BIOCLIME WP3 report Remote Sensing Solutions GmbH Final

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Survey of biomass, carbon stocks, biodiversity, and assessment of the historic fire regime for integration into a forest monitoring system in South Sumatra, Indonesia.

Project number: 12.9013.9-001.00

Survey of biomass, carbon stocks, biodiversity, and assessment of the historic fire

regime for integration into a forest monitoring system in the Districts Musi Rawas,

Musi Rawas Utara, Musi Banyuasin and Banyuasin, South Sumatra, Indonesia

Work Package 3

Aboveground biomass and tree community composition modelling

Prepared by:

Uwe Ballhorn, Kristina Konecny, Peter Navratil, Juilson Jubanski, Florian Siegert RSS – Remote Sensing Solutions GmbH

Isarstraße 3 82065 Baierbrunn Germany

Phone: +49 89 48 95 47 66 Fax: +49 89 48 95 47 67 Email:ballhorn@rssgmbh.de

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Table of Content

1. Introduction 2

2. Carbon and biodiversity plots 4

2.1. Inventory design 4

2.2. Aboveground biomass calculations 9

3. LiDAR data and aerial photos 13

3.1. LiDAR and aerial photo survey 13

3.2. LiDAR processing, filtering and interpolation 14

4. LiDAR based aboveground biomass model 17

4.1. Regression analysis and aboveground biomass model development 17

4.2. Determination of local aboveground biomass values 20

5. Analyses of tree community composition of lowland dipterocarp forests 23

5.1. Calculation of different LiDAR metrics for the biodiversity plots 23 5.2. Derivation of nMDS scores, biodiversity indices and pioneer/climax tree species ratio 23

5.3. LiDAR based tree community composition model 31

6. Conclusions 36

7. Outlook 36

Outputs / deliverables 37

References 38

Appendix A: Overview field plots 41

Appendix B: Overview biodiversity plots 47

Appendix C: Classification scheme 50

Appendix D: Overview LiDAR metrics lowland dipterocarp forest 52

Appendix E: Categorization of indetified tree species into pioneer or climax tree species. 54 Appendix F: Overview nMDS scores, biodiversity indices and ratios pioneer/climax tree species

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Executive summary

With the Biodiversity and Climate Change Project (BIOCLIME), Germany supports Indonesia's efforts to reduce greenhouse gas emissions from the forestry sector, to conserve forest biodiversity of High Value Forest Ecosystems, maintain their Carbon stock storage capacities and to implement sustainable forest management for the benefit of the people. Germany's immediate contribution will focus on supporting the Province of South Sumatra to develop and implement a conservation and management concept to lower emissions from its forests, contributing to the GHG emission reduction goal Indonesia has committed itself until 2020.

One of the important steps to improve land-use planning, forest management and protection of nature is to base the planning and management of natural resources on accurate, reliable and consistent geographic information. In order to generate and analyze this information, a multi-purpose monitoring system is required.

The concept of the monitoring system consists of three components: historical, current and monitoring. This report presents the outcomes of the work package 3 “Aboveground biomass and tree community composition modelling” which is part of the current component.

The main objectives of WP 3 were:

 Filtering of the LiDAR 3D point clouds (provided by the project) into vegetation and non-vegetation points.

 Derive Digital Surface Models (DSM), Digital Terrain Models (DTM) and Canopy Height Models (CHM) from the airborne LiDAR data.

 Advice BIOCLIME in the collection of forest inventory data to calibrate the LiDAR derived aboveground biomass model.

 Derive an aboveground biomass model from the airborne LiDAR data (provided by the project) in combination with forest inventory data (provided by the project).

 Derive local aboveground biomass values for different vegetation classes from this LiDAR based aboveground biomass model.

 Derive a tree a community composition model of Lowland Dipterocarp Forest at various degradation stages from LiDAR data (provided by the project) in combination with tree species/genera diversity data collected in the field (provided by the project).

The results of the workpackage were a set of local aboveground biomass (AGB) values (Emission factors) derived from the LiDAR based aboveground biomass model for almost all identified vegetation cover classes. It was shown that aboveground biomass variability within vegetation classes can be very high (e.g. Primary Dryland Forest has a standard deviation for aboveground biomass of ±222.5 t/ha). Areas with the highest aboveground biomass (AGB) values were located within and around the Kerinci Seblat National Park.

The tree community composition modelling results indicate that the similarity in tree community composition can be predicted and monitored by means of airborne LiDAR. In addition to using airborne LiDAR data as mapping tool for aboveground biomass this data could be further developed to provide a biodiversity mapping tool, so that biodiversity assessments could be carried out simultaneously with aboveground biomass analyses (same dataset). A further advantage of the approach is that the tree community composition can be carried out without identifying individual tree crowns in remotely sensed imagery.

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1. Introduction

This system will provide a variety of information layers of different temporal and geographic scales:

 Information on actual land-use and the dynamics of land-use changes during the past decades is considered a key component of such a system. For South Sumatra, this data is already available from a previous assessment by the World Agroforestry Center (ICRAF).

 Accurate current information on forest types and forest status, in particular in terms of

aboveground biomass, carbon stock and biodiversity, derived from a combination of remote sensing and field techniques.

 Accurate information of the historic fire regime in the study area. Fire is considered one of the

key drivers shaping the landscape and influencing land cover change, biodiversity and carbon stocks. This information must be derived from historic satellite imagery.

 Indicators for biodiversity in different forest ecosystems and degradation stages.

The objective of the work conducted by Remote Sensing Solutions GmbH (RSS) was to support the goals of the BIOCLIME project by providing the required information on land use dynamics, forest types and status, biomass and biodiversity and the historic fire regime. The conducted work is based on a wide variety of remote sensing systems and analysis techniques, which were jointly implemented within the project, in order to produce a reliable information base able to fulfil the project’s and the partners’ requirements on the multi-purpose monitoring system.

This report presents the results of Work Package 3 (WP 3): Aboveground biomass and tree community composition modelling. The main objectives of WP 3 were:

 Filtering of the LiDAR 3D point clouds (provided by the project) into vegetation and non-vegetation points.

 Derive Digital Surface Models (DSM), Digital Terrain Models (DTM) and Canopy Height Models (CHM) from the airborne LiDAR data.

 Advice BIOCLIME in the collection of forest inventory data to calibrate the LiDAR derived aboveground biomass model.

 Derive an aboveground biomass model from the airborne LiDAR data (provided by the project) in combination with forest inventory data (provided by the project).

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 Deduce local aboveground biomass values for different vegetation classes from this LiDAR based aboveground biomass model.

Figure 1 shows the flowchart of the activities carried out in Work Package 3 (WP 3).

Figure 1: Flow chart of the activities carried out in Work Package 3 (WP 3): Aboveground biomass and tree community composition modelling.

2. Carbon and biodiversity plots

2.1. Inventory design

112 so called carbon inventory plots were recorded within the four districts of Banyuasin, Musi Banyasin, Musi Rawas Utara and Musi Rawas. All these districts are located in the province of South Sumatra. The planning and collection of these carbon inventory plots was conducted by the BIOCLIME’s survey teams, consisting of GIZ staff, researchers of the Forest Research Agency in Palembang, forestry officers and local people from South Sumatra. The distribution of these carbon inventory plots is based on a systematic sampling design. In order, to assure that statistically enough carbon inventory plots are located within the airborne LiDAR transects to generate the LiDAR based aboveground biomass model (see Chapter 4 LiDAR based aboveground biomass model) some of these

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112 carbon inventory plots where spatially shifted into the nearest LiDAR transect, consequently now not fitting into the systematic sampling design anymore.

The stratification of the forest areas was based on a combination forest type, altitude and soil type. The 112 carbon inventory plots were located in 10 different strata (see Table 1).

Table 1: Number of sample plots for each stratum.

Stratum1 Number of carbon inventory plots Primary Dryland Forest

(Hutan Lahan Kering Primer) 8 Secondary / Logged over Dryland Forest

(Hutan Lahan Kering Sekunder) 33 Primary Mangrove Forest

(Hutan Mangrove Primer) 13 Secondary / Logged over Mangrove Forest

(Hutan Mangrove Sekunder) 7 Primary Peat Swamp Forest

(Hutan Rawa Gambut Primer) 5 Secondary / Logged over Peat Swamp Forest

(Hutan Rawa Gambut Sekunder) 9 Plantation Forest

(Hutan Tanaman) 8

Tree Crop Plantation

(Perkebunan) 15

Shrubs

(Semak Belukar) 6

Swamp Shrubs

(Semak Belukar Rawa) 8

Sum 112

1_{in brackets Bahasa Indonesia}

In natural forests a nested rectangular plot design was chosen. Figure 2 displays the layout of such a nested rectangular plot and which Diameter at Breast Height (DBH) ranges were measured in which subplot. In plantations a circular plot design was applied, where the size of the circle depended on the age of the plantation (age of plantation < four years: radius = 7.98 m, area = 0.02 ha; age of plantation ≥ four years: radius = 11.29 m, area = 0.04 ha). Figure 3 shows the layouts of these circular plots. The determination of these plot sizes (nested rectangular and circular) and diameter thresholds is based on experiences from previous field inventories in Indonesia.

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Figure 2: Layout of a rectangular nested carbon plot located in natural forests. Diameter at Breast Height (DBH) ranges in cm measured within the different rectangular nests (A, B, C, D and E) and the spatial orientation (N = North) are also given.

Figure 3: Layout of a circular plots in plantations. If the plantation is younger than four years a radius (r) of 7.98 m (plot size = 0.02 ha) is used and if the plantation is 4 years or older a radius of 11.29 m (plot size = 0.04 ha). Within these circular plots all trees with a Diameter at Breast Height (DBH) of more than 5 cm were measured.

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For all “in” trees within the carbon plots following parameters where recoded (an “in” tree was defined as a tree where the center of the stem at DBH is within the boundaries of the respective (sub)plot):

 Diameter at Breast Height(DBH) at 1.3 m above the ground (in centimeter)

 Total tree height(in meter) measured with a Haga instrument or a Suunto clinometer

 Tree species(scientific names in Latin): All “in” trees were identified up to the species level by a trained botanist. This was necessary to determine wood densities. Ideally it would be good to have an identification up to the species level as wood density can strongly vary within genus level. If it was not possible to identify up to the species level it was at least tried to record the genus or the family.

 Four dead wood classes (for the aboveground biomass estimates all dead trees were excluded):

 1 = many branches and twigs but without leaves

 2=large branches are still there but without a branch / small twigs and leaves

 3=almost no branches / twigs but still there are rods that may be broken

 4=just a broken rod topped resemble stumps

Additionally, to the carbon plots 58 so called biodiversity plots were recorded. The layout of these biodiversity plots is shown in Figure 4. The spatial locations of these biodiversity plots are exactly the same as the ones of the respective carbon plot.

