The main aim of this thesis was to evaluate the potential and accuracy of airborne and space-based LiDAR data in measuring peat topography, peat fire burn depth, peat fire carbon emissions and forest AGB in Central Kalimantan, Indonesia. Study planning, LiDAR data processing, derivation of peat burning depths, calculation of carbon emission from peat fires and writing of the publication were carried out by Uwe Ballhorn.
Derivation of burn scar depths and estimation of carbon
Pre-fire surface 3D modeling of tropical peatland burn scars based on airborne LiDAR in Central Kalimantan, Indonesia 47
ICESat/GLAS data as a measurement tool for peatland
Above ground biomass estimation across forest types at
65 Figure III-7: Results of the box plot analyses....67 Figure III-8: Historical results of the fire scar classification for the years 1990 through 2009. RMSE Root Mean Square Error SAR Synthetic Aperture Radar SEE standard error of the estimate SEM standard error of the average SLC scan line corrector.
Introduction
1 The tropical peatlands of Indonesia 1.1 Characteristics
Degradation and the impact on the global climate
In particular, drainage and forest clearance disturb their hydrological stability (Page & Rieley, 1998) and make these otherwise waterlogged ecosystems susceptible to fire (Langner et al. 2007) (Figures I-3 and I-4). Moreover, drained and deforested peatlands release large amounts of carbon due to micro-biotic decomposition (Hooijer et al., 2006; Hooijer et al., 2009; Hooijer et al., 2010).
2 Market based mechanisms for forest conservation
Voluntary carbon markets, on the other hand, help governments, organizations, businesses and individuals reduce carbon emissions outside of regulatory mandates (Portela et al., 2008). The regulatory carbon market is dominated by the Kyoto Protocol of the UNFCCC (Portela et al., 2008).
3 The use of remote sensing data to monitor Indonesian peatlands 3.1 Introduction to remote sensing
LiDAR data
First-feedback LiDAR devices only record the position of the first object hit by the laser beam. Last return LiDAR devices on the other hand record the position of the last object hit by the laser beam and are particularly useful for topographic mapping.
Optical satellite data
RADAR satellite data
19 transmitted in the direction of interest and the strength and origin of the reflections received are recorded (Lillesand et al., 2008). The absolute horizontal and vertical accuracy of the data is better than 20 and 16 m respectively (Lillesand et al., 2008).
4 Approach and specific objectives
Therefore, ICESat/GLAS data may be an adequate tool to measure peatland topography and forest AGB. The red rectangle in the upper left depicts the location of the study area within Central Kalimantan.
5 Structure of the thesis
AGB-prediction models were established for each forest type using statistical values of LiDAR point clouds and forest inventory plots. A possible improvement of the regression models using LiDAR point density as weights was tested.
Derivation of burn scar depths and
Abstract
1 Introduction
We focused our investigation on 2.79 million ha of a peat-dominated landscape in Central Kalimantan, Borneo, where in 2006 severe wildfires destroyed large parts of the peat swamp forest (Figure II-1a). Three hundred meter LiDAR traversal through a burn scar within a peat bog forest; vegetation feedbacks are indicated in green and soil feedbacks are indicated in magenta; in peat bogs about 1.0 ±0.5% and in burns about 6.4 ±2.1% of the return signal was classified as soil.
2 Results
Based on a digital map of the study area showing burned peat forest from the year 2007, peat fires released Mt carbon (Table II-1) (see Materials and Methods). Derived from the object-oriented classification of the Landsat ETM+ 7 image August 2007, gap filled) (see Materials and Methods).
3 Discussion
Average peat thickness of the three peat domes in Central Kalimantan (Block B, Block C and Sebangau) modeled by Jaenicke et al. To estimate Indonesia's contributions to global carbon emissions from peatland fires, we calculated the approximate emissions for Indonesia in 2006 based on (a) active fire surveys from the MODIS (Davies et al., 2009), (b) a correction factor for the MODIS burned area determined from a correlation with Landsat-derived burned areas, (c) peatland maps of Indonesia (Wetlands International and (d) the burned depth measurements described here.
4 Materials and methods
Satellite data processing and classification
In addition, deforested and drained peatlands release significant amounts of carbon due to bacterial oxidation (Hooijer et al., 2009). The MODIS system detects active burning fires, so-called hotspots, at a spatial resolution of 1 km in tropical regions (Langner et al., 2007; Davies et al., 2009; Langner & Siegert, 2009).
Light Detection and Ranging (LiDAR) data processing, filtering and Digital Terrain Model (DTM) generation
Finally, the ground points were interpolated using an inverse distance weighted (IDW) GIS interpolation model (Figure II-1c). In transects 1 and 2, 48% of the original ground return signals were below the interpolated DTM and 52% above it with a mean difference of m and.
