mc rmp hotspots kalimantan

MINISTRY OF MARINE AFFAIRS AND FISHERIES

Directorate General of Coastal and Small Islands

Jl. M.T. Haryono Kav, 52-53, Jakarta Selatan, Indonesia. Fax.+62 (21) 79180456

BLACK & VEATCH (International) Ltd In association with: Nusantara Systems International

MARINE & COASTAL RESOURCES MANAGEMENT PROJECT

Package B

SPATIAL DATA AND INFORMATION MANAGEMENT

ADB Loan No. 1770-INO (SF)

PART A – Hotspot Analysis Kalimantan & Kabupaten Kotawaringan Training Manual MCRMP-B – Spatial Modeling Hotspots

Hotspots (10-Jan-99 to 21-Dec-03), elevation, national parks and Kabupaten Kotawaringan Kalimantan

May 2005

PT EXSA Internasional PT Multi Tehniktama Prakarsa

MCRMP - Marine and Coastal Resources Management Project

Component B: Spatial Data and Information Management

TABLE OF CONTENTS TABLE OF CONTENTS...................................................................................................................................... 2 EXECUTIVE SUMMARY................................................................................................................................... 3 PART A – SPATIAL MODELING HOTSPOTS KALIMANTAN.................................................................. 3 1. INTRODUCTION......................................................................................................................................... 3 2. MATERIALS AND METHODS.................................................................................................................. 3

2.1 STUDY AREA ............................................................................................................................................. 3 2.2 DATA......................................................................................................................................................... 4

2.2.1 Hotspots .......................................................................................................................................... 4 2.2.2 Forestry Concessions and National Parks ....................................................................................... 4 2.2.3 Land Systems .................................................................................................................................. 4

2.3 SPATIAL MODELING .................................................................................................................................. 4 2.3.1 Density Mapping............................................................................................................................. 4 2.3.2 Overlay Analysis ............................................................................................................................. 4

2.4 STATISTICAL ANALYSIS ............................................................................................................................ 4 3. RESULTS ...................................................................................................................................................... 5

3.1 KALIMANTAN ............................................................................................................................................ 5 3.1.1 Hotspot Density............................................................................................................................... 5 3.1.2 Tests for Independence.................................................................................................................... 8

3.2 KABUPATEN KOTAWARINGIN.................................................................................................................... 9 3.2.1 Hotspot Density............................................................................................................................... 9 3.2.2 Relation to Roads & Elevation...................................................................................................... 10 3.2.3 Roads & Elevation ........................................................................................................................ 13

4. DISCUSSION .............................................................................................................................................. 14 5. REFERENCES............................................................................................................................................ 14 ANNEX 1 – LAND SYSTEMS........................................................................................................................... 15 ANNEX 2 – FORESTRY CONCESSIONS ...................................................................................................... 18 ANNEX 3 – NATIONAL PARKS ..................................................................................................................... 28

Black & Veatch (International) Ltd in association with PT Nusantara Systems Int., PT Exsa Int. and PT Multi Tehniktama Prakarsa

HotspotsKalimantan.doc 2 BL/18-May-05 6:43:00 PM



EXECUTIVE SUMMARY MCRMP-B consultants were requested to visit Sampit to assist with computer networks and software installation and commissioning and to contribute to a training workshop designed to socialize the use of GIS for BAPPEDA and other invited staff.

Discussions with the PIMPRO indicated that forest and peat fires were a major environmental problem in Kabupaten Kotawaringan. In response MCRMP-B consultants analyzed hotspot data recorded from 1999 to end of 2003 in relation to spatial density and other factors such as distance from road and land systems. The purpose of this study was to socialize and demonstrate the use of GIS as a powerful tool for exploring, visualizing, modeling and analyzing spatial patterns in distribution and abundance of a phenomenon (in this case hotspots) using a real-world example of a local environmental problem that has high negative impacts both locally and on nearby neighbors Singapore and Malaysia. The uncontrolled destruction of forests in Kalimantan has also generated high interest internationally because of the rapidly diminishing areas of unique tropical rainforest there.

The results of this analysis are presented in two parts: the first describes the methodology used to analyze the data and reports the results; the second outlines step-by-step the procedures used to do the analysis.

It is important to stress that GIS analysis itself, although interesting, is not the end product which must usually be a report that describes and synthesizes the results of the GIS application.

PART A – SPATIAL MODELING HOTSPOTS KALIMANTAN

1. INTRODUCTION Deforestation in tropical areas is occurring at a rapid rate and there is growing awareness that deforestation is a global concern as shown by the outcomes of the United Nations Conference on Environment and Development (UNCED) held at Rio de Janeiro in 1992. Indonesia has vast areas of natural tropical forest that represents 10% of the world’s total tropical forest area. Large areas are under private concessions who use selective cutting and planting silviculture methods to log these areas; maps from the Department of Forestry indicate that 126 companies have concession rights to log 10,3682 km2 in Kalimantan alone with an additional 318 plantations covering 62,896 km2. More recently, with ongoing implementation of plans to foster regional autonomy after the economic crisis that hit Indonesia in 1998 and subsequent “reformasi” there has been debate specifically concerning an increase in uncontrolled legal and illegal logging because district governments can now issue their own small forest concessions and collect some revenue.

Clearing of land using fires is used in Kalimantan to improve access for logging and developing plantations of oil palm. The smoke arising from burn offs causes respiratory problems not only on local populations but on communities as far away as Malaysia and Singapore. Smoke creates a hazard for air, land and river transportation because of reduced visibility.

The purpose of this study was to map and analyze in general the spatial patterns in distribution and abundance of hotspots in Kalimantan and then to explore these patterns in more detail for Kabupaten Kotawaringan. Understanding and describing these patterns in distribution and abundance are important for proper and cost effective planning for health, education, monitoring and surveillance programs.

2. MATERIALS AND METHODS 2.1 Study Area

The study area consisted of Kalimantan for the general analysis and Kabupaten Kotawaringan for more detailed analysis (Figure 1).





2.2 Data

2.2.1 Hotspots

Hotspot data was obtained from the University of Singapore who use NOAA data for mapping hotpots in the region.

2.2.2 Forestry Concessions and National Parks

Forestry concessions and National Parks shape files were obtained from Dept. Forestry Jakarta.

2.2.3 Land Systems

Land Systems data was digitized off hardcopy maps of ReppRot circa 1970’s.

2.3 Spatial Modeling

2.3.1 Density Mapping

Point Density calculates the density of point features around each output raster cell. Conceptually, a neighborhood is defined around each raster cell center, and the number of points that fall within the neighborhood is totaled and divided by the area of the neighborhood.