Figure 4: Layout of a biodiversity plot. Diameter at Breast Height (DBH) ranges in cm measured within the small and large plot and the spatial orientation (N = North) are also given.

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For all “in” trees within the biodiversity plots following parameters where recoded (here also an “in” tree was defined as a tree where the center of the stem at DBH is within the boundaries of the respective (sub)plot):

 Diameter at Breast Height (DBH) at 1.3 m above the ground (in centimeter)

 Tree species (scientific names in Latin): All “in” trees were identified up to the species level by a trained botanist. This was necessary to determine wood densities. If it was not possible to identify up to the species level it was at least tried to record the genus or the family.

Table 2 gives an overview on how many carbon and biodiversity plots were recorded and whether they are located within LiDAR transects or not. As can be seen in Table 2, five plots were recorded after the fires of 2015. These plots have to be treated with care, as the LiDAR data was recorded before the fires of 2015.

Table 2: Overview carbon and biodiversity plots recorded and whether they are located within LiDAR transects or not.

Carbon plots Biodiversity plots Amount plots Amount plots within_{LiDAR transects}

X 54 (521₎ _{15 (13}1₎

X X 58 (551₎ _{48 (45}1₎

Sum 112 (1071₎ _{63 (58}1₎ 1_{Amount of plots after subtracting plots that were recorded after the fires of 2015}

Figure 5 displays the location of the recorded carbon and biodiversity plots within the four districts of Banyuasin, Musi Banyasin, Musi Rawas Utara and Musi Rawas. It also shows which of these carbon and biodiversity plots are located within a LiDAR transect.

Appendix A (Overview field plots) gives a detailed overview of all the 112 recorded plots and Appendix B (Overview biodiversity plots) gives and overview of the 58 recorded biodiversity plots. An in-depth explanation of the inventory design and collection is provided in the BIOCLIME GIZ Final Report: Panduan Survei Cadangan Karbon dan Keanekaragaman Hayati di Sumatera Selatan (Rusolono et al.

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2.2. Aboveground biomass calculations

Rusolono et al. (2015) provide detailed procedures for analyzing the carbon stocks of the five carbon pools: aboveground biomass (AGB), belowground biomass (BGB), dead-wood, litter and soils. As the derived LiDAR AGB model is based on the estimated AGB from the carbon inventory plots only the derivation of AGB will be described in this report.

AGB consists of woody vegetation (saplings, poles and trees), non-woody vegetation (e.g. palms and bamboos) and understory vegetation. A detailed description of the analysis of woody vegetation biomass can be found in the BIOCLIME GIZ Final Report: Panduan Survei Cadangan Karbon dan Keanekaragaman Hayati di Sumatera Selatan (Rusolono et al. 2015). For each carbon inventory plot, the woody vegetation biomass of naturally grown trees species of saplings (DBH of 5-9 cm, within 0.0025 ha, subplot B), poles (DBH of 10-19 cm, within 0.01 ha, subplot C), small trees (DBH of 20-34 cm, within 0.04 ha, subplot D) and large tree (DBH ≥35 cm, within 0.1 ha, subplot E) as well as the woody vegetation biomass of planted tree species were estimated using allometric biomass models (Figure 2, Figure 3 and Table 3). Species-specific or genus-specific allometric biomass models were available only for some major tree species of plantation forest (i.e. Acacia mangium and Eucalyptus pellita), estate crops (i.e. Hevea brasiliensis and Elaesis guineensis) and mangrove species (i.e. Avicennia marina,

Avicennia alba, Bruguiera gymnorrhiza, Bruguiera parviflora, Bruguiera sexangula, Ceriops tagal,

Rhizophora apiculata, Rhizophora mucronata, Xylocarpus granatum, Excoecaria agallocha and

Sonneratia caseolaris). Therefore, some general allometric biomass models were used to estimate AGB of the other tree species. The allometric biomass models for mixed species from Chave et al. (2014), Figure 5: Location of all recorded carbon and biodiversity plots within the four districts of Banyuasin, Musi Banyasin, Musi Rawas Utara and Musi Rawas. Also shown is which of these carbon and biodiversity plots are located within a LiDAR transect.

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Manuri et al. (2014) and Komiyamaet al. (2005) were used to estimate tree AGB for dryland forests, peat swamp forests and other mangrove species (i.e. Excoecaria agallocha and Sonneratia caseolaris), respectively.

Table 3: Allometric biomass models for estimating tree aboveground biomass (AGB).

Tree species Allometric model Statistics Location Source Plantation forest

Acacia mangium Wag= 0.070*D2.580 D: 8–28 cm

R2_{= 0.97}

South Sumatra Wicaksono (2004) cited in Krisnawatiet al.(2012)

Eucalyptus pellita1 _W_ag_{= 0.0678*D}2.790 _D_{: 2–27 cm}

R2_{= 0.99}

South Sumatra Onrizal et al. (2009) cited in Krisnawatiet al.(2012) Estate crop

Hevea brasiliensis Wag= 0.2661*D2.1430 Elias (2014)

Elaeis guineensis Wag= 0.0706 + 0.0976*H Sumatra, Kalimantan ICRAF (2009) cited in

Hairiahet al.(2011) Dryland forest

Tropical mixed species Wag= 0.0673 (p*D2*H)0.976 D: 2–212 cm Africa, America, Asia Chaveet al.(2014)

Peat swamp forest

Mixed species Wag= 0.15 D2.0.95*p0.664*H05526 D: 2–167 cm

R2_{= 0.981}

Riau, South Sumatra,

West Kalimantan Manuriet al.(2014) Mangrove forest

Avicennia marina, Avicennia alba1

Wag= 0.1846*D2.352 D: 6–35 cm

R2_{= 0.98}

West Java Darmawan & Siregar (2008) cited in Krisnawatiet al.(2012)

Bruguiera gymnorrhiza Wag= 0.1858*D2.3055 D: 2–24 cm

R2_{= 0.989}

Australia Clough & Scott (1989) Bruguiera parviflora,

Bruguiera sexangula1

Wag= 0.1799*D2.4167 D: 2–21 cm

R2_{= 0.993}

Australia Clough & Scott (1989)

Ceriops tagal Wag= 0.1884*D2.3379 D: 2–18 cm

R2_{= 0.989}

Australia Clough & Scott (1989)

Rhizophora apiculate, Rhizophora mucronata1

Wag= 0.235*D2.42 D: 5–28 cm

R2_{= 0.98}

Malaysia Onget al.(2004)

Xylocarpus granatum Wag= 0.0823*D2.5883 D: 3–17 cm

R2_{= 0.994}

Australia Clough & Scott (1989) Other species

(Excoecaria agallocha,

Sonneratia caseolaris)

Wag= 0.2512*p*D2.46 D: 5–49 cm

R2_{= 0.979}

Indonesia, Thailand Komiyamaet al.(2005)

Wag= aboveground biomass (kg),D= Diameter at Breast Height (DBH, cm),H= tree height (m),p= wood density (gram/cm3),R2=

coefficient of determination

1_{The aboveground biomass of these tree species were estimated using the available allometric models for similar tree species.}

The diameter at breast height (D) and tree height (H) data were obtained from the field measurements in each carbon inventory plot. But, the height data of some trees in Secondary / Logged over Dryland Forests (at PT REKI and BKSDA Dangku) and Primary Mangrove Forests (at TN Sembilang) were not available, so that diameter-height models (Table 4), which were developed based on the available data, were applied to estimate missing tree height measurements. Wood density (p) data was obtained from ICRAF’s database (http://db.worldagroforestry.org). When a species-specific wood density value for a particular tree species was not available, then the genus or family level wood density value was used in the allometric models. For tree species that could not be identified by their scientific names (only 0.8%

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of the total tree species), the allometric models used average wood density values that were derived from identified tree species in a particular stratum and location (Table 5).

Table 4: Diameter-height models for estimating tree height in some surveyed areas.

Stratum1 _Location _Model _Statistics

Secondary / Logged over Dryland Forest (Hutan Lahan Kering Sekunder / Bekas Tebangan)

PT REKI H=D/(0.7707+0.0195*D) nAIC= 168,= 1032.21,D= 5–104 cm,RMSE= 5.58,

adj= 0.624

BKSDA Dangku H= exp(0.7071+0.6556*ln(D)) nAIC= 156,= 845.56,D= 6–69 cm,RMSE= 3.75,

adj= 0.690

Primary Mangrove Forest

(Hutan Mangrove Primer) TN Sembilang H= 28.1613*(1-exp(-D/27.0703))

n= 221,D= 5-77 cm,

AIC= 1098.86,RMSE= 2.89,

adj= 0.750

D= Diameter at Breast Height (DBH, cm),H= tree height (m),n= number of samples,AIC= Akaike Information Criterion,RMSE= root mean square error,R2

adj= coefficient of determination adjusted

1_{in brackets Bahasa Indonesia}

Table 5: Average wood density for unidentified tree species.

Startum1 _Location Mean

(g/cm3₎ _(g/cmSt.dev.3₎2

Primary Dryland Forest

(Hutan Lahan Kering Primer) TN Kerinci Seblat 0.615 0.142 Secondary / Logged over Dryland Forest

(Hutan Lahan Kering Sekunder / Bekas Tebangan) PT REKI 0.594 0.143 Primary Mangrove Forest

(Hutan Mangrove Primer) Banyuasin 0.702 0.077 Secondary / Logged over Swamp Forest

(Hutan Rawa Sekunder / Bekas Tebangan) TN Sembilang 0.643 0.115

1_{in brackets Bahasa Indonesia} 2_{standard deviation}

Some carbon inventory plots (i.e. 7 plots or 4.9% of the total sample plots) also contained non-woody vegetation (i.e. bamboo, palm or rattan). The quantity of such non-woody vegetation, however, was very low with incomplete measurements of diameter or height, which resulted in difficulties when estimating their biomass. Therefore, the quantification of this insignificant non-woody biomass for these 7 sample plots was ignored.

The biomass of understory vegetation in each carbon inventory plot was estimated based on the field measurements and a laboratory analysis of the understory vegetation samples. The field measurements provided data on the sample’s fresh weight and total fresh weight of the understory vegetation, while the laboratory analysis provided data on the sample’s dry weight of the understory vegetation. The aboveground biomass of the understory vegetation was then calculated on the ratio of dry and fresh weights of the sample, which was then multiplied with the total fresh weight of the understory vegetation within a sample plot.

Total biomass of saplings, poles, small trees, large trees und understory vegetation in each subplot were converted into a common unit (i.e. ton/ha) and then were added to derive total aboveground biomass for each sample plot. Aboveground biomass estimates per plot in tons per hectare (t/ha) for the carbon plots are shown in Appendix A. Table 6 summarizes the aboveground biomass estimates for the different strata. An in-depth explanation of the results and methods applied in the field

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inventory is provided in the BIOCLIME GIZ Final Report: Cadangan Karbon Hutan dan Keanekaragaman Flora di Sumatera Selatan (Tiryanaet al.2015).

