QUANTIFYING ABOVEGROUND BIOMASS OVER 50-HA TROPICAL FOREST DYNAMIC PLOT IN PASOH, MALAYSIA USING LIDAR AND CENSUS DATA
DOI:
https://doi.org/10.51200/bsj.v41i2.4448Keywords:
Center for Tropical Forest Science (CTFS), 50-ha dynamic plot, LiDAR, biomassAbstract
Airborne light detection and ranging (LiDAR) instruments have been widely used for quantification of forest biomass. This study investigated the relationships between LiDAR data and aboveground biomass (AGB). The study area is located at the 50-ha dynamic plot in a primary forest area of the Pasoh Forest Reserve, a lowland dipterocarp forest, a type of evergreen tropical moist forest. A number of variables have been produced from the LiDAR metrics. These variables were correlated with AGB that were derived from census data. The study found that the CHM and a few matrices are the best predictors for AGB and therefore used for the estimation of AGB in the entire study area. The estimated AGB ranged from 52 to 718 Mg ha-1, with a root mean square error (RMSE) of about 59 Mg ha-1. The study suggests that the AGB estimates produced by this study are the most accurate – with an accuracy of 83% based on the mean absolute percentage error (MAPE) – as compared to other remotely-sensed based estimates in the study area.
References
Ashton, P.S., Okuda, T. and Manokaran, N. (2003). Pasoh Research, Past and Present. In Pasoh: Ecology and natural history of a Southeast Asian lowland tropical rain forest. Okuda, T., Manokaran, N., Matsumoto, Y., Niiyama, K., Thomas, S.C., Ashton, P.S., Eds.; Springer: Tokyo, Japan. 1–13.
Chave, J., Rejou-Mechain, M., Burquez, A., Chidumayo, E., Colgan, M.S., Delitti, W.B.C., Duque, A., Eid, T., Fearnside, P.M., Goodman, R.C., Henry, M., Martinez-Yrizar, A., Mugasha, W.A., Muller-Landau, H.C., Mencuccini, M., Nelson, B.W., Ngomanda, A., Nogueira, E.M., Ortiz-Malavassi, E., Pelissier, R., Ploton, P., Ryan, C.M., Saldariagga, J.G. and Vielledent, G. (2014). Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biology, 20(10): 3177-3190.
Chen, Q., (2013). Lidar remote sensing of vegetation biomass. Remote Sensing of Natural Resources. 399–420.
Chirici, G., McRoberts, R.E., Fattorini, L., Mura, M. and Marchetti, M. (2016). Comparing echobased and canopy height model-based metrics for enhancing estimation of forest aboveground biomass in a model-assisted framework. Remote Sensing of Environment, 174: 1–9.
Condit, R. 1998. Tropical Forest Census Plots: Methods and Results from Barro Colorado Island, Panama and a comparison with other plots. Springer: University of Michigan, USA.
Dong, P. and Chen, Q. (2018). LiDAR Remote Sensing and Applications.
Taylor & Francis, Boca Raton, FL. Drake, J.B., Dubayah, R.O., Knox, R.G., Clark, D.B. and Blair, J.B. (2002). Sensitivity of largefootprint LiDAR to canopy structure and biomass in a neotropical rainforest. Remote
Sensing of Environment, 81: 378–392.
Frazer, G.W., Magnussen, S., Wulder, M.A. and Niemann, K.O. (2011 ). Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR derived estimates of forest stand biomass. Remote Sensing of Environment, 115: 636– 649.
Frolking, S., Palace, M.W., Clark, D.B., Chambers, J.Q., Shugart, H.H. and Hurtt, G.C. (2009). Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. Journal of Geophysical Research: Biogeosciences, 114: G00E02.
Hamdan, O., Muhamad Afizzul, M. and Abd Rahman, K. (2017). Synergetic of PALSAR-2 and Sentinel-1A SAR Polarimetry for Retrieving Aboveground Biomass in Dipterocarp Forest of Malaysia. Applied Sciences, 7(675).
Hamdan, O. and Muhamad Afizzul, M. (2018). Time series maps of aboveground biomass in dipterocarps forests of Malaysia from PALSAR and PALSAR-2 polarimetric data. Carbon Balance and Management, 13:19.