If a Population field setting other than NONE is used, items value determines the number of times to count the point. Thus an item value of 3 would cause the point to be counted as 3 points. The values can be integer or floating point. If an area unit is selected, the calculated density for the cell is multiplied by the appropriate factor before it is written to the output raster. For example, if the input ground units are meters, comparing a unit scale factor of meters to kilometers will result in the values being different by a multiplier of 1,000,000 (1000 x 1000).

Possible uses include finding density of houses, wildlife observations, or crime reports. The population field could be used to weigh some points more heavily than others, depending on their meaning, or to allow one point to represent several observations. For example, one address might represent a condominium with 6 units, or some crimes might be weighed more severely than others in determining overall crime levels.

Increasing the radius will not change the calculated density values very much. Although more points will fall inside the larger neighborhood, this number will be divided by a larger area when calculating density. The main effect of a larger radius is that density is calculated considering a larger number of points, which can be further from the raster cell. This results in a more generalized output raster.

Two point density maps were produced: the first was for all Kalimantan and the second was for Kabupaten Kotawaringin. To prevent edge effects a 20 km buffer was created around the Kabupatan.

For this study simple density was used with radius 10 km and raster cell size of 100 m for Kalimantan and Kabupaten Kotawaringin. The map was classified based on standard deviations into four categories: 0 – 1; 1 – 2; 2 – 3 and 3 – 4 using standard deviations (std). The raster map was then converted to polygons.

2.3.2 Overlay Analysis

Forestry concessions, national parks and land systems were overlain with the reclassified point density map. Area estimates for all combinations were calculated and presented as percentages of the total.

2.4 Statistical Analysis

Each polygon for national parks, forestry concessions and land systems belonged to a category. Each hotspot was spatially joined to the polygon it fell within and a sum was kept of the total number of hotspots in each polygon.

The feature class was next exported to an Excel workbook where a Chi-squared test was used to test for statistical independence. In this case the independence test was that hotspots were randomly distributed.





The function CHITEST(actual_range,expected_range) in Excel was used to test for spatial independence for land systems, forestry concessions and national parks. CHITEST returns the value from the chi-squared (c2) distribution for the statistic and the appropriate degrees of freedom. You can use c2 tests to determine whether hypothesized results are verified by an experiment.

The Actual_range is the range of data that contains observations to test against expected values.

Expected_range is the range of data that contains the ratio of the product of row totals and column totals to the grand total.

Remarks

• If actual_range and expected_range have a different number of data points, CHITEST returns the #N/A error value.

• The c2 test first calculates a c2 statistic using the formula:

where:

Aij = actual frequency in the i-th row, j-th column

Eij = expected frequency in the i-th row, j-th column

r = number or rows

c = number of columns

• A low value of c2 is an indicator of independence. As can be seen from the formula, c2 is always positive or 0, and is 0 only if Aij = Eij for every i,j.

• CHITEST returns the probability that a value of the c2 statistic at least as high as the value calculated by the above formula could have happened by chance under the assumption of independence. In computing this probability, CHITEST uses the c2 distribution with an appropriate number of degrees of freedom, df. If r > 1 and c > 1, then df = (r - 1)(c - 1). If r = 1 and c > 1, then df = c - 1 or if r > 1 and c = 1, then df = r - 1. r = c= 1 is not allowed and #N/A is returned.

3. RESULTS 3.1 Kalimantan

3.1.1 Hotspot Density

There was a total of 32,855 hotspots recorded in Kalimantan from 10-Jan-1999 to 21-Dec-2003 (Figure 1). Visual inspection and point density mapping indicated that most hotspots were located in southern and north western Kalimantan (Figure 2 & 3).





Figure 1. Elevation, National Parks and Hotspots of Kalimantan recorded from 1999 to end 2003. Blue

hatching: national parks. Red dots: hotspots; cyan: Kabupaten Kotawaringan





Figure 2. Hotspot point density West Kalimantan. Green: 0 – 1; Orange: 1 – 2; and Red: 2 – 3 standard

deviations.





N

High density of hotspots

Figure 3. Hotspot density shown as base heights overlaying land systems of Kalimantan.

3.1.2 Tests for Independence

Chi-squared tests were performed on hotspots grouped by land system description name, forestry concessions and national parks. The chi-squared test was for:

(Observed Number of Hotspots – Expected Number of Hotpots)2 / Expected Number of Hotpots summed over the categories.

The tables used for the Chi-square test are given in Annexes 1, 2 & 3.

Land Systems

There was a significant departure (P < 0.00001) from random distribution of hotspots for Land System. Examination of the table of observed and expected number of hotspots (Annex 1) indicated that large departures were present for a number of Land System categories. In particular, those land systems categories with peat or were flat / undulating low-lying coastal categories had higher than expected abundance of hotspots whereas mountainous categories or categories with steep slopes had lower than expected abundance of hotspots.

Forestry Concessions

The number of hotspots in Forestry Concessions also departed significantly from that expected from a random distribution. There were lower than expected abundance of hotspots for the majority of concession holders. However, there were a number of instances where the abundance of hotspots was much larger than expected based on random distribution (Annex 2). Many of these higher than expected number of hotspots were found in the Kotawaringan area of Kalimantan.





National Parks

With few exceptions national parks had lower than expected hotspots (P < 0.0001) (Annex 3). In a couple of cases the observed number of hotspots was higher than expected.

3.2 Kabupaten Kotawaringin

3.2.1 Hotspot Density

A total of 4,463 hotspots were recorded over the 1999 to 2003 time interval in Kabupaten Kotawaringin; this represented more than a tenth (13.58%) of the total number of hotspots recorded in Kalimantan for that time period (Figure 4). In contrast the area of Kabupaten Kotawaringin, 15,886 km2 was less than 3% (2.96%) of the total area, 536,865 km2, of Kalimantan.

The hotspots that fell within Kabupaten Kotawaringin were selected from the hotspots of Kalimantan and point density calculated using a 5 km radius and 50 m raster cell size. The resulting map was reclassified into 4 categories based on standard deviations (1 – 4 standard deviations). This more detailed modeling indicated that anomalously high areas of hotpots occurred in all Kecematan within Kabupaten Kotawaringin.

Figure 4. Hotspot point density mapping for Kabupaten Kotawaringin





The density mapping resulted in well over half (56.73%) the hotspots being located in 15.84% of the study area (Table 1). When the second standard deviation category was included then 83% of hotspots were within one third (32.5%) the study area.

