AIRBORNE LIDAR FOR ESTIMATING ABOVEGROUND BIOMASS IN DIPTEROCARP FORESTS OF MALAYSIA

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1 AIRBORNE LIDAR FOR ESTIMATING ABOVEGROUND BIOMASS IN DIPTEROCARP FORESTS OF MALAYSIA Hamdan O. 1, Nor Azura O. 2, Nurul Dasani A.D. 2, Mohd Azahari F. 1 and Abd Rahman K. 1 1 Forest Research Institute Malaysia (FRIM), Kepong, Selangor, Malaysia 2 TNB Research Sdn. Bhd., Kajang, Selangor, Malaysia hamdanomar@frim.gov.my KEY WORDS: LiDAR, dipterocarp forest, biomass, Malaysia ABSTRACT: Dipterocarp forest is an inland forest ecosystem that reside on land with elevation <1200 m is rich with numerous plant species and timber trees. Amount of biomass in this forest is typically high as compared to other ecosystems in the tropics. Given their prominent role in global biogeochemistry, and the likelihood that these high productivity areas will be prime areas for carbon sequestration efforts, better characterization of high biomass forests using remotely sensed data is desirable. However, direct estimation of carbon storage in high biomass forests is difficult for common space borne optical and radar sensors. Airborne light detection and ranging (LiDAR) instruments measure the vertical structure of forests and thus provide alternative for remotely sensed quantification of forest biomass. In this study, the relationships between LiDAR information and aboveground biomass (AGB) measured on the field were investigated. The study covered an area of about 12,000 ha of lowland and hill dipterocarp forests in Terengganu, Peninsular Malaysia. Eight variables were produced from the LiDAR point-cloud namely intensities from all returns, first return, second return, third return, last return, canopy density, digital surface model and canopy height model (CHM). These parameters were converted into raster format with pixel resolution of 1 m. The study found that the CHM is the best predictor variable for AGB and was therefore used for the estimation of AGB in the entire study area. The estimated AGB ranged from 55 to 497 Mg ha -1, which amounted about 2.5 million Mg of AGB for the entire study area with an average of about 206 Mg ha -1 and accuracy at about ±31 Mg ha INTRODUCTION Aboveground biomass (AGB) of forests is one of the key parameters for carbon accounting. It also plays crucial roles in mitigating effects of climate change and control the global carbon balance (Houghton, 2005). Deforestation and forest degradation have been identified as major contributors to global climate change producing nearly 20% of global anthropogenic greenhouse gas emissions (IPCC, 2007). Due to the high carbon content of vegetation biomass, accurate quantification and monitoring of forest biomass is necessary for evaluation of schemes aiming to reduce carbon emissions from changes in forest areas. Malaysia is one of the highest percentages of forested land among tropical countries with 19.5 million hectare of its land covered with natural forest. Out of this, about million hectare is reserved for production purposes. It means that more than 50% of the forest cover is managed principally for harvesting. It was estimated that more than 40% of the world s tropical forest cover is in secondary state where logging operations have been taking place (Brown and Lugo, 1990). In Malaysia, the timber extraction operations are mainly concentrating in lowland and hill dipterocarps forests. Consequently, the timber and timber products industry are very important and play a significant role in Malaysia s economy. As such, Malaysia has accorded the management of the forests on a sustainable basis a high priority. However, there are concerns that the assumptions for the implementation of selective management system (SMS) are not being met consistently and thus affecting the productivity of the residual stands. Consequently instead of deforestations that occur mainly from land conversion, which is more permanent in terms of land usage, forest degradation is also one of the great concerns as it also reduces the living biomass. Methods are therefore required to quantify and monitor the reduction of biomass resulted from these processes. Remote sensing has been recognized as one of the primary spatial inputs for quantifying and mapping of forest biomass (Goetz et al. 2009). However, estimating aboveground biomass (AGB) by using remote sensing approach has been challenging as it constrained by various limitations due to the limited data resource, accessibility, and numerous technical issues. Remote sensing has been used actively for forest biomass estimation since the last three decades and it is proven to be effective. Optical or synthetic aperture radar (SAR) system has its own potential in retrieving biomass but several issues remain unaddressed. While optical remote sensing is often hindered by cloud, SAR systems are always limited by signal saturation at high biomass levels (Hamdan et al. 2014). On the others hands, light detection and ranging (LiDAR) system was demonstrated to hold better promises than optical and SAR systems.

