Assessment of spatio - temporal changes in managroves of Kendrapara district, Orissa using remote sensing and geospatial tools
Student name: Ms Smita Bodhankar
Guide: Mr V.S.P.Sinha
Year of completion: 2009
Host Organisation: Defence Terrain Research Laboratory, DRDO
Supervisor (Host Organisation): Dr Sujata Dash
Abstract: Mangroves represent a specific ecosystem found in the inter-tidal zone along tropical and subtropical
coastlines, and are often located near estuaries and deltas. These ecosystems have the highest level of
productivity among natural ecosystems and are effective in storing large amounts of inorganic
nutrients which are washed into mangroves from the rivers and continental drainage. The threats to
mangroves can be consumptive, where mangroves are cut to be used as fuel, timber etc, and non
consumptive reason could be clear cutting of forests for settlements and polluted rivers. Satellite-based
remote sensing techniques have proved successful in providing comprehensive, reliable and up-to-date
information on land use, land cover and change dynamics periodically in cost-effective manner.
Therefore Remote Sensing is a good alternative to traditional field based surveys and methods, as it
allows information to be gathered from especially in the forbidding environment of mangroves. The
following study uses remote sensing to evaluate changes in mangrove forests of Kendrapara district,
Orissa and predict the changes. The study has also tried to explore the possibility of using NDVI as a
tool to distinguish between various classes which can also be used to give quantitative estimates of
mangrove vegetation parameters. Using LISS III data LULC maps were prepared for different data
sets, which were then integrated to get Annual LULC Maps for the years 96-97, 99, 2003 and 2006.
Change detection found that there has been increasing in dense mangroves as well as mangroves (less
dense) though constant exchanges between the three classes of mangroves did take place Tthere is
good probability that the change of mangroves to dense mangroves and scrub to mangroves will
continue in the future too. This means that the conservation efforts have been successful and continues
in future. The higher probability of fallow in last prediction is due to the timing of the image. NDVI
ranges found that Dense mangroves could be differentiated from mangroves (less dense), Agriculture,
fallow, although other vegetation class did mix with dense mangroves. NDVI calculated could be used
as a change detection tool and is helpful for analysis of various vegetation parameters such as density,
biomass, species richness etc. The range found out can further help in classification also.
