Skip to main content

Advertisement

SpringerLink
Log in

Quantification of carbon stock to understand two different forest management regimes in Kayar Khola watershed, Chitwan, Nepal

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

The impact of forest management activities on the ability of forest ecosystems to sequester and store atmospheric carbon is of increasing scientific and social concern. This is because a quantitative understanding of how forest management enhances carbon storage is lacking in most forest management regimes. In this paper two forest regimes, government and community-managed, in Kayar Khola watershed, Chitwan, Nepal were evaluated based on field data, very high resolution (VHR) GeoEye-1 satellite image and airborne LiDAR data. Individual tree crowns were generated using multi-resolution segmentation, which was followed by two tree species classification (Shorea robusta and Other species). Species allometric equations were used in both forest regimes for above ground biomass (AGB) estimation, mapping and comparison. The image objects generated were classified per species and resulted in 70 and 82 % accuracy for community and government forests, respectively. Development of the relationship between crown projection area (CPA), height, and AGB resulted in accuracies of R2 range from 0.62 to 0.81, and RMSE range from 10 to 25 % for Shorea robusta and other species respectively. The average carbon stock was found to be 244 and 140 tC/ha for community and government forests respectively. The synergistic use of optical and LiDAR data has been successful in this study in understanding the forest management systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Acharya, K. P. (2002). Twenty-four years of community forestry in Nepal. International Forestry Review, 4(2), 149–156.

    Article  Google Scholar 

  • Asner, G. P., & Warner, A. S. (2003). Canopy shadow in IKONOS satellite observations of tropical forests and savannas. Remote Sensing of Environment, 87, 521–533.

    Article  Google Scholar 

  • Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16. doi:10.1016/j.isprsjprs.2009.06.004.

    Article  Google Scholar 

  • Cartus, O., Kellndorfer, J., Rombach, M., & Walker, W. (2012). Mapping canopy height and growing stock volume using airborne LiDAR, ALOS PALSAR and Landsat ETM+. Remote Sensing, 4(12), 3320–3345. doi:10.3390/rs4113320.

    Article  Google Scholar 

  • Chaturvedi, A. N., & Khanna, L. (1982). Forest mensuration (Vol. 9, 3). Dehra Dun: International Book Distributors.

    Google Scholar 

  • Chave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J. Q., Eamus, D., Fölster, H., F. Fromard, F., Higuchi, N., Kira, T., Lescure, J.-P., Nelson, B. W., Ogawa, H., Puig, H., B. Riéra, B., & Yamakura, T. (2005). Tree allometry and improved estimation of carbon stocks and balance in tropical forest. Ecosystem Ecology, 145, 87–99.

  • Cho, M. A., Mathieu, R., Asner, G. P., Naidoo, L., van Aardt, J., Ramoelo, A., Debba, P., Wessels, K., Main, R., Smit, I. P. J., & Erasmus, B. (2012). Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system. Remote Sensing of Environment, 125, 214–226. doi:10.1016/j.rse.2012.07.010.

  • Coillie, F. M. B., Verbeke, L. P. C., & Wulf, R. R. (2008). Semi-automated forest stand delineation using wavelet based segmentation of very high resolution optical imagery. Berlin: Springer.

    Google Scholar 

  • Dare, P. M. (2005). Shadow analysis in high-resolution satellite imagery of urban areas. Photogrammetric Engineering Remote Sensing, 70(2), 169–177.

    Article  Google Scholar 

  • DeFries, R., Achard, F., Brown, S., Herold, M., Murdiyarso, D., Schlamadinger, B., & de Souza, C. Jr. (2007). Earth observations for estimating greenhouse gas emissions from deforestation in developing countries. Environmental Science & Policy, 10(4), 385–394. doi:10.1016/j.envsci.2007.01.010.

  • DoF. (2012). Department of Forest, Government of Nepal. (Retrieved from http://www.dof.gov.np/ on 28 January, 2013).

  • Drǎguţ, L., Tiede, D., & Levick, S. R. (2010). ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6), 859–871. doi:10.1080/13658810903174803.

    Article  Google Scholar 

  • Dubayah, R. O., & Drake, J. B. (2000). Lidar Remote Sensing for Forestry. Journal of Forestry, 98(6), 44–46.

    Google Scholar 

  • Eysn, L., Hollaus, M., Schadauer, K., & Pfeifer, N. (2012). Forest delineation based on airborne LIDAR data. Remote Sensing, 4(12), 762–783. doi:10.3390/rs4030762.

    Article  Google Scholar 

  • FAO. (2010). Global forest resources assessment 2010 country report. Nepal, Rome.

  • Fennerschool. (1997). Stand basal area. http://fennerschool-associated.anu.edu.au/mensuration/BrackandWood1998/S_BA.HTM.

  • Gibbs, H. K., Brown, S., Niles, J. O., & Foley, J. A. (2007). Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environmental Research Letters, 2(4), 045023. doi:10.1088/1748-9326/2/4/045023.