Table 6: Statistical results for the aboveground biomass (AGB) estimates for the different strata. Stratum1 Number of carbon

inventory plots Min AGB(t/ha)2 Max AGB_(t/ha)3 Mean AGB_(t/ha)4

Primary Dryland Forest

(Hutan Lahan Kering Primer) 8 196.1 637.0 335.6 ±153.8 Secondary / Logged over Dryland Forest

(Hutan Lahan Kering Sekunder) 33 69.1 560.7 259.0 ±129.0 Primary Mangrove Forest

(Hutan Mangrove Primer) 13 159.8 531.3 304.7 ±98.8 Secondary / Logged over Mangrove Forest

(Hutan Mangrove Sekunder) 7 44.9 342.5 174.0 ±114.1 Primary Peat Swamp Forest

(Hutan Rawa Gambut Primer) 5 442.4 616.0 538.1 ±64.9 Secondary / Logged over Peat Swamp

Forest

(Hutan Rawa Gambut Sekunder)

9 114.9 414.0 207.1 ±89.9

Plantation Forest

(Hutan Tanaman) 8 5.4 133.8 59.9 ±43.7

Tree Crop Plantation

(Perkebunan) 15 3.4 211.6 62.2 ±55.4

Shrubs

(Semak Belukar) 6 8.7 127.2 59.6 ±54.0

Swamp Shrubs

(Semak Belukar Rawa) 8 1.3 105.5 55.5 ±41.5

Sum 112

1_{in brackets Bahasa Indonesia}

2_{Minimum aboveground biomass (AGB) in tons per hectare for the stratum} 3_{Maximum aboveground biomass (AGB) in tons per hectare for the stratum}

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3. LiDAR data and aerial photos

3.1. LiDAR and aerial photo survey

In October 2014 15 transects of LiDAR data and aerial photos were captured for an area of approximately 43,300 ha. LiDAR data was acquired in two modes (a) LiDAR full waveform mode + aerial photos with an overlap of 60% and (b) LiDAR discrete return mode + aerial photo overlap 80%. Table 7 displays the technical specification of this LiDAR and aerial photo survey. A more detailed description of the survey can be found in the report of the surveying company PT Asi Pudjiastuti Geosurvey (PT Asi Pudjiastuti Geosurvey 2014).

Table 7: Technical specifications of the LiDAR and aerial photo survey (PT Asi Pudjiastuti Geosurvey 2014).

Parameter Flight plan Remark

LiDAR acquisition mode Full Waveform (FWF) Unlimited returns of laser reflectance_{Discrete Return 4 returns of laser reflectance}

Flying height 800 m

The survey was conducted at 800 m above ground level to get the accurate laser reflectance and minimize cloud cover. Laser pulse frequency 500 KHz Product specification in ALS70 Leica used_{for the project.} LiDAR point density Full Waveform (FWF) 8-15 points/m2

Discrete Return 6-8 points/m2

Aircraft speed 110 knots

Half scan angle 28 degrees

Field of view (FOV) 56 degrees. With this FOV LiDAR coverage will be embedded with aerial photo coverage.

Swath width 851 m

A scan angle (FOV) of 56 degrees and a flying height of 800 m will provide 851 m area coverage

Ground Sample Distance (GSD) 10-12.5 m

Forward overlap Full Waveform (FWF) 60% overlap Discrete Return 80% overlap

Aerial photo coverage 86 m x 644 m

Acquisition of aerial photos using a digital camera: Leica RCD 30 with 6 µm pixel resolution, with a GSD of 10 cm per pixel will results in a coverage of 860 m x 644 m.

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Figure 6: Location of the approximately 43,300 ha of LiDAR transects captured within the BIOCLIME study area.

3.2. LiDAR processing, filtering and interpolation

Different types of elevation models were generated from the airborne LiDAR 3D point clouds. Figure 7 shows some LiDAR 3D point could example sections representing different forest types (Lowland Dipterocarp Forest, Peat Swamp Forest and Mangrove). Figure 8 displays the location of these LiDAR 3D points clouds within the BIOCLIME study area and the corresponding LiDAR derived Canopy Height Models (CHM) (see next paragraph).

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Figure 7: Example of LiDAR 3D point clouds for Lowland Dipterocarp Forest, Peat Swamp Forest and Mangrove.

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Figure 8: Location of the LiDAR 3D points clouds shown in Figure 7 within the BIOCLIME study area and the corresponding LiDAR derived Canopy Height Models (CHM).

Products derived from these LiDAR 3D point clouds include a Digital Surface Model (DSM) which represents the elevation of the vegetation canopy, a Digital Terrain Model (DTM) which represents the ground elevation, and a Canopy Height Model (CHM) which is generated by subtraction of the DTM from the DSM and represents the vegetation height. The LiDAR data was processed using the Trimble Inpho software package.

A crucial step within the DTM generation is the LiDAR filtering. A hierarchic robust filter was applied to the LiDAR 3D point clouds, separating the ground and non-ground (vegetation) points (Pfeifer et al.

2001). The linear adaptable prediction interpolation (kriging) was utilized to generate the DTM (1 m spatial resolution). The DSM (1 m spatial resolution) was created by extracting the highest point of the 3D point cloud (the first returns of each laser beam) within a grid of 1 m which were then interpolated into a continuous raster. Pixels containing no data were filled using the highest point of the neighborhood and the morphological operator “closing” (Dougherty and Lotufo 2003). The CHM (1 m spatial resolution) was produced by calculating the difference between the elevation of the DSM and the underlying terrain of the DTM. Figure 9 exemplarily shows the resulting models for the BIOCLIME study area. Also shown are the positions of the field plots (n = 63) which are located within the LiDAR transects.

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Figure 9: Example from the LiDAR products generated for the BIOCLIME study area. Shown are examples for the Digital Terrain Model (DTM; 1 m spatial resolution) the Digital Surface Model (DSM; 1 m spatial resolution) and the Canopy Height Model (CHM; 1 m spatial resolution). Also shown are the position of the 63 carbon inventory plots that are located within the LiDAR transects.

4. LiDAR based aboveground biomass model

4.1. Regression analysis and aboveground biomass model development

Previous studies revealed that height metrics like the Quadratic Mean Canopy Height (QMCH) or the Centroid Height (CH) are appropriate parameters of the LiDAR 3D point cloud to estimate aboveground biomass in tropical forests (Jubanskiet al.2013, Englhartet al.2013, Ballhornet al.2011). LiDAR height histograms were calculated by normalizing all points within the carbon plot extent (usually 20x30 m except in plantations where, dependent on age, two different circular plot sizes were used; see Chapter 2.1 Inventory design) to the ground using the DTM as reference. A height interval of 0.5 m was defined and the number of points within this interval was stored in form of a histogram. The first (lowest) interval was considered as ground return and therefore excluded from further processing. The QMCH and the CH of the height histogram were calculated by weighing each 0.5 m height interval with the relative number of LiDAR points stored within this interval. QMCH and CH were related to field inventory estimated of aboveground biomass and regression models were developed.

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The commonly used power function resulted in significant overestimations in the higher biomass range within our study areas. For this reason, a more appropriate aboveground regression model was implemented, which is a combination of a power function (in the lower biomass range up to a certain threshold QMCH0; the example here uses QMCH but the same would be done with CH) and a linear

function (in the higher biomass range) (Englhartet al. 2013). The threshold of QMCH0 was determined

by increasing the value of QMCH0 in steps of 0.001 m through identifying the lowest Root Mean

Square Error (RMSE). The linear function is the tangent through QMCH0 and was calculated on the

basis of the first derivative of the power function:

Where QMCH is the quadratic Mean Canopy Height (the example here uses QMCH but the same would be done with CH), QMCH0is the threshold of function change and a, b are coefficients. Next the

aboveground regression with the highest coefficient of determination (r2_{) based on the QMCH or the}

CH was chosen.

Of the 63 carbon plots that were located within the LiDAR transects 52 plots (after removal of obvious outliers) were used for calibration. The model based on QMCH achieved better results as the one based on CH. Figure 10 displays the results of the QMCH based LiDAR regression model.

Figure 10: Predictive aboveground biomass (AGB) model used for the BIOCLIME study area based on carbon plot data and airborne LiDAR data. The Quadratic Mean Canopy Height (QMCH) was chosen as it had a higher coefficient of determination (r2_{) as the Centroid Height (CH) based model.}

Of the 63 carbon plots that were located within the LiDAR transects 52 plots (after removal of obvious outliers) were used for calibration.

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Next a spatially explicit aboveground biomass model was created by applying the above described regression model. The LiDAR based aboveground biomass model was created at 5 m spatial resolution i.e. each pixel represents an area of 0.1 ha. For ease of interpretation the cell values were scaled to represent aboveground biomass in tons per hectare. Figure 11 displays the final LiDAR based aboveground biomass model and gives examples of Lowland Dipterocarp Forest, Peat Swamp Forest and Mangrove.

Figure 11: Final LiDAR based aboveground biomass model (spatial resolution = 5 m; each pixel represents an area of 0.1 ha) and examples of Lowland Dipterocarp Forest, Peat Swamp Forest and Mangrove (lower three figures). The location of the lover three figures is shown as red rectangles in the upper figure.

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4.2. Determination of local aboveground biomass values

In order to derive local aboveground biomass values for the different land cover classes, the spatial aboveground biomass model was overlaid with the land cover classification from Work Package 2 (WP 2) and zonal statistics (minimum, maximum, mean and standard deviation) on aboveground biomass were extracted for the respective land cover class (see Figure 12). To avoid possible misclassifications at land cover class borders a buffer of 60 m was excluded from the zonal statistics.

Figure 12: Schematic representation of the extraction of zonal statistics. The aboveground biomass (AGB) model is overlaid with the land cover classification (from Work Package 2) and zonal statistics on aboveground biomass are extracted for the respective land cover class. In this example the mean AGB in tons per hectare (t/ha) for the respective land cover class (LCC) is shown.

Zonal statistics were extracted for the BAPLAN and BAPLAN enhanced land cover classes (see Appendix C). Table 8 and Table 9 display these derived local aboveground biomass values. For the land cover classes not present in the aboveground biomass model missing values were estimated based on existing values or missing values were based on values from scientific literature.

These local aboveground biomass values were used for the emission calculations in the other work packages.

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Table 8: Local aboveground biomass values derived from zonal statistics of the LiDAR aboveground biomass model for the different forest type / land cover classes based on BAPLAN.