Burn scar depth analysis and in situ measurements
We used the DTM instead of the original 3D point clouds because it facilitated all further data handling and analysis. With the exception of plots along the Sebangau River (10 measurements), all of these measurements were located on burns within Block C of the former MRP (Figure II-1a).
Acknowledgments
In situ peat fire depth data were collected in 2006 by CIMTROP of the University of Palangka Raya. Water extent in the peat dome of Block C during the 2006 peat fires that formed burn scars C1 and C2 was measured at 3 sites (2 in a burn scar from 2002 and 1 in an unburnt peat forest).
Pre-fire surface 3D modeling of tropical peatland burn scars based on airborne
As mentioned above, it is likely that the entire hydrological system of the peat dome was affected by the extensive drainage network that caused the overall subsidence of the peat dome. Also shown is the pre-fire peat area estimate (dashed-dotted line), modeled on the basis of the reference areas.
2 Materials and methods 2.1 Study area
Data
- Airborne LiDAR data and digital photos
- Landsat data
- MODIS hotspot, DGPS, water table, and rainfall data
To determine the length of individual fire seasons between 1997-2009, the MODIS 5 hotspot data collection was used to identify suitable image acquisition dates. To assess LiDAR accuracy, DTM points were measured with a Trimble 5700 Differential Global Positioning System (DGPS) device from May to August 2010.
Data analysis
- LiDAR data filtering and interpolation of DTMs
- Visual delineation of fire scars within the LiDAR tracks
- LiDAR based pre-fire peat surface modeling
- Peat loss calculation
- Relation of peat loss to burn frequency, water table measurements, and duration of dry season
- Object-oriented historical fire scar classification within the Kapuas district
- Estimation of peat volume loss and carbon emitted within the Kapuas district
For each of LiDAR tracks 5 to 9 (in LiDAR track 9 three virtual fire scars for validation purposes where delineated), a pre-burn peat surface was modeled based on unburned reference areas (Figure III-2). Initial profiles for the four LiDAR tracks based on the LiDAR-derived DTMs were assessed to evaluate the peat dome boundaries.
3 Results
- LiDAR derived DTMs
- Modeled pre-fire peat surfaces
- Peat loss
- Relation of peat loss to burn frequency, water table measurements, and duration of dry season
- Historical fire scar classification, estimation of the peat volume loss, and carbon emitted within the Kapuas district
In the areas where the heights of the peat surface were never burned before the fire and Heights of LiDAR-derived DTMs (gray lines) and modeled peat surface before fire (blue lines) are shown.
4 Discussion and conclusions
How important are the fuel loads on top of the peatland to the average peat loss. These results indicate that there is a relationship between the duration of the dry season and the average peat loss.
Acknowledgements
Additionally, only peat fires within previously undisturbed peat bog forest (78%, disturbed only by logging) or 10-year regenerated peat forest (16%) were analyzed. Furthermore, this method can be used as an input tool for future Reducing Emissions from Deforestation and Degradation in Developing Countries (REDD+) projects, which represent promising financial incentives to conserve remaining tropical peatland forests. .
ICESat/GLAS data as a measurement tool for peatland topography and peat swamp
Fire is particularly acute in Indonesia, where recurring fires release large amounts of carbon dioxide into the atmosphere (Page et al., 2002). On the other hand, systems controlled from aircraft have limitations due to large data volumes and high costs (Ranson et al., 2007).
2 Methodology 2.1 Study area
ICESat/GLAS data
The distance between the signal onset and the centroid of the ground return corresponds to the maximum canopy height and can be used as an estimate of AGB (Lefsky et al., 2005). A simplified overview of the different ICESat/GLAS heights and height measurements is given in Figure IV-2.
Airborne LiDAR data
The left side shows the location of the different ICESat/GLAS heights and the right side shows the varying ICESat/GLAS height statistics derived from them. For this study, 13,626 ha of LiDAR data were available, of which 9,702 ha of LiDAR transects were intersected by ICESat/GLAS data (Figure IV-1(B)).
SRTM data
MODIS data
Field inventory data
The following parameters were recorded for each tree: local species name, DBH in cm and tree height in m. AGB was calculated using an allometric model of Chave et al. 2005) for moist tropical forests that includes DBH and wood density, but not tree height.
Airborne LiDAR data processing and correlation with field inventory data
87 Figure IV-4: Overview of methodology for deriving aboveground biomass (AGB) values from field plots (left), development of AGB models with correlation of AGB from the field and airborne LiDAR 3D point cloud statistics (middle) and correlation of altimetry and ICESat altimetry /GLAS with LiDAR 3D point cloud statistics and AGB model development by correlating AGB results from airborne LiDAR AGB model with ICESat/GLAS height measurements (right). Canopy cover (CC), root mean square canopy height (QMCH) (Lefsky et al., 2002a) and the centroid of the LiDAR point cloud height histogram (CL) were determined as further possible predictors.
