Land Use / Land Cover Change in the Phoenix Metropolitan Area Lori Krider & Melinda Kernik

Introduction Why Phoenix? o One of 10 fastest growing cities from (Perry & Mackun, 2001) o Arid regions with high population are water stressed o Water use is reflected by how the land is used and managed o How is the landscape changing and how does this effect water use?

Objective Use remote sensing software to assess land use / land cover change in Phoenix from 1984 – 2011 o Expect to see dramatic changes due to rapid population growth  Increase in urban and suburban areas (sprawl)  Increase in cultivated areas on edges of metropolitan area  Decrease in natural vegetation

Objective Study Area o Phoenix-Mesa Metropolitan Area  South-central Arizona  16,200 km 2  Phoenix, Mesa, Tempe, Chandler, Gilbert, Scottsdale, Glendale, Sun City, Peoria, and Avondale Google Maps

Preparation Tools: ERDAS IMAGINE 2011, USGS GLOVIS, ArcGIS 10, Google Maps TM and Google Earth TM Materials: Landsat TM images from 1984 and 2011 (two from each year, 30 m res., 7 bands, June), 2006 NLCD Pre-classification processing o Stack bands, mosaic and crop images for each year o View NLCD o Unsupervised classification (5, 6 & 7 classes)

Analysis Supervised classification o Anderson Hierarchical Classification (levels 1 and 2)  Altered, unaltered, developed and water  Altered  Human-assisted: healthy and stressed crops, golf courses  Uncultivated: fields not reflecting in IR  Unaltered  Natural: upland and scrub/shrub (not in IR)  Hydrophillic vegetation: depressional vegetation often associated with water (in IR)  Water: lakes, rivers and large golf course water hazards  Developed  suburban (dwellings) & urban/roads (commercial/industrial)

Analysis Training Areas o o Why?  Errors in first run with less training areas  Combination of smaller category classes (i.e. healthy crop + stressed crop)  Reduce confusion and capture variety Change Detection o Thematic: > 2011 o Difference  to identify areas of significant change and overall patterns  10, 20, and 30% thresholds

Post-classification Accuracy Assessment o stratified random o same mosaics as reference  added Google Maps TM for 2011 o switched "trainers" o 140 reference points (20 per class) p

Purple: Change to Suburban Light Blue: Change to Urban Thematic Change Detection

1984

2011

Purple = changed to Suburban Blue = changed to Urban

Green = more than 20% increase in NIR Blue = more than 20% decrease in NIR

Thematic Change Detection

Limitations!

Accuracy Assessment

For future classifications: Clip to the smallest possible boundaries – More ontological classes = more classification confusion Complications using 30m resolution images for reference data and the same image. Use this technique, to generate water infrastructure policy for Phoenix  …probably not

References 1.Perry, M. J. & P. J. Mackun. Population Change and Distribution : Census 2000 Brief. April United States Census Bureau. 12 Nov