning, out of which 80% are in the tropical
regions (Kharol and Badarinath, 2006). Smoke
and haze are serious consequences of forest/
land ires in Indonesia and is arguably one of
Indonesia’s worst environmental problems.
done using the normalized diference image
calculated from the visible (channel 1; 0.58–
0.68 mm) and thermal infrared (channel 4;
10.3–11.3 mm) channels.
A variety of high temporal resoluion images
can be used for daily monitoring the smoke
and haze dispersion, such as, AVHRR, MODIS
and Muli-angle Imaging Spectroradiometer
(MISR). However, to acquire more detailed
informaion regarding burnt landcover type,
medium to high spaial resoluion data is
required. Landsat-8 has repeated acquisiion
for every 16 days with 30 m spaial resoluion.
The types of landcover which are burned can
be ideniied using visual interpretaion of such
satellite images. The repeiive coverage also
allows monitoring landcover changes before,
There are several methods that can be used during and ater burning periods. For precise
for detecion of smoke and haze. As a sample, locaion of burned area, the SPOT-6/7 with a
based on AVHRR image, the analysis can be 1.5 m spaial resoluion or other high resoluion
13
Lessons Learnt from Forest and Land Fires in Indonesia
images, e.g., WorldView-1/2/3 can also be
used. The visual interpretaion of the burned
area can be determined based on paricular
appearance depending on the usage of band
informaion and smoke discharged from the
land.
LAPAN has three ground staion located in Parepare (South Sulawesi), Rumpin - Bogor (West
Java) and Pekayon (Jakarta). Daily MODIS data,
Landsat-8 data and SPOT-6/7 from the ground
staions are received. The monitoring of smoke
and haze is done by using MODIS data on daily
basis by integraing the hotspot and red, green
and blue (RGB) composite of smoke/haze from
the MODIS data. LAPAN also complement the
monitoring by using Landsat-8 data for every
16 days. During the ire seasons, LAPAN also
acquire the SPOT-6/7 in order to map precise
locaion of the burned area.
Fire hotspot and smoke haze distribuion using MODIS data on September 23, 2015.
Source: LAPAN
Mulitemporal of Landsat-8 showing the changes before and ater burning episodes
in Ogan Komering Ilir, South Sumatera. Source: LAPAN
14
Lessons Learnt from Forest and Land Fires in Indonesia
Smoke/haze from forest/land ire September 23, 2015 in South Sumatera detected from
Landsat-8. Source: LAPAN
Smoke/haze from forest/land ire on September 17, 2015 in South Sumatera detected from
SPOT-7. Source: LAPAN
15
Lessons Learnt from Forest and Land Fires in Indonesia
5. Burned Area Mapping
Burned area mapping is performed to measure
the total area afected by ires. Its purpose is to
detect and describe the scars let by ires based
on their spectral signature and/or ire-induced
spectral changes.
There is a wide variety of satellite data sources
from opical and microwave instruments
used to map the burned area. A variety of
sensors can be used for mapping burned
area, e.g., Operaional Land Imager (OLI)
and Thermal Infrared Sensor (TIRS) aboard
Landsat8 and the MODIS on board Terra/Aqua
as well as high spaial resoluion satellites
such as WorldView-1/2/3, GF-2, Cartosat-1,
Seninel-1/2 etc. In general, coarse resoluion
sensors such as MODIS can cover extensive area
of land every day. Therefore, it is suitable for
the daily monitoring of burned area especially
in very large ires. The MODIS data is useful for
preliminary mapping of burned areas and it can
be used as reference for further observaion
with high resoluion images. By using mid to
high resoluion images, detailed informaion on
burned landcover types can be obtained. Such
informaion provides severity of burned areas
of each landcover type and helps in prioriising
rehabilitaion measures.
The remote sensing methods dealing with scar
mapping include: (i) diference of pre- and postires original bands or indices (Key and Benson,
1999; Loboda et al., 2007), (ii) thresholding
of original bands or indices (Hall et al., 1980;
Huesca et al., 2008; González-Alonso and
Merino-de-Miguel, 2009), (iii) unsupervised or
supervised classiicaion of original bands or
indices (Milne, 1986; Miller and Yool, 2002), (iv)
spectral mixing analysis (Cochrane and Souza,
1998; González-Alonso et al., 2007), (v) ime
series analysis (Milne, 1986; Roy et al., 2002,
2005), etc. At regional to global scales, the
detecion of burned areas by means of satellite
data has been tradiionally carried out by
AVHRR because of its high temporal resoluion
(Kaufman et al., 1990; Chuvieco et al., 2008).
However, the MODIS sensor has opened up a
new era in the remote sensing of burned areas
(Jusice et al., 2002; Roy et al., 2002, 2005). It
has more than 30 narrow bands at wavelengths
from the visible to the thermal infrared and
at variable spaial resoluions (250–1000 m),
which ofers great potenial for burned area
mapping.
Among the diferent methods of burned area
mapping by means of relectance satellite
Fires in Kalimantan in September 2014, leave the burned area which cause
environmental and land resources degradaion. Image: LAPAN
16
Lessons Learnt from Forest and Land Fires in Indonesia
cover or dense smoke (Roy et al., 2002). This
diiculty can be solved through the combinaion
of acive ire informaion together with spectral
indices. Following this model, Roy et al. (1999)
developed a muli-temporal burn scar detecion
algorithm using a ime series of burn scar index
data (based on a vegetaion index) derived from
AVHRR daily images, to compute a burn scar
index change map that was then classiied using
thresholds based on the output of an acive-ire
detecion algorithm. Fraser et al. (2000) also
used AVHRR data to develop their hotspot and
NDVI diferencing synergy (HANDS) algorithm,
an approach that combines the strengths of
hotspot detecion and NDVI diferencing for
boreal burned area mapping. Al-Rawi et al.
