A Feasibility Assessment For Calculating Carbon Stock in

The Batang Toru Forest Ecosystem for REDD Opportunity

A Final Technical Report Submitted to

Japan Bank for International Development

August, 2008

1

Photo on the report taken by

Erwin A Perbatakusuma, Martin Hardiyono, Mistar, Bonie Dewantara, Hasbi Hasbulah, Tantyo Bangun, Rondang Siregar

Maps on the report created by Bonie Dewantara

Quote as: Perbatakusuma et al (2008). A Feasibility Assessment for Calculating Carbon Stock in The Batang Toru Forest Ecosystem for REDD Opportunity. Research Report to Japan Bank for

International Development Conservation International, Jakarta, Indonesia

Email contact address: eperbatakusuma@conservation.org, bdewantara@conservation.org,

iwijayanto@conservation.org, ahamid@conservation.org, nkemp@conservation.org, y.tamura@ conservation.org, y.natori@conservation.org, y.hibi@conservation.org, onrizal03@yahoo.com, isamsoedin@yahoo.com, nurmheriyanto88@yahoo.com

AUTHORS :

Erwin A Perbatakusuma Bonie Dewantara Iwan H Wijayanto

Neville Kemp Abdulhamid Damanik

/Conservation International Indonesia/ Yoko Tamura Yoji Natori

Yasushi Hibi /Conservation International Japan/

Ismayadi Samsoedin N. M. Heriyanto

/Center for Forest and Conservation Research and Development, Department of Forestry, Indonesia/

Onrizal /Faculty of Agriculture, Department of Forestry, North Sumatra University, Medan, Indonesia/

2

1. INTRODUCTION

The Batang Toru Forest Ecosystem (BTFE) in North Sumatra Province, Indonesia, hosts some of the most biodiversity ecosystem on earth and unique wildlife species, such as the globally and critically endangered species Sumatran orangutan and Sumatran tigers. Comparative studies found Batang Toru dry lowland forest to have the higher tree plant diversities than other Sumatran and Indonesia forest. In mapping and priority setting conservation region across the world, Conservation International (CI) include the lowland forest as a part of Sundaland Hotspot and Sumatran Key Biodiversity Areas (Conservation International 2003, 2007). CI has been working in BTFE and Northern Sumatra Island since 2003, conducting conservation ground work to protect Sumatran orangutan Sumatran Tiger and their habitat, especially in tropical natural forest inside the Northern Sumatran Biodiversity Corridor (NSC) with a total area 4,5 million hectares and falls within the administrative boundaries of the Nanggroe Aceh Darussalam and North Sumatra provinces. CI has been monitoring the status of Sumatran orangutan and Sumatran tiger and also the threats to them and driver responsible for deforestation and forest degradation through field survey and remote sensing to detection of status of forest cover change in NSC, include BTFE areas. These species and their natural forest habitat have been under serious threat because of rapid large scale deforestation. The problem of deforestation is not only the loss of biodiversity values, but also environmental service values. Recently, the global significance green house emissions caused by forest deforestation, forest degradation, peat decomposition and forest burning in Indonesia particularly in Northern Sumatra Province has been generating attention. Besides unraveling the alarming relationship between palm oil large scale development, deforestation, biodiversity loss, and CO2 emissions, this research report aims to provide site specific input for an Indonesia REDD pilot and or REDD-like voluntary project, supporting actual definition process with a case study on land use dynamics in one Sumatra province. Only a relatively few studies on forest carbon stock have been undertaken in North Sumatra Province. This research report focus on the result of JBIC-funded project implementation entitled “A Feasibility Assessment for Calculating Carbon Stock in The Batang Toru Forest Ecosystem for REDD Opportunity” for cover period from July until Mid August 2008. This report will disseminate: a. Detail deforestation and forest degradation in BTFE North Sumatra Province and identify

the drivers of deforestation b. Describe existing forest structure and flora composition on classified forest of primary

and secondary forest c. Estimate carbon stock of live above forest biomass on classified forest and landscape in

BTFE d. Estimate CO2 emissions from deforestation at BTFE landscape level. The authors expect that REDD for Indonesia and involuntary inventors might be able to provide the necessary conservation-based incentives to stop further deforestation and forest

3

degradation in this province and thus reduce province’s contribution of climate change, reduce economic consequences of vast deforestation and forest degradation, maintain the forest biodiversity treasures and keep wild population Sumatran orangutan and Sumatran tiger alive.

2. BACKGROUND Tropical deforestation released ~1.5 billion metric tons of carbon (GtC) to the atmosphere anually throughout the 1990s, accounting for almost 20% of anthropogenic greenhouse gas emissions. Without implementation of effective policies and measures to slow deforestation, clearing of tropical forests will likely release an additional 87 to 130 GtC by 2100, corresponding to the carbon release of more than a decade of global fossil fuel combustion at current rates. Drought-induced tree mortality, logging, and fire may double these emissions and loss of carbon uptake (i.e., sink capacity) as forest area decreases may further amplify atmospheric CO2 levels (Gullison, et al, 2007). Despite policy attention on reducing deforestation, ≈13 million ha·yr−1 of forests continue to be lost. Deforestation could have the effect of cooling the atmosphere, but it also leads to reductions in biodiversity, disturbed water regulation, and the destruction of livelihoods for many of the world's poorest. Slowing down, or even reversing, deforestation is complicated by multiple causal factors, including conversion for agricultural uses, infrastructure extension, wood extraction, agricultural product prices, and a complex set of additional institutional and place-specific factors (Kindermann, et al, 2008) Indonesia LULUCF (Land Use, and Land Use Change and Forestry) emissions in 2000 were estimated to be 2,563 Mega Ton of CO2 (World Resources Institute, 2007), 34% of the global LULUCF emissions. (Peace, 2007), most of this was the result of deforestation and forest degradation (World Resources Institute, 2007) In addition, a recent preliminary study estimated peat decomposition and forest burning to have caused ca. 2000 MtCO2e per-year (Hoijer, et al, 2006), much of that ultimately triggered by deforestation. These two sources of CO2 emissions, combined with GHG emissions from energy, agriculture and waste (together 451 Mt CO2e), contributed Indonesia’s total GHG emissions reaching ca 5,000 Mt C02e This almost pars with the annual GHG emission of China (5.017 MtCO2e) Only the USA emitted more GHG (6,005 Mt CO2e) (World Resources Institute, 2007) Nawir et all (2002) reported that North Sumatra province is among the most severely degraded forested land areas. Based on studied by Sumantri, et al (2007), recorded a total cover deforestation is 422,099 hectares from 3,611,702 hectare forest cover in North Sumatra Province during year 1990 – 2000. Deforestation and degradation of these natural forests threaten ecosystem functions such as regularly water supply and carbon sequestration. Today, the existing natural forests in the BTFE has been severely degraded by various economic human pressure activities, from encroachment for subsistence agriculture expansion (at community levels) to forest clearing for palm oil plantation, logging and mining industries. The BTFE consists of around 150.000 hectares The Batang Toru catchments areas `currently are belong to four districts, i.e. the Districts of South Tapanuli, Central Tapanuli and North Tapanuli and Sibolga that located surround the BTFE. Based-on CI-I assessment result (2006), a total of 6,34 km2 of forest was cleared between Year 1990-2000 in BTFE,

4

Figure1. Map of deforestation from 1990-2000 for around Batang Toru area (subset from Sumatra deforestation map 1990-2000, WCS-CI-MoF, 2006)

BATANG TORU FOREST ECOSYSTEM

Figure 2. Deforestation likelihood map. Note : Result of MCE produces map of continuous values ranging from 0-255, with 255 (highest values) representing the highest likelihood of deforestation.

