INTERNATIONAL CLIMATE INITIATIVE Regional project Climate Protection through Forest Conservation in Pacific Island Countries

Forest Carbon Inventory In Proposed Central Suau REDD+ Area, Milne Bay Province, Papua New Guinea.

Forest Carbon Inventory in Proposed Central Suau REDD+ Area, Milne Bay Province, Papua New Guinea.

December 2014

Prepared by:

On behalf of: SPC/GIZ Regional Project ‘Climate Protection through Forest Conservation in Pacific Island Countries’ P.O. Box 14041, SUVA, Fiji Email: [email protected]

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Executive summary

The present report provides an overview on the methodology, results and discussion of the forest carbon inventory in Central Suau in Milne Bay province, Papua New Guinea conducted by Österreichische Bundesforste AG. The report is the deliverable 3 in the project “Project Design Document Development for Forest Carbon Project (Logged – Protected Forest) in Central Suau” financed by the Gesellschaft für Internationale Zusammenarbeit (GIZ) and supported by the Secretariat of the Pacific Community (SPC). The project is developed in close cooperation with the Papua New Guinea Forest Authority (PNGFA). The primary forest in the Central Suau Preliminary Project Area (PPA), located in Milne Bay Province, Papua New Guinea, defines the actual Project Area, as it is subject to conventional logging under a Forest Management Agreement (FMA) as planned by PNGFA. The PPA consists predominantly of hills and mountains with steep ridges. The central mountain range reaches an altitude of 1253m at its peak. Small areas of low relief exist in the lowlands and river valleys, particularly near Leleafa in the west, Saga-aho near the Segei River and Modewa in the south east. The methodology employed followed state-of-the-art approaches in land cover analysis to derive the operable forest area in commercially attractive forest strata. Nested Permanent Sample Plots (PSP) have been established in a purposively set cluster sampling design across three forest strata. The forest inventory was carried out in the PPA in September and October 2014. 141 plots were measured in seven clusters. In addition to assessing carbon stocks, the forest inventory also estimated standing volume of commercial sawlogs. Commercial sawlogs are defined as logs with minimum DBH of 50cm and with log form meeting minimum form criteria for each log grade. Three log grades were identified, with Grades A and B assumed to be export grade and Grade C suitable for domestic market only. The results of the land cover analysis show eleven land cover strata including three assessed to hold commercial sawlog stocks. These three land cover strata cover 43,374ha or 72.6% of Central Suau PPA area of 59,758ha. The dominant commercial forest strata is “Low Altitude Forest on Uplands < 550m altitude” (Hma), which covers 32,455ha or 75% of the production forest area. Of the total commercial forest area, 22,447ha (51.8%) is considered operable. The reminder is considered too steep for ground based logging operations (Hmb). As the Hmb strata is almost all located on steep land, the final operable area is dominated by the Hma strata. The results of the inventory indicate an average carbon stock in Aboveground Biomass of ~150 tons per ha across the three land cover strata sampled. In total, the carbon stock amounts to 6.2 million tons carbon across the commercial forest area of 43,374ha. The average sawlog stock constitute 42.0m3 net recoverable volume per ha across the three land cover strata sampled or 1.8 million m3 in total. Of this, 81% is considered suitable for export (Grade A and B). Poor weather conditions reduced the available time for plot measurement. As a result of this as well as several occurrences of landowner permission issues, most of the clusters were not completed to the target of 24 plots per cluster. Consideration should be given to completing the clusters during the next re-measurement, along with the establishment of the Cluster planned at point 4 (Silosilo), for which permission was withdrawn at the last minute. During the field work it became clear that there was a lack of information on the planned REDD+ project at the individual land owner level. Additional input is required to improve awareness at the grass roots level to ensure a smooth monitoring at PSPs in the future as required by VCS. This report will be used as an input into the preparation of the Project Description under VCS. The next steps to be taken comprehend the preparation of the indicative logging plan and the calculation of carbon emissions from logs harvested and associated harvest operations such as road construction, felling and extraction, transport to market, and other related support activities.

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Contents Executive summary ...... 2 Contents ...... 2 Abbreviations ...... 2 1 Background ...... 2 2 General Description of Project Area ...... 3 2.1 Location and Size ...... 3 2.2 General Physical Characteristics from PNGRIS Dataset ...... 4 2.3 Topography and Hydrology ...... 5 2.4 Accessibility ...... 5 3 Methodology ...... 6 3.1 Overview ...... 6 3.2 Land Cover Stratification ...... 6 3.3 Inventory ...... 9 3.4 Tree Height Estimation ...... 12 3.5 Estimation of Carbon Stock ...... 12 4 Area Statement...... 15 4.1 Area by Land Cover Strata...... 15 4.2 Slope Class Analysis ...... 16 4.3 Elevation Class Analysis ...... 17 4.4 Delineation of Steep Land Areas ...... 18 5 Inventory Results ...... 20 5.1 Clusters and Plots Measured ...... 20 5.2 Estimate of Total Carbon Stock and Stock per Hectare by Land Cover Strata ...... 21 5.3 Estimate of Total Sawlog Volume and Volume per Ha by Land Cover Strata ...... 21 6 Discussion and Conclusions ...... 23 7 Materials Delivered ...... 24 References ...... 25 Annex 1: Data Sources ...... 26 Annex 2 Definition of Land Cover Strata on Milne Bay Forest Base Map 2012 ...... 27 Annex 3 Field Measurement Procedure ...... 29 Annex 4 Plot Tallysheet Forms ...... 33 Annex 5 Cluster and Plot List and Plot Layout Maps ...... 36 Annex 6 Species List ...... 43 Annex 7 Detailed Results ...... 53

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Tables Table 1: PNGRIS data for the PPA ...... 4 Table 2: Land Cover Strata Used in the LCC Analysis ...... 8 Table 3: Regression Results ...... 12 Table 4: Area by Land Cover Strata ...... 15 Table 5: Slope Class Distribution ...... 16 Table 6: Elevation Class Distribution ...... 17 Table 7: Operable Area Estimation ...... 18 Table 8: Table of Clusters And Plots Measured by Land Cover Strata ...... 20 Table 9: Carbon Stock Statistics Summary ...... 21 Table 10: Sawlog Stock Statistics Summary ...... 22

Figures Figure 1: Situation Map ...... 3 Figure 2: Carbon Inventory Analysis Steps ...... 6 Figure 3: Inventory Plot Design...... 10 Figure 4: Selected Photos ...... 11 Figure 5: Land Cover Map ...... 15 Figure 6: Slope Class Distribution Map ...... 16 Figure 7: Elevation Class Distribution Map ...... 17 Figure 8: Areas Impacted by Steep Slope ...... 18 Figure 9: Map of Cluster Locations...... 20

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Abbreviations

ABLG Above Ground Live Biomass AGB Above-Ground Biomass CBD Convention on Biodiversity (1992) CCBS Climate, Community and Biodiversity Standards CDM Clean Development Mechanism of the Kyoto Protocol DBH Diameter at Breast Heigth GoPNG Government of Papua New Guinea FIM Forest Inventory Mapping FMA Forest Management Agreement GHG Greenhouse Gas GIS Geographic Information System GIZ Gesellschaft für Internationale Zusammenarbeit GPS Global Positioning System HCVF High Conservation Value Forest ILG Incorporated Land Group IPCC Intergovernmental Panel on Climate Change JICA Japanese International Cooperation Agency LCC Land Cover Change LLG Local Level Government LU/LC Land Use/Land Cover MRV Monitoring Reporting Verification PDD Project Design Document PMV Public Motor Vehicles PNG Papua New Guinea PNGFA Papua New Guinea Forest Authority PNGRIS Papua New Guinea Resource Information System PPA Preliminary Project Area PSP Permanent Sample Plots REDD+ Reducing Emmissions on Deforastation and Degradation SPC Secretariat of the Pacific Community SRTM Shuttle Radar Topography Mission TRP Timber Rights Purchase UNFCCC United Nations Framework Convention on Climate Change USGS United States Geological Survey VCS Verified Carbon Standard

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1 Background

Central Suau is located in Suau Rural Local Level Government (LLG) in Alotau District, Milne Bay Province and selected as a REDD+ pilot region by the Forest Authority (PNGFA). It covers an area of 59,758 ha. At its closest point, the Central Suau is approximately 30km straight line distance south west of Alotau, the provincial capital. The ex-ante carbon balance estimation in order to setup a REDD+ project under the Verified Carbon Standard methodology VM0011 “Improved Forest Management - Logged to Protected Forest” can be based on two methodological pathways. Either existing forest inventory datasets may be employed for calculating net GHG emissions from baseline logging versus the REDD+ project implementation or a forest inventory with permanent sample plots needs to be conducted. Two preceding exercises on forest data gathering and analysis have been concluded in other projects. The log volume of the standing stock has been estimated by log grades and merchantable species from the PNGFA forest inventory in 2010. The Biomass survey done by the Japanese International Cooperation Agency (JICA) in 2013 strived for generating accurate Above-Ground Biomass (AGB) data to examine the correlation between the forest carbon and forest canopy volume derived from airborne data. However, neither permanent sample plots have been established (PNGFA inventory 2010) nor does the sampling design meet the requirements of VCS (PNGFA inventory 2010, JICA survey 2013). Therefore a VCS-compliant forest inventory was designed and implemented. The criteria for compliance to VCS cover the prescribed level of accuracy, the minimum standard on the sampling design and plot design, the forest parameters to include in data collection, and the monitoring of parameters. The task needed to plan and implement a forest carbon inventory in order to estimate the above ground forest biomass and carbon stock in those land cover strata within the Central Suau REDD+ area that are assumed to contain commercial forest. The inventory was carried out in collaboration with the Papua New Guinea Forest Authority (PNGFA), who provided a group of eight experienced foresters and botanists for the length of the inventory. The inventory involved the establishment of Permanent Sample Plots (PSP) that can be relocated and re-measured as required in the future. The field work was carried out in September and October 2014. The report constitutes the deliverable 3 in the project “Project Design Document Development for Forest Carbon Project (Logged – Protected Forest) in Central Suau”. This report follows a common format. The Section 1 has provided the embedding of the forest inventory in the project context. The Preliminary Project Area is decribed in Section 2. The explanations on the methodology cover the land cover analysis, the ground-based inventory, the tree height estimation and derivation of forest carbon stocks in Section 3. The first set of results in Section 4 presents findings from the land cover analysis to derive the net operable forest area for planned logging. The second set of results in Section 5 comprehends the description of ground-based inventory results including the total Aboveground Biomass stock and sawlog standing stock. Section 6 provides discussions and conclusions while Section 7 lists the materials delivered to GIZ.

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2 General Description of Project Area

2.1 Location and Size The Central Suau Preliminary Project Area (PPA) is located in Milne Bay Province, Papua New Guinea (PNG). The PPA is on the south coast of the PNG mainland facing the Coral Sea. At its closest point, near Mila Village, the project area is approximately 30km straight line distance south west of Alotau, the capital of Milne Bay Province. The PPA covers an area of 59,758 ha.

Figure 1: Situation Map

The southern boundary of the project area runs along the coastline from the Modewa River mouth in the east to the end of the peninsula opposite Bona Bona Island. The western boundary follows the coastline along the southern coast of Mullins Bay. The northern boundary of the project area starting from Mullins Bay in the west, and runs east following two major ridges/catchment boundaries until it drops south from the ridge onto the top of the Segei River. The boundary continues east (upstream) to the head of the Segei River, crosses a short saddle over into a tributary of the Mila River and continues east until it meets another tributary flowing from the south. The Eastern boundary follows this tributary south then across the dividing ridge and drops down to the Modewa River and follows the river back to the south coast. PNGFA staff explained that the northern and eastern boundaries follow the boundaries of old timber extraction license areas, in particular:  the expired Sagarai TRP to the north  the expired Gara Modewa TRP to the east

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2.2 General Physical Characteristics from PNGRIS Dataset The Papua New Guinea Resource Information System (PNGRIS) dataset provides the following broad information on the PPA:

Landform Dominant: Mountains and hills with weak or no structural control. Other Minor: Mangrove swamps. Composite alluvial plains. Homoclinal ridges and cuestas: inclined asymmetrical structurally controlled ridges. Lithology Dominant: Basic to intermediate volcanic. Mixed sedimentary and limestone. Other Minor: Estuarine deposits. Alluvial deposits. Soils Dominant: Dystropepts - Moderately weathered soils with altered B horizons and low (<50%) subsoils base saturation values. Other Minor: Eutropepts - Slightly to moderately weathered soils with an altered B horizon and high (>50%) subsoils base saturation values. Tropofluvents - Mainly well drained undifferentiated soils with high (>0.2%) or fluctuating org C to > 125 cm. Slope Class Dominant: 20-30 degrees. Relief Dominant: Very high (>300m). High (100 - 300m). Other Minor: Very low (10 - 30m). Inundation Dominant: No flooding or inundation. Other Minor: Long-term inundation. Areas which are inundated for periods of up to 4 to 6 months but inundation is shallow (<0.25 m) and subject to drying out for short periods. Usually restricted to the wet season or to areas with high 'dry' seasonal rainfall. Areas subject to tidal flooding. Normally associated with mangroves. Rainfall 2500 - 3000 mm per year.

Table 1: PNGRIS data for the PPA

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2.3 Topography and Hydrology The PPA consists predominantly of hills and mountains with steep ridges and ravines accompanied by high to very high relief. In most areas the rise in slope starts from coastline and increases as one moves further inland to the north. The central mountain range reaches an altitude of 1253m at its peak. Small areas of low relief exist in the lowlands and river valleys, particularly near Leleafa in the west, Saga-aho near the Segei River and Modewa in the south east. Patches of limestone karst are found throughout the PPA. The dominance of steep land with high to very high relief and presence of karst on mountain ridges are seen as obstacles to timber harvesting operations. See calculation of operable area in Section 4.4.

2.4 Accessibility There are three main means of access to the PPA:  Villagers from the western parts of the PPA generally travel to and from Alotau by sea via Mariawate which is located just north of the entrance to Mullins Bay. From there Public Motor Vehicles (PMV) travel to Alotau.  Road access is available to Leleafa. PMVs are available to Alotau daily from Leleafa. This road is currently being extended to the Fyfe Bay Station. Villagers from Fyfe Bay commonly walk over to Leleafa to catch PMV to Alotau.  Villagers from the south-eastern coastal areas generally travel to and from Alotau directly by sea. A number of Anchorage points are available - the major ones are Mullins Harbour on the western end, Fyfe Bay and Baxter Harbour in central part and Dubaguri Bay north of Suau Island on the eastern side. A new concrete jetty has been constructed on Suau Island. The south coast of the mainland is open to the Coral Sea and as such the area frequently experiences rough seas. There are a number of reefs along the shore. The sheltered areas in the deep bays and major river mouths are mostly very shallow, particularly at low tide. Access to the shore for anything but dinghies and small runabouts is therefore limited.

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3 Methodology 3.1 Overview

Figure 2 sets out the carbon inventory analysis steps.

Figure 2: Carbon Inventory Analysis Steps 3.2 Land Cover Stratification

3.2.1 Approach In statistical surveys, when subpopulations within an overall population vary, it is advantageous to sample each subpopulation (stratum) independently. Stratification is the process of dividing members of the population into homogeneous subgroups before sampling. Then simple random sampling or systematic sampling is applied within each stratum. Land cover stratification is the process of dividing the land cover of a target area (the overall population) into relatively homogenous land cover strata (subpopulations). The process primarily consists of analysis of satellite imagery using Remote Sensing and Geographic Information Systems (GIS) tools for interpretation of vegetation cover. Forest condition characteristics that can be readily captured in imagery analysis include colour, canopy closure and roughness of the canopy layer. Stratification was applied for several reasons:  Forest condition is often correlated (but not always) with forest carbon stock and commercial timber stock. For example, dense fully stocked forest is usually associated with higher carbon stocks than open canopy low stocked forest. Therefore with stratification an improvement in the representativeness of the sample can be expected by reducing sampling error.

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 Stratification allows for more efficient sample design for ground survey, and clearer presentation of the results of the inventory. Broadly speaking there are three commonly used approaches to land cover stratification from satellite images: Unsupervised stratification involves software analysis of an image where the outcomes (groupings of pixels with common characteristics) are generated without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorithm the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground (such as wetlands, developed areas, forest classes, etc). Supervised stratification is based on the concept that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Training sites (also known as testing sets or input classes) are selected based on the knowledge of the user. The user also sets the bounds for how similar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the training area, plus or minus a certain increment (often based on "brightness" or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into. Many analysts use a combination of supervised and unsupervised classification processes to develop final output analysis and classified maps. Visual stratification is a manual digitisation process carried out by a user with excellent knowledge of the land cover conditions in the area. The user is able to determine each land cover class by on screen analysis of satellite images. Images are commonly enhanced to aid identification of classes. Recent satellite imagery available over the PPA suffered from various quality issues:  Landsat 7 images from 2011 - 2014 period all suffer from cloud cover over parts of the PPA as well as heavy striping. Multiple images were used to increase cloud free coverage.  A landsat 8 image from 2014 provides useful data over coastal areas but is cloud covered in the hilly interior of the PPA.  The steep topography over much of the PPA means there is a lot of shadow effect in the images. The condition of the data meant that manual heads up digitising on digitally enhanced images was the preferred stratification method for generating the land cover stratification.  The PPA is mostly forested. Non forest areas consist mostly of shifting cultivation areas along the coastal belt and close to villages. As such land cover patterns are relatively predictable.  The PPA is relatively small and as field teams spent 6 weeks in the field there is a high degree of familiarity with the area. Mature coconut plantation areas were digitised off a high resolution image on Google earth. It should be noted that the land cover stratification used in this report is a later version than that used in the Land Use Change analysis. However differences are minor. A complete list of the data used for the Land Cover Stratification is listed in Annex 1.

3.2.2 Definition of Forest PNG has not submitted an official forest definition (minimum indicator values for forest) to the UNFCCC. In the absence of this definition, we used the national definition (GoPNG 2014). The official definition of forests indicates the minimum area to distinct forest from non-forest, among other indicators, and is used to adjust the minimum mapping unit in historical land cover classification. As a consequence, the area of each polygon of forest mapped is at least as large as defined hereafter. The Government of PNG defines forest as follows: “Land spanning more than one (1) hectare with trees higher than three (3) meters and a canopy cover of more than ten (10) percent (%)” (GoPNG 2014).

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Note that the GoPNG definition of forest is not clear on restrictions to the definition of natural forest areas. In the Milne Bay Forest Base Map 2012 the following are classified as non-forest strata, although in theory they also fit the above criteria for forest:  Mature coconut plantation, which is classified as agriculture plantation  Shifting agriculture areas, which include patches of regenerating forest. These are classified under Agriculture land use.