In addiion to the use of spectral indices, several (2001) tried to go beyond designing a system
studies have showed the uility of acive ire for monitoring the status of a ire in real ime
detecion for burned area mapping. In acive by creaing a system that would also produce a
ire detecion, ire thermal energy, as measured near real-ime burned area map. To do so, Alby mid-infrared channels, is used to idenify Rawi et al. (2001) used NDVI – Maximum Value
acive ires. The scar mapping is developed Composites as well as acive ire locaions, both
based on examples in the total number of acive derived from AVHRR data. Another approach
ires (Pozo et al., 1997). However, the temporal taken by Pu et al. (2004) was an algorithm based
and spaial paterns of biomass burning cannot on ire dynamics on a daily basis to obtain daily
be esimated reliably from acive ire data, as informaion on acive ires and burned scars,
the satellite may not overpass at the ime of the using AVHRR data from California for NDVI
ire, or the ire may be obscured due to cloud calculaion and hotspot detecion.
data, the ones using spectral indices are the
most widespread. Vegetaion indices (e.g. NDVI
– Normalized Diference Vegetaion Index),
whose esimaion typically involves data from
the red and near-infrared (NIR) bands, have
been commonly used to derive vegetaion
properies but also to discriminate and map
burned areas. According to Lenile et al. (2006),
burned vegetaion results in a drasic reducion
in NIR relectance. This is typically accompanied
by a rise in shortwave-infrared relectance
(SWIR). This is the case of the Normalized Burn
Raio (NBR) and the diferenced NBR (dNBR),
developed by Key and Benson (1999) and the
MODIS Burned Area Index (BAI), developed by
Marín et al. (2006).
Burned area detecion using dNDVI and dNBR of SPOT-4 images in Riau. Image: LAPAN
17
Lessons Learnt from Forest and Land Fires in Indonesia
Density of Hotspot (October 21-27, 2015) zooming on SPOT6/7 mosaic
2014-2015 – OKI South Sumatera
18
Lessons Learnt from Forest and Land Fires in Indonesia
6. Information Dissemination
The results of remote sensing data processing
for forest/land ires monitoring and mapping
can be distributed to users through a web based
geographic informaion system (WebGIS). This
system is interacive, where users can select
diferent types of informaion and date, zoom
in/out locaions, measure distance and provide
markers. Users also have the opion to print or
download the informaion.
and zoom out the product informaion on their
mobile phones.
Informaion is delivered to the public via
newspaper (printed or online media), press
conference with the BNPB and TV interviews.
The users should be aware of limitaions of
the informaion derived from satellite remote
sensing data on forest/land ires. It may so
happen that the hotspot is not always the forest/
The informaion disseminaion system built land ire. It is only an indicaion of locaion that
by LAPAN displays a selecion of daily ire has higher temperature than its surroundings.
hotspots, forest ire danger raing system, as Further, hotspots could not be detected if the
well as informaion of the burned area. Users satellite does not pass the area or if the area
can easily access the informaion. The website is covered by clouds or thick haze. The user
of the system is as below.
should also noice that the burned area cannot
be esimated by summing the amount of
hotspots, but it should be done by following the
standard procedure and techniques to generate
measurable, reportable, veriiable data and
informaion. The standardized methodology is
useful in determining burned area comparison
on periodical basis.
htp://pusfatja.lapan.go.id/simba
In case of emergency response, LAPAN
disseminate the informaion to the naional
disaster management agency (Badan Nasional
Penanggulangan Bencana, BNPB) and other
stakeholders on a daily basis via applicaions on
mobile phones and emails. The key users are
the local BNPB groups that work on the frontline
to ight the ire and stakeholders in central
government, including BNPB, that provides
direcions and in ime report. Users can zoom in
19
Lessons Learnt from Forest and Land Fires in Indonesia
Emerging tools
Cloud based plaform services open up a new era for disaster management and
informaion disseminaion. Tomnod, a crowdsourcing plaform developed by
DigitalGlobe, uses geospaial databases and collect inputs from crowd to help in
disaster management and disaster risk reducion. Once a disaster happened, the
Tomnod program will make massive remote sensing data available and acivate the
crowdsourcing plaform to allow everyone around the world to tag visible damage
of that area on near real-ime satellite images and deliver the informaion to help
emergency response policy making.
Example: On April 25, 2015, Nepal experienced a 7.8 magnitude earthquake.
DigitalGlobe acivated a Tomnod crowdsourcing campaign to catalogue the extent of
the damage.
Such crowdsourcing plaforms have great potenial of mapping ire hotspots and
burned areas.
Sources:
htp://www.digitalglobeblog.com/2013/11/22/typhooninsight/
htp://emergencyjournalism.net/featured-tool-tomnod/
20
Lessons Learnt from Forest and Land Fires in Indonesia
7. Technical Assistance Meeting
In accordance with the mandate of Space
Law Number 21 Year 2013, remote sensing
data processing must be done with reference
to the method and the quality established by
LAPAN. LAPAN has a task for giving supervision
to other local and central insituions in how
to process and analyze the remote sensing
data for such applicaions. Related to forest/
land ire, LAPAN had conducted some trainings
in remote sensing applicaion for burned
area mapping, calculaing the forest danger
raing system, and also how to interpret the
hotspot data. By now, LAPAN have conducted
technical assistance meeing with following
agencies: the Plantaion Directorate of Ministry
of Agriculture of West Kalimantan, Forestry
Directorate of Central Kalimantan Province,
Local Disaster Management Body of South
Kalimantan, Forestry Directorate of Riau and
South Sumatera Province and also Plantaion
Directorate of Ministry of Agriculture of Jambi.
Technical assistance meeing developed by LAPAN and user agencies.
Image: LAPAN
21
Lessons Learnt from Forest and Land Fires in Indonesia
8. Conclusion
Peat land ires have reoccurred every year
in Indonesia. The severe ones were during
El Niño events in 1982, 1997 and 2015. It has
caused many serious problems to health,
transportaion, educaion and accounts for
high emissions of greenhouse gases that may
afect global warming.
Space-based informaion and technologies are
one of the most powerful tools to support the
full cycle of forest/land ire management such
as documening ire prone areas, providing
early warning, emergency response and post
ire assessment. In Indonesia, remote sensing
data ofered eicient alternaives for building up
the FDRS, especially in the regions with limited
weather staions. The advantages of remote
sensing data are disincive in ire monitoring
and haze/smoke detecion. For example, the
frequent revisit interval and extensive coverage
of remote sensing data provide possibiliies of
near real-ime monitoring of ires and enhance
early warning. As to post-ire ecosystem
restoraion, remote sensing data is capable of
mapping burned areas, esimaing vegetaion
condiions and monitoring the post-ire forest
paterns and changes over ime. All these
eforts will facilitate to miigate impacts and
occurrences of forest/land ires.
