A landscape classification approach for watersheds of the Pacific Northwest: is aquaticecosubregionalizationeven a word?

Chris Jordan, Steve Rentmeester, Carol Volk, Mimi DIorio, George Pess, Tim BeechieNOAA-NWFSC, Seattle

What are we doing, and why?Classify the aquatic-landscape of the Pacific Northwest based on relevant broad-scale characteristicsMajor determinants of watershed processesImmutable geomorphic characteristicsHuman impactData analysis supportEnvironmental variance partitioningEvaluation tool for site selection

A Made-up Example of What We Want the Output to Look Like

A couple of examples of something similar, but not quite the sameHessburg et al. 2000. Ecological subregions of the ICRB based on PVG, Temp-precip, solar radiation, elevation.Omernik et al. 1999+, US EPA Level III & IV Ecoregions based on terrestrial vegetation assemblages.

What are we doing, and why?Classify the aquatic-landscape of the Pacific Northwest based on relevant broad-scale characteristicsData analysis supportEvaluation tool for site selectionAssess representativeness of current monitoring and restoration efforts.Locate additional monitoring and restoration projects.

How are we doing this?Taking commonly available spatial data w/ consistent coverage across study area.Generating functional data layers from above.Attributing 6th field watersheds with a single value for each input data layer.Grouping watersheds into clusters of like, or classes.

Median Elevation Median Hill SlopeInput DataClimate Annual Precipitation Month of Max Precipitation Growing Degree Day

TopographyChannel NetworkGeology Stream sediment production Water chemistry Density (by gradient) Complexity (valley width) Stream power Tributary junctions Watershed shape

How are we doing this?Taking commonly available spatial data w/ consistent coverage across study area.Generating functional data layers from above.Attributing 6th field watersheds with a single value for each input data layer.Grouping watersheds into clusters of like, or classes.

How are we doing this?Taking commonly available spatial data w/ consistent coverage across study area.Generating functional data layers from above.Attributing 6th field watersheds with a single value for each input data layer.Grouping watersheds into clusters of like, or classes.

Hydrologic Unit Code6th field HUCsSub-watersheds (10,000-40,000 ac)

Five data layers: 6th field watersheds with a single values for each input characteristic.

Five data layers: 6th field watersheds with a single values for each input characteristic.

How are we doing this?Taking commonly available spatial data w/ consistent coverage across study area.Generating functional data layers from above.Attributing 6th field watersheds with a single value for each input data layer.Grouping watersheds into clusters of like, or classes.

Compile categorical data for 6th order HUCS and build as attributes into a GIS shapefile

Convert features from vectors to 200m raster grids

Stack separate raster integer grids into one multi-band raster file

Apply ISOCLUSTER and Maximum Likelihood Classification algorithms to separate classes based on pixel spectra

Evaluate spatial patterns using FragstatsSpatial Analyst : Convert Features to RasterSpatial Analyst: Zonal Statistics & Reclassify RasterRaster Calculator or Command Line:Make Grid Stack or Composite Bands ToolSpatial Analyst ToolsCommand LineISOCLUSTERProcessing StepProcessing ToolsFragstatsPatch Class and Landscape Metrics

Where are we and next steps Need to resolve 200m pixel v. 6th HUC grainNeed to clean up a few more data layersErosion potential v. Slope x Area%T, R, SMonth of max ppt v. hydro regimeNeed to resolve classification toolISODATA v. MCLUSTNeed to make maps and get feedbackNeed to move on to anthropogenic layers

The precipitation and growing degree day grids were interpolated from weather station data (COOP and SNOTEL) using the PRISM model. PRISM is an analytical model that uses point data and a digital elevation model (DEM) to generate gridded estimates of monthly and annual maximum temperature (as well as other climatic parameters). Data from 5000-6000 weather stations were utilized in developing the climate grids. The resolution of PRISM output is 2.5 arc-minutes (~4 km). In order to facilitate estimation of mean values at the 6th field HUC scale, climate grids were re-sampled to 30m resolution using a bilinear interpolation (The value of the output cell is determined using a weighted average of the 4 nearest cell centers.)

Standard USGS 30m DEMs were utilized to calculate median elevation and hill slope for all 6th field HUCs. We are currently investigating STRM derived DEMs for use in this project. The study area covers the US portion of the Columbia River Basin and the remainder of OR, WA, and ID. We are describing watershed characteristics at the 6th field HUC (sub-watershed) level. Currently, a single layer delineating all 6th field HUCs for the entire study area does not exist. Until a complete layer becomes available, we are working with a temporary layer that was generated by combining the REO 6th field watershed boundaries for OR and WA with the ICBEMP 6th field HUC boundaries for ID and the remaining portion of the CRB. When a completed 6th field HUC layer becomes available from NRCS, we can re-calculate watershed characteristics and re-run the classification.

FYI REO delineations were completed using 10m and 30m DEMs. ICBEMP delineations were determined from USGS quad maps, traced to mylar, and then digitized. Not surprisingly, the REO watershed boundaries match the DEM better than the ICBEMP boundaries. The data are compiled into the GIS and are reclassified into bins using the quantile classification scheme. The data are divided into 10 bins with the exception of the month of maximum precipiation (12 bins). Each attribute feature (i.e. data layer) is converted to a raster grid with a 200m cell size. The separate grids are stacked together into one multiband raster and processed using the IsoCluster algorithm. The IsoCluster function uses a modified iterative optimization clustering procedure, also known as the migrating means technique. The algorithm separates all cells into the user-specified number of distinct unimodal groups in the multidimensional space of a stack. The ISO prefix of the isodata clustering algorithm is an abbreviation for the Iterative Self Organizing way of performing clustering. This type of clustering uses a process such that during each iteration all samples are assigned to existing cluster centers and new means are recalculated for every class. This procedure results in a signature file that is then used to run a Maximum Likelihood Classification. The maximum-likelihood classifier considers both the variances and covariances of the class signatures when assigning each cell to one of the classes represented in the signature file. With the assumption that the distribution of a class sample is normal, a class can be characterized by the mean vector and the covariance matrix. Given these two characteristics for each cell value, the statistical probability is computed for each class to determine the membership of the cells to the class. Once classified, the ouput raster classification is interpreted using geostatistical tools in ArcGIS. The semivariance plots and covariance cloud are created and reviewed. The semivariogram and covariance functions quantify the assumption that things nearby tend to be more similar than things that are farther apart. Semivariogram and covariance both measure the strength of statistical correlation as a function of distance. Lastly, the classification results are run through a landscape ecology statistical analysis software called Fragstats. Fragstats is a software program used to quantify landscape structure through the calculation of landscape metrics based on the extent (area) and grain (resolution) of the landscape being evaluated.