Forest type / land cover BAPLAN1 _{Mean AGB (t/ha)}2 _{SD (t/ha)}3 _{Min AGB (t/ha)}4 _{Max AGB (t/ha)}5 _{Area (ha)}6

Primary Dryland Forest 586 ±157.8 38.2 1,404.8 2,285.2

Secondary / Logged over Dryland Forest 305 ±158.8 0.0 1,206.5 5,685.3

Primary Swamp Forest 279 ±97.8 4.7 709.0 1,806.5

Secondary / Logged over Swamp Forest 108 ±78.6 0.2 505.4 1,363.3

Primary Mangrove Forest 249 ±106.7 0.0 668.8 4,031.9

Secondary / Logged over Mangrove Forest 72 ±34.4 14.0 284.4 71.7 Mixed Dryland Agriculture / Mixed Garden 144 ±100.2 0.0 712.3 1,883.0

Tree Crop Plantation 49 ±63.2 0.0 428.9 442.2

Plantation Forest 64 ±45.8 0.0 406.5 517.5

Scrub 39 ±57.3 0.0 762.4 964.6

Swamp Scrub 15 ±19.1 0.2 123.1 3.3

Rice Field7 ₁₀ _- _- _-

-Dryland Agriculture 48 ±64.1 0.0 487.0 126.3

Grass8 ₆ _- _- _-

-Open Land9 _{(0) 29} _±75.7 _0.0 _749.0 _13.4

Settlement / Developed Land9 _{(0) 23} _±14.8 _0.2 _81.8 _1.3

Water Body9 _{(0) 164} _±68.8 _1.1 _469.3 _83.2

Swamp 22 ±20.7 0.3 80.8 1.3

Embankment9 _{(0) 2} _±4.1 _0.0 _25.1 _9.5

1_{Forest type/land cover class BAPLAN classification system}

2_{Mean aboveground biomass (AGB) in tons per hectare for the forest type/land cover class} 3_{Standard deviation (SD) in tons per hectare for the forest type/land cover class}

4_{Minimum aboveground biomass (AGB) in tons per hectare for the forest type/land cover class} 5_{Maximum aboveground biomass (AGB) in tons per hectare for the forest type/land cover class} 6_{Area in hectare from which zonal statistics are based on}

7_{Value for Rice Field from scientific literature (Confalonieri}_{et al.}₂₀₀₉₎ 8_{Value for Grass from scientific literature (IPCC 2006)}

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Table 9: Local aboveground biomass values derived from zonal statistics of the LiDAR aboveground biomass model for the different forest type / land cover classes based on BAPLAN enhanced.

Forest type / land cover BAPLAN enhanced1 Mean AGB

(t/ha)2 SD (t/ha)3 Min AGB_(t/ha)4 Max AGB_(t/ha)5 Area (ha)6

High-density Upper Montane Forest7 ₃₀₄ _- _- _-

-Medium-density Upper Montane Forest8 ₂₂₈ _- _- _-

-Low-density Upper Montane Forest7 ₁₉₂ _- _- _-

-High-density Lower Montane Forest 653 ±129.0 229.6 1106.7 52.0

Medium-density Lower Montane Forest 529 ±77.3 358.3 789.0 5.5

Low-density Lower Montane Forest7 ₂₆₈ _- _- _-

-High-density Lowland Dipterocarp Forest 584 ±158.0 38.2 1404.8 2,233.2 Medium-density Lowland Dipterocarp Forest 340 ±152.7 0.0 1206.5 4,536.6

Low-density Lowland Dipterocarp Forest 166 ±92.8 0.3 986.8 1,143.2

High-density Peat Swamp Forest 287 ±100.0 5.3 709.0 1,430.7

Medium-density Peat Swamp Forest8 ₂₁₆ _- _- _-

-Low-density (Regrowing) Peat Swamp Forest 110 ±86.6 0.9 505.4 590.7

Permanently Inundated Peat Swamp Forest 244 ±86.9 4.7 568.1 301.1

High-density Swamp Forest

(incl. Back- and Freshwater Swamp) 256 ±52.5 13.5 399.0 74.8

Medium-density Swamp Forest

(incl. Back- and Freshwater Swamp)8 192 - - -

-Low-density (Regrowing) Swamp Forest

(incl. Back- and Freshwater Swamp) 107 ±71.9 0.2 444.4 772.6

Heath Forest7 ₂₂₄ _- _- _-

-Mangrove 1 268 ±99.8 0.0 668.8 3,473.1

Mangrove 2 200 ±95.7 26.2 515.3 86.0

Nipah Palm 115 ±37.8 0.9 456.6 472.8

Degraded Mangrove 74 ±34.9 14.0 218.9 57.8

Young Mangrove 65 ±31.0 17.5 284.4 13.9

Dryland Agriculture mixed with Scrub 38 ±47.1 0.0 508.8 414.2

Rubber Agroforestry 174 ±90.4 0.0 712.3 1,468.8

Oil Palm Plantation 27 ±42.2 0.0 336.0 304.2

Coconut Plantation 58 ±27.1 2.5 132.2 94.1

Rubber Plantation 185 ±66.6 0.6 428.9 43.9

Acacia Plantation 64 ±48.6 0.0 236.6 360.2

Industrial Forest 63 ±38.7 0.5 406.5 157.3

Scrubland 39 ±57.3 0.0 762.4 964.6

Swamp Scrub 15 ±19.1 0.2 123.1 3.3

Rice Field9 ₁₀ _- _- _-

-Dryland Agriculture 48 ±64.1 0.0 487.0 126.3

Grassland10 ₆ _- _- _-

-Bare Area11 _{(0) 29} _±75.7 _0.0 _749.0 _13.4

Settlement11 _{(0) 10} _±14.2 _0.2 _81.8 _0.4

Road11 _{(0) 29} _±10.6 _0.3 _49.6 _0.9

Water11 _{(0) 164} _±68.8 _1.1 _469.3 _83.2

Wetland 22 ±20.7 0.3 80.8 1.3

Aquaculture11 _{(0) 2} _±4.1 _0.0 _25.1 _9.5

1_{Forest type/land cover class BAPLAN enhanced classification system} 7_{Values from FORCLIME (Navratil 2012)}

2_{Mean aboveground biomass (AGB) in tons per hectare for the forest type/land cover class} 8_{Calculated as 75% of respective high density class}

3_{Standard deviation (SD) in tons per hectare for the forest type/land cover class} 9_{Value for Rice Field from scientific literature (Confalonieri}_{et al.}₂₀₀₉₎ 4_{Minimum aboveground biomass (AGB) in tons per hectare for the forest type/land cover class} 10_{Value for Grass from scientific literature (IPCC 2006)}

5_{Maximum aboveground biomass (AGB) in tons per hectare for the forest type/land cover class} 11_{Value in brackets was finally used as local aboveground biomass value as} the

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5. Analyses of tree community composition of lowland dipterocarp forests

5.1. Calculation of different LiDAR metrics for the biodiversity plots

From the airborne LiDAR data following 19 LiDAR metrics per biodiversity plot located within a LiDAR transect (n = 28) were derived (Table 10).

Table 10: LiDAR metrics derived for each biomass plot located within the LiDAR transects (n = 28). Also shown in the respective abbreviation and which software method was used to derive them.

LiDAR metric Abbreviation Software / method used

Quadratic Mean Canopy Height (QMCH) QMCH in house script

Centroid Height (CH) CH in house script

Maximum height Max LASTools1

Mean height Mean LASTools1

Standard deviation height SD LASTools1

Forest cover at 1 m height cov 1m LASTools1

Forest cover at 2 m height cov 2m LASTools1

Forest cover at 5 m height cov 5m LASTools1

Forest cover at 7 m height cov 7m LASTools1

Forest cover at 10 m height cov 10m LASTools1

Forest cover at 12 m height cov 12m LASTools1

5th_{height percentile} _{p 5}th _LASTools1

10th_{height percentile} _{p 10}th _LASTools1

25th_{height percentile} _{p 25}th _LASTools1

50th_{height percentile} _{p 50}th _LASTools1

75th_{height percentile} _{p 75}th _LASTools1

90th_{height percentile} _{p 90}th _LASTools1

95th_{height percentile} _{p 95}th _LASTools1

99th_{height percentile} _{p 99}th _LASTools1

1_{https://rapidlasso.com/lastools/}

Appendix D displays these LiDAR metrics for all the biodiversity plots located in lowland dipterocarp forest (all further tree community composition analyses are for lowland dipterocarp forest only). These LiDAR metrics were then correlated to the nMDS scores, biodiversity indices and ratios between pioneer and climax tree species derived in Chapter 5.2 (Derivation of nMDS scores, biodiversity indices and pioneer/climax tree species ratio) in order to derive a predictive LiDAR based tree community composition model (see Chapter 5.3 LiDAR based tree community composition model).

5.2. Derivation of nMDS scores, biodiversity indices and pioneer/climax tree

species ratio

Within all the biodiversity plots 378 types of species where identified belonging to 192 genera. Table 11 displays the absolute numbers and percentage of trees within the biodiversity plots where the

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species could be identified, where only genus, family, common name was known and unidentified trees.

Table 11: Absolute and percentage of tree identification (species, only genus, only family, only common name and unidentified) within the biodiversity plots.

All trees

recorded identifiedSpecies Only genusidentified Only familyidentified Only commonname Unidentified Absolute

number 2733 2408 284 15 4 22

Percent (%) 100% 88% 10% 1% 0% 1%

All further analyses on tree community composition were conducted for lowland dipterocarp forest only. Mangrove was excluded as the variety of different tree species in the observed mangroves was very low (only up to six different tree species). Peat swamp forest was excluded because only three biodiversity plots were available and all were recorded after the fires of 2015.

Because some trees could not be identified to the species level all analyses on tree community composition are based on the genus level. Imaiat al.(2014) showed that results on the genus level are highly correlated with those at the species level.

The similarity in tree community composition for the different lowland dipterocarp forest density classes (low, medium and high) was not only assessed for the stratification of the biodiversity plots based on the satellite classification derived from Work Package 2 (WP 2) but also for a stratification based on the forest cover at 10 meter height above the ground (LiDAR metric was derived in the previous chapter). The thresholds for the two additional stratifications are shown in Table 12.

Table 12: Additional stratification of the biodiversity plots based on the forest cover at 10 meter height above the ground (LiDAR metric was derived in Chapter 5.1).

Lowland dipterocarp forest density class Stratification thresholds

Forest cover at 10 m height above ground (%) Low-density Lowland Dipterocarp Forest 0-<40

Medium-density Lowland Dipterocarp Forest 40≤-<80 High-density Lowland Dipterocarp Forest 80≤

To assess tree community composition nonmetric multidimensional (nMDS) scaling was applied and four biodiversity indicators per biodiversity plot were calculated (see below). All the statistics were calculated in PAST Version 3.13 (http://folk.uio.no/ohammer/past/) and were only based on the genera identified in the large plot of the biodiversity plots (see Chapter 2.1 Inventory design). Additionally, for each biodiversity plot the ratio between pioneer and climax tree species present was derived.

