3D Mapping - TINs and DEMs

Moving on from Spatial Data Quality in GIS Special Topics, the next Lab focuses on surfaces with a comparison of the Digital Elevation Model (DEM) and Triangular Irregular Network (TIN). A surface in GIS is a geographic phenomena represented as continuous data. Continuous spatial data references geographic objects characterized by very gradual boundaries such as temperature or elevation.

The most common way to represent elevation data is with contour lines. Contour lines are 2-dimensional features with attributes containing the value of the surface at a given location. They can be derived by the TIN vector model or the DEM raster model.

TINs are used exclusively to represent a 3-dimensional surface. A series of linked irregular triangles comprised from elevation points (nodes) in 3D (X,Y,Z) coordinates (Manandhar, 2005) occurring at any given location represent the 3D surface. The topological relationship of the network of triangles creates a continuous surface. The normal vector of each triangle is used to assign the properties of Slope and Aspect.

 

DEMs are the simplest way to represent a topographic surface. A DEM is a regular raster that uses a regular rectangular grid method (Manandhar, 2005) with cell values representing elevation or spot height. The cell size of a DEM determines the resolution. Therefore a DEM with a high number of smaller sized cells provides more accuracy than a DEM with less larger sized cells. Data becomes more implicit with larger cell sizes.

One part of this week's lab utilizes a DEM to develop a 3-dimensional Ski Run Suitability Map. Initially the supplied DEM was converted to a TIN for the 3D component for the Local Scene. The suitability parameters included Elevation where areas exceeding 2,500 meters are most favorable, Slope where angles between 30 and 45 degrees rank highest, and Aspect where south and west facing slopes are most preferred.

Following reclassification, respective rasters were generated from the DEM using geoprocessing tools in ArcGIS Pro. These in turn were input into the Weighted Overlay tool where the suitability rate for aspect is 25%, elevation is 40% and slope is 35%.

The output Ski Run Suitability Map. Lighting enhancements include shadowing and adjustment of the sun angle. The Vertical Exaggeration is 2.50.

The next part of the lab further explores TINs with adjustments to symbology between elevation, slope and aspect. The deliverable included the generation of contours and selecting appropriate colors.

Cividis color TIN with 50 meter contours and 250 meter index contours.

The last section of the lab provides a point feature class that will represent the mass points for a TIN. Geoprocessing of these points were input along with a study area soft clip polygon boundary in the Create TIN tool. The resulting TIN was modified symbolically to show contours set at an interval of 100 meters.

The same mass points feature class was input into the Spline tool to create a DEM. Contours were subsequently generated from the DEM with additional geoprocessing. The two contour feature classes were then compared.

While not necessarily more accurate, the DEM based contours have smoother curvature resulting from the implicit data values from each grid cell (Manandhar, 2005). Appearing more jagged in areas with less slope, the TIN based contours are derived from every node, where 3D coordinates are more explicit. There are less Faces (triangles) in flatter areas.

References:

Manandhar, N. (2005). Comparison of TIN and Grid Method of Contour Generation from Spot Height. Nepalese Journal on Geoinformatics, 4, 1-8.

https://www.nepjol.info/index.php/NJG/article/view/51271/38351

Corridor Suitability Analysis - Coronado National Forest

The final scenario for the lab of GIS Applications Module 6 is to determine a potential protected corridor linking two areas of black bear habitat in Arizona's Coronado National Forest. Data provided included the extent of the two existing areas of known black bear habitat, a DEM, a raster of land cover and a feature class of roads in the study area. Parameters required for a protected corridor facilitating the safe transit of black bear included land use away from population and preferably with vegetation, mid level elevations and distances far from roadways.

Geoprocessing flow chart for Scenario 4

The initial geoprocessing in our Corridor Suitability Analysis reclassifies the DEM and landcover rasters into suitability rasters using predetermined scales. The development of a suitability raster for the roads feature class commenced with creating a multi-ring buffer feature class, and then converting the derived polygons into a suitability raster using the Reclassify tool.

Reviewing the previous scenarios on outputting buffers from a polyline, I also ran the Euclidean Distance tool on the roads feature class. The succeeding output raster was then Reclassified using the distance suitability values that rank higher proximities with lower values. The results mirrored those using the Multi-Ring Buffer tool:

The suitability raster for the distance to roads derived from the raster output from the Euclidean Distance tool.

With suitability raster files finalized for elevation, landcover and proximity to roads, we can proceed with the analysis using the Weighted Overlay tool. The objective is to generate a cost raster using the integer scale of 1 through 10, based upon the influence percentages of 60% for land cover, 20% for elevation and 20% for distance to roads.