Insituional eforts are essenial as well for
implemening space-based technologies in
forest/land ires management. Based on the
experience from Indonesia, implementaion
of these tasks on an operaional basis requires
awareness raising among the user community,
public paricipaion and appropriate legislaion
framework. Advanced technologies combined
with local community-based forest/land ire
management makes usage of space-based
informaion to the fullest extent. WebGIS
based and cloud based plaforms are efecive
in disseminaing informaion to wide range of
users. Early warning messages can be issued in
22
local areas through web and communicaion
devices during dry season.
Incorporaing space-based informaion and
technologies in forest/land ire management
strengthens the capabiliies of disaster
management authoriies. It provides scieniic
basis for devising appropriate policies and
coningency plans to miigate and respond to
the forest/land ire disasters.
Image: merdeka.com
This document is aimed to showcase the
applicaion of remote sensing technologies in
the forest/land ires management in Indonesia.
We hope it provides ready reference to the
praciioners involved in forest ire monitoring.
Lessons Learnt from Forest and Land Fires in Indonesia
About UN-SPIDER Regional Support Oice in Indonesia
http://www.un-spider.org/network/regional-support-offices/
indonesia-regional-support-oice
A Regional Support Oice (RSO) is a regional or naional centre of experise that is set
up within an exising enity by a Member State or group of Member States that have
put forward an ofer to set up and fund the proposed RSO. An RSO can be hosted by
a space agency, a research center, a university, or a disaster management insituion,
to name some examples. These oices communicate and coordinate with UN-SPIDER
on a regular basis, covering the realms of Outreach and Capacity building, as well as
Horizontal Cooperaion and Technical Advisory Support.
The UN-SPIDER Regional Support Oice (RSO) in Indonesia is hosted by the Indonesian
Naional Insitute of Aeronauics and Space (LAPAN). The RSO established in 2013 under
a cooperaion agreement between LAPAN and the United Naions Oice for Outer Space
Afairs (UNOOSA) during The itieth session of the Scieniic and Technical Subcommitee
of the Commitee on the Peaceful Uses of Outer Space that was held on 11 -22 February
2013 at the United Naion Oice at Vienna, Austria. In the naional level, LAPAN has
legal authorize from President of Republic of Indonesia as the naional space agency
which listed in INPRES No. 6/2012 on the Provision, Use, Quality Control, Processing
and Distribuion of High Resoluion Remote Sensing Satellite Data. In order to support
disaster informaion using remote sensing satellite data, LAPAN coordinates with the
Indonesian Naional Board for Disaster Management (BNPB). LAPAN have implemented
several projects in the ield of disaster management and emergency response such
as lood, drought, ire hotspot, and climate monitoring or predicion, as well as the
assessment of those disasters and emergency response for other catastrophes such as
tsunami, earthquake, volcanic erupion, etc. As an RSO, LAPAN provides experts for UNSPIDER’s technical advisory support to countries within the region and to contribute to
capacity building eforts in the region.
23
Lessons Learnt from Forest and Land Fires in Indonesia
www.lapan.go.id
www.un-spider.org
Efective use of space-based
information to monitor
disasters and its impacts
Lessons Learnt from
Forest and Land Fires
in Indonesia
Prepared by LAPAN, Indonesia
Lessons Learnt from Forest and Land Fires in Indonesia
ISBN 978-602-72335-2-2
Lessons Learnt from Forest and Land Fires in Indonesia
Efective use of space-based information to monitor disasters
and its impacts
Lessons Learnt from Forest and Land Fires
in Indonesia
Suwarsono - LAPAN
Parwai Sofan - LAPAN
Dr. M. Rokhis Khomarudin - LAPAN
Dr. Shirish Ravan - UNOOSA/UN-SPIDER
Contributors (from LAPAN):
Winanto
Tauik Maulana
Yenni Vetrita
Any Zubaidah
M. Priyatna
Kusumaning Ayu DS
Tauik Hidayat
Iskandar Efendi
Ediing and booklet design by Hongmeng Liu and Nicole Ho Yan Lau
Lessons Learnt from Forest and Land Fires in Indonesia
About UN-SPIDER
www.un-spider.org
In its resoluion 61/110 of 14 December 2006 the United Naions General
Assembly agreed to establish the "United Naions Plaform for Spacebased Informaion for Disaster Management and Emergency Response
- UN-SPIDER" as a new United Naions programme, with the following
mission statement: "Ensure that all countries and internaional and
regional organizaions have access to and develop the capacity to use all
types of space-based informaion to support the full disaster management
cycle."
UN-SPIDER aims at providing universal access to all types of space-based
informaion and services relevant to disaster management by being a
gateway to space-based informaion for disaster management support;
serving as a bridge to connect the disaster management and space
communiies; and being a facilitator of capacity building and insituional
strengthening.
Whereas there have been a number of iniiaives in recent years that have
contributed to making space technologies available for humanitarian
and emergency response, UN-SPIDER is the irst to focus on the need
to ensure access to and use of such soluions during all phases of the
disaster management cycle, including the risk reducion phase, which
contributes signiicantly to reducing the loss of lives and property.
Lessons Learnt from Forest and Land Fires in Indonesia
Table of Contents
Foreword................................................................................................ 1
1. Forest /Land Fires in Indonesia and El Niño Southern
Oscillaion…………………………….................................................................2
2. Fire Danger Raing System……………………………….................................5
3. Fire Hotspot Monitoring...........................……….………........................ 11
4. Smoke and Haze Monitoring ……………………………............................... 13
5. Burned Area Mapping …………………………….........……...........................16
6. Informaion Disseminaion ……………………….........…….........................19
7. Technical Assistance Meeing.……………………….…................................21
8. Conclusion………………………………………................................................. 22
Lessons Learnt from Forest and Land Fires in Indonesia
Acronyms
AVHRR
Advanced Very High Resoluion Radiometer
ATSR
Along Track Scanning Radiometer
BAI
Burned Area Index
BNPB
Badan Nasional Penanggulangan Bencana
BUI
Buildup Index
DC
Drought Code
DMC
Duf Moisture Code
DNBR
Diferenced NBR
ENSO
El Nino-Southern Oscillaion
ESA
European Space Agency
FFMC
Fine Fuel Moisture Code
FDRS
Fire Danger Raing System
FWI
Fire Weather Index
GIS
Geographic Informaion System
HANDS
Hotspot and NDVII Diferencing Synergy
ISI
Iniial Spread Index
LAPAN
Indonesian Naional Insitute of Aeronauics and Space
MODIS
Moderate Resoluion Imaging Spectroradiometer
MISR
Muli-angle Imaging SpectroRadiometer
NASA
Naional Aeronauics and Space Administraion
NBR
Normalized Burn Raio
Lessons Learnt from Forest and Land Fires in Indonesia
NDMA
Naional Disaster Management Agency
NOAA
Naional Oceanic and Atmospheric Administraion
NIR
Near-Infrared
NDVI
Normalized Diference Vegetaion Index
RGB
Red, Green, Blue
SWIR
Shortwave-Infrared Relectance
UNOOSA
United Naions Oice for Outer Space Afairs
UN-SPIDER
United Naions Plaform for Space-based Informaion
for Disaster Management and Emergency Response
Lessons Learnt from Forest and Land Fires in Indonesia
Lessons Learnt from Forest and Land Fires in Indonesia
Foreword
The United Naions Plaform for Spacebased Informaion for Disaster Management
and Emergency Response (UN-SPIDER) was
established in 2006 by the United Naions
General Assembly in its resoluion 61/110. It is
a program implemented by the United Naions
Oice for Outer Space Afairs (UNOOSA). UNSPIDER aims to ensure that all countries and
internaional and regional organizaions have
access to and develop the capacity to use all
types of space-based informaion to support
the full disaster management cycle.
