ADIZA OWUSU ADUOMIH DECEMBER 06, 2011 NRS 509: CONCEPTS IN GIS AND REMOTE SENSING APPLICATION OF REMOTE SENSING AND GIS FOR WETLAND MAPPING

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1 ADIZA OWUSU ADUOMIH DECEMBER 06, 2011 NRS 509: CONCEPTS IN GIS AND REMOTE SENSING APPLICATION OF REMOTE SENSING AND GIS FOR WETLAND MAPPING Introduction Wetlands are areas that are transitional between terrestrial and aquatic systems, where the water table is usually at or near the surface or the land is covered by shallow water (Frohn et al., 2009). Wetlands provide a number of direct and indirect services, ranging from protection against coastal erosion by stabilizing sediments, furnishing nursery and spawning areas for commercially important coastal fish and shellfish species and providing stopover sites for migratory birds, fish, and mammals (Klemas, 2011). They are also important landscape features because they hold and slowly release flood water and snow melt, recharge groundwater, act as filters to cleanse water of impurities, recycle nutrients, and provide recreation and wildlife viewing opportunities for millions of people. Wetlands form an important component of the worlds coastal ecosystems. However, these ecosystems are being threatened by both natural and anthropogenic factors. The global loss of wetland ecosystems are more rapid than any other ecosystem (Agardy and Alder, 2005). Primary factors causing significant losses worldwide include long term sea level rise and its fluctuations, tidal activity, cyclonic storms and tsunamis, expansion by human settlements and conversion to fish and shrimp ponds. This affect the economic development and the environment as a whole. Owing to their importance and potential threats, many wetlands around the world have received international attention through the "Convention on Wetlands of International Importance" which opened for signature at Ramsar, Iran, in 1971 (Aheto et al.,2011) Application of Remote Sensing and GIS One of the major issues facing the management of the wetland ecosystem globally is the lack of effective management plan. This is often because wetland managers do not have access to information on the present conditions and also on the changes that occur in the wetland area over period of time, due to lack of reliable and up to date geospatial information (Kairo et al., 2002). However, accurate and up to date geospatial information on the current status of wetlands; their distribution as well as their abundance; are essential for monitoring the wetland ecosystem over time, and also for investigating linkages with other ecological systems that rely on them either directly or indirectly. Accurate wetland mapping has been identified as an effective way for monitoring wetland response to natural and anthropogenic actions (Baker et al., 2006). However detailed wetland maps are not easy to produce, mainly because wetland ecosystems are very difficult to access. Though traditional in situ data collection have been applied in some studies, it is becoming increasingly unpopular because of obvious reasons like

2 accessibility, labor and scale. Remote sensing and Geographic Information System technologies have provided an effective solution to these problems (Held et al., 2003). It has also proven to be very useful in areas of land cover change mostly for the identification of degraded areas, mapping regeneration and tracing major changes in the land cover (Ramachandran et al., 1998). This capability has been made possible by the use of Multi Temporal imagery for such applications. Remote sensing technology in recent years has proved to be of great importance in acquiring data for effective resources management. Multi Temporal data derived from different satellite such as Landsat series, IRS, SAR data which are independent of cloud cover and weather interference, provide information about the areal, extent, conditions and boundary of coastal wetlands (Ramachandran et al., 1998). This is also suitable for mapping major changes in land cover, provide opportunity to monitor changes in land use activities and estimate wetland biomass (Klemas, 2011). Though traditional remote sensing approaches can provide important information for mapping wetlands in general and detecting changes in coverage, new satellite sensors and techniques have considerably improved the accuracy of wetland classifications in detecting individual species as well as providing reliable estimates of features. There has been very rapid development of new remote sensing sensors and systems in recent years. The new types of satellite sensors include very high resolution systems like Quickbird, IKONOS, GeoEye 1,Worldview 2, and ALOS PRISM. Other imagery like RADAR and Hyperspectral image data have been explored in many instances because of their capability to overcome many of the limitations associated with the Landsat image series and other traditional approaches. Hyperspectral imagery, for instance may provide several hundreds of spectral bands resulting in detailed fine resolution data that can be used to identify small differences in species or features. The notable advantages of using GIS on the other hand include the ability to update the information rapidly, to undertake comparative analytical work and making this information available as required ( Long and Skewes, 1994). GIS in addition to providing efficient data storage and retrieval facilities also offers a cheaper option of monitoring wetland ecosystems conditions over time ( Ramachandran et al., 1998). It also provide the platform for complex analysis and operations. There is no doubt that the increasing quality and accessibility of geospatial data will assist the study of wetland ecosystems in the future, and it is clear that wetland managers have only begun to explore the potentials of this versatile tool. REFERENCES Agardy, T and Alder, J. (2005). Coastal systems. In Hassan, R., Scholes, R., and Ash, N. (eds). Ecosystems and Human Well being Volume 1, Current Status and Trends: Findings of the Condition and Trends Working Group. Island Press, Washington, DC. Aheto, D. W., Mensah, E., Aggrey Fynn, J., Obodai, E. A., Mensah, C. J., Okyere, I., and Aheto, S. P. K. (2011). Spatio temporal analysis of two coastal wetland systems in Ghana:

3 Addressing ecosystem vulnerability and implications for fisheries development in the context of climate and land use changes. Annals of Biological Research, 3 (3) : Baker, C., Lawrence, R., Montagne, C., Patten, D. (2006). Mapping wetlands and riparian areas using landsat etm+ Imagery and decision tree based models. The Society of Wetland Scientists. Wetlands, 26(2): Frohn, R.C., Reif, M., Lane, C., Autrey, B. (2009). Satellite remote sensing of isolated wetlands using object oriented Classification of landsat 7 data. The Society of Wetland Scientists. Wetlands, 29(3): Held, A., Ticehurst, C., Lymburner, L., Williams, N., High resolution mapping of tropical mangrove ecosystems using hyperspectral and radar remote sensing. International Journal of Remote Sensing 24, Kairo, J.G.,Kivyatu, B., and Koedam, N.(2002). Application of remote sensing and GIS in the management of mangrove forests within and adjacent to Kiunga marine protected area, Lamu, Kenya. Klemas, V., (2011). Remote Sensing Techniques for Studying Coastal Ecosystems. Journal of Coastal Research, 27(1):2 17. Long, B., G. and Skewes, T., D.(1994) GIS and remote sensing improves mangrove mapping. In. Kairo, J.G.,Kivyatu, B., and Koedam, N.(2002). Application of remote sensing and GIS in the management of mangrove forests within and adjacent to Kiunga marine protected area, Lamu, Kenya. Ramachandran. S, Sundramoorthy, S., Krishnamoorthy, R., Devasenapathy, J., Thanikachalam, M. (1998). Application of Remote Sensing and GIS to Coastal Wetland Ecology of Tamilnadu and Andaman and Nicobar group of Islands with special reference to Mangroves. Current Science,75(3) :

