The Landsat Thematic Mapper (Landsat-TM) data was taken as an input for the FCD (Forest Canopy Density) model. The FCD model comprises biophysical phenomenon modeling and analysis utilizing data derived from four indices: Advanced Vegetation Index (AVI), Bare Soil Index (BI), Shadow Index or Scaled Shadow Index (SI, SSI) and Thermal Index (TI). It determines FCD by modeling operation and obtaining from these indices. Landsat-TM (Path-Row 146-039) of 14-09-1996 and Enhanced Thematic Mapper (ETM+) data (Path-Row 146-039) of 14-10-2002 has been used for the digital analysis of forest canopy density. Phenology of the vegetation is one of the important factors to be considered for effective stratification of the forest density. Optimum season for the assessment of forest canopy density in the present study is August - November months of the year. Pre-processing is done in Erdas Imagine to enhance spectral signature of digital data and then enhanced image is imported into BIL format to make it compatible with FCD mapper.
The Forest Canopy Density (FCD) model combines data from the four indices (VI, BI, SI and TI) (Fig. 10). The canopy density is calculated in percentage for each pixel. Vegetation index response to all of vegetation cover such as the forest, scrub land and the grass land was computed. Advanced vegetation index (AVI) reacts sensitively the vegetation quantity. Shadow index increases as the forest density increases. Thermal index increases as the vegetation quantity increases. Black colored soil area shows a high temperature. Bare soil index increases as the bare soil exposure degree of ground increase. These index values are calculated for every pixel.
Note that as the FCD value increases there is a corresponding increase in the SI value. In other words where there is more tree vegetation there is more shadow. Concurrently, if there is less bare soil (i.e. a lower BI value) there will be a corresponding decrease in the TI value. It should be noted that the VI is "saturated" earlier than SI. This simply means that the maximum VI values that can be regardless of the density of the trees or forest. On the other hand, the SI values are primarily dependent on the amount of tall vegetation such as tree which casts a significant shadow.
Vegetation Density is calculated using vegetation index and bare soil index as a prime inputs. It is a pre-processing method which uses principal component analysis. Because essentially, VI and BI have high negative correlation. After that, set the scaling of zero percent point and a hundred percent point. The shadow index (SI) is a relative value. Its normalized value can be utilized for calculation with other parameters.
The SSI was developed in order to integrate VI values and SI values. In areas where the SSI value is zero, this corresponds with forests that have the lowest shadow value (i.e. 0%). Areas where the SSI value is 100, correspond with forests that have the highest possible shadow value (i.e.100%). SSI is obtained by linear transformation of SI. With development of SSI one can now clearly differentiate between vegetation in the canopy and vegetation on the ground. This constitutes one of the major advantages of the new methods. It significantly improves the capability to provide more accurate results from data analysis than was possible in the past. Integration of VD and SSI means transformation for forest canopy density value. Both parameters have dimension and percentage scale unit of density. It is possible to synthesize both these indices safely by means of corresponding scales and units of each
Forest canopy stratification is carried out using object oriented image analysis. In this approach, the tone and texture are considered for the base level segmentation. Segmented objects are again put into hierarchical stratification by selecting the test and training area based on the ground truth. Finally, forest densities have been stratified using standard nearest neighbour classification scheme. Semi-conventional onscreen visual interpretation of digital data is carried out to map the forest canopy density. Details about the methodology and techniques used for visual interpretation are discussed else where (Roy et al., 1989).
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