3.2.3 Definition of Land Cover Strata The Table below shows land cover strata identified in the stratification process: Land Cover Strata Code Description

Crown diameter >8m. Canopy is generally 30–35m high and irregular in both height and closure. Stem diameters generally range from large Low Altitude Forest on Plains P (70-89 cm) to small (30-49 cm) but very large stems (90+ cm) are not and Fans uncommon. The floristic composition is very mixed with no single- species dominance (definition for Pl). Low altitude forest on uplands. The canopy of this forest type is 25- 30m in height, is generally only slightly uneven and has a 60-80% Low Altitude Forest on Hma crown closure. Except for Araucaria, emergents rarely exceed 40m in Uplands < 550m altitude height. Very large stem diameters (90cm+) are rare except for Araucaria. Floristically the forest is very mixed (definition for Hm). Low Altitude Forest on Hmb Same as Hma but altitude >550m and dominance of Castanopsis spp Uplands 550-1000m altitude Lower montane forest (above 1000m). This forest has an even to slightly undulating canopy 20-30m in height. Canopy closure varies Lower Montane Forest L from dense to slightly open. The canopy height decreases with increasing altitude. Stem diameters are generally medium (50-69cm) to small (30-49cm). Mangrove and Nipah M Dominated by mangrove with small areas of associated nipah. Consists of old shifting cultivation areas where a tree canopy has regenerated and is dominant. Vegetation consists of a mix of pioneer Secondary Forest Se species, fruit trees, and scattered small residual pockets of forest. May also include scattered small pockets of recent clearance, and also small pockets of degraded primary forest. Consists of areas dominated by recent clearance for shifting Agriculture O agriculture. Land cover has no or a low canopy. May also include scattered small pockets of secondary forest. Agriculture plantation. In the project area this is limited to coconut Agriculture Plantation Qa plantations generally located around the villages. Grassland and Herbland G Lakes and Large Rivers E Coastal Open Land Col

Table 2: Land Cover Strata Used in the LCC Analysis

The strata and descriptions are based on those found in the Milne Bay Forest Base Map 20121 except for the Hmb, Se, and Col strata. Boundaries between strata have been updated using 2014 imagery and some adjustments to strata have been made. The Low Altitude Forest on Uplands (H) strata as described in Milne Bay Forest Base Map 2012 has an altitude limit of 1,000m. In the field a change in species composition was noticed at an altitude of around

1 The JICA - PNGFA Milne Bay Forest Base Map 2012. One of the key outcomes of the recently completed JICA- PNGFA Project is a national level forest base map known as the National Forest Base Map 2012.

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550m. From this altitude up Castanopsis spp began to dominate. It has therefore been decided to break the “Low Altitude Forest on Uplands” strata into the following two sub strata: Hma Medium Crowned Low Altitude Forest on Uplands up to 550m altitude. Hmb Low Altitude Forest on Uplands 550 - 1000m altitude. The Hmb strata could potentially be reclassified as HsCa (Small crowned forest with Castanopsis sp.) as described in the PNG HCVF toolkit Appendix 8. However this will need further checking on the ground due to the limited amount of inventory and reconnaissance done in these areas. Two forest classes identified in the Milne Bay Forest Base Map, namely Littoral Forests (coastal zone forests) and Seral Forest (regenerating disturbed forests) which covered a very small proportion of the PPA area have been merged into the other strata, primarily secondary forest. Creation of a Secondary Forest Strata. In the Central Suau area there are two main agricultural systems:  Coconut plantations for copra production  Shifting cultivation for food crops. Under the shifting cultivation system secondary forests regenerate in ex-cultivation areas and are commonly associated with new clearance and residual pockets of degraded primary forest in a heterogeneous mix. In the Milne Bay Forest Base Map these areas are predominantly found in the “Agriculture Land Use” strata. However significant portions of this class also qualify as forest following the above definition. In the absence of clear guidance, areas dominated by secondary forests have been separated from areas dominated by agriculture. The decision on whether to categorise these areas as forest or non-forest can be made at a later time. It should be noted that defining the cut-off between agriculture (non-forest) and secondary forest in shifting cultivation areas is somewhat subjective – in the long term guidance is required to address this issue.

3.3 Inventory

3.3.1 Inventory Planning Procedures for terrestrial field measurements have been designed according to requirements set out in the document “Approved Verified Carbon Standard VM0011 – Methodology for Improved Forest Management – Logged to Protected Forest: Calculation GHG Benefits from Preventing Planned Degradation” (Carbon Planet Ltd 2011). The inventory required establishment of Permanent Sample Plots (PSP) that can be relocated and re- measured as required in the future. Given the steep topography of the PPA and difficult access, it was decided to locate the PSPs in clusters. The basic cluster design decided upon was a group of 24 circular plots in a 3 x 8 or 6 x 4 grid, with a distance of 200m between plots in both directions. Prior to mobilisation, nine cluster locations were identified using GIS software covering the following three land cover strata considered to contain commercial log stocks.  Low Altitude Forest on Plains and Fans  Low Altitude Forest on Uplands < 550m altitude  Low Altitude Forest on Uplands 550-1000m altitude The survey was designed with the aim of attaining carbon stock estimates with 95% confidence intervals to within 10% of the total carbon stocks for the designated above ground carbon pools.

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3.3.2 Plot Design and Field Measurement Procedures A nested circular plot design was used (see Figure 3). Permanent plot center poles (1.3m long, 40mm diameter plastic pipes) were planted at each plot center point and the location captured by GPS. An aluminium plate with the plot number imprinted onto it was screwed onto each pole. A land Use and Biomass Field Evaluation Form was filled out for each plot describing the forest and plot site. All trees greater or equal to 20cm DBH were measured in the large plot. All trees greater than or equal to 5cm and less than 20cm DBH were measured in the small plot. Plastic labels were nailed onto all trees. Tree numbers were written on the label using permanent markers (see photo on cover). For all trees the measured, the following was recorded in tallysheets:  Genus and where known species  Diameter at breast height were measured using diameter tapes  Total tree heights were measured using laser rangefinders.  Commercial sawlog lengths and log grades were recorded for trees with diameter 50cm up that met the minimum log grade specifications.  Tree location in the plot (drawn on a tree map) Field measurement procedures are set out in Annex 3.

Figure 3: Inventory Plot Design

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3.3.3 Implementation of Inventory Field work was carried out from 7th of September until 10th October 2014. Two inventory teams were deployed, each with one inventory team leader from Ata Marie supported by 3-4 PNGFA rangers and 5 local labourers selected by the landowners at each cluster site. Field teams stayed in flying camps located close to the clusters. 1 cluster (target of 24 plots per cluster) generally took 5-6 days to complete weather permitting. In many cases individual land owners were not aware of the inventory program so time was required to carry out socialisation and seek permission to establish the plots. A number of cluster sites and also parts of clusters had to be dropped due to landowner permission issues, and replacement cluster sites generated and accessed. In addition poor weather conditions reduced the available time for plot measurement. The planned 24 plots per cluster was only achieved in one of the seven clusters. In most clusters 15-20 plots were measured. Finally seven clusters and a total of 141 plots were established (see Section5.1).

Photos: Above Left: Tree height measurement using laser rangefinder. Above right: Plot center pole with labelled trees in background. Right: Measurement of diameter above buttress. Below: Typical topographic conditions found in the PPA.

Figure 4: Selected Photos

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3.4 Tree Height Estimation Total tree heights were directly measured for most trees. Unfortunately total tree heights in Cluster 13 were not measured due to failure of the laser height measuring devices due to moisture damage. The tree heights for these species were estimated using DBH height diameter regression equation derived from the direct tree diameter and height measurements. The derived equation was as follows:

Tree height = α1+ α2*DBH- α3*DBH2+ α4*DBH3 Table 3 shows the results of the selected tree height regression equation.

Coefficient Estimates Estimate Std. Error Wald test p-value

α1 3.27846 0.197330 16.614 0.0000

α2 0.776157 0.0144124 53.853 0.0000

α3 -0.00633336 0.000241003 -26.279 0.0000

α4 0.0000162342 8.509073E-7 19.079 0.0000

Summary Analysis of Variance Table Source df SS MS F p-value Regression 4 1114410. 278603. 12810.58 0.0000 Residual 3264 70985. 21.7478 Lack of fit 630 20507.2 32.5511 1.70 0.0000 Pure Error 2634 50477.8 19.1639 Reason for termination: Converged normally.

Table 3: Regression Results

3.5 Estimation of Carbon Stock

3.5.1 Definition of Carbon Stocks The carbon stocks measured in the forest inventory is limited to the carbon pool of above-ground live biomass of large plant species (defined as having diameter at breast height greater than or equal to 5cm). This includes both tree and non-tree species. The following carbon pools have been excluded:  Forest understory including plant species having diameter at breast height below 5cm, vines, epiphytes, and other non-tree vegetation components.  Below ground biomass, i.e. living biomass of roots.  Deadwood.  Litter.  Soil organic matter.  Harvested wood products

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3.5.2 Allometrics for Calculation of Above Ground Live Tree Biomass The carbon stock was estimated for the living trees with DBH larger or equal to 5 cm using the Allometric Equations method. In the absence of species specific or national specific allometric equations in Papua New Guinea, the Chave, et. al. (2005) equation for wet tropical forests was applied. This widely used equation relates DBH and species specific wood density (ρ) to estimate Above Ground Live Biomass (AGLB) per tree measured in the forest plots.

AGLB = Above ground live biomass in kilograms D = Diameter at breast height (1.3m above ground) in centimetres H = Tree height in metres ρ = Specific gravity in grams per cubic centimetre The resulting AGLB is the total biomass of the stem, crown, and leaves for trees in kilograms. Chave et al. (2005) found that locally, the error on the estimation of a tree’s biomass was in the order of ± 5%. In order to validate the applicability of the Chave equations used to estimate AGB, the source data used to develop the equation was reviewed. The Chave equation collates destructive sampling data from 27 different tropical forest sites, and it was confirmed that one of these sites was a wet, old growth forest type measured at Marafunga in Papua New Guinea. The latitude and longitude of these measurements was entered into Google Earth, and the site was found to be located 313km north-west of the PPA. It can be concluded that the Chave equation is representative of the forest type/species and conditions in the Project Area, and that it covers the range of potential independent variable values. Furthermore, the Chave equation is listed as one of the preferred equation in the parameters section of the CP-AB module.

3.5.3 Wood Density Genus and/or species specific wood density values were determined for the species observed in the inventory from the following sources in order of priority: 1. Eddowes (1977) - The utilisation of Papua New Guinea timbers. This was used as the leading source of timber density estimates for PNG. 2. Global Wood Density Database. Chave J, Coomes DA, Jansen S, Lewis SL, Swenson NG, Zanne AE (2009) Towards a worldwide wood economics spectrum. Ecology Letters 12(4): 351-366. Preference is given wood density estimates from PNG/Australia and South East Asia, in order of priority. 3. IPCC (2006): 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 4. Table 4.13 – Basic Wood Density of Tropical Tree Species. 4. Where no wood density was available for the species, there were assigned a wood density value of 0.585 g/cm3. This figure was derived from the weighted average wood density of tree species in the forest inventory with identified wood density estimates.

A total of 200 Genera were identified in the inventory. A complete list of species identified in the inventory and their respective densities can be found in Annex 6.

3.5.4 Carbon Stocks The C fraction of biomass are reported in tons of C/ha (Mg C/ha) assuming that dry biomass is 50 percent C (Clark et al. 2001, Houghton et al. 2001, Malhi et al. 2004). This is an acceptable approximation; however, the wood C fraction does exhibit some small variation across species and tree ages (Elias & Potvin 2003).

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The 95% confidence intervals (CI) were calculated from the calculated carbon per ha in each plot using the following standard formula: CI = tα/2 ∙ s/√n Where t is the Student’s t value, α determines the level of confidence s is the standard deviation of the sample and n is the sample size. The average carbon stock densities and their variability (in units of tons C per hectare) in the aboveground live biomass (AGLB) were calculated for the three forest strata present in the Project Area.

3.5.5 Estimation of Harvestable Sawlog Volume The equation used for estimation of commercial sawlog volume calculation is as follows: V = 0.189523 + 0.0000547982 x (D – 2.4)² - 0.00892138 x L + 0.0000528219 x (D – 2.4)² x L Where: D is diameter in cm L is log length in meters V is log volume in cubic meters under bark This equation was developed by the PNG Forestry Department as part of the Inventor program which has been used in PNG for calculating standing log volumes for a long period of time. The equation generates a gross estimate of yield per log. Net volume on a per hectare basis is calculated by applying a 35% reduction to account for merchantability, log defect and breakage. All species have been treated as commercial. A maximum sawlog diameter limit of 65cm was used on Eucalyptopsis spp logs as this species is known for high frequency of defect (hollow logs) in large diameter logs.

3.5.6 Analysis of Data

3.5.6.1 Analysis of Biomass and Total Carbon Stocks The following results are provided in the report: 1. Average ABLG per hectare and 95% confidence limits for each vegetation stratum. 2. Average carbon stocks per hectare and 95% confidence limits for each vegetation stratum. 3. Multiple comparison of vegetation cover classes carbon stock estimates by region using Scheffé's values.

3.5.6.2 Biomass and Total Carbon Stocks by Diameter Classes Biomass and carbon stocks are reported by diameter classes as follows: 1. Average diameter class frequencies by strata and species groups. 2. The average biomass per hectare by diameter class, strata and species groups. 3. The average carbon stocks per hectare by diameter class, strata and species groups.

3.5.6.3 Commercial Sawlog Volume Estimation 1. Total commercial sawlog volume by grade, strata and species groups. 2. Total commercial sawlog volume by diameter class, strata and species groups. 3. Grade A commercial sawlog volume by diameter class, strata and species groups. 4. Grade B commercial sawlog volume by diameter class, strata and species groups. 5. Grade C commercial sawlog volume by diameter class, strata and species groups.

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4 Area Statement

4.1 Area by Land Cover Strata. The PPA covers an area of 59,758ha. Table 4 shows the area distributed by land cover strata. Three of the strata are considered to hold commercial log stocks cover a total area of 43,374ha or 72.6% of the PPA area. The main strata considered to hold commercial log stocks is the “Low Altitude Forest on Uplands < 550m altitude” strata. Figure 5 shows the distribution of the land cover strata across the PPA.

Area % of Total Forest / Non-Forest Land Cover Strata Code (ha) Area Low Altitude Forest on Plains and Fans P 1,347 2.3% Forest Strata containing Low Altitude Forest on Uplands < 550m Altitude Hma 32,455 54.3% Commercial Log Stock Low Altitude Forest on Uplands 550-1000m Altitude Hmb 9,571 16.0% Sub Total 43,374 72.6% Lower Montane Forest L 609 1.0% Forest Strata not Mangrove and Nipah M 3,004 5.0% containing Commercial Log Stock Secondary Forest Se 3,186 5.3% Sub Total 6,798 11.4% Agriculture O 8,374 14.0% Agriculture Plantation Qa 527 0.9% Grassland and Herbland G 283 0.5% Non Forest Strata Lakes and Large Rivers E 375 0.6% Coastal Open Land Col 28 0.0% Sub Total 9,587 16.0% Grand Total 59,758 100%

Table 4: Area by Land Cover Strata

Figure 5: Land Cover Map

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4.2 Slope Class Analysis Table 5 shows the estimated slope class distribution generated from analysis of 90m pixel SRTM radar data. Based on findings in the PPA and experience on other sites, slopes generated using SRTM data are under – estimated. Slopes have been increased 10% to allow for this. Slopes over 45% can be viewed as extreme and generally not suitable for ground based logging. Even slopes in the 35-45% class are extremely challenging operationally in high rainfall tropical conditions. Figure 6 shows the distribution of the slope classes across the PPA.

Slope Area % of Total Class (ha) Area 0-15% 9,492 16% 15-25% 10,879 18% 25-35% 10,908 18% 35-45% 10,499 18% 45% up 17,980 30% Total 59,758 100%

Table 5: Slope Class Distribution

Figure 6: Slope Class Distribution Map

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4.3 Elevation Class Analysis Table 6 shows the elevation range in the PPA. The majority of the PPA is in the 25-550m strata. However the Eastern area of mountains contains a large contiguous area of land in the 550-1000m range. Figure 7 shows the distribution of the elevation classes across the PPA.

Elevation Area Class (m) (ha) % of Area 0-25m 7,891 13% 25-550m 41,687 70% 550-1000m 9,571 16% 1000m up 609 1% Total 59,758 100%

Table 6: Elevation Class Distribution

Figure 7: Elevation Class Distribution Map

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4.4 Delineation of Steep Land Areas Figure 8 shows a map of the three land cover strata considered to contain commercial log stocks, overlayed (hatch) with areas judged to be impacted by steep slope and relief to the point that ground based logging systems are not suitable. The steep land areas consist of the larger contiguous blocks of land with slopes over 45%, plus associated small patches of less steep areas where access requires passing through steep land. Ground based logging in these steep land areas would be limited to harvesting of trees that can be reached from major ridge lines where road construction is feasible i.e. ridges that do not have large steep sections and are not blocked by limestone outcrops.

Figure 8: Areas Impacted by Steep Slope Table 7 shows the area calculation – approximately 48% of the productive forest area is located on steep land.

Steep Non Steep Total % Steep Land Cover Strata Code Land (ha) Land (ha) Area (ha) Land Low Altitude Forest on Plains and Fans P 6 1,340 1,347 0.5% Low Altitude Forest on Uplands < 550m altitude Hma 12,775 19,680 32,455 39.4% Low Altitude Forest on Uplands 550-1000m altitude Hmb 8,145 1,427 9,571 85.1% Total Area (ha) 20,926 22,447 43,374 48.2%

Table 7: Operable Area Estimation

The PNG Code of Logging Practise forbids ground based logging on land with slope over 30 degrees (approximately 57.74%), but in practise very few ground based operators would operate on slopes above 25 degrees (46%) for safety reasons alone, except for on short slopes where the steeper parts can be avoided. The final decision on whether logging operators push further into steep areas also depends heavily on economics. Building roads in steep land areas is expensive and may not be justified if the recoverable volume is low, as is the case in the Hmb strata. Nevertheless it is reasonable to assume that some proportion of this the steep land area is operable. This needs to be addressed in the preparation of the harvest plan.

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In addition, a reduction factor will need to be applied to the gross area to represent likely losses of area due to the following:  River Buffers. PNG standard river buffer width for major rivers is 50m left and right and for minor rivers a 10m buffer should be conserved. The river data available for the PPA is not sufficiently detailed to allow calculation of buffer area for individual rivers, so the buffer area has been included in a blanket reduction factor applied to gross area.  Cultural sites  High conservation value areas  Other non-productive land that cannot be detected by the satellite imagery  Allowance for landowners that may not wish to be involved in the project.

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5 Inventory Results

5.1 Clusters and Plots Measured A total of 141 plots were measured across seven clusters covering the three land cover strata considered to contain commercial log stocks. Figure 9 shows the location of the seven clusters measured.

Figure 9: Map of Cluster Locations.

Table 8 shows the number of plots measured by Cluster and Land Cover Strata. The majority of plots were measured in Hma, the largest strata in area terms. Additional measurement was planned in the Hmb strata but unfortunately this plan had to be dropped after encountering permission issues with landowners. Areas in the Hmb strata are predominantly on steep land and are difficult to access.