Hamdan Omar, Muhamad Afizzul M. and Y.T. Leong Houghton, R.A., Hall, F. and Goetz, S.J. (2009). Importance of biomass in the global carbon cycle. Journal of Geophysical Research, 114: G00E03.
Kochummen, K.M., LaFrankie, J.V. and Manokaran, N. (1990). Floristic Composition of Pasoh Forest Reserve a lowland rainforest in Peninsular Malaysia. Journal of Tropical Forest Science, 3: 1–13.
Lefsky, M.A., Cohen, W.B., Harding, D.J., Parker, G.G., Acker, S.A. and Gower, S.T. (2002). LiDAR remote sensing of aboveground biomass in three biomes. Global Ecology and Biogeography, 11: 393–399.
Lu, D., Chen, Q., Wang, G., Liu, L., Li, G. and Moran, E. (2016). A survey of remote sensing based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth, 9: 63–105.
Lu, D., Chen, Q., Wang, G., Moran, E., Batistella, M., Zhang, M., Vaglio Laurin, G. and Saah, D. (2012). Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates. International Journal of Forestry Research, Article ID 436537.
Magnussen, S., Næsset, E., Kändler, G., Adler, P., Renaud, J.P. and Gobakken, T. (2016). A functional regression model for inventories supported by aerial laser scanner data or photogrammetric point clouds. Remote Sensing of Environment, 184: 496–505.
Mallet, C. and Bretar, F. (2009). Full-waveform topographic LiDAR: State-of-the-art. ISPRS Journal of Photogrammetry and Remote Sensing, 64: 1–16.
Manokaran, N. and LaFrankie, J.V. (1990). Stand structure of Pasoh Forest Reserve, a lowland rainforest in Peninsular Malaysia. Journal of Tropical Forest Science, 3: 14–24.
Manokaran, N., LaFrankie, J.V., Kochummen, K.M., Quah, E.S., Klahn, J., Ashton, P.S. and Hubbell, S.P. (1990). Methodology for 50-ha research plot at Pasoh Forest Reserve. FRIM Research Pamphlet, No. 104. FRIM: Kepong, Malaysia.
Manokaran, N., Quah, E.S., Ashton, P.S., LaFrankie, J.V., Nur Supardi, M.N., Wan Shukri, W.A. and Okuda, T. (2003). Pasoh Forest Dynamic Plot, Peninsular Malaysia. In Tropical Forest Diversity and Dynamism, findings from a Large-Scale Plot Network. (Eds. Losos, E.C. and Leigh, Jr. E.G.). The University of Chicago Press: Chicago, USA.
McGaughey, R. (2009). FUSION/LDV: Software for LiDAR Data Analysis and Visualization. US Department of Agriculture, Forest Service, Pacific Nortwest Research Station.
Reutebuch, S.E., McGaughey, R.J. and Strunk, J.L. (2010). Sherman Pass LIDAR Forest Inventory Project. United States Department of Agriculture, Forest Service. Pacific Northwest Research Station. 80.
Roussel, J.R., Caspersen, J., Béland, M., Thomas, S. and Achim, A. (2017). Removing bias from LiDAR-based estimates of canopy height: Accounting for the effects of pulse density and footprint size. Remote Sensing of Environment, 198: 1–16.
Wan Shafrina, W.M.J., Woodhouse, I.H., Silva, C.A., Omar, H. and Hudak, A.T. (2017). Modelling Individual Tree Aboveground Biomass Using Discrete Return LiDAR in Lowland Dipterocarp Forest of Malaysia. Journal of Tropical Forest Science 29(4): 465– 484.
Wan Shafrina, W.M.J., Woodhouse, I.H., Silva, C.A., Omar, H., Khairul Nizam, A.M., Hudak, A.T., Klauberg, C., Cardil, A. and Mohan, M. (2018). Improving Individual Tree Crown Delineation and Attributes Estimation of Tropical Forests Using Airborne LiDAR Data. Forests. 9(759).
Wyatt-Smith, J. (1987). Manual of Malayan silviculture for inland forest, Part 3-Chapter 8. Red meranti-keruing forest. FRIM Research Pamphlet No. 101; FRIM: Kepong, Malaysia.
Zolkos, S.G., Goetz, S.J. and Dubayah, R. (2013). A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. Remote Sensing of Environment, 128: 289–298.