BATANG TORU FOREST ECOSYSTEM

5

equivalent to an average deforestation rate of 0,05% per year. Although the current condition is not yet available, visual observation in the field indicates that the deforestation is continually taking place. During the period of 1990-2000, the probability of an area being cleared of forest was found to be significantly and negatively related to elevation, slope, distance to plantation areas, distance to transmigration (P>z = 0.000) and distance to logging concession .The result of this process is a map of continuous values representing a composite index of suitability (or likelihood) for deforestation The analysis indicated that most of all forests in the West Batang Toru area were facing high deforestation threat, so that the BTFE area has been as well under high pressure. In addition, this prediction can occur in the future since the area is surrounded by provincial and regency level roads, new settlements are continued to appear upward penetrating the forest. Disturbance to orangutan habitat made by human in the area through forest conversion to agriculture and plantation areas, either legal or illegal and logging and mining activities have occurred as well as. Therefore, monitoring forest conversion to agricultural land and modeling future threats for the network of protected areas are very important to the maintenance of the BTFE areas. An integrated management body that considers the needs to deal with forest functions as carbon storage, the source of energy (via water supply for both hydro-electricity and geothermal powers), the main source of water supply for daily local livelihood (via sustainable agriculture development), the agents for soil fertility maintenance and climatic equilibrium. Without an appropriate conservation intervention, an estimated loss of natural forest in the Angkola Ecosystem including the BTFE areas could reach an area of 3,000 ha annually, which is approximately equal to 1,200,000 tones of CO2 annually released to the atmosphere. Although the carbon trade mechanisms such as Kyoto Protocol’s CDM or Voluntary Emission Reduction schemes could create a great financial incentive to protect the forest area, currently there has been no attempt in determining the target areas in BTFE. Reducing emissions from tropical deforestation, a major source of CO2, could potentially be a highly cost-effective option for climate policy. Deforestation means the conversion of forest to non-forest, and is associated with land use change. Degradation in general results from unsustainable management or use of forest land. In addition to their critical role in the global carbon cycle and climate system, tropical forests are home to about half of the world's species and provide a livelihood for millions of people. Recognizing the importance of tropical forests and the value of developing country participation in global climate change mitigation efforts, proposals are now being rapidly advanced to compensate tropical forest countries for REDD, as part of a future international climate agreement At the United Nations Framework Convention on Climate Change Conference of the Parties that took place in Bali in December of 2007, the parties agreed that reduced emissions from deforestation would be included in the post-2012 agreements (Olander and Murray 2007, Gullison et al 2007). But, REDD also has limitations in that the only countries that may qualify are those with high deforestation rates. Countries with low deforestation rates will see little funding under the proposed system since carbon credits are only issued for emissions reductions, not carbon stored. REDD is not primarily designed to save ecosystems, it is designed to cut emissions. That means that the countries with low deforestation rates are not the primary target, because they are not emitting. Beyond protecting the climate, reducing tropical deforestation has the potential to eliminate many negative impacts that may compromise the ability of tropical countries to develop

6

sustainability, including reduction in rainfall, loss of biodiversity, degraded human health from biomass burning pollution, and the unintentional loss of productive forests.. Providing economic incentives for the maintenance of forest cover can help tropical countries avoid these negative impacts and meet development goals, while also complementing aggressive efforts to reduce fossil fuel emissions. Industrialized and developing countries urgently need to support the RED policy process and develop effective and equitable compensation schemes to help tropical countries protect their forests, reducing the risk of dangerous climate change and protecting the many other goods and services that these forests contribute to sustainable development. One principal issues at 2007 of United Nations Framework Convention for Climate Change COP 13 was the political framework for the policies process “Reducing Emission from Deforestation in Developing Countries” (REDD). Parties agreed that REED is an important component of a future climate change regime beyond 2012, in both mitigation and adaptation. A financial scheme for trading carbon of “avoided deforestation” will be developed and international funds will be established. Currently, 1st Kyoto Protocol only recognizes reforestation and a-forestation as a mechanism to mitigate climate change. Can the Indonesian government commit to REDD for Indonesian to avoid deforestation and forest degradation throughout the country and reduce CO2 emissions? Can REDD put the “right value “on natural forest for the services they provide so it become more profitable to protect than to clear them?

3. PURPOSE OF THE STUDY This study goals only to cover the assessment of feasibility (Part of Phase I) by estimating the carbon credit potential from the avoided deforestation in BTFE, the study will also propose to continue towards remaining phases to reach the overall project goal is the creation of voluntary carbon market opportunities to generate financial value through REDD credits. In the next phase II and III, the project will prepare and validate the carbon credits to be suitable for registration. After the registration, the project will looks into commercialization and monitoring of the generated carbon credits to ensure sustainability and conservation of BTFE. This study suggests the feasibility assessment for calculating carbon stock in the Batang Toru Forest Ecosystem (BTFE) to prepare for Reduced carbon Emissions from Deforestation and Land Degradation (REDD) opportunities to further develop financial profit from ecosystem conservation. Within the BTFE, this study especially targets the forest concession area of Teluk Nauli logging concession, about 30,000 hectares. In this study we seek to evaluate the current carbon stock status focus on two classified forests . e.g primary forest and secondary forest. Another human made land use type is not including this study, such as agro-forestry, plantation and grazing land. The consequences on carbon stocks of potential REED project activities such as conservation of forests under threat of logging or deforestation, afforestation or reforestation of degraded lands, or changing harvest practices (e.g. from conventional logging to reduced impact logging) For measuring REDD, we will need to know: (1) the aerial extent of deforestation and forest degradation (hectares), (2) for degradation, the proportion of forest biomass lost (percentage), (3) where the deforestation or forest degradation occurred (which forest type), (4) the carbon content of each forest type (metric tons of carbon per hectare), and (5) the process of forest loss which affects the rate and timing of emissions (Ramankutty et al 2007).

7

With this preparation for REDD, this project looks into the long-term primary goal to create voluntary carbon market opportunities at BTFE for carbon sequestration through REDD. Using the REDD scheme to generate financial profit will make it possible to provide alternative incomes for the local communities and to increase conservation awareness of local government while at the same time protecting the natural forest and contributing to the reduction of greenhouse gas emissions. 4. DESCRIPTION OF THE STUDY AREAS The study area is located in Batang Toru Forest Ecosystem (BTFE) where consisted two forest blocks named West and East Batang Toru Forest Range or East Sarulla. The BTFE is located in North Sumatra Province south of the second world largest lake of Lake Toba at 980 50’ - 99018’ East Longitude and 10 26’ - 1 0 56’ North Latitude and situated along the main road between the main towns of Padangsidempuan, Tarutung and Sibolga . This site is accessible from capital town of North Sumatra - Medan, 1 hour by plane, and 10 – 12 hours by car. Roads separate West Batang Toru from the East Batang Toru or Sarulla area.

Map 1. Proposing location of Batang Toru Carbon Project Area related to watershed and forest cover. (© CI Indonesia)

8

Picture 1. Pristine forest in remote areas of Batang Toru Forest, presented based on over flight result

These study area was chosen based on the following: • Land use

change/deforestation and forest degradation are major drivers to these areas;

• Key data (e.g. remote sensing products and biodiversity data) were available for these areas;

• Strong interest in designing carbon sequestration projects in these areas, thereby providing a rich base of information with which to work and direct relevance of the results;

• The forest types at these areas differ— part of BTFE is dominated by primary tropical forest whereas the other part of its is dominated by secondary tropical forest, that would provide a broader bio-physical base upon which to

draw lessons regarding the development of baselines; The Batang Toru is a water catchments area that encompasses four regencies: North Tapanuli, Central Tapanuli, Sibolga and South Tapanuli. Primary rain forest dominates the vegetation cover, which grows on steep hillsides with more than a 60-degree slope and mountainous area with highest peat at Mt. Lubuk Raya (1.856 meter above sea level). The region is mountainous and the results of historic volcanic activity called Toba Super Volcano and the formation of geology is Volcanic Toba Tuff as the dominant geology rock type. Soil type is dominated by Podsolic Red-Yellow and Aluvial (Center for Soil and Agroclimate Research, 1997). The BTFE holds at least six principal habitat types including moss forest (above 600 meter), hill side moist forest (dominant between 200 m -600 m), lowland, cliffs and talus slopes, secondary forest, and riparian forest. Forest landscape of Batang Toru has 234,399 ha which included four districts Tapanuli Utara (North Tapanuli), Sibolga, Tapanuli Tengah (Middle Tapanuli), and Tapanuli Selatan (South Tapanuli). Based on satellite imagery classification that was analyzed by Conservation International Indonesia, primary forest cover in 2000 was 148.000 ha. with infrastructure undeveloped or frontier area and consisted by various officially forest status includes nature reserve, protection forest and production forest. The forest area is covered by five big watershed: Batang Toru with 92.121 ha wide, Aek Kolang 42.663 ha, Watershed Bila, Barumun,and Batang Gadis This various habitat type is a suitable habitat for many endangered wildlife species through providing a habitat principal requirement, such as food or prey resources, nest sites, forest space for seasonal and arboreal and terrestrial movement and protection from predator. Total

9

Picture 4. Deforestation areas in primary forest occurred in remote areas of Batang Toru Forest Block based on over flight result on August 2008.

Picture 5. The world tallest flower and globally endangered species - Amorphophalus baccari founded at primary forest in Batang Toru Forest Block

Picture 2. Sumatran Orangutan, one of globally endangered species occurred in Batang Toru Forest Block (©Tantyo Bangun)

extant habitat covers approximately 148,000 ha with total perimeter along 312 km. The forest areas within BTFE also have a high scientific value and are important to be protected, because it is though to be a biogeography transition area between the convergence point of southern and northern Toba Lake biogeographic assemblages, where distributions of species such as orangutan, mitered leaf-monkeys (Presbytis melalophos), Malayan tapir (Tapirus indicus), Sumatran serow (Capricornis sumatraensis sumatraensis) and Raflessia gadutensis overlap. This situation is of course thought to have big consequences for the value and uniqueness of its biodiversity. The BTFE will provide effective management for an area of a globally biodiversity significance, and was defined one of the remaining a Key Biodiversity Area in Sumatra Island based on the CI’s Conservation Outcomes Definition Process. Laumonier (1997a, b) employs a total of 47 mapping units and 9 bioclimatic regimes in Sumatra to categorize the plant cover (both spontaneous and anthropogenic) over the entire island. Within the BTFE, the Laumonier system recognizes the following principal vegetation types: 1) Western plain and foothill formations <300 m: secondary and derived types mosaic, mainly

shrubby; 2a) Medium elevation western hill formations (300 m–1000 m): secondary and derived types mosaic, mainly shrubby; 2b) ditto, forest from Airbangis to Bakongan regions

10

Picture 6. Primary forest deforestation areas drive-led by timber concession - Teluk Nauli Ltd. with total cover 30,000 ha in remote areas of Batang Toru Forest Block based on over flight result on August 2008.

