Quantifying Dynamics in Tropical Peat Swamp Forest Biomass with Multi- Temporal LiDAR Datasets

Tropical peat swamp forests in Indonesia store huge amounts of carbon and are responsible for enormous carbon emissions every year due to forest degradation and deforestation. These forest areas are in the focus of REDD+ (reducing emissions from deforestation, forest degradation, and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks) projects, which require an accurate monitoring of their carbon stocks or aboveground biomass (AGB). Our study objective was to evaluate multi-temporal LiDAR measurements of a tropical forested peatland area in Central Kalimantan on Borneo. Canopy height and AGB dynamics were quantified with a special focus on unaffected, selective logged and burned forests. More than 11,000 ha were surveyed with airborne LiDAR in 2007 and 2011. In a first step, the comparability of these datasets was examined and canopy height models were created. Novel AGB regression models were developed on the basis of field inventory measurements and LiDAR derived height histograms for 2007 (r(2) = 0.77, n = 79) and 2011 (r(2) = 0.81, n = 53), taking the different point densities into account. Changes in peat swamp forests were identified by analyzing multispectral imagery. Unaffected forests accumulated on average 20 t/ha AGB with a canopy height increase of 2.3 m over the four year time period. Selective logged forests experienced an average AGB loss of 55 t/ha within 30 m and 42 t/ha within 50 m of detected logging trails, although the mean canopy height increased by 0.5 m and 1.0 m, respectively. Burned forests lost 92% of the initial biomass. These results demonstrate the great potential of repetitive airborne LiDAR surveys to precisely quantify even small scale AGB and canopy height dynamics in remote tropical forests, thereby featuring the needs of REDD+

Biomass estimation model for peat swamp forest ecosystem using light detection and ranging

Peat swamp forest plays a very important role in absorbing and storing large amounts of terrestrial carbon, both above ground and in the soil. There has been a lot of research on the estimation of the amount of biomass above the ground, but a little on peat swamp ecosystems using light detection and ranging (LiDAR) technology, especially in Indonesia. The purpose of this study is to build a biomass estimation model based on LiDAR data. This technology can obtain information about the structure and characteristics of any vegetation in detail and in real time. Data was obtained from the East Kotawaringin Regency, Central Kalimantan. Biomass field was generated from the available allometry, and Point cloud of LiDAR was extracted into canopy cover (CC), and data on tree height, using the FRCI and local maxima (LM) method, respectively. The CC and tree height data were then used as independent variables in building the regression model. The best-fitted model was obtained after the scoring and ranking of several regression forms such as linear, quadratic, power, exponential and logarithmic. This research concluded that the quadratic regression model, with R2 of 72.16 % and root mean square error (RMSE) of 0.0003% is the best-fitted estimation model (BK). Finally, the biomass value from the models was 244.510 tons/ha

Enhanced Systems for measuring and monitoring REDD+: opportunities to improve the accuracy of emission factor and activity data in Indonesia

