Detecting Land Cover Land Use Change in Las Vegas

Sarah Belcher

Grant Cooper

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Introduction

Urban development in the Las Vegas Valley has seen unprecedented growth over the last fifty years. The population of Clark County has increased from 48,589 in 1950 to 741,459 in 1990 and 1,375,765 in 2000 according to historical census data (Clark County, 2005). Population in the Las Vegas valley urban area reached 1,685,197 in 2004 (Xian, George Z., 2005). The most current census from 2010 has the population of Clark County at 1,951,269 and the estimate for 2013 is 2,027,868 (U.S. Census).

Land use land cover change over time is important to look at because of its implications regarding increases in impervious surfaces, overall quality of life for residents, and water management (Xian, 2005). During this growth the region has seen a decrease in the clean water supply, yet an increase in demand for water (Ranatunga, Thushara, 2014). The valley has also seen increases in emissions of carbon monoxide, ozone and other pollutants of particulate matter (Xian, G., 2007).

Quantifying the amount of change among land use and land cover types across multiple years can provide valuable data that can be used to further analyze the environmental impacts of urban growth. This study addresses the urban growth of Las Vegas over a 15 year span by performing post- classification thematic change detection for four years of interest: 1999, 2005, 2010, and 2014.

Study Area

Las Vegas Valley falls within Clark County in Southern Nevada. The major cities within Clark County include Las Vegas, Henderson, North Las Vegas, and Boulder City. The area is characterized by a desert climate with extremely hot, dry summers and cool, wet winters (Xian, George Z., 2005). Given that there is little vegetation in the region, landscaping rock and bare alluvial soils can be mistaken for concrete when classifying an image (Xian, George Z., 2005).

Methods

Landsat imagery with a spatial resolution of 30m was gathered from the USGS Global Visualization Viewer. Images from four different years were downloaded; 1999, 2005, 2010 and 2014. These images were preprocessed by the U.S. Geological Survey (USGS) to correct for radiometric and geometric distortions in the images. All images were rectified to the Universal Transverse Mercator coordinate system, zone 11 north. Bands 1-4 (R, G, B, Near IR) were utilized for 1999, 2005 and 2010. For the 2014 image, bands 2-5 (R, G, B, Near IR) were used. All images for each year were collected in the month of July only few days apart from one another. Once all of the images were downloaded they were stacked in ERDAS Imagine. Given the expanse of data that was included in the Landsat imagery, a clip was necessary to include only

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the area of interest.

To establish this area, a 2010 census tract for Clark County was used to select the polygons (tracts) that encompassed the city of Las Vegas. The 2010 census tract file for Clark County was found on the GeoSpatial Data Gateway website offered through the Department of Agriculture’s Natural Resource Conservation Service. Once this was completed, the tracts within the area of interest were exported to a separate file and then dissolved, creating one polygon to be added into ERDAS Imagine. The selected tracts were dissolved in ArcGIS 10.2 using the Data Management Toolbox (Generalization -> Dissolve) to create one, large polygon.

In order to clip the Landsat imagery to the updated census shapefile it was necessary that both the shapefile and the Landsat imagery had the same projection. The Landsat imagery had a WGS 1984 UTM projection, so this was used to update the coordinate system for the shapefile. This was done in ArcGIS 10.2 using the Projections and Transformations Toolbox (Feature -> Project) before adding the shapefile to ERDAS by creating a separate output file with the new projection.

The shapefile with the updated projection was then added to ERDAS Imagine. Each of the Landsat images were clipped using the subset and clip feature in ERDAS based on the shapefile or polygon (figure 1), creating AOIs for our study area. This reduction of data was necessary to improve the speed and accuracy of the supervised classification.

Figure 1: Clipped area of Las Vegas, based on census tracts in Clark County, Nevada.

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To determine the land cover classes present in each image a supervised classification was utilized. Reference data was obtained from My Digital Globe. World View 1 imagery was downloaded as a series of TIFF files for our area of interest, which was determined by downloading a tile shapefile. The imagery was collected on September 27, 2014. This imagery was then opened in ERDAS imagine, along with the Landsat imagery for 2014 and the two views were linked and synced. An AOI layer was created for each year of Landsat imagery and 20 training sites were selected for each class of interest (figure 2). The classes of interest were: Water, Undeveloped Areas, Impervious Surfaces, Structures, and Vegetation.

A housing class was added after the initial classification with only 5 classes. This was done because the roofs of houses were being mistaken for undeveloped areas. Housing was added to improve the classification and to further separate developed or urban area from undeveloped (figure 3).

Figure 2: 2014 World View 1 imagery alongside completed supervised classification for 2014 Landsat imagery. Undeveloped areas are in maroon and can be seen widely dispersed throughout the city.

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Figure 3: 2014 World View 1 imagery alongside completed supervised classification for 2014 including a housing class in yellow.