2 In Malaysia there are limited studies published on the applications of LiDAR data for estimating biomass. Out of many studies conducted worldwide (i.e. Drake et al. 2003; He et al. 2013; Gregory et al. 2014; Hovi and Korpela, 2014; Brovkina et al. 2015), very few have been done in Malaysia (i.e. Mohd Hasmadi and Manaf, 2011). This indicates that the potential, limitations and advances of LiDAR in estimating AGB in tropical forests in Malaysia are not explored extensively. Methods for applying this data are also scarcely exploited. This study is therefore conducted to explore the potential uses and focus on the roles of airborne LiDAR remote sensing in assessing biomass of forest in lowland and hill dipterocarp forests ecosystems. The objectives of this study are; (i) to develop empirical model for estimating AGB in dipterocarp forests and (ii) to identify LiDAR variables that have the best correlation with AGB. 2. MATERIALS AND METHODS 2.1 The Study Area The study area (Figure 1) comprises lowland (within elevation of <300 m) and hill dipterocarp (within elevation of m) forests, occupying about 12,000 ha, located inside the Tembat Forest Reserve. These forests embrace all the well-drained primary forests of the plains, undulating land, and foothills and hilly terrain up to about 900 m altitude. The area has a typical tropical monsoon climate with uniformly high temperatures (from 24.2 o C to 29.9 o C), high humidity (from 70% to 98%) and a relatively high rainfall of up to in excess of 4,000 mm per year. The topography in the study area was heterogeneous, comprises flat river plains and swamps, undulating hills and mountainous areas, especially in the western areas. It is dominated by hill dipterocarp forest, which is mainly comprises species from Dipterocarpaceae family such as Shorea curtisii, Anisoptera curtisii and Vatica lowii. There is a small extent of lowland dipterocarp forest, with common species of Shorea spp., Dipertocapus spp., Dryobalanops aromatica and Neobalanocapus heimeii. Other than these, genera/species that show some degrees of abundance and dominance include: Syzigium spp. Dillenia spp. Lauraceae, Mangifera spp. (rengas), Melanochylla spp., Pometia spp., Elateriospermum tapos, Myristicaceae, Palaquium spp. and Xanthophyllum ecarinatum. There are also areas that have been dedicated for hydro-electric dam construction and most of the trees in this area were harvested and has been inundated in late Figure 1. Location of the study area

