Application of Geospatial Ecological Tools and Data in the Planning and Programming Phases of Delivering New Highway Capacity: Proof of Concept—East-West Gateway Council of Governments (2014) / Chapter Skim
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CHAPTER 4 Enhancements to Ecological Initiative Geospatial Tool The assessment indicated the need to refine the Ecological Initiative tool to extract a finerresolution land cover, including impervious surfaces, in the area identified as urban low intensity and urban high intensity in the existing classification. The goal of the tool refinement is to identify the softscape amidst the urban landscape, to map green corridors and nodes that, while they have likely been impacted by urbanization, still contribute to the ecological health of the region.
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Figure 4.1. Study areas I-44 in Southern Saint Louis County, Missouri, and IL-158 in Central Saint Clair County, Illinois.
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Figure 4.2. Digital elevation model (DEM)
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Figure 4.4. Vegetation and structure height surface developed by subtracting lidar DEM from DSM (MoRAP 2014)
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imagery for a given location within the same calendar year. The use of this imagery allowed for an accurate classification of surface cover conditions as of summer 2012.
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Figure 4.7. MSD impervious surface polygons aided in mapping roads, building footprints, and other impervious surfaces for the I-44 study area (MoRAP 2014)
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training sample points were used to inform the classifier and produce a six-class LULC map. The original 15 LULC classes were aggregated to fit within the six mapped classes (Table 4.1)
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Table 4.1. Crosswalk of 15-Class LULC Used to Generate Training Samples and Six LULC Classes to be Mapped for this Project 6 Class LULC to be mapped for 2012 2008-2009 15 Class LULC Water Open Water Urban/Impervious Impervious High Density Urban Low Density Urban Barren/Sparsely Vegetated Barren or Sparsely Vegetation Forest Deciduous Forest Evergreen Forest Mixed Forest Deciduous Woody/Herbaceous Evergreen Woody/Herbaceous Woody-Dominated Wetland Grass Grass Crop Crop Modeling Six classes were mapped at 1-meter spatial resolution using a supervised CART modeling approach with boosted regression trees with See5 statistical software.
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Figure 4.9. Six-class, 1-meter supervised LULC in the158 study area.
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Figure 4.10. Objects (black)
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Figure 4.11. Building shadows mapped as water was a common error fixed by manual inspection (MoRAP 2014)
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Success and Challenges The 2012 1-meter, six-class LULC improved upon the detail of LULC mapping that previously existed in the Saint Louis region by using higher spatial resolution imagery and lidar data, yet reducing the thematic resolution. The goal of improving the mapping of urban vegetation and riparian corridors within the metro area was achieved (Figures 4.15 and 4.16)
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Figure 4.16. 2012 MoRAP 1-meter LULC in I-44 study area (same extent as Figure 4.15)
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Figure 4.17. Comparison of LULC composition mapped of 2008–09 30-meter LULC and 2012 1-meter LULC in I-44 study area.
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Often large-scale national data sets do not reflect environmental conditions on the ground in urban areas. That was the case with the 2008–2009 LULC, which was based on input data sets (e.g.
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