decades, especially related to the aspects
of environments and natural resources
degradaion. In Indonesia region, especially
Sumatera (Sumatra) and Kalimantan, almost
every year, thousands of hectares of forest and
lands are consumed by wildire resuling in loss
of property with signiicant economic costs
and environmental impacts. The phenomena
is related to land uilizaion paterns and
deforestaion. Land and forest ires can be
detected, ideniied, monitored and mapped by
using satellite images.
One of the main tasks of UN-SPIDER is knowledge
management and transfer. This publicaion is
prepared as one of the main forms of support
for the exchange of knowledge about the
applicaion of remote sensing for forest and
land ire monitoring. This publicaion will be
incorporated into the UN-SPIDER Knowledge
Portal (www.un-spider.org), a web-based
plaform for informaion, communicaion and
process support and fosters the exchange of
informaion for sharing experiences, capacitybuilding and Technical Advisory Support.
This booklet contains some applicaions
of remote sensing for forest and land ires
monitoring which includes ire hotspot
monitoring, smoke and haze monitoring, burned
area mapping and ire danger raing systems.
Several scieniic publicaions relaing to the
use of remote sensing for monitoring forest
and land ires are referred while preparing this
booklet. The images used in the booklet are the
results of a work carried out in LAPAN.
Forest and land ire has become a serious
threat to global communiies for the last two
Special acknowledgement to all contributors in
the preparaion of this publicaion.
Dr. M. Rokhis Khomarudin
Director, Remote Sensing Applicaion Center,
Indonesian Naional Insitute of Aeronauics
and Space (LAPAN)
Forest ire in peatlands, Central Kalimantan,
September 18, 2014
1
Lessons Learnt from Forest and Land Fires in Indonesia
1. Forest/Land Fires in Indonesia and
El Niño Southern Oscillation
Forest/land ires is a major source of concern for
both environmental, public health and safety
reasons in many parts of the world. This is due
to the fact that they cause casualies as well as
important economic, social and environmental
losses. These events play an important role in
global climate change as they are responsible
for a signiicant amount of greenhouse gases,
pariculates and aerosol emissions into the
atmosphere (Merino et al., 2008). Throughout
the world, forest/land ires are also a cause
of deforestaion, land clearing, grassland
management, pest control and other agricultural
applicaions. As a natural disturbance agent,
forest/land ires not only work destrucively
but also as construcive power in terms of
maintaining the health of ecosystems. Through
the process of consumpion of biomass,
recycling nutrients and carbon, forest/land
ires have strong impacts on the structure and
funcions of ecosystems and determine the
secondary succession and restoraion over ime,
especially for ire-prone ecosystems (Chen,
2006). For both purposes of ire management
and facilitaing post-ire restoraion, it is
essenial to well understand forest/land ire
characterisics and other relevant factors such
as ire regime, meteorology, vegetaion type,
topography and the temporal and spaial scale
of ire combusion. Space-based technologies
can serve as an eicient and efecive tool in
this process.
Current esimates suggest that globally over 85%
of ires occur in the equatorial and subtropical
regions, primarily in Africa, South America
and Southeast Asia. Much of this acivity is
concentrated in developing countries where
extent of burning is not always adequately
documented (Prins et al., 2002).
Fires in Indonesia occur especially in Sumatera
and Kalimantan during the dry season months
of June-September and are more frequent and
severe during El Niño years due to a pronounced
rainfall deicit (Chandra et al., 1998; Wooster et
al., 1998; Kitaet al., 2000; Siegert and Hofmann,
2000; Roswiniari and Raman, 2003).
In 1982 - 1983, parts of the region were afected
by large scale vegetaion ires. This occurred
due to interacions between land clearance
aciviies and the abnormally dry weather
condiions associated with the ongoing El NiñoSouthern Oscillaion (ENSO) event. During this
earlier period, the largest uncontrolled forest/
land ires currently documented reportedly
damaged around 5 million hectares of primary
and secondary rain forest on Borneo (Wooster
& Strub, 2002).
A ireighter from the
Ministry of Forestry of
Indonesia, together with
army, spray water into
peat land forests, Riau
September 9, 2015.
Image: REUTERS
2
Lessons Learnt from Forest and Land Fires in Indonesia
Historical sea surface temperature anomaly in Niño 3.4 (red colour
refer to El Niño; blue colour refer to La Nina). Source: IRI
The Niño3.4 region in the east-central tropical Paciic Ocean for sea
surface temperature (red dashed line). Source: NOAA Climate.gov
3
Lessons Learnt from Forest and Land Fires in Indonesia
The 1997-1998 ires were apparently of a similar
magnitude. The 1997-1998 ires generated
an unprecedented of-site efect in terms of
the thick smog that blanketed Sumatera and
Borneo and neighbouring regions of Malaysia
and Singapore. The total ire-afected land area
during the 1997/1998 ENSO was about 9.75
million ha (BAPPENAS-ADB, 1999).The economic
losses due to the 1997/98 ires that resulted
in forest degradaion and deforestaion were
esimated between $1.6 and 2.7 billion. While
the smoke haze polluion costs were $674 - 799 President Joko Widodo checking the forest/land
million, and the carbon emissions costs were ires and haze in South Sumatera on September
around $2.8 billion (Taconi, 2003).