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Nonmetric multidimensional scaling (nMDS)

To assess the effects of different degradation levels on forest biodiversity the degree of similarity in tree community composition has gained increasing attention (Ioki et al.2016, Barlow et al.2007, Imai

et al. 2012, Imai et al. 2014, Magurran and McGill 2011, Su et al. 2004, Ding et al. 2012). Nonmetric multidimensional scaling (nMDS) was applied to assess the differences in tree community composition among the biodiversity plots. The number of trees of each genus within the 38 biodiversity plots located in lowland dipterocarp forest was used as input to the Bray-Curtis similarity index to calculate the nMDS scores of axis 1 and axis 2. Figure 13 and Figure 14 display the resulting scatter plots from the nMDS calculation for the two density stratifications (base on the satellite classification of Work Package 2 and the forest cover at 10 m height). In both scatterplots the nMDS axis 1 scores of High-density Lowland Dipterocarp Forest and Low-High-density Lowland Dipterocarp Forest are mostly located at the opposite ends of nMDS axis 1 indicating a difference in tree community composition of these two classes. The nMDS axis 1 scores of Medium-density Lowland Dipterocarp Forest is mostly located between the scores of the two other density classes.

Also indicated is the stress value. The stress value is used as an indicator of the performance of the nMDS (the lower the stress value the better). In our analyses the nMDS based on the forest cover at 10 m stratification had, with 0.235, the lowest stress value, so that for all further analyses the nMDS scores from this stratification were used.

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Figure 13: Nonmetric multidimensional scaling (nMDS) ordination diagram for the biodiversity plots located in the Lowland Dipterocarp Forest class. The stratification into different density classes was based on the satellite classification conducted in Work Package 2 (WP 2). The number of plots going into the ordination was 35. Shown are the scores of axis 1 and axis 2, with axis 1 indicating the similarity in tree community composition among the 35 plots. The nMDS axis 1 scores of High-density Lowland Dipterocarp Forest and Low-High-density Lowland Dipterocarp Forest are located at the opposite ends of nMDS axis 1 indicating a difference in tree community composition of these two classes. The nMDS axis 1 scores of Medium-density Lowland Dipterocarp Forest is mostly located between the scores of the two other density classes. Also indicated is the stress value (0.240).

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Figure 14: Nonmetric multidimensional scaling (nMDS) ordination diagram for the biodiversity plots located in the Lowland Dipterocarp Forest class. The stratification into different density classes was based the forest cover at 10 m height (derived from LiDAR). The number of plots going into the ordination was 28. Shown are the scores of axis 1 and axis 2, with axis 1 indicating the similarity in tree community composition among the 28 plots. The nMDS axis 1 scores of High-density Lowland Dipterocarp Forest and Low-density Lowland Dipterocarp Forest are located at the opposite ends of nMDS axis 1 indicating a difference in tree community composition of these two classes. The nMDS axis 1 scores of Medium-density Lowland Dipterocarp Forest is mostly located between the scores of the two other density classes. Also indicated is the stress value (0.235).

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Simpson index 1-D

The Simpson index 1-D was calculated for each biodiversity plot. This index measures ‘evenness’ of the community from 0 (one taxon dominates the community completely) to 1 (all taxa are equally present).

whereniis the number of individuals of taxoni.

Shannon index (entropy)

The Shannon index (entropy) was calculated for each biodiversity plot. The Shannon index (entropy) is a diversity index, taking into account the number of individuals as well as the number of taxa. The index increases as both the ‘richness’ and the ‘evenness’ of the community increases. It varies from 0 for communities with only a single taxon to high values for communities with many taxa, each with few individuals.

whereniis the number of individuals of taxoni.

In most ecological studies the values are generally between 1.5 and 3.5 and the index is rarely greater than 4.

Margalef’s richness index

The Margalef’s ‘richness’ index was calculated for each biodiversity plot. This index is a ‘richness’ index that attempts to compensate for sampling effects such as sample size. The higher the index the higher the ‘richness’.

Equitability

Equitability was calculated for each biodiversity plot. Equitability is the Shannon diversity divided by the logarithm of number of taxa. This measures the ‘evenness’ with which individuals are divided among the taxa present. The higher the index the higher the ‘evenness’.

Ratio pioneer/climax tree species

For each biodiversity plots the ratio between pioneer and climax tree species present was calculated. The identification which tree is a pioneer or climax species was based on information provided by a botanical expert from LIPI. Where available tree species were categorized into pioneer and climax species or species that are both (pioneer and climax species). To calculate the ratio, species that were

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categorized as pioneer and climax species were treated as climax species. An overview of this categorization is shown in Appendix E. The ratios calculated for the respective biodiversity plots lie between 0 (only climax species) and 1 (only pioneer species).

Appendix F gives an overview of the two forest stratifications (satellite classification and forest cover at 10 m height), the nMDS scores of axes 1 and 2 for the respective forest stratifications, the four biodiversity indices and the ratio between pioneer and climax tree species.

As there was no statistical significant correlation between the forest density classes and the nMDS scores of axis 2, only scores from axis 1 very implemented in subsequent analyses.

Table 13 displays the descriptive statistics on the nMDS axis 1 scores, the four biodiversity indicators and the ration between pioneer and climax tree species for the different Lowland Dipterocarp Forest density classes (Low, Medium and High). The stratification of the density classes is based on the forest cover at 10 m height. These results show that there is a gradient in the mean nMDS axis 1 scores where Low-density Lowland Dipterocarp Forest with -0.214 had the lowest mean and High-density Lowland Dipterocarp Forest the highest with 0.109. Looking at the biodiversity indicators the two indices for ‘richness/diversity’ (Shannon index and Margelef’s index) also had a similar gradient where the Low-density Lowland Dipterocarp Forest had the lowest and the High-density Lowland Dipterocarp Forest had the highest mean values indicating that High-density Lowland Dipterocarp Forest has the highest biodiversity. Also the other two biodiversity indicators for ‘evenness’ (Simpson index 1-D and Equitability) have a similar gradient which indicates that the High-density Lowland Dipterocarp Forest has the highest ‘evenness’ (all taxa are more equally present). With regard to the ratio between pioneer and climax tree species also a gradient could be observed where Low-density Lowland Dipterocarp Forest with a mean of 0.504 had the highest and High-density Lowland Dipterocarp Forest with a mean of 0.097 had the lowest value. This indicates that Low-density Lowland Dipterocarp Forest compared to High-density Lowland Dipterocarp Forest feature proportionally more pioneer tree species and vice versa.

All these findings point to the conclusion that high nMDS axis 1 scores go hand in hand with higher ‘richness/diversity’, ‘evenness’ and that the occurrence of climax tree species in comparison to pioneer tree species is favoured.

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Table 13: Descriptive statistics on the nMDS axis 1 scores and the four biodiversity indicators for the different Lowland Dipterocarp Forest density classes: Low, Medium and High. The density classes are based on the forest cover at 10 m height stratification.

Lowland Dipterocarp Forest

Low-density Medium-density density

High-nMDS axis 1 score

n 4 16 8

Min1 _-0.384 _-0.225 _0.008 Max2 _-0.107 _0.206 _0.224 Mean -0.214 -0.001 0.109 SD3 _±0.123 _±0.120 _±0.065

Simpson index 1-D

n 4 16 8

Min1 _0.392 _0.720 _0.870 Max2 _0.810 _0.955 _0.964 Mean 0.677 0.898 0.935 SD3 _±0.193 _±0.069 _±0.029

Shannon index

n 4 16 8

Min1 _0.807 _1.632 _2.383 Max2 _2.069 _3.310 _3.453 Mean 1.623 2.703 3.008 SD3 _±0.560 _±0.490 _±0.318

Margelef’s index

n 4 16 8

Min1 _1.039 _2.424 _3.938 Max2 _3.376 _8.266 _8.384 Mean 2.463 5.384 6.300 SD3 _±1.046 _±1.574 _±1.312

Equitability

n 4 16 8

Min1 _0.501 _0.667 _0.880 Max2 _0.840 _0.987 _0.964 Mean 0.725 0.892 0.932 SD3 _±0.154 _±0.089 _±0.027

Ratio pioneer/climax tree

species

n 4 16 8

Min1 _0.071 _0.000 _0.000 Max2 _0.787 _0.923 _0.361 Mean 0.504 0.304 0.097 SD3 _±0.328 _±0.226 _±0.113

1_{Minimum (Min)} 2_{Maximum (Max)} 3_{Standard deviation (SD)}

Next, to test whether there is a statistical significant difference between the different density classes (density stratification based on forest cover at 10 m height) with regard to nMDS scores axis 1, the biodiversity indicators and the ratio between pioneer and climax tree species a One-way ANOVA was performed. When the ANOVA results were significant, a Tukey’s pairwise post-hoc test was used to identify the different pairs of groups. Before the ANOVA, normality of data was tested by means of the Shapiro-Wilk test (Shapiro and Wilk 1965). Table 14 shows the results of these statistical analyses. As can be seen the One-way ANOVA showed that there was a statistical significant (p < 0.05) difference

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between the means of the different density classes (Low, Medium and High) for the nMDS axis 1 scores, Shannon index and Margelef’s index. Further, for all these three indicators the Tukey’s pairwise post-hoc test showed there was a statistical significant (p< 0.05) difference between the density pairs Low vs Medium and Low vs High but not for Medium vs High. For the Simpson index 1-D, the Equitability and the ratio between pioneer and climax tree species no statement on the difference between the density classes could be made as one or more of the density groups was not normally distributed, which is a requirement for a One-Way ANOVA.

