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Lesley Bross - Portland State University lbross@pdx.edu August 1, 2014 Using multispectral imagery to monitor vegetation change following flow restoration to the Lower Owens River, California

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Research question(s) Can Landsat scenes be used to monitor riparian vegetation change following flow restoration to the Lower Owens River ? 1.Compare different change detection techniques 2.Calculate and analyze landscape pattern metrics 3.Compare results from study plots with different vegetation communities Source: OVC 2008

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Riparian zones Source: Ecosystem Sciences 2008B Interface between terrestrial and aquatic ecosystems Diverse habitat = relatively high species richness Provide important ecological functions About 2% of original riparian land cover remains in western United States (Jones et al. 2008)

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Restoration efforts Source: Coffin 2013 River restoration projects are expensive (time and money) Monitoring success with ground surveys is also costly Is there an alternative?

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Satellite multispectral imagery Ground-truth data has limited spatial and temporal resolutions Landsat images cover a large areas inexpensively Unique landscape patterns are revealed at larger scale Regularly scheduled satellite images can monitor landscape change Archived remotely sensed data provides views of pre-restoration landscape

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Owens River Watershed Source: Ecosystem Sciences 2008B

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Lower Owens River Source: Risso 2007

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Hydrologic history 1913: First Los Angeles aqueduct opens 1929: Tinemaha Dam completed 1970: Second Los Angeles aqueduct opens 1991: LADWP and Inyo County approve Long Term Water Agreement; EIR completed 1997: Memorandum of Understanding 2006: Base flow restored to Lower Owens

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2008: Seasonal habitat flow Source: OVC 2008

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NDVI NDVI: Normalized Difference Vegetation Index represents greenness: ((IR - R)/(IR + R)) * 100 + 100 Ratio minimizes multiplicative noise from topographic factors (shadows, aspect) when comparing images Source: ESRI 2010

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Landscape pattern metrics Tool to explain effects of landscape patterns on biological processes and vice versa Patch-corridor-matrix is a popular model for quantifying landscape change Source: Barnes 2000

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Fragmentation and connectivity Fragmentation: breaking up of land cover type into smaller, isolated plots Connectivity: spatial continuity of land cover type across a landscape; How easy is it to move among patches? Source: Fahrig 2003

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Select metrics appropriate for scale and research question Freeman et al. recommend: percent of landscape occupied (per class) number of patches mean patch size patch density edge density How did land cover patterns change between 2002 and 2009? Which metrics to calculate? Source: ESRI 2010

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Plot 2: Vegetation maps Source: Ecosystem Sciences 2002, 2010 Ground surveys conducted in 2001, 2002, and 2009 Vegetation complexes reclassified to NLCD classes

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Plot 3: Vegetation maps Source: Ecosystem Sciences 2002, 2010

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Acquire Landsat images for summers of 2002 and 2009 Source data for 1.Post-classification change detection 2.Image differencing (NDVI) Landsat multispectral imagery Source: USGS 2014

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#1: Post-classification change detection 1.Unsupervised classification => land cover map (5 NLCD classes) 2.Perform accuracy assessment using ground truth polygons 3.Accuracy = important! Every error in both classifications = error in final results 4.Collapse classes if needed 5.Analyze from-to maps (percent occupied)

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Spatial pattern analysis 2002 and 2009 land cover maps = input for pattern analysis Class-level metrics for: forest, wetlands, and grasslands Calculate: number of patches mean patch size patch density edge density Plot mean patch size against density (connectivity) Source: Freeman et al. 2003

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#2: NDVI image differencing 1.Preprocessing: atmospheric correction? 2.Calculate NDVI for both dates 3.Subtract 2009 NDVI from 2002 NDVI 4.Difference values indicate: a)Direction of change (bare earth -> grasses) b)Magnitude of change 5.Set threshold values for change (difficult) Source: ESRI 2010 2002 2009

