Advanced GIS Lab7

Objectives

1) Estimation of coyote's home ranges

MBG – Minimum Bounding Geometry (Convex Polygons)
KDE – Kernel Density Estimator

2) Calculate home range area

3) Extract raster values to points

4) Manipulate and run Python script

5) Calculate availability and utilization

Introduction

This lab practiced visualize spatial probabilities using density surfaces. I conducted estimating and calculating home ranges for coyotes using ArcMap 10.7.1. In the first part, I conducted to calculate home rage area with define extent and density tools. In the second part, I run the python script and examined them and calculated availability and utilization using Excel and Pivot table. The study area is the Dugway region in central Utah, and coordinate system is WGS 84 UTM Zone 12N.

Lab07-95.pdf

Methods

I used the coyote.csv file for the Part 1. This csv file contains High Frequency Radio Collar data and XY locations. Thus, I could add the data and visualize in ArcMap. After adding csv file, I conducted Minimum Convex Polygon (MCP) and Fixed Kernel Density Estimate (KDE) for identifying coyote density and calculating 50% and 95 quantile areas. I used Minimum Bounding Geometry (MBG)tool to create individual home range polygon by the outside extent of the data points. In this process, it needed to be careful to choose geometry type (Convex Hull). Also, I exploited Kernel Density Estimates tool to get a density surface raster file. Finally, I calculated the core and general population home range using Extract Values to Points tool and Raster to Polygon tool.

In the second lab, I manipulated coyote python script and calculated use and availability of coyote home range. After adding landcover data (us_140evt raster file), clipped the landcover data using the extent of coyote’s 95 percentile area. Also, I conducted the Extract Values to Pints tool for summarizing vegetation counts in clipped area. Finally, I figured out counts of available and used area, and then calculated the expected count and preference ratio.

The Estimation of Coyote Home Ranges Download

Result & Discussion

I could visualize spatial probabilities of coyote’s home range with several ArcMap tools. MCP is good for calculation of home range based on the spatial extent of data. However, it is likely to overestimate home range area and do not provide probability. KDE can calculate the entire extent of data and estimate home ranges using calculating quantiles and mapping. Notably, Animal code of the result means the animal, not FID or ID number. In the part 2, I could learn some code of Python using coyote script. Especially, the code “import arcpy” which is starting to talk to ArcMap is informative. During the process of calculating availability in ArcMap, I joined the table to include the class name in the clipped file. Finally, it was not easy to figure out Pivot Tables in Excel because it was my first time to use. However, this function was helpful to calculate a lot of numbers after figuring out.

Figure.3

This table displays the calculated proportion of general land cover and utilized land cover. I calculated the expected count and preference ratio after identifying the used counts from the Pivot table. The result shows that the Barren type is greater than 1, and thus we could say that coyotes differently prefer the Barren type.

Availability