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Transcript of WDCAG
Högskolan i Gävle - 2011 Content Background
Materials and Methods
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
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.
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