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Utilization of Remotely Sensed Data for Targeting and Evaluating Implementation of Best Management Practices within the Wister Lake Watershed, Oklahoma Scott Stoodley, Brenda Berasi AMEC Earth and Environmental Phillip Busteed, Michael White, Daniel Storm Oklahoma State University
Introduction
Wister Lake Watershed
Located in southeast Oklahoma and Arkansas Eutrophic Listed as a Nutrient Limited Watershed (NWL) Listed on the State’s 303(d) list in 1998 for nutrients, siltation, suspended solids, flow alteration, taste and odor Oklahoma Conservation Commission (OCC) has begun using satellite imagery with water quality modelling to identify potential critical source areas of pollutants
Objectives
Utilize Landsat TM (30 meter resolution) imagery to: 1. Target Best Management Practices (BMPs) Map landcover types for use in SWAT modeling to target critical source areas of phosphorus from pasture systems 2. Evaluate BMPs Evaluate the effectiveness of the OCC’s Section 319 cost share program by analyzing vegetation and pasture grazing intensity for the summer of 2000 through the summer of 2004
Study Area
Landsat TM Imagery August/September 2004
Landcover Classification
Categories of interest:
Forest Pasture, high biomass Pasture, low biomass Shrub / Range Bare Soil Clear-Cut Urban, high density Urban, low density Rock Outcropping Mining Water Clouds
Landcover Classification
Methods
Unsupervised classification ISODATA clustering algorithm Iterative process Established set of decision criteria for each landcover category 3x3 Majority spatial filter used on large macro classes Change detection conducted using categorical image differencing
Landcover Classification
Methods
Ground-truth data Collected by OCC and Oklahoma State University (OSU) GPS coordinates, description of landcover type, digital photos 2 Data sets 255 locations surveyed in July and August 2005 100 locations surveyed in November 2005
2004 Landcover Classification Land Cover Categories
Water Forest High Biomass Pasture Low Biomass Pasture Shrub / Range Bare Soil Clear-cut Rock Outcropping High Density Urban Low Density Urban Mining Clouds
2000 Landcover Classification Land Cover Categories
Water Forest High Biomass Pasture Low Biomass Pasture Shrub / Range Bare Soil Clear-cut Rock Outcropping High Density Urban Low Density Urban Mining Clouds
Change Detection
Land Cover Change from 2000 to 2004
Unchanged Water Unchanged Forest Unchanged Pasture Forest → Clear-cut Pasture → Forest Bare Soil → Forest Unchanged Shrub/Range Clouds Unchanged Bare Soil Other Unchanged Urban
Riparian Buffer Zone
Land Cover
Water Forest High Biomass Pasture Low Biomass Pasture Shrub / Range Bare Soil Clear-cut Rock Outcropping High Density Urban Low Density Urban Mining
Landcover Classification
Accuracy Assessment
100 sites selected: Stratified random sampling design Within ½ mile buffer of roads Minimum of 10 sites per category
Ground Truth
Need for temporally coincident ground truth data 21 July 2005 Shrub / Range 02 November 2005 Pasture
Vegetation Analysis
Objective
Analyze vegetation density to evaluate BMP effectiveness
Methods
Vegetation indices Normalized Difference Vegetation Index Normalized Difference Senescent Vegetation Index Fractional cover of green vegetation Fractional cover of senescent vegetation Change detection based on percent difference from baseline
Vegetation Analysis
Multi-temporal analysis
31 Aug 2004 28 Jul 2003 27 Sep 2002 22 Jul 2001 20 Aug 2000
Vegetation Indices
Normalized difference vegetation index NDVI = (NIR-Red) (NIR+Red) Normalized difference senescent vegetation index NDSVI = (SWIR-Red) (SWIR+Red)
Vegetation Indices
Fractional cover of green vegetation FC gv = (NDVI-NDVI soil )/(NDVI veg -NDVI soil ) Fractional cover of senescent vegetation FC sv = (NDSVI-NDSVI soil )/(NDSVI veg -NDVSI soil ) NDVI soil and NDVI veg are the NDVI values of an area of only bare soil and an area of total vegetation, respectively, as derived from an image or measured in the field
Vegetation Analysis
All four indices computed for the pasture areas of the Oklahoma portion of the watershed Change depicted as percent departure from 2000
Vegetation Analysis
Vegetation Analysis
Vegetation Analysis
Evaluation of BMP effectiveness: Mean FC gc share fields and FC sv computed for 255 individual cost Computed ratio of the mean FC for each cost-share field to that of all other pastures within a given year Ratio 2000 – Ratio 2004 computed for each field ∑ Ratio 2000 – Ratio 2004 for all fields that had implemented the same combination of BMPs Area weighted average was also computed for the sum of the differences
Evaluation of BMPs
Some BMPs, such as a fence only, resulted in an overall increase in FC FC 2004 compared to 2000. gv and Others, such as the combination of lime and fertilizer, resulted in an overall increase in FC gv and an overall decrease in FC cost share fields for 2004 compared to 2000. sv relative to all of the non-
Conclusions: Land cover classification
The land cover classification indicates a watershed dominated by forest, followed by pasture. The change detection reveals a dynamic environment in which nearly 19% of the Oklahoma portion of the watershed underwent a change in land cover from 2000 to 2004.
Overall accuracy of 2004 land cover classification was 93.0%
Conclusions: Landcover Classification
Classification within the riparian zone offers a qualitative targeting method to spatially locate high risk landcover types. When combined with estimates of nonpoint source loadings through SWAT modeling, the landcover classification provides a mechanism by which to proactively identify and target areas that are likely contributing to water quality degradation.
Conclusions: Vegetation Analysis
No clear trend was found in overall FC for the cost share fields relative to the non-cost share fields in 2004 compared to 2000.
Some BMPs resulted in increased FC, whereas others resulted in decreased FC. The overall effect of BMP implementation on vegetation density may be obscured by changes in land-use management decisions.
Conclusions: Vegetation Analysis
By gathering data on the land management practices that had been implemented between 2000 and 2004, a better understanding of the effectiveness of the BMPs may be attained. The OCC should consider the possibility of placing management restrictions in conjunction with Section 319 funding for BMP implementation.