Aboveground biomass mapping using wall-to-wall LiDAR data in peat swamp forest, Central Kalimantan, Indonesia

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1 Aboveground biomass mapping using wall-to-wall LiDAR data in peat swamp forest, Central Kalimantan, Indonesia Solichin Manuri, Cris Brack, Laura Graham, Bruce Doran Introduction Objectives Methods Time line Chapters The role of remote sensing in AGB mapping The importance of tropical peat swamp forests (PSF) as a key ecosystem in the tropic The ability to better understand the magnitude of tropical peat ecosystem role hampered by the unavailability of accurate characterization of the ecosystem The need for accurate and actual forest monitoring system is increasing Remote sensing technology has proven to be the most feasible approach for assessing vast area of inaccessible forests Airborne light detection and ranging (Lidar) system become an emergent technology 2 1

2 Objectives assessment of airborne lidar technology for measuring characteristics of tropical peat swamp ecosystem in Central Kalimatan Indonesia. Specific objectives of the study include (1) to generate wall-to-wall maps of AGB and timber stock density of tropical peat swamp ecosystem using high resolution lidar data, (2) to compare with AGB estimates calculated using land cover maps and (3) to estimate AGB stock from each land cover types. 3 Materials Acquisition date 21 September 2 October 2011 Size (ha) 119,000 Laser pulse frequency (KHz) 100 Scan frequency 45 Half scan angle ( o ) 22 Flying height (m) 800 Speed (knots) 110 Side overlap (%) 30 Net swath width (m) 450 Calculated point density (point.m -2)

3 LiDAR metrics Mean aboveground height (MAH) Quadratic mean aboveground height (QMAH) = (MAH) 2 Variance of aboveground height (VAH) Percentile height values of aboveground height (P1, P5, P10, P20, P25, P 30 P40, P50, P60, P70, P75, P80, P90, P95, P99) Return density above 1, 5, 10, 15, 20, 25 and 30 m heights (RD1, RD5, RD10 RD15, RD20, RD25 and RD30) Return proportion above 1, 5, 10, 15, 20, 25 and 30 m heights (RP1, RP5, RP10 RP15, RP20, RP25 and RP30) Cumulative return proportions (CRP) is the sum of all RPs value Quadratic cumulative return proportions (QCRP) = (CRP) 2 Return proportion within certain canopy height strata (Str1-5; Str5-10; Str10-15 Str15-20; Str20-25; Str25-30) Cumulative return proportion of canopy height strata (CStr) The best LiDAR models Model No Model a b R2 RMSE AGB1 a+b*qcrp ns *** AGB2 a*crp b BA1 a+b*qcrp ns 2.870*** BA2 a*crp b

4 Data processing For generating lidar metric from all point cloud lidar data, we used LTK-processing tool from FUSION v3.42 software We computed CRP and QCRP metric for the whole project area with 30 meters grid resolution. We normalized the height-related lidar metric with 1- meter resolution DTM to removed terrain effect. AGB and BA maps were generated through applying the selected regression models with the CRP and QCRP layer, respectively. All spatial processing was carried out using ArcGIS Land cover AGB (t/ha) Source AGB map comparisons Two land cover maps generated using Landsat imageries were selected -the Indonesia land cover map 2011 produced by the Ministry of Forestry (MoF, 2012) - the Central Kalimantan land cover map 2010 generated by KFCP (Siegert, Navratil, Franke, & Kronseder, 2013). For calculating total AGB, we multiplied the area of each land cover with the associated AGB values. MoF map Area (ha) KFCP map Primary Swamp Forest Krisnawati et al (2014) Secondary Swamp Forest (Krisnawati et al., ) Secondary Dryland Forest Krisnawati et al (2014) Shrubs in swamp 54 Roberts et al. (2012) Shrubs 54 Roberts et al. (2012) Swamp 15 Solichin et al (2011) Bareland 34 Roberts et al. (2012) Settlement 10 based on assumption Water 0 based on assumption Shrub-Mixed Dryland Farm 54 Roberts et al. (2012)

5 Results Comparison of mean and total AGB estimates Approach mean AGB Total AGB Difference (%) Lidar (this study) ,511,230 0 MoF ,144, KFCP ,505,

6 KFCP map MoF map ,5 311, Forest 42,3 61,1 Non Forest 11 Conclusions In this study we estimated AGB and BA of peat swamp forest using lidar data. Wall-to-wall map derived from lidar data, we estimated total AGB and BA of 24 million ton and 373 m -2, respectively. The estimated mean AGB was 205 ton.ha -1 for the whole study area. The landcover classification map based on landsat imageries, should be used carefully to estimating AGB. The maps were unable to distinguish the variation of AGB accurately in non forest area, whereas the standard deviation of the mean were relatively high. However, we found that simple classification of forest and non-forest classes still produced similar estimates of AGB distribution between the maps. 12 6

7 Acknowledgement Kalimantan Forest Partnership Project 13 Thank you 14 7