Department of Forestry and Range Management, PMAS Arid Agriculture University, Rawalpindi, Pakistan


Forest ecosystems are among the largest terrestrial carbon reservoirs on our planet earth thus playing a vital role in global carbon cycle. Presently, remote sensing techniques provide proper estimates of forest biomass and quantify carbon stocks. The present study has explored Landsat-8 sensor product and evaluated its application in biomass mapping and estimation. The specific objectives were estimation of above ground biomass and carbon stocks using field data, assessing relationships of Landsat-8 spectral indices and field data and  modeling of biomass and carbon stocks based on best linear regression model. Results showed that the highest aboveground biomass and below ground biomass was recorded as 246 t/ha and 64 t/ha whereas the lowest aboveground biomass and below ground biomass was 55 t/ha and 14 t/ha, respectively. Similarly, the highest above ground carbon and below ground carbon (t/ha) were 116 t/ha and 30 t/ha respectively while the lowest above ground carbon and below ground carbon (t/ha) were estimated as 26 t/ha and 6.7 t/ha respectively. Indices computed from Landsat-8 included normalized difference vegetation index, difference vegetation index, soil adjusted vegetation index, perpendicular vegetation index and atmospherically resistant vegetation index. Regarding relationship between aboveground biomass and vegetation indices, the coefficient of correlation (R2) were 0.67, 0.68, 0.65, 0.58 and 0.23 for normalized difference vegetation index, soil adjusted vegetation index, Perpendicular vegetation index, difference vegetation index and atmospherically resistant vegetation index respectively. The stepwise correlation between aboveground biomass (dependent variable) and five indices (Normalized difference vegetation index; soil adjusted vegetation index; Perpendicular vegetation index; difference vegetation index; atmospherically resistant vegetation index). Among five vegetation indices, only soil adjusted vegetation index was selected in stepwise method, satisfying the criteria and the overall model R2 was 0.63 and its adjusted R2 was 0.60. Simple linear regression model between aboveground biomass and single predictor index was better than stepwise regression model with (R2= 0.68) and (Root mean square error = 33.75 t/ha). Thus, soil adjusted vegetation index was considered best for biomass mapping. The study concluded that Landsat-8 product has considerable potential for biomass and carbon stocks estimation and can be expanded to national and regional forest inventories, modeling and future reducing emission from deforestation and forest degradation+ implementation.


Main Subjects

Adan, M.S., (2017). Integrating Sentinel-2A derived indices and terrestrial laser scanner to estimate above ground biomass/carbon in Ayer Hitam tropical forest, Malaysia, Master of Science, University of Twente, The Netherlands.

Afzal, M.; Akhter, A.M., (2011). Estimation of biomass and carbon stock: Chichawatni Irrigated Plantation in Punjab, Pakistan.” SDPI’s Fourteenth Sustainable Development Conference.

Ali, A.; Ullah, S.; Bushra, S.; Ahmad, N.; Ali, A.; Khan, M.A., (2018). Quantifying forest carbon stocks by integrating satellite images and forest inventory data. Aus. J. For. Sci., 135(2): 93–117 (15 pages).

Ali, A., (2015), Biomass and carbon tables for major tree species of Gilgit Baltistan, Pakistan, GilgitBaltistan Forests, Wildlife and Environment Department, Gilgit-Baltistan and Pakistan Forest Institute, Peshawar.

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. Adv. Meteor., 2018, Article ID 3095891., (8 pages).

Bellassen, V.; Luyssaert, S., (2014). Carbon sequestration: Managing forests in uncertain times. Nature News, 506(7487): 153.

Carreiras, J.M.; Pereira, J.M.; Pereira, J.S., (2006). Estimation of tree canopy cover in evergreen oak woodlands using remote sensing. For. Ecol. Manage., 223(1-3): 45-53 (09 pages). Das, S.; Singh, T.P., (2012). Correlation analysis between biomass and spectral vegetation indices of forest ecosystem. Int. J. Eng. Res. Technol., 1(5).

Denman, K.L.; Brasseur, G.; Chidthaisong, A.; Ciais, Ph.; Cox, P.; Dickinson, R.E.; Hauglustaine, D.; Heinze, C.; Holland, E.; Jacob, D.; Lohmann, U.; Ramachandran, S.; da Silva Dias, P.L.; Wofsy, S.C.; Zhang, X., (2007). Couplings between changes in the climate system and biogeochemistry, Chapter 7 in: Climate Change 2007: The Physical Science Basis, The IPCC Fourth Assessment Report, Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge.

