To access information about the study area and background knowledge/rationale of the study please navigate from the menu bar located at the top of the page.

The blog can be read continuously from top to bottom, or you can use the jump-links given below to jump to a certain section.

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• Selection of Sampling Plots with ArcGIS: ArcMap
• Projecting each tree using its unique XY position
• Spatial extraction of elevation, slope, aspect values, and range coding (LIDAR-DEM)
• Modeling for suitable habitat sites of oaks (Model Builder)
• 3D rendering and animation of the modeling process (ArcScene)

Goals of the study

This study aims to create a snapshot of the current composition of upland forest at Palisades-Kepler State Park based on a survey of trees in random plots across varying topography of the upland areas in the park. The goal of the study is to answer the following questions

• Can ArcGIS be used to model possible viable sites in Palisades-Kepler State Park suitable for a certain tree species, especially white oak, from the field data collected in the study? (Click to jump to results of this question)

• Can ArcGIS be used to add value to the study by providing a better visualization of results of the study through 3D renders? (Click to jump to results of this question)

Methodology

GIS project planning was performed to select study sites and random plot centers. The XY positions were transferred to GPS receivers that support navigation to the selected plots in Palisades-Kepler State Park. A high accuracy survey file was recorded at each of the plot centers. This point anchored laser survey recorded precise location of each tree sampled. Finally, spatial analysis of the data collected was performed. Detailed breakdown of specific methods are presented below.

Selection of Sampling Plots with ArcGIS: ArcMap

The sampling plots were selected ex-situ with the help of ArcGIS-ArcMap. The mapping software used in this research project was ArcMap version 10.6 from the ArcGIS software package by the Environmental Systems Research Institute (ESRI). A 2-feet interval LIDAR-based contour map was overlaid on top of the USGS digital orthophotos derived from LANDSAT Thematic mapper satellite imagery. The contour map was used to digitize ridgelines and slopes by drawing polylines and polygons respectively on the map as a layer feature.

From the digitized ridgelines and slopes, study site sections were constructed by drawing a polygon feature around topographically diverse areas in the state park. The areas selected were based on the relative distance from the road, camping settlement and river; some sites near to them and some far. Each study section was given a number.

A dot density map was created from the symbology tab of the polygon-feature layer indicating the study areas. The dot value of the layer was set such that each section (and each side of the slope of each section) had about equal number of potential plots. Point features were digitized from a dot density map and a random plot-center point-feature layer was created

The coordinates (northing and easting) of each of the point features was calculated and extracted using the ‘calculate geometry’ tool. The attribute tables with the XY data were exported to a .csv excel sheet. The spatial information in the .csv excel sheet was converted into GPX format, uploaded to a Garmin Montana 610t receiver, and was used to navigate to the sampling plots on the field.

Projecting Each Tree Using its Unique XY position

Data was collected on trees that fell within a 7 meter radius of the plot center. The data included: 15-minute GPS data file, the species of tree, diameter of tree, distance and azimuth of tree from the plot center. The data on the trees were entered on Excel sheets and the easting and northing of the plot center (post processed from the GPS data collected in the field) were added for each tree that fell in the same plot. The excel file was imported as a .csv file on ArcMap.

The ‘Bearing Distance to Line’ tool from the Features section of Arc Toolbox was used to project the distance and direction of each tree. Subsequently, the ‘Feature Vertices to Points’ tool from Arc Toolbox was used to represent each tree (at the end of the projected vertices) as a digitized point-feature. ‘Joins’ function was used to merge each point-feature with tree data (name of tree and diameter of tree). A sample of the projection on one of the study sites is shown below:

Spatial Extraction and Range Coding

Digital Elevation Model (DEM)

A LiDAR-based Digital Elevation Model (DEM) for Linn County, IA was to project slope and aspect values using the ‘slope’ and ‘aspect’ functions respectively from the ‘Spatial Analyst’ tool set from Arc Toolbox. The DEM, Slope, and Aspect layers were used to extract the elevation, slope, and aspect values of each projected tree using the Extract Values to Points’ function from the Extraction section of the ‘Spatial Analyst’ toolset of Arc Toolbox.

