Application of vegetation indexes to assess carbon stock: A study from hilly community forests, Nepal


This was objectively conducted to estimate the carbon stock, to show the relation between carbon stock and indices and to assess the factors affecting carbon stock in community forests. Three community forests (CFs) namely Gumalchowki, Mahakalsthan and Mahalaxmi of Chandragiri Municipality in Nepal were selected as research sites. Altogether 135 plots were randomly established to collect data from the field. The diameter at breast height and height of the trees were measured in the sample plot. The biomass was calculated using Chave et al., equation which was converted into carbon stock multiplying by default value 0.47. The values of Normalized Difference Vegetation Index  (NDVI), Difference Vegetation Index (DVI) and Infrared Percentage Vegetation Index (IPVI) were calculated and regression equation between the indices and carbon stock was performed. The result showed that total above ground carbon stock was highest in Mahalaxmi CF with 30.42 ton/ha, followed by Mahakalsthan CF with 22.62 ton/ha and comparatively lowest with 21.55 ton/ha in Gumalchowki CF. The regression analysis between carbon stock and indices showed significantly and positive correlation. The R2 value of NDVI of Gumalchowki, Mahakalsthan and Mahalaxmi CF were found to be 0.51, 0.54 and 0.58, also, RMSE value of CFs were 1.41, 1.36 and 1.91 respectively. Principal component analysis showed that road construction, transmission line expansion, soil erosion, encroachment, disease, weeds, recreation, illegal logging are the major factors affecting carbon stock in all three community forests. This study will be useful to show the correlation between indices and carbon stock.

Keywords: Carbon, Nepal, community forests.

Author Biographies

Richa Dhakal, Kathmandu Forestry College, Kathmandu, Nepal

Bachelor Degree of Science in Forestry. Kathmandu Forestry College, Kathmandu.

Ram Asheshwar Mandal, School of Environment Science, Kathmandu, Nepal

PhD in Climate Change Science. School of Environment Science, Kathmandu.


