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Inland habitat environmental sensitivity index mapping and modeling using geographic information systems and remote sensing technology
This study applies the Inland ESI mapping model developed by ERML and ESRI for the Niger Delta to the southeastern coastal region of Nigeria. Traditionally ESI mapping had been applied to shoreline areas and the maps typically contain three types of information: shoreline classification in terms of...
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| Format: | Conference Proceeding |
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| Published: |
2007
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| LEADER | 00000njm a2000000a 4500 | ||
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| 001 | oai:repository.ui.edu.ng:123456789/8502 | ||
| 042 | |a dc | ||
| 720 | |a Taiwo, O. |e author | ||
| 720 | |a Areola, O. |e author | ||
| 260 | |c 2007 | ||
| 520 | |a This study applies the Inland ESI mapping model developed by ERML and ESRI for the Niger Delta to the southeastern coastal region of Nigeria. Traditionally ESI mapping had been applied to shoreline areas and the maps typically contain three types of information: shoreline classification in terms of sensitivity to oiling, human-use resources, and biological resources. The ESI shoreline classification scheme is a numeric characterization of the sensitivity of coastal environments and wildlife to spilled oil. ESI was developed to reduce the environmental consequences of a spill and help prioritize the placement and allocation of resources during cleanup efforts. An improvement to the traditional ESI atlas has further been added through the development of ESI for inland/interior areas. This is particularly significant in the Nigeria context where many oil and gas facilities are located in the inland/interior habitats. This study shows that the model developed for the Niger delta is equally applicable to southeast coastal environment. The modeling is done using satellite imagery followed by rigorous field data collection and modeling within Arcview GIS environment. The GIS approach is quite ideal for ESI modeling because of its capability to sequentially overlay different data layers for various kinds of spatial statistical analysis and spatial modeling. The most critical element is the construction of the database: the relational database structure adopted greatly facilitates data search and analytical operations. | ||
| 024 | 8 | |a ui_inpro_taiwo_inland_2007 | |
| 024 | 8 | |a Proceedings of International Conference on Adaptive Science and Technology technology, held between 10-12 December, in Accra, Ghana | |
| 024 | 8 | |a http://ir.library.ui.edu.ng/handle/123456789/8502 | |
| 653 | |a Environmental Sensitivity Index | ||
| 653 | |a Geographic information systems | ||
| 653 | |a Remote sensing | ||
| 245 | 0 | 0 | |a Inland habitat environmental sensitivity index mapping and modeling using geographic information systems and remote sensing technology |