Mapping and monitoring deforestation and forest degradation in Sumatra (Indonesia) using Landsat time series data sets from 1990 to 2010

According to the FAO Global Forest Resource Assessment (FAO 2006a), forest is defined by the presence of trees with land covering more than 0.5 ha. The trees should be able to reach a minimum height of 5 m in situ and a canopy cover of at least 10%. For the purposes of national forest accounting and management, Indonesian forest is defined as an area with a minimum mapping unit of 0.25 ha that is covered by trees higher than 5 m with a canopy cover of more than 30% (MoF 2008a). For this study, we employed the Indonesian definition of forest with a focus on forests composed of indigenous tree species and lacking near-term evidence of stand-replacement disturbance (FAO 2005). Forest timber and pulp plantations, oil palm estates and secondary forest are excluded from the analysis. Our definition of primary forest includes intact and degraded states, or natural forests consisting of native tree species that have not been cleared and converted to other land uses.

Intact forest consists of native tree species where there are no clearly visible indications of human activities (MoF 1989) and the ecological processes are not significantly disturbed (FAO 2006b). We employed the IFL method to map the extent of primary intact forest, which is defined as an unbroken expanse of natural ecosystems within areas of current forest extent, without signs of significant human activity, and having an area of at least 500 km² (Potapov et al 2008). Degraded primary forest is a natural forest which has been fragmented or subjected to forest utilization including wood and or non-wood forest product harvesting that alters the canopy cover, and overall forest structure (ITTO 2002). Forest management practices leading to degradation, such as selective logging, are evidenced by the presence of logging roads, logging patios, or forest canopy gaps. Primary intact forest is mature forest absent of and removed from such disturbance features. For simplicity, we define forest cover loss as an area having experienced a stand-replacement disturbance, and define forest degradation as an area having experienced a transition from primary intact forest to primary degraded forest.

2.2.1. Satellite imagery.

2.2.2. GIS data sets.

Official provincial boundaries (figure 1(a)), forest land use zones, and land cover digital maps of Sumatra were obtained from the Ministry of Forestry of Indonesia (MoF 2010). According to Indonesian Forestry Law (article 6 UU-41, 1999) forest land is officially divided into three major land use zones based on purpose and function: protection forest (hutan lindung), conservation forest (hutan konservasi) and production forest (hutan produksi). Production forest is further subdivided into regular production forest (hutan produksi tetap), limited production forest (hutan produksi terbatas) and convertible production forest (hutan produksi konversi). A summary of the Indonesian forest land use zones is shown in table 1 along with a map of Sumatran forest land use zones in figure 1(b).

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Table 1.  A summary of Indonesian forest land use zones: forest classes, function, possible management practices and consequences of each class (percentage represents only Sumatra).

Forest land use	Code	%	Purpose/function	Possible management practices	Consequences (under sustainable forest management)
Forestland		59.2	Designed as a forestland	Forest uses	Dynamic forest
Conservation forest	HK	10.6	Preserving the biodiversity of flora fauna and their ecosystem	Forest preservation	Stable forests without any deforestation and forest degradation
Protection forest	HL	13.0	Protecting the water system to prevent flooding, control erosion, protect sea water intrusion and maintain soil fertility	Forest protection	Stable forests without any deforestation and very low intensity of forest degradation
Production forest		35.5	Providing the forest products mainly from timber extraction	Forest production	Dynamic deforestation and forest degradation
Limited production forest	HPT	9.0	Low intensity logging (due to topographical condition)	Limited logging	Forest degradation
				Very selective logging
				Very limited clear cutting
				Post-logging silvicultural treatments
Regular production forest	HP	15.5	Logging	Selective logging	Temporary deforestation
			Forest plantations	Post-logging silvicultural treatments	Forest degradation
				Clear cutting
Convertible production forest	HPK	11.1	Logging	Clear cutting	Permanent and temporary deforestation
			Agriculture estate		Forest degradation
			Other uses
Non-forestland	APL	40.8	Outside forestland and designated for other uses (agriculture land, settlements etc)		Permanent deforestation
					Forest degradation
					Reforestation

Primary forest cover mapping employed Landsat composites and multi-temporal metrics as input data and was performed using a two-step supervised classification. The first step of classification included mapping areas with tree canopy cover of 30% and above for the 1990 and 2000 reference years. We used a decision tree algorithm, a hierarchical classifier that splits independent data (Landsat inputs) into more homogeneous subsets regarding class membership (Breiman et al 1984). The training data were a binary training data set of tree cover and non-tree cover, created using photo-interpretation of the circa 1990 and 2000 image composites, respectively.

