## Friday, July 25, 2014

### Comparing Elevation and Temperature - Virgin Mountain Trip

This graph charts the percent change in temperature in relation to elevation as my boys and I headed for our picnic spot on the Virgin Mountain. The data collection process is explained below...

The boys and I had a great trip up the Virgin Mountain last week as we searched for cooler temperatures. As we went up the mountain we logged the elevation, temperature and location.  My Arduino sensors haven’t come yet which will help me with capturing and logging data so for now they boys help me with the process. They are the best data loggers a dad could as for.

The raw data is interesting because it shows the change in temperature as you climb in elevation. One little side note to explain part of the data is the dramatic drop in the temperature after we initially peak in elevation, and then drop back down. This is because we dropped down the back side of the mountain in the shade of the sun.

Here I just overlaid the two graphs to show the comparison

I also wanted to normalize the data a little bit so it would make the comparisons and analysis a little bit easier. To do this I calculated the percent in change of both the elevation and temperature.

We also tested the temperature of the water coming out of the spring.

Out data collection rig

Sure had a great trip with my boys up on the mountain.

## Saturday, July 19, 2014

### Fire Risk Map

This is Part IV in a series of articles on Wildfires in the desert region of Gold Butte in North Eastern Clark County, NV.

To read Part I click the following link: Defining the Study Area:
To read Part II click the following link: Defining the Study Area:
To read Part III click the following link: Defining the Study Area:
To read Part IV click the following link: Defining the Study Area:

Fire Risk Indicator
The goal of this research was to assess the Gold Butte region for risk of fire. To create this model, I used geospatial data available on the internet from various government agencies, to create a model to calculate risk. I used the fire perimeter data available from the BLM’s website to create my sample area. I used Soil, Geology, Landform, and two different vegetation datasets to analyze the area within Gold Butte that has already experienced a wild fire event to look for clues as to why the fire burned where it did.

After analyzing the data, I found there were strong colorations between the data and sample area that helped determine why did the fire burn where it did. After these indicators had been determined I developed model to classify the entire study area (Gold Butte region). The results of this model are as follows:
I created a ranking hierarchy that ranged from 1 to 15 with 1 being the lowest risk and 15 being the highest risk of fire.

Classification by Acreage:
1: 73,997.69
2: 48,133.22
3: 28,132.37
4: 31,386.28
5: 13,055.05
6: 16,103.41
7: 12,518.14
8: 38,121.84
9: 24,185.72
10: 14,517.97
11: 18,885.54
12: 37,333.26
13: 16,801.13
14: 36,380.47
15: 24,161.95

Acreage Statistics:
Count:  15
Minimum:           12518.147206
Maximum:          73997.693831
Sum:      433714.117105
Mean:   28914.274474
Standard Deviation:        15924.771133

With this information a person could then more easily determine which area were most at risk for a fire event and determine how to mitigate or better manage those risks. I plan to document the areas that are at most risk which haven’t burned yet so in case of a fire event the pre-fire landscape will be adequately documented.

This is not the end of this project but just another stepping stone to more research and better understanding of wild fires in a desert ecosystem. One interesting byproduct of this study has been to look more closely at the areas that are marked high risk and within close proximity to the fire boundary but yet didn’t burn. In many instances it is plainly clear the role that roads play as natural fire breaks to prevent the fire from spreading even farther within the desert ecosystem. I will continue to post data and information about my findings in researching the Gold Butte region…

## Monday, July 14, 2014

### Details Depicted - Doing the Analysis

This is Part IV in a series of articles on Wildfires in the desert region of Gold Butte in North Eastern Clark County, NV.

To read Part II click the following link: Defining the Study Area:

To read Part II click the following link: Defining the Study Area:

To read Part III click the following link: Defining the Study Area:

In this step of the research, I am delving into the details. With the following graphics I try to depict how I performed the analysis to create the risk index. In the previous step (Step III) I calculated the specific value for each type we are researching. In this step I am applying those values to a grid that I created within the area of interest. The following is how I apply those values:

The next step is to create the map that depicts all these values which represent the potential risk an area has to fire.