7, 2015. Image: ANTARA
The strong El Niño has reoccurred in 2015. The
sea surface temperature in Niño 3.4 region has
increased during April to November 2015.The
sea surface temperature in Indonesia region
was lower than the normal temperature.
There was almost no rainfall during September
2015. Severe ires occurred in the peat lands
of Sumatera and Kalimantan. Haze problems
existed around 2 months during September unil
October over Sumatera and Kalimantan regions
of Indonesia and also other neighbouring
countries. The social and economic aciviies Students walking in front of school buildings
were hampered over 2 months. The World Bank shrouded in smog in Palangkaraya, Central
esimated the economic cost to be around $15 Kalimantanon October 3, 2015. Image: ANTARA
billion.
The sea surface temperature anomaly on September 9, 2015. Source: NOAA NESDIS
4
Lessons Learnt from Forest and Land Fires in Indonesia
2. Fire Danger Rating System
Fire Danger Raing System (FDRS)
is a system for evaluaing the ire
environment (such as ease of
igniion, rate of spread, diiculty
of control and impact of ire) on
regular intervals and in an objecive
way (de Groot et al., 2006).
FDRS was developed from the
Canadian Forest Fire Weather Index
(FWI) System, and was adapted
to other global regions, e.g.,
New Zealand, Florida, Fiji, Spain,
Indonesia, Malaysia and South
Africa.
FWI system evaluates fuel moisture
content and relaive ire behaviour The Moisture Codes are an accouning method to keep
by using the past and present efect track of moisture content through the weing and drying
phases in the fuel layers (Groot et al., 2006)
of weather on forest loor fuels (de
Groot et al., 2006).
The FWI System components are
calculated using daily temperature,
relaive humidity, wind speed and
24-hour rainfall data collected at
1200 Local Standard Time.
Refer to de Groot et al. (2006),
the FWI System provides relaive
indicators of general ire danger
across the landscape based on
weather. It comprises three fuel
moisture
codes
represening
diferent layers in the forest loor
and three ire behaviour indices:
•
FWI System
Fine Fuel Moisture Code
(FFMC), a numerical raing of
the moisture content of surface
liter and other cured ine fuels
on the forest loor;
5
Lessons Learnt from Forest and Land Fires in Indonesia
•
•
Duf Moisture Code (DMC), a numerical
raing of the average moisture content
of loosely compacted organic layers of
moderate depth in the forest loor;
Drought Code (DC),
a numerical raing
of the average
moisture content
of deep, compact
organic layers in
the forest loor;
providing comprehensive and muli-temporal
coverage of large areas in real-ime at frequent
intervals. Remote sensing data of various
resoluion can be used to map and monitor
forest ire in a cost efecive way. Remote sensing
based FDRS does not consider meteorological
parameters directly, but the data processing
is more complex and clouds oten cover land
observaion.
Khomarudin et al. (2005) tried to esimate the
elements of meteorological for supporing the
Indonesian FDRS using Moderate Resoluion
Imaging Spectroradiometer (MODIS) data and
Noviar et al. (2005) atempted to operate the
Indonesian FDRS using the Naional Oceanic and
Atmospheric Administraion (NOAA) Advanced
Very High Resoluion Radiometer (AVHRR).
In order to implement the FWI system in
local area, Indonesian Naional Insitute of
Aeronauics and Space (LAPAN) has built the
FWI Calculator tool that can be easily run by
Source of ield images: Canadian Forest Services
anyone to calculate the value of the FDRS codes.
FWI Training Document
Through inpuing the coordinates of locaion
• Iniial Spread Index (ISI), a numerical raing and the value of meteorological parameters,
of the expected rate of ire spread;
one can obtain inal FDRS values. For warning
• Buildup Index (BUI), a numerical raing tool in the ield, the FDRS dashboard can be
of the total amount of fuel available for placed in ire-prone areas such as peat lands,
plantaions and forests.
combusion; and
•
FWI, a numerical raing of ire intensity that
is used as a general indicator of ire danger.
The development of remotely-sensed FDRS for
Western Indonesia was moivated by:
•
The limited number of weather staions in
Indonesia
•
The sparse/uneven distribuions of these
weather staions
•
The required Fire Danger Raing informaion
on local scales (provincial/district scales) by
the local government.
Thus, the use of satellite remote sensing data
becomes the best alternaive.
Remote sensing data has the advantage of
6
Lessons Learnt from Forest and Land Fires in Indonesia
FWI Calculator for local area implementaion and its dashboard in the ield. Source: LAPAN
7
Lessons Learnt from Forest and Land Fires in Indonesia
8
Lessons Learnt from Forest and Land Fires in Indonesia
FDRS Codes generated from remote sensing data. Source: LAPAN
9
Lessons Learnt from Forest and Land Fires in Indonesia
The comparison analysis between remotesensed FDRS evaluaion and FDRS generated
from
ground
climatological
staion
measurements by Indonesian Agency for
Meteorological, Climatological and Geophysics
(BMKG) showed a good relaionship.
DC and FFMC codes based on remote sensing data compared with measurement
climatological based.
10
Lessons Learnt from Forest and Land Fires in Indonesia
3. Fire Hotspot Monitoring
A variety of space-borne sensors are used to
map ires on a global scale. Fire hotspots can
be detected by using several remote sensing
data, i.e., NOAA-AVHRR, European Space
Agency (ESA) Along Track Scanning Radiometer
(ATSR) and the Naional Aeronauics and Space
Administraion (NASA) MODIS.
those are more likely to cause false alarms. This
relectance is denoted by ρ2.
Fire detecion from satellite-based sensors
is determined by sensor characterisics such
as spectral bands, data processing chains,
detecion algorithms and revisit frequency.
Fire detecion also depends on ire regimes as
these results in diferent spaial and temporal
paterns of burning (Stolle et al., 2004).
Global acive ire detecion is performed
using a hotspot algorithm based on the band
informaion of mid and thermal infrared
sensors from NOAA-AVHRR, ESA-ATSR and
NASA-MODIS.
Illustraion of how ire hotspots represent ire
locaion in the ield. Source: FIRMS
The ire hotspot algorithm of MODIS from
Jusice et al. (2002) is developed from heritage
algorithms which are based on AVHRR and
Tropical Rainfall Measuring Mission (TRMM)
Visible and Infrared Scanner (VIRS). The
algorithm uses brightness temperatures
derived from the MODIS 4 and 11 µm channels,
denoted by T4 and T11, respecively.