The importance of accurate measurement of forest biomass in Indonesia has been growing ever since climate change mitigation schemes, particularly the reduction of emissions from deforestation and forest degradation scheme (known as REDD+), were constitutionally accepted by the government of Indonesia. The need for an accurate system of historical and actual forest monitoring has also become more pronounced, as such a system would afford a better understanding of the role of forests in climate change and allow for the quantification of the impact of activities implemented to reduce greenhouse gas emissions. The aim of this study was to enhance the accuracy of estimations of carbon stocks and to monitor emissions in tropical forests. The research encompassed various scales (from trees and stands to landscape-sized scales) and a wide range of aspects, from evaluation and development of allometric equations to exploration of the potential of existing forest inventory databases and evaluation of cutting-edge technology for non-destructive sampling and accurate forest biomass mapping over large areas. In this study, I explored whether accuracy—especially regarding the identification and reduction of bias—of forest aboveground biomass (AGB) estimates in Indonesia could be improved through (1) development and refinement of allometric equations for major forest types, (2) integration of existing large forest inventory datasets, (3) assessing nondestructive sampling techniques for tree AGB measurement, and (4) landscape-scale mapping of AGB and forest cover using lidar. This thesis provides essential foundations to improve the estimation of forest AGB at tree scale through development of new AGB equations for several major forest types in Indonesia. I successfully developed new allometric equations using large datasets from various forest types that enable us to estimate tree aboveground biomass for both forest type specific and generic equations. My models outperformed the existing local equations, with lower bias and higher precision of the AGB estimates. This study also highlights the potential advantages and challenges of using terrestrial lidar and the acoustic velocity tool for non-destructive sampling of tree biomass to enable more sample collection without the felling of trees. Further, I explored whether existing forest inventories and permanent sample plot datasets can be integrated into Indonesia’s existing carbon accounting system. My investigation of these existing datasets found that through quality assurance tests these datasets are essential to be integrated into national and provincial forest monitoring and carbon accounting systems. Integration of this information would eventually improve the accuracy of the estimates of forest carbon stocks, biomass growth, mortality and emission factors from deforestation and forest degradation. At landscape scale, this study demonstrates the capability of airborne lidar for forest monitoring and forest cover classification in tropical peat swamp ecosystems. The mapping application using airborne lidar showed a more accurate and precise classification of land and forest cover when compared with mapping using optical and active sensors. To reduce the cost of lidar acquisition, this study assessed the optimum lidar return density for forest monitoring. I found that the density of lidar return could be reduced to at least 1 return per 4 m2. Overall, this study provides essential scientific background to improve the accuracy of forest AGB estimates. Therefore, the described results and techniques should be integrated into the existing monitoring systems to assess emission reduction targets and the impact of REDD+ implementation

Quantifying the aboveground biomass stock changes associated with oil palm expansion on tropical peatlands using plot-based methods and L-band radar

The recent rapid expansion of oil palm (OP, Elaeis guineensis) plantations into tropical forest peatlands has resulted in net ecosystem carbon emissions. However, quantifications of the net carbon flux from biomass changes require accurate estimates of the above ground biomass (AGB) accumulation rate of OP on peat in working plantations. Current efforts that aim to reduce the emissions from OP expansion would also benefit from the development of economically viable remote sensing approaches that enable the detection of OP plantation expansion and monitoring of AGB stocks across at a fine spatial and temporal resolution. Here, destructive harvest and non-destructive plot inventories are conducted across a chronosequence of OP planting blocks (3 to 12 years after planting (YAP)) in plantations on drained peat in Sarawak, Malaysia. The effectiveness of using a timeseries of L-band synthetic aperture radar (SAR) scenes (ALOS PALSAR-1/2) and a novel ‘biomass matching’ approach to detect, quantify and map the AGB stock changes associated with OP establishment and growth was then assessed. Peat specific allometric equations for palm (9 palms, R2 = 0.92) and frond biomass are developed and upscaled to estimate AGB at the plantation block-level (902 palms). Aboveground biomass stocks on peat accumulated at ~6.39 ± 1.12 Mg ha-1 per year in the first 12 years after planting. However, high inter-palm and inter-block AGB variability was observed in mature classes as a result of variations in palm leaning and mortality. The ‘biomass matching’ approach detected statistically significant deforestation associated with OP establishment. OP growth was well estimated between 4 and 10 YAP, however sensitivity to increases in AGB was lost at ~ 45 - 60 Mg ha. Validation of the allometric equations defined and expansion of non-destructive inventories across alternative plantations and age classes on peat would further strengthen our understanding of OP AGB accumulation rates. With further investigation into the relationship between OP structural characteristics and L-band radar cross section (RCS) in the HV and HH polarisations, ‘biomass matching’ could be a feasible tool for monitoring AGB stock changes to inform carbon emission mitigation strategies

Tropical Peatland Vegetation Structure and Biomass: Optimal Exploitation of Airborne Laser Scanning