(i.e.,one of the 16 main physiographic regions recognized for Sumatra); 3) Formations of the Barisan range above 1,000 m: submontane forest (1000 m–1800 m); 4) Cultivated types and plantations: mosaic of dryland rice, food crops and secondary growth.

Recent survey by Conservation International in 2006 and other institution revealed that the BTFE and adjacent areas are home to a rich variety of the Sumatran species, particularly mammals, birds and plants, which are globally threatened. Sixty-seven species of mammals, two hundred eighty-seven of birds and one hundred ten of herpetofauna have been recorded in the area. Of this total number of mammals species, twenty species are protected under Indonesian law and twelve are globally threatened. Among these are Sumatran Orangutan (Pongo abelli), Sumatran tiger (Panthera tigris sumatrae), serow (Capricornis sumatrensis), Malayan tapir (Tapirus indicus), Malayan sun bear (Helarctos malayanus), slow loris (Nycticebus coucang), Golden Cat (Pardofelis marmomata). The survey also discovered rich avifauna diversity in the region, including rare as well as threatened species.

Of this total number of bird species fifty-one species are protected under Indonesian law and sixty-one are globally threatened, such as Sunda Blue Flycatcher (Cyornis caerulatus), Wallace's Hawk-eagle (Spizaetus nanus), Blackcrowned (Pitta venusta). Initial data from the BTFE suggest that it holds some of the highest levels of vascular plant biodiversity, with 688 different species. Of this total number of plant species, 138 species of orangutan food resources, 8 species globally threatened, including Nepenthes sumatrana (Miq.), the largest flower in the world Rafflesia gadutensis Meijer,Becc and the tallest flower in world Amorphophalus baccari and Amorphophalus gigas (Perbatakusuma, et al, 2006). Based on historical climate data during more tha 20 years from Pinangsori Airport – Central Tapanuli, the climate in the study area in the BTFE can be categorized as Wet Tropical or Af Climate Type according to Koppen with no dry season. Annual rainfall is relatively high, ranging from 4,303 mm to 5,874 mm, with an average of 217 days of rain per year recorded over 25 year period with average temperature between 25° C to 28° C. The highest rainfall generally occurs in October-November (611-614 mm, while the lowest is in January-June (372-410 mm). The rainfall pattern in Pinangsori areas is bimodal, meaning that there are two main rainfall peaks each year: the first in March-April and a second in October-November. Humidity was high in the study area with an annual mean of 84% over 20 year’s periods. The highest monthly humidity occurred in November (86%) and the lowest in June (82%). As with temperature, annual variability was low. The average daylight period was moderate (55%), with a peak value of 64% in February and a low of 44% in October (Anonim, 2005b)

11

4. METHODS OF THE STUDY 4.1 Location of Forest Biomass Sampling For this study will apply stratified random and systematic sampling which is generally yields more precise estimates for fixed cost than the other options (Anonim, 2006). During fieldwork a total 26 plots were established and measured. Plot locations were chosen through purposive and systematic sampling. Areas selected to represent a range of forest types and or forest ages. A forest biomass sampling and field measurement was conducted in three locations, these locations are following: • Secondary forest in logged area of Teluk Nauli timber concession. Geographically

positioned in N 010, 37, 50 and E 990, 21, 22. Administratively, located in Anggoli Village Sibabangun Sub-district, Central Tapanuli District and two plots with size 50 X 50 meter square established in this area.

• Primary forest in Aek Silemes Area and Aek Game-game areas. Geographically

positioned in N 98◦59’11,4 and E 01◦47’42,3 for Aek Selemes and N 98◦57’12,6 and E 01◦44’22,9 for Aek Game-game. Administratively, Aek Silemes located in Simardingiang Village Pahae Julu Sub-district, North Tapanuli District and Aek Game-game located in Tapian Nauli Village, Tukka Subdistrict, Central Tapanuli District. Each location ten plots with size 20 X 20 meter square established.

• Secondary forest in logged area of Sibuali-buali Nature Reserve. Geographically

positioned in N 01◦32’27,2 and E 99◦12’57. Administratively, located in Aek Nabara Village, Marancar Sub-district, South Tapanuli District. In this location, four plots established with size 50 X 50 meter.

4.2 Methods There are some methods or approach for estimating biomass density from existing data, e.g. biomass density based on existing volume data; biomass density based on stand tables; biomass estimates from GIS modeling and biomass estimates with field studies and remote sensing. These studies will apply integrated field studies and remote sensing approaches to calculate forest carbon stock. These approach a more comprehensive and reliable approach for estimating biomass change is to combine a new field studies with analysis of high resolution remote sensing imagery. Given the preliminary of this study plus the extensive geographic scope, definitive carbon measurements were not possible. The alternative is applying a combination of short but intensive field measurement to direct measures parameters related directly to biomass e.g. DBH, Height, Basal Area, quantitative relationship or Allometric Equation and Remote sensing image classification and vegetation index, use previous CI forest inventories in some of the classified forests, are here combined to estimate actual and potential carbon stocks. See diagram above to summarize of research methodology and flow chart of project cycle. In the detail are following : 4.2.1. Forest Biomass Sampling and Carbon Stock Analysis Forest biomass is presented by weighting unit of oven dry per wide unit, consist of leaves weight, flower, fruits, branches, sprigs, stems, root and dead tress (Brown et al., 1989).

12

Diagram 1. Summary of research methodology

Diagram 2. Summary of research flow

13

Quantitiy of forest biomass is defined by diameter, height, bulk density, and crown cover density. Estimating of forest biomass especially in tropical zone is very influenced by carnon cycle (Morikawa, 2002). From all biomass, forest biomass have 45 - 50 percent carbon (Brown, 1997; International Panel on Climate Change, 2003). According to Nelson et al. (1999), that biomass data of an ecosystem is very useful for evaluating the production pattern for others ecosystem. The method of field sampling applied for collecting field data is quadrate sampling, with 50m x 50m plot size. In this study, to measure the whole tree biomass (dry weight) of the forest, it’s not necessary to cut all tress to weight them (non destructive sampling). Destructive sampling method applied only for undergrowth plants and necromass (decayed and non decayed plant litter fall) with 2 X 2 meter plot size. Stratification done for the forest cover becomes A, B, C, D type and systematic sampling way chosen to spread the plant in row evenly, and then the trees were measured for each row randomly. In tree measurement, techniques of tree measurements include DBH (diameter at breast height; typically measured at 1.3 m above ground) and merchantable/total height. The diameter breast height will calculated from the girth of the trees and converted to the trees diameter. Trees with a DBH of 10 cm and greater were marked and enumerated, measured for their diameters and heights and identified. Voucher specimens which were mostly sterile were collected for further identification at the Herbarium Bogoriense at Bogor. Nomenclature followed Whitmore & Tantra (1986 ) and Steenis et al. (1949 – 2004). Leaves and for wood biomass under 10 cm in diameter, undergrowth plants and necromass will be weighted in the field and will be transported from study sites to laboratory at Forestry Research and Development Agency Department of Forestry in Bogor. This study will apply a direct approach using Allometric Equations for estimating aboveground biomass in trees. One way to estimate how much biomass in a forest stand is the application of allometric equations. Allometric is the relation between the size of an organism and the size of any of its parts, and allometric equation is usually expressed in power-law form or in logarithmic form. Once an allometric equation has been developed, the biomass can be estimated in a forest stand using just the simple measurements of diameter. The allometric equation's general form is usually written as,

where b is a constant (called the "allometric coefficient") and a is the allometric exponent. The empirical model is chosen to estimate biomass and carbon content by using field data. Empirical model based on statistical analysis of observed data, and these were usually applicable

14

After transporting the samples from study sites to the laboratory, each sample are dried at 85 °C in a constant-temperature oven. Drying take 2 days for leaves and for wood biomass under 10 cm in diameter. After drying, conversion ratios between the oven-dry and fresh weights are calculated. To calculate whole dry weight of undergrowth plant and necromass, after measuring dry weight of each sample, whole dry weight was calculated as follows: where, DWW = whole dry weight, DWS = dry weight of sample, FWW = whole fresh weight and FWS = fresh weight of sample The general procedure for estimating the biomass is to apply the allometric equation to each tree, calculate the sum of the living biomass of all measured trees within a plot, and then convert into the biomass per unit area. All the biomass data obtained in the field measurements can be converted to carbon stock by multiplying the weight of oven-dry matter by the carbon fraction.