The resulting tree canopy cover class was subsequently classified into primary forest and other tree cover classes in a second procedure using a similarly created training data set representing primary forest and other tree cover classes. The classified areas of other tree cover and non-tree cover from the first step classification were aggregated into a non-primary forest class, which included non-treed lands as well as other non-primary forest types, such as timber plantations and oil palm estates. The same algorithm was applied and the same procedures repeated for the mosaics of year 1990 and 2000. We used reference data from GoogleEarth™ and local knowledge to create classification training data and to perform post-classification manual primary forest mask corrections for obvious commission and omission errors. The primary author was an image interpreter for the Indonesian forest resource mapping of 2003, 2005 and 2008 (MoF 2003b, 2005, 2008b). The algorithm flowchart is shown in figure 2.

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As seen in figure 2 primary forest cover loss from 1990 to 2000 was mapped by comparing primary forest extent of circa 1990 and 2000, each mapped independently. Change from 2000 to 2005, and from 2005 to 2010, was mapped with a decision tree model using forest cover loss and non-forest cover loss training sites manually created, as demonstrated by Broich et al (2011b), and applied to the multi-temporal metrics of the 2000–5 and 2005–10 intervals. Decision tree algorithms have been successfully used to characterize remote sensing data (Hansen et al 1996, 2003).

Two different methods were used in mapping primary forest extent and loss due to data limitations. Specifically, the lack of data for the 1990s was a limitation in applying a direct change detection mapping approach. Unlike Landsat 7 ETM+, which has a global acquisition strategy that ensures regular coverage of the global land surface, Landsat 5 TM data were not regularly acquired over Indonesia. As a result, for 1990, nearly 10% of the island was not covered by cloud-free data, and the data richness was limited precluding using multi-temporal metric approach for change detection. Thus, a simple post-classification comparison, with expert editing, was employed to map the 1990 to 2000 primary forest cover loss. In contrast, change detection mapping approach was employed to map the 2000–10 primary forest cover loss. The first complete image coverage was available for circa year 2000. From the year 2000 primary forest map, forest cover loss from the 2000–5 and 2005–10 intervals was subtracted to create the primary forest maps of 2005 and 2010. In figure 2, per-pixel mapping of primary forest extent and forest cover loss is shown in the box labeled (A).

The year 2000 IFL map used in this paper is a part of global map developed by a group of scientists and environmental NGOs and is available through a dedicated web site (www.intactforests.org). The IFL method is a fragmentation analysis based on a GIS-buffering approach further updated through expert visual interpretation, as described in Potapov et al (2008). The year 2000 IFL map has been updated at the national scale for 2005 and 2010 using time-sequential image composites. We used indications of recent human activity such as clearing for agricultural expansion and forest plantation establishment, logging roads and other infrastructure developments (Fuller 2006, Adeney et al 2009) to map the observable disturbances in each epoch. Buffering and patch analysis were also performed to update the IFL change circa 2005 and 2010. We subtracted the changes from the 2000 IFL map to create IFL for 2005 and 2010. In addition, a retrospective analysis of IFL change was performed for Sumatra using the circa 1990 Landsat image mosaic. The IFL method, in contrast to the approaches of Asner et al (2005) and Souza et al (2003), is an indirect characterization method that relies on the mapping of human-built infrastructure and other persistent signs of human activity within and adjacent to mature forests to infer degradation. For this study, fragmentation due to disturbances and logging roads is quantified using the IFL method, illustrated in figure 2, box (B).

We incorporated the two different approaches to quantify primary degraded forest extent and change over time. Primary intact forest was represented by the IFL. Remaining primary forest from the per-pixel mapping method is labeled as degraded forest. Figure 3 illustrates the primary (intact and degraded) forests maps for a subset of Riau and Jambi provinces. Four sets of primary forest maps of years 1990, 2000, 2005 and 2010 were produced (figure 4). The maps of primary forest and its change from 1990 to 2010 together with the map of IFL and its change from year 1990 to 2010 allow for the derivation of change estimates over the period of study. To study forest management practices, we examined the extent of primary intact and primary degraded forests and their change between epochs as a function of province boundary and forest land use zone.