Table 1. Hotspots and density mapping

HOTSPOT STDEV HOTSPOTS

HOTSPOT %TOTAL

AREA SQ KM

AREA %TOTAL

AREA CUM% HOTSPOT SQKM

1 43 0.96% 9,133.8 32.41% 32.41% 0.0047

2 719 16.11% 9,879.8 35.05% 67.46% 0.0728

3 1169 26.19% 4,706.6 16.70% 84.16% 0.2484

4 2532 56.73% 4,466.0 15.84% 100.00% 0.5670

TOTAL 4463 100.00% 28,186.1 100.00%

3.2.2 Relation to Roads & Elevation

Roads

There was a strong relationship between the number of hotspots and proximity to road (Figure 5 & 6).

Figure 5. Hotspot density (No. sq km) for road buffers.





Figure 6. Number of hotspots with distance from nearest road

Two thirds (63.61%) of the hotspots were located within 10 km of the nearest road which was half the total study area (49.8%) (Table 2). In contrast one percent (0.69%) were located more than 40 km away from the nearest road.

Table 2. Breakdown of number of hotspots with distance from road

DISTANCE (M) NO.

HOTSPOTS HOTSPOT %TOTAL

HOTSPOT CUM%

HOTSPOTS SQ KM

AREA SQ KM

AREA %TOTAL

AREA CUM%

5,000 1567 35.11% 35.11% 0.1828 8,571 30.41% 30.41%

10,000 1272 28.50% 63.61% 0.2327 5,465 19.39% 49.80%

15,000 848 19.00% 82.61% 0.2055 4,127 14.64% 64.44%

20,000 483 10.82% 93.43% 0.1487 3,249 11.53% 75.97%

30,000 262 5.87% 99.31% 0.0606 4,320 15.33% 91.29%

40,000 29 0.65% 99.96% 0.0142 2,045 7.26% 98.55%

50,000 2 0.04% 100.00% 0.0049 409 1.45% 100.00%

TOTAL 4463 100.00% 28,186 100.00%

Elevation

There was a strong relationship between the number of hotspots and elevation (Figure 7 & 8).





Figure 7. Hotspot density (No. sq km) for elevation categories

Figure 8. Hotspot density (No./sq km) for elevation categories





The density of hotspots (0.2309) was highest for elevations between 25 and 50 m and over half the hotspots (52.33%) occurred in this elevation range (Table 3). The hotspot density decreased rapidly after this such that elevations greater than 100 m had less than 2% of the hotspots.

Table 3. Breakdown of number of hotspots with elevation

ELEV. (M) NO.

HOTSPOTS HOTSPOT %TOTAL

HOTSPOT CUM%

HOTSPOT PER SQ

KM AREA SQ

KM AREA

%TOTAL AREA

CUM%

25 2335 52.33% 52.33% 0.2243 10,409 36.93% 36.93%

50 1669 37.40% 89.74% 0.2309 7,228 25.64% 62.58%

100 388 8.70% 98.43% 0.0801 4,846 17.19% 79.77%

150 59 1.32% 99.75% 0.0195 3,020 10.71% 90.49%

225 9 0.20% 99.96% 0.0060 1,493 5.30% 95.78%

325 1 0.02% 99.98% 0.0017 590 2.09% 97.88%

450 1 0.02% 100.00% 0.0029 342 1.21% 99.09%

600 0 0.00% 100.00% 0.0000 184 0.65% 99.74%

1050 0 0.00% 100.00% 0.0000 72 0.26% 100.00%

TOTAL 4462 100.00% 28,185 100.00%

3.2.3 Roads & Elevation

Two-thirds (60%) of the roads mapped in the study area occurred at elevations less than 25 m (Table 4).

Table 4. Roads breakdown by elevation

ELEVATION (M) KM %TOTAL CUM %

0-25 854.4 59.55% 59.55%

25-50 363.3 25.32% 84.87%

50-100 134.1 9.34% 94.22%

100-150 49.8 3.47% 97.69%

150-225 26.6 1.86% 99.55%

225-325 5.9 0.41% 99.96%

325-450 0.6 0.04% 100.00%

TOTAL 1,434.7 100.00%





4. DISCUSSION Simple GIS analysis of patterns in distribution and abundance of hotspots in Kalimantan from 1999 to 2003 was useful for exploring relationships between hotspots and proximity to roads, elevation, land systems, forestry concessions and national parks.

Overlay visualization of hotspots, national parks and elevation clearly showed that few hotspots were located in mountainous areas where most of the area of national parks occurred.

The relationship between forestry concessions and hotspots was not simple but must be analyzed on a concession-by-concession and location-by-location basis since some forestry concession areas had higher than expected hotspots and many other had lower than expected hotspots.

All comparisons and conclusions, however, must be tempered with the fact that the number of hotspots in Kalimantan is unnaturally high. Further research is required to estimate what the background natural density of hotspots should be for Kalimantan; fires caused mainly by lightning or accident.

At a more detailed level of analysis for Kabupaten Kotawaringan hotspot density was highest in the buffer area 5 – 10 km away from roads and not immediately adjacent roads in the 0 – 5 km buffer zone. Why this is so remains unclear, however, it is highly likely that not all roads were digitized since most of the roads in the study area were digitized using Landsat 7 ETM+ satellite imagery with 30 m pixels. Roads may have been missed because they were not visible in the imagery or clouds obscured the roads. Alternately clearing using burning may be occurring away from roads because areas adjacent roads have already been cleared and small access roads were not visible in the imagery. Regardless, the relationship between hotspots and proximity to roads remains unresolved and requires further research.

The vast majority of hotspots (98%) occurred in low areas less than 100 m elevation. This correlates strongly with the length of road in these areas with 94% of roads situated less than 100 m.

The high correlation of hot spots with proximity to roads underlines the importance roads and hence access by humans plays in explaining the patterns of distribution and abundance of hotspots in Kabupaten Kotawaringan.

Density mapping resulted in well over half (56.73%) the hotspots being located in 15.84% of the study area. When the second standard deviation category was included then 83% of hotspots were within one third (32.5%) the study area. This information can be used to tailor health, education, surveillance and monitoring programs to target areas worst affected and with highest hotspots.

5. REFERENCES Silverman, B.W. Density Estimation for Statistics and Data Analysis. New York: Chapman and Hall, 1986.





ANNEX 1 – LAND SYSTEMS

Table 5. Land System categories, number of hotspots, area (km2), hotspots km2 and expected number of hotspots under an assumption of independence. Categories with high departures of observed from

expected are shown in italics.