In this study, to estimate forest biomass using existing alometric model to calculate biomass for annual rain fall 1500-4000 mm/th. Table 1 presented allometric model to estimate forest biomass.

Tabel 1. Allometric model used to estimate live above ground biomass in Batang Toru’s primary forest

No Equation

Equation Reference

Eq. 1 W = 38,4908 - 11,7883 (D) + 1,1926 (D2) Brown et al. (1989) Eq. 2 W = 42,69-12,800 (D) + 1,242 (D2) Brown (1997) Eq. 3 W = 0,118 D2,53 Brown (1997) Eq. 4 W = 0,11 ρ D 2+c; c = 0,62 Katterings et al. (2001)

Remarks : • W = biomass per tree (kg), D = DBH (cm), ρ = (wood density; g/cm3 atau t/m3); c =

coeficient factor to describing relationship between tree diametre and tree hight (c used is 0,62 , this values founded by Katterings et al., [2001] for forest in Jambi Province Indonesia).

• Equation Eq. 1 and Eq.2, only valid for tree DBH ≥ 5 cm. • ρ values refer to Prosea (1994, 1995, 1996), Reyes et al. (1992), Newman et al. (1999),

Katterings et al. (2001) Values of plant carbon content calculated based on Brown (1997) or International Panel on Climate Change/IPPC (2003) formulation follow : Carbon matter = plant dry weight X 50% and to estimate carbon sequestration calculated follow IPPC formulation : MR CO2 / AR C X carbon matter = , where MR = Relative carbon, AR = Relative molecule or CO2 = C x

15

3,67 (Misbach, 2000) . Finally, to estimate carbon stock calculated follow : C = DW X CF , where C = Carbon Stock, DW = dry weight and CF = carbon fraction of dry matter . 4.2.2 Remote Sensing Analysis Deforestation and forest cover of Sumatera was mapped by analyzing Landsat satellite imagery from circa 1990 to circa 2000. The bulk of the image data are part of NASA’s global orthorectified dataset, freely available for download at the University of Marylands Global Land Cover Facility (www.landcover.org) . However, additional scenes were purchased in order to full in data gaps caused by cloud contaminations. The bulk of the image data are part of NASA’s global orthorectified dataset, freely available for download at the University of Marylands Global Land Cover Facility (www.landcover.org) . However, additional scenes were purchased in order to fill in data gaps caused by clouds. The analysis was conducted at a spatial resolution of 28.5 meters. All images were co-registered at sub-pixel precision to minimize classification error caused by positional error between dates. The classification process involved directly mapping both circa 1990 and circa 2000 image data simultaneously. This technique reduces errors caused by different in vegetation phenology and illumination conditions between image dates and also improves image interpretation by simultaneously interpreting two images of the same area rather than once.

A supervised training methodology was used to classify each two-date “image-pair”. Training areas were drawn on this image pairs for areas of known forest, non-forest, water, forest clearance, cloud and shadow. A decision tree classifier (http://rulequest.com/see5-win.html) was then used to devide the image data into appropriate classes. Multiple iterations were run for each image pair, with each new iteration either altering the existing training polygons or including additional subclasses until the classification reached the acceptable level of accuracy. The final classification results were also manually edited to correct misclassification in areas with complex terrain, cloud, shadow and haze and also for plantation agriculture and secondary growth that was misclassified as forest. (Diagram 3)

Classification for primary and secondary forest was referred from mosaic of Spot 4 imagery acquired in 2006 and 2007 (Map 2). These SPOT 4 imageries have 20 meter resolution that obtained from PT. Agincourt Resources, a coal mining company near by Batang Toru area.

16

Diagram 3. Flow chart of classification activities and procedures (Adopted from CABS)

Knowledge Base Classification

Ground Checking

Radiometric Correction

Image Stacking

Supervised Classification (Maximum Likelihood Classification) (ITERATION)

Geometric Correction

Image Pre-processing

Classification Processing

Image Analysis

Data Collecting

Satellite Imagery

Landsat TM: - 1990 - 2000 - 2006/2007

Digital/Hardcopy Maps

- Topographic/Contour Map - Thematic Map - Other Reference Classified

Secondary Data

Statistical Data

= Data = Process = Result

Batang Toru Changed Detection Map

1990 -2000

1990-2000 2006/2007

Landscape Level Carbon Baseline Analysis

Overlay Trimmed Edge using ERDAS Modeler

Maker

Verification Classified

Map

Yes

Display Stacked Dataset

Training Site by using Area of Interest (AOI)

(ITERATION)

Post Processing of

Classified Image Filtering

- Majority Filter - Clump - Eliminate

Signature/Class Recoding (ITERATION)

Raster Editing using AOI and REcode

Converting to Shapefile (ArcView)

Merging and Mozaicking Corrected Image

Overlaying Raster/image and Vector

= Decision = Process Direction = Main Process Boundary

Editing Misclassificationn

No

No

No

No

17

The baseline information for the deforestation and forest cover change of Batang Toru Forest was obtained from the Sumatra-wide deforestation map created by CI and WCS in 2005. This map was created by comparing Landsat satellite imagery from circa 1990 to circa 2000 (Map 2) Previous classification is from Deforestation map 1990 – 2000, that was created by CI and WCS in 2005, so forest cover series could be obtained from these results.

Remote sensing analysis also will using ASTER (Advanced Space borne Thermal Emission and Reflection Radiometer) imagery, at a resolution of 15 meters, was compared to a Landsat 5 baseline of forest cover for 2000 to detect more precise of forest land cover change. Observed areas of further clearance, defined as forest becoming non-forest, were detected using visual interpretation and digitized manually. The data used cover the years 2003, through 2007. Complete imagery coverage of the park does not exist for every year, however between all years all areas of the park were analyzed. Total areas do not include clearance smaller than 1/10 hectare. The data have not been validated and thus some of the clearings detected are likely due to natural disturbance such as tree fall and landslides. 4.2.3 Aerial Survey

Two hours over flight over and aerial photographs documentation was conducted by Helicopter. This flight aimed for obtaining visualization perspective about primary and secondary forest, and matching the same points of forest biomass ground survey condition. Over flight didn't get the sequentially aerial photos mosaic, but observing the points of interest and focus area in order defining both classes primary and secondary, also for proving

1990 - 2000 2006 - 2007

Map 2. Comparison between forest cover 2000 as the result of deforestation 1990 – 2000 period, and 2006/2007 forest cover classification. White and medium green colors indicated land use change to non forest land

18

Map 3. Over flight Route and aerial survey results

19

current status of illegal logging, illegal forest conversion and another deforestation drivers inside and remote areas in Batang Toru Forest Block. Flight over was directed to observe the whole both sides of West and East Batang Toru, Sipansihaporas Hydro Power Plant, Pahae Valley and Siondop areas. The actual route could be seen in Map. 3. The actual route was collected point by point with GPS (Global Position System) Garmin 76 CSx, and guided by Pocket PC HP IPAQ 2210 and GPS Receiver Global Sat BC-337 with SiRF STAR III sensor. Batang toru boundary map, plan route, and Landsat acquisition date 2006 in jpeg format was put all together into GPS PC Pocket. The helicopter flied referred to the planning route and coordinate of focus point (point of interest). Points of interest are representing of primary and secondary forest where ground survey conducted to collect data for each carbon plots. Map 3 shows the points of interest also included Sipansihaporas hydro power plant and peat land area for another potentially carbon project near by Batang Toru. 5. RESULTS AND DISCUSSION 5.1 Deforestation and its driver The Sumatra-wide deforestation map of 1990 - 2000 showed forest loss of 25%, representing over 5 million hectares. The total forest area declined from 20,6 million ha in 1990 to 15,5 ha in 2000 (Table 2). The largest percentage loss occurred in the province of Sumatera Selatan, where over half of the forest was cleared between 1990 and 2000. But the largest forest loss occurred in Riau: almost ,18 million ha loss in ten years. The lowest percentage losses were found in Aceh and Sumatera Barat, with just over 8% forest loss. Both areas retain a high percentage of their land cover as forest, and both areas contain forest within national parks. Table.2Forest cover and forest loss statistics by province of Sumatera.

Province Forest 1990 (ha) Forest 2000 (ha) Forest loss (ha) % Loss

Aceh 3,607,008 3,302,431 304,577 8.4Bengkulu 1,085,377 851,507 233,870 21.5Jambi 2,485,547 1,843,960 641,587 25.8Lampung 529,024 340,043 188,981 35.7Riau 5,609,316 3,825,715 1,783,601 31.8Sumatera Barat 2,401,657 2,192,883 208,774 8.7Sumatera Selatan 2,599,651 1,161,306 1,438,345 55.3Sumatera Utara 2,255,248 1,971,248 284,001 12.6 20,572,828 15,489,093 5,083,735 25.0 (Average)

In Batang Toru area, period 2000 – 2007 occurred forest loss 1,382 ha from almost 168,881 ha total area, or greater than 0.1 % per-year. Meanwhile in period 1990 – 2000, Batang Toru area lost 880 ha, or 88 ha per year. It means that forest loss from 88 ha per year in period 1990 – 2000, increases 197 ha per year. In Batang Toru area, 880 ha of forest was lost during 1990 to 2000, or 88 ha a year. During 2000 to 2007, however, the deforestation rate increased to 197 ha a year or total cover 1,382 ha total. See Table 3. The deforestation

20

driver-led by subsistence agriculture expansion, small holder plantation gold mining exploitation, illegal logging, unsustainable timber exploitation, hydro power plant construction, road infrastructure development, human settlement expansion and nature factors (land slide, earthquake)

. Forest clearing at 699 sites, totaling 882 hectares, were detected within the Batang Toru key biodiversity area from 2003 through 2007 (Map. 4). Of these, 532 clearings encompassed less than one hectare, 148 were between one and ten hectares, 12 covered between 10 and 100 hectares, and one clearing measured 138 hectares. The clearings of 1 to 10 ha accounted for 391 ha of deforestation total, while clearings of less than 1 ha and 10 to 100 ha accounted for the same amount of deforestation (Graph 1). With the exception of scattered clearings in the northeast section of Batang Toru, in Tapanuli Utara (North Tapanuli), most clearings occurred within three kilometers of the delineated area. The average slope of the clearings is 16 degrees, only slightly more than the average of 13 degrees for a random sample of 500 points. These occur at a mean elevation of 1000 meters, where the mean elevation of the random sample is 904 meters. The majority of the scattered clearings in Tapanuli Utara are from 2007 (Map 5). There are a few clearings from 2004 and 2005 near the forest edge in Tapanuli Tengah, and in 2004 a pre existing logging road network entering the forest block in Tapanuli Tengah shows a few extensions (Map 6). There are clearings from all five years along the southwestern forest edge in Tapanuli Selatan, with the largest clearings from 2007. There are a few additional

No Class No Class Description Area (ha) 1 0 Null 145,864.524 2 5 Cloud in 2007 392.526 3 6 Shadow in 2007 204.214 4 10 Secondary Forest 2007 3,909.986 5 11 Forest 2007 135,476.090 6 12 Deforestation 2007 1,381.634 7 13 Regrowth (Secondary Forest) 2000 - 2007 16,835.273 8 22 Non Forest in 2007 10,376.861 9 40 Water Body 303.920 T O T A L 168,880.504

Total Area 168,880.504 ha Total Secondary Forest 20,745.258 ha Total Primary Forest 135,476.090 ha Total Non Forest (Agricultural, Settlement) 10,376.861 ha Uncertainty 596.740 ha

Table 3. Show the recapitulation of forest cover, non forest cover and deforestation in Batang Toru area

21

Distribution of cle arings by s ize class

0

100

200

300

400

500

600

<1 1-10 10-100 >100

S ize Class (ha)

Cou

nt

Cumulative are a cleared for each s ize class

050

100150200250300350400450

<1 1-10 10-100 >100

S ize Class (ha)

Cum

ulat

ive

area

(ha)

Graph 1. Distribution of clearings by size class in Batang Toru and cumulative area cleared for each size class in Batang Toru

clearings along the eastern forest edge further north in Tapanuli Selatan from 2005 and 2006 (Map 7).

22

Map 4. Forest clearance in Batang Toru key biodiversity area, 2003-2007. Grey color inside BTFE indicated non forest cover or deforestation areas

23

Map 5. . Scattered forest clearance in Tapanuli Utara from 2007. Grey color inside BTFE indicated non forest cover or deforestation areas

24

Map 6. Clearance along forest edge in Tapanuli Tengah. Grey color inside BTFE indicated non forest cover or deforestation areas

25

.

Map 7. Clearance along forest edge in Tapanuli Selatan. Grey color inside BTFE indicated non forest cover or deforestation areas

26

5.2. Forest composition and structure The results of the inventory of trees with DBH of 10 cm at primary forest in Aek Silemes and Aek Game-game areas and greater shows, that Batang Toru Forest Block is biodiversity rich area with 332 plant species in 161 genus, 70 families.. A total number of tree species a relative higher compare another natural forest land in Sumatra and outside Sumatra islands. Table 4 and also Graph 2 reveals that the forest is richer in tree species than other natural forests in North Sumatra and outside Sumatra, but poorer than those in Borneo and the Malay Peninsula; while tree densities were higher than those in Ketambe and Batang Gadis National Park, Tesso Nilo National Park and Rimbo Panti Nature Reserve (Sumatra), Bukit Lagong and Sungai Menyala (the Malay Peninsula), and Ladan and Belalong (Brunei), but lower than those in Malinau and Sebulu (East Kalimantan), Gunung Mulu (Sarawak) and Andulau (Brunei). Table 4. Comparison of densities and number of species in the present studies with those conducted Sumatra, Malay Peninsula and Borneo.

Locality Alt (m)

Plot size (ha)

Mean Density (tree/ha)

No of species

Source

East Kalimantan Seturan, Malinau Malinau Sebulu

100 100 70

1.0 1.0 1.0

759 640 592

221 ca. 211 276

Kartawinata (unpublished) Ismayadi (unpublished ) Sukardjo et al. (1990)

Brunei Belalong Ladan Andulau

250 70 60

1.0 0.96 0.96

550 480 596

231 194 256

Poulsen et al. (1996) Davies & Becker (1996) Davies & Becker (1996)

Sarawak Gunung Mulu

50

1.0

615

223 Proctor et al. (1983)

Malay Peninsula Bukit Lagong Sungei Menyala

460-550 30

2 2

494 476.5

253 232

Manokaran & Swaine (1994) Manokaran & Swaine (1994)

Sumatra Ketambe 1 Ketambe 2 Ketambe 3 Batang Gadis NP Tesso Nilo Rmbo Panti Batang Toru

350-450 350-450 350-450 650 25 -114 700 650 – 1000

1.6 1.6 1.6 1 1 0,8

538 420 475 583 557 429 846

116 94 127 168 215 154 332

Abdulhadi et al. (1989) Abdulhadi (1991) Abdulhadi et al. (1991) Kuswata et al (2004) Prawiradilaga, dkk (2003) Yusuf, et al (2005) Present study (2008)

27

0

50

100

150

200

250

Rimbo Panti Ketambe Tesso Nilo Aek Game-Game

Aek Silemes Batang Toru0.0

0.2

0.4

0.6

0.8

1.0

1.2

The study reveals that each tree plant species in Batang Toru Forest has low tree density per-species, only 1 - 10 individual per-hectare and non cluster distribution.( (≤ 20%). This ecological consequence is Batang Toru forest has high sensitive to disturbance due to species disappearances, in particularly for timber exploitation and forest conversion to various economic purposes. The total basal area (BA) in Batang Toru’s primary forest is 50,14 m2 and 51,95 m2 in Aek game game and Aek Silemes respectively (Onrizal, 2008). Its values more higher compare with total tree basal area Batang Gadis National Park with a total basal area is 4,56 m2 (Kuswata, et al, 2004). There are no most prominent species between dipterocarp and non dipterocarp species to dominate of tree basal area,. Its different in Batang Gadis National Park, dipterocarps are the most prominent species. The diameter class distribution of trees presented in Graph 3, which shows more or less a typical size class shaped “L” curve graphs of a tropical undisturbed primary forest and called balanced forest. It reveals that 68,2 % of the trees were less than 30 cm Only 31,82 % occurred in the diameters greater than 30 cm . It is interesting to note that there are no dominance the family trees with large DBH.

Number of Species Plot Area Wide

p l o t a r e a

n u mb e r o f s p e c i e s

Graph 2. Comparison number of species and plot area in various sites in Sumatra

28

0

100

200

300

400

500

600

10.0-

19.9

20.0-

29.9

30.0-

39.9

40.0-

49.9

50.0-

59.9

60.0-

69.9

70.0-

79.9

80.0-

89.9

90.0-

99.9

00.0-

109.9

Aek Game-gameAek Silemes

5.3. Tree Biomass, Carbon Content, and Equivalent CO2 Analysis result to calculating forest biomass in two locations indicate almost similar values, range 551,8-623,0 ton per –hectare or equivalent with 275,9-311,5 ton Carbon per-hectare. Thus number of CO2 can absorbed by forest stand is range 1012,6-1143,2 t CO2/ha. Graph 4 shows values of forest biomass, carbon and C02 absorbing. These biomass values is higher compare with biomass potency average values of Indonesian undisturbed above ground biomass circa 533 t per-hectare (Brown, 1997). Comparison with another sites in Amazon rainforest , forest biomass values in BTFE is higher. In Amazon forest biomass values is 356,2 – 376, 6 t per-year (Fearside et al., 1999, Nascimento & Laurance, 2002). Table 6,7 shows, the largest carbon deposits appeared in the tree with above 50 cm DBH or more 45% - 52 % of total carbon values in forest stand, however number of tress only 0,4 0,5 % from all trees forest stand. See Table 5. The ecological consequences is disappearing the tree above 50 cm DBH, meaning disappearing of carbon deposits from forest stand.