3 2.2 LiDAR Data Acquisition The acquisition LiDAR data was carried out by using airborne system. The sensor was mounted on fixed wing aircraft and the LiDAR observation was done in a number of flights to cover the study area. The sensor acquired LiDAR data in discrete form, which produced point-cloud information from four laser echoes or returns. Table 1 summarizes the specifications of the airborne LiDAR data acquired. Table 1. Summary of LiDAR data No. System Sensor Date of acquisition 1 Airborne Laser Scanning (ALS) ALTM Gemini September Ground Data Collection Ground data collection activities have been conducted between July 2014 and February A total of 60 sample plots have been surveyed within the period. Circular plots measuring 10 m in radius have been established in each study area. In each plot, every tree measuring 10 cm and above of diameter at breast height (dbh) were inventoried. Species of every stand were also recorded. Stratified random sampling approach was applied to estimate total biomass in the study area. Given the circular plot of 10 m in radius, the size will return for an approximately m 2 (or ha). The total AGB (in Mg) for each sample plot is divided with to obtain a measurement of Mg ha -1. Allometric function that was used in the estimation of AGB was adopted from Chave et al. (2014). Some improvements have been made to this function from the previous allometry proposed by Chave et al. (2005). The bioclimatic variable which takes into account factors of temperature seasonality, climatic water deficit, and precipitation seasonality were included in this function. The tree allometry can be expressed as AGB = [exp ( E+0.976ln(ρ) ln(d) [ln(d)] 2 ] (1) Where AGB E ρ D = Aboveground biomass (kg/tree) = bioclimatic variable (average value of -0.1 for the study area) = wood specific gravity/ wood density = dbh 2.4 LiDAR Data Processing Generally LiDAR signal is produced in point-cloud form (typically in *.LAS format). Each point in the point-cloud contains two major types of information, which are signal intensity (i.e. strength of return) and height of the object. From these information, several parameters can be produced and manipulated. In this study, eight (8) LiDAR variables were produced which are intensities from all returns, first return, second return, third return, last return, digital surface model (DSM), digital terrain model (DTM), canopy density (CD), and canopy height model (CHM). These parameter were correlated to the biomass acquired from sample plots information. An overall processes for LiDAR processing is depicted in Figure 2. Digital surface model is the first reflective surface model that contains elevations of natural terrain features in addition to vegetation and cultural features such as buildings and roads. Digital terrain model is a bare-earth model that contains elevations of natural terrain features such as bare land and river valleys (Figure 3). In DTM, elevations of vegetation and cultural features, such as buildings and roads, were removed. The DTM was generated by triangulating elevation values only from the bare earth LiDAR point cloud data, while the DSM was generated by triangulating elevation only from the first return LiDAR point cloud data. The first essential step in many LiDAR based applications is conversion of point clouds to uniform raster surfaces. Surfacing was used to interpolate the ground points and generate the DTM. The intensity image was generated by triangulating intensity from the first-return LiDAR points. Most of LiDAR processing software will have a graphical interface that will render the numerical point cloud into an image, but there is a great range of options and functionalities that will vary among the different options. In some of the cases, bare earth DTM does not represent true ground elevation. Hence, the model needs to clean and edit to obtain a representable DTM. Canopy height model is calculated from the LiDAR data by subtracting the DTM from the DSM. The CHM represents the absolute height of all above-ground features relative to the ground.

4 Figure 2. LiDAR point cloud data processing workflow for CHM extraction Digital surface model (DSM) Digital terrain model (DTM) Canopy height (m) Mean sea level (MSL) Figure 3. Example of cross-section through LiDAR point cloud 2.5 Correlation Analysis There are two methodological approaches for AGB assessment by utilizing LiDAR data. The (i) single-tree-based approach and the (ii) area-based approach. Both approaches mainly involve the use of empirical or semi-empirical models by using linear or non-linear regression analysis. An experiment has been conducted to test the strength of LiDAR signal towards AGB in the study areas. Regression analysis was carried out by correlating AGB against the parameters that have been derived from LiDAR point-cloud. Eight variables that have been derived from LiDAR signal were used in the initial analysis namely all return, first return, second return, third return, last return, CD, DSM, DTM and CHM. Further analysis has been conducted to determine the best spatial resolution for the estimation of biomass by using LiDAR parameters, in this case CHM. Pixel resolution of CHM image, which was originally 1 m has been degraded to 5, 10, 25, 50 and 100 m. Regression analysis was then carried out between AGB and the CHM at different resolutions. 2.6 Validation In order to validate the results, an absolute accuracy a measure of the error between a predicted biomass from satellite image and the actual biomass measured on the ground was calculated to validate the models. To produce a prediction with good absolute accuracy, reliable ground control can be used to reduce biases. Absolute accuracy is