Table 14: Results of the statistical analyses comparing the different Lowland Dipterocarp Forest density classes: Low, Medium and High. The density classes are based on the forest cover at 10 m height stratification. Numbers in the cells represent the p values. Cells in green show that the respective test results are positive and cells in red that the respective test results are negative. Shaded cells indicate that here a One-Way ANOVA could not be conducted as one or more of the density groups was not normally distributed. (p< 0.05;n= 28)

nMDS axis 1 Simpson index 1-D Shannon index Test normal

distribution1

Low Medium High Low Medium High Low Medium High

0.477 0.752 0.896 0.079 0.000 0.033 0.187 0.097 0.574

One-way ANOVA2 _0.000 _0.000 _0.000

Tukey’s pairwise3

Medium High Medium High Medium High Low 0.003 0.000 0.000 0.000 0.001 0.000

Medium X 0.161 X 0.716 X 0.445

Margelef’s index Equitability Ratio pioneer/climax_{tree species} Test normal

distribution1

Low Medium High Low Medium High Low Medium High

0.509 0.976 0.914 0.164 0.003 0.500 0.473 0.081 0.005

One-way ANOVA2 _0.001 _0.002 _0.014

Tukey’s pairwise3

Medium High Medium High Medium High Low 0.003 0.000 0.005 0.001 0.222 0.005

Medium X 0.481 X 0.694 X 0.202

1_{Shapiro-Wilk test on normal distribution: if}_p_{> 0.05 normal distribution (green); if}_p_{< 0.05 no normal distribution (red)}

2_if_p_{< 0.05 difference between means (green); if}_p_{> 0.05 no difference between groups (red); shaded: no One-Way ANOVA possible because} one or more of the density groups is not normally distributed

3_if_p_{< 0.05 difference between density pair (green); if}_p_{> 0.05 no difference between density pair (red)}

These statistical results indicate that there is a significant different with regard to tree community composition between these different density classes and that the density classes Low vs Medium and Low vs High could be best differentiated.

5.3. LiDAR based tree community composition model

First a correlation analysis was conducted to assess whether nMDS axis 1 scores, one of the four biodiversity indices or the ratio between pioneer and climax tree species correlated best with the derived LiDAR metrics. Table 15 displays the Spearman’s correlation coefficients (rs) and Table 16 the

corresponding p values. Overall the nMDS axis 1 scores correlated best with the LiDAR metrics with regard to thersand the correspondingpvalues. Except for the LiDAR metric p 99th(rs= 0.69) allrswere

equal to or higher than 0.70. For the LiDAR metrics QMCH (rs= 0.83), CH (rs= 0.82), Mean (rs= 0.82), p

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Table 15: Results of the correlation analyses displaying the Spearman’s correlation coefficient (rs).

Shown are the correlation results between the nMDS axis 1 scores, the four biodiversity indicies and the ratio pioneer and climax tree species and the 19 LiDAR metrics. Cells in (absolute values): green

rs≥ 0.70; orangers< 0.70 - ≥ 0.50; redrs< 0.50 (n= 28).

nMDS axis 1 _{index 1-D}Simpson Shannon_index Margelef’s_index Equitability pioneer/climaxRatio tree species

QMCH 0.83 0.60 0.55 0.55 0.62 -0.61

CH 0.82 0.59 0.53 0.54 0.62 -0.60

Max 0.70 0.57 0.51 0.51 0.67 -0.48

Mean 0.82 0.59 0.53 0.53 0.61 -0.63

SD 0.72 0.57 0.51 0.50 0.64 -0.45

cov 1m 0.71 0.49 0.47 0.48 0.47 -0.54

cov 2m 0.74 0.51 0.48 0.50 0.49 -0.57

cov 5m 0.74 0.54 0.50 0.51 0.51 -0.58

cov 7m 0.74 0.56 0.52 0.53 0.53 -0.58

cov 10m 0.79 0.56 0.52 0.52 0.55 -0.60

cov 12m 0.80 0.55 0.51 0.51 0.55 -0.61

p 5th _0.73 _0.51 _0.50 _0.51 _0.48 _-0.60

p 10th _0.77 _0.57 _0.55 _0.56 _0.53 _-0.66

p 25th _0.77 _0.57 _0.53 _0.53 _0.54 _-0.63

p 50th _0.80 _0.57 _0.52 _0.52 _0.58 _-0.59

p 75th _0.82 _0.59 _0.54 _0.53 _0.61 _-0.60

p 90th _0.79 _0.58 _0.53 _0.52 _0.64 _-0.56

p 95th _0.71 _0.56 _0.51 _0.50 _0.64 _-0.47

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Table 16: Results of the correlation analyses displaying the pvalues. Shown are the correlation results between the nMDS axis 1 scores, the four biodiversity indicies and the ratio pioneer and climax tree species and the 19 LiDAR metrics. Cells in: greenp≤ 0.001; orangep> 0.001 -≤ 0.05 (n= 28).

nMDS axis 1 Simpson index 1-D

Shannon index

Margelef’s

index Equitability

Ratio pioneer/climax

tree species

QMCH 0.0000 0.0009 0.0032 0.0029 0.0006 0.0006

CH 0.0000 0.0013 0.0042 0.0039 0.0006 0.0010

Max 0.0000 0.0018 0.0068 0.0069 0.0001 0.0116

Mean 0.0000 0.0013 0.0041 0.0042 0.0007 0.0005

SD 0.0000 0.0018 0.0068 0.0084 0.0003 0.0190

cov 1m 0.0000 0.0092 0.0144 0.0106 0.0140 0.0038

cov 2m 0.0000 0.0063 0.0110 0.0080 0.0094 0.0019

cov 5m 0.0000 0.0034 0.0082 0.0067 0.0069 0.0015

cov 7m 0.0000 0.0026 0.0060 0.0047 0.0043 0.0015

cov 10m 0.0000 0.0025 0.0060 0.0050 0.0027 0.0010

cov 12m 0.0000 0.0030 0.0072 0.0068 0.0030 0.0007

p 5th _0.0000 _0.0066 _0.0084 _0.0065 _0.0121 _0.0009

p 10th _0.0000 _0.0018 _0.0028 _0.0022 _0.0041 _0.0002 p 25th _0.0000 _0.0018 _0.0046 _0.0047 _0.0039 _0.0005 p 50th _0.0000 _0.0017 _0.0050 _0.0050 _0.0014 _0.0012 p 75th _0.0000 _0.0012 _0.0037 _0.0041 _0.0007 _0.0009 p 90th _0.0000 _0.0016 _0.0047 _0.0055 _0.0003 _0.0023 p 95th _0.0000 _0.0026 _0.0072 _0.0085 _0.0003 _0.0127 p 99th _0.0000 _0.0017 _0.0069 _0.0076 _0.0001 _0.0115

Due to this high overall correlation of the LiDAR metrics to nMDS axis 1 scores it was decided that these scores are used to derive the predictive LiDAR based tree community composition model.

Next a stepwise forward and backward multiple regression was performed (R software was used for this). The final model included three significant variables (Mean, cov 12m and p 50th_{) and four}

biodiversity plots were excluded (outliers) from the model development. An r2 _{of 0.72 was obtained}

(36)

Figure 17: Predictive tree community composition model.

This final model was then applied (spatial resolution 31.25 m) to the areas of the LiDAR transects that cover Lowland Dipterocarp Forest (based on the land cover classification from Work Package 2) (Figure 18). To exclude non-forested areas all areas where the LiDAR metric Max was smaller than 6 m were excluded. The predicted nMDS axis 1 scores of this map ranged from -0.264 to 0.741. The highest nMDS axis 1 scores were found in Kerinci Sebelat National Park and the lowest in eastern lowlands of the Musi Banyuasin district. These results indicate that the areas within the Kerinci Sebelat National Park have tree community compositions that indicate high biodiversity compared to the ones in the eastern lowlands of the Musi Banyuasin District.

(37)

Figure 18: Final predictive LiDAR based tree community composition model. The predictive map shown here was reclassified into three classes using the Natural Breaks (Jenks) provided in ArcGIS

(www.esri.com). The predicted nMDS axis 1 scores of this map ranged from -0.264 to 0.741. The

lower three figures exemplarily show areas with low, medium and high nMDS axis 1 scores. The location of the lover three figures is shown as red rectangles in the upper figure. The highest nMDS axis 1 scores were found in Kerinci Sebelat National Park which indicates that this area has tree community compositions that indicate high biodiversity compared to the eastern lowland (e.g. District of Musi Banyuasin).

(38)

6. Conclusions

Following conclusions could be drawn (separated into the aboveground biomass and the tree community composition modelling).

Aboveground biomass modelling

 Local aboveground biomass (AGB) values could be derived from the LiDAR based aboveground biomass model for almost all identified vegetation cover classes.

 High aboveground biomass variability within vegetation classes could be identified (e.g. Primary Dryland Forest has a standard deviation for aboveground biomass of ±222.5 t/ha).

 Areas with the highest aboveground biomass (AGB) values were located within and around the Kerinci Seblat National Park.

Tree community composition modelling

 The findings of this study indicate that the similarity in tree community composition can be

predicted and monitored by means of airborne LiDAR.

 In addition to using airborne LiDAR data as mapping tool for aboveground biomass this data

could be further developed to provide a biodiversity mapping tool, so that biodiversity assessments could be carried out simultaneously with aboveground biomass analyses (same dataset).

 A further advantage of the approach is that the tree community composition can be carried out without identifying individual tree crowns in remotely sensed imagery.

7. Outlook

Interesting research topics for the future would be:

 A spatial comparison of the LiDAR based aboveground biomass model with the LiDAR based tree community composition model.

 Further, it would be of interest to analyse what influence do different historical land use patterns (e.g. logging) have on the aboveground biomass and tree community composition for similar forest classes (e.g. Secondary Dryland Forest), that were classified on the basis of multispectral satellite imagery.

(39)

Outputs / deliverables

 Processed and filtered LiDAR data (.las format)

 Digital Surface Model (DSM) in 1 m spatial resolution (.img format)  Digital Terrain Model (DTM) in 1 m spatial resolution (.img format)

 Canopy Height Model (CHM) in 1 m spatial resolution (.img format)

 LiDAR based aboveground biomass model in 5 m spatial resolution (.img format)

 Local aboveground biomass values (tables in final report)

 LiDAR based tree community composition model for Lowland Dipterocarp Forest in 31.25 m spatial resolution (.img format)

(1)

Species1 _Family2

species3 _species4 Species1 Family2 _species3 _species4

Gymnacranthera bancana(Miq.) J.Sinclair Myristicaceae no yes Lithocarpus bancanus(Scheff.) Rehder Fagaceae no yes

Gymnacranthera forbesii(King) Warb. Myristicaceae no yes Tetranthera angulate(Blume) Nees Lauraceae no yes

Gymnacranthera paniculata(A.DC.) Warb. Myristicaceae no yes Litsea diversifoliaBlume Lauraceae no yes

Gynotroches axillarisBlume Rhizophoraceae yes yes Litsea firma(Blume) Hook.f. Lauraceae no yes

Hevea brasiliensis(Willd. ex A.Juss.) Müll.Arg. Euphorbiaceae yes no Litsea forsteniiBoerl. Lauraceae no yes

Hibiscus tilliaceus L. Malvaceae yes no Litsea lancifolia(Roxb. ex Nees) Fern.-Vill. Lauraceae yes yes

Homalanthus populneus(Geiseler) Pax Euphorbiaceae yes no Litsea ellipticaBlume Lauraceae yes yes

Hopea mengarawanMiq. Dipterocarpaceae no yes Litsea oppositifoliaGibbs Lauraceae no yes