The result shows the highest suitability score for mid level elevations representative of undeveloped forest land that mostly avoids roads. Low level elevations represented by urban areas, agriculture and barren land factor into low suitability areas:

Weighted Overlay raster with the values of 1-10 where lighter colors represent lower suitability scores

Utilizing the Weighted Overlay raster, a cost surface raster is generated by using the Raster Calculator geoprocessing tool. The cost surface values were obtained by inverting the suitability model so that higher habitat suitability values translated into lower travel costs:

Cost Surface raster where the darker colors represent higher costs

With the Cost Surface raster, the Corridor Suitability Analysis continues with the Cost Distance tool run on the two Coronado National Forest black bear habitat area feature classes. This outputs Cost Distance and Cost Distance Backlink Rasters.

The cost distance raster for the northeastern unit of Coronado N.F.

The cost distance raster for the southwestern unit of Coronado N.F.

Together the two cost distance rasters for Coronado National Forest are the parameters for the Corridor geoprocessing tool, which generates the Least-Path Corridor raster. The threshold value for determining the best corridors was subjective, so I went with percentages used in the previous scenario, where the minimum destination cost value multiplied by 1.05 represented the optimal corridor. Chose a color scheme based upon the ColorBrewer web site.

The Least-Path Corridor for a protected Black Bear Corridor between Coronado National Forest units

Least-Cost Path and Corridor Analysis with GIS

The second half of Module 6 for GIS Applications conducts Least-Cost Path and Corridor Analysis on two scenarios. The first continues working with the Jackson County, Oregon datasets from Scenario 2.

There are several ways that GIS measures distance. Euclidean, the simplest, represents travel across a straight line or "as the crow flies". Manhattan distance simulates navigating along a city street grid, where travel is restricted to either north-south and east-west directions. Network Analysis models travel in terms of time, where travel is restricted by a road network or transit infrastructure.

Least-Cost Path Analysis models travel across a surface. It determines the single best course, a polyline, that has the lowest cost for a given source and destination, which are represented by points. This can be described as the routing over a landscape that is not restricted by road networks. 

The course through the landscape is modeled as a cost. More specifically each cell in a cost raster has a value which represents the cost of traveling through it.

Typical cost factors are slope and land cover. A cost surface can vary from just a single factor to a combination of them. Even if multiple factors are considered, the analysis only uses a single cost raster.

Least-Cost Path Analysis can be expanded to Corridor Analysis. Instead of resulting in a single base solution represented by a polyline, corridor analysis produces multiple solutions, representing a zone where costs are close to the least cost. The corridor width uses is somewhat subjective. It is controlled by deciding what range of cost to consider. Values of a few percentage points above the lowest cost to as much as 10% above the lowest cost are common.

Scenario 3 uses least-cost path analysis on an area of land in the planning for a potential pipeline. Cost factors include elevation, proximity to rivers and potential crossings of waterways. Datasets used for these cost factors include a DEM, a rivers feature class and feature classes determining the source and destination of the proposed pipeline. Analysis proceeds focusing on each cost factor individually.

Geoprocessing flowchart for least-cost path analysis factoring solely on slope

Focusing first on the DEM, the raster is converted to a slope raster, and subsequently reclassified using a cost factor range of eight values. The next analysis step utilizes the Cost Distance geoprocessing tool. Using an iterative algorithm, a cost distance raster is generated that represents the accumulated cost to reach a given cell from the source location point.

A cost backlink raster is also created, which traces back how to reach a given cell from the source. This reveals the actual path utilized to obtain the lowest cost. The actual cell values of the backlink raster represent either cardinal directions or the intercardinal point (NE, NW, etc.) instead of cost. The combination of the two output rasters contain every least cost path solution from the single source to all cells within the study area.

Cost Distance Raster - Values represent the cost of traveling through a cell

Cost Distance Backlink Raster - Values correspond with compass directions

The final step of least cost path analysis obtains the least cost path from the source to one or more destinations. The result of the Cost Path geoprocessing tool, this consists of a single polyline representing the lowest accumulated cost.

The result of Least-Cost Path Analysis solely on slope

Continuing our analysis of a proposed pipeline in Jackson County, Oregon, we factor in river crossings as a cost factor.

Geoprocessing flowchart for least-cost path analysis factoring in both slope and river crossings

The result of factoring in river crossings to the cost analysis reduces potential crossings to five from the 16 when factoring in slopes alone:

Furthering our analysis, we change from factoring in river crossings to instead factor in the distance to waterways. Using a multiple ring buffer, cost factors are set high for areas within 100 meters of hydrology and moderate for areas within 500 meters. Distances beyond 500 meters from a waterway are zero, reflecting no cost.

Geoprocessing flowchart for least-cost path analysis factoring slope and proximity to waterways

As the cost factor criterion for the least-cost path analysis is adjusted to better compensate natural factors, the least cost path adjusts accordingly:

The final analysis for third scenario looks at Least-Cost Path Corridor Analysis, which not only includes the least-cost path, but also a multiple of other least-cost alternatives within a corridor determined on a case-by-case basis.