Graph. 3 Distribution of class of tree diameter in primary forest in Batang Toru

DBH CLASS

T R E E D E N S I T Y

29

100

300

500

700

900

1100

1300

Aek Game-Game Aek Silemes Aek Game-Game Aek Silemes Aek Game-Game Aek Silemes

Biomassa (t/ha) Karbon (t C/ha) CO2 (t CO2/ha)

ton/ha

Eq. 1 Eq. 2 Eq. 3 Eq. 4

Graph 4. Forest biomass, Carbon and CO2 equivalent in Batang Toru’s pimary forest

Table 5. Distribution of above ground biomass based on class diameter

Above Ground Biomass Values Aek Game-game Forest Aek Silemes Forest

Eq. 1 Eq. 2 Eq. 3 Eq. 4 Eq. 1 Eq. 2 Eq. 3 Eq. 4 No Diameter Class (cm)

(t/ha) % (t/ha) % (t/ha) % (t/ha) % (t/ha) % (t/ha) % (t/ha) % (t/ha) % 1 1,0 - 1,9 * * * * 1,8 0,3 1,2 0,2 * * * * 1,9 0,3 1,2 0,2 2 2,0 - 4,9 * * * * 7,6 1,2 5,8 1,0 * * * * 8,8 1,5 6,2 1,1 3 5,0 - 9,9 18,2 3,0 17,8 2,9 19,0 3,0 13,7 2,2 20,9 3,5 20,4 3,3 22,0 3,7 17,8 3,2 4 10,0 - 19,9 46,8 7,8 46,8 7,6 40,7 6,5 33,6 5,5 65,3 10,9 65,3 10,7 56,6 9,5 48,7 8,8 5 20,0 - 29,0 72,5 12,1 74,0 12,0 60,5 9,7 49,9 8,2 95,1 15,9 96,9 15,8 79,1 13,3 72,4 13,1 6 30,0 - 39,9 75,8 12,7 77,8 12,6 66,2 10,6 59,2 9,7 91,8 15,4 94,2 15,4 80,7 13,6 76,6 13,9 7 40,0 - 49,9 71,3 11,9 73,4 11,9 66,7 10,7 63,8 10,4 81,9 13,7 84,4 13,8 76,7 12,9 71,0 12,9 8 ≥ 50,0 314,1 52,5 325,2 52,9 360,5 57,9 384,3 62,8 241,8 40,5 250,1 40,9 267,9 45,1 257,8 46,7

Number (D<5) * * * * 9,4 1,5 7,0 1,2 * * * * 10,7 1,8 7,4 1,3 Numbe (D≥5) 598,7 100,0 615,0 100,0 613,6 98,5 604,5 98,8 596,7 100,0 611,5 100,0 583,0 98,2 544,4 98,7 Total 598,7 100,0 615,0 100,0 623,0 100,0 611,5 100,0 596,7 100,0 611,5 100,0 593,8 100,0 551,8 100,0

Table 6. Carbon content in above gound biomass based on diameter class in primary forest Batang Toru

Carbon Content in Biomass Aek Game-game Forest Aek SilemesForest

Eq. 1 Eq. 2 Eq. 3 Eq. 4 Eq. 1 Eq. 2 Eq. 3 Eq. 4 No Diameter Class (cm)

(t C/ha) % (t C/ha) % (t C/ha) % (t C/ha) % (t C/ha) % (t C/ha) % (t C/ha) % (t C/ha) % 1 1,0 - 1,9 * * * * 0,9 0,3 0,6 0,2 * * * * 0,9 0,3 0,6 0,2 2 2,0 - 4,9 * * * * 3,8 1,2 2,9 1,0 * * * * 4,4 1,5 3,1 1,1 3 5,0 - 9,9 9,1 3,0 8,9 2,9 9,5 3,0 6,8 2,2 10,4 3,5 10,2 3,3 11,0 3,7 8,9 3,2 4 10,0 - 19,9 23,4 7,8 23,4 7,6 20,4 6,5 16,8 5,5 32,6 10,9 32,7 10,7 28,3 9,5 24,3 8,8 5 20,0 - 29,0 36,3 12,1 37,0 12,0 30,2 9,7 25,0 8,2 47,5 15,9 48,5 15,8 39,5 13,3 36,2 13,1 6 30,0 - 39,9 37,9 12,7 38,9 12,6 33,1 10,6 29,6 9,7 45,9 15,4 47,1 15,4 40,4 13,6 38,3 13,9 7 40,0 - 49,9 35,6 11,9 36,7 11,9 33,3 10,7 31,9 10,4 41,0 13,7 42,2 13,8 38,4 12,9 35,5 12,9 8 ≥ 50,0 157,1 52,5 162,6 52,9 180,3 57,9 192,2 62,8 120,9 40,5 125,1 40,9 134,0 45,1 128,9 46,7

Number (D<5) * * * * 4,7 1,5 3,5 1,2 * * * * 5,4 1,8 3,7 1,3 Number(D≥5) 299,3 100,0 307,5 100,0 306,8 98,5 302,2 98,8 298,3 100,0 305,7 100,0 291,5 98,2 272,2 98,7 Total 299,3 100,0 307,5 100,0 311,5 100,0 305,7 100,0 298,3 100,0 305,7 100,0 296,9 100,0 275,9 100,0

Remarks: * = Equation Eq. 1 dan Eq. 2 not valid for tree DBH < 5 cm

1

Table 7 . Distribution of equvalent CO2 based on tree diameter in primary forest Batang Toru

Equavalent CO2 Aek Game-game Forest Aek Silemes forest

Eq. 1 Eq. 2 Eq. 3 Eq. 4 Eq. 1 Eq. 2 Eq. 3 Eq. 4

No Diameter Class (cm)

(t CO2/ha) % (t CO2/ha) % (t CO2/ha) % (t CO2/ha) % (t CO2/ha) % (t CO2/ha) % (t CO2/ha) % (t CO2/ha) % 1 1,0 - 1,9 * * * * 3,3 0,3 2,2 0,2 * * * * 3,4 0,3 2,3 0,2 2 2,0 - 4,9 * * * * 14,0 1,2 10,7 1,0 * * * * 16,2 1,5 11,3 1,1 3 5,0 - 9,9 33,3 3,0 32,7 2,9 34,8 3,0 25,1 2,2 38,3 3,5 37,4 3,3 40,3 3,7 32,7 3,2 4 10,0 - 19,9 85,9 7,8 85,9 7,6 74,7 6,5 61,6 5,5 119,8 10,9 119,9 10,7 103,8 9,5 89,3 8,8 5 20,0 - 29,0 133,1 12,1 135,7 12,0 111,0 9,7 91,6 8,2 174,5 15,9 177,8 15,8 145,1 13,3 132,9 13,1 6 30,0 - 39,9 139,0 12,7 142,8 12,6 121,5 10,6 108,7 9,7 168,4 15,4 172,9 15,4 148,1 13,6 140,6 13,9 7 40,0 - 49,9 130,8 11,9 134,7 11,9 122,3 10,7 117,0 10,4 150,3 13,7 154,9 13,8 140,8 12,9 130,4 12,9 8 ≥ 50,0 576,4 52,5 596,7 52,9 661,6 57,9 705,2 62,8 443,6 40,5 459,0 40,9 491,7 45,1 473,1 46,7

Number (D<5) * * * * 17,3 1,5 12,9 1,2 * * * * 19,7 1,8 13,6 1,3 Number(D≥5) 1098,5 100,0 1128,5 100,0 1125,9 98,5 1109,2 98,8 1094,9 100,0 1122,0 100,0 1069,9 98,2 999,0 98,7 Total 1098,5 100,0 1128,5 100,0 1143,2 100,0 1122,1 100,0 1094,9 100,0 1122,0 100,0 1089,6 100,0 1012,6 100,0

Remarks: * = Equation Eq. 1 dan Eq. 2 not valid for tree DBH < 5 cm

Tabel 8. Biomass Estimation, carbon content, and equivalent CO2 in Batang Toru areas in various forest types

Biomass (ton/ha) Carbon Content (ton C/ha) Equivalent CO2

Forest Type Trees

Density (N/ha) BD Brown’s BD Brown’s BD Brown’s

SECONDARY FOREST

Secondary Forest of Ex Timber Concession - Teluk Nauli, 1984

1,010 80,37 123,54 40,18 61,77 147,46 226,70

Secondary Forest 1 of Aek Nabara 926 125,61 140,96 62,81 70,48 230,51 258,66

Secondary Forest 2 of Aek Nabara 654 209,56 210,11 104,78 105,06 384,54 385,57

Averages of Secondary Forest 863 138,51 158,2 69,26 79,1 244,17 290,31

Average of Approach - 148,36 74,18 267,24

PRIMARY FOREST Mean of E1 + E2 + E3 + E4 Mean of E1 + E2 + E3 + E4 Mean of E1 + E2 + E3 + E4

Primary Forest of Aek Game-game 742 612,05 321,75 1122,83

Primary Forest of Aek Silemes 950 588,45 289,7 1079,76

Averages of Primary Forest 846 600,25 305,73 1011,3

Table 8 show comparison of forest biomass estimation, carbon content and equivalent CO2 in primary and secondary forest. This study revealedt that tree forest biomass estimation, carbon content and equivalent CO2 in primary and secondary forest in primary forest more higher compare with secondary forest. Table 8 reveals that the carbon content in Batang Toru forest is higher than those in other primary forests in Gunung Gede National Park West Java circa 275,56 ton C per-hectare (Siregar, 2007), and Siberut Island Wes Sumatra circa 65,96 ton C per-hectare (Bismark et al., 2008).

