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Remotely sensed forest cover loss shows high spatial and temporal variation across Sumatera and Kalimantan, Indonesia 2000-2008

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The Indonesian islands of Sumatera and Kalimantan (the Indonesian part of the island of Borneo) are a center of significant and rapid forest cover loss in the humid tropics with implications for carbon dynamics, biodiversity conservation, and local livelihoods. The aim of our research was to analyze and interpret annual trends of forest cover loss for different sub-regions of the study area. We mapped forest cover loss for 2000–2008 using multi-resolution remote sensing data from the Landsat enhanced thematic mapper plus (ETM+) and moderate resolution imaging spectroradiometer (MODIS) sensors and analyzed annual trends per island, province, and official land allocation zone. The total forest cover loss for Sumatera and Kalimantan 2000–2008 was 5.39 Mha, which represents 5.3% of the land area and 9.2% of the year 2000 forest cover of these two islands. At least 6.5% of all mapped forest cover loss occurred in land allocation zones prohibiting clearing. An additional 13.6% of forest cover loss occurred where clearing is legally restricted. The overall trend of forest cover loss increased until 2006 and decreased thereafter. The trends for Sumatera and Kalimantan were distinctly different, driven primarily by the trends of Riau and Central Kalimantan provinces, respectively. This analysis shows that annual mapping of forest cover change yields a clearer picture than a one-time overall national estimate. Monitoring forest dynamics is important for national policy makers, especially given the commitment of Indonesia to reducing greenhouse gas emissions as part of the reducing emissions from deforestation and forest degradation in developing countries initiative (REDD+). The improved spatio-temporal detail of forest change monitoring products will make it possible to target policies and projects in meeting this commitment. Accurate, annual forest cover loss maps will be integral to many REDD+ objectives, including policy formulation, definition of baselines, detection of displacement, and the evaluation of the permanence of emission reduction.
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Remotely sensed forest cover loss shows high spatial and temporal variation across
Sumatera and Kalimantan, Indonesia 2000–2008
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IOP PUBLISHING ENVIRONMENTAL RESEARCH LETTERS
Environ. Res. Lett. 6(2011) 014010 (9pp) doi:10.1088/1748-9326/6/1/014010
Remotely sensed forest cover loss shows
high spatial and temporal variation across
Sumatera and Kalimantan, Indonesia
2000–2008
Mark Broich1, Matthew Hansen1, Fred Stolle2, Peter Potapov1,
Belinda Arunarwati Margono1and Bernard Adusei1
1South Dakota State University, Brookings, SD 57007, USA
2World Resources Institute, Washington, DC 20002, USA
E-mail: mark.broich@sdstate.edu
Received 12 October 2010
Accepted for publication 3 February 2011
Published 23 February 2011
Online at stacks.iop.org/ERL/6/014010
Abstract
The Indonesian islands of Sumatera and Kalimantan (the Indonesian part of the island of Borneo) are a
center of significant and rapid forest cover loss in the humid tropics with implications for carbon
dynamics, biodiversity conservation, and local livelihoods. The aim of our research was to analyze and
interpret annual trends of forest cover loss for different sub-regions of the study area. We mapped forest
cover loss for 2000–2008 using multi-resolution remote sensing data from the Landsat enhanced
thematic mapper plus (ETM+) and moderate resolution imaging spectroradiometer (MODIS) sensors
and analyzed annual trends per island, province, and official land allocation zone. The total forest cover
loss for Sumatera and Kalimantan 2000–2008 was 5.39 Mha, which represents 5.3% of the land area
and 9.2% of the year 2000 forest cover of these two islands. At least 6.5% of all mapped forest cover
loss occurred in land allocation zones prohibiting clearing. An additional 13.6% of forest cover loss
occurred where clearing is legally restricted. The overall trend of forest cover loss increased until 2006
and decreased thereafter. The trends for Sumatera and Kalimantan were distinctly different, driven
primarily by the trends of Riau and Central Kalimantan provinces, respectively. This analysis shows
that annual mapping of forest cover change yields a clearer picture than a one-time overall national
estimate. Monitoring forest dynamics is important for national policy makers, especially given the
commitment of Indonesia to reducing greenhouse gas emissions as part of the reducing emissions from
deforestation and forest degradation in developing countries initiative (REDD+). The improved
spatio-temporal detail of forest change monitoring products will make it possible to target policies and
projects in meeting this commitment. Accurate, annual forest cover loss maps will be integral to many
REDD+objectives, including policy formulation, definition of baselines, detection of displacement,
and the evaluation of the permanence of emission reduction.
