CARBON STOCK ASSESSMENT IN PINE FOREST OF KEDUNG BULUS SUB-WATERSHED (GOMBONG DISTRICT) USING REMOTE SENSING AND FOREST INVENTORY DATA

Autor(s): Tyas Mutiara Basuki, Nining Wahyuningrum
DOI: 10.20886/ijfr.2013.10.1.21-30

Abstract

Carbon stock in tree biomass can be quantified directly by cutting and weighing trees. It is assumed that 50% of the dry weight of biomass consists of carbon. This direct measurement is the most accurate method, however for large areas it is considered time consuming and costly. Remote sensing has been proven to be an important tool for mapping and monitoring carbon stock from landscape to global scale in order to support forest management and policy practices. The study aimed to (1) develop regression models for estimating carbon stock of pine forests using field measurement and remotely sensed data; and (2) quantify soil carbon stock under pine forests using field measurement. The study was conducted in Kedung Bulus sub-watershed, Gombong - Central Java. The derived data from Satellite Probatoire d'Observation de la Terre (SPOT) included spectral band 1, 2, 3, and 4, Normalized Differences Vegetation Index (NDVI), and Principle Component Analysis (PCA) images. These data were integrated with field measurement to develop models. Soil samples were collected by augering for every 20 cm until a depth of  100 cm. The potential of  remote sensing to estimate carbon stock was shown by the strong correlation between multiple bands of SPOT (band 2 , 3; band 1, 2, 3; band 1, 3, 4; and band 1, 2, 3, 4) and carbon stock with r = 0.76, PCA (PC1, PC2, PC3) and carbon stock with r = 0.73. The role of pine forest to reduce CO2 in the atmosphere was demonstrated by the amount of carbon in the tree and the soil. Carbon stock in the tree biomass varied from 26 to 206 Mg C ha-1 and in the soil under pine forest ranged from 85 to 194 Mg C ha-1.

Keywords

Remote sensing; carbon stock; field measurement

Full Text:

PDF

References

Alewell, C., M. Schaub, and F. Conen. 2009. A method to detect soil carbon degradation during soil erosion. Biogeosciences 6: 2541-2547.

Austin, J.M., B.G. ,Mackey, and K.P. van Nief. 2003. Estimating forest biomass using satellite radar: An exploratory study in a temperate Australia Eucalyptus forest. Forest Ecology and Management 176: 5755-83.

Basuki, T.M., A.K. Skidmore, P.E., van Laake, I. van Duren, and Y.A. Hussin. 2011. The potential of spectral mixture analysis to improve the estimation accuracy of tropical forest biomass. Geocarto International 27(4):

-345.

Foody, G.M., D.S. Boyd, and M.E.J. Cutler. 2003. Predictive relations of tropical forest biomass from Landsat TM data and their transferability between region. Remote Sensing of Environment 85: 463-474.

Gibbs, H.K., S. Brown, J.O. Niles, and J.A. Foley. 2007. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ.Res.Lett.2. doi:10.1088/1748-9326/4/045023.

Harrison, R.B., P.W. Footen, and B.D. Strahm. 2011. Deep soil horizons: contribution and importance to soil carbon pools and in assessing whole-ecosystem response to management and global change. Forest Science 57(1): 67 - 76.

IPCC (Intergovernmental Panel on Climate Change). 2003. Good practice guidance for land use, land-use change and forestry (GPG-LULUCF). Edited by Penman, J., M. Gystarsky, , T. Hiraishi, T. Krug, D. Kruger, R. Pipatti, L. Buendia, K. Miwa, T. Ngara, K. Tanabe, and F. Wagner. IPCC National Greenhouse Gas Inventories Program.

Lal, R. 2005. Forest soils and carbon sequestration. Forest Ecology and Management 220: 242 - 258.

Lillesand, T.M and R.W. Kiefer. 2004. Remote Sensing and Image Interpretation, John Wiley and Sons. Inc.

Loaiza, U.J.C., T.J.A. Rodriquez, A.M.V. Ramirez, and J.L.T. Alvaro. 2010. Estimation of biomass and carbon stocks in plants, soil, and forest floor in different tropical forests. Forest Ecology and Management 260(10): 1906 -1913.

Lorenz, K., R. Lal, and M.J. Shipitalo. 2011. Stabilized soil organic carbon pools in sub soils under forest are potential sinks for atmospheric CO2. Forest Science 57(1): 19 -25.

Lu, D., P. Mausel, Brondízio, and E., Moran. 2004. Relationship between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management 198: 149-167.

Lucas, R.M., N. Cronin, A. Lee, M. Moghaddam, C. Witte, and P. Tickle. 2006. Empirical relationships between AIRSAR backscatter and LiDAR-derived forest biomass, Queensland, Australia. Remote Sensing of Environment 100: 407 - 425.

Riao, D., E. Chuieco, J. Salas, and I. Aguado. 2003. Assessment of different topographic corrections in Landsat TM data for mapping vegetation types. IEEE transactions on Geoscience and Remote Sensing 41(5): 1056 - 1061.

Rosenqvist, Å., A. Milne, R. Lucas, M. Imhoff, and C. Dobson. 2003. A review of remote sensing technology in support of the Kyoto Protocol. Environmental Science & Policy 6:441-455.

Steininger, M.K., 2000. Satellite estimation of tropical secondary forest above-ground biomass data from Brazil and Bolivia. Int.J. Remote Sensing 21(6&7): 1139-1157.

Tangki, H. andChappell. 2008. Biomass variation across selectively logged forest within a 225-km2 region of Bor neo and its prediction by Landsat TM. Forest Ecology and Management 256: 1960-1970.

Tokola, T., J. Sarkeala, and M. van der Linden, M.,2001. Use of topographic correction in Landsat TM based forest interpretation on

Nepal 22 (4): 551 - 563.

DOI: https://doi.org/10.20886/ijfr.2013.10.1.21-30

Refbacks

  • There are currently no refbacks.


Bookmark and Share