5 expressed as the vertical root mean square error (RMSE) of the vertical error measured at geographic coordinates given by where n = the number of check plots c i = measured TC at check plot i c i = derived/predicted TC at position i μ = average of TC difference n 1 ' 2 RMSE (ci c i ) μ (2) n i 1 For this particular purpose, nine (9) independent sample plots have been set aside. These sample plots were not used in regression analysis for empirical models development. The sample plots were selected based on AGB values and each represents low, medium and high AGB and distributed well in the study areas. 3. RESULTS AND DISCUSSION 3.1 Sample Plots Data The values of AGB that have been obtained from the sample plots are ranging from to Mg ha -1. It implies that the distribution of the sample plots is representative, which cover a wide range of AGB densities in the study area. Figure 4 shows the breakdown of AGB densities of all sample plots. 3.2 LiDAR Image Variables Figure 4. Summary of the AGB measured at sample plots The most important LiDAR variable that was used in this study is the CHM since it has relation with the biometric properties of a forest (Hyyppä et al. 2008). Figure 4 shows a sample of CHM that was derived from the point cloud data. The pixel resolution was prepared at 1 meter resolution and individual canopy of trees are visible clearly on the CHM images. 3.3 Correlation Analysis and Modelling Correlations between AGB from all sample plots and all LiDAR variables have been tested with linear correlations and the results is summarized in matrix form. The strength of each correlation is measured by using coefficient of determination (R 2 ) as summarized in Table 2. The initial analysis indicated that all variables can be used to produce empirical model. The same scatterplots were used and again tested with 2 nd order polynomial regression form. It was found that the correlation between AGB and CHM showed the strongest relationship as compared to the other variables. Table 3 summarizes the empirical functions that were produced from the correlations and Figure 6 shows the scatterplots of the correlations.

6 Table 2. Coefficient of determination (R 2 ) of linear correlations between LiDAR variables and the measured AGB Variable AGB All First Second Third Forth CD CHM DSM returns return return return return AGB All returns First return Second return Third return Forth return CD CHM DSM Note: AGB = aboveground biomass; CD = canopy density; CHM = canopy height model; DSM = digital surface model Table 3. Empirical functions produced from correlations between AGB and LiDAR variables LiDAR variable Equation R 2 All returns y = 0.072x x First return y = 0.017x x Second return y = x x Third return y = x x Forth return y = x x CD y = 1317x x DSM y = 0.002x x CHM y = 0.456x x Digital surface model (DSM) Digital Terrain Model (DTM) Canopy height model (CHM) Figure 5. Extraction of CHM, DSM and DTM from point cloud LiDAR data Another experiment confirmed that the values of R 2 that were produced from correlation between AGB and CHM (with 1-m resolution) and CHM (with 10-m resolution) produced almost the same results. This indicates that the pixel degradation process (i.e. from 1 m to 10 m) did not affect the accuracy of the estimation. Therefore this is considered to be the optimum spatial resolution when generating AGB maps for the investigated study area. Among the benefits of reducing the resolution of CHM are, it can reduce the storage space of the data and thus fasten the processing time for modelling and final estimation processes. From this information, regression analysis was then performed again by using CHM with 10-m resolution, but with different mathematical functions. The best-fit equations and the results are summarized in Table 4. It was found that exponential form of expression produced the highest correlation as compared to the other expressions. The equation with the highest R 2 was then used to estimate AGB for the entire study area.

7 Figure 6. Scatterplots of correlations between AGB and LiDAR variables

8 Table 4. Empirical functions produced from correlations between AGB and CHM at 10-m resolution Correlation types Equation R 2 Linear y = 17.73x Exponential y = 63.09e 0.081x Polynomial y = 0.221x x Logarithmic y = 185.1ln(x) Power y = 19.15x Estimation of Aboveground Biomass The estimation of total AGB have been carried out by using the model developed from the CHM. The results indicated that the total AGB in the study area was at 2,489,973 Mg with an average of Mg ha -1. The AGB was ranging from to Mg ha -1. Pattern of distribution of AGB is shown in Figure 7. It also shows the composition of AGB in the entire study area, which is divided into intervals, which are; < 100 Mg ha -1, Mg ha -1, Mg ha -1, and > 300 Mg ha -1. Although the frequency of occurrence is relatively low, the majority (36%) of the study area is occupied by high density forest, which has the AGB density of > 300 Mg ha -1. A spatially distributed map of AGB in the entire study area is depicted in Figure 8. It is notable the low lying areas along the rivers in the south parts are covered by relatively low AGB. It was because the forests were cleared for a dam constructions and the areas has been inundated since October Figure 7. Pattern of distribution of AGB in the entire study area 3.5 Accuracy of Estimation Figure 9 shows the propagation of errors produced from the estimation. The scatterplots is produced from the RMSE of AGB calculated from the validation plots. The red line indicates perfect agreement between predicted and observed AGB and the bold black line represent the amount of errors resulted from the estimation. It indicates that the estimation that has been produced from the LiDAR data has underestimated the AGB at average of about 11.3 Mg ha -1. The RMSE shows that the estimation has an uncertainty of ±30.88 Mg ha -1. This confirms that the LiDAR data is viable and reliable for the estimation of AGB in dipterocarp forests of Malaysia.