Keywords: tropical forest, deforestation, REDD+, deforestation drivers, remote sensing, Landsat,
MODIS
1. Introduction
Carbon emissions from deforestation and forest degradation
are the second largest source of anthropogenic carbon emission
(Intergovernmental Panel on Climate Change 2007,LeQuere
et al 2009, van der Werf et al 2009). Considered a
cost effective way to mitigate anthropogenic greenhouse gas
emissions, the reducing emissions from deforestation and
1748-9326/11/014010+09$33.00 ©2011 IOP Publishing Ltd Printed in the UK1
Environ. Res. Lett. 6(2011) 014010 MBroichet al
forest degradation in developing countries (REDD+) policy
framework plans to financially compensate developing tropical
nations for reducing their deforestation and forest degradation
(Gullison et al 2007, Baker et al 2010). For the first
time, the 16th Conference of the Parties (COP16) formally
established a REDD+mechanism under the auspices of the
United Nations Framework Convention on Climate Change
(UNFCCC). Based on the COP16’s decisions, developing
countries are encouraged to contribute to their reduction of
emissions from deforestation and forest degradation (REDD),
conservation of forest carbon stocks, sustainable management
of forest, and enhancement of forest carbon stocks (REDD
+’). Developing countries are further encouraged to develop
a robust and transparent national forest monitoring system to
evaluate the above-mentioned activities (COP16/CMP6 2010).
Indonesia has the third largest extent of the world’s
remaining humid tropical forests, extensive forested peatlands,
and a high rate of deforestation (Achard et al 2002,FAO
2005, Mayaux et al 2005,Hansenet al 2009,Forest
Watch Indonesia/Global Forest Watch 2002), which makes
the country highly significant in the context of REDD+.
Indonesia’s forests are rich in biological diversity and provide
ecosystem goods and services such as watershed protection
and erosion prevention. Indonesian tropical forests also
play a critical role for the livelihood of local communities
and the national economy (Forest Watch Indonesia/Global
Forest Watch 2002). In recognition of the importance of
carbon sequestration and other important ecosystem services,
anewREDD
+initiative, the ‘Norway–Indonesia REDD+
Partnership’ aims at reducing emissions from deforestation
and degradation in Indonesia3. For Indonesia, the lack of
timely, spatially and temporally detailed information on forest
cover loss rates hampers forest management and governance
(Fuller 2006,Hansenet al 2009), posing a challenge in
meeting the objectives of REDD+. A monitoring system
that fits the objectives of REDD+to calculate greenhouse gas
emissions requires two inputs: activity data and emissions
factors (Intergovernmental Panel on Climate Change 2003,
2006). Activity data quantify the area of forest land cover
classes that have been converted to non-forest land cover
classes. Emissions factors refer to emissions of greenhouse
gases per unit area of a given forest class that was converted
to a non-forest class. Quantifying activity data is critical for
the formulation and evaluation of REDD+policy, including
the specification of deforestation baselines, quantification of
displacement of deforestation, and evaluation of persistence
of reduced deforestation rates (Miles and Kapos 2008). For
example, deforestation has been displaced to Indonesia from
countries that have slowed their deforestation rates or increased
their forest area (Meyfroidt et al 2010). Annual updates of
forest change are a REDD+objective (Baker et al 2010)that
has not been met in Indonesia.
Our overall objective of this research was to quantify
and interpret interannual gross forest cover loss trends at
the island and regional level from 2000 to 2008. While
‘deforestation’ implies a permanent conversion to non-forest,
3Letter of Intent between the Government of the Kingdom of Norway and the
Government of the Republic of Indonesia, http://www.norway.or.id/.
we use the term ‘forest cover loss’ to refer to areas that have
been converted from a forest cover to a non-forest cover class
within the study interval. The quantification of high spatial
and temporal variations in forest cover loss trends, as they
have been documented for the Brazilian Amazon (INPE 2010),
requires detailed annual maps. Previous work quantifying
Indonesian forest cover loss provided multi-year average maps
(Government of Indonesia/World Bank 2000), sample-based
estimates (e.g. Hansen et al 2009), and annual maps for specific
regions (Gaveau et al 2009a,2009b,2009c,Uryuet al 2008,
van der Werf et al 2008). Large area national-scale efforts to
map forest cover loss at moderate spatial resolution4and at
annual time steps are missing (Sanchez-Azofeifa et al 2009).
Consequently, spatial and temporal variations in Indonesia’s
forest cover loss trends have not been quantified in detail.
Quantifying such trends is needed for targeting and evaluating
policy aiming to reduce forest cover loss. Our analysis focuses
on the island groups of Sumatera and Kalimantan (Indonesian
Borneo), an area of 100 Mha and the primary area of recent
forest clearing activities (Hansen et al 2009, Mayaux et al
2005, Curran et al 2004, Fuller et al 2004, Gaveau et al 2009a,
2009c).
While other countries in the tropics, such as Brazil and
India, produce periodic maps of deforestation at moderate
spatial resolution (Forest Survey of India 2004,INPE2010), a
monitoring system for Indonesia has not yet been developed.