Plots Measured by Cluster and Land Cover Strata Low Altitude Low Altitude Low Altitude Forest on Uplands Forest on Uplands Forest on Plains Cluster < 550m altitude 550-1000m altitude and Fans Total Plots Number Cluster Name Hma Hmb P Measured 1 Leleafa 23 23 3 Enala 19 19 5 Sigei 24 24 6 Gunaheda 18 18 7 Gunaheda North 22 22 10 Lalawasa 17 17 13 Mila 8 10 18 Total 94 10 37 141

Table 8: Table of Clusters And Plots Measured by Land Cover Strata

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5.2 Estimate of Total Carbon Stock and Stock per Hectare by Land Cover Strata Table 9 shows the tree stocking and biomass and carbon stock related statistics for the three land cover strata considered to contain commercial log stocks. Total carbon stocks are generated by multiplication of the average tons per ha by the gross strata area shown in Table 4. Total carbon stock across the three strata is estimated to be 6,514,283 tons, which corresponds to an average of 150.2 tons of carbon per ha. Analysis of significant difference of average carbon stock per ha between the strata using the Scheffe methodology shows there is no significant difference between the strata. Tables showing a more detailed breakdown by genus and diameter class, and the Scheffe test results can be found in Annex 7. Forest Statistics Carbon stock (tC per ha) Lower Upper Number Stocking Biomass Average 95% CL 95% CL Stratum of plots Stems/ha kg/ha tons/ha Hma 94 1,528 295,443 147.7 135.9 159.5 Hmb 10 1,683 237,477 118.7 87.1 150.4 P 37 1,290 328,762 164.4 120.7 208.1

Stand Table Diameter class (cm) Number Total 05.0-14.9 15.0-29.9 30.0-49.9 50.0+ Stratum of plots stems/ha Hma 94 1,527.8 1,281.9 119.5 86.9 39.5 Hmb 10 1,683.3 1,360.0 173.3 135.0 15.0 P 37 1,289.6 1,086.5 85.1 78.8 39.2

Carbon stock per diameter class Diameter class (cm) Number Total 05.0-14.9 15.0-29.9 30.0-49.9 50.0+ Stratum of plots tons/ha Hma 94 147.7 22.3 19.3 41.7 64.4 Hmb 10 118.7 24.9 26.1 51.6 16.1 P 37 164.4 15.8 11.6 35.0 102.0

Gross

Stratum Carbon Total % of Area Stock Carbon Total Stratum (ha) (tons/ha) (Tons) Carbon Hma 32,455 147.7 4,794,371 73.6%

Hmb 9,571 118.7 1,439,688 22.1%

P 1,347 164.4 280,224 4.3% Total 43,374 6,514,283 100.0%

Average Carbon Stock Tons/ha 150.2

Table 9: Carbon Stock Statistics Summary A total of 200 Genera were identified in the inventory. A complete list of species identified in the inventory and their respective densities can be found in Annex 6.

5.3 Estimate of Total Sawlog Volume and Volume per Ha by Land Cover Strata Table 10 shows the sawlog volume per ha and total sawlog volume by stratum, diameter class and grade. Total sawlog stocks are generated by multiplication of the average volume per ha by the gross strata area. Total sawlog stock across the three strata is estimated at 1,798,048 m3, which corresponds to an average of 41.5 m3 per ha. Grade A and Grade B logs are assumed to be exported in log form, except for Podocarpaceae and rosewood (Pterocarpus spp) logs which fall under an export ban. Grade C logs are assumed to be sold into the local market. Tables showing a more detailed breakdown by genus and diameter class can be found in Annex 7.

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Total Sawlog Stock Table Diameter class (cm) Total Sawlog Gross Total 50.0-59.9 60.0-69.9 70.0+ Stock Stratum 3 3 Stratum Area (ha) m /ha m Hma 32,455 47.9 17.1 16.1 14.6 1,553,070 Hmb 9,571 16.3 7.7 2.0 6.6 155,797 P 1,347 66.2 20.2 9.5 36.6 89,180 Total 43,374 1,798,048

Total Sawlog Stock Table Log Grade Total Sawlog Gross Stock Stratum Total Grade A Grade B Grade C 3 3 Stratum Area (ha) m /ha m Hma 32,455 47.9 20.9 18.2 8.8 1,553,070 Hmb 9,571 16.3 11.8 4.5 0.0 155,797 P 1,347 66.2 14.2 27.1 25.0 89,180 Total 43,374 1,798,048

Total Sawlog Stock Table

Log Grade Gross Stratum Total Grade A Grade B Grade C Total m3 Stratum Area (ha) 32,455 1,553,070 677,728 591,313 284,030 Hma 9,571 155,797 112,491 43,306 0 Hmb 1,347 89,180 19,122 36,451 33,607 P Total 43,374 1,798,048 809,340 671,070 317,637 % of Total 100% 45% 37% 18% Average Sawlog Volume m3/ha 41.5

A Grade Sawlog Stock Table Diameter class (cm) Total Sawlog Gross Total 50.0-59.9 60.0-69.9 70.0+ Stock Stratum 3 3 Stratum Area (ha) m /ha m Hma 32,455 20.9 6.8 8.3 5.8 677,728 Hmb 9,571 11.8 5.2 0.0 6.6 112,491 P 1,347 14.2 5.6 1.9 6.7 19,122 Total 43,374 809,340

B Grade Sawlog Stock Table Diameter class (cm) Total Sawlog Gross Total 50.0-59.9 60.0-69.9 70.0+ Stock Stratum 3 3 Stratum Area (ha) m /ha m Hma 32,455 18.2 7.4 4.8 5.9 591,313 Hmb 9,571 4.5 2.6 2.0 0.0 43,306 P 1,347 27.1 9.4 4.4 13.3 36,451 Total 43,374 671,070

C Grade Sawlog Stock Table Diameter class (cm) Total Sawlog Gross Total 50.0-59.9 60.0-69.9 70.0+ Stock Stratum 3 3 Stratum Area (ha) m /ha m Hma 32,455 8.8 2.9 3.0 2.9 284,030 Hmb 9,571 0.0 0.0 0.0 0.0 0 P 1,347 25.0 5.2 3.2 16.6 33,607 Total 43,374 317,637 Table 10: Sawlog Stock Statistics Summary

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6 Discussion and Conclusions Of the total PPA area of 59,758 ha, 43,374 ha or 72.6% is within the three forest land cover strata that contain commercial log stocks. A total of 141 plots were measured during the inventory, targeting these three strata. Results of the carbon inventory are sufficiently positive for the project to proceed to preparation of the PDD. The “Low Altitude Forest on Uplands < 550m altitude” stratum (code Hma) covers 32,455ha - 75% of the forest area. This is the dominant land cover stratum in the PPA. Inventory results indicate this stratum holds carbon stocks of 147.7 tons C/ha. The 95% confidence limits for this strata fall within the targeted maximum of +10% of the mean. The “Low Altitude Forest on Plains and Fans” (code P) stratum covers 1,347ha - 3% of the forest area. P was treated as a separate land cover stratum primarily because the PNGFA 2012 Forest Base Map recognises this as a separate land cover stratum. However inventory results indicate there is no significant difference between the average carbon stock of the P and Hma strata so they be could in fact be combined for the purposes of carbon stock estimation in the Central Suau PPA. As it stands the 95% confidence limits for the P strata do not meet the target due to the variability in the population and the relatively small sample size (37 plots). The “Low Altitude Forest on Uplands 550 - 1000m altitude” (code Hmb) stratum covers 22% of the forest area. The sample size (10 plots) and spread of plots for this stratum is less than optimal as a result of landowner permission issues which cut short field work. As a result the 95% confidence limits for the P strata do not meet the target. However as the majority (85%) of the stratum area is found on steep land which is unsuitable for ground based logging, most of this area is likely to be excluded from the harvesting model to be prepared for the PDD. In addition to the technical harvesting issues, the stocking per ha of commercial logs (16.3 m3/ha including C grade logs) and species mix in this stratum would normally be considered not commercially attractive. During the field work it became clear that despite the previous workshops and awareness work carried out by various parties, there was still a lack of information at the individual land owner level. In many cases individual land owners were not aware of the project’s existence or the inventory program so time was required to carry out socialisation and seek permission to establish the plots. A number of cluster sites and also parts of clusters had to be dropped due to landowner permission issues, and replacement cluster sites generated and accessed. Poor weather conditions reduced the available time for plot measurement. As a result of this as well as several occurrences of landowner permission issues, most of the clusters were not completed to the target of 24 plots per cluster. Consideration should be given to completing the clusters during the next re-measurement. Also the establishment of the planned Cluster at point 4 (Silosilo), for which permission was withdrawn at the last minute, should also be considered. Tree height measurement was carried out using laser height measurement instruments. Unfortunately at Cluster 13 (Mila) the instruments suffered water damage and stopped working. Consideration should be made to going and measuring the heights of these trees. This report will be used as an input into preparation of the PDD. The next steps to be taken are as follows:  Preparation of the harvest plan  Calculation of carbon emissions from logs harvested and associated harvest operations such as road construction, felling and extraction, transport to market, and other related support activities. The procedures for maintenance and re-measurement of the PSPs will be documented in the MRV report.

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7 Materials Delivered The following material will delivered in addition to this report: Electronic Form: 1. Inventory data collated into a spreadsheet, including the final species list. 2. Access database containing all inventory data and calculations. 3. Scanned copies of plot tally sheets. 4. Satellite imagery files 5. GIS dataset (shp files) used in the preparation of the various reports prepared. 6. Soft copies of reports and maps.

Hard Copy Form: 1. The original plot tally sheets (hard copies) bound into a separate report.

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References

1. CHAVE, J., C. ANDOLO, S. BROWN, M. A. CAIRNS, J. Q. CHAMBERS, D. EAMUS, H. FOLSTER, F. FROMARD, N. HIGUCHI, T. KIRA, J. P. LESCURE, B. W. NELSON, H. OGAWA, H. PUIG, B. RIERA, and T. YAMAKURA. 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145: 87–99. 2. CLARK, D. A., S. BROWN, D. W. KICKLIGHTER, J. Q. CHAMBERS, J. R. THOMLINSON, J. NI, AND E. A. HOLLAND. 2001. Net primary production in tropical forests: An evaluation and synthesis of existing field data. Ecol. Appl. 11: 371–384. 3. Eddowes, P.J., The utilisation of Papua New Guinea timbers. Forest Products Research Centre (Papua New Guinea). Port Moresby : Dept. of Primary Industry, Office of Forests, Forest Products Research Centre, 1977 4. ELIAS, M., AND C. POTVIN. 2003. Assessing inter- and intra-specific variation in trunk carbon concentration for 32 neotropical tree species. Can. J. For. Res. 33: 1039–1045. 5. HOUGHTON, R. A., K. T. LAWRENCE, J. L. HACKLER, AND S. BROWN. 2001. The spatial distribution of forest biomass in the Brazilian Amazon: A comparison of estimates. Glob. Change Biol. 7: 731–746. 6. MALHI, Y., T. R. BAKER, O. L. PHILLIPS, S. ALMEIDA, E. ALVAREZ, L. ARROYO, J. CHAVE, C. I. CZIMCZIK, A. DI FIORE, N.HIGUCHI, T. J. KILLEEN, S.G. LAURANCE, W. F. LAURANCE, S. L. LEWIS, L. M. M. MONTOYA, A.MONTEAGUDO,D. A. NEILL, P.N.VARGAS, S.PATINO, N.C. A. PITMAN, C. A. QUESADA, R. SALOMAO, J. N. M. SILVA, A. T. LEZAMA, R. V. MARTINEZ, J. TERBORGH, B. VINCETI, AND J. LLOYD. 2004. The aboveground coarse wood productivity of 104 Neotropical forest plots. Glob. Change Biol. 10: 563–591.

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Annex 1: Data Sources The land cover stratification utilised the following data sources.  The JICA - PNGFA Milne Bay Forest Base Map 2012. One of the key outcomes of the recently completed JICA-PNGFA Project2 is a national level forest base map known as the National Forest Base Map 2012. The Base Maps were developed using optical satellite imagery (RapidEye), Radar satellite data (ALOS-PALSAR) and existing data3.  A range of Landsat 7 and Landsat 8 imagery downloaded from the USGS website.  Images on Google Earth  Boundary of the PPA from PNGFA (central_suau_new_poly.shp)  Forest Inventory Mapping (FIM) System - definitions of forest strata.

Table 1: Satellite Imagery No Item Condition Source

1 Landsat 7 path/row 94/67 date Generally clear over project area. Heavy October 11, 2011 striping. 2 Landsat 8 path/row 94/67 date Project area significantly cloud affected. Heavy December 27, 2013 striping. 3 Landsat 7 path/row 94/67 date May Project area significantly cloud affected. Heavy 12 , 2014 striping. 4 Landsat 7 path/row 94/67 date Project area significantly cloud affected. Heavy August 16 , 2014 striping. 5 Google Earth Imagery date April 6 High resolution imagery of unknown source Google Earth 2010. over the part of the study area. Image not available for download. Use limited to identification of coconut plantations and mangrove areas.

Table 2: Other Data Sources No Item Source

1 JICA - PNGFA Milne Bay Forest Base Map 2012. JICA / PNGFA Project Office Raster image stratifying the area into Land Cover Classes based on Port Moresby Remote Sensing Analysis of RapidEye Imagery 2010-2011. 2 Raster image slope class – GeoSar 5 tiles covering the project area. JICA / PNGFA Project Office Port Moresby 3 Boundary of project area - central_suau_new_poly.shp JICA / PNGFA Project Office Port Moresby

2 JICA-PNGFA PROJECT: Capacity Development on Forest Resource Monitoring for Addressing Climate Change, 2011-14. 3 JICA-PNGFA Brochure_1312 26 SPC/GIZ Regional REDD+ Project

Annex 2 Definition of Land Cover Strata on Milne Bay Forest Base Map 2012

Forest / Non Present in PPA Forest Code LU/LC Strata Definition from the Forest Inventory Mapping (FIM) System (y/n)

The forest has an irregularly open, irregularly uneven canopy of medium (8-15m) crowns 20-30m in B Littoral Forest y height. Low altitude forest on plains and fans. Crown diameter >8m. Canopy is generally 30–35m high and Low Altitude Forest on irregular in both height and closure. Stem diameters generally range from large (70-89 cm) to small (30- P y Plains and Fans 49 cm) but very large stems (90+ cm) are not uncommon. The floristic composition is very mixed with no single-species dominance (definition for Pl). Low altitude forest on uplands. The canopy of this forest type is 25-30m in height, is generally only Low Altitude Forest on slightly uneven and has a 60-80% crown closure. Except for Araucaria, emergents rarely exceed 40m H y Uplands in height. Very large stem diameters (90cm+) are rare except for Araucaria. Floristically the forest is very mixed (definition for Hm). Lower montane forest (above 1000m). This forest has an even to slightly undulating canopy 20-30m in height. Canopy closure varies from dense to slightly open. The canopy height decreases with increasing L Lower Montane Forest y altitude. Stem diameters are generally medium (50-69cm) to small (30-49cm). The forest occurs throughout the mountain ranges in the 1400-3400m altitude range. Covers a wide range of communities from almost bare tidal flats with scattered halophytic herbs, to M Mangrove y Forest Land mangrove forest over 30m in height. This forest “mossy forest” has a dense, even, dark toned, almost velvety textured canopy 5-15m in Mo Montane n height, usually without emergents. Stems are very thin and crooked. (FIM mentions altitude >3000m) This forest has a fairly open canopy 20-25m in height with emergents to 30m and occasionally to 40m. D Dry Seasonal Forest n Stems are often low-branched and crooked. This forest has an irregularly open to open, irregularly uneven, medium (8-15m) to small (<8m) crowned canopy up to 30m in height. Large crowned (>15m) emergents, may be present. The forest is Fri Seral Forest n heterogeneous, comprising many seral stages, from low forest to original levee forest, following changes in the course of a river. Riverine successions with Casuarina grandis The forest has an irregularly open, almost even canopy of medium (8-15m) to very small (<8m) crowns Fsw Mixed Swamp forest n 20-30m in height. A dense under-storey of sago palms is often visible. The tree layer is low and open but the ground layer is usually dense and may include shrubs, herbs or W Woodland n grasses, or any combination of these three. Sc Scrub n

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Forest / Non Present in PPA Forest Code LU/LC Strata Definition from the Forest Inventory Mapping (FIM) System (y/n)

O Agriculture Landuse y Qa Agriculture Plantation y Qf Forest Plantation n Sa Savannah n Grassland and G n Herbland Non Forest Land Ga Alpine Grassland n Gi Sub-alpine Grassland n Z Bare Areas n Settlements and U n Larger Urban Areas Lakes and Large E y Rivers

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Annex 3 Field Measurement Procedure Plot Layout A nested circular plot design has been chosen with two plot sizes as shown in the diagram below. All trees greater or equal to 20cm DBH shall be measured in the large plot. In addition, all trees greater than or equal to 5cm and less than 20cm DBH shall be measured in the small plot.

Navigating to Plots 1. Team leaders will be provided with instructions for each plot including:  Map of plot locations  Plot Center Point co-ordinates (in GPS) 2. Team leaders should navigate to the Plot Center Point using the GPS. 3. Establish the plot center by planting the plastic pipe with the aluminium plot number plate attached. 4. Plot Center Point should only be moved due to safety reasons.

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Land Use and Biomass Field Evaluation Form 1. Fill out every detail of the form. 2. Capture GPS waypoint at the center point of the plot and record the waypoint number. Waypoint number recorded should be the running number produced by the GPS. Do not edit this number. 3. The slope is measured with the laser rangefinder steps of 2 percent. 4. Local guides will usually know the name of the area which might help to find the point again during a monitoring phase. Identification of “In” Trees and Tree Labelling 1. An “In” tree is defined as a tree where the centre of the tree stem at DBH height is within the boundaries of the plot. 2. Trees on the edge of the plot will be checked using a tape measure and a slope correction table. Where trees are on a slope, the slope should be measured using the laser rangefinder, and the distance to the boundary adjusted using the slope correction table. 3. A plastic label will be attached to each tree at the DBH measurement height using a hammer and nail. The label shall record the plot and tree number. 4. It is imperative that the label shall record the same plot and tree number as recorded in the field book to enable diameter increment to be monitored in the future. 5. The nail should not be put in completely – the tag should hang from the nail approximately 1cm from the bark. Tree Measurement 1. Plot Measurement should be carried out quadrant by quadrant starting from Quadrant 1 (north-west quadrant). Plot Quadrant Boundaries will be established prior to commencement of tree measurement. 2. All tree measurements shall be recorded in the Plot Tree Measurement Forms provided. There is one form for quadrants 1 and 2, and another form for quadrants 3 and 4. Plot Identification Data: The following plot identification data shall be recorded at the top of the form: 1. Plot number 2. Date 3. Team Leader 4. Plot Center Waypoint number Note this data must be the same as the data recorded in the Land Use and Biomass Form. Individual Tree Measurement Data: The following data shall be recorded for each tree: 1. Tree number. The tree number shall be a running number starting at one for each plot. 2. Species code. All trees measured in the plot should be assessed for species. Species will be identified in the field according to their common name (local name). A list of 3 letter codes shall be kept by the team leader, and cross referenced with other team leaders on a regular basis to ensure all teams are using the same codes. 3. Diameter at breast height (DBH) (cm). See definition below. 4. Commercial Log Length (m) and Grade. This will be measured using the laser rangefinder for trees minimum 50cm DBH, which have a minimum of 6m of commercial log length. Minimum top diameter for a commercial log is assumed to be 30cm. 5. Total Tree Height (m) will be measured for all trees in the plot using the laser rangefinder.

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Tree Map Tree location and tree number must be drawn on the tree map. This is imperative to enable re-identification of trees should the label be unreadable in the subsequent measurement. The following is an example of how the tree map should be marked:

Quadrant 1 Quadrant 2

 3

 2

 1

Definition of Diameter at breast height (DBH) Diameter at breast height (DBH) is defined as follows: Tree form Measurement method

Well formed tree Stem diameter is measured at 1.3m above ground from the uphill side of the tree. Tree forks below 1.3m The diameter of each stem is measured separately at 1.3m above ground from the uphill side of the tree. Tree has large deformity at The stem diameter is measured at 0.5m above the point where the deformity 1.3m terminates. Buttressing occurs above The stem diameter is measured at 0.5m above the point where the buttressing 1.3m terminates.

The DBH point (1.3m from the ground) should be defined on the body of each person measuring. Alternatively use a stick marked at 1.3m.

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Tree Stem Form Classes for trees DBH 50cm up containing sawlog: The tree stem form classes are denoted by ranking between form A – D based on the straightness and roundness of the bole. The form classes to be recorded are as follows: Class Description

A Round and straight. A good peeler. Round but not straight. Logs are allowable if they bend in one direction only, provided that the B amount of sweep is less than a quarter of the diameter of the log, in any 6 m section of the log. Such logs may be suitable for peeling. C Straight but not round. These are good saw logs. Neither straight nor round. The amount of allowable sweep is as stated in “B” above. These may be D suitable for sawlogs.