9 Figure 8. Spatial distribution of AGB in the study area Figure 9. Scatterplot of error propagation obtained from the prediction AGB map

10 4. CONCLUSION The study demonstrated that the use of LiDAR data for AGB estimation in dipterocarp forests of Malaysia is viable. Results indicated that the estimation was reliable at ±30.88 Mg ha -1. Canopy height model was found to be the best predictor for AGB. Therefore the empirical function that has been derived from the CHM of LiDAR data was used to estimate the AGB in the entire study area. The estimated AGB ranged from 55 to 497 Mg ha -1, which amounted about 2.5 million Mg of AGB in the entire study area with an average of about 206 Mg ha -1 and accuracy at about ±31 Mg ha -1. The effects of varying pixel sizes were also investigated to find the optimum spatial resolution of the CHM-based AGB retrieval. The results show that the CHM at 10-m resolution is the optimum spatial resolution for the estimation of AGB. In summary, the study research shows that there is potential for improvement of AGB assessment through airborne LiDAR data. LiDAR data is also important when an accurate AGB estimation is required. The method presented can replicated to other sites that have similar characteristics with lowland and hill dipterocarp forests. The AGB estimation based on LiDAR data could serve as an alternative or complement approach to existing inventory data. REFERENCES Brovkina, O., Zemek, F. & Fabiánek, T Aboveground biomass estimation with airborne hyperspectral and LiDAR data in Tesinske Beskydy Mountains. Beskydy, 8(1), pp Brown, S. & Lugo, A.E Tropical secondary forests. Journal of Tropical Ecology, 6 (1), pp Chave J. & co-authors Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biology, pp Chave, J., Andalo, C., Brown, S., Cairns, M.A., Chambers, J.Q., Eamus, D., Fölster, H., Fromard, F., Higuchi, N., Kira, T., Lescure, J.P., Nelson, B.W., Ogawa, H., Puig, H., Riéra, B., & Yamakura, T Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 145(1), pp Drake, J.B. and co-authors Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships. Global Ecology and Biogeography, 12, Gregory, P., Asner, G.P. & Mascaro, J Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric. Remote Sensing of Environment, 140, pp Hamdan, O, Mohd Hasmadi, I & Ismail, H Advance and challenge of remote sensing in assessing tropical forest biomass of Malaysia. International Forestry Review, 16(5), pp 217. Hasmadi, M.I. & Manaf M.S The Potential of LiDAR Application in Malaysia. International Journal of Remote Sensing Applications, 1(1), pp 1-5. He, Q., Chen, E., An, R. & Li, Y Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest. Forests, 4, pp Houghton, R.A Tropical deforestation as a source of greenhouse gas emissions. In Mutinho & Schwartzman (Ed.) Tropical deforestation and climate change, Instituto de Pesquisa Ambiental Amazônia (IPAM), Belém, Brazil. Hovi, A. & Korpela, I Real and simulated waveform-recording LiDAR data in juvenile boreal forest vegetation. Remote Sensing of Environment, 140, pp Hyyppä, J., Hyyppä, H., Leckie, D., Gougeon, F., Yu, X. & Maltamo, M., Review of methods small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. International Journal of Remote Sensing, 29(5), pp IPCC Climate Change 2007: Synthesis Report. Contribution of Working Group I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. In Pachauri R.K. et al. (Ed.) IPCC, Geneva.