Optical remote sensing-based deforestation monitoring in
Indonesia is challenging due to persistent cloud cover. Annual
monitoring using single best Landsat images during a local
dry season, as applied for Brazil, is not feasible. For
Indonesia, moderate spatial resolution Landsat images with
low cloud cover were photo-interpreted to map forest cover
and change for the 1985–1998 time intervals (Government of
Indonesia/World Bank 2000). Yet, data gaps due to clouds
adversely impact the reliability of deforestation estimates
(Ministry of Forestry Indonesia 2003b) derived from this most
recent (1985–1998) Landsat-based multi-year average map
for Indonesia. Coarse spatial resolution remote sensing data
streams, such as those provided by MODIS, have a higher
observation frequency than moderate resolution data streams,
therefore providing more frequent cloud-free observations
(Global Observation of Forest and Land Cover Dynamics
2009). However, MODIS data are considered inadequate for
accurate estimation of deforestation area as most deforestation
occurs at subpixel scale (Global Observation of Forest and
Land Cover Dynamics 2009,Mortonet al 2005, Sanchez-
Azofeifa et al 2009,Hansenet al 2008a). Moderate spatial
resolution data are recommended for quantifying deforestation
area (Global Observation of Forest and Land Cover Dynamics
2009). For Indonesia, Broich et al (2011) showed that
even when combining cloud-free data from large numbers
of Landsat 7 imagery, annual, gap-free forest cover loss
maps over large areas cannot be derived. However, accurate,
internally consistent, large area maps of forest cover loss
based on Landsat 7 time-series data over a multi-year interval
are feasible (Broich et al 2011). Freely available Landsat
4Moderate spatial resolution is defined in Global Observation of Forest and
Land Cover Dynamics (2009) as 10–60 m.
2
Environ. Res. Lett. 6(2011) 014010 MBroichet al
images represent the most complete remotely sensed dataset
at moderate spatial resolution for the past decade.
Here we quantified forest cover loss trends by disaggregat-
ing Landsat-based moderate spatial, coarse temporal resolution
maps, which represent forest cover loss area over a multi-
year interval, to individual years. We used annual forest cover
loss indicator maps derived from high temporal, coarse spatial
resolution MODIS data5to disaggregate Landsat-based forest
cover loss maps to individual years. This data integration
was needed to produce annual maps of forest cover loss
over large areas that cannot be derived solely from Landsat
or MODIS data. We investigated the following research
questions using multi-source satellite and forest land use data
to improve current understanding of Indonesian forest cover
loss dynamics:
(1) What was the spatio-temporal variation in forest cover loss
for Sumatera and Kalimantan from 2000 to 2008?
(2) What amount of forest cover loss occurred in land
allocation zones where clearing is restricted or prohibited
(Ministry of Forestry Indonesia 2008)?
2. Methods
2.1. Definitions
Forest was defined as >25% canopy cover of trees 5min
height. Forest cover loss was measured without regard to
forest land use, and included plantation and palm estate change
dynamics. Using the above definition of forest, forest cover
loss was defined as the conversion of forest to non-forest, and
represents a stand-replacement disturbance at a 60 m spatial
resolution. By using the term forest cover loss, we emphasize
that we did not differentiate between a temporary loss of
forest and a permanent conversion to a non-forest land cover.
Consequently, ‘forest cover loss’ represents gross rather than
net area and rate. Selective logging, defined as tree harvesting
that does not result in conversion of forest to non-forest, was
not part of our forest cover loss definition and was not mapped.
Throughout this research, the term ‘trend’ is used to describe a
‘general tendency’. The term, as used here, does not refer to a
‘statistical trend’, as the small sample size of eight observations
(2000–2008) precluded the robust identification of statistically
significant trends.
2.2. Data
We utilized data from the Landsat 7 enhanced thematic mapper
plus (ETM+) and MODIS sensors and digital elevation
data from the Shuttle Topography Radar Mission (SRTM).
Landsat 7 data were downloaded from USGS/EROS (WWW1)
and resampled to 60 m ×60 m spatial resolution. MODIS 16
day composites of the MODIS land bands (Vermote et al 2002)
and the thermal bands (10 780–11 280 nm) were obtained for
2000–2008 (WWW2). SRTM-obtained digital elevation data
(Rabus et al 2003,USGS2006) were downloaded for the study
area (WWW3).
5Coarse spatial resolution is defined in Global Observation of Forest and
Land Cover Dynamics (2009) as 250–1000 m.