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Sumatra primary forest extent for 1990, 2000, 2005 and 2010 is shown in table 2 and figures 4–6. By definition, total primary forest and primary intact forest can only lose or maintain areal extent. Remaining total primary forest cover in 2010 was 30.4% of the total land area. Primary forest extent was nearly halved over the 20 yr study period.

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As summarized in table 2, primary forest cover loss in Sumatra from 1990 to 2010 totaled 7.54 Mha. An additional 2.31 Mha of primary forest was degraded by 2010. The total primary forest area lost was 35.7% of 1990 primary forest area. An additional 11% of 1990 primary intact forest was degraded. In total, nearly half (47%) of 1990 Sumatran primary forest was either cleared or degraded during the study period. Primary forest cover loss in the 1990s was far greater than in the 2000s. The 1990s total of primary forest cover loss of 5.43 Mha is more than double the 2000s total of 2.11 Mha. Of cleared primary intact forest cover, the 1990s experienced over ten times the area converted compared to the 2000s (0.26–0.02 Mha).

For the entire study period, the rate of forest loss was 0.38 Mha/yr, and the rate of forest degradation was 0.12 Mha/yr. The first decade of analysis (1990–2000) contributed 72% of forest loss and 83% of forest degradation. The rate of loss was about 0.54 Mha/yr and comparable to the estimated rate of forest cover loss for Sumatra from 1985 to 1997 of 0.56 Mha/yr (Holmes 2000a, 2000b). The second decade (2000–10) accounted for 28% of forest loss and 17% of forest degradation for the two-decade study period. The rate of forest loss was 0.21 Mha/yr, less than half of the rate of the 1990s.

Each province in Sumatra has its own history of forest cover change. For example, forest fires played major role in forest clearing in South Sumatra (Tacconi 2003), while rubber plantations and 'jungle rubber' collection were the primary sources of forest degradation in Jambi (Tomich and Van Noorwijk 1995, Ketterings et al 1999). Additionally, in 2000 Indonesia applied a new decentralization policy (Seymour and Turner 2002) providing regional autonomy (Law No. 22/1999) and authority to provinces and districts in sharing revenue from land use fees and taxes (Law No. 25/1999). The decentralization policy provides a rationale for quantifying provincial-scale information pertinent to forest management.

The primary forest cover loss over two decades for eight provinces in Sumatra was summarized in table 3. The per cent primary forest loss for the provinces of Riau and South Sumatra exceeded 50% of their 1990 primary forest extent; the province of Jambi experienced a primary forest loss in excess of 40%. For these three provinces, as in Sumatra as whole, most forest loss was within forests already degraded by 1990. Only Riau experienced a significant forest cover loss within primary intact forests. Primary intact forest loss in Riau accounted for nearly 68% of all primary intact forest loss in Sumatra. Riau contributed 46% of total Sumatran forest degradation, followed by Nanggroe Aceh Darussalam/Aceh (23%) and Jambi provinces (12%). For all provinces, there was a dramatic decline in primary intact forest loss between the 1990s and 2000s, reflecting the near exhaustion of intact lowland forests. Clearing of degraded forests also declined for all provinces, with Riau coming closest to a sustained inter-decadal rate; for Riau, primary degraded forest loss in the 2000s was 85% that of the 1990s. Remaining primary intact forest in 2010 was located largely in Aceh (40%), West Sumatra (15%), and Bengkulu (12%) provinces, which are all located along the Sumatra uplands.

Sumatra has always been a key area for oil palm production in the country (Tomich et al 2001), with Riau as the leading province (Tambunan 2006). The very high rates of Riau primary forest loss over the study period were likely due to the intensive establishment of oil palm and forest timber and pulp plantations (Holmes 2000a, 2000b, Nawir et al 2007, Uryu et al 2008). By the late 1990s, most of the Riau lowland forests had been converted, leaving mainly peat swamps (Holmes 2000a) as the remaining natural intact forest cover, which in the 2000s have been the location of forest clearing and conversion. In contrast to Riau is Aceh province. In 1990, Aceh's primary forest extent was second to Riau's. By 2010, the primary forest of Aceh was the greatest (24% of the island total), consisting of the largest remaining primary intact forest and second largest extent of primary degraded forests. These forests, in particular primary intact forest, have been preserved by the less accessible upland landforms and their conservation and protection land use status (figure 1). Aceh also had been a place of conflict between a local separatist group and the government of Indonesia from 1976 to 2005 (Ross 2005). Political instability most likely also limited the secure access to the primary forest.