Amir, M., Liu, X., Ahmad, A., Saeed, S., Mannan, A., & Muneer, M. A. (2018). Patterns of Biomass and Carbon Allocation across Chronosequence of Chir Pine ( Pinus roxburghii ) Forest in Pakistan: Inventory-Based Estimate. Advances in Meteorology, 2018, 1–8.
Asner, G. P., Powell, G. V. N., Mascaro, J., Knapp, D. E., Clark, J. K., Jacobson, J., Kennedy-Bowdoin, T., Balaji, A., Paez-Acosta, G., Victoria, E., Secada, L., Valqui, M., & Hughes, R. F. (2010). High-resolution forest carbon stocks and emissions in the Amazon. Proceedings of the National Academy of Sciences, 107(38), 16738–16742.
Bhandari, M. (2012). International Centre for Integrated Mountain Development. In G. Ritzer (Ed.), The Wiley-Blackwell Encyclopedia of Globalization (p. wbeog308). John Wiley & Sons, Ltd.
Boyd, D. S., Foody, G. M., & Curran, P. J. (Centre for E. and E. S. R. (1999). The relationship between the biomass of Cameroonian tropical forests and radiation reflected in middle infrared wavelengths (3.0-5.0 micro m). International Journal of Remote Sensing (United Kingdom).
Cao, J., Wang, X., Tian, Y., Wen, Z., & Zha, T. (2012). Pattern of carbon allocation across three different stages of stand development of a Chinese pine (Pinus tabulaeformis) forest. Ecological Research, 27(5), 883–892.
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. (2005). Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 145(1), 87–99.
Cui, L., Jiao, Z., Dong, Y., Sun, M., Zhang, X., Yin, S., ... & Xie, R. (2019). Estimating forest canopy height using MODIS BRDF data emphasizing typical-angle reflectances. Remote Sensing, 11(19), 2239.
Foody, G. M., Boyd, D. S., & Cutler, M. E. J. (2003). Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sensing of Environment, 85(4), 463–474.
Grierson, P., Adams, M., & Attiwill, P. (1992). Estimates of Carbon Storage in the Aboveground Biomass of Victorias Forests. Australian Journal of Botany, 40(5), 631.
Gunawardena, A., Iftekhar, S., & Fogarty, J. (2020). Quantifying intangible benefits of water sensitive urban systems and practices: an overview of non-market valuation studies. Australasian Journal of Water Resources, 24(1), 46-59.
Hunt, C. A. G. (2009). Carbon Sinks and Climate Change: Forests in the Fight Against Global Warming. Edward Elgar Publishing.
Ingram, J. C., Dawson, T. P., & Whittaker, R. J. (2005). Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks. Remote Sensing of Environment, 94(4), 491–507.
IPCC. (2006). Forestland. In: IPCC Guidelines for National Greenhouse Gas Inventories .eds Eggleston HS., Buendia L., Miwa K., Ngara T. and Tanabe K), pp. 4.1–4.83. IPCC, Japan.
Jordan, C. F. (1969). Derivation of Leaf-Area Index from Quality of Light on the Forest Floor. Ecology, 50(4), 663–666.
K. C., A. (2019). Forest as a Sink of Carbon in Global and Nepalese Context. In M. K. Jhariya, A. Banerjee, R. S. Meena, & D. K. Yadav (Eds.), Sustainable Agriculture, Forest and Environmental Management (pp. 223–249). Springer Singapore.
Karnell, M. P., Melton, S. D., Childes, J. M., Coleman, T. C., Dailey, S. A., & Hoffman, H. T. (2007). Reliability of Clinician-Based (GRBAS and CAPE-V) and Patient-Based (V-RQOL and IPVI) Documentation of Voice Disorders. Journal of Voice, 21(5), 576–590.
Kumar, R., & Saikia, P. (2020). Forest resources of Jharkhand, Eastern India: socio-economic and bio-ecological perspectives. In Socio-economic and Eco-biological Dimensions in Resource use and Conservation (pp. 61-101). Springer, Cham.
Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote Sensing and Image Interpretation. John Wiley & Sons.
Lu, D., Mausel, P., Brondızio, E., & Moran, E. (2004). Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management, 198(1–3), 149–167.
Mandal, R. A., Jha, P. K., Dutta, I. C., Thapa, U., & Karmacharya, S. B. (2016). Carbon Sequestration in Tropical and Subtropical Plant Species in Collaborative and Community Forests of Nepal. Advances in Ecology, 2016, 1–7.
Mandal, R. A., Khadka, G. B., Shrestha, M., Sah, P., & Lamichhane, A. (2020). Modeling the diameter at breast height (DBH) with height and volume of Shorea robusta using destructive method: A study from Banke District, Nepal. 15.
Mandal, R., Aryal, K., & Gupta, J. (2017). Effects of Hilly Aspects on Carbon Stock of Pinus roxburghii Plantations in Kaleri, Salyan Salleri and Barahpakho Community Forests. Journal of Climate Change Research, 3, 816–824.
Maynard, C. L., Lawrence, R. L., Nielsen, G. A., & Decker, G. (2007). Modeling vegetation amount using bandwise regression and ecological site descriptions as an alternative to vegetation indices. GIScience & Remote Sensing, 44(1), 68–81.
Megevand, C., & Mosnier, A. (2013). Deforestation trends in the Congo Basin: reconciling economic growth and forest protection. World Bank Publications.
Mfon, P., Akintoye, O. A., Mfon, G., Olorundami, T., Ukata, S. U., & Akintoye, T. A. (2014). Challenges of deforestation in Nigeria and the Millennium Development Goals. International Journal of Environment and Bioenergy, 9(2), 76-94.
Näsi, R., Honkavaara, E., Lyytikäinen-Saarenmaa, P., Blomqvist, M., Litkey, P., Hakala, T., & Holopainen, M. (2015). Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level. Remote Sensing, 7(11), 15467-15493.
Palmer, C. (2011). Property rights and liability for deforestation under REDD+: Implications for permanence’ in policy design. Ecological economics, 70(4), 571-576.
Ravindranath, N. H., & Ostwald, M. (Eds.). (2008). Methods for Estimating Above-Ground Biomass. In Carbon Inventory Methods Handbook for Greenhouse Gas Inventory, Carbon Mitigation and Roundwood Production Projects (pp. 113–147). Springer Netherlands.
Schlerf, M., Atzberger, C., & Hill, J. (2005). Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sensing of Environment, 95(2), 177–194.
Sharma, I., & Kakchapati, S. (2018). Linear Regression Model to Identify the Factors Associated with Carbon Stock in Chure Forest of Nepal. Scientifica, 2018, 1–8.
Sharma, K. P., Bhatta, S. P., Khatri, G. B., Pajiyar, A., & Joshi, D. K. (2020). Estimation of Carbon Stock in the Chir Pine (Pinus roxburghii Sarg.) Plantation Forest of Kathmandu Valley, Central Nepal. Journal of Forest and Environmental Science, 36(1), 37–46.
Shrestha, S., Karky, B. S., Gurung, A., Bista, R., & Vetaas, O. R. (2013). Assessment of Carbon Balance in Community Forests in Dolakha, Nepal. Small-Scale Forestry, 12(4), 507–517.
Smith, B., Knorr, W., Widlowski, J. L., Pinty, B., & Gobron, N. (2008). Combining remote sensing data with process modelling to monitor boreal conifer forest carbon balances. Forest Ecology and Management, 255(12), 3985-3994.
Soenen, S. A., Peddle, D. R., Hall, R. J., Coburn, C. A., & Hall, F. G. (2010). Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain. Remote Sensing of Environment, 114(7), 1325-1337.
Steininger, M. K. (2000). Satellite estimation of tropical secondary forest above-ground biomass: Data from Brazil and Bolivia. International Journal of Remote Sensing, 21(6–7), 1139–1157.
Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150.
Vicharnakorn, P., Shrestha, R., Nagai, M., Salam, A., & Kiratiprayoon, S. (2014). Carbon Stock Assessment Using Remote Sensing and Forest Inventory Data in Savannakhet, Lao PDR. Remote Sensing, 6(6), 5452–5479.
Volkanovski, A., Čepin, M., & Mavko, B. (2009). Application of the fault tree analysis for assessment of power system reliability. Reliability Engineering & System Safety, 94(6), 1116–1127.
Zheng, D., Rademacher, J., Chen, J., Crow, T., Bresee, M., Le Moine, J., & Ryu, S.-R. (2004). Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment, 93(3), 402–411.
Zhong, X., Li, J., Li, X., Ye, Y., Liu, S., Hallett, P. D., Ogden, M. R., & Naveed, M. (2017). Physical protection by soil aggregates stabilizes soil organic carbon under simulated N deposition in a subtropical forest of China. Geoderma, 285, 323–332.
Zianis, D., Muukkonen, P., Mäkipää, R., & Mencuccini, M. (2005). Biomass and stem volume equations for tree species in Europe. FI.
How to Cite
DHAKAL, Richa; MANDAL, Ram Asheshwar. APPLICATION OF VEGETATION INDEXES TO ASSESS CARBON STOCK. Mercator, Fortaleza, v. 21, dec. 2022. ISSN 1984-2201. Available at: <>. Date accessed: 12 june 2024. doi: