Mapping Vegetation and Forest Types using Landsat TM in the Western Ghat Region of Maharashtra, India

Vegetation plays a key role in reducing ambient temperature, moisture and pollutant capture, energy use and subsequent ground level ozone reduction. In recent years vegetation mapping has become increasingly important, especially with advancements in environmental economic valuation. The spatial information from the remote sensing satellites enables researchers to quantify and qualify the amount and health of vegetation. The present study highlights significance of remote sensing in the vegetation mapping of western ghat region of Maharashtra using satellite imageries from Landsat TM. A supervised (full Gaussian) maximum likelihood classification was implemented in our approach. The final classification product provided identification and mapping of dominant land cover types, including forest types and nonforest vegetation. Remote sensing data sets were calibrated using a variety of field verification measurements. Field methods included the identification of dominant forest species, forest type and relative state-of-health of selected tree species. Ground truth information was used to assess the accuracy of the classification. The vegetation type map was prepared from the classified satellite image. The moist deciduous forests constitute major portion of the total forest area. The application of remote sensing and satellites imageries with spatial analysis of land use land cover provides policy and decision makers with current and improved data for the purposes of effective management of natural resources.

[1]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[2]  S. G. Champion,et al.  A revised survey of the forest types of India. , 1968 .

[3]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[4]  Raisa Mäkipää,et al.  Biomass and stem volume equations for tree species in Europe , 2005, Silva Fennica Monographs.

[5]  P. Shi,et al.  Zoning grassland protection area using remote sensing and cellular automata modeling—A case study in Xilingol steppe grassland in northern China , 2005 .

[6]  Elizabeth Thompson,et al.  Evaluating Error in Using the National Vegetation Classification System for Ecological Community Mapping in Northern New England , USA , 2005 .

[7]  G. Foody,et al.  Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions , 2003 .

[8]  Martin Jung,et al.  Exploiting synergies of global land cover products for carbon cycle modeling , 2006 .

[9]  M. Schlerf,et al.  Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods , 2005 .

[10]  Ana M. Cingolani,et al.  Mapping vegetation in a heterogeneous mountain rangeland using landsat data: an alternative method to define and classify land-cover units , 2004 .

[11]  John J. A. Ingram,et al.  Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks , 2005 .

[12]  R. Hall,et al.  Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume , 2006 .

[13]  J. Heiskanen Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data , 2006 .

[14]  Dengsheng Lu,et al.  Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin , 2004 .

[15]  J. W. Bruce,et al.  The causes of land-use and land-cover change: moving beyond the myths , 2001 .

[16]  Richard G. Lathrop,et al.  Sub-pixel estimation of urban land cover components with linear mixture model analysis and Landsat Thematic Mapper imagery , 2005 .

[17]  Keith T. Weber,et al.  Challenges of Integrating Geospatial Technologies Into Rangeland Research and Management , 2006 .

[18]  S. Wofsy,et al.  Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data , 2004 .

[19]  C. Woodcock,et al.  Forest biomass estimation over regional scales using multisource data , 2004 .

[20]  Kevin P. Price,et al.  Using conservation reserve program maps derived from satellite imagery to characterize landscape structure , 2002 .

[21]  G. A. Nielsen,et al.  Modeling Vegetation Amount Using Bandwise Regression and Ecological Site Descriptions as an Alternative to Vegetation Indices , 2007 .