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CHAPTER 2 Research Approach: Methodology Geospatial and tabular data were collected from the three Caltrans districts for planned and programmed transportation projects over the next 10 to 20 years. The assessment methods used were similar to impacts that were accounted for in a statewide assessment for Caltrans that was loaded into a database intended for desktop use (Thorne et al. 2009a). That database did not generally make it into wide use due to the difficulty of adding updates and the overhead to remove projects from the database as they were implemented. Some of the projects provided by Caltrans were not included in this analysis if, for example, the project was not likely to require mitigation (such as landscape projects), or if the project already had its mitigation identified or was closed out. The remaining projects were categorized using a standardized set of project types with estimated footprints from the California Transportation Investment System (CTIS), which is a spatial data viewer and database that was created with input from Caltrans and Regional Transportation Planning Agency representatives (Table 2.1). CTIS also contains a list and GIS data of programmed transportation projects in California over the next 20 years, which was also accessed to compare their list of projects with the lists received from the individual Caltrans districts. The following steps were performed for each Caltrans district. Data Review The regional offices (District 1, Arcata; District 5, San Luis Obispo; and District 4, Oakland) provided information on transportation projects, which were then reviewed using a GIS. The data received portrayed each project as a general point or line. For road projects such as adding lanes or installing median barriers, line segments were used; for interchanges, a central point was used. From these generalized data, the project boundaries were transcribed to a transportation network in order to include both sides of the road. For some projects, this step was time intensive and created a bottleneck in the process. Discussions are currently underway with Caltrans to see if this step could be automated more easily in their system. However, the transformation of generalized line segments to a transportation network would normally not occur at Caltrans until a later stage in the planning and programming timeline. 11
Table 2.1. CTIS Estimated Footprints Project Type Estimated Footprint Width (ft) New alignment 500 Reconstruct interchange and access ramps 200 Construct expressway 200 Construct new bridge 150 Widen roadway 100 Remove rail trestle 100 Realign curve 100 Grade separation improvements 100 Construct expressway existing alignment 100 Slow-vehicles lane 50 Passing lanes 50 Construct lane 50 High-occupancy lanes 40 Stabilize slope 30 Rehabilitate roadway 30 Construct noise barrier 30 Construct left-turn lane 30 Construct retaining wall 20 Install median barrier 20 Roadside rest areas 10 Install warning devices 5 Install message signs/traffic operation systems 5 Install ramp metering 5 Transportation Project Preparation From the list of selected transportation projects, the potential spatial extent was assessed, and a GIS footprint for each project was created to represent the total area affected. This estimate was created by combining estimates of the distance from the road centerline that different types of projects require for construction with a visual observation of each project using National Agricultural Inventory Program aerial imagery. The existing widths of roads were measured, and added lanes or new alignment widths were assessed. These measurements were made for each project. The centerline or center point of each project was then buffered by the width determined by the estimated footprint table (Table 2.1) to identify the area, or footprint, of impact associated with that project (Figure 2.1). 12
This method identifies only the extent of new pavement or roadway and is perhaps conservative relative to the construction process, particularly for new alignments and interchanges. Figure 2.1. An example of how buffers are used to estimate the extent of impact from a new project. Black dotted lines represent road centerlines, blue lines represent the edges of existing roads, and red lines are the estimated buffered impacts. Biological Data Preparation Because not all suitable habitats are occupied by vertebrate species, an additional analysis was added that used terrestrial vertebrate location records from the state-managed California Natural Diversity Database (CNDDB). Buffers 2 and 4 miles long were applied to the known point locations of threatened and endangered species data from the 13