Accurate estimation of above ground biomass (AGB) is required to better understand the variability and dynamics of tropical peat swamp forest (PSF) ecosystem function and resilience to disturbance events. The objective of this work is to examine the relationship between tropical PSF AGB and small-footprint airborne Light Detection and Ranging (LiDAR) discrete return (DR) and full waveform (FW) derived metrics, with a view to establishing the optimal use of this technology in this environment. The study was undertaken in North Selangor peat swamp forest (NSPSF) reserve, Peninsular Malaysia. Plot-based multiple regression analysis was performed to established the strongest predictive models of PSF AGB using DR metrics (only), FW metrics (only), and a combination of DR and FW metrics. Overall, the results demonstrate that a Combination-model, coupling the benefits derived from both DR and FW metrics, had the best performance in modelling AGB for tropical PSF (R2 = 0.77, RMSE = 36.4, rRMSE = 10.8%); however, no statistical difference was found between the rRMSE of this model and the best models using only DR and FW metrics. We conclude that the optimal approach to using airborne LiDAR for the estimation of PSF AGB is to use LiDAR metrics that relate to the description of the mid-canopy. This should inform the use of remote sensing in this ecosystem and how innovation in LiDAR-based technology could be usefully deployed

Ecological impacts of deforestation and forest degradation in the peat swamp forests of northwestern Borneo

Tropical peatlands have some of the highest carbon densities of any ecosystem and are under enormous development pressure. This dissertation aimed to provide better estimates of the scales and trends of ecological impacts from tropical peatland deforestation and degradation across more than 7,000 hectares of both intact and disturbed peatlands in northwestern Borneo. We combined direct field sampling and airborne Light Detection And Ranging (LiDAR) data to empirically quantify forest structures and aboveground live biomass across a largely intact tropical peat dome. The observed biomass density of 217.7 ± 28.3 Mg C hectare-1 was very high, exceeding many other tropical rainforests. The canopy trees were ~65m in height, comprising 81% of the aboveground biomass. Stem density was observed to increase across the 4m elevational gradient from the dome margin to interior with decreasing stem height, crown area and crown roughness. We also developed and implemented a multi-temporal, Landsat resolution change detection algorithm for identify disturbance events and assessing forest trends in aseasonal tropical peatlands. The final map product achieved more than 92% user’s and producer’s accuracy, revealing that after more than 25 years of management and disturbances, only 40% of the area was intact forest. Using a chronosequence approach, with a space for time substitution, we then examined the temporal dynamics of peatlands and their recovery from disturbance. We observed widespread arrested succession in previously logged peatlands consistent with hydrological limits on regeneration and degraded peat quality following canopy removal. We showed that clear-cutting, selective logging and drainage could lead to different modes of regeneration and found that statistics of the Enhanced Vegetation Index and LiDAR height metrics could serve as indicators of harvesting intensity, impacts, and regeneration stage. Long-term, continuous monitoring of the hydrology and ecology of peatland can provide key insights regarding best management practices, restoration, and conservation priorities for this unique and rapidly disappearing ecosystem