5.4. Ground vegetation and necromass Ground vegetation is kind of vegetation which diameter less then 2 cm, and necromass is part of vegetation organs such as leaves, barnches, roots, and flowers where as decay or still alive where above the soil surface. Necromass in carbon cycle will produce CO2 emission in decaying process and can be absorbed by flora as fertelizing (Morikawa, 2002). Tabel 9. Potential of ground vegetation and necromass in Batang Toru area.

Biomass (ton/ha) Carbon Content ( ton C/ha) eCO2 (ton/ha)

Forest Type Ground

Vegetation Necromass Ground Vegetation Necromass Ground

Vegetation Necromass

Primary Forest ND ND ND ND ND ND

Secondary Forest of Ex Concession Teluk Nauli, 1984

2,39 5,55 1,20 2,77 4,40 10,17

Aek Nabara Secondary Forest 1

2,11 4,08 1,05 2,04 3,85 7,49

Aek Nabara Secondary Forest 2

2,17 7,00 1,08 3,50 3,96 12,84

Averages of Secondary Forest

2,22 5,54 1,111 2,77 4,07 10,17

Remarks : ND : No data available

Table 9 shows that potential of necromass as a CO2 source in the primary forest is higher than others forest type, cause of the necromass are not decomposed yet, and referring to that necromass increase as forests mature this is one of the characteristic of tropical rain forest.

Table 10. Biomass, Carbon Content, and Necromass recapitulation from average methodology approached. .

Forest

Type

Biomass (ton/ha) Carbon Content (t C/ha) eCO2 (t /ha)

Tree DBH ≥ 2 cm

Ground Vegetation Necro mass

Tree DBH ≥ 2 cm

Ground Vegetation

Necro mass

Tree DBH ≥ 2 cm

Ground Vegetation Necro mass

Secondary Forest

148,36 2.25 4.82 74,18 1.13 2.41 267,24 4.13 8.83

Total of Secondary Forest

155,43 77,72 280,2

Total of Primary Forest

600,25 305,73 1011,3

5.5 Carbon stock estimation

Based on ground survey, the sum of biomass in trees (DBH ≥ 2 cm), ground vegetation, and necromass yielded the averages of carbon content from secondary forest as 77.72 ton C/ha. This is equivalent to 280,2 CO2 ton per-hectare (Table 10).Primary forest in Batang Toru obtains _305,73 ton C per-hectare, equivalent to 101,3 ton CO2 per-hectares.

According to the classification imagery process, Batang Toru area has secondary forest 20,747.26 hectare, so it means that Batang Toru Forest Block has stored 1,612,477 ton Carbon, or 5,813,382 ton equivalent CO2. Primary forest classification from imagery process, Batang Toru area has 135,476.090 hectare. This reveals that carbon content in primary forest in the BTFE is 4,149,105 ton Carbon and or 137,006,970 ton equivalent CO2

Finally, Average of deforestation in BTFE from 1990 - 2007, is about 142 ha per year, if assumed that deforestation driving factor is linear and without conservation incentives to avoid deforestation, in 2015 will be estimated BTFE area will loose the forest cover around 700 - 1600 hectares. This is equivalent with 707,910 – 1,618,080 ton CO2 and will contribute significant to increasing global warming. 6. ACKNOWLEDGEMENTS We thank the Japan Bank for International Development for providing the financial support to undertake the study. Our gratitude goes also to the Conservation International Indonesia and Japan, Forest Research and Development Agency Minister of forestry and University of North Sumatra and local community in Aek Nabara and Sibulan bulan for various supports that made the study possible. Members of the carbon study team of Conservation International Indonesia and Conservation International Japan have provided the many valuable information and comments, for which we gratefully acknowledge their cooperation. 7. REFERENCES Abdulhadi, D.,1991. A Meliaceae forest in Ketambe, G. Leuser National Park, Sumatera, Indonesia, with special reference to the status of Dipterocarp species. In SOERIANEGARA, I., TJITROSOMO, S., UMALY, R.C. & I. UMBOH (eds.), Proceedings of the Fourth Round-Table Conference on Dipterocarps, Bogor, Indonesia, 12-15 December 1989. BIOTROP Special Publication No. 41: 307-315. Abdulhadi, R., Kartawinata, K. & Yusup, R. 1984. Pola hutan di Ketambe, Taman Nasional G. Leuser, Aceh. Hal. 207-214. Dalam: WIRJOATMODJO, S. (ed.) Laporan Teknik 1982-1983, LBN-LIPI, Bogor. Abdullhadi , R, Mirmanto, E. & Kartawinata, K. 1987. A lowland dipterocarp forest in Sekundur, North Sumatra, Indonesia: five years after mechanized logging. In Kostermans, A.J.G.H. (ed), Proceedings of the Third Round Table Conference on Dipterocarps, UNESCO/ ROSTSEA, Jakarta. pp. 255-273. Anonim a. 1973. Daftar nama pohon-pohonan Sumatera Utara (Sumatera Timur dan Tapanuli). Laporan No. 171 revisi. Bagian botani hutan. Lembaga Penelitian Hutan. Dirjen Kehutanan, Departeman Pertanian.

1

__________ b. 1983. Jenis-jenis pohon di susun berdasarkan nama daerah dan nama botaninya. Sumatera Utara. Edisi khusus No. 58 F (buku 6). Dirjen Bina Program Kehutanan. Departeman Pertanian. Anonim. 2005a : Manual of Biomass Survey and Analysis. Forestry Research and Development Agency (FORDA) and Japan International Cooperation Agency (JICA). Anonim. 2005b : Survey of Teresterial Ecology, Air Quality and Noise for the Martabe Project Area, North Sumatra Indonesia. PT. Newmont Horas Nauli, LIPI, Hatfield, 2005 Bismark, M., N.M. Heriyanto dan S. Iskandar. 2008. Biomasa dan kandungan karbon pada hutan produksi di Cagar Biosfer Pulau Siberut, Sumatera Barat. Jurnal Penelitian Hutan (dalam proses percetakan). Bismark, M dan N. M. Heriyanto. 2007. Dinamika Potensi dan Struktur Tegakan Hutan Produksi Bekas Tebangan Dalam Cagar Biosfer Siberut. Info Hutan, Pusat Litbang Hutan dan Konservasi Alam. Vol. IV. No. 6 (553-564). Bismark, M., N.M. Heriyanto dan S. Iskandar. 2008. Biomasa dan kandungan karbon pada hutan produksi di Cagar Biosfer Pulau Siberut, Sumatera Barat. Jurnal Penelitian Hutan (dalam proses percetakan

Brown, S., J. Sathaye., M. Canel and P. Kauppi. 1996. Mitigation of carbon emission to the atmosphere by forest management, Commonwealth Forestry Review 75 : 80 – 91. Brown, S. 1997. Estimating biomass and biomass change of tropical forest. A primer, FAO. Forestry paper No. 134. FAO, USA. Brown, S. Gillespie, A.J.R. and Lugo, A.E. 1989 : Biomass Estimation Methods of Tropical Forests with Application to Forest Inventory Data. Forest Science 35 (4): 881-902. Brown, S. 1997 : Estimating Biomass and Biomass Change of Tropical Forests. FAO Forestry Paper 134. A forest resources assessment publication, Rome. Pp. 1. Brown, S. 1999. Guidelines for inventorying and monitoring carbon offsets in forest-based projects. Winrock International. Forest Carbon Monitoring Program, Winrock International, Airlington, VA, USA. Bustomi, S., D. Wahjono dan N. M. Heriyanto. 2006. Klasifikasi Potensi Tegakan Hutan Alam Berdasarkan Citra Satelit di Kelompok Hutan Sungai Bomberai – Sungai Besiri di Kabupaten Fakfak, Papua. Jurnal Penelitian Hutan dan Konservasi Alam Vol. III. No. 4. (437-458).

