Above ground biomass estimation across forest types at different degradation levels
2.3 LiDAR data
2.3.1 Acquisition and processing of airborne laser scanner data
The airborne LiDAR data set was acquired in a flight campaign by Milan Geoservice GmbH and Kalteng Consultants from the 5th to 10th August 2007. During the campaign 13,626ha were scanned. A Riegl LMS-Q560 Airborne Laser Scanner was mounted to a Bell 206 helicopter. Small-footprint full-waveform LiDAR data was collected from a flight altitude of ±500m above ground over a scan angle of ±30 degrees (swath width ±500m). The laser sensor had a pulse rate of up to 100,000 pulses per second with a footprint of 0.25m and a wavelength of 1.5µm (near Infrared). To avoid noise and outliers only echoes with intensity higher than 9 were used in this study. This resulted in an average of 1.4 echoes per square meter. The corresponding GPS ground station for differential geo-correction was located at Palangka Raya airport at an elevation of 25m above sea level (a.s.l.). Position and116
orientation of the aircraft and LiDAR measurement system were measured in-flight by GPS and an Inertial Measurement Unit (IMU). The Riegl LMS-Q560 Airborne Laser Scanner system allows height measurements of ±0.02m. Single beam measurements have an absolute horizontal accuracy of ±0.50m and vertical accuracy of ±0.15m Root Mean Square Error (RMSE).
A filtering algorithm based on Kraus & Pfeifer (1998) was applied to differentiate between ground and vegetation points. The algorithm is based on linear prediction with an individual accuracy for each measurement. Digital Terrain Models (DTMs) and Canopy Surface Models (CSMs) were generated by interpolating the filtered ground and vegetation points respectively. Ordinary Kriging interpolation method was selected to generate the DTM (cell size 1m). It showed the best results with fewest artefacts. CSMs were generated using the Inverse Distance weighted interpolation (cell size 1m) as point clouds exceeded feasible data size for Kriging. Subtracting the DTM from the CSM resulted in the Canopy Height Model (CHM) which provides an estimate of vegetation height.
2.3.2 Generation of multiple regression models: Plot level approach
The plot level approach of biomass estimation focuses on the direct correlation of the LiDAR 3D point cloud statistics within a defined polygon with the corresponding ground-based AGB value. Multiple regression analysis was applied to create AGB estimation models. Our analysis follows the principles of Magnussen & Boudewyn (1998) and its application follows Lim & Treitz (2004), Patenaude (2004), and Lucas (2006). As the angle count method is designed to extrapolate measurements to 1ha values, a circle of 1ha (56.42m radius around the sample plot center) was used to clip the LiDAR point cloud. The height above the terrain (absolute vegetation heights) for each point within the cloud was determined by subtracting the corresponding pixel value of the DTM. Only points with a value greater than 0.5m were included in the analysis (Lucas et al., 2006). LiDAR point height distributions of each sample plot were analyzed statistically and following metrics were derived and used as predictors: (1) mean hmean, (2) measures of dispersion including the Standard Error of the Mean (SEM) hSEM, standard deviation (σ) hσ, variance hvar, range hrange and
117 maximum hmax, and (3) the quantiles corresponding to the 5, 10, ..., 95 percentiles of the distributions (h5,..,.95). As a further potential predictor, Canopy Cover (CC) was determined. For every pixel of a certain size (5m), CC was calculated by dividing the number of points above a certain height threshold (10m) by the number of points below the threshold. The 10-meter-threshold was assumed to be appropriate for getting significant differences between the plots. A small cell size of 5m was used in order to avoid large errors at the borders of the plot circle, since the cut pixels along the border are either counted completely or not.
All above variables within the sampling area of the angle count method were correlated to the corresponding estimated AGB values per ha. Multiple linear regression analysis was conducted for all sample plots as well as for different forest and land use types. Stepwise selection was performed to determine which independent variables should be included in the final models. Further, a log- transformation of the predictors and an exponential regression were tested. For final model validation, the coefficient of determination (R²), the adjusted coefficient of determination (R²adj), the Standard Error of the Estimate (SEE), and absolute as well as relative Root Mean Square Error (RMSE) were used.
2.3.3 Application of the regression models
Fitted regression models were applied to six selected LiDAR tracks which together have a size of 5,241ha (adding up to 93,221m length and on average 562m wide). In lowland dipterocarp forest area, tracks 4a (236ha, Tumbang Danau) and 5a (462ha, Tewaibaru) were analysed. The other four tracks cover peat swamp forest within the Sebangau National Park (parts of track 1a, 556ha and 2, 472ha), Block C (track 2b, 1,280ha) and Block B (track 3a, 2,234ha) (Figure V-1). Each track was overlaid by a grid with a cell size of 100m representing forest inventory plot size (1ha). The point cloud statistics for each grid cell including mean height, canopy cover, standard error of the mean, standard deviation, variance, range, maximum height, percentiles, and standard error of the mean was calculated. Based on these statistics and the regression models developed (see section V-2.3.2) the AGB values for each 1ha grid cell were calculated.
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For comparison, a Landsat image (ETM+ 118-62, 2007-08-05) was classified. Prior to the classification the Landsat imagery was geometrically corrected by automated image to image matching techniques. Afterwards a radiometric correction was applied in order to compensate atmospheric distortions, resulting from water vapour, viewing geometry, and other physical parameters. The land cover classification of the imagery was implemented using an object oriented approach, applying a segmentation algorithm prior to the classification. Classification itself corresponds in fact a database query by formulating rule bases on how the objects should be evaluated. The AGB values of the different land cover types were based on standard values of the IPCC (2006) for insular Asia: tropical rain forest (350Mg ha-1, here:
pristine peat swamp forest) and tropical shrubland (70Mg ha-1, here: bushland, shrub, regrowth). The class ‘open peat swamp forest’ was assumed to be 75% of the pristine class based on the canopy closure maximum of this class.
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