DOI: https://doi.org/10.14710/geoplanning.3.1.1-14

INCREASING ACCURACY VALUE IN THE ESTIMATES OF CARBON STOCK BY USING VEGETATION INDEX FROM ALOS AVNIR 2 SATELLITE IMAGERY

*Irland Fardani  -  Bandung Islamic University (UNISBA), Indonesia
Soni Darmawan  -  National Institute of Technology, Indonesia
Dudung M Hakim  -  Bandung Institute of Technology, Indonesia
Agung Budi Harto  -  Bandung Institute of Technology, Indonesia
Ketut Wikantika  -  Bandung Institute of Technology, Indonesia

Citation Format:
Abstract
The existence of carbon stock began to be noticed by the public, especially after the global warming phenomenon, because one of the causes of global warming is the increasing amount of carbon in the atmosphere. There are several approaches that can be used to calculate carbon stocks, one of which is through remote sensing. In the study of carbon stocks in Meru Betiri National Park Indonesia, the vegetation index from ALOS-AVNIR satellite imagery is used to estimate carbon reserves by finding an exact equation. If it uses the Modified Soil Adjusted Vegetation Index (MSAVI) only, the correlation value is 0.49. Meanwhile, if Infrared Percentage Vegetation Index (IPVI) is used, the correlation value is 0.47. However, if some vegetation indices such as Soil-Adjusted Vegetation Index (SAVI), Normalize Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) are combined, the correlation value of the equation is 0.63. The comparison showed that by combining several variables of vegetation indices will increase the value of the correlation equation significantly.
Fulltext View|Download
Keywords: Accuracy Value; Carbon Stock; Vegetation Index; ALOS AVNIR; Meru Betiri

Article Metrics:

Article Info
Section: Original Research
Language : EN
Recent articles
INCREASING ACCURACY VALUE IN THE ESTIMATES OF CARBON STOCK BY USING VEGETATION INDEX FROM ALOS AVNIR 2 SATELLITE IMAGERY RELATIONSHIP BETWEEN URBANIZATION AND DENGUE HAEMORRHAGIC FEVER INCIDENCE IN SEMARANG CITY MONITORING THE LAND USE CHANGE IN CAMPUS 2 STKIP PGRI PONTIANAK More recent articles
Most cited articles
MONITORING THE LAND USE CHANGE IN CAMPUS 2 STKIP PGRI PONTIANAK LAND PRICE MAPPING OF JABODETABEK, INDONESIA THE PERFORMANCE OF LAND USE CHANGE CAUSATIVE FACTOR ON LANDSLIDE SUSCEPTIBILITY MAP IN UPPER UJUNG-LOE WATERSHEDS SOUTH SULAWESI, INDONESIA ACCURACY ASSESSMENT OF HEIGHTS OBTAINED FROM TOTAL STATION AND LEVEL INSTRUMENT USING TOTAL LEAST SQUARES AND ORDINARY LEAST SQUARES METHODS MODEL OF CLIMATE AND LAND-USE CHANGES IMPACT ON WATER SECURITY IN AMBON CITY, INDONESIA More cited articles
  1. Akumu, C. E., & McLaughlin, J. W. (2014). Modeling peatland carbon stock in a delineated portion of the Nayshkootayaow river watershed in Far North, Ontario using an integrated GIS and remote sensing approach. Catena, 121, 297–306. http://doi.org/10.1016/j.catena.2014.05.025
  2. Ardiansyah, D. M., & Buchori, I. (2014). Pemanfaatan Citra Satelit Untuk Penentuan Lahan Kritis Mangrove Di Kecamatan Tugu, Kota Semarang. Geoplanning Journal of Geomatics and Planning, 1(1), 1–12
  3. Cantarello, E., Newton, A. C., & Hill, R. A. (2011). Potential effects of future land-use change on regional carbon stocks in the UK. Environmental Science and Policy, 14(1), 40–52. http://doi.org/10.1016/j.envsci.2010.10.001
  4. Elias, & Wistara, I. N. J. (2009). Inovasi Metodologi dan Metode Estimasi Cadangan Karbon dalam Hutan dalam Rangka Reduced Emissions From Deforestation and Degradation (REDD) Indonesia. In Prosiding Seminar Hasil - Hasil Penelitian IPB (pp. 288–307). Bogor: IPB Bogor
  5. Filzmoser, P. (2004). A Multivariate Outlier Detection Method. Vienna-Austria
  6. Hairiah, K., et. al. (2001). Methods for sampling carbon stocks above and below ground. ICRAF Bogor, Indonesia
  7. Humpenöder, F., et. al. (2013). Effects of land-use change on the carbon balance of 1st generation biofuels: An analysis for the European Union combining spatial modeling and LCA. Biomass and Bioenergy, 56, 166–178. http://doi.org/10.1016/j.biombioe.2013.05.003
  8. Lévesque, J., & King, D. J. (2003). Spatial analysis of radiometric fractions from high-resolution multispectral imagery for modelling individual tree crown and forest canopy structure and health. Remote Sensing of Environment, 84(4), 589–602
  9. Lu, D. (2006). The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing, 27(7), 1297–1328
  10. Myeong, S., Nowak, D. J., & Duggin, M. J. (2006). A temporal analysis of urban forest carbon storage using remote sensing. Remote Sensing of Environment, 101(2), 277–282
  11. Ponce-Hernandez, R., Koohafkan, P., & Antoine, J. (2004). Assessing carbon stocks and modelling win-win scenarios of carbon sequestration through land-use changes (Vol. 1). Food & Agriculture Org
  12. Riaño, D., et. al. (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
  13. Rosenqvist, A., et. al. (1999). Remote sensing and the Kyoto protocol: a review of available and future technology for monitoring treaty compliance. In Workshop Report, Ann Arbor, MI, USA (pp. 20–22)
  14. Seo, S. (2006). A review and comparison of methods for detecting outliers in univariate data sets. University of Pittsburgh
  15. Sohl, T. L., et. al. (2012). A land-use and land-cover modeling strategy to support a national assessment of carbon stocks and fluxes. Applied Geography, 34, 111–124
  16. Sularso, G. N. M., Hermawan, R., & Prasetyo, L. B. (2011). Pendugaan Perubahan Cadangan Karbon di Taman Nasional Meru Betiri. In Semiloka Nasional : Implementasi RAN-GRK untuk bidang berbasis Lahan. Bogor: IPB Press
  17. Sun, H., et. al. (2015). Increasing the Accuracy of Mapping Urban Forest Carbon Density by Combining Spatial Modeling and Spectral Unmixing Analysis. Remote Sensing, 7(11), 15114–15139. http://doi.org/10.3390/rs71115114
  18. Ulumuddin, Y. I., et. al. (2005). Korelasi stok karbon dengan karakteristik spektral citra landsat: studi kasus gunung papandayan. In Presented on Pertemuan Ilmiah Tahunan MAPIN XIV,“Pemanfaatan Efektif Pengideraan Jauh Untuk Peningkatan Kesejahteraan Bangsa”, Surabaya
  19. Widayati, A., Ekadinata, A., & Syam, R. (2005). Land use change in Nunukan: Estimating landscape level carbon stocks through land cover types and vegetation density. Carbon stocks monitoring in Nunukan, East Kalimantan: A spatial and modelling approach. Report from FORMACS project

Last update:

No citation recorded.

Last update: 2024-11-20 12:20:58

  1. The Impact of Urbanization to Forest Degradation in Metropolitan Semarang: A Preliminary Study

    A W Sejati, I Buchori, I Rudiarto. IOP Conference Series: Earth and Environmental Science, 123 , 2018. doi: 10.1088/1755-1315/123/1/012011

  2. Landuse change prediction model based on Cellular Automata (CA) method in Bandung City

    Fardani I.. Journal of Physics: Conference Series, 127 (1), 2020. doi: 10.1088/1742-6596/1469/1/012030