CNDDB records (see bullets below for the selection criteria for CNDDB records). These buffers (Figure 2.1) were then overlaid on project footprints, and suitable habitat within the buffer ring was identified. This method of identifying the suitable habitat of mitigation species that might be affected is a conservative measure because, although a transportation project might be longer, and have more suitable habitat beyond the buffer, there is no assurance that it is occupied by the listed species. Therefore, the analysis identifies the locations (and amount) of habitat that are most likely to have the species present because they are relatively near to an observed occurrence. This type of analysis has the effect of reducing the overall lands identified as potential impacts for long transportation projects, but it also recognizes that not all habitat is actually occupied, therefore providing a more realistic estimate of the lands that might require mitigation due to project impacts. The 2- and 4-mile buffers themselves also provide a range of predicted impacts that can be further refined as projects are later reviewed on the ground. ⢠Existing data for threatened and endangered species were compiled, along with the distribution of land cover or habitat, and the locations of important agricultural lands. ⢠CNDDB points were selected that occurred within and up to 5 miles beyond the US-101 corridor. The CNDDB data represent the known and available inventory of threatened and endangered species and some habitats. ⢠From the CNDDB points, those that were listed as âpresumed extantâ and from 1980 to the present were selected. ⢠Federally listed and/or state-listed species and other species requiring mitigation were selected. ⢠The subset of CNDDB points were buffered by 2 and 4 miles. ⢠Buffered points were overlaid on land cover maps depicting vegetation type. ⢠Appropriate habitat types for each mitigation species were selected using the California Wildlife Habitat Relationships model for terrestrial vertebrate species, Calflora-listed land cover types for plant species, and various online sources for invertebrate species. U.S. Fish and Wildlife Service and National Marine Fisheries Service data, along with National Hydrography data on waterways, were used for fish species. ⢠Only the specified habitat types for each specific species were selected from the land cover overlay to ensure a conservative estimation of species occupancy (Figure 2.2). Figure 2.2 shows the use of CNDDB records with transportation project impacts to predict the extent of suitable habitat that could be affected by a project. Green represents suitable habitat for a species, although it is not known if the species is occupying that entire habitat. The red cross represents a known occurrence record for that species. The 14
black outlines depict a transportation corridor and its estimated impact footprint. The green areas within the red circles of (a) a 2-mile and (b) a 4-mile buffer from the CNDDB point represent the suitable habitat that falls within the project footprint. Any appropriate habitat patches within the circles are summed to provide the impact estimate for that species on that project. Figure 2.2. Use of California Natural Diversity Database records with transportation project data to help predict potential impacts on habitat. Calculation of Biological Impacts The project footprints were overlaid with the biological and agricultural data to estimate impacts. These were then summarized for each project, and can be summed across multiple projects, such as by county, by road, or by types of projects. Calculation of Aquatic Impacts Some projects in each district include modifications to existing bridges over streams. Examples of these projects include new alignments, repairing channel paving, bridge widening, and reconstructing interchanges. Several maps of wetlands and riverine features, including the National Wetland Inventory (NWI), the CALVEG wildlife habitat relationships (WHR) classification, and the Conservation Lands Network (CLN) in the Bay Area, were used to examine potential impacts from the portfolio of transportation (a) (a) (b) 15
projects. CALVEG and the CLN use the same land cover classification system; NWI has its own classification. Impact assessments for anadromous fish with the GIS forecasting tool is a portion of the project that is still under development. Current practice within Caltrans is to assess the potential of a transportation project to directly affect fish mortality. Without a base of population numbers for different fish species to use, the research team assessed the number of projects that cross streams with a known presence of listed fish species, all in the anadromous fish category. Calculation of Urban Impacts Caltrans District 4 includes the San Francisco Bay Area and is unique in that many of the transportation projects occur in highly urbanized areas. Although transportation projects in urban areas generally aim at solving problems of safety and capacity, such as reducing congestion, improving walking and bicycling, and possibly reducing vehicle travel to minimize air pollution and greenhouse gas emissions, some of the impacts that can affect urban areas include community cohesion, economic development, traffic noise, visual quality, and property value (Forkenbrock and Weisbrod 2001). In addition, various population groups within the community may be affected quite differently. The impacts of transportation products to urban and farmland areas were calculated using a parcel- level land use data set (from Digital Map Products, 2013). Using parcel-level data also allows for the examination of land use types that are affected the most by projects. 16