Cluster Description Report Team leaders are required to submit a short 1-2 page description of each cluster covering the following aspects:  Forest condition  Topographic condition  Evidence of human activity  Landowners / ILG group controlling the cluster site  Any issues encountered in implementation of the inventory  Any other relevant comments for the cluster. Materials and Tools for a Measurement Team:  Waterproof paper tally sheet forms, clipboard and pencils  GPS x 2 and spare batteries  Laser rangefinder and spare batteries for tree height measurement  Compass  Clinometer  2 x Measuring Tapes 50m  2 x Diameter Tapes  Poles for marking plot center point (1.3m long, 40mm diameter plastic pipes)  Labels for plot center point  Tree labels  Hammer and nails  Bushknives and file  Species lists  Slope correction table for checking trees close to plot boundary  Coloured nylon rope plastic string marked at plot radius length

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Annex 4 Plot Tallysheet Forms Page 1 – Land Use and Biomass Field Evaluation Form

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Page 2 – Plot Tree Measurement Form Quadrant 1 and 2

34 SPC/GIZ Regional REDD+ Project

Page 3 – Plot Tree Measurement Form Quadrant 3 and 4

35

Annex 5 Cluster and Plot List and Plot Layout Maps Cluster Location: Cluster Name Cluster 1: Leleafa Cluster 3: Enala Cluster 5: Sigei Cluster 6: Gunaheda Cluster 7: Gunaheda North Cluster Lalawasa 10: Cluster Mila 13:

Plot Co-ordinates: Cluster Plot Co-ordinates No Plot No X Y 1 001 150° 5' 38.436" E 10° 30' 47.592" S 1 002 150° 5' 44.724" E 10° 30' 47.408" S 1 003 150° 5' 51.576" E 10° 30' 47.700" S 1 004 150° 5' 57.740" E 10° 30' 48.374" S 1 005 150° 5' 38.724" E 10° 30' 41.184" S 1 006 150° 5' 45.312" E 10° 30' 41.256" S 1 007 150° 5' 38.364" E 10° 30' 54.072" S 1 008 150° 5' 44.952" E 10° 30' 54.144" S 1 009 150° 5' 51.504" E 10° 30' 54.216" S 1 010 150° 5' 58.291" E 10° 30' 54.237" S 1 011 150° 5' 51.864" E 10° 30' 41.328" S 1 012 150° 5' 58.452" E 10° 30' 41.364" S 1 013 150° 5' 38.333" E 10° 31' 0.435" S 1 014 150° 5' 44.989" E 10° 31' 0.257" S 1 015 150° 5' 51.288" E 10° 31' 1.169" S 1 016 150° 5' 57.731" E 10° 31' 0.701" S 1 017 150° 5' 38.485" E 10° 31' 13.014" S 1 018 150° 5' 44.899" E 10° 31' 13.490" S 1 019 150° 5' 38.093" E 10° 31' 6.456" S 1 020 150° 5' 44.899" E 10° 31' 6.696" S 1 021 150° 5' 51.396" E 10° 31' 7.212" S 1 022 150° 5' 56.924" E 10° 31' 7.154" S 1 023 150° 5' 50.395" E 10° 31' 14.675" S 3 041 150° 0' 11.961" E 10° 33' 37.020" S 3 042 150° 0' 7.331" E 10° 33' 41.563" S 3 043 150° 0' 11.851" E 10° 33' 46.104" S 3 044 150° 0' 16.592" E 10° 33' 41.601" S 3 045 150° 0' 21.290" E 10° 33' 37.109" S 3 046 150° 0' 16.741" E 10° 33' 32.472" S 3 047 150° 0' 12.056" E 10° 33' 27.885" S 3 048 150° 0' 7.327" E 10° 33' 32.354" S 3 050 150° 0' 16.844" E 10° 33' 23.247" S 3 051 150° 0' 21.397" E 10° 33' 18.624" S 3 052 150° 0' 21.153" E 10° 33' 28.252" S 3 053 150° 0' 26.032" E 10° 33' 23.298" S

36 SPC/GIZ Regional REDD+ Project

Cluster Plot Co-ordinates No Plot No X Y 3 054 150° 0' 30.733" E 10° 33' 28.086" S 3 055 150° 0' 25.991" E 10° 33' 32.473" S 3 056 149° 59' 48.449" E 10° 33' 50.471" S 3 057 149° 59' 53.196" E 10° 33' 55.224" S 3 058 149° 59' 57.680" E 10° 34' 0.183" S 3 059 150° 0' 2.744" E 10° 33' 55.329" S 3 060 149° 59' 57.876" E 10° 33' 50.652" S 5 061 150° 7' 45.505" E 10° 36' 15.660" S 5 062 150° 7' 45.491" E 10° 36' 21.881" S 5 063 150° 7' 52.014" E 10° 36' 22.187" S 5 064 150° 7' 58.483" E 10° 36' 22.302" S 5 065 150° 8' 5.186" E 10° 36' 22.234" S 5 066 150° 7' 52.201" E 10° 36' 15.502" S 5 067 150° 7' 57.788" E 10° 36' 15.815" S 5 068 150° 8' 4.787" E 10° 36' 15.660" S 5 069 150° 8' 12.116" E 10° 36' 15.793" S 5 070 150° 8' 18.402" E 10° 36' 15.847" S 5 071 150° 8' 18.413" E 10° 36' 22.421" S 5 072 150° 8' 11.771" E 10° 36' 22.237" S 5 073 150° 7' 45.527" E 10° 36' 9.173" S 5 074 150° 7' 52.414" E 10° 36' 9.108" S 5 075 150° 7' 58.278" E 10° 36' 8.770" S 5 076 150° 8' 4.758" E 10° 36' 8.899" S 5 077 150° 8' 11.443" E 10° 36' 8.618" S 5 078 150° 8' 18.294" E 10° 36' 9.234" S 5 079 150° 8' 18.359" E 10° 36' 2.988" S 5 080 150° 8' 11.922" E 10° 36' 2.790" S 5 081 150° 8' 5.226" E 10° 36' 2.776" S 5 082 150° 7' 58.703" E 10° 36' 2.610" S 5 083 150° 7' 52.068" E 10° 36' 2.639" S 5 084 150° 7' 45.556" E 10° 36' 2.642" S 6 091 150° 17' 55.699" E 10° 38' 50.258" S 6 092 150° 17' 55.692" E 10° 38' 56.756" S 6 093 150° 17' 55.614" E 10° 39' 3.029" S 6 094 150° 17' 55.104" E 10° 39' 10.155" S 6 095 150° 17' 55.331" E 10° 39' 16.845" S 6 096 150° 17' 48.120" E 10° 39' 16.680" S 6 097 150° 18' 1.646" E 10° 38' 50.962" S 6 098 150° 18' 1.800" E 10° 38' 57.300" S 6 099 150° 18' 1.380" E 10° 39' 3.420" S 6 100 150° 18' 1.740" E 10° 39' 10.260" S 6 103 150° 18' 8.655" E 10° 38' 51.084" S 6 104 150° 18' 8.641" E 10° 38' 57.344" S 6 105 150° 17' 47.600" E 10° 39' 10.072" S 6 106 150° 17' 49.056" E 10° 39' 3.762" S 6 107 150° 17' 48.142" E 10° 38' 57.037" S 6 108 150° 17' 48.723" E 10° 38' 50.769" S 6 109 150° 18' 14.926" E 10° 38' 50.915" S 6 110 150° 18' 14.917" E 10° 38' 57.434" S 7 111 150° 16' 0.206" E 10° 37' 48.896" S 7 112 150° 16' 1.355" E 10° 37' 54.750" S 7 113 150° 16' 0.062" E 10° 38' 2.008" S 7 114 150° 16' 0.541" E 10° 38' 6.648" S 7 115 150° 15' 59.548" E 10° 38' 15.986" S 7 116 150° 16' 0.419" E 10° 38' 21.548" S 7 117 150° 16' 13.768" E 10° 37' 47.896" S 7 118 150° 16' 13.138" E 10° 37' 54.944" S

37

Cluster Plot Co-ordinates No Plot No X Y 7 119 150° 15' 55.836" E 10° 38' 2.778" S 7 120 150° 15' 53.831" E 10° 38' 8.207" S 7 121 150° 15' 53.122" E 10° 38' 14.384" S 7 122 150° 15' 53.536" E 10° 38' 21.372" S 7 123 150° 16' 10.682" E 10° 37' 47.838" S 7 124 150° 16' 6.949" E 10° 37' 53.929" S 7 125 150° 16' 7.122" E 10° 38' 1.018" S 7 126 150° 16' 7.504" E 10° 38' 7.505" S 7 127 150° 16' 7.651" E 10° 38' 13.949" S 7 128 150° 16' 7.727" E 10° 38' 20.900" S 7 129 150° 16' 11.500" E 10° 38' 0.244" S 7 130 150° 16' 14.041" E 10° 38' 7.321" S 7 131 150° 16' 13.987" E 10° 38' 13.924" S 7 132 150° 16' 14.819" E 10° 38' 20.940" S 10 024 150° 7' 6.011" E 10° 33' 6.989" S 10 025 150° 7' 6.068" E 10° 33' 0.572" S 10 026 150° 7' 6.285" E 10° 32' 53.398" S 10 027 150° 7' 6.416" E 10° 32' 47.708" S 10 028 150° 6' 59.797" E 10° 32' 47.297" S 10 029 150° 6' 53.205" E 10° 32' 47.097" S 10 030 150° 6' 46.554" E 10° 32' 47.121" S 10 031 150° 6' 59.163" E 10° 33' 6.654" S 10 032 150° 6' 59.182" E 10° 33' 0.695" S 10 033 150° 6' 59.386" E 10° 32' 53.897" S 10 034 150° 6' 53.155" E 10° 32' 53.614" S 10 035 150° 6' 47.330" E 10° 32' 53.839" S 10 036 150° 6' 39.784" E 10° 32' 53.487" S 10 037 150° 6' 39.771" E 10° 32' 47.005" S 10 038 150° 6' 53.398" E 10° 33' 6.855" S 10 039 150° 6' 53.173" E 10° 33' 0.203" S 10 040 150° 6' 46.362" E 10° 33' 0.242" S 13 140 150° 16' 53.760" E 10° 34' 12.576" S 13 141 150° 16' 52.988" E 10° 34' 6.691" S 13 142 150° 17' 0.617" E 10° 34' 6.430" S 13 143 150° 17' 8.331" E 10° 34' 5.811" S 13 144 150° 17' 7.152" E 10° 34' 12.267" S 13 145 150° 17' 7.260" E 10° 33' 58.987" S 13 146 150° 17' 7.681" E 10° 33' 53.179" S 13 147 150° 17' 13.780" E 10° 33' 53.073" S 13 148 150° 17' 13.914" E 10° 34' 0.162" S 13 149 150° 17' 13.578" E 10° 34' 7.008" S 13 150 150° 17' 0.719" E 10° 34' 11.996" S 13 153 150° 17' 0.536" E 10° 33' 46.571" S 13 154 150° 17' 7.519" E 10° 33' 46.637" S 13 155 150° 17' 13.453" E 10° 33' 46.768" S 13 156 150° 16' 53.918" E 10° 33' 47.048" S 13 157 150° 16' 46.645" E 10° 33' 46.576" S 13 158 150° 16' 47.431" E 10° 33' 53.224" S 13 160 150° 16' 54.177" E 10° 33' 53.272" S

38 SPC/GIZ Regional REDD+ Project

Cluster and Plot Maps:

39

40 SPC/GIZ Regional REDD+ Project

41

42 SPC/GIZ Regional REDD+ Project

Annex 6 Species List Genus Species Tree or Specific Family Genus Species Code Code Palm Density Acanthaceae Acanthaceae sp ACANT SP Tree Elaeocarpaceae Aceratium oppositifolia ACERA OPPOS Tree 0.568 Elaeocarpaceae Aceratium sp ACERA SP Tree 0.568 Lauraceae Actinodaphne nitida ACTIN NITID Tree 0.510 Fabaceae Adenanthera parvonica ADENA PARVO Tree 0.775 Fabaceae Adenanthera sp ADENA SP Tree 0.775 Meliaceae Aglaia cf.sapindina AGLAI CF.SA Tree 0.420 Meliaceae Aglaia cucullata AGLAI CUCUL Tree 0.456 Meliaceae Aglaia goebeliana AGLAI GOEBE Tree 0.735 Meliaceae Aglaia mackiana AGLAI MACKI Tree 0.623 Meliaceae Aglaia sapindina AGLAI SAPIN Tree 0.420 Meliaceae Aglaia sp AGLAI SP Tree 0.735 Meliaceae Aglaia subcuprea AGLAI SUBCU Tree 0.730 Simaroubaceae Ailanthus integrifolia AILAN INTEG Tree 0.305 Simaroubaceae Ailanthus sp AILAN SP Tree 0.320 Lauraceae Alseodaphne archboldiana ALSEO ARCHB Tree 0.562 Lauraceae Alseodaphne nitida ALSEO NITID Tree 0.562 Lauraceae Alseodaphne sp ALSEO SP Tree 0.562 Apocynaceae Alstonia scholaris ALSTO SCHOL Tree 0.336 Dipterocarpaceae Anisoptera sp ANISO SP Tree 0.520 Dipterocarpaceae Anisoptera thurifera ANISO THURI Tree 0.590 Annonaceae Annonaceae sp ANNON SP Tree Rubiaceae Anthocephalus chinensis ANTHO CHINE Tree 0.365 Rubiaceae Anthocephalus sp ANTHO SP Tree 0.365 Moraceae Antiaris toxicaria ANTIA TOXIC Tree 0.327 Phyllanthaceae Antidesma olivaceum ANTID OLIVA Tree 0.703 Phyllanthaceae Antidesma sp ANTID SP Tree 0.703 Meliaceae Aphanamixis sp APHAN SP Tree 0.547 Euphorbiaceae Apo sp APO SP Tree Apocynaceae Apocynaceae sp APOCY SP Tree Euphorbiaceae Aporusa brassii APORU BRASS Tree 0.628 Euphorbiaceae Aporusa carrii APORU CARRI Tree 0.628 Euphorbiaceae Aporusa longicaudata APORU LONGI Tree 0.628 Euphorbiaceae Aporusa nigrapunctata APORU NIGRA Tree 0.628 Euphorbiaceae Aporusa papuana APORU PAPUA Tree 0.628 Euphorbiaceae Aporusa petiolaris APORU PETIO Tree 0.628 Euphorbiaceae Aporusa praegrandiflora APORU PRAEG Tree 0.628 Euphorbiaceae Aporusa sp APORU SP Tree 0.628 Araliaceae Aralia sp ARALI SP Tree Fabaceae Archidendron sp ARCHI SP Tree 0.502 Primulaceae Ardisia forbesii ARDIS FORBE Tree 0.568 Primulaceae Ardisia sp ARDIS SP Tree 0.568 Arecaceae Arenga sp ARENG SP Palm Moraceae Artocarpus sepicanus ARTOC SEPIC Tree 0.350 Moraceae Artocarpus sp ARTOC SP Tree 0.350 Chloranthaceae Ascarina philippinensis ASCAR PHILI Tree Chloranthaceae Ascarina sp ASCAR SP Tree Melastomataceae Astronia atriviridis ASTRO ATRIV Tree 0.460 Melastomataceae Astronia montana ASTRO MONTA Tree 0.460 Melastomataceae Astronia sp ASTRO SP Tree 0.460 Lecythidaceae Barringtonia calyptocalyx BARRI CALYP Tree 0.453 Lecythidaceae Barringtonia novae-hiberiae BARRI NOVAE Tree 0.453 Lecythidaceae Barringtonia sp BARRI SP Tree 0.453

43

Genus Species Tree or Specific Family Genus Species Code Code Palm Density Lauraceae Beilschmiedia sp BEILS SP Tree 0.614 Phyllanthaceae Bischofia javanica BISCH JAVAN Tree 0.570 Euphorbiaceae Blumeodendron sp BLUME SP Tree 0.576 Malvaceae Bombax ceiba BOMBA CEIBA Tree 0.280 Malvaceae Bombax sp BOMBA SP Tree 0.220 Arecaceae Brassiophoenix drymophloeides BRASS DRYMO Palm Arecaceae Brassiophoenix schumannii BRASS SCHUM Palm Arecaceae Brassiophoenix sp BRASS SP Palm Phyllanthaceae Bridelia macrocarpa BRIDE MACRO Tree 0.470 Phyllanthaceae Bridelia sp BRIDE SP Tree 0.470 Anarcardiaceae Buchanania arborescens BUCHA ARBOR Tree 0.405 Anarcardiaceae Buchanania macrocarpa BUCHA MACRO Tree 0.284 Anarcardiaceae Buchanania mollis BUCHA MOLLI Tree 0.295 Anarcardiaceae Buchanania sp BUCHA SP Tree 0.295 Sapotaceae Burckella obovata BURCK OBOVA Tree 0.629 Sapotaceae Burckella sp BURCK SP Tree 0.590 Salicaceae Caesearia sp CAESE SP Tree Euphorbiaceae Caldcluvia celebica CALDC CELEB Tree 0.390 Euphorbiaceae Caldcluvia sp CALDC SP Tree 0.535 Rhizophoraceae Callaria sp CALLA SP Tree Verbenaceae Callicarpa sp CALLI SP Tree 0.350 Calophyllaceae Calophyllum papuana CALOP PAPUA Tree 0.594 Calophyllaceae Calophyllum soulattri CALOP SOULA Tree 0.430 Calophyllaceae Calophyllum sp CALOP SP Tree 0.495 Calophyllaceae Calophyllum vaxens CALOP VAXEN Tree 0.594 Acanthaceae Calycacanthus magnusianus CALYC MAGNU Tree Anacardiaceae Campnosperma sp CAMPN SP Tree 0.350 Burseraceae Can sp CAN SP Tree 0.480 Annonaceae Cananga odorata CANAN ODORA Tree 0.377 Burseraceae Canarium indicum CANAR INDIC Tree 0.560 Burseraceae Canarium lamii CANAR LAMII Tree 0.589 Burseraceae Canarium maluensis CANAR MALUE Tree 0.589 Burseraceae Canarium oleseum CANAR OLESE Tree 0.480 Burseraceae Canarium schlechteri CANAR SCHLE Tree 0.589 Burseraceae Canarium sp CANAR SP Tree 0.480 Burseraceae Canarium vitiensis CANAR VITIE Tree 0.589 Rubiaceae Canthium barbatum CANTH BARBA Tree 0.603 Rubiaceae Canthium sp CANTH SP Tree 0.603 Rubiaceae Canthium valetoniana CANTH VALET Tree 0.603 Rhizophoraceae Carallia brachiata CARAL BRACH Tree 0.630 Rhizophoraceae Carallia sp CARAL SP Tree 0.630 Arecaceae Caryota rumphiana CARYO RUMPH Palm Arecaceae Caryota sp CARYO SP Palm Salicaceae Casearia sp CASEA SP Tree 0.588 Fagaceae Castanopsis accuminatissima CASTA ACCUM Tree 0.520 Fagaceae Castanopsis sp CASTA SP Tree 0.520 Cannabaceae Celtis latifolia CELTI LATIF Tree 0.420 Cannabaceae Celtis philipensis CELTI PHILI Tree 0.584 Cannabaceae Celtis sp CELTI SP Tree 0.640 Cunoniaceae Ceratopetalum sp CERAT SP Tree 0.510 Cunoniaceae Ceratopetalum succirubrum CERAT SUCCI Tree 0.529 Apocynaceae Cerbera brassii CERBE BRASS Tree 0.436 Apocynaceae Cerbera floribunda CERBE FLORI Tree 0.456 Apocynaceae Cerbera sp CERBE SP Tree 0.395 Oleaceae Chionanthus ramiflorus CHION RAMIF Tree 0.753

