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WDCAG

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Steven Lee

on 19 February 2013

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Transcript of WDCAG

Detecting Wetland Change through Supervised Classification of Landsat Satellite Imagery within the Tunkwa Watershed of British Columbia, Canada Steven Lee
Högskolan i Gävle - 2011 Content Background
Purpose
Materials and Methods
Results
Discussion Background Why are wetlands important? Biodiversity
Regulation of watershed hydrology
Carbon sequestration Diminishing trends identified in Canada Anthropogenic interaction
Climate change Remote Sensing Data collection without direct contact with site
Allows user to monitor change over time and potentially predict future changes
Landsat satellite missions Wetland inventory progress in Canada (Ducks Unlimited Canada, 2011) General Scope and Aim of Study Identify wetlands in Tunkwa watershed and detect change over time
Assess feasibility and practicality of using remote sensing and ''gapped" images for this study
Determine if Landsat imagery is adequate
Identify limitations of study and suggest potential improvements Materials and Data ArcMap 10
PANCROMA
USGS Landsat database
Land cover map from GeoBase (2000) Data Preparation [PANCROMA] Clipping imagery to appropriate sizes
Gap-filling the gapped images
Pan-sharpening Gap-filling Permanent failure of scan-line corrector (SLC) on Landsat 7 satellite in 2003 Source: PANCROMA user’s manual (Childs, 2011) Repair Process The PANCROMA software uses the blue, green, red, near infrared, and panchromatic multispectral bands from a non-gapped (2001) image
Cell reflectance values are estimated based on the non-gapped image and interpolation of adjacent cells on the gapped image Pan-sharpening Use of the higher resolution panchromatic band to 'sharpen' each image
Reduces 2001, 2005, and 2008 cell resolutions from 30m to 15m 2008 image - before & after Image Classification Training Stage Supervised classification requires manual input
Polygons digitized within the visible boundaries of each feature class
Polygon placement based on spectral reflectance of pixels under different wavelength combinations
Training sites attributed to feature types from pre-classified land use layer from 2000 Image Classification Maximum likelihood supervised classification for each image (1990, 1995, 2001, 2005, 2008) 1990 2008 Discussion Comparison of remotely sensed images proved to be a good, cost-effective, and efficient approach to monitoring wetlands. The use of Landsat data is appropriate and feasible for this purpose. However, limitations with these methods and data are apparent. Use of Landsat Data Appropriate for this study
Easy to obtain, free of charge
Used by Ducks Unlimited Canada
Land cover map from GeoBase was crucial for determining land cover classes during training stage Limitations SLC-off gap-filled images still of lesser quality than SLC-on
Minimal misclassification is likely
Post-classification processing employed to reduce pixel noise Limitations Retrieval of imagery
Cloud presence problematic
Must be same month/season
2009 and 2010 lacked good images from the USGS Landsat database
High resolution data is expensive Limitations Inclusion of an image from 1976
Low (60 m) resolution
Classification would likely be too inaccurate
However, aesthetically, the image is useful Land Use Change
Correlation vs. Causation Land Use Change
Correlation vs. Causation Timber harvests in BC have been associated with trends in rising CO2 levels
Wetlands are a contributor to carbon sequestration
Both of these trends negatively contribute to greenhouse gas levels in the atmosphere
Deforestation and expansion of Highland Valley mine can be correlated with wetland loss, but they may not be the cause
On-site environmental monitoring is necessary Value of Remote Sensing Remote sensing is a valuable tool for monitoring wetlands. Depending on the aims of the study, higher resolution imagery may be very useful for studies at a more localized scale. LiDAR data has also been proven useful for wetland studies. Conclusion Comparison of moderate resolution satellite imagery has shown to be an appropriate and feasible method for measuring wetland health over time. Considering the value of wetlands, generating a comprehensive wetland database for Canada should be pursued. Further measurements of wetlands in the Tunkwa watershed using either fine or moderate resolution imagery is encouraged. References Baker, C., Lawrence, R., Montagne, C., Patten, D. (2006). Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision-tree-based models. Wetlands, 26(2), 465-474.