Stolle et al. (2004) atempted to verify the ire
hotspot by using mulisource satellite data.
The AVHRR, ATSR, Defense Meteorological
Satellite Program-Operaional Linescan System
(DMSP-OLS) and MODIS ire datasets for
Indonesia were compared with each other
and with burnscar maps derived from high
spaial resoluion data. Results show that each
The MODIS instrument has two 4-µm channels, dataset detects diferent ires. More than twonumbered 21 and 22, both of which are used by thirds of the ires detected by one dataset are
the detecion algorithm. Channel 21 saturates not detected by any other datasets. None of
at nearly 500 K; channel 22 saturates at 331 K. the datasets detect all ires in test areas. Fire
Since the low-saturaion channel is less noisy and datasets were not complemening each other
has a smaller quanizaion error, T4 is derived as they all had commission as well as omission
from this channel whenever possible. However, errors. The detecion of ires is determined by
when channel 22 saturates, or has missing data, ire regime, sensor characterisics and the ire
it is replaced with the high saturaion channel detecion algorithm used.
to derive T4. T11 is computed from the 11- Several organisaions provide ire hotspot
µm channel (channel 31), which saturates at informaion on their websites for free. The
approximately 400 K. The 250-m near-infrared following are some web addresses that provide
band (0.86 µm), averaged to 1-km resoluion, hotspot data:
is also used to idenify highly relecive surfaces
11
Lessons Learnt from Forest and Land Fires in Indonesia
Avalibility of Fire Hotspot data on market
Insituion
LAPAN
FIRMS NASA
GIC - AIT
ASMC
Data Source
MODIS, VIIRS
MODIS
MODIS
NOAA/AVHRR
Web Address
htp://modis-catalog.lapan.go.id/
htps://earthdata.nasa.gov/data/near-real-ime-data/irms
htp://www.geoinfo.ait.ac.th/modis/
htp://asmc.asean.org/home/
Daily ire hotspot informaion derived from MODIS and VIIRS in Indonesia on
September 22-23, 2015. Image: LAPAN
12
Lessons Learnt from Forest and Land Fires in Indonesia
Sentinel (online hotspot detection system), Australia
Seninel is a naional bushire monitoring system that provides imely informaion
about hotspots to emergency service managers across Australia. The mapping
system allows users to idenify ire locaions with a potenial risk to communiies
and property.This system was iniially developed during the bushires in New South
Wales and the Australian Capital Territory in early 2002.
It uilizes satellites with thermal infra-red sensors to detect hotspots which indicate
bushires. The satellites pass over Australia each morning and aternoon, observing
the land surface and beaming that informaion to Geoscience Australia's ground.
The informaion is analysed automaically by a computer to detect hotspots. Within
an hour of the satellite overpass the hotspots appear on the internet site. The
system is funcioning as a powerful online tool for bushire mapping for Australian
Government Geoscience Australia.
Sources:
htp://www.ga.gov.au/ausgeonews/ausgeonews200906/ire.jsp
htp://seninel.ga.gov.au/#/announcement
4. Smoke and Haze Monitoring
Atmospheric aerosols play a potenially
important role in the Earth’s climate system.
They have direct interacion with solar and
terrestrial radiaion through scatering and
absorpion thereby modifying the Earth’s
radiaion budget. Biomass burning is recognized
as an important source of atmospheric polluion
giving rise to the release of large quaniies
of gaseous emissions and pariculate mater
(Crutzen and Andreae, 1990). Each year, more
than 100 million tons of smoke aerosols are
released into the atmosphere from biomass
burning, out of which 80% are in the tropical
regions (Kharol and Badarinath, 2006). Smoke
and haze are serious consequences of forest/
land ires in Indonesia and is arguably one of
Indonesia’s worst environmental problems.
done using the normalized diference image
calculated from the visible (channel 1; 0.58–
0.68 mm) and thermal infrared (channel 4;
10.3–11.3 mm) channels.
A variety of high temporal resoluion images
can be used for daily monitoring the smoke
and haze dispersion, such as, AVHRR, MODIS
and Muli-angle Imaging Spectroradiometer
(MISR). However, to acquire more detailed
informaion regarding burnt landcover type,
medium to high spaial resoluion data is
required. Landsat-8 has repeated acquisiion
for every 16 days with 30 m spaial resoluion.
The types of landcover which are burned can
be ideniied using visual interpretaion of such
satellite images. The repeiive coverage also
allows monitoring landcover changes before,
There are several methods that can be used during and ater burning periods. For precise
for detecion of smoke and haze. As a sample, locaion of burned area, the SPOT-6/7 with a
based on AVHRR image, the analysis can be 1.5 m spaial resoluion or other high resoluion
13
Lessons Learnt from Forest and Land Fires in Indonesia
images, e.g., WorldView-1/2/3 can also be
used. The visual interpretaion of the burned
area can be determined based on paricular
appearance depending on the usage of band
informaion and smoke discharged from the
land.
LAPAN has three ground staion located in Parepare (South Sulawesi), Rumpin - Bogor (West
Java) and Pekayon (Jakarta). Daily MODIS data,
Landsat-8 data and SPOT-6/7 from the ground
staions are received. The monitoring of smoke
and haze is done by using MODIS data on daily
basis by integraing the hotspot and red, green
and blue (RGB) composite of smoke/haze from
the MODIS data. LAPAN also complement the
monitoring by using Landsat-8 data for every
16 days. During the ire seasons, LAPAN also
acquire the SPOT-6/7 in order to map precise
locaion of the burned area.
Fire hotspot and smoke haze distribuion using MODIS data on September 23, 2015.
Source: LAPAN
Mulitemporal of Landsat-8 showing the changes before and ater burning episodes
in Ogan Komering Ilir, South Sumatera. Source: LAPAN
14
Lessons Learnt from Forest and Land Fires in Indonesia
Smoke/haze from forest/land ire September 23, 2015 in South Sumatera detected from
Landsat-8. Source: LAPAN
Smoke/haze from forest/land ire on September 17, 2015 in South Sumatera detected from
SPOT-7. Source: LAPAN
15
Lessons Learnt from Forest and Land Fires in Indonesia
5. Burned Area Mapping
Burned area mapping is performed to measure
the total area afected by ires. Its purpose is to
detect and describe the scars let by ires based
on their spectral signature and/or ire-induced
spectral changes.
