Comparison of Different Resolution Satellite Imageries for Forest Carbon Quantification


  • Him Lal Shrestha Ministry of Population and Environment, Nepal



Forest carbon, satellite imageries, image processing, crown projection areas (CPA), NDVI


The current trend of the monitoring of the forest involves the measurement of aboveground forest biomass carbon using the multisource forest inventory techniques. The multisource forest inventory techniques involve the multiple data inputs such as GIS, Remote sensing, GPS, field measurement and existing information. The remote sensing data are useful for the quantification of aboveground forest carbon using the spectral and spatial characteristics of the data. The application of remote sensing data for the forest carbon quantification may enhance the efficiency in terms of resource allocation, time spent and interoperability and ultimately support the efficient National Forest Monitoring System as a basis for the REDD (+) implementation in future. The study tried to compare the methods and results from the multispatial resolution satellite imageries for the quantification of forest biomass carbon i.e. Landsat TM(30m), RapidEye (5/6.5m) and WorldView PAN (0.5m). The medium resolution imageries like Landsat and RapidEye images have scope to process at the plot level where as WorldView PAN has scope to process upto the tree by tree level. Thus the methods of operation involves mainly two stream i.e. NDVI extraction for the plot average for Landsat and RapidEye data and CPA analysis for the individual tree for WorldView data. The result shows the higher R2 (0.6) relation in CPA method rather than the NDVI relation with the total forest carbon if we see the linear relations. While we see the polynomial relation, we get the R2 value of 0.77 from the NDVI value of RapidEye which support conclude the use of WorldView PAN image and RapidEye image for the quantification of forest carbon.


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How to Cite

Shrestha, H. L. (2016). Comparison of Different Resolution Satellite Imageries for Forest Carbon Quantification. Journal on Geoinformatics, Nepal, 15(1), 23–26.