LAND SYSTEM DESCRIPTION NO.

HOTSPOTS

EXPECTED NO.

HOTSPOTS AREA SQ

KM

HOTSPOTS PER SQ

KM

Alluvial floodplains between swamps 1,305 775 12,595.92 0.103605

Asymmetric, non-orientated sedimentary hills 734 1,991 32,376.66 0.022671

Back Swamps 22 19 316.27 0.069561

Back Swamps of inland floodplain 1 10 167.25 0.005979

Coalescent estuarine/riverine plains 516 665 10,805.98 0.047751

Coalescent inland riverine plains 132 194 3,148.77 0.041921

Coastal beach ridges 109 34 553.66 0.196872

Coastal beach ridges and swales 24 46 741.89 0.032350

Deeper peat swamps, commonly domed 368 659 10,718.51 0.034333

Dissected dip slopes of cuestas 6 143 2,324.98 0.002581

Dissected volcanic cones with radial drainage pattern 10 102 1,664.51 0.006008

Dissected volcano 0 9 145.70 0.000000

Eroded mountainous strato-volcanoes with radial drainage 0 394 6,413.01 0.000000

Extremely steep-sides, finger-like volcanic plug 27 31 502.95 0.053683

Flat sandy terraces covered by deeper peat 95 215 3,493.74 0.027191

Flat sandy terraces covered by shallow peat 75 22 365.02 0.205467

High sandstones plateau 0 4 70.90 0.000000

High undulating riverine terraces 46 24 391.68 0.117442

Hillocky acid igneous/metamorphic plains 265 341 5,538.25 0.047849

Hillocky basaltic plains 0 72 1,167.84 0.000000

Hillocky karstic plains 4 102 1,659.86 0.002410

Hillocky plain 2,380 2,702 43,926.30 0.054182

Hillocky plains with cuesta-shaped ridges 25 110 1,788.28 0.013980

Hillocky plains with steep parallel ridges 20 152 2,473.89 0.008084

Hillocky sedimentary plains 2,427 2,903 47,207.11 0.051412

Hillocky sedimentary plains with steep parallel ridges 1 3 51.01 0.019605

Hillocky ultrabasic plains 0 6 92.62 0.000000

Hills with moderately steep dip and steep scarp slopes 21 162 2,642.14 0.007948





Inter-tidal mudflats under mangrove and nipah 253 648 10,539.71 0.024004

Linear, sedimentary ridge systems with steep dip slopes 72 203 3,303.81 0.021793

Long, narrow-crested, steep-sided sedimentary ridges 28 87 1,413.43 0.019810

Marine Terraces 94 164 2,674.13 0.035152

Meander belt or large river with broad levees 79 128 2,086.33 0.037866

Meander belts within very wide river floodplains 89 160 2,599.52 0.034237

Minor valley floors 48 55 886.58 0.054141

Minor valley floors within hills 89 131 2,138.14 0.041625

Moderately dissected lava flows 65 180 2,933.96 0.022154

Moderately steep hills 274 639 10,390.44 0.026370

Mountainous cuestas and circular basin 0 500 8,122.31 0.000000

Mountainous ridges 16 47 758.79 0.021086

Mountainous sandstone cuestas with dissected dip slopes 28 293 4,768.52 0.005872

Mountains 86 2,188 35,582.02 0.002417

No Data 2 63 1,027.25 0.001947

Non-orientated, non-sedimentary hills 363 830 13,497.57 0.026894

Non-orientated, sedimentary mountains 162 2,926 47,580.71 0.003405

Orientated, granite mountain ridge systems 91 286 4,650.61 0.019567

Peat basin margins 2,338 517 8,400.09 0.278330

Peat basins or domes 3,621 822 13,361.40 0.271005

Peat-covered sandy terraces 1,397 545 8,856.65 0.157735

Peat-filled valleys within terraces 933 152 2,476.66 0.376718

Permanently waterlogged floodplains 206 198 3,223.67 0.063902

Permanently waterlogged plains 163 61 984.28 0.165604

Rolling plain and sandy remnant 274 59 953.98 0.287218

Rolling plain and sandy remnants 3,173 987 16,052.64 0.197662

Rolling volcanic plains 22 54 871.05 0.025257

Rugged karst ridges and mountains 70 216 3,517.27 0.019902

Sandstone cuestas with relatively gentle dip slopes 2 21 345.32 0.005792

Shallower peat swamps 1,070 622 10,119.91 0.105732

Steep hills 82 501 8,151.49 0.010060

Steep hills and sub parallel ridges 31 15 246.25 0.125888

Steep karstic blocks and ridges 0 5 74.60 0.000000

Steep narrow ridges 31 121 1,970.79 0.015730





Steep volcanic plugs 0 6 96.70 0.000000

Sub parallel, metamorphic ridge systems 9 8 135.70 0.066323

Swampy floodplains mainly within terraces 122 61 990.06 0.123225

Swampy floodplains of narrow valleys 77 71 1,149.64 0.066977

Terrace remnant 1 8 127.38 0.007851

Tidal (mangrove and nipah) 53 61 984.68 0.053825

Ultrabasic hills 13 13 205.46 0.063272

Ultrabasic/basic mountains 31 112 1,822.91 0.017006

Undulating karst plains 12 24 387.99 0.030929

Undulating karstic plains with hums 188 232 3,777.31 0.049771

Undulating plains (PO2) with several wide valleys 963 524 8,518.17 0.113052

Undulating plains with several wide valleys 227 204 3,322.71 0.068318

Undulating sandy terraces 1,752 695 11,297.81 0.155074

Undulating to rolling non-sedimentary plain 112 162 2,629.03 0.042601

Undulating to rolling sedimentary plain 4,043 2,673 43,469.70 0.093007

Water 29 57 923.86 0.031390

Waterlogged sandy terraces 914 273 4,433.35 0.206165

Waterlogged sandy terraces 354 150 2,437.24 0.145247

Weakly orientated, metamorphic mountain ridge systems 43 143 2,323.89 0.018503

Weakly orientated, non sedimentary mountain ridge systems 44 1,058 17,203.14 0.002558

Wide valley floors containing hillock, within hills 0 2 26.65 0.000000

TOTAL 32,852 32,852 534,167.95





ANNEX 2 – FORESTRY CONCESSIONS Table 6. Forestry concession name, number of hotspots, area sq km, Hotspots per sq km, and expected

number of hotspots under spatial independence.