Fares, S.; Paoletti, E.; Calfapietra, C.; Mikkelsen, T.N.; Samson, R.; Le Thiec, D., (2017). Carbon sequestration by urban trees. In The Urban Forest. Springer, Cham. 31-39 (9 pages).

Foody, G.M.; Boyd, D.S.; Cutler, M.E., (2003). Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sens. Environ., 85(4): 463-474 (12 pages).

Gasparri, N.I.; Parmuchi, M.G.; Bono, J.; Karszenbaum, H.; Montenegro, C.L., (2010). Assessing multi-temporal Landsat 7 ETM+ images for estimating above-ground biomass in subtropical dry forests of Argentina. J. Arid Environ., 74(10), 1262-1270 (09 pages).

Gibbs, H.K.; Brown, S.; Niles, J.O.; Foley, J.A., (2007). Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ. Res. Lett., 2(4): 045023.

Hansen, M.C.; Potapov, P.V.; Moore, R.; Hancher, M.; Turubanova, S.A.A.; Tyukavina, A.; Thau, D.; Stehman, S.V.; Goetz, S.J.; Loveland, T.R.; Kommareddy, A., (2013). High-resolution global maps of 21st-century forest cover change. Science, 342(6160): 850-853 (04 pages).

Ismail, I.; Sohail, M.; Gilani, H.; Ali, A.; Hussain, K., Hussain,  Karky, B.S.; Qamer, F.M.; Qazi, W.; Ning, W.;  Kotru, R., (2018). Forest inventory and analysis in Gilgit-Baltistan: A contribution towards developing a forest inventory for all Pakistan. Int. J. Clim. Change Strategies Manage., 10(4): 616-631 (16 pages).

Jordan, C.F., (1969). Derivation of leaf‐area index from quality of light on the forest floor. Ecology, 50(4), 663-666 (04 pages).

Kaufman, Y.J.; Tanre, D., (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens., 30(2): 261-270 (10 pages).

Kongwongjan, J.; Suwanprasit, C.; Thongchumnum, P., (2012). Comparison of vegetation indices for mangrove mapping using THEOS data. Proceedings of the Asia-Pacific Advanced Network, 33: 56-64 (9 pages).

Kumar, D.; Shekhar, S., (2015). Statistical analysis of land surface temperature–vegetation indexes relationship through thermal remote sensing. Ecotoxicol. Environ. Saf., 121: 39-44 (6 pages).

Lazaridou, M.A.; Karagianni, A.C., (2016, July). Landsat 8 multispectral and pansharpened imagery processing on the study of civil engineering issues. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences-ISPRS Archives, 23rd ISPRS Congress, 12-19 (8 pages).

Lu, D., (2005). Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. Int.  J. Remote Sens., 26(12): 2509-2525 (17 Pages).

Malhi, Y.; Baker, T.R.; Phillips, O.L.; Almeida, S.; Alvarez, E.; Arroyo, L.; Chave, J.; Czimczik, C.I.; Fiore, A.D.; Higuchi, N. Timothy J. Killeen; Susan G. Laurance;  William F.; Laurance  Simon L.; Lewis  Lina; María Mercado Montoya; Abel Monteagudo;  David A. Neill;  Percy Núñez Vargas;  Sandra Patiño;  Nigel C.A.; Pitman Carlos; Alberto Quesada;  Rafael Salomão ; José Natalino; Macedo Silva;  Armando Torres; Lezama Rodolfo; Vásquez Martínez  John Terborgh;  Barbara Vinceti;  Jon Lloyd., (2004). The above-ground coarse wood productivity of 104 Neotropical forest plots. Global Change Biol., 10(5): 563-591 (29 pages).

Molto, Q.; Rossi, V.; Blanc, L., (2013). Error propagation in biomass estimation in tropical forests. Methods Ecol. Evol., 4(2): 175-183 (9 pages).

Navar, J., (2009). Allometric equations for tree species and carbon stocks for forests of northwestern Mexico. For. Ecol. Manage., 257(2): 427-434 (8 pages).

Nelson, R.F.; Kimes, D.S.; Salas, W.A.; Routhier, M., (2000). Secondary forest age and tropical forest biomass estimation using thematic mapper imagery: single-year tropical forest age classes, a surrogate for standing biomass, cannot be reliably identified using single-date TM imagery. Bioscience, 50(5), 419-431 (13 pages).

