1. Detecting Land Cover Land Use Change in Las Vegas Sarah Belcher & Grant Cooper December 8, 2014

2. Introduction Goals: 1.To quantify land use/land cover change for Las Vegas over time 2.Collect necessary data 3.Determine class scheme 4.Use skills obtained through lab exercises 5.Show Results 6.Validation 7.Report

3. Rapid Population Growth 1950: 48,589 1990: 741,459 2000: 1,375,765 2004: 1,685,197 2013: 2,027,868 (Est.) Desert Climate Alluvial Soils Sparse Vegetation Hot, dry summers http://cdn.teslarati.com/wp- content /uploads/2014/02/LasVegasStrip.jpg Satellite imagery courtesy of Digital Globe Inc. Study Area

4. Southern Nevada receives 90% of its water supply from the Colorado River Area has been experiencing drought for the last 14 years Per capita water use has dropped 40% in the past two decades in Las Vegas http://www.activistangler.com/storage/Lake_Mead_after_11_years_of_drought. jpg?__SQUARESPACE_CACHEVERSION=1395066723992 http://earthobservatory.nasa.gov/Features/LakeMead/images/mead_2000_lrg.jpg

5. Study Area Previous studies have been conducted on ISA (impervious surface areas) ISA indicator of non-point source pollution or polluted runoff Changes in ISA useful indicators of spatial extent, intensity and potentially types of LULC change Source: Xian, G. Analysis of Urban Land Use Change in the Las Vegas Metropolitan Area Using Multitemporal Satellite Imagery. ASPRS 2006 Annual Conference.

6. Methods 30m Landsat (5, 7 & 8) imagery Utilized bands R, G, B, and near IR All collected in the month of July All images stacked in ERDAS Imagine Landsat imagery 7/4/1999

7. Methods 2010 Census tract for Clark County Arc Map 10.2 used to select tracts for study area and dissolve boundaries Projection changed to WGS 1984 UTM completed in Arc Map 10.2

8. Methods Each Landsat image subset/clipped based on census tract polygon (AOI) Improve speed for processing and accuracy of supervised classification

9. Methods Classes: Structures Impervious Undeveloped Vegetation Water Housing 20 training sites per class

10. Supervised Classification, Maximum Likelihood 6 Classes with housing added

11. Methods 1999-20052005-20102010-2014

12. 1999-2014

13. 1999-2005 Thematic Change

14. Results Fda 1999 2014 1999 Total VegetationUndevelopedUrbanWater Vegetation1,560.7845.14430.791.982,038.69 Undeveloped726.1223,559.2630,370.825.0454,661.24 Urban1,153.356,524.4649,490.517.2957,175.61 Water0.815.9448.51100.91156.17 2014 Total3,441.0630,134.880,340.63115.22114,031.71 *All areas in hectares

15. Results Undeveloped areas decreased 45% Urban areas increased 41% Water decreased 26% Vegetation increased 69%

16. Accuracy Assessment Classified Data Reference Data Total VegetationUrbanUndevelopedWater Vegetation70007 Urban0548264 Undeveloped0519024 Water00055 Total760277100

17. Limitations Reference data for classification should have should have had imagery for all four years of interest Accuracy assessment should have used an independent source, not the World View 1 imagery Mixed pixels on edges and with roofs/buildings and bare soil

18. If we Knew What we Know Now… Data can be very challenging to track down Scope creep With more time, we could have: Obtained high resolution imagery for all four years of interest, possibly more to not used Landsat all together Done more detailed analysis – added NDVI’s to look at percentage of vegetation over time