Aboveground biomass calculations Carbon and biodiversity plots 1. Inventory design

RSS - Remote Sensing Solutions GmbH 10 Manuri et al. 2014 and Komiyama et 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 W ag = 0.070D 2.580 D: 8–28 cm R 2 = 0.97 South Sumatra Wicaksono 2004 cited in Krisnawati et al. 2012 Eucalyptus pellita 1 W ag = 0.0678D 2.790 D: 2–27 cm R 2 = 0.99 South Sumatra Onrizal et al. 2009 cited in Krisnawati et al. 2012 Estate crop Hevea brasiliensis W ag = 0.2661D 2.1430 Elias 2014 Elaeis guineensis W ag = 0.0706 + 0.0976H Sumatra, Kalimantan ICRAF 2009 cited in Hairiah et al. 2011 Dryland forest Tropical mixed species W ag = 0.0673 pD 2 H 0.976 D: 2–212 cm Africa, America, Asia Chave et al. 2014 Peat swamp forest Mixed species W ag = 0.15 D 2.0.95 p 0.664 H 05526 D: 2–167 cm R 2 = 0.981 Riau, South Sumatra, West Kalimantan Manuri et al. 2014 Mangrove forest Avicennia marina, Avicennia alba 1 W ag = 0.1846D 2.352 D: 6–35 cm R 2 = 0.98 West Java Darmawan Siregar 2008 cited in Krisnawati et al. 2012 Bruguiera gymnorrhiza W ag = 0.1858D 2.3055 D: 2–24 cm R 2 = 0.989 Australia Clough Scott 1989 Bruguiera parviflora, Bruguiera sexangula 1 W ag = 0.1799D 2.4167 D: 2–21 cm R 2 = 0.993 Australia Clough Scott 1989 Ceriops tagal W ag = 0.1884D 2.3379 D: 2–18 cm R 2 = 0.989 Australia Clough Scott 1989 Rhizophora apiculate, Rhizophora mucronata 1 W ag = 0.235D 2.42 D: 5–28 cm R 2 = 0.98 Malaysia Ong et al. 2004 Xylocarpus granatum W ag = 0.0823D 2.5883 D: 3–17 cm R 2 = 0.994 Australia Clough Scott 1989 Other species Excoecaria agallocha, Sonneratia caseolaris W ag = 0.2512pD 2.46 D: 5–49 cm R 2 = 0.979 Indonesia, Thailand Komiyama et al. 2005 W ag = aboveground biomass kg, D = Diameter at Breast Height DBH, cm, H = tree height m, p = wood density gramcm 3 , R 2 = 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 RSS - Remote Sensing Solutions GmbH 11 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. Stratum 1 Location Model Statistics Secondary Logged over Dryland Forest Hutan Lahan Kering Sekunder Bekas Tebangan PT REKI H = D0.7707+0.0195D n = 168, D = 5–104 cm, AIC = 1032.21, RMSE = 5.58, R 2 adj = 0.624 BKSDA Dangku H = exp0.7071+0.6556lnD n = 156, D = 6–69 cm, AIC = 845.56, RMSE = 3.75, R 2 adj = 0.690 Primary Mangrove Forest Hutan Mangrove Primer TN Sembilang H = 28.16131-exp-D27.0703 n = 221, D = 5-77 cm, AIC = 1098.86, RMSE = 2.89, R 2 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, R 2 adj = coefficient of determination adjusted 1 in brackets Bahasa Indonesia Table 5: Average wood density for unidentified tree species. Startum 1 Location Mean gcm 3 St.dev. gcm 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. tonha and then were added to derive total aboveground biomass for each sample plot. Aboveground biomass estimates per plot in tons per hectare tha 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 RSS - Remote Sensing Solutions GmbH 12 inventory is provided in the BIOCLIME GIZ Final Report: Cadangan Karbon Hutan dan Keanekaragaman Flora di Sumatera Selatan Tiryana et al. 2015. Table 6: Statistical results for the aboveground biomass AGB estimates for the different strata. Stratum 1 Number of carbon inventory plots Min AGB tha 2 Max AGB tha 3 Mean AGB tha 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 4 Mean aboveground biomass AGB in tons per hectare for the Stratum class ± = standard deviation RSS - Remote Sensing Solutions GmbH 13 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 pointsm 2 Discrete Return 6-8 pointsm 2 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. Figure 6 shows the location of the LiDAR transects within the BIOCLIME study area. RSS - Remote Sensing Solutions GmbH 14 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.