The latest update of the Digital Map of Indonesian
Forest Land Use vector6dataset was obtained from the
Ministry of Forestry Indonesia (2008). Indonesia has
three official function-based ‘forest’ land allocation zones,
which include production forest (including regular production,
limited production, and conversion), watershed protection
forest, and biodiversity conservation forest7(Law Republik
Indonesia 1999). Selective logging and permanent forest
clearing are allowed in conversion forests. Selective logging
and temporary forest clearing, followed by sufficient replanting
are allowed in regular production forests. Selective logging
and restricted forest clearing are allowed in limited production
forests (Ministry of Forestry Indonesia 2003a). Selective
logging and clearing are also legal outside the forest zone
on ‘other use’8lands that are not under the jurisdiction of
the Ministry of Forestry. However, clearing is prohibited
in watershed protection forest and biodiversity conservation
forest (Ministry of Forestry Indonesia 2003a). For our analysis,
we used the following six land uses zones: conversion forest,
regular production forest, limited production forest, watershed
protection forest, biodiversity conservation forest, and other
use (figure 1).
2.3. Annual forest cover loss mapping
For this study, we used the method described in Broich et al
(2011) to automatically map forest cover loss at 60 m ×
60 m spatial resolution, for the 2000–2008 time interval,
using Landsat ETM+and MODIS imagery as inputs. Due
to persistent cloud cover and limited acquisitions per year,
multiple years of Landsat 7 imagery have to be combined to
derive complete cloud-free forest cover loss maps (Broich et al
2011). Landsat images were first radiometrically normalized
using MODIS-mapped dark forest as a reference (Broich et al
2011,Hansenet al 2008b). A radiometric normalization
was required to allow the application of generic per-pixel
classification algorithms. Each pixel in each Landsat image
was automatically flagged as being influenced by clouds or
assigned a probability of the pixel being forest. Forest
probability classification was based on the Landsat bands
and normalized differences of bands, which have long been
known to partially compensate for the effect of rugged terrain
(Vincent 1973, Cheng et al 2004). Clear surface observations,
acquired over multiple years, were assembled into per-pixel
forest probability time series. These time series were then
classified into either forest cover loss or no change. Cloud-free
Landsat images are rare over Indonesia and clouds cannot be
flagged with absolute certainty. As a consequence, individual
observations are unreliable and the accurate, spatially explicit
characterization of forest cover loss, as demonstrated by Broich
et al (2011), relies on multi-year per-pixel trajectories. All
characterization models were based on supervised decision tree
6‘Peta Penunjukan Kawasan Hutan’ in Indonesian.
7‘Hutan Produksi Tetap (HP), Hutan Produksi Terbatas (HPT), Hutan
Produksi Konversi (HPK), Hutan Lindung (HL), and Hutan Konservasi
(HK)’ are the Indonesian terms for regular production, limited production,
conversion, watershed protection, and biodiversity conservation forests,
respectively.
8‘Areal Penggunaan Lain (APL)’ in Indonesian.
3
Environ. Res. Lett. 6(2011) 014010 MBroichet al
Figure 1. Land allocation zones (Ministry of Forestry Indonesia 2008) and upland areas for Sumatera and Kalimantan.
Figure 2. Forest cover loss for Sumatera and Kalimantan mapped at moderate spatial resolution for the 2000–2008 interval superimposed on a
Landsat image composite (bands 5/7/4 as R/G/B). The box marks the zoom-in area displayed in figure 3.
algorithms (Breiman 1996,Prasadet al 2006) that have been
previously used to successfully characterize remote sensing
data (Michaelsen et al 1994,Hansenet al 1996,Friedlet al
1999,Hansenet al 2003).
MODIS data were classified with a decision tree algorithm
following the approach of the standard vegetation continuous
field product of the MODIS land science team (Hansen et al
2003) resulting in annual maps of per cent forest cover per
250 m ×250 m pixel. Pixels that lost more than 70% of their
forest cover in subsequent years were identified for producing
annual forest cover loss maps.
We disaggregated forest cover loss mapped at 60 m×60 m
spatial resolution for the 2000–2008 time interval, by year,
using the annual MODIS detections. Each 60 m ×60 m forest
cover loss pixel overlapping with a 250 m ×250 m MODIS
forest cover loss pixel was allocated to the year of the MODIS
detection. MODIS-mapped forest cover loss pixels or parts of
such pixels that did not overlap with Landsat-mapped forest
4
Environ. Res. Lett. 6(2011) 014010 MBroichet al
Figure 3. Temporal disaggregation of the moderate spatial resolution forest cover loss map for Riau province, Sumatera. Landsat band 5 is
displayed in grayscale with dark tones representing forest cover. Colors mark the year of MODIS-detected forest cover loss.
cover loss pixels were disregarded. Landsat forest cover loss
pixels that did not overlap with a MODIS forest cover loss
pixel were allocated to individual years proportional to the
temporal distribution of clearly allocated forest cover loss. We
determined area of forest cover loss and forest cover loss trends
for various sub-regions of the study area, namely Sumatera and
Kalimantan, provinces, and land allocation zones.