There is a wide variety of satellite data sources
from opical and microwave instruments
used to map the burned area. A variety of
sensors can be used for mapping burned
area, e.g., Operaional Land Imager (OLI)
and Thermal Infrared Sensor (TIRS) aboard
Landsat8 and the MODIS on board Terra/Aqua
as well as high spaial resoluion satellites
such as WorldView-1/2/3, GF-2, Cartosat-1,
Seninel-1/2 etc. In general, coarse resoluion
sensors such as MODIS can cover extensive area
of land every day. Therefore, it is suitable for
the daily monitoring of burned area especially
in very large ires. The MODIS data is useful for
preliminary mapping of burned areas and it can
be used as reference for further observaion
with high resoluion images. By using mid to
high resoluion images, detailed informaion on
burned landcover types can be obtained. Such
informaion provides severity of burned areas
of each landcover type and helps in prioriising
rehabilitaion measures.
The remote sensing methods dealing with scar
mapping include: (i) diference of pre- and postires original bands or indices (Key and Benson,
1999; Loboda et al., 2007), (ii) thresholding
of original bands or indices (Hall et al., 1980;
Huesca et al., 2008; González-Alonso and
Merino-de-Miguel, 2009), (iii) unsupervised or
supervised classiicaion of original bands or
indices (Milne, 1986; Miller and Yool, 2002), (iv)
spectral mixing analysis (Cochrane and Souza,
1998; González-Alonso et al., 2007), (v) ime
series analysis (Milne, 1986; Roy et al., 2002,
2005), etc. At regional to global scales, the
detecion of burned areas by means of satellite
data has been tradiionally carried out by
AVHRR because of its high temporal resoluion
(Kaufman et al., 1990; Chuvieco et al., 2008).
However, the MODIS sensor has opened up a
new era in the remote sensing of burned areas
(Jusice et al., 2002; Roy et al., 2002, 2005). It
has more than 30 narrow bands at wavelengths
from the visible to the thermal infrared and
at variable spaial resoluions (250–1000 m),
which ofers great potenial for burned area
mapping.
Among the diferent methods of burned area
mapping by means of relectance satellite
Fires in Kalimantan in September 2014, leave the burned area which cause
environmental and land resources degradaion. Image: LAPAN
16
Lessons Learnt from Forest and Land Fires in Indonesia
cover or dense smoke (Roy et al., 2002). This
diiculty can be solved through the combinaion
of acive ire informaion together with spectral
indices. Following this model, Roy et al. (1999)
developed a muli-temporal burn scar detecion
algorithm using a ime series of burn scar index
data (based on a vegetaion index) derived from
AVHRR daily images, to compute a burn scar
index change map that was then classiied using
thresholds based on the output of an acive-ire
detecion algorithm. Fraser et al. (2000) also
used AVHRR data to develop their hotspot and
NDVI diferencing synergy (HANDS) algorithm,
an approach that combines the strengths of
hotspot detecion and NDVI diferencing for
boreal burned area mapping. Al-Rawi et al.
In addiion to the use of spectral indices, several (2001) tried to go beyond designing a system
studies have showed the uility of acive ire for monitoring the status of a ire in real ime
detecion for burned area mapping. In acive by creaing a system that would also produce a
ire detecion, ire thermal energy, as measured near real-ime burned area map. To do so, Alby mid-infrared channels, is used to idenify Rawi et al. (2001) used NDVI – Maximum Value
acive ires. The scar mapping is developed Composites as well as acive ire locaions, both
based on examples in the total number of acive derived from AVHRR data. Another approach
ires (Pozo et al., 1997). However, the temporal taken by Pu et al. (2004) was an algorithm based
and spaial paterns of biomass burning cannot on ire dynamics on a daily basis to obtain daily
be esimated reliably from acive ire data, as informaion on acive ires and burned scars,
the satellite may not overpass at the ime of the using AVHRR data from California for NDVI
ire, or the ire may be obscured due to cloud calculaion and hotspot detecion.
data, the ones using spectral indices are the
most widespread. Vegetaion indices (e.g. NDVI
– Normalized Diference Vegetaion Index),
whose esimaion typically involves data from
the red and near-infrared (NIR) bands, have
been commonly used to derive vegetaion
properies but also to discriminate and map
burned areas. According to Lenile et al. (2006),
burned vegetaion results in a drasic reducion
in NIR relectance. This is typically accompanied
by a rise in shortwave-infrared relectance
(SWIR). This is the case of the Normalized Burn
Raio (NBR) and the diferenced NBR (dNBR),
developed by Key and Benson (1999) and the
MODIS Burned Area Index (BAI), developed by
Marín et al. (2006).
Burned area detecion using dNDVI and dNBR of SPOT-4 images in Riau. Image: LAPAN
17
Lessons Learnt from Forest and Land Fires in Indonesia
Density of Hotspot (October 21-27, 2015) zooming on SPOT6/7 mosaic
2014-2015 – OKI South Sumatera
18
Lessons Learnt from Forest and Land Fires in Indonesia
6. Information Dissemination
The results of remote sensing data processing
for forest/land ires monitoring and mapping
can be distributed to users through a web based
geographic informaion system (WebGIS). This
system is interacive, where users can select
diferent types of informaion and date, zoom
in/out locaions, measure distance and provide
markers. Users also have the opion to print or
download the informaion.
and zoom out the product informaion on their
mobile phones.
Informaion is delivered to the public via
newspaper (printed or online media), press
conference with the BNPB and TV interviews.
The users should be aware of limitaions of
the informaion derived from satellite remote
sensing data on forest/land ires. It may so
happen that the hotspot is not always the forest/
The informaion disseminaion system built land ire. It is only an indicaion of locaion that
by LAPAN displays a selecion of daily ire has higher temperature than its surroundings.
hotspots, forest ire danger raing system, as Further, hotspots could not be detected if the
well as informaion of the burned area. Users satellite does not pass the area or if the area
can easily access the informaion. The website is covered by clouds or thick haze. The user
of the system is as below.
should also noice that the burned area cannot
be esimated by summing the amount of
hotspots, but it should be done by following the
standard procedure and techniques to generate
measurable, reportable, veriiable data and
informaion. The standardized methodology is
useful in determining burned area comparison
on periodical basis.
htp://pusfatja.lapan.go.id/simba
In case of emergency response, LAPAN
disseminate the informaion to the naional
disaster management agency (Badan Nasional
Penanggulangan Bencana, BNPB) and other
stakeholders on a daily basis via applicaions on
mobile phones and emails. The key users are
the local BNPB groups that work on the frontline
to ight the ire and stakeholders in central
government, including BNPB, that provides
direcions and in ime report. Users can zoom in
19
Lessons Learnt from Forest and Land Fires in Indonesia
Emerging tools
Cloud based plaform services open up a new era for disaster management and
informaion disseminaion. Tomnod, a crowdsourcing plaform developed by
DigitalGlobe, uses geospaial databases and collect inputs from crowd to help in
disaster management and disaster risk reducion. Once a disaster happened, the
Tomnod program will make massive remote sensing data available and acivate the
crowdsourcing plaform to allow everyone around the world to tag visible damage
of that area on near real-ime satellite images and deliver the informaion to help
emergency response policy making.
