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Posted by Bechu

Forests play an important role in regional and global carbon (C) cycles as they store large quantities of carbon invegetation (Brownetal., 1996). Because of their importance in the global C cycle, there is an increasing need to accurately estimate the amount of C (or biomass) forests contain (Brown and Lugo,1982; Birdsey, 1992). Information about above ground biomass (AGB) is necessary for estimating and forecasting ecosystem productivity, carbon budgets, nutrient allocation and fuel accumulation (Brown et al., 1999). Mapping forest biomass is of fundamental importance for estimating CO2 emissions due to land use and land cover changes (Sales etal., 2007), for estimating bioenergy potentials and monitoring carbon stocks (Powell et al., 2010) and for planning and managing of forest operations for commercial use or the study of ecosystem productivity (Parresol, 1999; Brown et al., 1996).

Remote sensing (RS) has opened an effective way to estimate forest biomass and carbon (Rosenqvist et al., 2003 )and is especially suitable for estimating the C pool – particularly the AGB (IPCC, 2003). Estimation of AGB has been made by a range of methods, from field measurements to RS - based methods, as well as GIS-based modeling approaches using auxiliary data (Lu, 2006). With the increasing availability of satellite imagery, remote sensing based methods have been the most widely used approach to predict AGB (Viana et al., 2012). In recent years, geostatistics has been used for mapping forest variables (Akhavan and Kia-Daliri, 2010). Combining data from remote sensing imagery with field measurements and geostatistical interpolation techniques/models can lead to accurate mapping of forest biomass.

Objectives

  • To estimate above ground woody biomass (AGWB) from field inventory data.
  • To map AGWB combining field inventory data, remote sensing and geo-statistical models viz., k-NN, DRR and co-kriging.
  • To evaluate/compare the AGWB maps derived from the different geo-statistical models

Research Questions

  • How much above ground woody biomass is stored in each forest type of the study area?
  • What is the distribution of aboveground woody biomass in the study area?
  • Which model can provide the highest accuracy in predicting biomass?

Study Area

  • Western Doon valley
  • Geog. extent: 29.7 - 30.7°N & 77.4 - 78.2°E
  • Avg. annual rainfall: 1550mm
  • Temp.: 02 - 40 °C
  • Forest type: Tropical moist deciduous forest (sal dominated)

Materials

  • Data
    • Field inventory data
    • Satellite data
      • IRS P6 LISS-III (ordered on 02.01.2013)
    • Ancillary data
      • Topographic maps
      • Volumetric equations
      • Specific gravity
  • Instruments
    • GPS
    • Measuring tape
    • D-tape
    • Silva Rangers Compass
    • Laser Range Finder
    • Nylon rope
    • Densitometer
    • Hypsometer
  • Software
    • ERDAS IMAGINE
    • ArcGIS
    • ENVI
    • k-NN FOREST
    • R-statistics

Methodology

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