6th International Young Scientist Congress (IYSC-2020) will be Postponed to 8th and 9th May 2021 Due to COVID-19. 10th International Science Congress (ISC-2020).  International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Estimation of above ground Forest biomass and Carbon stock by Integrating LiDAR, satellite image and field measurement in Nepal

Author Affiliations

  • 1Deparment of Soil Conservation and Watershed Management, Ministry of Forests and Soil Conservation, Kathmandu, NEPAL
  • 2Institute of Forestry, Pokhara NEPAL
  • 3Arbonaut, FINLAND
  • 4Arbonaut, FINLAND
  • 5Arbonaut, FINLAND
  • 6International Union for Conservation of Nature (IUCN), NEPAL

Res. J. Agriculture & Forestry Sci., Volume 2, Issue (8), Pages 1-6, August,8 (2014)

Abstract

For the first time in South Asia, the model-based Lidar Assisted Multisource Program (LAMP) was tested in 23500 km2 Terai arc landscape (TAL) area of Nepal by integrating 5% airborne light detection and ranging (LiDAR) sampling, wall-to-wall Rapid Eye satellite image and a representative field inventory to estimate above ground biomass (AGB) and carbon stock. The average 1.26/mLiDAR point density recorded by the scanner was used to measure canopy height and build a model using LiDAR variables and model coefficients. The developed LAMP model successfully estimated the AGB of the study area. The research tells that the study area comprises almost 50% forest cover with an average 211.63 t/ha AGB. Standing carbon stock was converted from AGB by multiplying the 0.47 which is default carbon fraction. Average standing carbon stock is 99.47 t/ha in the study area. The LAMP method found that the standing total AGB was 214.85-208.41 t/ha at a 95% confidence level and thefield-based Forest Resource Assessment (FRA) Nepal field-plot AGB estimate is 210.09/ha. This correspondence at this level of confidence means that the LAMP estimates are as accurate as those of the field-based inventory.

References

  1. FAO, Global Forest Resource Assessment (FRA), FAOforestry paper 163, Food and Agriculture Organization(FAO) of the United Nations Rome (2010)
  2. IPCC, Summary for policymakers in climate change: Thephysical science basis, Contribution of working group 1 tothe fourth assessment report of Intergovernmental Panel onClimate Change (2007)
  3. Kandel P.N., Monitoring above ground forest biomass: Acomparison of cost and accuacy between Lidar AssistedMultisource Program and field based forest resourceassessment in Nepal, Banko Janakari: A journal of forestryinformation for Nepal, Department of Forest Research andSurvey of Nepal, 23(1), (2013)
  4. Asner G.P., Clark J.K., Mascaro J., Vaudry R., ChadwickK.D., Vieilledent G. and Knapp D.E., Human andenvironmental controls over aboveground carbon storage inMadagascar, Carbon Balance and Management, 7,2.doi:10.1186/1750-0680-7-2 (2012)
  5. Gautam B., Tokola T., Hämäläinen J., Gunia M.,Peuhkurinen J., Parviainen H. and Sah B., Integration ofairborne LiDAR, satellite imagery, and field measurementusing a two-phase sampling method for forest biomassestimation in tropical forests, International Symposium onBenefiting from Earth Observation, 4-6 October 2010,Kathmandu, Nepal, 1-7 (2010)
  6. Gatziolis D. and Andersen H.E., A guide to LiDAR dataacquisition and processing for the forests of PacificNorthwest, United State Department of Agriculture (USDA)forest service Pacific Northwest research station, Generaltechnical report (2008)
  7. Næsset E., Estimating timber volume of forest stands usingairborne laser scannerdata, Remote Sensing of Environment,51, 246–253 (1997)
  8. Hummel S. and O’hara K.L., Forest management. P 1653–1662 in Ecological engineering, Encyclopedia of ecology,Vol. 2, Jørgensen, S.E., and B.D. Fath(editors-in-chief),Elsevier. Oxford, England (2008)
  9. Næsset E., Airborne laser scanning as a method inoperational forest inventory: status of accuracy assessmentsaccomplished in Scandinavia, Scandinavian Journal ofForest Research, 22, 433-442 (2007)
  10. Asner G.P., Tropical forest carbon assessment: integratingsatellite and airbornemapping approach, Environ.lett.4:034009(11) (2009)
  11. Asner G.P., Powell G.V.N., Mascaro J., Knapp D.E., ClarkJ.K., Jacobson J., Kennedy-Bowdoin Ty., Arvindh B.,Paez-Acosta G., Victoria E., Secada L., Valqui M. andHughes R.F., High- resolution forest carbon stocks andemission in theAmazon, http://www.pnas.org/content/early/2010/08/30/1004875107. (2010)
  12. Arbonaut, Arbolidar: Monitoring change in carbon stocksand achieving REDD+ targets, Arbonaut Ltd., FIN-80130,Joensuu, Finland,http://www.arbonaut.com/files/Arbonaut_REDD_en%281%29.pdf (2010)
  13. HMGN/MFSC, Nepal Biodiversity Strategy, His Majesty’sGovernment of Nepal, Ministry of Forests and SoilConservation (HMGN/MFSC) (2004)
  14. Junttila V., Kauranne T. and Leppänen V., Estimation offorest stand parameters from air bornelaser scanning usingcalibrated lot databases, ForestScience, 56(3), 257-270(2010)
  15. Næsset E., Predicting forest stand characteristics withairborne scanning laser using apractical two-stageprocedure and field data, Remote Sensing of Environment,80, 88-99 (2002)
  16. Sharma E.K. and Pukkala T., Volume equations andbiomass prediction of forest trees of Nepal, Ministry ofForests and Soil Conservation, Forest Survey and StatisticsDivision, publication, 47, 1-16 (1990)
  17. Eerikäinen K., Predicting the height-diameter pattern ofplanted Pinuskesiya stands inZambia and Zimbabwe, Forest Ecology and Management, 175, 355-366 (2003)
  18. Junttila, V., Maltamo M., Kauranne T., Sparse Bayesianestimation of forest stands characteristics from airbornelaser scanning, The Society of American Foresters, Forestscience, 54(5), 543–552 (2008)
  19. Moore David S. and McCabe George P., Introduction to thepractice of statistics, thirdedition, (1998)