ABOVEGROUND BIOMASS AND CARBON STOCK ESTIMATION USING DOUBLE SAMPLING APPROACH AND REMOTELY-SENSED DATA

Authors

  • Nurul Ain Mohd Zaki Applied Remote Sensing & Geospatial Research Group (ARSG), Centre of Studies for Surveying Science and Geomatics, Faculty of Architecture ,Planning and Surveying, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Zulkiflee Abd Latif Applied Remote Sensing & Geospatial Research Group (ARSG), Centre of Studies for Surveying Science and Geomatics, Faculty of Architecture ,Planning and Surveying, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Mohd Zainee Zainal Centre of Studies for Surveying Science and Geomatics, Faculty of Architecture ,Planning and Surveying, Universiti Teknologi MARA, 02600 Arau, Perlis, Malaysia

DOI:

https://doi.org/10.11113/jt.v78.8551

Keywords:

Tropical Rain Forest, Double sampling approach, LiDAR, Aboveground Biomass, Carbon

Abstract

Tropical forest embraces a large stock of carbon and contributes to the enormous amount of aboveground biomass (AGB) in the global carbon cycle. In order to quantify the carbon inventory, field data is vital for accurately determining the forest parameter such as diameter at the breast height (DBH), height  of the tree (h) ,crown diameter (CD) and tree species. The merging of the multi-sensory remote sensing which is LiDAR (Light Detection and Ranging) and very high resolution satellite imagery can reduce the labor intensive of field sampling for a large area of carbon inventory data. Double sampling approach which is combination of the field sampling plot measurement with ancillary remote sensing data used to improve the precision of AGB estimation compared by using field data alone. Hence, this study aims: (1) to describe the use of field data plots in a statistical way, and (2) to determine the potential of LiDAR data in a double sampling forest aboveground biomass and carbon stock inventories and (3) to compare the used of field data plot itself or combination with LiDAR data to quantify the aboveground biomass and carbon stock for upcoming inventories.

References

Clark, M. L., Roberts, D. a., Ewel, J. J., and Clark, D. B. Estimation Of Tropical Rain Forest Aboveground Biomass With Small-Footprint Lidar And Hyperspectral Sensors. Remote Sens. Environ. 115(11): 2931-2942.

Asner, G. P. and Mascaro, J. 2014. Mapping Tropical Forest Carbon: Calibrating Plot Estimates To A Simple Lidar Metric. Remote Sens. Environ. 140: 614-624.

Fayolle, A., Doucet, J.-L., Gillet, J.-F., Bourland, N. and Lejeune, P. 2013. Tree allometry in Central Africa: Testing The Validity Of Pantropical Multi-Species Allometric Equations For Estimating Biomass And Carbon Stocks. For. Ecol. Manage. 305: 29-37.

Chave, J., Réjou-Méchain, M., Búrquez, A., Chidumayo, E., Colgan, M. S., Delitti, W. B. Duque, A., Eid,T., Fearnside, P., Goodman, M. R. C., Henry, M., Martínez-Yrízar, A., Mugasha, W. a, Muller-Landau, H. C., Mencuccini, M., Nelson, B. W., Ngomanda, A., Nogueira, E. M., Ortiz-Malavassi, E., Pélissier, R., Ploton, P., Ryan,C. M., Saldarriaga, J. G., and Vieilledent, G. 2014. Improved Allometric Models To Estimate The Aboveground Biomass Of Tropical Trees. Glob. Chang. Biol.

Baccini, A. and Asner, G. P. 2013. Improving Pantropical Forest Carbon Maps With Airborne Lidar Sampling. Carbon Manag. 4(6): 591-600.

Basuki, T. M., van Laake, P. E., Skidmore, A. K. and Hussin, Y. a., 2009. Allometric Equations For Estimating The Above-Ground Biomass In Tropical Lowland Dipterocarp Forests. For. Ecol. Manage. 2578: 1684-1694.

Malhi, Y., Aragão, L. E. O. C., Metcalfe, D. B., Paiva, R. Quesada, C. a., Almeida, S., Anderson, L., Brando, P., Chambers, J. Q., da COSTA A. C. L., Hutyra,L. R., Oliveira, P., Patiño, S., Pyle, E., Robertson, H. A. L. and Teixeira, L. M. 2009. Comprehensive Assessment Of Carbon Productivity, Allocation And Storage In Three Amazonian Forests. Glob. Chang. Biol. 15(5): 1255-1274.

Gonzalez, P., Asner, G. P., Battles, J. J., Lefsky, M. a., Waring, K. M. and Palace, M. 2010. Forest Carbon Densities And Uncertainties From Lidar, Quickbird, And Field Measurements In California. Remote Sens. Environ. 114(7): 1561-1575.

Carreiras, J. M. B., Vasconcelos, M. J. and Lucas, R. M. 2012. Understanding The Relationship Between Aboveground Biomass And ALOS PALSAR Data In The Forests Of Guinea-Bissau (West Africa). Remote Sens. Environ. 121: 426-442.

Alves, L. F., Vieira, S. a., Scaranello, M. a., Camargo, P. B., Santos, F. a M., Joly, C. a. and Martinelli, L. a. 2010. Forest Structure And Live Aboveground Biomass Variation Along An Elevational Gradient Of Tropical Atlantic Moist Forest (Brazil). For. Ecol. Manage. 260(5): 679-691.

Mohd Zaki, N. A., Abdul Latif, Z., Zainal, M. Z. and Zainuddin, K. 2015. Individual Tree Crown (ITC) Delineation Using Watershed Transformation Algorithm For Tropical Lowland Dipterocarp, Proc. IEEE 2015. 2: 237-242.