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Spatial pattern analysis (redux) Determine NDVI threshold value indicating green vegetation Use value to create data layers with vegetation/no- vegetation parcels 2002 and 2009 data layers = input for pattern analysis Calculate: number of patches mean patch size patch density edge density Plot mean patch size against density (connectivity)

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Anticipated outcomes 1.Evaluate two common change detection methods and report their benefits and limitations 2.Assess the usability of Landsat-derived vegetation maps for riparian (narrow) landscapes 3.Demonstrate an economical, repeatable process that can be used to monitor vegetation change following river rehabilitation efforts Source: Ecosystem Sciences 2008A

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Timeline Fall 2014: Literature review and technical tasks Winter 2015: Write-up results Spring 2015: Thesis presentation

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References ArcGIS 10. 2010. ArcGIS 10 Help. Redlands, CA: Environmental Systems Research Institute (ESRI). Barnes, T. 2000. Landscape ecology and ecosystems management. Lexington, KY: University of Kentucky College of Agriculture. Available at http://www2.ca.uky.edu/agc/pubs/for/for76/for76.htm (last accessed 10 July 2014). Coffin, B. 2013. Hemlock Dam removalClearing a path for steelhead recovery in Trout Creek. Vancouver, WA: Salmon Recovery Conference. Available at: http://www.rco.wa.gov/documents/SalmonConference/presentations/TuesdayPMCoffin.pdf (last accessed 13 September 2013). Danskin, W. 1998. Evaluation of the hydrologic system and selected water-management alternatives in the Owens Valley, California. United States Geological Survey (USGS), Water-supply Paper 2370:1-175. Ecosystem Sciences. 2002. Lower Owens River Project Baseline polygons. Prepared for the site scale mapping component of the LORP Monitoring and Adaptive Management program. (Received from Timothy Maguire on April 21, 2014). Ecosystem Sciences. 2008A. Lower Owens River project 2008 annual monitoring draft report. Prepared for Los Angeles Department of Water and Power and Inyo County Water Department. Available at http://www.inyowater.org/projects/lorp/ (last accessed 10 July 2014). Ecosystem Sciences. 2008B. Lower Owens River Project Monitoring, Adaptive Management and Reporting Plan. Prepared for Los Angeles Department of Water and Power and Inyo County Water Department. Available at http://www.inyowater.org/projects/lorp/ (last accessed 12 June 2014).

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Ecosystem Sciences. 2010. Lower Owens River Project 2010 polygons. Prepared for the site scale mapping component of the LORP Monitoring and Adaptive Management program. (Received from Timothy Maguire on April 21, 2014). Fahrig, L. 2003. Effects of Habitat Fragmentation on Biodiversity. Annual Review of Ecology, Evolution, and Systematics 34(2003): 487-515. Freeman, R., E. Stanley, and M. Turner. 2003. Analysis and conservation implications of landscape change in the Wisconsin River floodplain, USA. Ecological Applications 13(2): 416-31. Jones, K., C. Edmonds, E. Slonecker, J. Wickham, A. Neale, T. Wade, K. Riiters, and W. Kepner. 2008. Detecting changes in riparian habitat conditions based on patterns of greenness change: A case study from the Upper San Pedro River Basin, USA. Ecological Indicators 8(1): 89-99. Naiman, R. and H. Decamps. 1997. The ecology of interfaces: riparian zones. Annual Review of Ecology & Systematics 1997(28):621-58. The Owens Valley Committee (OVC). 2008. Lower Owens River Project. Bishop, CA: OVC. Available at http://www.ovcweb.org/issues/lorp%20overview.html (last accessed 10 July 2014). Risso, D. 2007. Floodplain vegetation following over 80 years of intensive land use and de-watering: Lower Owens River, California. Masters Thesis. Department of Fisheries and Wildlife, Oregon State University. Turner, M., Gardner, R., and R. ONeill. 2001. Landscape ecology in theory and practice pattern and process. New York: Springer-Verlag. United States Geological Service (USGS). 2014. EarthExplorer. Washington D.C.: USGS. Available at http://earthexplorer.usgs.gov/ (last accessed 24 January 2014). References