## Friday, July 4, 2014

### Spatial Analysis - Sifting Statistics

This is Part III in a series of articles on Wildfires in the desert region of Gold Butte in North Eastern Clark County, NV. To read Part II click the following link: Defining the Study Area:

In the field of Spatial Analysis and Statistics you use geographic data, which is data that has a fixed location within the real world, to find trends and correlations between the data. The goal of my fire analysis project is to find try and find correlations between the fire area and the rest of the gold butte region to create a risk index to identify the most at risk areas in Gold Butte that haven’t yet burned but are likely to burn. Once this is complete I will then document those areas current habitat for future restoration plans. I will also look for ways to protect these desert ecosystems from the devastating consequences of a wildfire event.

In step 1 I defined the sampling area as the boundary of the Forks and Tramp fire within the Gold Butte Region and presented the summary five different datasets including soil, geology, vegetation types and landforms or slope classifications within the burned area (sample).

In step 2 I defined the study area to which I would scale my analysis to. I then presented the summary results of the same datasets that I presented for the sample area in step 1.

In Step 3 I am looking for recurring patterns and relationships between the burned area and the total study area. In this step I am looking for scale invariance or the lack of scale invariance to try and see if I can determine why the fire burned where it did through analyzing the results of both step 1 and step 2.

For example, if all of the data from the sample area scaled perfectly to the study area, all that it would tell me is that this fire burned consistent with the statistical distribution across the total study area.  If 30 percent of the study area was made up of creosote and 30 percent of the burn area was also creosote the only inference that could be made is that the fire burned consistent with the overall makeup of the study area. However if 30 percent of the study area was made up of creosote but 5 percent of the burn area consisted of creosote then we could deduce that the creosote vegetation is not as susceptible to fire.  The following is the breakdown of the previously defined data:

Landform (Slope Classifications)
Looking at the total Landform distribution across the entire study area you can see that the gently sloping ridges and hills make up the majority of the study area. However that same classification only makes up 20% of the burn area meaning that this landform classification is not as susceptible to fire as other types.  The landform types that have the smallest percent of the total study area but the largest percentage of the burn area are the indicators of the most susceptible landform types:
·         very dry steep slopes
·         very moist steep slopes
·         hot aspect scarps, cliffs, canyons
·         cool aspect scarps, cliffs, canyons

Geologic Types
Again here I am looking for the geologic types that have the smallest percent of the total study area but the largest percentage of the burn area.  The most susceptible geologic types:
·         very dry steep slopes
·         very moist steep slopes
·         hot aspect scarps, cliffs, canyons
·         cool aspect scarps, cliffs, canyons

Soil Types
After analyzing the soil types the indicators are not as significant as other data is at showing correlation however there are still relationships that exist that will help add to the modeling. Again I am looking for the types that have the smallest percent of the total study area but the largest percentage of the burn area.  The most susceptible soil types are:
·         Water (this is the land that has been exposed by the drop in lake water levels)
·         lithic torriorthents-rock outcrop-lithic and deep calciorthids
·         deep and shallow paleorthids-calciorthids-haplargids

Vegetation Types
Again I am looking for the types that have the smallest percent of the total study area but the largest percentage of the burn area.  The most susceptible Vegetation types are:
·         Artemisia tridentata ssp. (tridentata, wyomingensis) – This type was only found in burn area
·         Inter-Mountain Basins Montane Sagebrush Steppe
·         Great Basin Pinyon-Juniper Woodland
·         Inter-Mountain Basins Big Sagebrush Shrubland
·         Agriculture-Cultivated Crops and Irrigated Agriculture
·         Mojave Mid-Elevation Mixed Desert Scrub

Vegetation 2
Again I am looking for the types that have the smallest percent of the total study area but the largest percentage of the burn area.  The most susceptible Vegetation types are:
·         Juniper II
·         Mountain shrub

·         Blackbrush