Davies, S.J. & Becker, P.1996. Floristic composition and stand structure of mixed dipterocarp forest and heath forests in Brunei Darussalam. Journal of Tropical Forest Science 8(4): 542-569. Fearnside, P.M., P.M.L.A. Graca, N.L. Filho, F.J.A. Rodrigues, & J.M. Robinson. 1999. Tropical forest burning in Brazilian Amazonia: measurement of biomass loading, burning efficiency and charcoal formation at Altamira, Para. For. Ecol. & Manage. 123: 35-79. Gullison RE, Frumhoff PC, Canadell JC, Field CB, Nepstad DC, et al. 2007 Tropical forests and Climate Policy. Science 316: 985–986. Heriyanto, N.M., I. Heriansyah, C.A. Siregar and K. Miyakuni. 2002 : Measurement of biomass in forest. Demonstration Study on Carbon Fixing Forest Management in Indonesia. Callaboration Project

2

between Forest Research Development Agency (FORDA) with Japan International Cooperation Agency (JICA). Bogor. Unpublished.

Heriyanto, N.M., I. Heriansyah, C.A. Siregar and K. Miyakuni. 2002. Measurement of biomass in forest. Demonstration study on carbon fixing forest management in Indonesia. Callaboration Project between Forest Research Development Agency (FORDA) with Japan International Cooperation Agency (JICA). Bogor. Unpublished. International Panel on Climate Change. 2003. IPPC guidelines for nation greenhouse inventories : Reference manual IPCC. Hooijer, A, Silvus M, Hosten, H and Page, S .2006. : Peat –CO2 : Assessment of CO2 emissions from drained peat lands in South East Asia. Delft Hydraulics Report Q30943. ICRAF SE Asia. Wood densitiy online databese: International Panel on Climate Change. 2003 : IPPC Guidelines for Nation Greenhouse Inventories : Reference Manual IPCC. Kartawinata, K, Afriastini, J.J, Heriyanto, M and Samsoedin, I. 2004. A Tree Species Inventory in A One-Hectare Plot at the Batang Gadis National Park, North Sumatra, Indonesia. Reinwardtia 12(2)_145 Ketterings QM, Coe R, van Noordwijk M, Ambagau Y and Palm CA. 2001. Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests. Forest Ecology and Management 146: 199-209 Kindermann, G, Michael Obersteiner, Brent Sohngen , Jayant Sathaye,`Kenneth Andrasko , Ewald Rametsteiner, Bernhard Schlamadinger, Sven Wunder, and Robert Beach. 2008 Global cost estimates of reducing carbon emissions through avoided deforestation The National Academy of Sciences of the USA

Lamounier, Y., Purnajaya and Setiabudi.: International Map of Vegetation and Environmental Conditions: Northern Sumatra, Scale 1:1,000,000. Institut de la Carte Internationale du Tapis Végétal and SEAMEO-BIOTROP, Toulouse and Bogor.

Manokaran, N. & Swaine, M.D. 1994. Population dynamics of trees in dipterocarp forests of Peninsular Malaysia. Malaysian Forest Records No. 41, Forest Research Institute Malaysia, Kepong Morikawa, Y. 2002. Biomass measurement in planted forest in and around Benakat. Fiscal report of assessment on the potentiality of reforestation and afforestation activities in mitigating the climate change 2001, 58-63. JIFPRO, Tokyo, Japan. Morikawa, Y. 2002. Biomass measurement in planted forest in and around Benakat. Fiscal report of assessment on the potentiality of reforestation and afforestation activities in mitigating the climate change 2001, 58-63. JIFPRO, Tokyo, Japan. Nascimento, H.E.M., & W.F. Laurance. 2002. Total aboveground biomass in central Amazonian rainforests: a landscape-scale study. For. Ecol. & Manage. 168: 311-321. Nelson, B.W., R.Mesquita., J.L.G. Periera., S.G.A. De Souza., G.T. Batista and L. B. Couto. 1999. Allometric regressions for improved estimate of secondary forest biomass in the central Amazon. Forest ecology and management 117 : 149-167.

3

Newman, M.F., P.F. Burgess, & T.C. Whitmore. 1999. Pedoman identifikasi pohon-pohon Dipterocarpaceae Sumatera. Prosea Indonesia. Bogor. Olander LP, Holly K Gibbs, Marc Steininger, Jennifer J Swenson and Brian C Murray 2007 Reference scenarios for deforestation and forest degradation in support of REDD: a review of data and methods Environ. Res. Lett. 3 (April-June 2008) Onrizal dan Ismail (2007) Keanekaragaman Flora Pohon di Kawas Batang Toru. Departemen Kehutanan, Fakultas Pertanian, Universitas Sumatera Utara, Unpublised Report Onrizal 2008 Simpanan Karbon Hutan Hujan Tropia Primer di Kawasan Batang Toru. Uniiversittas Sumatra Utara Unpublised Report Perbatakusuma, EA, Supriatna, J, Siregar, R,.S.E, Wurjanto, D, Sihombing, L, and Sitaparasti, D : Mainstreaming Biodiversity Conservation and Life Support System Policies in Natural Forest in Batang Toru Water Cachment Area North Sumatra Province. Policy Technical Report. Conservation International Indonesia and Department of Forestry. Pandan. 2006. In Bahasa Indonesia. Poilsen, A.D., Nielsen, I.C., Tan S. & Balslev, H. 1996. A quantitative inventory of trees in one hectare of mixed dipterocarp forest in Temburong, Brunei Darussalam. In EDWARDS, D.S., BOOTH, W.E. & CHOY, S.C. (eds.) Tropical Rain Forest Research – Current Issues. Dodrecht, The Netherlands, Kluwer Academic Press. PEACE (2007). Indonesia and Climate Change. Current Status andol Policies. Prawiradilaga, D. M., A. Suyanto, W. A. Noerdjito, A. Saim, Purwaningsih, I. Rachmatika, S. Susiarti, I. Sidik, A. Marakarmah, A. Mun’im, M. H. Sinaga, E. Cholik, Ismail, M. Maharani, Y. Purwanto, dan E.. B. Waluyo. (2003): Survey Report on Biodiversity of Tesso Nilo. LIPI-WWF Indonesia. Tidak Dipublikasikan Proctor, J., Anderson , J.M. Chai, P. & Wallack, H.W. 1983. Ecological studies in four contrasting tropical lowland rain forests in Gunung Mulu National Park. I. Forest Environment, structure and floristics. Journal of Ecology 71: 237-260 Prosea. 1994. Plant resources of South-east Asia 5. (1) Timber trees: major commercial timbers. Prosea Foundation, Bogor, Indonesia, 610 pp Prosea. 1995. Plant resources of South-east Asia 5. (2) Timber trees: minor commercial timbers. Prosea Foundation, Bogor, Indonesia, 655 pp Prosea. 1996. Plant resources of South-east Asia 5. (2) Timber trees: lesser-know timbers. Prosea Foundation, Bogor, Indonesia, 655 pp Reyes, G., S. Brown, J. Chapman, and A. E. Lugo, 1992. Wood densities of tropical tree species. USDA Forest Service, General Technical Report SO-88, Southern Forest Experiment Station, New Orleans, Louisiana, USA Siregar, C. A. 2007. Potensi serapan karbon di Taman Nasional Gede Pangrango, Cibodas, Jawa Barat. Info Hutan. Vol. IV, No. 3 (233-244). Pusat Penelitian dan Pengembangan Hutan dan Konservasi Alam. Bogor Samsoedin, I dan Heriyanto, NM (2008) Biomassa dan Kandungan Karbon da Daerah Aliran Sungai Batang Toru Sumatera Utara. Unpublised Report

4

Sukardjo, S., Hagihara, A.,Yamakuta T. & Ogawa, H. 1990. Floristic composition of a tropical rain forest in Indonesian Borneo. Bull. Nagoya Univ. For. No. 10:1-43. Steenis, C.G.G.J. VAN et al. (Eds.). 1948-2002. Flora Malesiana, I, 1-16. Noordhoff-Kolf, Noordhoff, Nijhoff, National Herbarium Nederland, etc., Netherland. Sumantri, H 2007: Change Detection Mapping for Sumatra 1990-2000 Paper presented Forum Remote Sensing & GIS Indonesia, Conservation International, Recontruction and Rehabilitation Agency. Banda Aceh. UNFCCC 2006 Good practice guidance and adjustments under Article 5, paragraph 2, of the Kyoto Protocol FCCC/KP/CMP/2005/8/Add.3 Decision 20/CMP.1 World Resources Institute 2007 Climate Analysis Indicator Tools (Version 5.0) Washington DC Whitmore HITMORE, T.C. & Tantra, I.G.M. (ed.) 1986. Tree flora of Indonesia: Check List for Sumatra. Forest Research and Development Center, Bogor. Yusuf, R., Purwaningsih, & Gusman. 2005. Komposisi dan struktur hutan vegetasi hutan alam Rimbo Panti, Propinsi Sumatera Barat. Biodiversitas 6 (4): 266-271