Hopea semicuneataSymington Dipterocarpaceae no yes Lophopetalum beccarianumPierre Celastraceae no yes

Horsfieldia crassifolia(Hook.f. & Thomson) Warb. Myristicaceae no yes Lophopetalum multinerviumRidl. Celastraceae no yes

Horsfieldia glabra(Reinw. ex Blume) Warb. Myristicaceae no yes Macaranga conifer(Rchb.f. & Zoll.) Müll.Arg Euphorbiaceae yes no

Horsfieldia gracilisW.J.de Wilde Myristicaceae no yes Macaranga denticulate(Blume) Müll.Arg. Euphorbiaceae yes no

Horsfieldia irya(Gaertn.) Warb. Myristicaceae no yes Macaranga gigantean(Rchb.f. & Zoll.) Müll.Arg. Euphorbiaceae yes no

Ilex cymosaBlume Aquifoliaceae no yes Macaranga hispida(Blume) Müll.Arg. Euphorbiaceae yes no

Irvingia malayana Oliv. ex A.W.Benn. Simarubaceae yes yes Macaranga hypoleuca(Rchb.f. & Zoll.) Müll.Arg. Euphorbiaceae yes no

Ixonanthes icosandraJack Ixonanthaceae yes yes Macaranga peltata(Roxb.) Müll.Arg. Euphorbiaceae yes no

Ixora miqueliiBremek Rubiaceae yes yes Macaranga conifer(Rchb.f. & Zoll.) Müll.Arg Euphorbiaceae yes no

Knema cinereaWarb Myristicaceae no yes Macaranga triloba(Thunb.) Müll.Arg. Euphorbiaceae yes no

Knema furfuracea(Hook. f. & Thomson) Warb Myristicaceae no yes Mallotus paniculatus(Lam.) Müll.Arg. Euphorbiaceae yes no

Knema globularia(Lam.) Warb. Myristicaceae no yes Manilkara kauki(L.) Dubard Sapotaceae yes yes

Knema latifoliaWarb. Myristicaceae no yes Melaleuca cajuputiPowell Myrtaceae yes no

Knema laurinaWarb. Myristicaceae no yes Melanorrhoea wallichiiHook.f Anacardiaceae no yes

Koompassia excels(Becc.) Taub. Leguminosae no yes Melicope latifolia(DC.) T.G. Hartley Rutaceae yes no

Koompassia malaccensisBenth. Leguminosae no yes Melicope lunu-ankenda(Gaertn.) T.G. Hartley Rutaceae yes no

Lansium parasiticum(Osbeck) K.C.Sahni & Bennet Meliaceae yes yes Memecylon eduleRoxb. Melastomataceae no yes

1_{Scientific name species} 2_{Scientific name family} 3_{Pioneer species (yes/no)} 4_{Climax species (yes/no)}

(2)

Species1 _Family2 Pioneer

species3 _speciesClimax4 Species1 Family2 _speciesPioneer3 _speciesClimax4

Memecylon lilacinumZoll. & Moritzi Melastomataceae no yes Parastemon urophyllus(Wall. ex A.DC.) A.DC. Chrysobalanaceae no yes

Millettia atropurpureaBth. Leguminosae no yes Parkia speciosaHassk. Leguminosae yes yes

Mussaendopsis beccarianaBaill. Rubiaceae yes no Payena acuminata(Blume) Pierre Sapotaceae no yes

Myrciaria vexatorMcVaugh Myrtaceae no yes Pellacalyx axillarisKorth. Rhizophoraceae yes yes

Myristica ellipticaWall. ex Hook.f. & Thomson Myristicaceae no yes Peltophorum pterocarpum(DC.) K.Heyne Leguminosae yes no

Virola surinamensis(Rol. ex Rottb.) Warb. Myristicaceae no yes Peronema canescensJack Lamiaceae yes no

Myristica lowianaKing Myristicaceae no yes Pertusadina eurhyncha(Miq.) Ridsdale Rubiaceae no yes

Myristica maximaWarb. Myristicaceae no yes Pertusadina multifolia(Havil.) Ridsdale Rubiaceae no yes

Nauclea orientalis(L.) L. Rubiaceae yes yes Phoebe elliptica(Blume) Blume Lauraceae no yes

Nauclea subdita(Korth.) Steud. Rubiaceae yes no Pimelodendron griffithianum(Müll.Arg.) Benth. ex Hook.f. Euphorbiaceae no yes

Neolamarckia cadamba(Roxb.) Bosser Rubiaceae yes no Piper aduncumL. Piperaceae yes no

Nephelium cuspidatumBlume Sapindaceae no yes Podocarpus neriifoliusD.Don Torricelliaceae no yes

Nephelium lappaceumL. Sapindaceae no yes Polyalthia caulifloraHook.f. & Thomson Annonaceae no yes

Nephelium mangayiHiern Sapindaceae no yes Maasia hypoleuca(Hook.f. & Thomson) Mols, Kessler &_Rogstad Annonaceae yes yes

Ochanostachys amentaceaMast. Olacaceae no yes Polyalthia rumphii(Blume ex Hensch.) Merr. Annonaceae no yes

Ormosia sumatrana(Miq.) Prain Leguminosae yes yes Maasia sumatrana(Miq.) Mols, Kessler & Rogstad Annonaceae yes yes

Palaquium confertumH.J.Lam Sapotaceae no yes Pometia alnifolia(Blume) King Sapindaceae no yes

Palaquium gutta(Hook.) Baill. Sapotaceae no yes Pometia pinnataJ.R.Forst. & G.Forst. Sapindaceae no yes

Palaquium hexandrum(Griff.) Baill. Sapotaceae no yes Porterandia anisophylla(Jack ex Roxb.) Ridl. Rubiaceae yes yes

Palaquium leiocarpumBoerl. Sapotaceae no yes Pouteria reticulata(Engl.) Eyma Sapotaceae no yes

Palaquium obovatum(Griff.) Engl. Sapotaceae no yes Prunus arborea(Blume) Kalkman Rosaceae no yes

Palaquium rostratum(Miq.) Burck Sapotaceae no yes Psychotria viridifloraReinw. ex Blume Rubiaceae no yes

Palaquium sumatranumBurck Sapotaceae no yes Pternandra azurea(DC.) Burkill Melastomataceae no yes

Parashorea lucidaKurz Dipterocarpaceae no yes Pternandra caerulescensJack Melastomataceae no yes

Parashorea malaanonanMerr. Dipterocarpaceae no yes Pternandra cordataBaill. Melastomataceae no yes

1_{Scientific name species} 2_{Scientific name family} 3_{Pioneer species (yes/no)} 4_{Climax species (yes/no)}

(3)

Species1 _Family2

species3 _species4 Species1 Family2 _species3 _species4

Pterocymbium tinctoriumMerr. Sterculiaceae yes no Shorea paucifloraKing Dipterocarpaceae no yes

Pterospermum celebicumMiq. Malvaceae yes yes Shorea platycladosSlooten ex Endert Dipterocarpaceae no yes

Pterospermum javanicumJungh Malvaceae yes yes Shorea teysmannianaDyer ex Brandis Dipterocarpaceae yes yes

Quercus argentataKorth. Fagaceae no yes Shorea uliginosaFoxw. Dipterocarpaceae yes yes

Lithocarpus sundaicus(Blume) Rehder Fagaceae no yes Sindora beccarianade Wit Leguminosae no yes

Aidia densiflora(Wall.) Masam. Rubiaceae yes yes Sindora bruggemaniide Wit Leguminosae no yes

Rhizophora apiculataBlume Rhizophoraceae yes yes Sonneratia caseolaris(L.) Engl. Lythraceae yes yes

Rhizophora mucronataLam. Rhizophoraceae yes yes Spondias pinnata(L. f.) Kurz Anacardiaceae yes yes

Rhodamnia rubescens(Benth.) Miq. Myrtaceae no yes Stemonurus secundiflorusBlume Stemonuraceae yes yes

Ryparosa javanicaKoord. & Valeton Flacourtiaceae no yes Sterculia cordataBlume Malvaceae yes yes

Sandoricum beccarianumBaill. Meliaceae yes yes Sterculia laevisWall. Malvaceae yes yes

Santiria laevigataBlume Burseraceae no yes Syzygium acuminatissimum(Blume) DC. Myrtaceae no yes

Sapium macrocarpumMüll.Arg. Euphorbiaceae no yes Syzygium claviflorum(Roxb.) Wall. ex A.M.Cowan & Cowan Myrtaceae no yes

Sapium lateriflorumHemsl. Euphorbiaceae no yes Syzygium conglobatumMerr. Myrtaceae no yes

Scaphium macropodum(Miq.) Beumée ex K.Heyne Malvaceae yes yes Syzygium laxiflorum(Blume) DC. Myrtaceae no yes

Shorea beccarianaBurck Dipterocarpaceae no yes Syzygium lineatum(DC.) Merr. & L.M.Perry Myrtaceae no yes

Shorea dasyphyllaFoxw. Dipterocarpaceae no yes Syzygium magnoliifolium(Blume) DC. Myrtaceae yes yes

Shorea gibbosaBrandis Dipterocarpaceae no yes Syzygium palawanense(C.B.Rob.) Merr. & L.M.Perry Myrtaceae yes no

Shorea hemsleyanaKing ex Foxw. Dipterocarpaceae no yes Syzygium palembanicumMiq. Myrtaceae yes yes

Shorea javanicaKoord. & Valeton Dipterocarpaceae no yes Syzygium polyanthum(Wight) Walp. Myrtaceae yes yes

Shorea laevifolia(Parijs) Endert Dipterocarpaceae no yes Syzygium pseudoformosum(King) Merr. & L.M.Perry Myrtaceae no yes

Shorea leprosulaMiq. Dipterocarpaceae yes yes Syzygium racemosum(Blume) DC. Myrtaceae yes yes

Shorea ovalisBlume Dipterocarpaceae no yes Syzygium samarangense(Blume) Merr. & L.M.Perry Myrtaceae yes no

Shorea palembanicaMiq. Dipterocarpaceae no yes Syzygium siamense(Craib) Chantaran. & J.Parn. Myrtaceae yes yes

Shorea parvifoliaDyer Dipterocarpaceae yes yes Tarenna sambucina(G.Forst.) T.Durand ex Drake Rubiaceae yes no

1_{Scientific name species} 2_{Scientific name family} 3_{Pioneer species (yes/no)} 4_{Climax species (yes/no)}

(4)

Species1 _Family2 Pioneer

species3 _speciesClimax4 Species1 Family2 _speciesPioneer3 _speciesClimax4

Teijsmanniodendron pteropodum(Miq.) Bakh. Verbenaceae no yes Vatica wallichiiDyer Dipterocarpaceae no yes

Terminalia bellirica (Gaertn.) Roxb. Combretaceae yes yes Vitex glabrataR.Br. Verbenaceae yes no