    Article  Google Scholar 

  • Goetz, S. J., Baccini, A., Laporte, N. T., Johns, T., Walker, W., Kellndorfer, J., Houghton, R. A., & Sun, M. (2009a). Mapping and monitoring carbon stocks with satellite observations: a comparison of methods. Carbon Balance and Management, 4(1), 1–7. doi:10.1186/1750-0680-4-2.

  • Goetz, S. J., Baccini, A., Laporte, N. T., Johns, T., Walker, W., Kellndorfer, J., Houghton, R. A., & Sun, M. (2009b). Mapping and monitoring carbon stocks with satellite observations: a comparison of methods. Carbon Balance and Management, 4, 2. doi:10.1186/1750-0680-4-2.

  • Gonzalez, P., Asner, G. P., Battles, J. J., Lefsky, M. A., Waring, K. M., & Palace, M. (2010). Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California. Remote Sensing of Environment, 114(7), 1561–1575. doi:10.1016/j.rse.2010.02.011.

    Article  Google Scholar 

  • Huang, W., Sun, G., Dubayah, R., Cook, B., Montesano, P., Ni, W., & Zhang Z. (2013). Mapping biomass change after forest disturbance: applying LiDAR footprint-derived models at key map scales. Remote Sensing of Environment, 134, 319–332. doi:10.1016/j.rse.2013.03.017.

  • Husch, B., Beers, T. W., & Kershaw, J. A. (2003). Forest mensuration. Hoboken: Wiley.

    Google Scholar 

  • Hyde, P., Nelson, R., Kimes, D., & Levine, E. (2007). Exploring LiDAR–RaDAR synergy—predicting aboveground biomass in a southwestern ponderosa pine forest using LiDAR, SAR and InSAR. Remote Sensing of Environment, 106(1), 28–38. doi:10.1016/j.rse.2006.07.017.

    Article  Google Scholar 

  • Imhoff, M. L. (1995). Radar backscatter and biomass saturation: ramifications for global biomass inventory. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 511.

    Article  Google Scholar 

  • IPCC. (2003). Good Practice Guidance for Land Use, Land-Use Change and Forestry, IPCCC National Greenhouse Gas Inventories Programme. Kanagawa: Institute for Global Environment Strategies.

    Google Scholar 

  • IPCC. (2006). Guidelines for National Greenhouse Gas Inventories, IPCCC National Greenhouse Gas Inventories Programme. Kanagawa: Institute for Global Environment Strategies.

    Google Scholar 

  • Jensen, J. R. (2005). Introductory digital image processing: A remote sensing perspective (3rd ed.). USA: Pearson Prentice Hall.

    Google Scholar 

  • Kajisa, T., Murakami, T., Mizoue, N., Top, N., & Yoshida, S. (2009). Object-based forest biomass estimation using Landsat ETM plus in Kampong Thom Province, Cambodia. Journal of Forest Research, 14(4), 203–211. doi:10.1007/s10310-009-0125-9.

    Article  Google Scholar 

  • Ke, Y., Quackenbush, L. J., & Im, J. (2010). Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification. Remote Sensing of Environment, 114(6), 1141–1154. doi:10.1016/j.rse.2010.01.002.

    Article  Google Scholar 

  • Kim, S. R., Kwak, D. A., Lee, W. K., Son, Y., Bae, S. W., Kim, C., & Yoo S. (2010). Estimation of carbon storage based on individual tree detection in Pinus densiflora stands using a fusion of aerial photography and LiDAR data. [Research Support, Non-U.S. Gov’t]. Science China. Life Sciences, 53(7), 885–897. doi:10.1007/s11427-010-4017-1.

  • Kronseder, K., Ballhorn, U., Böhm, V., & Siegert, F. (2012). Above ground biomass estimation across forest types at different degradation levels in Central Kalimantan using LiDAR data. International Journal of Applied Earth Observation and Geoinformation, 18, 37–48. doi:10.1016/j.jag.2012.01.010.

    Article  Google Scholar 

  • Kwak, D.-A., Lee, W.-K., Lee, J.-H., Biging, G. S., & Gong, P. (2007). Detection of individual trees and estimation of tree height using LiDAR data. Journal of Forest Research, 12(6), 425–434. doi:10.1007/s10310-007-0041-9.

    Article  Google Scholar 

  • Leckie, D. G., Gougeon, F. A., Walsworth, N., & Paradine, D. (2003). Stand delineation and composition estimation using semi-automated individual tree crown analysis. Remote Sensing of Environment, 85, 355–369.

    Article  Google Scholar 

  • Möller, M., Lymburner, L., & Volk, M. (2007). The comparison index: a tool for assessing the accuracy of image segmentation. International Journal of Applied Earth Observation and Geoinformation, 9, 311–321.

    Article  Google Scholar 

  • Namaalwa, J., Sankhayan, P. L., & Hofstad, O. (2007). A dynamic bio-economic model for analyzing deforestation and degradation: an application to woodlands in Uganda. Forest Policy and Economics, 9(5), 479–495.

    Article  Google Scholar 

  • Niraula, R. R., Gilani, H., Pokharel, B. K., & Qamer, F. M. (2013). Measuring impacts of community forestry program through repeat photography and satellite remote sensing in the Dolakha district of Nepal. Journal of Environmental Management, 126, 20–29. doi:10.1016/j.jenvman.2013.04.006.

