Sarah Belcher amp Grant Cooper December 8 2014 Introduction Goals To quantify land useland cover change for Las Vegas over time Collect necessary data Determine class scheme Use skills obtained through lab exercises ID: 564986
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Slide1
Detecting Land Cover Land Use Change in Las Vegas
Sarah Belcher & Grant Cooper
December 8, 2014Slide2
Introduction
Goals:
To quantify land use/land cover change for Las Vegas over time
Collect necessary data
Determine class scheme
Use skills obtained through lab exercises
Show Results
Validation
ReportSlide3
Rapid Population
G
rowth
1950: 48,589
1990: 741,459
2000: 1,375,7652004: 1,685,1972013: 2,027,868 (Est.)Desert ClimateAlluvial SoilsSparse VegetationHot, dry summers
http://cdn.teslarati.com/wp-
content/uploads/2014/02/LasVegasStrip.jpg
Satellite imagery courtesy of Digital Globe Inc.
Study AreaSlide4
Study Area
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
Slide5
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. Slide6
Methods
30m Landsat (5, 7 & 8) imagery
Utilized bands R, G, B, and
near IR
All collected in the month of JulyAll images stacked in ERDAS ImagineLandsat imagery 7/4/1999Slide7
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 10.2Slide8
Methods
Each Landsat image subset/clipped based on census tract polygon (AOI)
Improve speed for processing and accuracy of supervised classificationSlide9
Methods
Classes:
Structures
Impervious
Undeveloped
VegetationWaterHousing 20 training sites per classSlide10
Supervised Classification, Maximum Likelihood
6 Classes with housing addedSlide11
Methods
1999-2005 2005-2010 2010-2014Slide12
1999-2014Slide13
1999-2005 Thematic ChangeSlide14
Results
Fda
1999
2014
1999 Total
Vegetation
Undeveloped
Urban
Water
Vegetation
1,560.78
45.14
430.79
1.98
2,038.69
Undeveloped
726.12
23,559.26
30,370.82
5.04
54,661.24
Urban
1,153.35
6,524.46
49,490.51
7.29
57,175.61
Water
0.81
5.94
48.51
100.91
156.17
2014 Total
3,441.06
30,134.8
80,340.63
115.22
114,031.71
*All areas in hectaresSlide15
Results
Undeveloped areas decreased 45%
Urban areas increased 41%
Water decreased 26%
Vegetation increased 69%Slide16
Accuracy Assessment
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
100Slide17
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 Slide18
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 togetherDone more detailed analysis – added NDVI’s to look at percentage of vegetation over time