3. Results
3.1. Spatio-temporal variation in forest cover loss
Forest cover loss for Sumatera and Kalimantan mapped at
moderate spatial resolution for the 2000–2008 interval was
5.39 Mha, 5.3% of the land area, and 9.2% of the year
2000 forest cover (figure 2). An example of the temporal
disaggregation of the moderate spatial resolution map using
annual MODIS-mapped forest cover loss for Riau province,
Sumatera is shown in figure 3.
The overall trend of forest cover loss of the two islands
groups increased until 2006 and decreased thereafter. The
individual trends for Sumatera and Kalimantan are different
from the overall trend (figure 4). Forest cover loss on Sumatera
increased almost monotonically until 2005 and decreased
thereafter, whereas forest cover loss exhibited peaks in 2002–
2003 and 2005–2007 in Kalimantan.
The analysis of per province forest cover loss revealed that
the combination of Central Kalimantan and Riau provinces
totaled 46% of all forest cover loss. On an annual basis,
these two provinces accounted for a minimum of 40% of all
forest cover loss (2000–2001) and a maximum of 55% of
all forest cover loss (2004–2005) for the study area. Riau
0
100
200
300
400
500
600
700
800
900
1,000
2000 2001 2002 2003 2004 2005 2006 2007 2008
Forest cover loss ha*1000
year
Sumatra & Kallmantan Sumatra Kallmantan
Figure 4. Forest cover loss, 2000–2008 for Sumatera and
Kalimantan.
province had the highest rates of forest cover loss in the
study area, except for 2002–2003 and 2006–2007 when Central
Kalimantan exceeded Riau’s rate (figure 5).
3.2. Forest cover loss in different land allocation zones
The analysis of forest cover loss within the official land
allocation zones showed that 79.9% of all mapped forest cover
loss occurred in land allocation zones that permit permanent
or temporary clearing (table 1). However, 6.5% of forest
cover loss occurred in zones where clearing is prohibited,
with 4.4% occurring in watershed protection zones, and 2.1%
in biodiversity conservation zones. An additional 13.6% of
forest cover loss occurred in limited production forests, where
clearing is restricted.
5
Environ. Res. Lett. 6(2011) 014010 MBroichet al
Table 1. Area, forest area, and forest cover loss area within the official land allocation zones on Sumatera and Kalimantan (zones according
to the Ministry of Forestry Indonesia 2008, Ministry of Forestry Indonesia 2003a).
Land allocation zone Legal clearing
Area
(Mha)
Forest area
2000 (Mha)
Area of forest
cover loss (Kha)
% of all forest
cover loss
% of forest cover
loss in forest use
zones
Biodiversity conservation forest No 8.7 7.3 112.2 2.1 8.9
Watershed protection forest No 12.5 10.3 237.3 4.4
Limited production forest Restricted clearing 15.3 12.1 734.2 13.6 18.7
Conversion forest Yes 10.3 4.6 890.8 16.5 72.4
Regular production forest Temporary clearing 21.0 12.9 1952.6 36.1
Other use Yes 32.1 10.6 1475.0 27.3
0
50
100
150
200
250
300
350
2000 2001 2002 2003 2004 2005 2006 2007 2008
year
Forest cover loss ha*1000
Central Kallmantan Riau
Figure 5. Forest cover loss trends 2000–2008 for Riau and Central
Kalimantan provinces.
Clearing in watershed protection and biodiversity conser-
vation forests did not show a distinct trend except for a year
of maximum clearing in 2005 (figure 6). Clearing in limited
production forests generally increased over the 8-year period of
the study. As much as 27.3% of all forest cover loss occurred
outside the official forest zone (table 1), where a generally
increasing trend in clearing was observed (figure 6).
4. Discussion
4.1. Spatio-temporal variation in forest cover loss
Our results extend those of Hansen et al (2009), who identified
a near-monotonic increase in forest cover loss from 2000–
2005. Our results reveal a peak in forest cover loss in 2006
followed by a gradual reduction thereafter, and provide a
spatio-temporally explicit quantification of forest cover loss in
Sumatera and Kalimantan.
Forest cover loss within Sumatera and Kalimantan is
a largely a function of clearing within Riau and Central
Kalimantan provinces, respectively. Forest cover loss within
Riau and Central Kalimantan provinces accounts for nearly
half of the total forest cover loss in the study area; their
proportion of total change did not increase or decrease over
time. The individual trends for Sumatera and Kalimantan
were different and reflected the dynamics of Riau and Central
Kalimantan provinces, respectively. The trend for Central
Kalimantan corresponds to van der Werf et al’s (2008)
MODIS-derived deforestation trend for South Borneo that
hasbeenlinkedtofireandElNi˜no droughts (van der Werf
Figure 6. Forest cover loss trends 2000–2008 within the official land
allocation zones on Sumatera and Kalimantan (Ministry of Forestry
Indonesia 2008).
et al 2008). Dry season precipitation in Sumatera is spatially
more variable than that of Borneo, and interannual variation
in Sumatera’s precipitation is less distinct (van der Werf
et al 2008). Field et al (2009) identified differences in
the relationship between drought and fire for Sumatera and
Kalimantan before the 1980s and attributed them to differences
in population growth and transition to large plantations.