Example: On April 25, 2015, Nepal experienced a 7.8 magnitude earthquake.
DigitalGlobe acivated a Tomnod crowdsourcing campaign to catalogue the extent of
the damage.
Such crowdsourcing plaforms have great potenial of mapping ire hotspots and
burned areas.
Sources:
htp://www.digitalglobeblog.com/2013/11/22/typhooninsight/
htp://emergencyjournalism.net/featured-tool-tomnod/
20
Lessons Learnt from Forest and Land Fires in Indonesia
7. Technical Assistance Meeting
In accordance with the mandate of Space
Law Number 21 Year 2013, remote sensing
data processing must be done with reference
to the method and the quality established by
LAPAN. LAPAN has a task for giving supervision
to other local and central insituions in how
to process and analyze the remote sensing
data for such applicaions. Related to forest/
land ire, LAPAN had conducted some trainings
in remote sensing applicaion for burned
area mapping, calculaing the forest danger
raing system, and also how to interpret the
hotspot data. By now, LAPAN have conducted
technical assistance meeing with following
agencies: the Plantaion Directorate of Ministry
of Agriculture of West Kalimantan, Forestry
Directorate of Central Kalimantan Province,
Local Disaster Management Body of South
Kalimantan, Forestry Directorate of Riau and
South Sumatera Province and also Plantaion
Directorate of Ministry of Agriculture of Jambi.
Technical assistance meeing developed by LAPAN and user agencies.
Image: LAPAN
21
Lessons Learnt from Forest and Land Fires in Indonesia
8. Conclusion
Peat land ires have reoccurred every year
in Indonesia. The severe ones were during
El Niño events in 1982, 1997 and 2015. It has
caused many serious problems to health,
transportaion, educaion and accounts for
high emissions of greenhouse gases that may
afect global warming.
Space-based informaion and technologies are
one of the most powerful tools to support the
full cycle of forest/land ire management such
as documening ire prone areas, providing
early warning, emergency response and post
ire assessment. In Indonesia, remote sensing
data ofered eicient alternaives for building up
the FDRS, especially in the regions with limited
weather staions. The advantages of remote
sensing data are disincive in ire monitoring
and haze/smoke detecion. For example, the
frequent revisit interval and extensive coverage
of remote sensing data provide possibiliies of
near real-ime monitoring of ires and enhance
early warning. As to post-ire ecosystem
restoraion, remote sensing data is capable of
mapping burned areas, esimaing vegetaion
condiions and monitoring the post-ire forest
paterns and changes over ime. All these
eforts will facilitate to miigate impacts and
occurrences of forest/land ires.
Insituional eforts are essenial as well for
implemening space-based technologies in
forest/land ires management. Based on the
experience from Indonesia, implementaion
of these tasks on an operaional basis requires
awareness raising among the user community,
public paricipaion and appropriate legislaion
framework. Advanced technologies combined
with local community-based forest/land ire
management makes usage of space-based
informaion to the fullest extent. WebGIS
based and cloud based plaforms are efecive
in disseminaing informaion to wide range of
users. Early warning messages can be issued in
22
local areas through web and communicaion
devices during dry season.
Incorporaing space-based informaion and
technologies in forest/land ire management
strengthens the capabiliies of disaster
management authoriies. It provides scieniic
basis for devising appropriate policies and
coningency plans to miigate and respond to
the forest/land ire disasters.
Image: merdeka.com
This document is aimed to showcase the
applicaion of remote sensing technologies in
the forest/land ires management in Indonesia.
We hope it provides ready reference to the
praciioners involved in forest ire monitoring.
Lessons Learnt from Forest and Land Fires in Indonesia
About UN-SPIDER Regional Support Oice in Indonesia
http://www.un-spider.org/network/regional-support-offices/
indonesia-regional-support-oice
A Regional Support Oice (RSO) is a regional or naional centre of experise that is set
up within an exising enity by a Member State or group of Member States that have
put forward an ofer to set up and fund the proposed RSO. An RSO can be hosted by
a space agency, a research center, a university, or a disaster management insituion,
to name some examples. These oices communicate and coordinate with UN-SPIDER
on a regular basis, covering the realms of Outreach and Capacity building, as well as
Horizontal Cooperaion and Technical Advisory Support.
The UN-SPIDER Regional Support Oice (RSO) in Indonesia is hosted by the Indonesian
Naional Insitute of Aeronauics and Space (LAPAN). The RSO established in 2013 under
a cooperaion agreement between LAPAN and the United Naions Oice for Outer Space
Afairs (UNOOSA) during The itieth session of the Scieniic and Technical Subcommitee
of the Commitee on the Peaceful Uses of Outer Space that was held on 11 -22 February
2013 at the United Naion Oice at Vienna, Austria. In the naional level, LAPAN has
legal authorize from President of Republic of Indonesia as the naional space agency
which listed in INPRES No. 6/2012 on the Provision, Use, Quality Control, Processing
and Distribuion of High Resoluion Remote Sensing Satellite Data. In order to support
disaster informaion using remote sensing satellite data, LAPAN coordinates with the
Indonesian Naional Board for Disaster Management (BNPB). LAPAN have implemented
several projects in the ield of disaster management and emergency response such
as lood, drought, ire hotspot, and climate monitoring or predicion, as well as the
assessment of those disasters and emergency response for other catastrophes such as
tsunami, earthquake, volcanic erupion, etc. As an RSO, LAPAN provides experts for UNSPIDER’s technical advisory support to countries within the region and to contribute to
capacity building eforts in the region.
23
Lessons Learnt from Forest and Land Fires in Indonesia
www.lapan.go.id
www.un-spider.org