Once the housing class was added to the classification a thematic recode (Raster -> Thematic -> Recode) was applied to each year’s classified Landsat image. The classes of impervious, surfaces structures, and houses were combined into one general class called urban (figure 4).

Figure 4. World View 1 imagery alongside completed supervised classification for 2014 with thematic recode to four classes, with the urban class in gray.

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With all four years now classified into urban, undeveloped, water, and vegetation, a thematic change detection was done for the years 2014 and 2010; 2010 and 2005; 2005 and 1999; 2014 and 1999 (Raster -> Thematic -> Matrix Union). The least recent year was selected for the Vector #1 file and the most recent year was selected for the Vector #2 file. A separate output was created for each thematic change operation.

Results

To quantify the change in land use land cover across the years of interest a summary by zone report was created for each thematic change detection (Raster tab -> Thematic -> Summary Report by Matrix). Below is a summary by zone table for the thematic change detection done for 1999 to 2014 (figure 5). Tables for the remaining thematic change detection can be found in appendix A at the end of this report. A map for the thematic change detection done for 1999 to 2014 can be found in appendix B at the end of this report as well.

1999 2014 1999 Total

Vegetation Undeveloped Urban Water

Vegetation 1560.78 45.14 430.79 1.98 2038.69

Undeveloped 726.12 23559.26 30370.82 5.04 54661.24

Urban 1153.35 6524.46 49490.51 7.29 57175.61

Water 0.81 5.94 48.51 100.91 156.17

2014 Total 3441.06 30134.8 80340.63 115.22 114031.71

Figure 5. Summary by zone table for years 1999-2014.

From 1999 to 2014 most of the change in hectares for each class was what we expected. There was a decrease in undeveloped, an increase in urban, a decrease in water and surprisingly a large increase in vegetation. In 1999, the Southern Nevada Water Authority (SNWA) implemented the Water Smart Landscapes Rebate to provide an incentive to property owners to remove grass lawns and put in more drought resistant, water efficient landscaping. Since its implementation, more than 168 million square feet of lawn have been replaced with water-efficient landscaping (SNWA). With this program in place and the severe drought, we would have expected the amount of vegetation to decrease in hectares from 1999 to 2014. It’s possible that the vegetation increased due to the increase in urban development. There may still be a fair amount of trees being planted in housing developments even if lawns are not

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being installed. More golf courses were also built from 1999 to 2014 which may have also helped to offset any decrease in lawn vegetation.

From 2010 to 2014 there was a decrease in vegetation, a decrease in undeveloped, an increase in urban and decrease in water. This is more in line with what we expected for the overall change from 1999-2014.

From 2005 to 2010 there was an increase in vegetation, a decrease in undeveloped, an increase in urban and a decrease in water. So, it appears that the amount of vegetation fluctuates between the thematic change detections, possibly due to problems with mixed pixels.

From 1999 to 2005 there was an increase in vegetation, a decrease in undeveloped, an increase in urban and an increase in water. It would be interesting to look at the number of new housing developments built between each time span to see if there is any correlation with the increase in vegetation that was seen from 1999 to 2005, as well as from 2005 to 2010. Given that before houses were put in most undeveloped areas had almost no vegetation, any type of vegetation would have increased the total.

Accuracy Assessment

For our accuracy assessment, the 2014 World View 1 imagery was utilized as reference data. This imagery was also used for the classification of the Landsat imagery. It is recognized that this would not be standard protocol and that other imagery should have been used that was independent of the classification. The requested IKONOS2 imagery from Digital Globe when received, was for the wrong geographic area entirely. Therefore the 2014 World View 1 imagery, which was reserved for the accuracy assessment reference data, became the reference data.

Due to the fact that we were unable to download a World View 1 image of our study area in its entirety for one date, the classified Landsat image needed to be clipped to the extent of the World View 1 imagery before proceeding with the accuracy assessment (Raster tab -> Supervised -> Accuracy Assessment). After linking the accuracy assessment to the viewer with the 2014 classified Landsat image, Create -> Add Random Points was selected from the edit menu. 100 points were selected with a stratified random sampling scheme. Originally 10 points per class were going to be selected, however due to the clip that was necessary Lake Mead was no longer part of the study area, therefore greatly reducing the options for sampling for the water class. Instead, a minimum of 5 points per class was selected.

Below is the error matrix and users/producers accuracy for the 2014 classified Landsat imagery (figure 6).

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Classified Data

Reference Data Total

Vegetation Urban Undeveloped Water

Vegetation 7 0 0 0 7

Urban 0 54 8 2 64

Undeveloped 0 5 19 0 24

Water 0 0 0 5 5

Total 7 60 27 7 100

Figure 6a. Error matrix for 2014 classified landsat imagery and 2014 World View 1 imagery.

