Methodology
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The Figure shows the schematic diagram of the methodology. Using Digital Image Processing (DIP) software, the satellite data in digital form was downloaded on the Workstations from the CDs procured from the NRSA. Radiometric and contrast corrections were applied for removing radiometric defects and for improving visual impact of the False Colour Composites (FCC).

Schematic diagram showing Methodology of Forest Cover Mapping

Geometric rectification of the data is carried out with the help of scanned and geo-referenced Survey of India (SOI) toposheets on 1:50,000 scale. The methodology of interpretation involves a hybrid approach in which unsupervised classification (ISODATA algorithm) aided on-screen visual interpretation of forest cover was done.

Normalized Difference Vegetation Index (NDVI) transformation was used for removing non-vegetated areas from the scene. Areas of less than one hectare, whether classified as forest cover within non-forest areas or blanks within forest cover, were excluded by appropriate DIP techniques.

Degraded forests with tree canopy density less than 10 percent have been classified as scrubs, which do not form part of the forest cover.

Shadow areas in the scenes have been treated separately. Shadow regions on the images are highlighted using band ratio techniques. Forest cover classification of the totally obscure areas due to shadow or cloud cover has been done using the ground truth information.

Mangrove forests have characteristic tone and texture on the satellite image. Their presence on the coastal areas makes them even more conspicuous. They have been, therefore, separately classified.

This was then followed by extensive ground verification which takes more than six months. All the necessary corrections were subsequently incorporated. Reference data collected through ground truth and field experience of the interpreter played an important role in delineating the forest cover patches and classifying them into three canopy density classes.
 

 

 
Forest Cover Mapping

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