The DEM, Slope, and Aspect layers are shown below:

Using Python to sort extracted elevation, slope, aspect values of projected trees.

The elevation, slope, and aspected values extracted from the respective rasters were coded into the ranges with the ‘Field Calculator’ tool using a Python-parser pre-logic script. The ranges for the elevation values were in 10-meter intervals, with the value falling within the 10-meter range coded as the mid value of the lowest and highest values of the class. The ranges for the slope values were in 5-degree intervals, with the value falling within the 5-degree range coded as the mid value of the lowest and highest values of the class. The aspects extracted from the aspect layer were coded into the 8 cardinal ranges: North (N), North-East (NE), East (E), South-East (SE), South (S), South-West (SW), West and North-West (NW)

Model Builder was used in ArcMap to model optimal topographical niches (suitable habitats) in Palisades-Kepler State Park for tree species.

‘Optimal’, for this study, is defined as the range (elevation, slope, and aspect range) in which the highest percentage of a specific tree species was found in the field data. The highest percentage of each tree species in each topographical range was determined using pivot tables and filters in Excel.

The optimal topographical niches modeled for a tree species in ArcMap is a layer consisting of polygon patches that represent areas of terrain in Palisades-Kepler State Park that have the optimal elevation, slope, and aspect ranges of the respective tree species as their topographical attribute.

Conversion of raster dataset to polygon features

The elevation, slope, and aspect raster were converted to into polygon layers using the ‘Raster to Polygon’ tool the ‘Conversion Tools’ toolset in Arc Toolbox. An example of the conversion of the DEM raster to an elevation polygon layer is shown below, the area covered by a single polygon represents the elevation of that area in a 10 m range.

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Model Builder

The Model Builder Window in the Toolbar in ArcMap was used to create optimal topographic niche layers for each tree species.

The figure above shows the model run to create the optimal topographic niche for white oaks, Q. alba. The model is run from left to right. The polygon layers (blue oval shapes on left-most side), constructed from raster data sets, are the input layers for the ‘Make Feature Layer’ tool represented as yellow-rounded rectangles following the blue oval objects. The tool acts as a filter, extracting just the optimal ranges and screening the remaining ranges. The ‘Intersect’ tool produces a layer consisting of polygons with the optimal ranges of elevation, slope, and aspect as their topographic attribute.

According to the field data collected, oaks were observed the most on south-west (SW) facing slopes, with a steepness of 10°-14.9°, and an altitude of 250 m-259.9 m above sea level. The model builder would find land patches in the Park that were SW facing, patches that had a steepness of 10°-14.9°, and patches that were on an altitude of 250 m-259.9 m above sea level (as shown below).

The intersection these three layers would be patches of land in the park that had all three topographical characteristics, i.e. south-west facing with a steepness of 10°-14.9°, and an altitude of 250 m-259.9 m above sea level. Click Here for a 3D visualization of the modeling process.

The same process was repeated for other species of trees encountered in the study (shown below).

The 2D data in ArcMap was rendered into a 3D model in ArcScene with triangular irregular network (TIN) layers. The TIN layers created from ArcMap were imported in ArcScene. The TIN layer made with the contour map of the park was set as the base layer. The optimal topographic niche TINs were drape over the Park TIN layer.

Animating 3D layers to represent the modeling process

The 3D layers projected on ArcScene were animated using the Animation Toolbar by altering the time and location of each layer and recording it.

The optimal suitable habitats for white oaks and sugar maple (their competition) were projected on ArcScene. A visual presentation of the 3D-projected sites is shown in the animation below.

The 3D render produced in ArcScene can help better visualize the output layers of the modelling run in ArcMap as a longitudinal cross-section perspective (oblique view), compared to the traditional perpendicular view of 2D maps.

The topographic characteristics of the terrain viewed as a 3D render, with its curvatures and gradients, provide a better understanding of the topography of the park in contrast to attempting to understand it by constantly referring to labeled legends on a 2D map.

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