Geoprocessing flowchart for least-cost path corridor analysis

The geoprocessing to develop the least-cost path corridor utilizes the previously generated cost raster factoring in the proximity to waterways and slope. Instead of using the source point as the feature source data, the Cost Distance tool is based off the destination point feature class.

Together the two cost distance rasters, one based off the "destination" feature class and the other off the "source" feature class, are input into the Corridor geoprocessing tool. This outputs the Least-Path Corridor raster, which areas of least-cost paths symbolized based upon a percentage from the minimum cost value:

Suitability Modeling with GIS

Module 6 for GIS Applications includes four scenarios conducting Suitability and Least-Cost Path and Corridor analysis. Suitability Modeling identifies the most suitable locations based upon a set of criteria. Corridor analysis compiles an array of all the least-cost paths solutions from a single source to all cells within a study area.

For a given scenario, suitability modeling commences with identifying criteria that defines the most suitable locations. Parameters specifying such criteria could include aspects such as percent grade, distance from roads or schools, elevation, etc.

Each criteria next needs to be translated into a map, such as a DEM for elevation. Maps for each criteria are then combined in a meaningful way. Often Boolean logic is applied to criteria maps where suitability is assigned the value of true and non suitable is false. Boolean suitability modeling overlays maps for all criteria and then determines where all criterion is met. The result is a map showing areas suitable versus not suitable.

Another evaluation system in suitability modeling use Scores or Ratings. This scenario expresses criterion as a map showing a range of values from very low suitability to very high, with intervening values in between. Suitability is expressed as a dimensionless score, often by using Map Algebra on associated rasters.

Scenario 1 for lab 6 analyzes a study area in Jackson County, Oregon for the establishment of a conservation area for mountain lions. Four sets of criterion area are specified. Suitable areas must have slopes exceeding 9 degrees, be covered by forest, be located within 2,500 feet of a river and more than 2,500 feet from highways. 

Flowchart outlining input data and geoprocessing steps.

Working with a raster of landcover, a DEM and polyline feature classes for rivers and highways, we implement Boolean Suitability modeling in Vector. The DEM raster is converted to a slope raster, so that it can be reclassified into a Boolean raster where slopes above 9 feet are assigned the value of 1 (true) and those below 0 (false). The landcover raster is simply reclassified where cells assigned to the forest land use class are true in the Boolean.

Buffers were created on the river and highway feature classes, where areas within 2,500 feet of the river are true for suitability and areas within 2,500 feet of the highway are false for suitability. Once the respective rasters are converted to polygons and the buffer feature classes clipped to the study area, a criteria union is generated using geoprocessing. The suitability is deduced based upon the Boolean values of that feature class and selected by a SQL query to output the final suitability selection.

We repeat this process, but utilizing Boolean Suitability in Raster. Using the Euclidean Distance tool in ArcGIS Pro, buffers for the river and highway feature classes were output as raster files where suitability is assigned the value of 1 for true and 0 for false. Utilized the previously created Boolean rasters for slope and landcover.

Obtaining the suitable selection raster with the four rasters utilizes the Raster Calculator geoprocessing tool. Since the value of 1 is true for suitability in the four rasters, simply adding the cell values for all result in a range of 0 to 4, where 4 equates to fully suitable. The final output was a Boolean where 4 was reclassified as 1 and all other values were assigned NODATA.

Scenario 2 determines the percentage of a land area suitable for development in Jackson County, Oregon. The suitability criteria ranks land areas comprising meadows or agricultural areas as most optimal. Additional criterion includes soil type, slopes of less than 2 degrees, a 1,000 foot buffer from waterways and a location within 1,320 feet of existing roads. Input datasets consist of rasters for elevation and landcover, and feature classes for rivers, roads and soils.

Flowchart of the geoprocessing for Scenario 2

With all five criteria translated into respective maps, we proceed with combining them into a final result. However with Scenario 2, the Weighted Overlay geoprocessing tool is implemented. This tool utilizes a percentage influence on each input raster corresponding to the raster's significance to the criterion. The percentages of each raster input must total 100 and all rasters must be integer-based.

Cell values of each raster are multiplied by their percentage influence and the results compiled in the generation of an output raster. The first scenario evaluated for lab 6 includes an equal weight scenario, where the 5 raster files have the same percentage influence (20%). The second scenario assigned heavier weight to slope (40%) while retaining 20% influence to land cover and soils criterion, and decreasing the percentage influence of road and river criterion to 10%. The final comparison between the two scenarios:

Opted to symbolize the output rasters using a diverging color scheme from ColorBrewer.