Abd Latif, Z., Aman, S. N. A. and Ghazali, R. Delineation Of Tree Crown And Canopy Height Using Airborne Lidar And Aerial Photo. 2011 IEEE 7th Int. Colloq. Signal Process. its Appl. 2: 354-358.

Latif, Z. A., Zamri, I. and Omar, H. 2012. Determination Of Tree Species Using Worldview-2 Data. 2012 IEEE 8th Int. Colloq. Signal Process. its Appl. 383-387.

Blackburn, G. A., Abd Latif, Z. and Boyd, D. S. 2014. Forest Disturbance And Regeneration: A Mosaic Of Discrete Gap Dynamics And Open Matrix Regimes? J. Veg. Sci. 25(6): 1341-1354.

Stephens, P. R., Kimberley, M. O., Beets, P. N., Paul, T. S., Searles, H. N., Bell, A., Brack, C. and Broadley, J. 2012. Airborne scanning Lidar In A Double Sampling Forest Carbon Inventory. Remote Sens. Environ. 117: 348-357.

Mascaro, J., Detto, M., Asner, G. P., and Muller-Landau, H. C. 2011. 2011. Evaluating Uncertainty In Mapping Forest Carbon With Airborne LiDAR. Remote Sens. Environ. 115(12): 3770-3774.

Mitchard, E. T. a, Feldpausch, T. R., Brienen, R. J. W., Lopez-Gonzalez, G., Monteagudo, A., Baker, T. R. 2014. Markedly Divergent Estimates Of Amazon Forest Carbon Density From Ground Plots And Satellites. Glob. Ecol. Biogeogr. 23(8): 935-946.

Saeidi, V., Pradhan, B., Idrees, M. O. and Abd Latif, Z. 2014. Fusion of Airborne LiDAR With Multispectral SPOT 5 Image for Enhancement of Feature Extraction Using Dempster-Shafer Theory, Geosci. Remote Sensing, IEEE Trans. 99:1-9.

Fernow, B. E. 1906. Forest Mensuration. 24(624).

Chave, J., Andalo, C., Brown, S., Cairns, M. a, Chambers, J., Eamus, Q. D., Fölster, H., Fromard, F., Higuchi, N., Kira, T., Lescure, J.-P., Nelson, B. W., Ogawa, H., Puig, H., Riéra, B. and Yamakura, T. 2005. Tree Allometry And Improved Estimation Of Carbon Stocks And Balance In Tropical Forests. Oecologia. 145(1): 87-99.

Kenzo, T., Furutani, R., and Hattori, D. 2009. Allometric Equations For Accurate Estimation Of Above-Ground Biomass In Logged-Over Tropical Rainforests In Sarawak, Malaysia. J Res. 14365-372: 365-372.

IPCC. 2006. Guielines for National Greenhouse Gas Inventories Volume 4 Agriculture,Forestry and other Land Use. 4.

Ketterings, Q. M., Coe, R., van Noordwijk, M., Ambagau, Y. and Palm, C. a. 2001. Reducing Uncertainty In The Use Of Allometric Biomass Equations For Predicting Above-Ground Tree Biomass In Mixed Secondary Forests. For. Ecol. Manage. 146(1-3): 199-209.

Kato, R., Tadaki, Y., and Ogawa, H. 1978. Plant Biomass And Growth Increment Studies In Pasoh Forest. Malayan Nature Journal. 30(2): 211-224.

Gitelson, A. a., Peng, Y., Arkebauer, T. J., and Schepers, J. 2014. Relationships Between Gross Primary Production, Green LAI, And Canopy Chlorophyll Content In Maize: Implications For Remote Sensing Of Primary Production. Remote Sens. Environ. 144: 65-72.

Asner, G. P. and Mascaro, J. 2014. Remote Sensing of Environment Mapping tropical forest carbon : Calibrating plot estimates to a simple LiDAR metric. Remote Sens. Environ. 140: 614-624.

Claverie, M., Demarez, V., Duchemin, B., Hagolle, O., Ducrot, D., Marais-Sicre, C., Dejoux, J.-F., Huc, M., Keravec P., Béziat, P., Fieuzal, R., Ceschia, E., and Dedieu, G. 2012. Maize And Sunflower Biomass Estimation In Southwest France Using High Spatial And Temporal Resolution Remote Sensing Data. Remote Sens. Environ. 124: 844-857.

T. Ahamed, Tian, L., Zhang, Y. and Ting, K. C. 2011. A Review Of Remote Sensing Methods For Biomass Feedstock Production. Biomass and Bioenergy. 35(7): 2455-2469.

IUCN. 2014. The IUCN Red List of Threatened Species., Version 2014.2., 2014. [Online]. Available: <http://www.iucnredlist.org>.

Rutishauser, E., Noor’an, F., Laumonier, Y., Halperin, J., Hergoualc’h, K. and Verchot, L. 2013. Generic Allometric Models Including Height Best Estimate Forest Biomass And Carbon Stocks In Indonesia. For. Ecol. Manage. 307: 219-225.

Omar, H., Ismail, M. H., Hamzah,K. A. and Mohd Shafri, H. Z. 2014. Factors Affecting L-Band Alos Palsar Backscatter on Tropical Forest Biomass, Glob. J. Sci. Front. Res. 14(3).

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Published

2016-05-09

Issue

Vol. 78 No. 5-4: Sciences and Mathematics

Section

Science and Engineering

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How to Cite

ABOVEGROUND BIOMASS AND CARBON STOCK ESTIMATION USING DOUBLE SAMPLING APPROACH AND REMOTELY-SENSED DATA. (2016). Jurnal Teknologi, 78(5-4). https://doi.org/10.11113/jt.v78.8551