British Columbia Ministry of Environment. (2006). Develop with Care: Environmental Guidelines for Urban and Rural Land Development in British Columbia. Retrieved from http://www.env.gov.bc.ca/wld/documents/bmp/devwithcare2006/DWC%202006%20Sec%201%20Introduction.pdf

British Columbia Ministry of Environment. (n.d.). Riparian Areas Regulation. Retrieved from http://www.env.gov.bc.ca/habitat/fish_protection_act/riparian/riparian_areas.html

Bunnel, F.L., Kremsater, L.L, Moy, A., Wells, R. (2011). Future vegetation structure and vertebrate distributions based on changes in
moisture balance and temperature. Retrieved from http://www.for.gov.bc.ca/hfd/library/FIA/2011/FSP_Y113120.pdf

Chen, J., Zhu, X., Vogelmann, J.E., Gao, F., Jin, S. (2011). A simple and effective method for filling gaps in Landsat ETM+ SLC-off images. Remote Sensing of Environment, 115(4), 1053-1064.

Childs, 2011. PANCROMA pan sharpening [computer software]. Available from http://www.pancroma.com/

Childs, 2011. PANCROMA Instruction Manual. Retrieved from http://www.pancroma.com/downloads/PANCROMAHelpFile_v3.pdf

Cook, B.D., Bolstad, P.V., Næsset, E., Anderson, R.S., Garrigues, S., Morisette, J.T., Nickeson, J., Davis, K.J. (2009). Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations. Remote Sensing of Environment, 113(11), 2366-2379.

Ducks Unlimited Canada. (2011). About DUC – A Wetland and Wildlife Conservation Organization. Retrieved from http://www.ducks.ca/aboutduc/index.html Elmahboub, W.M. (n.d.). A simulated Linear Mixture Model to Improve Classification Accuracy of Satellite Data Utilizing Degradation of Atmospheric Effect. Retrieved from http://www.iiisci.org/journal/CV$/sci/pdfs/P642150.pdf

Engels, J., Dixon-Hardy, D. (2010). Highland Valley Cooper Mine, Logan ake, BC, Canada. Retrieved from http://www.tailings.info/highland.htm
Environment Canada. (2007). Environmental Trends in British Columbia: Climate Change. Retrieved from http://www.env.gov.bc.ca/soe/et07/04_climate_change/technical_paper/climate_change.pdf

Environment Canada. (n.d.). Wetlands. Retrieved from http://www.ec.gc.ca/default.asp?lang=en&n=540B1882-1

Eppink, F. V., van den Bergh, J.C.J.M., Rietveld, P. (2004). Modelling biodiversity and land use: urban growth, agriculture and nature in a wetland area. Ecological Economics, 51(3-4), 201-216.

Fourie, A. (2009). Preventing catastrophic failures and mitigating environmental
impacts of tailings storage facilities. Procedia Earth and Planetary Science, 1(1), 1067-1071.

Garzelli, A., Nencini, F. (2007). Panchromatic sharpening of remote sensing images using a multiscale Kalman filter. Pattern Recognition, 40(12), 3568-3577.

GeoBase. Land Cover, Circo 2000 – Vector. [Land cover shapefile]. Retrieved from http://www.geobase.ca/geobase/en/data/landcover/index.html;jsessionid=6D22C0891BB6155C921EDE3DEA0064F1

Gunnarson, U., Löfroth, M., (2009). Våtmarksinventeringen – resultat från 25 års inventeringar. Naturvårdsverket. Retrieved from http://www.naturvardsverket.se/Documents/publikationer/978-91-620-5925-5.pdf

Hartig, E.K., Gornitz, V., Kolker, A., Mushacke, F., Fallon, D. (2002). Anthropogenic and climate-change impacts on salt marshes of Jamaica Bay, New York City. Wetlands, 22(1), 71-89. Huang, N., Wang, Z., Liu, D., Niu, Z. (2010). Selecting Sites for Converting Farmlands to Wetlands in the Sanjiang Plain, Northeast China, Based on Remote Sensing and GIS.