Catastrophic El Ni˜no fires have been interpreted as people
using the opportunity of anomalous climatic conditions to
clear forests via fire (Fuller et al 2004, Casson 2000), an
effect that possibly occurs when population density and the
landscape proportion of large plantations reach a certain level.
The deforestation curve for Riau province, as published by
Uryu et al (2008), shows similar patterns to the trend we
derived for the same area. Riau province has the largest pulp
mill capacity in Indonesia (Uryu et al 2008). Relatively low
deforestation rates have been interpreted by Uryu et al (2008)
as a consequence of Riau’s pulp and paper industry defaulting
on its debt in the early 2000s and a large police investigation
concerning illegal logging in Riau in 2007. In the interim
period, our results illustrate Riau’s rapid and extensive forest
cover clearing. Variation in governance from one district to
another has been debated as a region-specific deforestation
driver (Smith et al 2003, McCarthy 2002). Variation in
governance increased with Indonesia’s decentralization after
the years of the Suharto regime and has been held responsible
for an increase in illegal logging (Casson and Obidzinski 2002,
Smith et al 2003).
6
Environ. Res. Lett. 6(2011) 014010 MBroichet al
4.2. Forest cover loss in different land allocation zones
Our analysis showed that the majority of all mapped forest
cover loss (79.9%) occurred in land allocation zones that
permit permanent or temporary clearing, while 20.1% occurred
where clearing is either prohibited or restricted. Effective
enforcement of existing biodiversity conservation, watershed
protection, and limited production forest land use designations
could significantly reduce forest cover loss. Furthermore, the
increasing trend in forest cover loss in the limited production
zone is of concern as clearing within this zone is restricted
(Ministry of Forestry Indonesia 2003a). The trend within this
zone mirrored that of the regional total, reflecting increased
exploitation of limited production forests. We also found that a
large and increasing proportion of forest cover loss occurred
outside the official forest zone, an area that we mapped as
40% forest covered in 2000. Based on visual inspection of
composite imagery, we interpreted forest clearance activity in
this zone as replacement of oil palm plantation and small scale
agriculture.
Both conversion and regular production forest zones
exhibited a general increasing trend over the study period,
with production forests having a higher overall rate of increase
compared to conversion forests. Commercial forest land
uses, such as timber plantations, are allowed within both
production and conversion forests. Thus, while forest cover
loss within biodiversity conversation and watershed protection
zones implies the loss of intact forest, such a determination is
not possible in this study for conversion and regular production
forests.
4.3. Quantifying activity data in support of REDD+
Annual quantification of activity data is considered an
important objective under REDD+(Baker et al 2010). For
Indonesia, annual maps of forest cover loss have not been
previously provided. For Sumatera and Kalimantan, our results
illustrate an interannual forest cover loss dynamic similar to
that found in the Brazilian states of Mato Grosso and Para
(INPE 2010). However, our estimate includes all forest cover
loss, not just intact forest loss as with INPE’s product. Given
the identified high spatial and temporal variation in forest
cover loss trends, annual change estimates will be needed
for determining baselines and evaluating policy impacts, more
specifically quantifying the displacement of forest cover loss
and permanence of forest cover loss reduction. While the
specific integration of our annual map products into the policy
process is yet to be determined, we believe that using maps
with lower temporal resolution (multi-year averages) would
lead to an inaccurate evaluation of policy impacts. Total
mapped forest cover loss from 2000–2008 was derived from
Landsat imagery. The temporal disaggregation of this total
was based on annual detections of a MODIS forest cover loss
product. Small and isolated Landsat-mapped forest cover loss
patches could not be clearly allocated using the coarse spatial
resolution MODIS product. Instead, those areas were allocated
to individual years proportional to the temporal distribution of
clearly allocated forest cover loss. We assume that the Landsat-
derived totals and the inter annual trend per province, which
was driven by industrial-scale clearing, closely approximated
reality. However, the trends within certain forest land uses, for
example watershed protection forests that are located mostly in
uplands and characterized by small clearing patches, were less
certain.
In this work, we did not differentiate the type of forest
that has been cleared. The results of this study quantify the
dynamics of all forest cover that has been lost between 2000
and 2008 for the study area including old growth, degraded,
and secondary forest as well as timber plantations and oil palm
estates. Differentiating forest types is important in determining
emissions factors in a carbon accounting framework, but also
critical in quantifying other ecosystem goods and services
such as biodiversity conservation. Remote sensing-based
identification of forest type is technically more challenging
than forest cover loss mapping and is the subject of ongoing
research.