Terminalia subspathulataKing Combretaceae yes yes Vitex pinnataL. Verbenaceae yes no

Tetramerista glabraMiq. Theaceae yes yes Xanthophyllum flavescensRoxb. Polygalaceae no yes

Timonius wallichianus(Korth.) Valeton Rubiaceae yes yes Xanthophyllum obscurumA.W.Benn. Polygalaceae no yes

Toona sureni(Blume) Merr. Meliaceae yes yes Xanthophyllum vitellinum(Blume) D.Dietr. Oleaceae no yes

Trema cannabinaLour. Ulmaceae yes no Xerospermum laevigatumRadlk. Sapindaceae no yes

Triadica cochinchinensisLour. Euphorbiaceae yes no Xerospermum muricatumRadlk. Sapindaceae no yes

Discospermum abnorme(Korth.) S.J.Ali & Robbr. Rubiaceae yes no Xerospermum noronhianumBlume Sapindaceae no yes

Stemonurus secundiflorusBlume Icacinaceae yes yes Xylocarpus granatumJ.Koenig Meliaceae yes yes

Vatica rassakBlume Dipterocarpaceae no yes Xylopia altissimaBoerl. Annonaceae yes yes

Vatica venulosaBlume Dipterocarpaceae no yes

1_{Scientific name species} 2_{Scientific name family} 3_{Pioneer species (yes/no)} 4_{Climax species (yes/no)}

(5)

61 Appendix F: Overview nMDS scores, biodiversity indices and ratios pioneer/climax tree species lowland dipterocarp forest

Plot ID1 Forest type

Classification2 _{Forest cover 10m startification}Forest type 3

nMDS scores

class. stratification4 _{forest cover 10m stratification}nMDS scores 5 Simpson

index 1-D Shannon index(entropy) richness indexMargalef’s Equitability

Ratio pioneer/climax

tree species

Axis 1 Axis 2 Axis 1 Axis 2

1-LD-RKI Low-density Lowland Dipterocarp Forest Medium-density Lowland Dipterocarp Forest -0.075 -0.013 -0.052 -0.108 0.875 2.412 4.219 0.870 0.50

2-LD-RKI High-density Lowland Dipterocarp Forest High-density Lowland Dipterocarp Forest 0.121 0.009 0.084 0.062 0.941 3.062 6.518 0.929 0.04

4-LD-RKI Medium-density Lowland Dipterocarp Forest Medium-density Lowland Dipterocarp Forest 0.084 -0.030 0.079 0.013 0.949 3.310 8.266 0.924 0.06

4A-KS Medium-density Lowland Dipterocarp Forest High-density Lowland Dipterocarp Forest 0.127 -0.058 0.140 -0.037 0.936 2.946 6.005 0.940 0.08

4-KS Medium-density Lowland Dipterocarp Forest Medium-density Lowland Dipterocarp Forest 0.061 -0.041 0.042 -0.010 0.952 3.138 6.493 0.963 0.10

5-LD-RKI Medium-density Lowland Dipterocarp Forest Medium-density Lowland Dipterocarp Forest 0.037 -0.186 0.085 -0.163 0.896 2.631 5.223 0.878 0.13

5-KS Medium-density Lowland Dipterocarp Forest Medium-density Lowland Dipterocarp Forest 0.061 -0.042 0.078 -0.036 0.932 2.926 5.728 0.909 0.25

6-LD-RKI Low-density Lowland Dipterocarp Forest Low-density Lowland Dipterocarp Forest -0.097 0.138 -0.107 0.156 0.810 2.069 3.100 0.807 0.43

7-LD-RKI Low-density Lowland Dipterocarp Forest Medium-density Lowland Dipterocarp Forest -0.196 -0.026 -0.225 0.021 0.720 1.632 2.424 0.709 0.92

8-KS Medium-density Lowland Dipterocarp Forest Medium-density Lowland Dipterocarp Forest 0.018 -0.047 0.031 -0.064 0.934 2.980 6.342 0.926 0.31

9-LD-RKI Medium-density Lowland Dipterocarp Forest Medium-density Lowland Dipterocarp Forest 0.017 0.032 -0.002 0.019 0.942 3.014 6.154 0.948 0.27

9-KS Low-density Lowland Dipterocarp Forest Low-density Lowland Dipterocarp Forest -0.188 -0.145 -0.221 -0.061 0.725 1.869 3.376 0.752 0.73

10-LD-RKI Medium-density Lowland Dipterocarp Forest Medium-density Lowland Dipterocarp Forest 0.054 -0.028 0.061 -0.032 0.955 3.276 7.669 0.954 0.15

10-KS Low-density Lowland Dipterocarp Forest Low-density Lowland Dipterocarp Forest -0.321 -0.207 -0.384 -0.033 0.392 0.807 1.039 0.501 0.79

11-KS Medium-density Lowland Dipterocarp Forest High-density Lowland Dipterocarp Forest 0.101 -0.029 0.100 -0.007 0.949 3.121 6.777 0.958 0.11

12A-KS High-density Lowland Dipterocarp Forest Medium-density Lowland Dipterocarp Forest 0.160 -0.009 0.153 0.034 0.936 2.886 5.285 0.948 0.00

13-KS High-density Lowland Dipterocarp Forest High-density Lowland Dipterocarp Forest 0.230 0.094 0.224 0.122 0.870 2.383 3.938 0.880 0.00

15-KS Medium-density Lowland Dipterocarp Forest High-density Lowland Dipterocarp Forest 0.010 -0.071 0.008 -0.052 0.934 2.921 5.746 0.932 0.36

18-KS Low-density Lowland Dipterocarp Forest Medium-density Lowland Dipterocarp Forest -0.101 -0.114 -0.135 -0.038 0.889 2.605 4.864 0.843 0.43

1_{The plot ID from the tally sheets}

2_{Forest type at plot location based on the land cover classification derived in WP 2}

3_{Forest type at plot location based on the forest cover at 10m height (from LiDAR) stratification} 4_{nMDS scores based on the land cover classification derived in WP 2}

(6)

Plot ID1 Forest type

Classification2 _{Forest cover 10m startification}Forest type 4

nMDS scores

class. stratification5 _{forest cover 10m stratification}nMDS scores 6 Simpson

index 1-D Shannon index(entropy) richness indexMargalef’s Equitability

Ratio pioneer/climax

tree species

Axis 1 Axis 2 Axis 1 Axis 2

19-KS Low-density Lowland Dipterocarp Forest Low-density Lowland Dipterocarp Forest -0.387 0.110 -0.143 -0.351 0.780 1.748 2.337 0.840 0.07

110A-KS Medium-density Lowland Dipterocarp Forest Medium-density Lowland Dipterocarp Forest 0.143 -0.225 0.206 -0.184 0.893 2.272 3.753 0.987 0.13

207-KS Medium-density Lowland Dipterocarp Forest High-density Lowland Dipterocarp Forest 0.095 -0.065 0.070 0.009 0.930 2.894 5.689 0.911 0.02

273 NoData7 _{Medium-density Lowland Dipterocarp Forest} _- _- _-0.211 _0.220 _0.750 _1.759 _2.950 _0.667 _0.44

290 Medium-density Lowland Dipterocarp Forest NoData -0.134 0.091 - - 0.859 2.436 4.492 0.843 0.63

291 Low-density Lowland Dipterocarp Forest NoData -0.126 0.101 - - 0.900 2.568 4.465 0.906 0.74

316 NoData Medium-density Lowland Dipterocarp Forest - - -0.043 0.027 0.932 2.819 5.242 0.957 0.40

319 Medium-density Lowland Dipterocarp Forest Medium-density Lowland Dipterocarp Forest -0.082 0.180 -0.074 0.179 0.885 2.571 4.905 0.890 0.37

351 NoData NoData - - - - 0.891 2.535 4.578 0.877 0.57

354 Medium-density Lowland Dipterocarp Forest NoData -0.042 0.035 - - 0.902 2.710 5.624 0.864 0.59

391 Medium-density Lowland Dipterocarp Forest NoData 0.057 0.036 - - 0.958 3.371 8.206 0.948 0.13

393 Medium-density Lowland Dipterocarp Forest High-density Lowland Dipterocarp Forest 0.135 0.068 0.158 0.086 0.955 3.286 7.345 0.940 0.07

414 Medium-density Lowland Dipterocarp Forest NoData 0.016 0.078 - - 0.914 2.717 5.021 0.907 0.38

415 Medium-density Lowland Dipterocarp Forest Medium-density Lowland Dipterocarp Forest -0.016 0.089 -0.011 0.125 0.928 3.020 6.622 0.906 0.42

416 Medium-density Lowland Dipterocarp Forest NoData 0.096 0.036 - - 0.949 3.138 6.871 0.952 0.06

417 Medium-density Lowland Dipterocarp Forest High-density Lowland Dipterocarp Forest 0.093 0.082 0.088 0.102 0.964 3.453 8.384 0.964 0.09

431 Low-density Lowland Dipterocarp Forest NoData 0.064 0.060 - - 0.916 2.916 6.547 0.866 0.18

446 Medium-density Lowland Dipterocarp Forest NoData -0.044 -0.016 - - 0.935 2.935 5.814 0.936 0.44

1_{The plot ID from the tally sheets}

2_{Forest type at plot location based on the land cover classification derived in WP 2}

3_{Forest type at plot location based on the forest cover at 10m height (from LiDAR) stratification} 4_{nMDS scores based on the land cover classification derived in WP 2}

BIOCLIME WP3 report Remote Sensing Solutions GmbH Final

Survey of biomass, carbon stocks, biodiversity, and assessment of the historic fire

regime for integration into a forest monitoring system in the Districts Musi Rawas,

Musi Rawas Utara, Musi Banyuasin and Banyuasin, South Sumatra, Indonesia

Work Package 3

Aboveground biomass and tree community composition modelling

Table of Content

Executive summary

1.

Introduction

2.

Carbon and biodiversity plots

2.1. Inventory design

2.2. Aboveground biomass calculations

3.

LiDAR data and aerial photos

3.1. LiDAR and aerial photo survey

3.2. LiDAR processing, filtering and interpolation

4.

LiDAR based aboveground biomass model

4.1. Regression analysis and aboveground biomass model development

4.2. Determination of local aboveground biomass values

5.

Analyses of tree community composition of lowland dipterocarp forests

5.1. Calculation of different LiDAR metrics for the biodiversity plots

5.2. Derivation of nMDS scores, biodiversity indices and pioneer/climax tree

species ratio

5.3. LiDAR based tree community composition model

6.

Conclusions

7.

Outlook

Outputs / deliverables

61

Appendix F: Overview nMDS scores, biodiversity indices and ratios pioneer/climax tree species lowland dipterocarp forest

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