Data Acquisition through Remote Sensing for Management Planning of National Parks and Protected Areas
Subsequently the analysis and classification of the same data into 10 spectral classes differentiated the dense forest from other vegetation types and also into 10 spectral differentiated the dense forest from other vegetation types and also overcame the problem of the division of the dense forest into two distinct spectral classes. The second analysis gave six different spectral classes for the study area (2). They are: Dense forest, open forest, bare rocks/bare soil, grass cover with sparse tree vegetation, paddy and water bodies. The false colour composite (4,3,1) of IRS of 1992 March 10 confirmed the spectral identifiable on low altitude aerial photographs and found that open forest including the secondary forest growth in abandoned chena lands.
The visual interpretation of the imagery HRV I XS of 1:50,000 scale of the spot data of 1986 recognized eight different ecological units for the same study area one (1).They are (1) Dense forest, (2) Open forest, (3) Secondary vegetation, (4) Scrub on river levee, (5) Chena/abandoned chena, (6) Scrub, (7) Homesteads (8) Paddy, (9) Rock out corps and (10) identified on the 1:50,000 image used. Further forest plantations could be separated from other types of forest.
the ecological features clearly identified in both the computer-aided analyses and the visual interpretations were, dense forest, paddy, bare rock/bare soil and water bodies. In the unsupervised spectral analysis, the open forest, secondary vegetation, secondary vegetation in river levee, chena/abandoned chena, homesteads, upland croplands were classified in one spectral class. Further, study is required to separate these units by computer-aided analysis. The manual training field selection, that is “supervised training field selection” with ground observations would perhaps be able to separate these ecological units. The ecological unit, grassland with spare tree vegetation appears to include more area than the damana lands that is found in the study area. Further, field work is necessary to confirm this observation.
A major problem for the conservation of biodiversity in the protected areas is the encroachment of agricultural uses. Even the unsupervised classification of the spectral signatures was able to show the extent of encroachment. The irrigation structure (tanks), paddy cultivation, upland croplands and settlements showed distinct spectral signatures, and such activities were readily identified. These observations were confirmed by the false colour composite image of IRS data. Unless the low altitude photography is very recent, the extent of encroachments into the protected areas can not be accurately mapped.
The digital processing of IRS data appears to be very useful in the inventory of resources for protected area management. It is very rapid method, Major ecological differences could be easily distinguished and mapped. However, for wildlife management mere inventory of the natural resources is not adequate. The identification and characterization of habitat types are very necessary. The interpretation of low altitude photographs of a suitable scale provided a very good basis for the identification of habitat types. Such habitat types included more than one spectral class, but in different proportions. A method involving a combination of the interpretation of low altitude photographs, visual
interpretation of spot imagery and computer-aided analysis of IRS data together is most fruitful in the identification and characterization of the wildlife habitat type.
The multi-temporal satellite data is useful for assessment of stress conditions, arising from droughts, fires etc. what would to develop management plans for protection, and of vulnerable areas. To monitor deforestation and detect areas vulnerable to encroachment can be made with the
I am very grateful to Mr. Sarath Jayatilake and his staff for inviting me and giving me the opportunity to use their data. Mr. Ranjith Premalal of the Faculty of Agricultural Engineering readily assistant me in the computer – aided classification of the IRS data. I wish to thank Mr. T.M.J. Bandara of Natural Resources Management Centre for the useful suggestion made in the preparation of this paper.
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