5. Conclusion
The integrated use of multi-resolution remote sensing data
is required for monitoring forest cover loss in many humid
tropical forest zones with highly variable loss rates. Cloud
contamination and restricted image acquisition strategies limit
annual monitoring based only on Landsat 7 data for many
humid tropical regions. However, Landsat 7-based maps over
multi-year time steps are feasible. To date, annual forest cover
loss maps have not been available for Indonesia, a country
second only to Brazil in terms of area of humid tropical forest
cover loss. Quantifying the spatial and temporal variation
of forest loss ‘activity data’ within Indonesia is necessary in
light of the ambitious goals of the Norway–Indonesia REDD+
Partnership.
Previous work quantifying Indonesian forest cover loss
provided multi-year average maps (Government of Indone-
sia/World Bank 2000), sample-based estimates (e.g. Hansen
et al 2009), and annual maps for specific regions (Gaveau
et al 2009a,2009b,2009c,Uryuet al 2008, van der Werf
et al 2008). Our method is the first to map gross forest
cover loss at moderate spatial resolution annually for Sumatera
and Kalimantan, Indonesia. For Sumatera and Kalimantan, a
large and rapidly changing area in Indonesia, the combined
use of spatially detailed Landsat data with temporally detailed
MODIS data allowed the disaggregation of Landsat-mapped
forest cover loss to individual years. This was possible
due to the predominance of industrial-scale forest clearing in
Sumatera and Kalimantan. The analysis of our annual spatially
explicit datasets based on multi-resolution optical remote
sensing data revealed large variations in the spatio-temporal
trends of forest cover loss. The high observed fraction of
forest cover loss in zones where clearing should be restricted,
or where clearing is prohibited, points towards a significant
potential for reducing forest cover loss in Indonesia via the
effective enforcement of existing forest land use designations.
The results of such enforcement should be verified using
remotely sensed data sets. Operational satellite monitoring of
national-scale forest dynamics will also provide information
for the detection of displacement and the assessment of
7
Environ. Res. Lett. 6(2011) 014010 MBroichet al
permanence in response to changes in forest governance.
Annual monitoring is required to identify the impact of new
policy and law enforcement in an environment characterized
by high spatial–temporal variations in forest cover loss rates.
Results from this study illustrate the value and important
contribution of multi-source approaches to forest monitoring.
Acknowledgments
Funding from the National Aeronautics and Space Admin-
istration supported this research under grant NWX08AL99G
managed under the NASA Land Cover Land Use Change
program (manager: Dr Garik Gutman).
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... Ha (28.4%) of the total area of Makassar, and water body 854.2 Ha (4.9%), which is Jeneberang watershed and Tallo river. The presence of clouds in Landsat satellite images requires the clouds cover class in the land cover classification because Indonesia has no seasonal cloud-free moment offering the opportunity to capture cloud-free Landsat satellite image [8]. In 2019, Figure 1c clearly showed that the built-up areas in the downtown area have increased; on the contrary, vegetation has decreased. ...
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Makassar City, the capital of South Sulawesi Province, is the largest metropolitan city in the eastern part of Indonesia, with a population development rate of 1.19% in 2019. An increase in population impacts city development and results in land use and land cover changes. Changes in land use and land cover pattern bring impact to Land Surface Temperature (LST). This study examines land cover’s influence on land surface temperature in Makassar City using multi-temporal satellite data. Land cover and LST data were extracted using Landsat 7 and Landsat 8 over the period of 1999, 2009, and 2019. The result shows that the highest increase in land cover changed was a built-up area of 13.1%, and vegetation decreased by 8.6%. The change in average LST value in the last 20 years was 0.39°C with the highest LST distribution areas was in 30-32°C and 32-34°C classes. The result of LST analysis in 2019 shows that the Urban Heat Island phenomenon has occurred in Makassar in the downtown area and several areas with the densely built-up area. With an overview of the UHI phenomenon in Makassar, the government is expected to raise public awareness of this phenomenon so that preventive actions can be taken, so the effects of UHI do not spread more widely.
... Jumlah studi kasus tentang pendorong perubahan lahan dan dampaknya di Sumatra dan Kalimantan telah berkembang pesat (Syam et al. 1997;Suyanto et al. 2004;Obidzinski dan Chaudhury 2009;Broich et al. 2011a;. Namun, pemahaman tentang faktor penyebab perubahan tutupan dan penggunaan lahan memiliki tantangan tersendiri karena variasi yang kuat dari studi kasus yang ada, serta memiliki relevansi terbatas karena ruang lingkup dan konteksnya (Gough et al. 2017). ...