44 SPC/GIZ Regional REDD+ Project

Genus Species Tree or Specific Family Genus Species Code Code Palm Density Oleaceae Chionanthus sp CHION SP Tree 0.710 Meliaceae Chisocheton ceramicus CHISO CERAM Tree 0.450 Meliaceae Chisocheton sp CHISO SP Tree 0.450 Meliaceae Chisocheton weinlandii CHISO WEINL Tree 0.495 Sapotaceae Chrysophyllum sp CHRYS SP Tree 0.400 Euphorbiaceae Claoxylon polot CLAOX POLOT Tree 0.360 Euphorbiaceae Claoxylon sp CLAOX SP Tree 0.355 Euphorbiaceae Cleistanthus marianthus CLEIS MARIA Tree 0.576 Euphorbiaceae Cleistanthus myrianthus CLEIS MYRIA Tree 0.535 Euphorbiaceae Cleistanthus sp CLEIS SP Tree 0.576 Arecaceae Cocos nucifera COCOS NUCIF Palm Boraginaceae Cordia sp CORDI SP Tree 0.420 Corynocarpaceae Corynocarpus cribbianus CORYN CRIBB Tree 0.594 Corynocarpaceae Corynocarpus sp CORYN SP Tree 0.594 Fabaceae Crudia papuana CRUDI PAPUA Tree 0.760 Fabaceae Crudia sp CRUDI SP Tree 0.760 Lauraceae Cryptocarya caloneura CRYPT CALON Tree 0.578 Lauraceae Cryptocarya clauneura CRYPT CLAUN Tree 0.578 Lauraceae Cryptocarya densiflora CRYPT DENSI Tree 0.616 Lauraceae Cryptocarya depressa CRYPT DEPRE Tree 0.465 Lauraceae Cryptocarya gigantocarpa CRYPT GIGAN Tree 0.465 Lauraceae Cryptocarya magnifolia CRYPT MAGNI Tree 0.465 Lauraceae Cryptocarya massoy CRYPT MASSO Tree 0.530 Lauraceae Cryptocarya multipaniculata CRYPT MULTI Tree 0.380 Lauraceae Cryptocarya sp CRYPT SP Tree 0.465 Lauraceae Cryptocarya weinlandii CRYPT WEINL Tree 0.578 Cyatheales Cyathea contaminans CYATH CONTA Palm Annonaceae Cyathocalyx polycarpum CYATHOCALYX POLYC Tree 0.510 Cycadaceae Cycas circinalis CYCAS CIRCI Cycas Cycadaceae Cycas sp CYCAS SP Cycas Myrtaceae Decaspermum sp DECAS SP Tree 0.702 Podocarpaceae Decussocarpus sp DECUS SP Tree 0.460 Urticaceae Dendrocnide elliptica DENDR ELLIP Tree 0.207 Urticaceae Dendrocnide frutescens DENDR FRUTE Tree 0.207 Urticaceae Dendrocnide sp DENDR SP Tree 0.207 Dilleniaceae Dillenia papuana DILLE PAPUA Tree 0.551 Dilleniaceae Dillenia sp DILLE SP Tree 0.480 Euphorbiaceae Dimorphocalyx australense DIMOR AUSTR Tree 0.820 Ebernaceae Diospyros ferrae DIOSP FERRA Tree 0.980 Ebernaceae Diospyros hebecarpa DIOSP HEBEC Tree 0.842 Ebernaceae Diospyros sp DIOSP SP Tree 0.980 Primulaceae Discocalyx sp DISCO SP Tree Rubiaceae Dolicholobium sp DOLIC SP Tree Dracaenaceae Dracaena sp DRACA SP Tree 0.420 Anarcardiaceae Dracontomelon dao DRACO DAO Tree 0.400 Anarcardiaceae Dracontomelon sp DRACO SP Tree 0.470 Putranjivaceae Drypetes bordenii DRYPE BORDE Tree 0.793 Putranjivaceae Drypetes lasiogynoides DRYPE LASIO Tree 0.771 Putranjivaceae Drypetes sp DRYPE SP Tree 0.670 Meliaceae Dysoxylum aboros DYSOX ABORO Tree 0.620 Meliaceae Dysoxylum arborescens DYSOX ARBOR Tree 0.470 Meliaceae Dysoxylum archboliana DYSOX ARCHB Tree 0.630 Meliaceae Dysoxylum micranthum DYSOX MICRA Tree 0.630 Meliaceae Dysoxylum parasiticum DYSOX PARAS Tree 0.600 Meliaceae Dysoxylum pettigravianum DYSOX PETTI Tree 0.630

45

Genus Species Tree or Specific Family Genus Species Code Code Palm Density Meliaceae Dysoxylum sp DYSOX SP Tree 0.620 Edinantea sp EDINA SP Tree Elaeocarpaceae Elaeocarpus amplifolius ELAEO AMPLI Tree 0.491 Elaeocarpaceae Elaeocarpus cf.sayeri ELAEO CF.SA Tree 0.491 Elaeocarpaceae Elaeocarpus culminicola ELAEO CULMI Tree 0.491 Elaeocarpaceae Elaeocarpus meigei ELAEO MEIGE Tree 0.491 Elaeocarpaceae Elaeocarpus murukkai ELAEO MURUK Tree 0.491 Elaeocarpaceae Elaeocarpus nouhuysii ELAEO NOUHU Tree 0.491 Elaeocarpaceae Elaeocarpus sayeri ELAEO SAYER Tree 0.491 Elaeocarpaceae Elaeocarpus sp ELAEO SP Tree 0.375 Elaeocarpaceae Elaeocarpus sphaericus ELAEO SPHAE Tree 0.327 Elaeocarpaceae Elaeocarpus womersleyii ELAEO WOMER Tree 0.491 Elaeocarpaceae Eleaocarpus sphaericus ELEAO SPHAE Tree Lauraceae Endiandra brassii ENDIA BRASS Tree 0.663 Lauraceae Endiandra forbesii ENDIA FORBE Tree 0.663 Lauraceae Endiandra fragrans ENDIA FRAGR Tree 0.663 Lauraceae Endiandra fulva ENDIA FULVA Tree 0.663 Lauraceae Endiandra sp ENDIA SP Tree 0.550 Euphorbiaceae Endospermum medullosum ENDOS MEDUL Tree 0.327 Euphorbiaceae Endospermum sp ENDOS SP Tree 0.385 Myrtaceae Eucalyptopsis papuana EUCAL PAPUA Tree 0.460 Myrtaceae Eucalyptopsis pinnata EUCAL PINNA Tree 0.460 Myrtaceae Eucalyptopsis sp EUCAL SP Tree 0.460 Rutaceae Euodia elleryana EUODI ELLER Tree 0.327 Rutaceae Euodia sp EUODI SP Tree 0.672 Anacardiaceae Euroschinus papuanus EUROS PAPUA Tree 0.360 Anacardiaceae Euroschinus sp EUROS SP Tree 0.414 Gentianaceae Fagraea monticola FAGRA MONTI Tree 0.835 Gentianaceae Fagraea sp FAGRA SP Tree 0.835 ---- Fern sp FERN SP Fern Moraceae Ficus arfakensis FICUS ARFAK Tree 0.411 Moraceae Ficus bernaysii FICUS BERNA Tree 0.411 Moraceae Ficus erythrosperma FICUS ERYTH Tree 0.345 Moraceae Ficus hispidioides FICUS HISPI Tree 0.411 Moraceae Ficus polyantha FICUS POLYA Tree 0.411 Moraceae Ficus pungens FICUS PUNGE Tree 0.345 Moraceae Ficus sp FICUS SP Tree 0.345 Moraceae Ficus tinctoria FICUS TINCT Tree 0.411 Moraceae Ficus variegata FICUS VARIE Tree 0.344 Proteaceae Finschia chlorosantha FINSC CHLOR Tree Proteaceae Finschia sp FINSC SP Tree Salicaceae Flacourtia sp FLACO SP Tree 0.771 Salicaceae Flacourtia zippelii FLACO ZIPPE Tree 0.771 Rutaceae Flindersia laevicarpa FLIND LAEVI Tree 0.593 Rutaceae Flindersia pimenteliana FLIND PIMEN Tree 0.531 Rutaceae Flindersia sp FLIND SP Tree 0.830 Cluciaceae Garcinia celebica GARCI CELEB Tree 0.760 Cluciaceae Garcinia hollrungii GARCI HOLLR Tree 0.645 Cluciaceae Garcinia hunstenii GARCI HUNST Tree 0.833 Cluciaceae Garcinia latissima GARCI LATIS Tree 0.770 Cluciaceae Garcinia ledermanii GARCI LEDER Tree 0.833 Cluciaceae Garcinia maluensis GARCI MALUE Tree 0.645 Cluciaceae Garcinia schraderi GARCI SCHRA Tree 0.790 Cluciaceae Garcinia sp GARCI SP Tree 0.645 Rubiaceae Gardenia hansemannii GARDE HANSE Tree 0.697

46 SPC/GIZ Regional REDD+ Project

Genus Species Tree or Specific Family Genus Species Code Code Palm Density Rubiaceae Gardenia papuana GARDE PAPUA Tree 0.697 Rubiaceae Gardenia sp GARDE SP Tree 0.697 Phyllanthaceae Glochidion insectum GLOCH INSEC Tree 0.620 Phyllanthaceae Glochidion sp GLOCH SP Tree 0.465 Lamiaceae Gmelina moluccana GMELI MOLUC Tree 0.396 Lamiaceae Gmelina sp GMELI SP Tree 0.400 Gnetaceae Gnetum costatum GNETU COSTA Tree 0.610 Gnetaceae Gnetum gnemon GNETU GNEMO Tree 0.610 Gnetaceae Gnetum sp GNETU SP Tree 0.610 Stemonuraceae Gomphandra montana GOMPH MONTA Tree 0.456 Annonaceae Goniothalamus imbricathus GONIO IMBRI Tree 0.440 Annonaceae Goniothalamus sp GONIO SP Tree 0.440 Cardiopteridaceae Gonocaryum littorale GONOC LITTO Tree 0.662 Cardiopteridaceae Gonocaryum sp GONOC SP Tree 0.662 Thymelaeaceae Gonystylus macrophyllus GONOS MACRO Tree 0.560 Theaceae Gordonia papuana GORDO PAPUA Tree 0.577 Theaceae Gordonia sp GORDO SP Tree 0.530 Arecaceae Gronophyllum sp GRONO SP Palm Sapindaceae Guioa sp GUIOA SP Tree 0.525 Arecaceae Gulubia costata GULUB COSTA Palm Myristicaceae Gymnacranthera paniculata GYMNA PANIC Tree 0.460 Myristicaceae Gymnacranthera sp GYMNA SP Tree 0.551 Rhizophoraceae Gynotroches axillaris GYNOT AXILL Tree 0.520 Rhizophoraceae Gynotroches sp GYNOT SP Tree 0.520 Rutaceae Halfordia sp HALFO SP Tree 0.770 Burseraceae Haplolobus floribundus HAPLO FLORI Tree 0.710 Burseraceae Haplolobus sp HAPLO SP Tree 0.504 Annonaceae Haplostichanthus sp HAPLOSTI SP Tree 0.621 Sapindaceae Harpullia longipetala HARPU LONGI Tree 0.714 Sapindaceae Harpullia sp HARPU SP Tree 0.714 Proteaceae Helicia latifolia HELIC LATIF Tree 0.606 Proteaceae Helicia sp HELIC SP Tree 0.606 Malvaceae Heritiera littoralis HERIT LITTO Tree 0.730 Malvaceae Heritiera sp HERIT SP Tree 0.660 Salicaceae Homalium foetidum HOMAL FOETI Tree 0.728 Salicaceae Homalium sp HOMAL SP Tree 0.680 Dipterocarpaceae Hopea grabrifolia HOPEA GRABR Tree 0.769 Dipterocarpaceae Hopea grandiflora HOPEA GRAND Tree 0.769 Dipterocarpaceae Hopea iriana HOPEA IRIAN Tree 0.852 Dipterocarpaceae Hopea light HOPEA LIGHT Tree 0.769 Dipterocarpaceae Hopea papuana HOPEA PAPUA Tree 0.603 Dipterocarpaceae Hopea sp HOPEA SP Tree 0.785 Myristicaceae Horsfieldia hellwigii HORSF HELLW Tree 0.387 Myristicaceae Horsfieldia irya HORSF IRYA Tree 0.387 Myristicaceae Horsfieldia sp HORSF SP Tree 0.360 Myristicaceae Horsfieldia spicata HORSF SPICA Tree 0.360 Myristicaceae Horsfieldia subtilis HORSF SUBTI Tree 0.387 Arecaceae Hydriastele sp HYDRI SP Palm Aquifoliaceae Ilex ledermanii ILEX LEDER Tree 0.443 Aquifoliaceae Ilex sp ILEX SP Tree 0.443 Fabaceae Inocarpus sp INOCA SP Tree 0.357 Fabaceae Intsia bijuga INTSI BIJUG Tree 0.722 Fabaceae Intsia sp INTSI SP Tree 0.645 Itonalium sp ITONA SP Tree Rubiaceae Ixora sp IXORA SP Tree 0.728

47

Genus Species Tree or Specific Family Genus Species Code Code Palm Density Lauraceae Kibara laurifolia KIBAR LAURI Tree 0.530 Lauraceae Kibara sp KIBAR SP Tree 0.530 Fabaceae Kingiodendron novoguineensis KINGI NOVOG Tree 0.490 Fabaceae Kingiodendron sp KINGI SP Tree 0.490 Rubiaceae Lasianthus sp LASIA SP Tree Lauraceae Lauraceae sp LAURA SP Tree Leeaceae Leea indica LEEA INDIC Tree 0.460 Leeaceae Leea sp LEEA SP Tree 0.570 Lepania sp LEPAN SP Tree Sapindaceae Lepisanthes sp LEPIS SP Tree 0.620 Arecaceae Licuala sp LICUA SP Palm Fagaceae Lithocarpus celebica LITHO CELEB Tree 0.700 Fagaceae Lithocarpus sp LITHO SP Tree 0.580 Lauraceae Litsea collina LITSE COLLI Tree 0.426 Lauraceae Litsea elliptica LITSE ELLIP Tree 0.450 Lauraceae Litsea firma LITSE FIRMA Tree 0.408 Lauraceae Litsea globosa LITSE GLOBO Tree 0.426 Lauraceae Litsea guppyii LITSE GUPPY Tree 0.426 Lauraceae Litsea laurifolia LITSE LAURI Tree 0.426 Lauraceae Litsea ledermanii LITSE LEDER Tree 0.426 Lauraceae Litsea sp LITSE SP Tree 0.400 Celastraceae Lophopetalum sp LOPHO SP Tree 0.345 Celastraceae Lophopetalum torriensis LOPHO TORRI Tree 0.526 Euphorbiaceae Macaranga inermis MACAR INERM Tree 0.380 Euphorbiaceae Macaranga punctata MACAR PUNCT Tree 0.431 Euphorbiaceae Macaranga sp MACAR SP Tree 0.300 Magnoliaceae Magnolia tsiampacca MAGNO TSIAM Tree 0.510 Calophyllaceae Mammea cordata MAMME CORDA Tree 0.827 Calophyllaceae Mammea sp MAMME SP Tree 0.827 Fabaceae Maniltoa lenticellata MANIL LENTI Tree 0.663 Fabaceae Maniltoa megalocephala MANIL MEGAL Tree 0.663 Fabaceae Maniltoa psilogyne MANIL PSILO Tree 0.663 Fabaceae Maniltoa sp MANIL SP Tree 0.620 Chrysobalanaceae Maranthes corymbosa MARAN CORYM Tree 0.847 Cornaceae Mastixia kaniensis MASTI KANIE Tree 0.472 Stemonuraceae Medusanthera laxiflora MEDUS LAXIF Tree 0.390 Stemonuraceae Medusanthera sp MEDUS SP Tree 0.390 Sapindales Meliaceae sp MELIA SP Tree Rutaceae Melicope pullei MELIC PULLE Tree 0.509 Rutaceae Melicope sp MELIC SP Tree 0.509 Melastomataceae Memecylon hepaticum MEMEC HEPAT Tree 0.456 Melastomataceae Memecylon sp MEMEC SP Tree 0.456 Icacinaceae Merrilliodendron megacarpum MERRI MEGAC Tree Icacinaceae Merrilliodendron sp MERRI SP Tree Tiliaceae Microcos grandiflora MICRO GRAND Tree 0.495 Tiliaceae Microcos schlechteri MICRO SCHLE Tree 0.495 Tiliaceae Microcos sp MICRO SP Tree 0.495 Sapindaceae Mischocarpus sp MISCH SP Tree 0.697 Myristicaceae Myristica buchneriana MYRIS BUCHN Tree 0.387 Myristicaceae Myristica fatua MYRIS FATUA Tree 0.385 Myristicaceae Myristica globosa MYRIS GLOBO Tree 0.481 Myristicaceae Myristica sp MYRIS SP Tree 0.385 Myristicaceae Myristica subalulata MYRIS SUBAL Tree 0.290 Myrsinaceae Myrsinaceae sp MYRSI SP Tree Podocarpaceae Nageia sp NAGEI SP Tree 0.496

48 SPC/GIZ Regional REDD+ Project

Genus Species Tree or Specific Family Genus Species Code Code Palm Density Podocarpaceae Nageia walichianus NAGEI WALIC Tree 0.496 Rubiaceae Nauclea orientalis NAUCL ORIEN Tree 0.482 Rubiaceae Nauclea sp NAUCL SP Tree 0.482 Lauraceae Neolitsea sp NEOLI SP Tree 0.581 Rubiaceae Neonauclea obversitifolia NEONA OBVER Tree 0.703 Rubiaceae Neonauclea orientalis NEONA ORIEN Tree 0.703 Rubiaceae Neonauclea sp NEONA SP Tree 0.580 Loganiaceae Neuburgia corynocarpa NEUBU CORYN Tree 0.460 Loganiaceae Neuburgia sp NEUBU SP Tree 0.460 Lauraceae Nothophoebe sp NOTHO SP Tree Datiscaceae Octomeles sumatrana OCTOM SUMAT Tree 0.310 Annonaceae Oncodostigma sp ONCOD SP Tree Arecaceae Orania sp ORANI SP Palm Araliaceae Osmoxylon novoguineensis OSMOX NOVOG Tree 0.370 Araliaceae Osmoxylon sp OSMOX SP Tree 0.370 Owt sp OWT SP Tree Sapotaceae Palaquium amboinense PALAQ AMBOI Tree 0.380 Sapotaceae Palaquium sp PALAQ SP Tree 0.525 Sapotaceae Palaquium warburgiana PALAQ WARBU Tree 0.525 Arecaceae Palm sp PALM SP Palm Pandanaceae Pandanus contaminans PANDA CONTA Palm 0.331 Pandanaceae Pandanus deckelmannianus PANDA DECKE Palm 0.331 Pandanaceae Pandanus sp PANDA SP Palm 0.331 Moraceae Paratocarpus sp PARAT SP Tree Moraceae Paratocarpus venenosus PARAT VENEN Tree Moraceae Paratocarpus venosus PARAT VENOS Tree Phyllanthaceae Phyllanthus sp PHYLL SP Tree 0.670 Euphorbiaceae Pimelodendron amboinicum PIMEL AMBOI Tree 0.534 Euphorbiaceae Pimelodendron sp PIMEL SP Tree 0.480 Pinaceae Pinus sp PINUS SP Tree 0.482 Nyctaginaceae Pisonia longilostris PISON LONGI Tree 0.301 Nyctaginaceae Pisonia sp PISON SP Tree 0.301 Lecythidaceae Planchonia papuana PLANC PAPUA Tree 0.646 Lecythidaceae Planchonia sp PLANC SP Tree 0.595 Icacinaceae Platea excelsa PLATE EXCEL Tree 0.363 Icacinaceae Platea sp PLATE SP Tree 0.358 Podocarpaceae Podocarpus neriifolius PODOC NERII Tree 0.370 Podocarpaceae Podocarpus sp PODOC SP Tree 0.510 Annonaceae Polyalthia discolor POLYA DISCO Tree 0.510 Annonaceae Polyalthia glauca POLYA GLAUC Tree 0.450 Annonaceae Polyalthia lateriflora POLYA LATER Tree 0.500 Annonaceae Polyalthia oblongifolia POLYA OBLON Tree 0.508 Annonaceae Polyalthia sp POLYA SP Tree 0.480 Escalloniaceae Polyosma integrifolia POLYO INTEG Tree 0.530 Escalloniaceae Polyosma sp POLYO SP Tree 0.620 Sapindaceae Pometia pinnata POMET PINNA Tree 0.594 Sapindaceae Pometia sp POMET SP Tree 0.580 Fabaceae Pongamia pinnata PONGA PINNA Tree 0.580 Fabaceae Pongamia sp PONGA SP Tree 0.580 Annonaceae Popowia sp POPOW SP Tree 0.545 Sapotaceae Pouteria anteridifera POUTE ANTER Tree 0.684 Sapotaceae Pouteria chartacea POUTE CHART Tree 0.740 Sapotaceae Pouteria lateriflora POUTE LATER Tree 0.684 Sapotaceae Pouteria maclayana POUTE MACLA Tree 0.420 Sapotaceae Pouteria monticola POUTE MONTI Tree 0.684

