Ecosystem evaluation and probable land use projections on Deepor Beel, a Ramsar Wetland in North East India | |
| Author | Mozumder, Chitrini |
| Call Number | AIT Diss. no.RS-14-03 |
| Subject(s) | Land use--Remote sensing--Evaluation Ramsar Wetland (India) Wetland ecology--Remote sensing--Evaluation--Ramsar Wetland (India) |
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering in Remote Sensing & Geographic Information Systems |
| Publisher | Asian Institute of Technology |
| Series Statement | Dissertation ; no. RS-14-03 |
| Abstract | Wetlands, the natural or artificial transition zones between land and water are those areas which permanently or seasonall y get sa turated with water and support the ecosystem. However, with rapid urbanization and population growth, most of the wetlands in cities and outskirts, are turning into “wastelands”. International treaties, like Ramsar convention have therefore put effort to c onserve and wise use of these wetlands and related resources. Such efforts demand long term monitoring of wetland ecosystems for the sustainable development plans of conservation measures. Deepor Beel, a Ramsar wetland and major storm - water - basin of River Brahmaputra in northeastern region of India, particularly needs attention due to its constant degradation over past decades. This freshwater wetland is just 10 km away from the fastest growing city Guwahati of North East India and lacks a proper management policy with adequate institutional arrangements. This study mainly aims in top - down assessment of the wetland and its upland in the past 23 years and evaluation of the effect of land use conversions on the wetland in next 40 years. Geospatial technologi es offer cost effective methods for identifying and monitoring wetlands and their uplands. However, due to unavailability of adequate field information as well as complexity in the composition, extraction of cover types within a wetland is still a major ch allenge among many others. In this study, Landsat, ALOS AVNIR and SRTM DEM (Digital Elevation Model) satellite data have been used for analyzing spatio - temporal changes in 2 spatial scales of Deepor Beel and its upland. For the wetland scale, a rule based classification algorithm was employed using 6 satellite indices which include NDVI (Normalized Difference Vegetation Index), NDWI - 1 (Normalized Difference Water Index), NDWI - 2, MNDWI (Modified Normalized Difference Water Index), NDPI (Normalized Difference Pond Index), and NDTI (Normalized Difference Turbidity Index). The wetland was classified into six major cover types, namely open water, transition zone, aquatic vegetation, vegetated land, grass land, and mudflat. A fuzzy accuracy assessment of the class ified datasets showed an overall accuracy of 82% for MAX criteria and 90% for RIGHT criteria. Using the classified datasets, historical and seasonal changes of the various cover types were evaluated. For the catchment scale, 12 LULC (Land Use Land Cover) t ypes were identified. The hilly forests could be easily separated from plain forests due to the use of the DEM into the rules along with bands and band ratios. The water indices helped to accurately differentiate between water logged areas and urban areas. The validation using field data proved an accuracy of 94% and kappa coefficient of 0.91 in case of the catchment scale classification. The study showed highest historical change in case of hilly forests and built ups implying the deforestation and rapid u rbanization in the area. Climatic, physical, geographic and demographic factors were analys ed by using multivariate regression analysis and Cramer‟s V analysis for two periods of 1989 - 2001 and 2001 - 2012 to quantify the impacts of the explanatory factors on the wetland changes. In both periods, out of 24 climatic factors only 3 were found effective in driving the changes. The quantitative analysis with the driving factors revealed that in the period of 1989 - 2001, factors related to agricultural lands and u rbanization were most influential. The period 2001 - 2011 was crucial for the factors such as distance to built - up, road, garbage dump, built - up growth rate and elevation contributing to direct or indirect changes in the wetland. The study showed that the De epor Beel is highly eutrophicated in the last 23 years and it is further getting degraded due to a number of factors, such as croplands, urban growth, garbage dump, roads etc. in the catchment. The seasonal and flow accumulation analysis revealed that the wetland, which barely covers an area of 927 ha, inundates over 6000 ha during monsoon flooding every year. It was found that about 4400 ha of the inundated area is mostly covered by rice fields which are fed with fertilizers (P, K, N) 1 to 2 times per year , which significantly contributes to the eutrophication in the Ramsar wetland. An Artificial Neural Network (ANN) based model was developed to predict future impacts of urban and agricultural expansion on the uplands of Deepor Beel, by 2025 and 2035 respe ctively. Simulations were carried out for three different transition rates as determined from the changes during 2001 - 2011, namely simple extrapolation, Markov Chain (MC), and System Dynamic (SD) modeling, using projected population growth, which were furt her investigated based on three different zoning policies. The first zoning policy employed no restriction while the second conversion restriction zoning policy restricted urban - agricultural expansion in the Guwahati Municipal Development Authority (GMDA) proposed green belt, extending to a third zoning policy providing wetland restoration in the proposed green belt. The prediction maps were found to be greatly influenced by the transition rates and the allowed transitions from one class to another within e ach sub - model. The model outputs were compared with GMDA land demand as proposed for 2025 whereby the land demand as produced by MC was found to best match the projected demand. Regarding the conservation of Deepor Beel, the Landscape Development Intensity (LDI) Index revealed that wetland restoration zoning policies may reduce the impact of urban growth on a local scale, but none of the zoning policies was found to minimize the impact on a broader base. The results from this study may assist the planning a nd reviewing of land use allocation within Guwahati city to secure ecological sustainability of the wetlands. |
| Year | 2014 |
| Corresponding Series Added Entry | Asian Institute of Technology. Dissertation ; no. RS-14-03 |
| Type | Dissertation |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Remote Sensing (RS) |
| Chairperson(s) | Tripathi, Nitin K. |
| Examination Committee(s) | Ebbers, Theo ;Taravudh Tipdecho ;Kawasaki, Akiyuki |
| Scholarship Donor(s) | Government of Japan |
| Degree | Thesis (Ph. D.) - Asian Institute of Technology, 2014 |