FORESTRY CONCESSION NO.

HOTSPOTS EXPECTED HOTSPOTS

AREA SQ KM

HOTSPOTS PER SQ

KM

PT. ARIA JAYA RAYA 1 1.6 26 0.038091

PT. DAHIAN TIMBER 1 41.1 668 0.001497

PT. GUNUNG MERANTI 1 55.3 899 0.001112

PT. INOCIN ARIA BIMA SARI 1 103.2 1,678 0.000596

PT. KARDA TRADEST LTD 1 58.5 951 0.001052

PT. KAYU ARA JAYA TBR 1 83.4 1,356 0.000738

PT. KUSUMA PERKASA TIMBER 1 45.5 741 0.00135

PT. MARUWEI TIMBER 1 46.8 761 0.001314

PT. NARA KALIMANTAN 1 44.1 717 0.001394

PT. SARANG SAPTA PUTRA 1 67.9 1,105 0.000905

PT. SINAR BARITO INDAH PLY 1 10.0 163 0.006144

PT. SIPO JAYA TBR 1 37.0 601 0.001664

PT. SUMBER JAYA UTAMA BARU 1 8.9 144 0.006945

PT. TUNGGAL PAMENANG 1 5.5 89 0.011264

PT.BIRAWA TIMBER CO. LTD. 1 19.6 318 0.003143

PT.DAISY TIMBER 1 35.5 577 0.001733

PT.DAYAK BESAR 1 4.7 76 0.013121

PT.KARYA PARAKAWAN 1 24.6 400 0.002501

PT.KEMAKMURAN 1 82.8 1,346 0.000743

PT.LESTARI GREEN LAND 1 43.9 713 0.001402

PT.PUJI SAMPURNA RAHARJA 1 30.6 497 0.002012

PT.RODA MAS 1 31.6 515 0.001944

PT.SUMALINDO HUTANI JAYA 1 7.2 117 0.008518

PT.TANJUNG REDEP HUTANI 1 58.1 944 0.001059

PT. KARUNIA WANA IKA 2 29.9 485 0.004121

PT. KAYU TRIBUANA 2 74.1 1,204 0.001661

PT. MANIMBUN JAYA 2 65.8 1,070 0.001869

PT. PONTI JAYA I 2 5.8 94 0.021247





PT. SINAR BARITO INDAH 2 8.1 131 0.01526

PT. TIMBER DANA 2 5.9 96 0.020857

PT.BDBJR 2 8.4 137 0.014651

PT.INHUTANI I/PT.POROSIDA TRADING 2 68.0 1,105 0.00181

PT.INHUTANI II Ex. PT.TANAH GROGOT 2 45.6 741 0.002699

PT.KALIMEX 2 6.2 100 0.019956

PT.KARTIKA KAPUAS SARI 2 31.1 506 0.003953

PT.METRO DAYA BUANA 2 47.8 777 0.002573

PT.PAKAR MULA BHAKTI 2 26.6 433 0.004617

PT.SYLVIA ERY TIMBER 2 34.2 556 0.003599

PT. BUMI RAYA UTAMA WOOD INDUSTRIES 3 103.8 1,687 0.001778

PT. DACRYDIUM II 3 43.9 714 0.004201

PT. HALISA 3 45.5 740 0.004051

PT. LANJAK DERAS 3 37.6 611 0.004907

PT. SBIP 3 34.4 559 0.005365

PT. SIKATAN WANA RAYA 3 32.4 527 0.005688

PT.BALTIMUR TIMBER 3 46.2 751 0.003993

PT.BERAU TIMUR 3 14.5 235 0.012749

PT.BINA LESTARI 3 51.8 843 0.00356

PT.ESSAM TIMBER 3 225.2 3,661 0.000819

PT.GELORA DAYAK BESAR 3 40.5 659 0.004553

PT.GIRI EKA WANA 3 64.2 1,044 0.002874

PT.INDONESIA MERANTI TIMBER 3 22.5 365 0.008209

PT.LIMBANG PRAJA COMPANY LTD 3 33.3 541 0.005543

PT.OCEANIAS TIMBER PRODUCT 3 128.2 2,084 0.001439

Ex. PT.ALAS KUSUMA 4 24.8 403 0.009927

PT. ANEKA MIRI 4 17.3 281 0.014249

PT. ANTANG KALIMANTAN 4 29.0 472 0.008473

PT. BUMI INDAH JAYA 4 5.9 95 0.042

PT. ERNA DJULIAWATI 4 101.4 1,649 0.002426

PT.DUTA RENDRA MULYA 4 130.8 2,126 0.001881





SEJAHTERA

PT.PANTAI HARAPAN 4 10.5 170 0.023517

PT.SUMALINDO LESTARI JAYA II 4 173.7 2,824 0.001417

HUTAN NEGARA 5 16.8 273 0.018338

PT. DAYA SAKTI TIMBER CORP 5 7.9 128 0.038981

PT.BENGEN TIMBER 5 41.4 674 0.00742

PT.DAYAK BESAR AGUNG CORP 5 118.9 1,933 0.002587

PT.DAYAK BESAR VINCENT 5 81.1 1,319 0.003792

PT.EDI MULYA 5 46.0 747 0.00669

PT.GUNUNG PUTIH INDAH 5 11.2 181 0.027549

PT.INHUTANI II 5 37.8 615 0.008132

PT.LESTARI JAYA UTAMA 5 19.0 308 0.01622

PT.MERANTI SAKTI SAKTI INDONESIA 5 18.8 306 0.016328

PT. BERKAT CAHAYA 6 107.9 1,754 0.003421

PT. KARTIKA BORNEO 6 6.9 113 0.0533

PT. KAYU MAS RATU 6 61.3 997 0.006018

PT. SIPO JAYA 6 8.2 133 0.045233

PT.RANGGA KUSUMA 6 20.8 339 0.017708

PT.SEGARA INDOCHEM 6 18.1 294 0.020399

PT. BARITO PACIFIC LUMBER 7 44.5 723 0.009678

PT. BERKAT CAHAYA TIMBER 7 21.7 353 0.019818

PT. CARUSINDO 7 36.8 599 0.011686

PT. HUTAN DOMAS RAYA 7 148.2 2,410 0.002904

PT. KURNIA KAPUAS 7 23.2 377 0.018558

PT. WANA SAWIT 7 4.3 70 0.100674

PT.INHUTANI 7 14.4 235 0.029799

PT.DJAYANTI DJAYA 8 24.2 393 0.020342

PT. INKA RAYA 9 11.4 185 0.048766

PT. TANJUNG RAYA 9 50.2 817 0.01102

PT. TRIKAKA 9 60.5 983 0.009152

PT.HANURATA 9 194.3 3,159 0.002849

PT.KIANI HUTANI LESTARI (Ex. PT.GEORGIA PACIFIC IND.) 9 45.6 742 0.012125