Environmental Management, 46, 790-800. doi: 10.1007/s00267-010-9547-6

Jogo, W., Hassan, R. (2010). Balancing the use of wetlands for economic well-being and ecological security: The case of the Limpopo wetland in southern Africa. Ecological Economics, 69(7), 1569-1579.

Karakatsoulis, J., Paul, S., Osborne, R., Ortner, C., Anderson, M. (2005). Skeetchestn Indian Band: Research and Development in Riparian Zone Management. Retrieved from http://www.skeetchestn.ca/download/document/SIBRDRZM.pdf

Kashaigili, J.J., Mbilinyi, B.P., Mccartney, M., Mwanuzi, F.L. (2006). Dynamics of Usangu plains wetlands: Use of remote sensing and GIS as management decision tools. Physics and Chemistry of the Earth, 31(15-16), 967-975.

Konrad, S.R., Rempel, R.S. (1990). Cost-effectiveness Of Landsat TM Classification By Operations Staff. Geoscience and Remote Sensing, 28(4), 769-771.

Lee, T., Yeh, H. (2009). Applying remote sensing techniques to monitor shifting wetland vegetation: A case study of Danshui River estuary mangrove communities, Taiwan. Ecological Engineering, 35(4), 487-496.

Lillesand, T.M., Kiefer, R.W., Chipman, J.W. (2004). Remote Sensing and Image Interpretation. University of Michigan: John Wiley and Sons.

May, A.M.B., Pinder III, J.E., Kroh, G.C. (1997). A comparison of Landsat Thematic Mapper and SPOT multi-spectral imagery for the classification of shrub and meadow vegetation in northern California, U.S.A.. International Journal of Remote Sensing, 18(18), 3719-3728.

Melendez-Pastor, I., Navarro-Pedreno, J., Gomez, I., Koch, M. (2010). Detecting drought induced environmental changes in a Mediterranean wetland by remote sensing. Applied Geography, 30(2), 254-262.

Moody, A. (1998). Using Landscape Spatial Relationships to Improve Estimates of Land-Cover Area from Coarse Resolution Remote Sensing. Remote Sensing of Environment, 64(2), 202-220. Morisette, J.T., Nickeson, J.E., Davis, P., Wang, Y., Tian, Y., Woodcock, C.E., Hansen, M., Cohen, W.B., Oetter, D.R., Kennedy, R.E. (2003). High spatial resolution satellite observations for validation of MODIS land products: IKONOS observations acquired under the NASA Scientific Data Purchase. Remote Sensing of Environment, 88(1-2), 100-110.

NASA. (2011). Landsat Data Continuity Mission. Retrieved from http://ldcm.nasa.gov/
National Wetlands Working Group, 1997. The Canadian Wetland Classification System. Retrieved from http://www.portofentry.com/Wetlands.pdf
Natural Resources Canada. (n.d.). Wetlands. Retrieved from http://atlas.nrcan.gc.ca/sites/english/learningresources/theme_modules/wetlands/index.html

Nielsen, E.M., Prince, S.D., Koeln, G.T. (2008). Wetland change mapping for the U.S. mid-Atlantic region using an outlier detection technique. Remote Sensing of Environment, 112(11), 4061-4074.

Olewiler, N. (2004). The Value of Natural Capital in Settled Areas of Canada. Retrieved from http://www.ducks.ca/aboutduc/news/archives/pdf/ncapital.pdf

Poulin, B., Davranche, A., Lefebvre, G. (2010). Ecological assessment of Phragmites australis wetlands using multi-season SPOT-5 scenes. Remote Sensing of Environment, 114(7), 1602-1609.

Pringle, M.J., Schmidt, M., Muir, J.S. (2009). Geostatistical interpolation of SLC-off Landsat ETM+ images. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6), 654-664.