Ni, Y., (2014). Global potential for carbon storage based on forest ecosystems. Master's thesis.

Nizami, S.M.; Mirza, S.N.; Livesley, S.; Arndt, S.; Fox, J.C.; Khan, I.A.;  Mahmood, T., (2009). Estimating carbon stocks in sub-tropical pine (Pinus roxburghii) forests of Pakistan. Pak. J. Agric. Sci., 46(4): 266-270 (5 pages).

Paustian, K.; Ravindranath, N.H.; van Amstel, A.R., (2006). IPCC Guidelines for National Greenhouse

Gas Inventories. Volume 4: Agriculture, Forestry and Other Land Use Part 2.

Perry, C .Jr; Lautenschlager, L.F., (1984). Functional equivalence of spectral vegetation indices. Remote Sens. Environ., 14(1-3): 169-182 (14 pages).

Pravalie, R., (2018). Major perturbations in the Earth's forest ecosystems. Possible implications for global warming. Earth-Science Reviews.

Prentice, I.C.; G.D.; Farquhar, M.J.R.; Fasham, M.L.; Goulden, M.; Heimann, V.J.; Jaramillo, H.S.; Kheshgi, C.; Le Que´re´, R.J.; Scholes, D.; Wallace, W.R., (2001). Pages 183–237 in J. T. Houghton and D. Yihui, editors. Climate change 2001: the scientific basis. The Intergovernmental Panel on Climate Change (IPCC) third assessment report. Cambridge University Press, Cambridge, UK. 183-237 (45 pages).

Qi, J.; Chehbouni, A.L.; Huete, A.R.; Kerr, Y.H.; Sorooshian, S., (1994). A modified soil adjusted vegetation index (MSAVI). Remote Sens. Environ. 48: 119–126 (8 pages).

Rouse,  J.W.; Haas, R.H.; Schell,  J.A.; Deering, D.W., (1973).  Monitoring vegetation systems in the Great Plains with ERTS. In: Third ERTS Symposium. NASA, 309–317 (9 pages).

Ryan, C.M.; Williams, M.; Grace, J., (2011). Above‐and belowground carbon stocks in a miombo woodland landscape of Mozambique. Biotropica, 43(4): 423-432 (10 pages).

Segura, M.; Kanninen, M., (2005). Allometric models for tree volume and total aboveground biomass in a tropical humid forest in Costa Rica 1. Biotropica. J. Biol. Conserv., 37(1): 2-8 (7 pages).

Seidel, D.; Fleck, S.; Leuschner, C.; Hammett, T., (2011). Review of ground-based methods to measure the distribution of biomass in forest canopies. Ann. For. Sci., 68: 225–244 (20 pages).

Shaheen, H.; Khan, R.W.A.; Hussain, K.; Ullah, T.S.; Nasir, M.; Mehmood, A., (2016). Carbon stocks assessment in subtropical forest types of Kashmir Himalayas. Pak. J. Bot, 48(6): 2351-2357 (7 pages).

Streck, C.; Scholz, S.M., (2006). The role of forests in global climate change: whence we come and where we go. Int. Affairs., 82(5): 861-879 (19 pages).

Tian, H.; Lu, C.; Ciais, P.; Michalak, A.M.; Canadell, J.G.; Saikawa, E.; Huntzinger, D.N.; Gurney, K.R.; Sitch , S.; Zhang, B.; Yang, J., (2016). The terrestrial biosphere as a net source of greenhouse gases to the atmosphere. Nature, 531(7593): 225.

Vidhya, R.; Vijayasekaran, D.; Farook, M.A.; Jai, S.; Rohini, M.; Sinduja, A., (2014). Improved classification of mangroves health status using hyperspectral remote sensing data. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., 40(8): 667.

Working Plan, (2012). Forest Planning and Monitoring Center, Working Plan of Siran Forests, Kyber Pukhtunkhwa, Pakistan.

Zhu, X.; Liu, D., (2015). Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. ISPRS J. Photogramm. Remote Sens., 102: 222 - 231 (10 pages).


International Journal of Human Capital in Urban Management (IJHCUM) welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in IJHCUM should be sent to the editorial office of IJHCUM within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.

[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.

[3] Letters can be no more than 300 words in length.

[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.

[5] Anonymous letters will not be considered.

[6] Letter writers must include their city and state of residence or work.

[7] Letters will be edited for clarity and length.