PT.TUNGGAL YUDI TIMBER UNIT I 9 20.5 333 0.027062

PT. DWIMA JAYA UTAMA 10 97.7 1,588 0.006297

PT. HARJOHN TIMBER 10 53.9 877 0.011404

PT. PELITA RIMBA ALAM 10 32.7 532 0.018788

PT. PONTI JAYA II 10 10.6 172 0.05822

PT. TALAWANG 10 13.0 212 0.04713

PT. WIRA SARAYA UTAMA 10 14.2 231 0.043217

PT.MSK TIMBER 10 14.8 241 0.041531

PT.RATAH TIMBER 10 95.4 1,551 0.006449

PT. ALAM INDAH RAYA 11 66.6 1,084 0.010152

PT. BULIND TRADING COMPANY 11 39.6 644 0.017086

PT. JAYANTI JAYA 11 162.7 2,646 0.004157

PT. SUMBER BARU JAYA UTAMA 11 21.3 347 0.031746

PT. TIGA BADANG SANAK III 11 9.5 154 0.071419

PT.INHUTANI I (Ex. PT.SANI MULYA ABADI) 11 13.6 221 0.049761

PT.WEYER HAUSER 11 16.6 270 0.040701

PT. CAKRA ALAM 12 45.0 731 0.016415

PT. KAWEDAR MUKTI 12 58.7 954 0.012579

PT. KLI 12 21.0 341 0.035148

PT. PONTI JAYA III 12 3.3 54 0.221503

PT. SAJ 12 9.1 149 0.080705

PT. BUMI INDAH RAYA 13 51.2 833 0.015608

PT. GOOD TMBER CORP 13 38.3 623 0.020868

PT.KARYAYASA KENCANA 13 25.3 411 0.031637

PT. AYA TIMBER 14 37.6 612 0.022894

PT.GUNUNG RAYA UTAMA TIMBER INDUSTRIES 14 35.4 575 0.024355

PT. FIRST RACHMAT 15 45.2 735 0.020398

PT. KAYU WAJA 15 49.0 796 0.018838

PT.INHUTANI Ex. PT.INNE DONG HWA DEVELEPMENT CO. LTD. 15 77.3 1,258 0.011927

PT. BENUA INDAH 16 85.7 1,393 0.011484





PT. PAPA GUNA 16 50.7 825 0.019396

PT. PURUK CAHU JAYA 16 173.8 2,825 0.005663

PT.SUMBER MAS TIMBER I 16 33.0 536 0.029854

PT.ALAS HELAU 17 207.5 3,374 0.005039

PT.PERSADA BUMI HIJAU 17 91.4 1,487 0.011436

PIR.PERKEBUNAN 18 2.9 47 0.382022

PT. HASAL AGUNG LUMBER 18 23.8 387 0.046572

PT. KALIMANTAN SARI 18 40.3 656 0.027451

PT. TRI IKA SARI 18 49.3 802 0.022441

PT.KAYAN RIVER TIMBER 19 175.1 2,847 0.006675

PT. PUNCAK SAWMILL 20 61.6 1,002 0.019968

PT. RAJA RIMBA 20 58.7 955 0.020943

PT.KAYU MAHAKAM 21 27.6 448 0.046837

EX PT. KRIDA PERKASA 22 6.0 98 0.224213

PT. HUTAN MULYA 22 56.1 913 0.024102

PT. MARISON II 22 12.6 205 0.107152

PT. SARITAMA INDAH RAYA 22 28.5 463 0.047516

PT. SUMPOL TIMBER 22 42.1 685 0.032116

PT. YOHANES ARNOLD P 22 42.6 693 0.031762

PT. FAJAR KAHAYAN 23 46.4 755 0.030453

PT. PALANGKA NUSANTARA 23 60.3 981 0.02345

PT.ALAS KUSUMA 23 26.7 434 0.053004

PT.KIANI LESTARI (Ex. PT.GEORGIA PASIPFIC IND.) 24 188.6 3,066 0.007827

PT. GIAT 25 90.9 1,478 0.016919

PT. RIMBA RAMIN PONTIANAK 25 63.3 1,029 0.024294

PT.BALIKPAPAN FORREST INDUSTRIES 25 141.1 2,294 0.010898

PT.ROSMATIKA UNIT I 25 33.9 551 0.045405

BUKIT RAYA (HSA-W) 26 262.7 4,272 0.006086

PT. ADO JAYA 26 64.8 1,054 0.024677

PT. HENDRATNA PLYWOOD 26 15.7 255 0.101984

PT. YAYANG INDONESIA 26 26.7 435 0.059816

PT.TELAGA MAS KALIMANTAN COMPANY 26 61.4 998 0.02606

TN KUTAI 26 134.4 2,185 0.0119





PT.INHUTANI I (Ex. PT.SANGKULIRANG) 27 61.7 1,003 0.026908

PT.LIMBANG GANECA 27 105.9 1,721 0.015684

PT. BRATAJAYA UTAMA 28 30.2 491 0.056982

PT. MELANT 28 38.0 618 0.045283

PT. SUKA JAYA MAKMUR 28 183.2 2,979 0.009401

PERK.PT. SAPTA KARYA DAMAI 29 6.5 105 0.275759

PT. BATARUNG 29 38.3 622 0.046611

PT. EMIL TIMBER 29 27.7 450 0.064486

PT SUKA JAYA MAKMUR 30 14.1 229 0.131265

PT. BRAJATAMA 30 51.4 836 0.035903

PT. INYUNITAS 30 72.0 1,170 0.025638

PT.TRIWIRA ASTRA BHARATA 30 40.3 655 0.04583

PT. DAYAK BESAR TIMBER COY 31 102.9 1,673 0.018533

PT. GELORA AGUNG 31 29.2 475 0.065236

PT. INHUTANI 31 37.6 612 0.050673

PT. SEBANGAU BESAR 32 42.7 695 0.04604

PT.RIMBA AGUNG 32 10.3 167 0.191111

PT. FIRST LAMANDAU TIMBER INT 34 81.1 1,319 0.025779

PT. DAYA SAKTI TMB 35 27.2 442 0.079134

PT. LAMANDAU UTAMA JAYA 35 85.9 1,397 0.025051

PT.IHM 36 139.2 2,263 0.015905

PT. BATARUNG INH III 37 43.7 711 0.052023

PT. ERNA JULIAWATI 37 67.8 1,102 0.033585

PT. ANURAGA 38 28.1 456 0.083315

HL 39 1,671.0 27,170 0.001435

PT. WANAWATI UTAMA 39 112.5 1,830 0.021312

PT. KAYU LAPIS INDONESIA 40 80.9 1,315 0.030407

PT. SEHATI RUNGAN 41 25.4 413 0.099192

PT. KAYU PAPA ENTI 42 43.9 713 0.058901

PT. KAYU BATANG KARANG 44 15.2 248 0.177752

PT. RIMBAYU BARITO 44 69.9 1,136 0.038735

PT. ANTANG PERMATA TIMBER 45 32.2 524 0.085869





PT. BINA PRAJA INDAH 45 16.6 269 0.167011

PT. PUSAKA JAYA AGUNG 45 24.8 404 0.111459

PT. SARI BUMI KUSUMA 45 174.1 2,831 0.015894

PT.NATA MARGA BUANA 45 22.3 362 0.1242

PT.GOMPU INDONESIA 48 68.9 1,120 0.042857

PT. SINDO LUMBER 50 69.4 1,128 0.044338

PT.TIMBER DANA 50 96.7 1,572 0.031808

PT. ALAM UNDA 51 35.2 572 0.08912

PT. DAYA SAKTI 52 14.1 229 0.227392

PT. DAYA SAKTI TMB CORP 52 9.8 159 0.327485

PT.KAYUMAS TIMBER 53 128.2 2,085 0.025421

PT. SATYA JAYA RAYA 54 39.5 642 0.08405

PT. PULAU MAYA 55 25.3 412 0.133537

PT.JATITRIN CO. LTD. 55 14.3 233 0.236459

PT. KALANG MURNI 57 61.0 992 0.057459

PT.SENTOSA KALIMANTAN JAYA 57 20.0 325 0.175256

PT. KAPUAS SAKTI UTAMA 58 32.6 530 0.109495

Ex. PT.HAYAM WURUK 64 79.4 1,291 0.049592

PT. TIGA BADANG SANAK 64 50.8 827 0.077406

PT.INH III(EX.PT.KATINGAN TMB) 71 83.7 1,361 0.052165

PT. SUNGAI SEBELONGAN JAYA 74 48.9 795 0.093124

Ex. PT.SYLVA DUTA CORP 75 31.4 510 0.146975

PT. SETIA ALAM JAYA 75 24.0 390 0.192176

PT. MARSELA WANA SEKAWAN 76 38.1 620 0.122572

PT. AUSTRAL BINA 81 156.8 2,549 0.031772

PT.INHUTANI II Ex. PT.SUMBER RAYON RAYA 81 79.1 1,286 0.062984

PT. SUKA BUDI MULYA 85 30.7 499 0.170306

PT. DUAJA CORP 86 112.5 1,830 0.046996

PT.DHARMA SATYA NUSANTARA 87 127.7 2,077 0.041897

PT.MERATUS KALIMANTAN TIMBER 87 58.2 947 0.091866





PT. MEDANG KARANG 88 71.4 1,161 0.075765

PT. KOREA DEVELOPMENT CO LTD 92 171.5 2,788 0.033001

PT. BPL 93 27.0 439 0.211621

PT. KURNAIN 93 26.3 427 0.217626

PT.NEW TIMBER RAYA CORP 96 42.7 694 0.138419

PT. PERSADA KAWI INDUSTRIAL & TRAD. 101 66.9 1,089 0.092782

PT. MENTAYA KALANG 102 41.9 682 0.14957

PT.MARIMUN TIMBER 102 26.6 433 0.235826

PT. BATASAN 106 83.7 1,361 0.077863

PT. DELAPAN DELAPAN 107 148.5 2,415 0.044306

HSA-W 109 126.9 2,064 0.052809

PT. GEMPITA KALTENG 113 34.6 563 0.200754

PT. TANJUNG PURA SAKTI 113 90.6 1,473 0.076698

PT. HUTAN KINTAP 114 86.6 1,408 0.080971

PT. PAMUKAN JAYA 114 80.2 1,304 0.087452

PT. BINTANG ARUT 116 51.6 838 0.138352

PT. TAWANG MERANTI 119 70.4 1,144 0.104013

PT.ITCI 123 286.4 4,657 0.026412

PT. MARAGA DAYA WOOD WORKS 126 107.0 1,740 0.072405

PT. YUSMIN TRADING 134 53.9 876 0.152972

PT. RIMBA KARYA KALIMANTAN 135 75.3 1,225 0.110224

PT.HARIATI TIMBER INDS. 135 127.6 2,074 0.065092

PT.INHUTANI I 138 1,044.7 16,986 0.008124

PT. MARISON I 149 47.1 766 0.194544

PT. PAMINGGIR 151 65.7 1,069 0.141294

PT. GUNTUR GEMPITA 152 44.1 717 0.21199

PT. TALAWANG INHUTANI III 154 42.2 686 0.224452

PT. SARMIENTO PARAKANCA TMB 156 210.6 3,425 0.045546

PT.JAMAKER 170 118.2 1,922 0.088449

PT. HARAPAN KITA UTAMA 185 121.4 1,974 0.093737

PT. GELORA DAYAK BESAR 187 225.3 3,663 0.05105

PT. ANDALAN RAYA 189 48.0 780 0.242164





PT. BINA SAMAKTHA 190 109.8 1,785 0.106468

PT. KATUNEN 194 45.0 732 0.264981

PT. MENGKATIP 197 29.4 478 0.411741

PT. DAYA BUDHI DHARMA 198 44.4 722 0.274121

PT. BINA DAYA TETRA 210 125.5 2,040 0.102933

PT. KAYU PESAGUHAN 222 86.2 1,402 0.158323

PT. BARITO BARU UTAMA 236 89.2 1,450 0.162771

PT. HUTAN RAYA UTAMA 237 58.2 946 0.250495

PT. PERKASA WANA 243 79.2 1,288 0.188638

PT. ARIA JAYA 249 57.2 929 0.267887

PT.INH III(EX.KAYU KLABAN TMB) 266 55.1 895 0.297163

PT. NUSANTARA PLYWOOD 267 123.5 2,008 0.13299

PT. TUNAS INDO TIMBER 274 61.6 1,001 0.273725

PT. ANGKASA WANA 315 58.1 944 0.333689

PT. INHUTANI II 369 294.5 4,789 0.077049

PT. SINJAL DJAYA LTD 412 51.5 838 0.491832

PT. SUMBER ALAM MURNI 413 43.5 707 0.584005

PT. SEHATI BARITO 421 100.3 1,631 0.258161

PT. SIMAANGGANGHAYU 440 128.4 2,087 0.210813

PT. DJAYANTI 456 119.5 1,943 0.234728

PT. INDO KAYU 509 115.4 1,876 0.271261

PT. GAJAH SENO SAKTI 515 93.7 1,523 0.33806

PT. INHUTANI III 546 460.4 7,487 0.07293

PT. DAYA SAKTI TIMBER 560 190.9 3,103 0.18045

PT. BALAMBIT 570 66.7 1,084 0.525629

PT. SALAWATI MAKMUR 605 219.3 3,566 0.169677

PT. LAMJAYA UTAMA 755 70.7 1,150 0.65642

TN. TANJUNG PUTING 775 179.0 2,911 0.266248

PT.INH III EX.KARANG S.M 802 245.3 3,989 0.201055

PT. SINAR WEST KALIMANTAN 891 136.7 2,222 0.40092

PT. KAHAYAN LUMBER 1,303 115.0 1,870 0.696966

PT. INHUTAN III 2,036 525.8 8,549 0.238155

PT.SUMBER SEJAHTERA 0 3.3 54 0

PT.SURAPATI PERKASA CORP 0 76.8 1,249 0





PT.SUWARAN JAYA KUSUMA 0 21.6 352 0

PT.TABALAR WOOD INDUSTRIES 0 40.8 663 0

PT.TUNGGAL YUDI TIMBER 0 11.3 184 0

PT.TUNGGAL YUDI TIMBER SP UNIT III 0 35.0 570 0

PT.TUNGGAL YUSI TIMBER 0 5.6 92 0

PT.TWI 0 1.0 16 0

PT.WANA RIMBA KENCANA 0 45.7 743 0

No forestry concession 8,762 9,581.1 155,787

Forestry Concessions Combined 24,090 23,270.9 378,381

TOTAL 32,852 32,852.0 534,168

Black & Veatch (International) Ltd in association with

PT Nusantara Systems Int., PT Exsa Int. and PT Multi Tehniktama Prakarsa




ANNEX 3 – NATIONAL PARKS

Table 7. National Parks, number of hotspots, area sq km, Hotspots per sq km, and expected number of hotspots under spatial independence.

NATIONAL PARK NO.

HOTSPOTS AREA SQKM

HOTSPOTS PER SQKM

EXPECTED HOTSPOTS

Apar Besar 125 2,370.38 0.05273 145.8

Apo Kayan NR/BR 2 948.02 0.00211 58.3

Batu Kristal 0 37.29 0.00000 2.3

Bentuang Karimun 2 7,858.62 0.00025 483.3

Bukit Baka 0 535.05 0.00000 32.9

Bukit Batikap I,II & III 1 8,434.46 0.00012 518.7

Bukit Batutenobang 3 4,643.16 0.00065 285.6

Bukit Perai 23 1,825.68 0.01260 112.3

Bukit Raya 0 1,325.45 0.00000 81.5

Bukit Raya (Perluasan) 17 3,290.94 0.00517 202.4

Bukit Rongga 13 1,376.58 0.00944 84.7

Bukit Soeharto 55 835.92 0.06580 51.4

Bukit Tangkiling 0 50.59 0.00000 3.1

Danau Sentarum 19 897.86 0.02116 55.2

Gunung Berau 1 1,591.07 0.00063 97.9

Gunung Lumut 0 501.56 0.00000 30.8

Gunung Niut/Gunung Becapa 9 1,407.18 0.00640 86.5

Gunung Palung 14 1,073.60 0.01304 66.0

Gunung Raya Pasi 0 52.80 0.00000 3.2

Gunung Sebatung NR (Pulau Laut) 0 18.28 0.00000 1.1

Gunung Tunggal 52 637.88 0.08152 39.2

Hutan Kapur Sangkulirang 3 2,149.37 0.00140 132.2

Kaya Kuku NR (Kalimantan) 6 69.69 0.08609 4.3

Kayan Mentarang 0 1,316.91 0.00000 81.0

Kelompok Hutan Kahayan 409 1,391.21 0.29399 85.6

Kelompok Hutan Sambas 0 395.84 0.00000 24.3

Kelumpang Baai 4 245.61 0.01629 15.1

Kep Karimata 0 903.11 0.00000 55.5





Kuala Kayan 2 613.50 0.00326 37.7

Kutai 13 2,247.21 0.00578 138.2

Long Bangun 0 3,550.01 0.00000 218.3

Mandor 42 53.67 0.78261 3.3

Meratus Hulu Barabai 24 978.87 0.02452 60.2

Muara Kaman Sedulang 13 862.44 0.01507 53.0

Muara Kayan 28 915.53 0.03058 56.3

Muara Kendawangan 138 1,376.10 0.10028 84.6

Muara Sebuku 9 570.28 0.01578 35.1

Pamukan 29 317.07 0.09146 19.5

Pantai Samarinda 36 325.43 0.11062 20.0

Pararawen Baru 7 813.69 0.00860 50.0

Pararawen I,II 0 39.23 0.00000 2.4

Perairan Sungai Mahakam 51 1,226.34 0.04159 75.4

Pleihari Martapura 148 1,349.46 0.10967 83.0

Pleihari Tanah Laut 11 406.50 0.02706 25.0

Pulau Maratua-Karang Muaras 0 1,822.20 0.00000 112.1

Pulau Sebuku 7 169.82 0.04122 10.4

Pulau Suwangi 1 201.40 0.00497 12.4

Sesulu 3 1,353.82 0.00222 83.3

Sungai Berambai 0 780.15 0.00000 48.0

Sungai Kayan Sungai Mentarang 9 18,102.82 0.00050 1,113.3

Tanjung Dewa Barat 3 237.63 0.01262 14.6

Tanjung Penghujan NR/RP 25 276.82 0.09031 17.0

Tanjung Puting 829 3,971.21 0.20875 244.2

Rest 30,666 445,422.63 0.05741 27,394.0

National Parks Combined 2,186 88,745.32 5,457.9

TOTAL 32,852 534,167.95 32,852.0



mc rmp hotspots kalimantan

Documents

national parks

overlay analysis

statistical analysis

hotspot density

ministry of marine affairs

land systems

density mapping

black veatch international