4 Annotated Bibliography Frohn, R.C., Reif, M., Lane, C., Autrey, B. (2009). Satellite remote sensing of isolated wetlands using object oriented Classification of landsat 7 data. The Society of Wetland Scientists. Wetlands, 29(3): In this study, Remote sensing technology was applied in the mapping of isolated wetlands in the St. Johns River Water Management District of Alachua County, Florida, which has high density of wetlands and high diversity of mixed land cover. The authors applied both image segmentation and object oriented analyzes to multi seasonal Landsat 7 imagery to map the wetlands. Other ancillary GIS data were used by the authors to validate their results. Accuracy for individual isolated wetlands was then determined based on the intersection of reference data and the extracted polygons. The authors demonstrated that, satellite remote sensing provides an accurate, relatively inexpensive, and timely means for classifying isolated depressional wetlands on a regional or national basis. Tiner, R.W. (2003). Estimated extent of Geographically Isolated Wetlands in Selected areas of The United States. The Society of Wetland Scientists. Wetlands, 23(3): In a similar study, the U.S Fish and Wildlife (FWS) estimated the extent of geographically isolated wetlands in selected areas of the United States with a Geographic Information System technology. In this study, GIS was used to analyze existing digital data (e.g., National Wetlands Inventory data and U.S. Geological Survey hydrologic data) to predict the extent of isolated wetlands in 72 study areas. The study concluded that isolated wetlands constituted a significant proportion of the wetland resource in arid and semi arid to sub humid regions and in karst topography. However, the author accepts that the results of this GIS analysis present only one perspective on the extent of geographically isolated wetlands in the country and represent a starting point for more detailed assessments. Baker, C., Lawrence, R., Montagne, C., Patten, D. (2006). Mapping wetlands and riparian areas using Landsat ETM+ Imagery and decision tree based models. The Society of Wetland Scientists. Wetlands, 26(2): Baker et al discussed how multi season Landsat ETM+ imagery can be combined with ancillary topographic and soils data to map wetland and riparian systems in the Gallatin Valley of Southwest Montana, USA. In this paper, a combination of the Classification Tree Analysis (CTA) and Stochastic Gradient Boosting (SGB) decision tree based classification algorithms were used to distinguish wetlands and riparian areas from the rest of the landscape. CTA creates a single classification tree using a one step look ahead procedure to reduce variance. SGB on the other hand uses classification errors to refine tree development and incorporates multiple tree results into a single best classification. The result of the study showed that, the SGB classification (86.0% overall accuracy) was more effective than CTA (73.1% overall accuracy) at detecting a variety of wetlands and riparian zones present on this landscape.

5 Hernández Cornejo, R., Koedam, N., Ruiz Luna, A., Troell, M., Dahdouh Guebas, F. (2005). Remote sensing and ethnobotanical assessment of the mangrove forest changes in the Navachiste San Ignacio Macapule lagoon complex, Sinaloa, Mexico. Ecology and Society 10(1): 16. In this paper, Remote sensing technology was applied in the evaluation of the socioeconomic, ecological functions and the spatiotemporal changes in the mangrove wetland vegetation cover. This study was conducted on the Navachiste San Iganacio Macapule lagoon complex in northwest Mexico over a 27 year period using Landsat MSS and TM imagery. Additionally, local ethnobotanical uses of the mangrove forest and local perceptions about the status and recent development of the mangrove forest cover were also analyzed. The results show an appreciable increase in the mangrove area over the period. Though the authors acknowledge that, the rate of spatial increase is not always a good indicator of a positive trend, because it can mask the qualitative degradation of the mangrove area at different places, they demonstrate the need for its application. In this paper, the authors demonstrates how knowledge and perception of local folks could be incorporated in mangrove mapping. Zhang, S., Xiaodong, N., Kong, B., Wang, Z., Jiang, H., Huan,Y., Zhao, Z., Xiaofeng, L., Chunyue, L., Dale, P. (2009). Identifying Wetland Change in China's Sanjiang Plain Using Remote Sensing. The Society of Wetland Scientists. Wetlands, 29(1): Zhang et al discussed how Remote Sensing is used to map and examine demographic and socioeconomic drivers of land use and cover (LULC) change in Sanjiang plain in China. The authors applied a maximum likelihood supervised classification and post classification change detection techniques to Landsat MSS/TM historical images acquired in 1976, 1986, 1995, 2000, and From the results, it showed that the Sanjiang plain had been subject to much change and the drivers for LULC patterns was associated with widespread reclamation for agriculture and alterations to the marsh hydrology. This is a typical application of multi temporal Landsat imagery establish changes in the areal coverage of wetlands over time. Hirano, A., Madden, M., Welch, R. (2003). Hyperspectral Image Data for mapping wetland vegetation. The Society of Wetland Scientists. Wetlands, 23(2): The objectives of this study were in two folds; to investigate the possibilities for differentiating wetland vegetation communities; and assess the potential uses of hyperspectral image data along with automated classification methods for constructing wetland vegetation. The authors used Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) with 224 bands, each with 0.01 mm spectral resolution and 20 meter spatial resolution, to produce a vegetation map for a portion of Everglades National Park, Florida, USA. The hyperspectral image data proved suitable for detecting invasive exotic species lather leaf (Colubrina asiatica) that is sometimes difficult to differentiate on aerial photographs. The result of this paper reinforces the recent development in Remote sensing technology for detecting subtle differences in features or species.