49

Genus Species Tree or Specific Family Genus Species Code Code Palm Density Sapotaceae Pouteria obovata POUTE OBOVA Tree 0.620 Sapotaceae Pouteria obovoidea POUTE OBOVO Tree 0.630 Sapotaceae Pouteria sp POUTE SP Tree 0.684 Sapotaceae Pouteria thyrsoidea POUTE THYRS Tree 0.684 Burseraceae Protium macgregorii PROTI MACGR Tree 0.750 Burseraceae Protium sp PROTI SP Tree 0.750 Amygdaloideae Prunus gazelle-peninsulae PRUNU GAZEL Tree 0.400 Amygdaloideae Prunus sp PRUNU SP Tree 0.448 Rubiaceae Psychotria sp PSYCH SP Tree 0.450 Fabaceae Pterocarpus indicus PTERO INDIC Tree 0.525 Fabaceae Pterocarpus sp PTERO SP Tree 0.500 Malvaceae Pterocymbium beccarii PTEROCYM BECCA Tree 0.310 Malvaceae Pterocymbium sp PTEROCYM SP Tree 0.360 Malvaceae Pterygota horsfieldii PTERY HORSF Tree 0.663 Malvaceae Pterygota sp PTERY SP Tree 0.595 Arecaceae Ptychosperma sp PTYCH SP Palm Cunoniaceae Pullea glabra PULLE GLABR Tree 0.688 Cunoniaceae Pullea sp PULLE SP Tree 0.688 Simaroubaceae Quassia indica QUASS INDIC Tree 0.331 Rubiaceae Randia sp RANDI SP Tree 0.618 Primulaceae Rapanea leucantha RAPAN LEUCA Tree 0.608 Primulaceae Rapanea sp RAPAN SP Tree 0.608 Arecaceae Rhopaloblaste sp RHOPA SP Palm Anacardiaceae Rhus taitensis RHUS TAITE Tree 0.370 Violaceae Rinorea bengalensis RINOR BENGA Tree Violaceae Rinorea sp RINOR SP Tree Rubiaceae Rubiaceae sp RUBIA SP Tree Rutaceae Rutaceae sp RUTAC SP Tree Achariaceae Ryparosa javanica RYPAR JAVAN Tree 0.450 Achariaceae Ryparosa sp RYPAR SP Tree 0.584 Sapindaceae Sapindaceae sp SAPIN SP Tree Actinidiaceae Saurauia sp SAURA SP Tree 0.426 Araliaceae Schefflera sp SCHEF SP Tree 0.413 Ochnaceae Schuurmansia henningsii SHUUR HENIN Tree Anacardiaceae Semecarpus cassiveum SEMEC CASSI Tree 0.400 Anacardiaceae Semecarpus cassuvium SEMEC CASSU Tree 0.400 Anacardiaceae Semecarpus forsternii SEMEC FORST Tree 0.400 Anacardiaceae Semecarpus sp SEMEC SP Tree 0.400 Elaeocarpaceae Sloanea forbesii SLOAN FORBE Tree 0.482 Elaeocarpaceae Sloanea sogeriensis SLOAN SOGER Tree 0.514 Elaeocarpaceae Sloanea sp SLOAN SP Tree 0.485 Monimiaceae Steganthera hirsuta STEGA HIRSU Tree 0.551 Monimiaceae Steganthera sp STEGA SP Tree 0.551 Malvaceae Sterculia ampla STERC AMPLA Tree 0.280 Malvaceae Sterculia conwentzii STERC CONWE Tree 0.276 Malvaceae Sterculia schumanniana STERC SCHUM Tree 0.280 Malvaceae Sterculia sp STERC SP Tree 0.280 Symplocaceae Symplocos cochinchinensis SIMPL COCHI Tree 0.440 Symplocaceae Symplocos cochinchinensis SYMPL COCHI Tree 0.440 Symplocaceae Symplocos sp SIMPL SP Tree 0.496 Symplocaceae Symplocos sp SYMPL SP Tree 0.496 Myrtaceae Syzygium buttnerianum SYZYG BUTTN Tree 0.645 Myrtaceae Syzygium cf.effussum SYZYG CF.EF Tree 0.645 Myrtaceae Syzygium cratermontensis SYZYG CRATE Tree 0.645 Myrtaceae Syzygium decipiens SYZYG DECIP Tree 0.645

50 SPC/GIZ Regional REDD+ Project

Genus Species Tree or Specific Family Genus Species Code Code Palm Density Myrtaceae Syzygium effusium SYZYG EFFUS Tree 0.645 Myrtaceae Syzygium furfuraceum SYZYG FURFU Tree 0.645 Myrtaceae Syzygium gonatanthum SYZYG GONAT Tree 0.645 Myrtaceae Syzygium hylophilum SYZYG HYLOP Tree 0.645 Myrtaceae Syzygium longipes SYZYG LONGI Tree 0.610 Myrtaceae Syzygium pachycladum SYZYG PACHY Tree 0.645 Myrtaceae Syzygium pyrocarpum SYZYG PYROC Tree 0.645 Myrtaceae Syzygium sp SYZYG SP Tree 0.610 Myrtaceae Syzygium subcorymbosa SYZYG SUBCO Tree 0.645 Myrtaceae Syzygium thornei SYZYG THORN Tree 0.610 Myrtaceae Syzygium versteegii SYZYG VERST Tree 0.645 Primulaceae Tapeinosperma magnifica TAPEI MAGNI Tree Primulaceae Tapeinosperma sp TAPEI SP Tree Rubiaceae Tarenna sp TAREN SP Tree Lamiaceae Teijsmanniodendron bogoriense TEIJS BOGOR Tree 0.426 Lamiaceae Teijsmanniodendron sp TEIJS SP Tree 0.370 Combretaceae Terminalia complanata TERMI COMPL Tree 0.412 Combretaceae Terminalia kaenbachii TERMI KAENB Tree 0.472 Combretaceae Terminalia kaernbachii TERMI KAERN Tree 0.396 Combretaceae Terminalia megalocarpa TERMI MEGAL Tree 0.472 Combretaceae Terminalia sp TERMI SP Tree 0.515 Pentaphylacaceae Ternstroemia cherryi TERNS CHERR Tree 0.581 Pentaphylacaceae Ternstroemia merrilliodendron TERNS MERRI Tree 0.581 Pentaphylacaceae Ternstroemia sp TERNS SP Tree 0.581 Tetramelaceae Tetrameles nudflora TETRA NUDFL Tree 0.307 Tetramelaceae Tetrameles nudiflora TETRA NUDIF Tree 0.307 Tetramelaceae Tetrameles sp TETRA SP Tree 0.270 Rubiaceae Timonius pulposus TIMON PULPO Tree 0.551 Rubiaceae Timonius sp TIMON SP Tree 0.551 Rubiaceae Timonius timon TIMON TIMON Tree 0.551 Cannabaceae Trema orientalis TREMA ORIEN Tree 0.333 Achariaceae Trichadenia philippinensis TRICHA PHILI Tree 0.715 Achariaceae Trichadenia sp TRICHA SP Tree 0.715 Malvaceae Trichospermum philippinensis TRICH PHILI Tree 0.328 Malvaceae Trichospermum pleiostigma TRICH PLEIO Tree 0.328 Malvaceae Trichospermum sp TRICH SP Tree 0.328 Myrtaceae Tristania sp TRIST SP Tree 0.640 Staphylaceae Turpenia pentandra TURPE PENTA Tree Staphyleaceae Turpinia sp TURPI SP Tree 0.360 ---- Unknown sp UNKNO SP Tree Dipterocarpaceae Vatica papuana VATIC PAPUA Tree 0.521 Dipterocarpaceae Vatica sp VATIC SP Tree 0.485 Meliaceae Vavaea amicorum VAVAE AMICO Tree 0.626 Meliaceae Vavaea sp VAVAE SP Tree 0.626 Asteraceae Vernonia sp VERNO SP Tree 0.330 Rubiaceae Versteegia cauliflorus VERST CAULI Tree Rubiaceae Versteegia sp VERST SP Tree Lamiaceae Vitex cofassus VITEX COFAS Tree 0.650 Lamiaceae Vitex coffasus VITEX COFFA Tree 0.610 Lamiaceae Vitex sp VITEX SP Tree 0.610 Polygalaceae Xanthophyllum papuana XANTHOPHY PAPUA Tree 0.689 Polygalaceae Xanthophyllum sp XANTHOPHY SP Tree 0.620 Myrtaceae Xanthostemon brassii XANTH BRASS Tree 0.918 Myrtaceae Xanthostemon sp XANTH SP Tree 0.850 Annonaceae Xylopia papuana XYLOP PAPUA Tree 0.536

51

Genus Species Tree or Specific Family Genus Species Code Code Palm Density Annonaceae Xylopia sp XYLOP SP Tree 0.536 Rhamnaceae Ziziphus acutangular ZIZIP ACUTA Tree 0.577 Rhamnaceae Ziziphus angustifolia ZIZIP ANGUS Tree 0.577 Rhamnaceae Ziziphus sp ZIZIP SP Tree 0.577 Winteraceae Zygogynum calothrysa ZYGOG CALOT Tree 0.495 Winteraceae Zygogynum sp ZYGOG SP Tree 0.495

52 SPC/GIZ Regional REDD+ Project

Annex 7 Detailed Results Table 1: Forest Carbon Statistics Diameter class (cm) Stocking Biomass Total 05.0-14.9 15.0-29.9 30.0-49.9 50.0+ Stratum Species Group Stems/ha kg/ha Tons per ha Hopea 128 65,054 32.5 2.9 4.9 11.5 13.3 Pterocarpus 4 9,400 4.7 0.0 0.1 0.7 4.0 Intsia 2 3,329 1.7 0.0 0.0 0.5 1.1 Anisoptera 15 9,929 5.0 0.4 0.3 1.4 2.8 Eucalyptopsis 24 27,895 13.9 0.1 0.1 0.6 13.2 Vatica 35 17,336 8.7 0.3 0.7 4.1 3.5 Pometia 12 9,898 4.9 0.1 0.1 1.3 3.4 Canarium 32 8,283 4.1 0.3 0.7 1.8 1.3 Hma Syzygium 72 11,605 5.8 1.1 0.7 1.3 2.6 Dracontomelon 1 980 0.5 0.0 0.1 0.2 0.2 Castanopsis 24 2,339 1.2 0.3 0.5 0.3 0.0 Podocarpaceae 8 3,404 1.7 0.1 0.1 0.6 0.9 Export Group 1 60 11,593 5.8 0.8 0.8 1.4 2.8 Export Group 2 30 7,309 3.7 0.5 0.3 1.8 1.1 Export Group 3 131 18,581 9.3 1.8 1.4 2.6 3.5 Export Group 4 878 87,503 43.8 13.1 8.4 11.4 10.8 Palm 71 1,006 0.5 0.4 0.1 0.0 0.0 Stratum Total 1,528 295,443 147.7 22.3 19.3 41.7 64.4 Hopea 0 0 0.0 0.0 0.0 0.0 0.0 Pterocarpus 0 0 0.0 0.0 0.0 0.0 0.0 Intsia 0 0 0.0 0.0 0.0 0.0 0.0 Anisoptera 73 25,138 12.6 0.6 1.1 5.2 5.6 Eucalyptopsis 0 0 0.0 0.0 0.0 0.0 0.0 Vatica 0 0 0.0 0.0 0.0 0.0 0.0 Pometia 0 0 0.0 0.0 0.0 0.0 0.0 Canarium 20 783 0.4 0.4 0.0 0.0 0.0 Hmb Syzygium 75 17,985 9.0 1.6 1.5 1.7 4.2 Dracontomelon 0 0 0.0 0.0 0.0 0.0 0.0 Castanopsis 428 74,179 37.1 7.7 8.8 16.5 4.2 Podocarpaceae 10 1,020 0.5 0.5 0.0 0.0 0.0 Export Group 1 135 19,458 9.7 1.5 2.4 4.8 1.0 Export Group 2 3 1,999 1.0 0.0 0.0 1.0 0.0 Export Group 3 203 14,362 7.2 1.4 1.5 3.1 1.1 Export Group 4 665 81,746 40.9 10.9 10.7 19.3 0.0 Palm 70 808 0.4 0.4 0.0 0.0 0.0 Stratum Total 1,683 237,477 118.7 24.9 26.1 51.6 16.1 Hopea 36 26,471 13.2 0.9 1.4 4.5 6.5 Pterocarpus 19 81,204 40.6 0.2 0.1 2.1 38.3 Intsia 3 4,030 2.0 0.0 0.0 0.6 1.4 Anisoptera 50 26,299 13.1 1.1 0.7 5.6 5.8 Eucalyptopsis 3 54 0.0 0.0 0.0 0.0 0.0 Vatica 9 5,644 2.8 0.1 0.2 0.0 2.5 Pometia 5 11,809 5.9 0.0 0.1 1.2 4.6 Canarium 27 19,647 9.8 0.1 0.4 1.3 8.0 P Syzygium 54 3,778 1.9 0.7 0.1 1.2 0.0 Dracontomelon 0 17,298 8.6 0.0 0.0 0.0 8.6 Castanopsis 0 0 0.0 0.0 0.0 0.0 0.0 Podocarpaceae 3 132 0.1 0.1 0.0 0.0 0.0 Export Group 1 46 15,523 7.8 0.4 0.6 3.4 3.3 Export Group 2 11 7,505 3.8 0.2 0.1 0.9 2.5 Export Group 3 45 13,967 7.0 0.8 0.8 2.3 3.1 Export Group 4 700 88,941 44.5 8.8 6.8 11.5 17.3 Palm 279 6,459 3.2 2.5 0.4 0.3 0.0 Stratum Total 1,290 328,762 164.4 15.8 11.6 35.0 102.0

53

Table 2: Diameter Distribution by Strata and Species Group Diameter class (cm) Species Total 05.0-14.9 15.0-29.9 30.0-49.9 50.0+ Stratum Group stems/ha Hopea 128 86 20 16 6 Pterocarpus 4 0 1 1 2 Intsia 2 0 0 1 1 Anisoptera 15 9 2 3 2 Eucalyptopsis 24 16 1 1 6 Vatica 35 18 5 10 3 Pometia 12 6 1 2 2 Canarium 32 22 5 4 1 Syzygium 72 63 4 3 2 Hma Dracontomelon 1 0 1 1 0 Castanopsis 24 19 4 1 0 Podocarpaceae 8 5 1 1 1 Export Group 1 60 50 5 3 2 Export Group 2 30 23 2 4 1 Export Group 3 131 112 10 6 3 Export Group 4 878 782 60 29 7 Palm 71 70 1 0 0 Stratum Total 1,528 1,282 120 87 40 Hopea 0 0 0 0 0 Pterocarpus 0 0 0 0 0 Intsia 0 0 0 0 0 Anisoptera 73 50 8 12 3 Eucalyptopsis 0 0 0 0 0 Vatica 0 0 0 0 0 Pometia 0 0 0 0 0 Canarium 20 20 0 0 0 Syzygium 75 60 8 3 3 Hmb Dracontomelon 0 0 0 0 0 Castanopsis 428 320 58 45 5 Podocarpaceae 10 10 0 0 0 Export Group 1 135 100 17 17 2 Export Group 2 3 0 0 3 0 Export Group 3 203 180 13 8 2 Export Group 4 665 550 68 47 0 Palm 70 70 0 0 0 Stratum Total 1,683 1,360 173 135 15 Hopea 36 22 5 6 3 Pterocarpus 19 5 1 6 7 Intsia 3 0 0 1 1 Anisoptera 50 32 5 10 3 Eucalyptopsis 3 3 0 0 0 Vatica 9 5 1 0 2 Pometia 5 0 0 2 2 Canarium 27 19 2 3 2 Syzygium 54 51 0 2 0 P Dracontomelon 0 0 0 0 0 Castanopsis 0 0 0 0 0 Podocarpaceae 3 3 0 0 0 Export Group 1 46 32 5 7 2 Export Group 2 11 8 0 2 0 Export Group 3 45 30 6 6 3 Export Group 4 700 603 54 32 12 Palm 279 273 5 1 0 Stratum Total 1,290 1,086 85 79 39

54 SPC/GIZ Regional REDD+ Project

Table 3: Total Sawlog Volume per ha by Strata, Species Group and Grade Diameter class (cm) Total Grade A Grade B Grade C Stratum Species Group m3 per ha Hopea 11.7 5.6 5.0 1.1 Pterocarpus 0.5 0.0 0.3 0.2 Intsia 0.6 0.3 0.3 0.0 Anisoptera 3.2 2.6 0.3 0.3 Eucalyptopsis 1.8 0.0 1.3 0.4 Vatica 4.6 2.5 1.7 0.4 Pometia 3.7 1.1 1.7 1.0 Canarium 1.6 1.0 0.3 0.2 Syzygium 2.8 0.0 0.8 2.0 Hma Dracontomelon 0.4 0.0 0.0 0.4 Castanopsis 0.0 0.0 0.0 0.0 Podocarpaceae 1.3 1.3 0.0 0.0 Export Group 1 3.6 3.1 0.5 0.0 Export Group 2 1.3 0.5 0.6 0.2 Export Group 3 3.9 1.2 1.4 1.3 Export Group 4 7.1 1.8 3.9 1.4 Palm 0.0 0.0 0.0 0.0 Stratum Total 47.9 20.9 18.2 8.8 Hopea 0.0 0.0 0.0 0.0 Pterocarpus 0.0 0.0 0.0 0.0 Intsia 0.0 0.0 0.0 0.0 Anisoptera 8.1 8.1 0.0 0.0 Eucalyptopsis 0.0 0.0 0.0 0.0 Vatica 0.0 0.0 0.0 0.0 Pometia 0.0 0.0 0.0 0.0 Canarium 0.0 0.0 0.0 0.0 Syzygium 2.0 0.0 2.0 0.0 Hmb Dracontomelon 0.0 0.0 0.0 0.0 Castanopsis 3.1 1.7 1.3 0.0 Podocarpaceae 0.0 0.0 0.0 0.0 Export Group 1 1.2 0.0 1.2 0.0 Export Group 2 0.0 0.0 0.0 0.0 Export Group 3 1.9 1.9 0.0 0.0 Export Group 4 0.0 0.0 0.0 0.0 Palm 0.0 0.0 0.0 0.0 Stratum Total 16.3 11.8 4.5 0.0 Hopea 4.4 0.9 3.5 0.0 Pterocarpus 5.0 0.0 1.8 3.2 Intsia 0.9 0.0 0.9 0.0 Anisoptera 8.7 3.4 4.3 1.0 Eucalyptopsis 0.0 0.0 0.0 0.0 Vatica 2.1 1.0 1.1 0.0 Pometia 3.0 0.0 1.6 1.5 Canarium 9.8 0.7 8.6 0.6 Syzygium 0.0 0.0 0.0 0.0 P Dracontomelon 8.2 0.0 0.0 8.2 Castanopsis 0.0 0.0 0.0 0.0 Podocarpaceae 0.0 0.0 0.0 0.0 Export Group 1 5.3 3.4 0.5 1.4 Export Group 2 4.6 0.0 0.0 4.6 Export Group 3 4.6 2.2 0.9 1.5 Export Group 4 9.6 2.7 3.8 3.1 Palm 0.0 0.0 0.0 0.0 Stratum Total 66.2 14.2 27.1 25.0