Class Reference Classified Number Producers Users

Name Totals Totals Correct Accuracy Accuracy

Vegetation 7 7 7 100.00% 100.00%

Urban 59 64 54 91.53% 84.38%

Undeveloped 27 24 19 70.37% 79.17%

Water 7 5 5 71.43% 100.00%

Totals 100 100 85

Figure 6b: Overall Producers and Users accuracy for each class for 2014 classified landsat imagery and 2014 World View 1 imagery.

Discussion

It was initially planned to study five year increments, however we needed to select imagery from 1999 versus 2000 due to processing being required for our month of interest in the 2000 imagery.

The 2014 World View 1 imagery obtained from My Digital Globe was used as the reference data for the classification of all four years in the study. This was due to limited access to high

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resolution imagery. The Digital Globe Foundation was originally contacted for IKONOS2 imagery for all four years of interest to serve as our classification reference data, but when the imagery came through it was for an entirely different area. High resolution imagery should have been collected for each year of interest to be used as reference data for each supervised classification. Given that it took over two weeks for the request for imagery to be processed and the incorrect files were not received until November 21st, 2014; submitting another request for the correct imagery was not a feasible option at the time.

Given our limited access to imagery in general for our study area, it was planned early on that the accuracy assessment would only be for the 2014 classification. If the duration of this project would have been longer, more avenues could have been sought out to locate reference data for all four years of interest and for the entire study area. The Digital Globe Foundation could have been contacted a second time to gain access to the correct data.

Another issue was with mixed pixels throughout the study area. Our urban area is speckled with undeveloped pixels and in the 1999-2014 change detection some pixels reverted back to undeveloped inside the city. While this could be due to construction, most are probably due to misclassification. Also, the bare ground in the northern part of the study area was misclassified as urban because of its spectral similarity to our housing class. With better resolution imagery both of these problems could potentially have been solved.

With the limitations stated, this data could be utilized for further research in the Las Vegas Valley region to study how urban growth relates to water quality and quantity throughout the Las Vegas watershed. City managers and planners could use this data to identify areas of growth. They could also compare this land use land cover with an existing land use land cover of undeveloped or natural areas to identify where urban development may be encroaching on sensitive habitat.

Appendix A:

1999 2005 1999 Total

Vegetation Undeveloped Urban Water

Vegetation 1769.99 109.51 158.92 0.18 2038.6

Undeveloped 639.09 34057.06 19915.47 15.66 54627.28

Urban 1416.6 10223.57 45494.53 25.02 57159.72

Water 0.63 4.14 17.46 133.92 156.15

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2005 Total 3826.31 44394.28 65586.38 174.78 113981.75

Figure 7. Summary by zone table for years 1999-2005.

2005 2010 2005 Total

Vegetation Undeveloped Urban Water

Vegetation 3164.76 41.76 622.08 6.12 3834.72

Undeveloped 93.78 27990.9 16467.93 1.44 44554.05

Urban 998.46 8633.52 56026.53 10.89 65669.4

Water 0.36 10.71 42.48 121.95 175.5

2010 Total 4257.36 36676.89 73159.02 140.4 114233.67

Figure 8. Summary by zone table for years 2005-2010.

2010 2014 2010 Total

Vegetation Undeveloped Urban Water

Vegetation 3112 32.76 1104.1 0 4248.86

Undeveloped 34.31 22976.1 13522.64 3.1 36536.15

Urban 294.39 7099.54 65656.35 6.84 73057.12

Water 0.09 1.19 33.12 105.3 139.7

2014 Total 3440.79 30109.59 80316.21 115.24 113981.83

Figure 9. Summary by zone table for years 2010-2014.

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Appendix B:

Figure 10. Change detection map for all of the years studied, 1999-2014.

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Division of Work:

Our division of work was pretty equally divided between us. There were only two or three instances were one of us worked on the project without the other team member present.

Classification of 2014 and 1999 Landsat imagery - Sarah

Classification of 2010 and 2005 Landsat imagery - Grant

Subsets and clips of each year of interest - Grant

Selection of Accuracy Assessment points - Grant

Study Area/Methods/Discussion section of paper - Sarah

Other sections of paper and editing - Joint effort

Data collection - Joint Effort

Presentation - Joint Effort

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References

Ranatunga, T., T.Y. Tong, S., Sun, Y., & Jeffrey Yang, Y. (2014). A total watershed management

analysis of the las vegas watershed. Physical Geography, 35(3), 220.

Southern Nevada Water Authority. (2014). SNWA Milestones. Retrieved Oct/28 2014, from

http://www.snwa.com/about/history_milestones.html.

Xian, G. Z. (2007). Analysis of impacts of urban land use and land cover on air quality in the las

vegas region using remote sensing information and ground observations. International

Journal of Remote Sensing, 28(24), 5427.

Xian, G. Z., Crane, M., & McMahon, C. Assessing urban growth and environmental change using

remotely sensed data. Pecora 16 “Global Priorities in Land Remote Sensing”, Sioux Falls,

South Dakota.

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