The primary forest change over the two decades for each forest land use zone in Sumatra was quantified (table 4). Within the forest land uses, the highest rates of forest loss were in primary degraded forests of the regular production forest (HP), convertible production forest (HPK) and limited production forest (HPT) land uses. These land uses accounted for 32.5%, 17.1% and 15.8% of the total loss, respectively. For primary intact forest, about 50% of the loss occurred within the regular production forest land use (HP). Forest degradation rates were the highest within regular production (HP), protection forests (HL) and conservation forests (HK). It is worth noting that logging is not allowed within protection and conservation forests (HL and HK). Thus this degradation is an indication of illegal logging occurrence within the protection and conservation forests, particularly in the second decade of the study (Broich et al 2011a).

The proportion of forest loss in the three official land uses that either prohibit (HL and HK), or severely restrict clearing (HPT), increased over the study period (from 24% to 29% of total forest loss). The forest land use with the highest proportion of 2000s forest loss to 1990s forest loss was the limited production forest (HPT); these data indicate pressure on an increasingly rare primary forest resource base. In 1990, 14% of primary forests were located in the outside forest land use (APL); 96% of these forests were degraded. By 2010, outside forest land use (APL) accounted for 8.7% of Sumatran primary forest.

Within the forest land uses, production forests as a whole (HP, HPT and HPK) accounted for 65.8% of the total forest cover loss, comparable to 5% of protection forests (HL), and 4% of conservation forests (HK). These data designate the importance of establishing forest management units over production and protected areas, as encouraged by the Government of Indonesia (Forestry Laws UU 41/1999, Government regulations PP 44/2004, PP 6/2007), as one of the REDD and REDD+ strategies in Indonesia (MoF 2008a).

For assessment of our results, we employed the Ministry of Forestry of Indonesia (MoF) land cover maps year 2000 (figure 7(b)) derived from photo-interpretation (MoF 2011). This is the only MoF product coincident with our epochal map products and it was previously and independently assessed by intensive field verification, yielding an accuracy of 88% for all 23 land cover classes and 98% for forest and non-forest classes (MoF 2011). We regrouped the classes of the MoF map into primary intact forest, primary degraded forest, and non-primary forest classes. The overall agreement of total primary forest (combined primary degraded and primary intact forest) for year 2000 was 92% (producer's agreement 86% and user's agreement 93%). When treating primary degraded and primary intact forest classes separately, the overall agreement was 79% (producer's agreement 56%, user's agreement 71% for primary intact class, and producer's agreement 90%, user's agreement 82% for primary degraded class).

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We also employed the GLAS data set (figure 7(a)) to evaluate our map product. Canopy height was useful for assessing differences between primary intact and degraded forest classes and the HOME metric was also used given previous evidence of its relationship to forest canopy structure and aboveground biomass (Drake et al 2002a, Baccini et al 2008). GLAS data from 2006 were analyzed together with primary intact, primary degraded and non-primary forest areas in 2005, assuming that forest change between 2005 and 2006 would be small and not deleteriously affect the comparison. Results indicated a structural difference between the primary intact (mean value of tree height 28 m ± 8.7 m), primary degraded forests (19 m ± 8.2 m), and non-primary forest covered with trees/other tree cover (13 m ±5.2 m), as characterized using the combination of primary forest mapping and the IFL buffering of forest near human infrastructure (figure 8). A t-test with 95% confidence interval applied and indicated the significant difference of tree height from GLAS shots between primary intact and primary degraded; between primary degraded and non-primary classes covered with trees; and between primary intact and non-primary classes covered with trees. While per-pixel processing of the Landsat time series data cannot be used to discriminate these forest types, the addition of the IFL analysis enabled their characterization, as both GLAS metrics capture a structural difference between primary intact and primary degraded forests.