Biomass Representation in Synthetic Aperture Radar Interferometry Data Sets

This work makes an attempt to explain the origin, features and potential applications of the elevation bias of the synthetic aperture radar interferometry (InSAR) datasets over areas covered by vegetation. The rapid development of radar-based remote sensing methods, such as synthetic aperture radar (SAR) and InSAR, has provided an alternative to the photogrammetry and LiDAR for determining the third dimension of topographic surfaces. The InSAR method has proved to be so effective and productive that it allowed, within eleven days of the space shuttle mission, for acquisition of data to develop a three-dimensional model of almost the entire land surface of our planet. This mission is known as the Shuttle Radar Topography Mission (SRTM). Scientists across the geosciences were able to access the great benefits of uniformity, high resolution and the most precise digital elevation model (DEM) of the Earth like never before for their a wide variety of scientific and practical inquiries. Unfortunately, InSAR elevations misrepresent the surface of the Earth in places where there is substantial vegetation cover. This is a systematic error of unknown, yet limited (by the vertical extension of vegetation) magnitude. Up to now, only a limited number of attempts to model this error source have been made. However, none offer a robust remedy, but rather partial or case-based solutions. More work in this area of research is needed as the number of airborne and space-based InSAR elevation models has been steadily increasing over the last few years, despite strong competition from LiDAR and optical methods. From another perspective, however, this elevation bias, termed here as the “biomass impenetrability”, creates a great opportunity to learn about the biomass. This may be achieved due to the fact that the impenetrability can be considered a collective response to a few factors originating in 3D space that encompass the outermost boundaries of vegetation. The biomass, presence in InSAR datasets or simply the biomass impenetrability, is the focus of this research. The report, presented in a sequence of sections, gradually introduces terminology, physical and mathematical fundamentals commonly used in describing the propagation of electromagnetic waves, including the Maxwell equations. The synthetic aperture radar (SAR) and InSAR as active remote sensing methods are summarised. In subsequent steps, the major InSAR data sources and data acquisition systems, past and present, are outlined. Various examples of the InSAR datasets, including the SRTM C- and X-band elevation products and INTERMAP Inc. IFSAR digital terrain/surface models (DTM/DSM), representing diverse test sites in the world are used to demonstrate the presence and/or magnitude of the biomass impenetrability in the context of different types of vegetation – usually forest. Also, results of investigations carried out by selected researchers on the elevation bias in InSAR datasets and their attempts at mathematical modelling are reviewed. In recent years, a few researchers have suggested that the magnitude of the biomass impenetrability is linked to gaps in the vegetation cover. Based on these hints, a mathematical model of the tree and the forest has been developed. Three types of gaps were identified; gaps in the landscape-scale forest areas (Type 1), e.g. forest fire scares and logging areas; a gap between three trees forming a triangle (Type 2), e.g. depending on the shape of tree crowns; and gaps within a tree itself (Type 3). Experiments have demonstrated that Type 1 gaps follow the power-law density distribution function. One of the most useful features of the power-law distributed phenomena is their scale-independent property. This property was also used to model Type 3 gaps (within the tree crown) by assuming that these gaps follow the same distribution as the Type 1 gaps. A hypothesis was formulated regarding the penetration depth of the radar waves within the canopy. It claims that the depth of penetration is simply related to the quantisation level of the radar backscattered signal. A higher level of bits per pixels allows for capturing weaker signals arriving from the lower levels of the tree crown. Assuming certain generic and simplified shapes of tree crowns including cone, paraboloid, sphere and spherical cap, it was possible to model analytically Type 2 gaps. The Monte Carlo simulation method was used to investigate relationships between the impenetrability and various configurations of a modelled forest. One of the most important findings is that impenetrability is largely explainable by the gaps between trees. A much less important role is played by the penetrability into the crown cover. Another important finding is that the impenetrability strongly correlates with the vegetation density. Using this feature, a method for vegetation density mapping called the mean maximum impenetrability (MMI) method is proposed. Unlike the traditional methods of forest inventories, the MMI method allows for a much more realistic inventory of vegetation cover, because it is able to capture an in situ or current situation on the ground, but not for areas that are nominally classified as a “forest-to-be”. The MMI method also allows for the mapping of landscape variation in the forest or vegetation density, which is a novel and exciting feature of the new 3D remote sensing (3DRS) technique. Besides the inventory-type applications, the MMI method can be used as a forest change detection method. For maximum effectiveness of the MMI method, an object-based change detection approach is preferred. A minimum requirement for the MMI method is a time-lapsed reference dataset in the form, for example, of an existing forest map of the area of interest, or a vegetation density map prepared using InSAR datasets. Preliminary tests aimed at finding a degree of correlation between the impenetrability and other types of passive and active remote sensing data sources, including TerraSAR-X, NDVI and PALSAR, proved that the method most sensitive to vegetation density was the Japanese PALSAR - L-band SAR system. Unfortunately, PALSAR backscattered signals become very noisy for impenetrability below 15 m. This means that PALSAR has severe limitations for low loadings of the biomass per unit area. The proposed applications of the InSAR data will remain indispensable wherever cloud cover obscures the sky in a persistent manner, which makes suitable optical data acquisition extremely time-consuming or nearly impossible. A limitation of the MMI method is due to the fact that the impenetrability is calculated using a reference DTM, which must be available beforehand. In many countries around the world, appropriate quality DTMs are still unavailable. A possible solution to this obstacle is to use a DEM that was derived using P-band InSAR elevations or LiDAR. It must be noted, however, that in many cases, two InSAR datasets separated by time of the same area are sufficient for forest change detection or similar applications