55

Table 4: Total Sawlog Volume per ha by Strata, Species Group and Diameter Class Total Sawlog Volume Table Diameter class (cm) Total 50.0-59.9 60.0-69.9 70.0+ Stratum Species Group m3 per ha Hopea 11.7 4.3 3.6 3.8 Pterocarpus 0.5 0.3 0.2 0.0 Intsia 0.6 0.1 0.4 0.0 Anisoptera 3.2 0.5 1.5 1.2 Eucalyptopsis 1.8 1.3 0.5 0.0 Vatica 4.6 1.7 2.1 0.9 Pometia 3.7 1.0 1.4 1.4 Canarium 1.6 0.7 0.9 0.0 Syzygium 2.8 0.6 0.4 1.8 Hma Dracontomelon 0.4 0.0 0.4 0.0 Castanopsis 0.0 0.0 0.0 0.0 Podocarpaceae 1.3 0.6 0.6 0.0 Export Group 1 3.6 0.8 1.8 0.9 Export Group 2 1.3 0.4 0.9 0.0 Export Group 3 3.9 1.9 1.0 1.0 Export Group 4 7.1 2.9 0.5 3.7 Palm 0.0 0.0 0.0 0.0 Stratum Total 47.9 17.1 16.1 14.6 Hopea 0.0 0.0 0.0 0.0 Pterocarpus 0.0 0.0 0.0 0.0 Intsia 0.0 0.0 0.0 0.0 Anisoptera 8.1 1.5 0.0 6.6 Eucalyptopsis 0.0 0.0 0.0 0.0 Vatica 0.0 0.0 0.0 0.0 Pometia 0.0 0.0 0.0 0.0 Canarium 0.0 0.0 0.0 0.0 Syzygium 2.0 0.0 2.0 0.0 Hmb Dracontomelon 0.0 0.0 0.0 0.0 Castanopsis 3.1 3.1 0.0 0.0 Podocarpaceae 0.0 0.0 0.0 0.0 Export Group 1 1.2 1.2 0.0 0.0 Export Group 2 0.0 0.0 0.0 0.0 Export Group 3 1.9 1.9 0.0 0.0 Export Group 4 0.0 0.0 0.0 0.0 Palm 0.0 0.0 0.0 0.0 Stratum Total 16.3 7.7 2.0 6.6 Hopea 4.4 2.0 2.3 0.0 Pterocarpus 5.0 0.0 2.2 2.9 Intsia 0.9 0.9 0.0 0.0 Anisoptera 8.7 3.5 0.0 5.2 Eucalyptopsis 0.0 0.0 0.0 0.0 Vatica 2.1 0.0 0.0 2.1 Pometia 3.0 0.0 0.5 2.5 Canarium 9.8 2.0 0.0 7.8 Syzygium 0.0 0.0 0.0 0.0 P Dracontomelon 8.2 0.0 0.0 8.2 Castanopsis 0.0 0.0 0.0 0.0 Podocarpaceae 0.0 0.0 0.0 0.0 Export Group 1 5.3 0.5 2.4 2.3 Export Group 2 4.6 0.0 0.0 4.6 Export Group 3 4.6 4.6 0.0 0.0 Export Group 4 9.6 6.6 2.1 1.0 Palm 0.0 0.0 0.0 0.0 Stratum Total 66.2 20.2 9.5 36.6

56 SPC/GIZ Regional REDD+ Project

Table 5: Grade A Sawlog Volume per ha by Strata, Species Group and Diameter Class Sawlog Grade A Volume Table Diameter class (cm) Total 50.0-59.9 60.0-69.9 70.0+ Stratum Species Group m3 per ha Hopea 5.6 2.4 2.2 1.0 Pterocarpus 0.0 0.0 0.0 0.0 Intsia 0.3 0.0 0.3 0.0 Anisoptera 2.6 0.4 1.0 1.2 Eucalyptopsis 0.0 0.0 0.0 0.0 Vatica 2.5 1.3 0.7 0.5 Pometia 1.1 0.2 0.0 0.8 Canarium 1.0 0.2 0.9 0.0 Syzygium 0.0 0.0 0.0 0.0 Hma Dracontomelon 0.0 0.0 0.0 0.0 Castanopsis 0.0 0.0 0.0 0.0 Podocarpaceae 1.3 0.6 0.6 0.0 Export Group 1 3.1 0.3 1.8 0.9 Export Group 2 0.5 0.3 0.3 0.0 Export Group 3 1.2 0.9 0.3 0.0 Export Group 4 1.8 0.2 0.2 1.3 Palm 0.0 0.0 0.0 0.0 Stratum Total 20.9 6.8 8.3 5.8 Hopea 0.0 0.0 0.0 0.0 Pterocarpus 0.0 0.0 0.0 0.0 Intsia 0.0 0.0 0.0 0.0 Anisoptera 8.1 1.5 0.0 6.6 Eucalyptopsis 0.0 0.0 0.0 0.0 Vatica 0.0 0.0 0.0 0.0 Pometia 0.0 0.0 0.0 0.0 Canarium 0.0 0.0 0.0 0.0 Syzygium 0.0 0.0 0.0 0.0 Hmb Dracontomelon 0.0 0.0 0.0 0.0 Castanopsis 1.7 1.7 0.0 0.0 Podocarpaceae 0.0 0.0 0.0 0.0 Export Group 1 0.0 0.0 0.0 0.0 Export Group 2 0.0 0.0 0.0 0.0 Export Group 3 1.9 1.9 0.0 0.0 Export Group 4 0.0 0.0 0.0 0.0 Palm 0.0 0.0 0.0 0.0 Stratum Total 11.8 5.2 0.0 6.6 Hopea 0.9 0.0 0.9 0.0 Pterocarpus 0.0 0.0 0.0 0.0 Intsia 0.0 0.0 0.0 0.0 Anisoptera 3.4 0.0 0.0 3.4 Eucalyptopsis 0.0 0.0 0.0 0.0 Vatica 1.0 0.0 0.0 1.0 Pometia 0.0 0.0 0.0 0.0 Canarium 0.7 0.7 0.0 0.0 Syzygium 0.0 0.0 0.0 0.0 P Dracontomelon 0.0 0.0 0.0 0.0 Castanopsis 0.0 0.0 0.0 0.0 Podocarpaceae 0.0 0.0 0.0 0.0 Export Group 1 3.4 0.0 1.0 2.3 Export Group 2 0.0 0.0 0.0 0.0 Export Group 3 2.2 2.2 0.0 0.0 Export Group 4 2.7 2.7 0.0 0.0 Palm 0.0 0.0 0.0 0.0 Stratum Total 14.2 5.6 1.9 6.7

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Table 6: Grade B Sawlog Volume per ha by Strata, Species Group and Diameter Class Sawlog Grade B Volume Table Diameter class (cm) Total 50.0-59.9 60.0-69.9 70.0+ Stratum Species Group m3 per ha Hopea 5.0 1.5 0.7 2.8 Pterocarpus 0.3 0.3 0.0 0.0 Intsia 0.3 0.1 0.2 0.0 Anisoptera 0.3 0.1 0.2 0.0 Eucalyptopsis 1.3 0.8 0.5 0.0 Vatica 1.7 0.4 1.4 0.0 Pometia 1.7 0.6 0.6 0.5 Canarium 0.3 0.3 0.0 0.0 Syzygium 0.8 0.2 0.2 0.3 Hma Dracontomelon 0.0 0.0 0.0 0.0 Castanopsis 0.0 0.0 0.0 0.0 Podocarpaceae 0.0 0.0 0.0 0.0 Export Group 1 0.5 0.5 0.0 0.0 Export Group 2 0.6 0.2 0.5 0.0 Export Group 3 1.4 0.2 0.5 0.7 Export Group 4 3.9 2.1 0.2 1.5 Palm 0.0 0.0 0.0 0.0 Stratum Total 18.2 7.4 4.8 5.9 Hopea 0.0 0.0 0.0 0.0 Pterocarpus 0.0 0.0 0.0 0.0 Intsia 0.0 0.0 0.0 0.0 Anisoptera 0.0 0.0 0.0 0.0 Eucalyptopsis 0.0 0.0 0.0 0.0 Vatica 0.0 0.0 0.0 0.0 Pometia 0.0 0.0 0.0 0.0 Canarium 0.0 0.0 0.0 0.0 Syzygium 2.0 0.0 2.0 0.0 Hmb Dracontomelon 0.0 0.0 0.0 0.0 Castanopsis 1.3 1.3 0.0 0.0 Podocarpaceae 0.0 0.0 0.0 0.0 Export Group 1 1.2 1.2 0.0 0.0 Export Group 2 0.0 0.0 0.0 0.0 Export Group 3 0.0 0.0 0.0 0.0 Export Group 4 0.0 0.0 0.0 0.0 Palm 0.0 0.0 0.0 0.0 Stratum Total 4.5 2.6 2.0 0.0 Hopea 3.5 2.0 1.5 0.0 Pterocarpus 1.8 0.0 0.8 0.9 Intsia 0.9 0.9 0.0 0.0 Anisoptera 4.3 2.5 0.0 1.8 Eucalyptopsis 0.0 0.0 0.0 0.0 Vatica 1.1 0.0 0.0 1.1 Pometia 1.6 0.0 0.0 1.6 Canarium 8.6 0.7 0.0 7.8 Syzygium 0.0 0.0 0.0 0.0 P Dracontomelon 0.0 0.0 0.0 0.0 Castanopsis 0.0 0.0 0.0 0.0 Podocarpaceae 0.0 0.0 0.0 0.0 Export Group 1 0.5 0.5 0.0 0.0 Export Group 2 0.0 0.0 0.0 0.0 Export Group 3 0.9 0.9 0.0 0.0 Export Group 4 3.8 1.8 2.1 0.0 Palm 0.0 0.0 0.0 0.0 Stratum Total 27.1 9.4 4.4 13.3

58 SPC/GIZ Regional REDD+ Project

Table 7: Grade C Sawlog Volume per ha by Strata, Species Group and Diameter Class Sawlog Grade C Volume Table Diameter class (cm) Total 50.0-59.9 60.0-69.9 70.0+ Stratum Species Group m3 per ha Hopea 1.1 0.4 0.7 0.0 Pterocarpus 0.2 0.0 0.2 0.0 Intsia 0.0 0.0 0.0 0.0 Anisoptera 0.3 0.0 0.3 0.0 Eucalyptopsis 0.4 0.4 0.0 0.0 Vatica 0.4 0.0 0.0 0.4 Pometia 1.0 0.2 0.8 0.0 Canarium 0.2 0.2 0.0 0.0 Syzygium 2.0 0.3 0.2 1.4 Hma Dracontomelon 0.4 0.0 0.4 0.0 Castanopsis 0.0 0.0 0.0 0.0 Podocarpaceae 0.0 0.0 0.0 0.0 Export Group 1 0.0 0.0 0.0 0.0 Export Group 2 0.2 0.0 0.2 0.0 Export Group 3 1.3 0.8 0.2 0.3 Export Group 4 1.4 0.6 0.0 0.8 Palm 0.0 0.0 0.0 0.0 Stratum Total 8.8 2.9 3.0 2.9 Hopea 0.0 0.0 0.0 0.0 Pterocarpus 0.0 0.0 0.0 0.0 Intsia 0.0 0.0 0.0 0.0 Anisoptera 0.0 0.0 0.0 0.0 Eucalyptopsis 0.0 0.0 0.0 0.0 Vatica 0.0 0.0 0.0 0.0 Pometia 0.0 0.0 0.0 0.0 Canarium 0.0 0.0 0.0 0.0 Syzygium 0.0 0.0 0.0 0.0 Hmb Dracontomelon 0.0 0.0 0.0 0.0 Castanopsis 0.0 0.0 0.0 0.0 Podocarpaceae 0.0 0.0 0.0 0.0 Export Group 1 0.0 0.0 0.0 0.0 Export Group 2 0.0 0.0 0.0 0.0 Export Group 3 0.0 0.0 0.0 0.0 Export Group 4 0.0 0.0 0.0 0.0 Palm 0.0 0.0 0.0 0.0 Stratum Total 0.0 0.0 0.0 0.0 Hopea 0.0 0.0 0.0 0.0 Pterocarpus 3.2 0.0 1.3 1.9 Intsia 0.0 0.0 0.0 0.0 Anisoptera 1.0 1.0 0.0 0.0 Eucalyptopsis 0.0 0.0 0.0 0.0 Vatica 0.0 0.0 0.0 0.0 Pometia 1.5 0.0 0.5 1.0 Canarium 0.6 0.6 0.0 0.0 Syzygium 0.0 0.0 0.0 0.0 P Dracontomelon 8.2 0.0 0.0 8.2 Castanopsis 0.0 0.0 0.0 0.0 Podocarpaceae 0.0 0.0 0.0 0.0 Export Group 1 1.4 0.0 1.4 0.0 Export Group 2 4.6 0.0 0.0 4.6 Export Group 3 1.5 1.5 0.0 0.0 Export Group 4 3.1 2.1 0.0 1.0 Palm 0.0 0.0 0.0 0.0 Stratum Total 25.0 5.2 3.2 16.6

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Table 8: Scheffe Analysis of Significant Difference in Average Carbon Stock per ha Between Strata Sample Statistics Variables N SS Avg Hma 94 308,458 147.7 Hmb 10 17,646 118.7 P 37 617,976 164.4 SSE 944,079 MSE 6,841 p 0.05 k 3 N 141 F(p, k-1, N-k) 3.06

Pair Wise Differences Between Sample Means Type Hma Hmb P Hma 29.0 16.7 Hmb 45.6 P

Scheffe Comparison Values Type Hma Hmb P Hma 68.1 39.7 Hmb 72.9 P

Signficant Differences Type Hma Hmb P Hma No No Hmb No P

60 SPC/GIZ Regional REDD+ Project

Table 9: Statistics per Plot Total Total Total SPH Carbon Carbon Carbon Carbon Total Sawlog Sawlog Sawlog Sawlog Sawlog Sawlog LC SPH SPH 30- SPH Biomass Carbon 5-20 20-30 30-50 50+ Sawlog 50-60 60-70 70+ A B C Strata Cluster Plot SPH 5-20 20-30 50 50+ kg/ha t/ha t/ha t/ha t/ha t/ha m3/ha m3/ha m3/ha m3/ha m3/ha m3/ha m3/ha Hma 1 001 2,233 1,900 83 233 17 390,422 195.2 47.5 17.0 93.6 37.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 1 002 967 700 117 133 17 199,186 99.6 6.7 10.8 59.9 22.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 1 003 1,217 900 183 133 0 219,183 109.6 18.9 36.8 53.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 1 004 1,200 1,000 83 67 50 223,575 111.8 16.1 10.0 38.6 47.1 20.6 20.6 0.0 0.0 0.0 20.6 0.0 Hma 1 005 1,467 1,300 33 83 50 209,818 104.9 7.5 3.8 34.4 59.2 86.6 35.3 51.3 0.0 51.3 35.3 0.0 Hma 1 006 1,417 1,200 67 117 33 365,665 182.8 14.2 15.4 76.3 77.0 146.7 0.0 66.9 79.9 146.7 0.0 0.0 Hma 1 007 1,683 1,400 117 100 67 425,898 212.9 24.9 29.2 55.3 103.5 61.7 0.0 61.7 0.0 38.5 23.2 0.0 Hma 1 008 1,550 1,400 100 33 17 108,714 54.4 16.2 14.6 9.9 13.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 1 009 900 700 133 17 50 191,498 95.7 3.8 28.2 5.6 58.1 41.7 41.7 0.0 0.0 0.0 20.9 20.7 Hma 1 010 1,933 1,700 117 83 33 278,992 139.5 19.0 25.5 43.7 51.3 98.2 39.1 59.1 0.0 59.1 39.1 0.0 Hma 1 011 900 700 50 100 50 366,781 183.4 22.3 6.7 60.7 93.6 86.0 86.0 0.0 0.0 43.8 42.1 0.0 Hma 1 012 717 500 117 100 0 133,784 66.9 3.6 21.6 41.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 1 013 1,567 1,400 50 33 83 327,020 163.5 27.9 8.7 9.9 116.9 192.7 24.5 90.1 78.1 78.1 90.1 24.5 Hma 1 014 5,983 5,900 67 17 0 169,127 84.6 72.5 9.2 2.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 1 015 1,317 1,100 183 33 0 160,107 80.1 24.6 29.2 26.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 1 016 2,133 1,900 100 117 17 264,451 132.2 28.4 16.1 73.8 14.0 18.6 18.6 0.0 0.0 0.0 18.6 0.0 Hma 1 017 1,183 1,000 117 17 50 220,462 110.2 23.5 18.5 9.9 58.2 162.0 43.8 66.8 51.4 0.0 110.6 51.4 Hma 1 018 1,500 1,100 233 150 17 240,412 120.2 15.5 37.2 46.5 20.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 1 019 1,583 1,300 133 67 83 509,101 254.6 31.8 22.0 26.2 174.6 221.7 76.3 145.3 0.0 143.9 77.8 0.0 Hma 1 020 683 400 167 50 67 401,184 200.6 13.3 30.7 21.0 135.5 172.3 35.1 0.0 137.1 137.1 0.0 35.1 Hma 1 021 1,217 1,000 83 83 50 392,044 196.0 17.5 15.1 32.7 130.7 22.8 22.8 0.0 0.0 0.0 22.8 0.0 Hma 1 022 1,083 800 133 100 50 360,287 180.1 12.1 28.8 38.1 101.2 210.2 0.0 120.3 90.0 179.7 30.6 0.0 Hma 1 023 983 700 167 117 0 163,658 81.8 8.4 26.2 47.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 P 3 041 1,133 900 133 100 0 167,380 83.7 12.3 15.8 55.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 P 3 042 1,183 1,000 83 67 33 126,094 63.0 7.7 13.9 13.4 27.9 100.8 41.1 59.7 0.0 59.7 41.1 0.0 P 3 043 2,350 1,900 200 250 0 381,821 190.9 45.2 30.2 115.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 P 3 044 1,400 1,200 117 50 33 174,985 87.5 17.3 13.0 28.5 28.6 52.0 21.8 30.2 0.0 0.0 21.8 30.2 P 3 045 1,650 1,200 217 167 67 338,184 169.1 26.5 27.0 61.9 53.7 101.6 101.6 0.0 0.0 40.5 0.0 61.1 P 3 046 1,150 900 167 0 83 364,392 182.2 12.0 20.5 0.0 149.7 82.5 34.6 47.9 0.0 0.0 82.5 0.0 P 3 047 1,633 1,500 117 17 0 59,746 29.9 9.4 9.8 10.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 P 3 048 1,183 1,000 67 100 17 215,712 107.9 18.8 9.0 64.2 15.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0