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Our study produced a new set of maps quantifying the extent of primary degraded and primary intact forests in Sumatra over a 20 yr period using a hybrid approach of per-pixel mapping and GIS-buffering of observable disturbances and human infrastructure. This hybrid approach integrated supervised classification methods with expert-based image interpretation of Landsat 'wall-to-wall' mapping. Comparisons with official MoF maps and forest structure metrics from GLAS data indicate a viable quantification of total primary forest extent and primary intact and degraded forest subclasses.

GLAS-derived tree height indicated significant differences of forest vertical structure. Mean values of tree height from GLAS shots for primary intact, primary degraded and non-primary forest are significantly differ to each other, as we excluded the non-primary/other tree cover and no-trees areas. The height of median energy (HOME) which is calculated by finding the median of the entire LiDAR signal both from canopy and ground (Drake et al 2002a) also shown similar indication. Majority of the HOME waveform come from the upper portion of the canopy profile (Drake et al 2002b). Thus, forest with higher trees and large canopy like primary intact forest gives higher HOME compare to the forests with shorter trees and smaller canopy like primary degraded forests.

Landsat data capture sufficient spatial detail to derive reliable change area estimates in Indonesia (Achard et al 2002, Curran et al 2004, Hansen et al 2009, Miettinen and Liew 2010). Reliable maps of forest cover disturbance can be achieved through the combined use of medium spatial resolution satellite images, such as Landsat or Satellite Pour l'Observation de la Terre (SPOT), if interpreted by experts with local knowledge (Liew et al 1998, Tucker and Townshend 2000). The global acquisition strategy of Landsat 7 (Arvidson and Gasch 2001) facilitated mapping for the 2000–10 epoch, while a traditional post-classification approach with expert intervention was required to map the comparatively data poor 1990–2000 epoch. The 'wall-to-wall' mapping approach overcomes limitations of sampling methods in the estimation of forest cover loss (Tucker and Townshend 2000), and also provides a more application-ready product in assessing impacts, such as fragmentation and forest degradation (Steininger et al 2001), on aboveground carbon stock dynamics and ecosystem services and co-benefits for forest conservation (Stickler et al 2009).

Over the past 60 yr, Sumatra has experienced intensive industrial forestry and agricultural development that has significantly reduced the area of natural forest. In 1950, forest covered 71.2% of Sumatra (Hannibal 1950 as reported by FWI/GWF 2002), which was reduced to 49% by 1985 and to 35% by 1997 (Holmes 2000a). We estimated that remaining 1990 primary forest extent covered 47% of Sumatra, and that this was reduced to 33% by 2000 and to 30% by 2010. Slowing of primary forest cover loss is partly the result of a greatly diminished resource base, particularly of lowland primary forests. Hansen et al (2009) highlighted a recent increase in primary forest loss in the upland forests of Sumatra and Kalimantan, possibly in response to an exhausted lowland forest resource.

Of the dominant drivers of forest cover loss, including agricultural expansion, wood extraction and infrastructure extension (Curran et al 2004, Fuller et al 2004, Mayaux et al 2005), the underlying causes of forest cover loss in Sumatra are related to the expanding global markets for pulp, timber and oil palm (Holmes 2000a, 2000b, Nawir et al 2007, Uryu et al 2008, Hansen et al 2008b). In addition to mechanical clearing of forests to establish agroforestry projects, other direct causes for Sumatra include fires (Holmes 2000a, FWI/GWF 2002, Tacconi 2003, Uryu et al 2008), illegal logging (Nawir et al 2007, Tacconi 2007), transmigration programs (FWI/GWF 2002), and smallholder clearance for tree crops (Holmes 2000a, 2000b). A summary of the direct causes of primary forest cover change for the past 60 yr in Sumatra is provided in table 5. Considering the scale of the observed changes over the study period, it is clear that large-scale commercial logging and agro-industrial development are the main drivers of Sumatran forest loss. Illegal logging also occurred during the study period, as illustrated by an increasing rate of forest degradation within protection and conservation forests compared to other forest land uses (table 4). We note that illegal logging, by some estimates, accounts for more than half of total domestic timber production (FWI/GWF 2002). A high rate of forest degradation within protected forests (HL) was possibly triggered by the breakdown of centralized political authority in early 2000, and the inability of provincial governments to adequately enforce forest codes (Nawir et al 2007, Uryu et al 2008).