Pypker, T.G., Fredeen, A.L. (2003). Below ground CO2 efflux from cut blocks of varying ages in sub-boreal British Columbia. Forest Ecology and Management, 172(2-3), 249-259.

Rebelo, L.-M., Finlayson, N., Nagabhatla, N. (2009). Remote Sensing and GIS for wetland inventory, mapping and change analysis. Journal of Environmental Management, 90(7), 2144-2153.

Reimer, K. (2009). The Need for a Canadian Wetland Inventory. Retrieved from http://www.ducks.ca/aboutduc/news/archives/2009/pdf/301-cwi.pdf Sader, S. A., Ahl, D., Liou, W. (1995). Accuracy of Landsat-TM and GIS rule-based methods for forest wetland classification in Maine. Remote Sensing of Environment, 53(3), 133-144.
Sawaya, K.E., Olmanson, L.G., Heinert, N.J., Brezonik, P.L, Bauer, M.E. (2003). Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery. Remote Sensing of Environment, 88(1-2), 144-156.

Symmetree Consulting Group. (2009). Adapting Forest Management in the Kamloops TSA to Address Climate Change. Retrieved from http://www.for.gov.bc.ca/hcp/ffs/KFFS_REPORT_ALL_June25-09.pdf

Taylor, B. (2004). Climate Trends in BC, Canada and the World. Retrieved from http://www.ser.org/serbc/pdf/Climate_trends_in_British_Columbia,_Canada,_and_the_world.pdf

Töyrä, J., Pietroniro, A. (2005). Towards operational monitoring of a northern wetland using geomatics-based techniques. Remote Sensing of Environment, 97(2), 174-191.

U.S. Geological Survey. USGS Global Visualization Viewer. [1976-2009 satellite imagery]. Retrieved from http://glovis.usgs.gov/

Wakelyn, L.A. (1990). Wetland inventory and mapping in the Northwest Territories using digital Landsat data. Retrieved from http://www.enr.gov.nt.ca/_live/documents/content/96.pdf

Wang, M., Howarth, P.J. (1993). Modeling errors in remote sensing image classification. Remote Sensing of Environment, 45(3), 261-271.

Weiss, D.J., Crabtree, R.L. (2011). Percent surface water estimation from MODIS BRDF 16-day image composites. Remote Sensing of Environment, 115(8), 2035-2046.

Whigham, D.F. (1999). Ecological issues related to wetland preservation, restoration, creation, and assessment. The Science of The Total Environment, 240(1-3), 31-40.

Williams, D. C., Lyon, J. G. (1997). Historical aerial photographs and a geographic information system (GIS) to determine effects of long-term water level fluctuations on wetlands along the St. Marys River, Michigan, USA. Aquatic Botany, 58(3-4), 363-378. Withey, P., Cornelis van Kooten, G. (2011). The effect of climate change on optimal wetlands and waterfowl management in Western Canada. Ecological Economics, 70(4), 198-805.

World Wildlife Fund. (2000, November 9-10). Implementing the EU Water Framework Directive: A seminar series on water. Retrieved from http://assets.panda.org/downloads/sem2-proc.pdf

Wright, C., Gallant, A. (2007). Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data. Remote Sensing of Environment, 107(4), 582-605.

Wulder, M. (2003). EOSD Land Cover Classification Legend Report. Retrieved from http://cfs.nrcan.gc.ca/files/501

Wulder, M.A., White, J.C., Goward, S.N., Masek, J.G., Irons, J.R., Herold, M., Cohen, W.B., Loveland, T.R., Woodcock, C.E. (2008). Landsat continuity: Issues and opportunities for land cover monitoring. Remote Sensing of Environment, 112(3), 955-969.

Zomer, R.J., Trabucco, A., Ustin, S.L. (2009). Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing. Journal of Environmental Management, 90(7), 2170-2177. After filtering and smoothing Before processing
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