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As a country considered as land-based economic resources, Indonesia has shown that the expansion of plantation areas and Industrial Plantation Forests (HTI) in this country is growing rapidly. These sectors provide economic solutions and fulfill the market needs. However, environmental degradation and social conflicts caused by the land changes are against the goal of sustainability. The use of the land itself is a manifestation of power competition between actors with an interest in land. In the process of land allocation, various actors with an interest in land will compete with the power they have. The most powerful actors will take control of the land-use based on their decisions. Therefore, to obtain a comprehensive understanding of Indonesia's land cover and land-use changes, identification is needed to reveal the trends. It will also show how big the land cover and land-use changes, the land-use conditions at the site level as well as the understanding of the strong actors determining the process. This study has a general objective to explain land-use and land cover changes spatially and to describe the dynamics of the actors’ power in the land-use for oil palm and HTI plantations. These research objectives can be achieved through intermediate objectives namely: (1) Understanding the process of land cover and land-use change and its actors in an empirical case study; (2)Produce a map of land-use change from 1990-2019; (3) Describing the powerdynamics of actors through ACP and SNA approach and identify the most powerfulactors. This study was conducted in three stages; systematic review, spatial analysis, analysis of the actors’ strength and social networks. This systematic review is an approach to determine the limits of existing knowledge so that further research builds on that knowledge. This study used a spatial approach to determine the locations that have experienced changes, the extent to which changes in the landscape have occurred, transitions of land-use changes, and the most critical changes in a certain period. It aims to understand the magnitude of the impact caused by the actor and what conditions affect the land change over a certain period. Image interpretation carried out in this study is visual interpretation (digitization on-screen) to classify land cover into several classes and perform accuracy tests based on the coordinates taken in the field. The final stage of this research is the analysis of the actors’ power and their networks by combining the Actor Centered Power (ACP) and Social Network Analysis (SNA) approaches. The combination of these approaches is a form of developing an analysis of the actors’ power in contesting the use of land resources. The case study was conducted in Bengkalis Island, Riau. 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... Kalimantan and Sumatra have been the islands experiencing the greatest impact of this spike in deforestation. As much as 5.4 million ha of the forests on these islands were Figure 1: Ecozones in Indonesia, from Ruesch and Gibbs, 2008 cleared between 2000 and 2010, 80 percent of which were on land concessions that allow for forest clearing (Broich et al. 2011). ...
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... For example, while extensive Fornace et al. Malar J (2021) 20:69 deforestation occurred in Indonesia between 2000 and 2012, commitments to global climate change agreements led to substantial decreases in forest loss in 2017 [23][24][25]. Conversely, policies may have unintended implications. Peace agreements between the Colombian government and armed groups led to land colonization in previously inaccessible areas of the Andean-Amazonian foothills of Colombia; deforestation has been further amplified by governmental programs building roads and fostering extractive and ranching industries [26]. ...
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... The causes for land use change are multifactorial, with underlying political, institutional and economic factors driving agricultural expansion, resource extraction and infrastructure development [20]. For example, while rapid and extensive deforestation occurred in Indonesia between 2000 and 2012, the commitment to a forest moratorium policy protecting forests and peatlands as part of the Indonesian Intended Nationally Determined Contributions to mitigate global climate change led to substantial decreases in forest loss in 2017 [9,21,22]. Conversely, economic and political policies may have unintended implications for deforestation and conservation. Peace agreements between the Colombian government and armed groups led to large increases in land colonisation in previously inaccessible areas of the Andean-Amazonian foothills of Colombia; deforestation has been further amplified by governmental programs building roads and fostering extractive and ranching industries [23]. ...
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... Sumatra has always been a key area for oil palm production in Indonesia, with Riau as the leading province [54]. The forest cover loss in Riau contributed half of the total forest loss in Sumatra [55]. Second, logging and wildfire were another major cause, accounting for 31% of the forest loss. ...
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Bays are some of the core areas for marine economic development. The South China Sea coast is one of the most developed and dynamic places in the Asia-Pacific. In this study, we focused on the large bays surrounding the South China Sea. The techniques of image segmentation and supervised classification as well as image interpretation were used to acquire land-use data of 41 bays from 1988 to 2018. Then, we quantified the intensity and pattern of land-use and land-cover change during the two periods. Plantation land was the dominant agriculture land type as well as the second land use type after natural forest. Agriculture land cover increased from 29.8% to 40.9% and the growth was driven by plantation expansion. Deforestation was serious, including both natural forests and mangroves. Natural forest cover decreased by 31.6% and mangrove cover decreased by 16.2%. The vast majority of forest loss occurred in Sumatra and western Kalimantan. Commodity-driven deforestation for plantations was the major reason for forest loss.
... Djaenudin [2] reported that Jambi Province was crowned as the province with a highest deforestation class (-5.73%/year). Reports related to forest loss in Sumatra Island could also be found in several other studies [3], [5] . ...
Chapter
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