ICESat/GLAS data processing and analysis
Additionally, an ATMOSPHERE filter was established, defined by the i_FRir_qaFlag of the GLA14 data product, which indicates the presence of clouds. Using the GLA14 i_rng_UQF flag, the RANGE filter is defined, which indicates the quality of the range steps.
Comparison ICESat/GLAS and airborne LiDAR data
The filters VCF CHANGE 0% to VCF CHANGE 25% show the woody vegetation change in percentage between the years 2000 and 2003 defined by the MODIS VCF product. By visual comparison of the ICESat/GLAS footprints and Landsat images, the vegetation change between the acquisition of the ICESat/GLAS data and the acquisition of the airborne LiDAR data was assessed and the footprints were classified into 8 vegetation change classes (no change, forest – degraded forest, forest-deforested area, degraded forest-deforested area, degraded forest-forest, deforested area-forest, deforested area-degraded forest, water).
Development of above ground biomass prediction models from ICESat/GLAS data
Conceptual overview
Comparison ICESat/GLAS, SRTM data, and SRTM 3D peatland elevation models
Also evident is a variation in the forest canopy height of the peat bog forest related to different subtypes of peat bog forests (low polar, medium and high). The penetration depth of the SRTM C-band phase center in the forest canopy can be assessed.
Comparison ICESat/GLAS and airborne LiDAR data
97 Statistics of the normalized airborne LiDAR point clouds (z-values of the airborne LiDAR points minus the corresponding DTM values) were then compared to the ICESat/GLAS height measurements H1-H7 (Figure IV-2). The highest R2 values were found when correlating percentile 95 with the ICESat/GLAS height metrics, with the exception of H7 where 80%.
Above ground biomass predictions models from airborne LiDAR data and ICESat/GLAS data
The comparison of ICESat/GLAS heights with the average SRTM height showed a very high correlation of the waveform centroid (R² = 0.92). These results indicate that ICESat/GLAS data can be used to validate and improve SRTM-derived 3D peat height models.
Above ground biomass estimation across forest types at different degradation levels
Field inventory
For each tree selected by either the angle count or the nested plot method, the following parameters were recorded: Local species name, DBH in cm (at 1.3m above the ground), and tree height in m. Two models are proposed for moist forest, one of which includes tree height, DBH and wood density, the other includes DBH and wood density, but no tree height.
LiDAR data
- Acquisition and processing of airborne laser scanner data The airborne LiDAR data set was acquired in a flight campaign by Milan Geoservice
- Generation of multiple regression models: Plot level approach
- Application of the regression models
The height above the terrain (absolute vegetation heights) for each point in the cloud was determined by subtracting the corresponding pixel value from the DTM. All the above variables within the sampling area of the angle counting method were correlated to the corresponding estimated AGB values per ha.
Conceptual overview
Field inventory analysis
- Angle count versus nested plot method
- Comparison of forest types at different degradation levels In total 2,788 trees were measured during the two field inventories. The means of
When comparing unlogged lowland dipterocarp forest with unlogged peat swamp forest, all differences are significant, except the average number of stems per ha. The differences between the logged lowland dipterocarp forest and the logged peat swamp forest are all significant, except for the basal area.
LiDAR data analysis
- Multiple regression analysis: Plot level approach
- Application of the regression models
In lowland dipterocarp forest, the elevation range and peak position are clearly higher than in peat swamp forests. The results of multiple regression analysis of the corner count graphs are shown in Table V-3 and Figure V-5.
Deriving forest above ground biomass in Central Kalimantan (Indonesia) using
The regression models can be improved by using the LiDAR point densities as weights. Furthermore, the new approach presented here, by using CH and the LiDAR point densities as weights, has great potential to improve current estimates of carbon stocks in these highly inaccessible tropical rainforests.
1 Summary and conclusions
As an additional parameter to improve the robustness of the models, the LiDAR point density per square meter (pt/m2) at each plot was treated as a weight during the regression (see methods). In both cases, using the LiDAR point densities as a weight improved the regression models (9% and 8% for the CH and QMCH respectively).
2 Methods
- Field inventory
- Acquisition and processing of airborne laser scanner data
- Generation of the regression models
- Covariance propagation analysis
- Comparison between optical remote sensing and LiDAR approach for AGB estimation
Since point density directly affects the quality of the height histogram, it also affects the metrics derived from it (i.e. CH and QMCH). To avoid artifacts caused by filtering problems, 20 meters from the boundaries of the LiDAR track were excluded from processing.
Synthesis
1 Summary and main conclusions
Objective (1): Assess the potential and accuracy of airborne LiDAR data for measuring peat burn depth for single and multiple fire events. This result indicates that there is a relationship between the duration of the dry season and the average peat loss after fire.