    Article  Google Scholar 

  • Patenaude, G., Milne, R., & Dawson, T. P. (2005). Synthesis of remote sensing approaches for forest carbon estimation: reporting to the Kyoto Protocol. Environmental Science & Policy, 8(2), 161–178. doi:10.1016/j.envsci.2004.12.010.

    Article  Google Scholar 

  • Pouliot, D., King, D., Bell, F., & Pitt, D. (2002). Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration. Remote Sensing of Environment, 82(2), 322–334.

    Article  Google Scholar 

  • Sagar, R., & Singh, J. S. (2006). Tree density, basal area and species diversity in a disturbed dry tropical forest of nothern India: implications for conservation. Environmental Conservation, 33(3), 256–262.

    Article  Google Scholar 

  • Sapkota, I. P., Tigabu, M., & Oden, P. C. (2009). Tree diversity and regression of community-managed Bhabar lowland and Hill Sal forests in central region of Nepal. Bois et Forêts des Tropiques, 63, 57–68.

  • Shrestha, R., Shrestha, S. L., Acharya, S. G., & Adhikari, S. (2009). Improving community level governance: adaptive learning and action in community forest user groups in Nepal. Journal of Forest and Livelihood, 8(2), 67–77.

    Article  Google Scholar 

  • Singh, B. K., & Chapagain, D. P. (2006). Trends in forest ownership, forest resources tenure and institutional arrangements: are they contributing to better forest management and poverty reduction?. Understanding forest tenure in South and Southeast Asia. Forestry Policy and Institutions Working Paper 14 (pp. 115-151). Rome: FAO.

  • Skole, D. L., & Tucker, C. J. (1993). Tropical deforestation and habitat fragmentation in the Amazon: satellite data from 1978 to 1988. Science, 260, 1905–1910.

    Article  Google Scholar 

  • St Onge, B., Vega, C., Fournier, R. A., & Hu, Y. (2008). Mapping canopy height using a combination of digital stereo photogrammetry and lidar. International Journal of Remote Sensing, 29(11), 3343–3364. doi:10.1080/01431160701469040.

    Article  Google Scholar 

  • Toan, T., Quegan, S., Woodward, I., Lomas, M., Delbart, N., & Picard, G. (2004). Relating radar remote sensing of biomass to modelling of forest carbon budgets. Climatic Change, 67(2–3), 379–402. doi:10.1007/s10584-004-3155-5.

    Article  Google Scholar 

  • Wagner, W., Ullrich, A., Melzer, T., Briese, C., & Kraus, K. (2004). From single-pulse to full-waveform airborne laser scanners: potential and practical challenge. International Archives of Photogrammetry and Remote Sensing, 35(Part B), 201–206. doi:10.1007/s10584-004-3155-5.

    Google Scholar 

  • Yang, X., Strahler, A. H., Schaaf, C. B., Jupp, D. L. B., Yao, T., Zhao, F., Wang, Z., Culvenor, D. S, Newnham, G. J., Lovell, J. L., Dubayah, R. O., Woodcock, C. E., & Ni-Meisteri W. (2013). Three-dimensional forest reconstruction and structural parameter retrievals using a terrestrial full-waveform lidar instrument (Echidna®). Remote Sensing of Environment, 135, 36–51. doi:10.1016/j.rse.2013.03.020.

Download references

Acknowledgments

This publication is reporting a joint work of ITC, University of Twente, Netherlands, and the International Centre for Integrated Mountain Development (ICIMOD) under the REDD+ project which is being carried out in three watersheds of Nepal. Special thanks to Mr. Basanta Shrestha, Dr. MSR Murthy, Dr. Bhaskar Karki, Ms. Seema Karki and Mr. Govinda Joshi from ICIMOD for their logistic support during the data collection period in Nepal. Finally, the immense support of Prof. Gilbert Nduru of KARATINA University is highly appreciated. The findings reported stand as scientific study and observations of the authors and do not necessarily reflect as the views of ITC and ICIMOD.

Author information

Authors and Affiliations

  1. Chuka University, P.O. BOX 109, 60400, Chuka, Kenya

    Purity Rima Mbaabu

  2. Faculty of Geo-Information Science and Earth Observation (ITC) – University of Twente, Enschede, The Netherlands

    Yousif Ali Hussin & Michael Weir

  3. International Centre for Integrated Mountain Development (ICIMOD), GPO Box 3226, Khumaltar, Lalitpur, Nepal

    Hammad Gilani

  4. School of Geomatics, Liaoning Technical University, 47 Zhonghua Road, Fuxin, Liaoning Province, China

    Hammad Gilani

Authors
  1. Purity Rima Mbaabu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yousif Ali Hussin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael Weir
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hammad Gilani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Purity Rima Mbaabu.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mbaabu, P.R., Hussin, Y.A., Weir, M. et al. Quantification of carbon stock to understand two different forest management regimes in Kayar Khola watershed, Chitwan, Nepal. J Indian Soc Remote Sens 42, 745–754 (2014). https://doi.org/10.1007/s12524-014-0379-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-014-0379-3

Keywords

Search

Navigation