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Total Total Total SPH Carbon Carbon Carbon Carbon Total Sawlog Sawlog Sawlog Sawlog Sawlog Sawlog LC SPH SPH 30- SPH Biomass Carbon 5-20 20-30 30-50 50+ Sawlog 50-60 60-70 70+ A B C Strata Cluster Plot SPH 5-20 20-30 50 50+ kg/ha t/ha t/ha t/ha t/ha t/ha m3/ha m3/ha m3/ha m3/ha m3/ha m3/ha m3/ha P 3 050 1,733 1,500 133 67 33 216,327 108.2 32.4 15.8 28.2 31.9 35.3 35.3 0.0 0.0 0.0 0.0 35.3 P 3 051 767 400 150 150 67 375,412 187.7 14.4 23.2 53.5 96.6 147.8 147.8 0.0 0.0 0.0 90.6 57.2 P 3 052 2,000 1,700 100 200 0 291,268 145.6 30.0 14.2 101.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 P 3 053 1,500 1,200 17 200 83 509,578 254.8 18.0 2.5 117.6 116.7 233.6 143.0 0.0 90.5 90.5 111.6 31.4 P 3 054 1,950 1,700 117 100 33 278,313 139.2 14.7 20.0 48.6 55.9 157.1 52.9 0.0 104.2 0.0 157.1 0.0 P 3 055 1,217 1,000 100 117 0 134,678 67.3 10.1 12.2 45.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 P 3 056 1,317 1,100 83 67 67 296,417 148.2 10.1 9.3 21.1 107.7 203.2 87.5 54.7 61.0 0.0 54.7 148.5 P 3 057 2,850 2,700 100 33 17 222,223 111.1 49.8 9.5 22.7 29.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 P 3 058 1,683 1,500 100 50 33 268,095 134.0 30.2 13.3 17.9 72.6 150.5 0.0 0.0 150.5 102.6 0.0 47.8 P 3 059 1,917 1,600 133 83 100 618,786 309.4 23.4 36.9 46.7 202.4 250.0 116.6 133.4 0.0 49.9 200.1 0.0 P 3 060 867 700 50 50 67 294,531 147.3 9.1 9.4 23.8 104.9 137.7 83.7 0.0 54.0 83.7 54.0 0.0 Hma 5 061 1,383 1,200 67 50 67 239,674 119.8 19.6 8.5 10.0 81.7 147.6 118.4 29.3 0.0 0.0 112.3 35.3 Hma 5 062 1,750 1,500 150 83 17 141,371 70.7 13.9 12.4 26.5 17.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 5 063 1,767 1,400 167 167 33 216,072 108.0 9.0 19.9 56.0 23.1 39.7 0.0 39.7 0.0 0.0 39.7 0.0 Hma 5 064 1,633 1,300 117 133 83 408,393 204.2 29.9 21.2 53.8 99.3 252.7 72.3 180.4 0.0 165.2 34.6 53.0 Hma 5 065 1,417 1,200 50 117 50 256,988 128.5 22.4 6.9 52.6 46.6 103.3 103.3 0.0 0.0 36.9 66.3 0.0 Hma 5 066 2,000 1,700 183 67 50 374,987 187.5 18.8 29.4 39.9 99.4 38.2 38.2 0.0 0.0 0.0 0.0 38.2 Hma 5 067 1,400 1,100 183 67 50 390,524 195.3 20.3 29.4 34.4 111.2 105.4 0.0 52.3 53.1 52.3 0.0 53.1 Hma 5 068 2,333 2,100 200 33 0 95,490 47.7 26.2 16.1 5.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 5 069 983 700 150 117 17 302,352 151.2 6.1 37.4 87.4 20.3 72.1 0.0 72.1 0.0 0.0 72.1 0.0 Hma 5 070 783 500 217 50 17 120,197 60.1 6.7 27.0 14.5 12.0 60.3 60.3 0.0 0.0 60.3 0.0 0.0 Hma 5 071 1,117 900 50 50 117 587,899 293.9 15.3 5.9 31.3 241.4 187.1 67.4 69.1 50.7 50.7 82.1 54.4 Hma 5 072 1,250 1,000 83 100 67 367,571 183.8 17.5 14.0 46.2 106.1 75.5 75.5 0.0 0.0 75.5 0.0 0.0 Hma 5 073 1,583 1,200 217 117 50 372,777 186.4 23.4 38.5 53.2 71.4 42.5 0.0 42.5 0.0 0.0 42.5 0.0 Hma 5 074 1,350 1,100 183 50 17 319,584 159.8 21.0 26.8 26.7 85.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 5 075 1,450 1,200 100 117 33 339,870 169.9 19.2 19.3 61.3 70.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 5 076 1,967 1,700 150 117 0 160,886 80.4 28.4 22.6 29.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 5 077 1,250 1,000 133 83 33 202,432 101.2 9.2 17.6 45.4 29.1 49.9 49.9 0.0 0.0 0.0 24.7 25.2 Hma 5 078 1,033 900 33 0 100 439,539 219.8 14.1 4.1 0.0 201.6 89.7 89.7 0.0 0.0 0.0 64.6 25.2 Hma 5 079 1,883 1,700 100 50 33 195,023 97.5 25.8 15.5 17.7 38.5 93.8 93.8 0.0 0.0 34.8 59.0 0.0 Hma 5 080 1,300 1,100 83 33 83 334,265 167.1 10.8 10.7 14.1 131.5 238.2 41.4 117.9 78.9 0.0 159.1 79.1 Hma 5 081 1,833 1,600 100 67 67 577,874 288.9 44.3 15.4 25.5 203.6 24.3 24.3 0.0 0.0 0.0 24.3 0.0 Hma 5 082 867 600 100 33 133 630,267 315.1 21.9 14.1 8.0 271.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 62 SPC/GIZ Regional REDD+ Project

Total Total Total SPH Carbon Carbon Carbon Carbon Total Sawlog Sawlog Sawlog Sawlog Sawlog Sawlog LC SPH SPH 30- SPH Biomass Carbon 5-20 20-30 30-50 50+ Sawlog 50-60 60-70 70+ A B C Strata Cluster Plot SPH 5-20 20-30 50 50+ kg/ha t/ha t/ha t/ha t/ha t/ha m3/ha m3/ha m3/ha m3/ha m3/ha m3/ha m3/ha Hma 5 083 2,600 2,500 83 17 0 73,892 36.9 27.4 6.8 2.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 5 084 1,233 900 100 133 100 484,184 242.1 23.7 11.3 52.4 154.7 222.3 29.6 107.7 84.9 107.7 114.6 0.0 P 6 091 700 500 83 100 17 153,012 76.5 6.6 13.6 30.4 26.0 29.3 0.0 29.3 0.0 0.0 0.0 29.3 P 6 092 733 500 100 100 33 265,261 132.6 7.4 12.5 40.4 72.3 177.0 0.0 44.3 132.6 132.6 0.0 44.3 P 6 093 1,083 900 50 100 33 151,963 76.0 5.2 5.0 38.1 27.7 33.8 33.8 0.0 0.0 0.0 33.8 0.0 P 6 094 500 200 100 133 67 366,894 183.4 0.5 12.7 73.9 96.4 32.4 32.4 0.0 0.0 0.0 32.4 0.0 P 6 095 1,217 1,000 133 33 50 277,728 138.9 19.7 17.6 15.4 86.2 118.3 0.0 0.0 118.3 56.7 61.6 0.0 P 6 096 2,033 1,900 33 67 33 597,079 298.5 38.4 3.7 34.1 222.4 494.1 0.0 0.0 494.1 0.0 234.7 259.4 P 6 097 517 400 33 67 17 115,213 57.6 5.6 3.6 22.0 26.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 P 6 098 950 900 33 17 0 28,717 14.4 7.3 2.7 4.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 P 6 099 783 700 50 17 17 96,651 48.3 3.9 5.3 4.7 34.4 62.8 0.0 62.8 0.0 0.0 62.8 0.0 P 6 100 817 700 17 100 0 111,952 56.0 12.5 1.8 41.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 P 6 103 1,650 1,500 50 67 33 271,341 135.7 15.8 6.3 20.7 92.9 56.7 0.0 0.0 56.7 0.0 0.0 56.7 P 6 104 600 500 33 33 33 141,603 70.8 2.7 4.7 10.5 52.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 P 6 105 767 600 50 33 83 793,327 396.7 4.5 9.5 12.7 369.9 248.6 36.5 0.0 212.2 36.5 212.2 0.0 P 6 106 1,083 1,000 17 17 50 1,081,539 540.8 18.4 4.1 8.9 509.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 P 6 107 1,183 1,100 50 0 33 810,084 405.0 11.4 5.4 0.0 388.2 47.6 47.6 0.0 0.0 47.6 0.0 0.0 P 6 108 1,133 900 33 83 117 1,116,255 558.1 11.9 4.6 35.9 505.7 679.2 70.8 0.0 608.4 70.8 89.6 518.8 P 6 109 1,483 1,300 67 50 67 345,692 172.8 13.8 8.0 17.1 134.0 114.0 37.0 77.0 0.0 37.0 0.0 77.0 P 6 110 1,000 900 33 33 33 105,928 53.0 6.7 3.8 7.3 35.1 23.2 23.2 0.0 0.0 0.0 0.0 23.2 Hma 7 111 1,417 1,200 117 50 50 458,452 229.2 32.1 18.3 33.2 145.7 202.5 86.7 0.0 115.8 169.8 0.0 32.7 Hma 7 112 1,317 1,100 117 83 17 208,904 104.5 21.2 18.6 34.5 30.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 7 113 1,000 800 100 83 17 209,930 105.0 11.7 13.8 39.6 39.9 53.2 0.0 53.2 0.0 0.0 0.0 53.2 Hma 7 114 1,167 900 167 67 33 381,683 190.8 26.5 26.0 33.0 105.4 111.3 0.0 43.5 67.9 67.9 0.0 43.5 Hma 7 115 1,067 900 117 0 50 305,754 152.9 17.8 19.1 0.0 116.0 111.5 40.4 0.0 71.1 111.5 0.0 0.0 Hma 7 116 1,383 1,300 50 17 17 200,049 100.0 10.9 12.1 13.2 63.8 91.5 0.0 0.0 91.5 0.0 0.0 91.5 Hma 7 117 967 800 83 50 33 164,369 82.2 7.7 11.3 33.8 29.3 75.1 75.1 0.0 0.0 0.0 43.9 31.2 Hma 7 118 1,383 1,100 50 183 50 301,916 151.0 16.3 7.7 93.5 33.4 40.7 40.7 0.0 0.0 40.7 0.0 0.0 Hma 7 119 1,717 1,500 83 67 67 489,451 244.7 34.3 11.4 32.3 166.8 27.9 27.9 0.0 0.0 0.0 0.0 27.9 Hma 7 120 1,483 1,300 83 67 33 221,030 110.5 25.6 16.3 23.9 44.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 7 121 1,400 1,200 83 117 0 148,251 74.1 11.7 12.9 49.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 7 122 1,600 1,300 183 67 50 238,581 119.3 26.4 28.3 21.6 42.9 38.9 38.9 0.0 0.0 38.9 0.0 0.0 Hma 7 123 1,317 1,100 117 33 67 332,773 166.4 25.9 17.0 25.3 98.2 110.8 62.2 48.6 0.0 84.3 0.0 26.4 63

Total Total Total SPH Carbon Carbon Carbon Carbon Total Sawlog Sawlog Sawlog Sawlog Sawlog Sawlog LC SPH SPH 30- SPH Biomass Carbon 5-20 20-30 30-50 50+ Sawlog 50-60 60-70 70+ A B C Strata Cluster Plot SPH 5-20 20-30 50 50+ kg/ha t/ha t/ha t/ha t/ha t/ha m3/ha m3/ha m3/ha m3/ha m3/ha m3/ha m3/ha Hma 7 124 2,550 2,200 200 117 33 384,205 192.1 55.9 30.3 59.8 46.2 44.9 0.0 44.9 0.0 0.0 0.0 44.9 Hma 7 125 1,567 1,300 67 150 50 414,150 207.1 21.2 12.7 102.8 70.4 101.1 32.8 68.4 0.0 32.8 0.0 68.4 Hma 7 126 2,783 2,500 133 117 33 317,655 158.8 29.2 26.1 61.3 42.2 25.6 25.6 0.0 0.0 25.6 0.0 0.0 Hma 7 127 1,483 1,300 67 100 17 224,427 112.2 34.1 14.3 48.7 15.0 27.7 27.7 0.0 0.0 0.0 27.7 0.0 Hma 7 128 2,000 1,800 100 33 67 410,984 205.5 29.3 15.8 20.4 140.0 149.2 0.0 85.8 63.4 51.9 33.9 63.4 Hma 7 129 1,600 1,400 50 133 17 220,388 110.2 22.3 5.4 54.9 27.6 52.4 0.0 52.4 0.0 52.4 0.0 0.0 Hma 7 130 2,233 2,000 117 50 67 420,186 210.1 26.3 19.8 36.9 127.1 145.6 63.4 0.0 82.1 38.1 82.1 25.3 Hma 7 131 1,367 1,100 83 100 83 384,625 192.3 14.5 13.3 67.1 97.3 54.3 54.3 0.0 0.0 0.0 54.3 0.0 Hma 7 132 1,817 1,600 83 100 33 207,336 103.7 15.1 12.3 42.7 33.6 26.5 26.5 0.0 0.0 26.5 0.0 0.0 Hma 10 024 1,033 900 50 67 17 246,305 123.2 23.1 6.9 51.5 41.7 133.0 0.0 0.0 133.0 0.0 133.0 0.0 Hma 10 025 2,200 1,900 167 83 50 383,165 191.6 28.2 33.5 54.9 75.0 94.9 36.5 58.4 0.0 94.9 0.0 0.0 Hma 10 026 833 600 133 83 17 194,349 97.2 4.3 16.6 47.0 29.3 73.5 0.0 0.0 73.5 73.5 0.0 0.0 Hma 10 027 1,783 1,500 150 100 33 251,639 125.8 24.5 21.3 52.2 27.8 53.2 53.2 0.0 0.0 0.0 53.2 0.0 Hma 10 028 867 700 67 83 17 238,916 119.5 9.0 12.2 41.3 56.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 10 029 1,400 1,100 150 100 50 481,050 240.5 47.9 33.8 68.6 90.3 130.1 48.5 0.0 81.6 48.5 81.6 0.0 Hma 10 030 1,417 1,100 167 150 0 233,664 116.8 27.0 26.6 63.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 10 031 2,600 2,200 250 100 50 361,603 180.8 35.6 47.4 44.1 53.8 132.8 67.1 65.7 0.0 65.7 67.1 0.0 Hma 10 032 1,567 1,200 183 117 67 467,084 233.5 48.8 32.0 80.3 72.5 137.3 84.8 52.4 0.0 137.3 0.0 0.0 Hma 10 033 1,233 1,000 167 50 17 260,347 130.2 27.9 32.0 19.0 51.3 117.2 0.0 0.0 117.2 0.0 0.0 117.2 Hma 10 034 2,167 1,800 167 200 0 311,522 155.8 36.7 30.1 88.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 10 035 1,550 1,300 133 117 0 261,194 130.6 22.4 28.3 79.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 10 036 1,100 900 83 100 17 286,639 143.3 10.3 13.3 49.8 69.9 155.1 0.0 0.0 155.1 0.0 155.1 0.0 Hma 10 037 1,617 1,300 167 117 33 290,057 145.0 23.6 31.6 47.9 41.9 105.9 0.0 105.9 0.0 0.0 105.9 0.0 Hma 10 038 1,783 1,500 183 83 17 247,319 123.7 38.8 33.6 33.4 17.8 24.0 24.0 0.0 0.0 0.0 24.0 0.0 Hma 10 039 1,667 1,500 67 83 17 127,802 63.9 16.3 6.3 30.0 11.2 28.6 28.6 0.0 0.0 28.6 0.0 0.0 Hma 10 040 1,283 1,100 50 50 83 356,598 178.3 22.3 7.3 30.1 118.6 236.5 90.3 75.4 70.9 236.5 0.0 0.0 Hmb 13 140 1,317 1,100 117 83 17 144,936 72.5 22.4 14.0 24.6 11.5 28.9 28.9 0.0 0.0 28.9 0.0 0.0 Hmb 13 141 1,067 700 167 150 50 330,120 165.1 10.6 26.2 62.0 66.2 147.0 45.5 0.0 101.5 128.1 18.9 0.0 Hmb 13 142 2,583 2,100 300 183 0 292,240 146.1 50.9 41.0 54.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hmb 13 143 2,283 1,900 217 150 17 256,175 128.1 28.6 27.0 55.0 17.6 23.8 23.8 0.0 0.0 23.8 0.0 0.0 Hmb 13 144 1,483 1,200 167 117 0 206,266 103.1 34.9 27.0 41.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hmb 13 145 1,683 1,300 167 183 33 393,528 196.8 33.3 26.0 95.3 42.2 30.0 0.0 30.0 0.0 0.0 30.0 0.0 Hma 13 146 1,883 1,500 267 100 17 263,586 131.8 31.5 42.0 37.5 20.9 49.7 0.0 0.0 49.7 0.0 49.7 0.0 64 SPC/GIZ Regional REDD+ Project

Total Total Total SPH Carbon Carbon Carbon Carbon Total Sawlog Sawlog Sawlog Sawlog Sawlog Sawlog LC SPH SPH 30- SPH Biomass Carbon 5-20 20-30 30-50 50+ Sawlog 50-60 60-70 70+ A B C Strata Cluster Plot SPH 5-20 20-30 50 50+ kg/ha t/ha t/ha t/ha t/ha t/ha m3/ha m3/ha m3/ha m3/ha m3/ha m3/ha m3/ha Hmb 13 147 1,483 1,200 117 133 33 210,464 105.2 12.0 18.4 51.0 23.9 20.7 20.7 0.0 0.0 0.0 20.7 0.0 Hmb 13 148 1,417 1,200 100 117 0 163,876 81.9 15.7 16.6 49.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hmb 13 149 1,450 1,300 67 83 0 105,411 52.7 13.4 14.4 24.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hmb 13 150 2,067 1,600 317 150 0 271,754 135.9 27.2 50.2 58.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 13 153 1,500 1,200 150 100 50 256,772 128.4 17.3 22.0 50.7 38.3 51.6 18.3 33.3 0.0 0.0 0.0 51.6 Hma 13 154 1,417 1,100 133 150 33 333,467 166.7 33.9 24.1 70.3 38.4 68.5 0.0 27.8 40.6 0.0 27.8 40.6 Hma 13 155 1,267 1,000 117 67 83 281,396 140.7 21.6 18.8 35.8 64.5 132.8 23.0 109.8 0.0 132.8 0.0 0.0 Hma 13 156 1,700 1,400 83 150 67 407,730 203.9 31.3 8.8 83.7 80.1 128.4 43.8 32.7 51.9 0.0 75.7 52.7 Hma 13 157 1,167 900 133 117 17 228,138 114.1 18.7 16.9 46.0 32.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hma 13 158 817 600 100 83 33 177,915 89.0 7.8 13.8 30.5 36.9 75.7 24.7 0.0 51.0 0.0 75.7 0.0 Hma 13 160 1,567 1,400 33 100 33 224,827 112.4 20.3 4.4 48.6 39.2 116.7 27.7 0.0 89.0 0.0 116.7 0.0

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