The forest cover mapping involves a series of steps as shown in the Picture blow. The cloud free satellite data is procured from NRSC for the entire country for the period October to January. The geometric rectification (co-registration) of the image data has been carried out primarily in reference to the previous cycle geo-referenced imageries to ensure that the successive forest cover maps have a high degree of image to image correspondence from the point of view of mapped features.
The hybrid classification approach followed in forest cover mapping utilizes the potential of the algorithms to generate clusters of pixels having close association and then assigning information class, i.e., appropriate forest cover density class to each cluster. This is further supported by the interpreter's knowledge, information from collateral sources and the observations made during ground truthing. Periodic ground data collected by field parties and the other ground truth information form the basis for the training data generation and accuracy assessment of the interpreted image data.
Schematic Diagram of the Methodology followed in Forest Cover Mapping
Limitations of Remote Sensing Data
The remote sensing data has certain inherent limitations that affect the accuracy of the forest cover mapping. Some of these limitations are mentioned below:
·Since the resolution of the LISS-III sensor data is 23.5 m, the land cover having dimension less than the above are not captured.
·Young plantations and tree species with less chlorophyll or poor foliage are many a times not discernable on satellite images due to poor leaf area index and transmittance.
·Considerable ground details may sometimes be obscured due to clouds and shadows. Such areas are difficult to classify without the help of collateral data or ground truthing.
·Gregarious occurrence of weeds like lantana in forest areas and agricultural crops like sugarcane, cotton, etc. occurring in the vicinity of forest area cause mixing of the spectral signatures and often make forest cover delineation difficult.
·Where heterogeneity in crop composition is high, generalized classification may affect the accuracy level.
·Non-availability of appropriate season data sometimes leads to misinterpretation of the features.
Source: Forest Survey of India, Dehradun. State of Forest Report (2015)