(1) NOAA (Myint - PI) Evaluation of Drought Risks and its Impact on Agricultural

Land and Water Use to Support Adaptive Decision-making (2012-2014)

(1) TITLE: Evaluation of Drought Risks and its Impact on Agricultural Land and Water Use to Support Adaptive Decision-making

Program: NOAA Climate and Societal Interactions Program (CSI) - Water (FY 2012) CSI-Water Priority: Coping with Drought and NIDIS (FY 2012)

Soe W. Myint (Lead PI) Jan M. H. Hendrickx (Co-PI) Rimjhim Aggarwal (Co-PI) Professor Professor Associate Professor School of Geographical Dept of Earth and School of Sustainability Sciences and Urban Planning Environmental Science Global Institute of Sustainability Arizona State University New Mexico Tech Arizona State University Tempe, AZ 85287 Socorro, NM 87801 Tempe, AZ 85287 Email: soe.myint@asu.edu Email: hendrick@nmt.edu Email: rimjhim@asu.edu Nancy Selover (Co-PI) N.W. Bill Plummer (Co-PI) Elizabeth Wentz (Co-PI) State Climatologist President Professor AZ State Climate Office Agri-Business Council of Arizona School of Geographical Arizona State University 1819 E. Southern Ave., Suite E-10 Sciences and Urban Planning Tempe, AZ 85287 3550 N Central Ave Arizona State University Email: selover@asu.edu Mesa, Arizona 85204 Tempe, AZ 85287 Email: PlummerNW@aol.com Email: wentz@asu.edu Dianne Yunker (Collaborator) Prasad S. Thenkabail (Collaborator) Jim Holway (Collaborator) Water Resource Specialist Research Geographer-14 Western Lands and Arizona Department of U. S. Geological Survey Communities, Sonoran Institute Water Resources (ADWR) Southwest Science Team 11010 N. Tatum Blvd. 3550 N Central Ave 2255, N. Gemini Dr Ste. D101 Phoenix, AZ 85012 Flagstaff, AZ 86001 Phoenix, AZ 85028 Email: deyunker@azwater.gov Email: pthenkabail@usgs.gov E: jholway@sonoraninstitute.org Karin Park (ASU Org Rep) Chris Udall (ABC Org Rep) Norene Boykin (NMT Org Rep) Sponsored Projects Officer Executive Director Research Administrator Office of Research and Agri-Business Council of Arizona Sponsored Project Sponsored Projects 1819 E. Southern Ave., Ste E-10 Administration Arizona State University 3550 N Central Ave New Mexico Tech Tempe Arizona, 85287 Mesa, Arizona 85204 Socorro, NM 87801 Email: ASU.awards@asu.edu Email: chris@agribusinessarizona.org Email: nboykin@admin.nmt.edu

How does vulnerability to drought vary by crop types based on a large spatial scale analysis?

What is the impact of drought on agricultural water consumption at different spatial scales?

What adaptive options are available through changes in crop mixes and limited time market driven water transfers?

What are the economic cost-benefits of alternative adaptation strategies under different drought scenarios at the farm and watershed levels?

Research Questions

1. Prepare crop type maps (a) 2 wet years – 2001, 2005 (b) 2 drought years – 2002, 2010

2. Produce daily Evapotranspiration (ET) layer for 2001, 2002, 2005, and 2010

3. Determine which crop types are vulnerable and resilient to drought with regards to water use

4. Cost and benefit analysis of crop type, crop yield, water cost, and crop price in connection to climatic parameters

Objectives

5. Develop a spatial decision support system

Figure 1. Phoenix Active Management Area Basin.

Study Area (Phoenix AMA)

A false color composite of Landsat ETM+ 30 meter resolution data over

Phoenix metropolitan area

Landsat

1. Alfalfa 2. Wheat; Sorghum; & Fall Alfalfa 3. Double Wheat Cotton 4. Double Barley Sudan Grass 5. Vegetable & Cotton 6. Other Hay (non-alfalfa) 7. Flowers 8. Double vegetable/Fruits 9. Unknown might be other Hay 10. Triple vegetable crop 11. Vegetable Sorghum 12. Cotton 13. Unknown 14. Corn 15. Corn & Vegetable/Fruit 16. Corn & melon (not sure) 17. Double Barley Cotton 18. Double Barley Sorghum 19. Alfalfa & Late season crop 20. Unknown decrease NDVI 21. Wheat & Fall Alfalfa 22. Spring & Late fall vegetable crops 23. Quadruple vegetable crops 24. Barley 25. Sorghum 26. Winter vegetable 27. Alfalfa 28. Late fall crop 29. Spring vegetable 30. Alfalfa 31. Alfalfa 32. Spring vegetable & Late Fall crop 33. Hay 34. Corn & Late fall crop 35. Early spring vegetable 36. Potato 37. Double Wheat Sorghum 38. Early spring vegetable & Late fall crop 39. Alfalfa 40. Watermelon 41. Potato 42. Double Potato Sorghum 43. Early spring vegetable

40 Crop Types (plus Double & Triple)

43 Phenologial Spectral Patterns

Data Collection (very difficult) Spectro-radiometer readings 1. USDA 2. Irrigation District – General Managers 3. Interviews with local farmers 4. Interviews with farm owners

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Cotton

Watermelon

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Flowers

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Vegetables

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(1) Spectral Angle Mapper

(2) Regression Tree

(3) Decision Tree

(4) Neural Network?

Methods

Crop Type Map (2002)

24 NDVI images (2002)

Fourier Transform

freq

time

• Basis functions are smooth, analytic

• Solutions to natural differential equations

• Assumes signal is stationary

Short-Time Fourier Analysis

freq

time

• Windows of fixed size but varying shape

• Fixed size implies fixed time and frequency resolution

• Useful for analysis of narrowband processes

Wavelet Analysis

scale

time

Non-stationary and multiscale signal analysis

- Good time resolution at high frequencies

- Good frequency resolution at low frequencies

Fourier

Loses time (location) coordinate completely

Analyses the whole signal

Short pieces lose “frequency” meaning

Wavelets

Localized time-frequency analysis

Short signal pieces also have significance

Scale = Frequency band

Fourier versus Wavelets

Signal Decomposition

Signal

Detail

Coefficients (cD1)

Approximation

Coefficients (cA1)

2

2

downsample

Signal

Detail

Signal (D1)

Approximation

Signal (A1)

High Pass

Low Pass

f

f

These signals

are now

oversampled

High Pass

Low Pass

f

f

Discrete Wavelet Transform

• CWT: Scales = any value

• DWT: Scales = Dyadic scale 21 22 23 24 25 ...

• Equivalent to:

Signal Detail (level 1)

Approximation

(level 1) Detail (level 2)

Approximation

(level 2)

S = D1 + A1 = D1 + D2 + A2

H

L 2 1

2 1

1 2

1 2

1 2

1 2

A

DH

DV

DD H

L

L

H

Approximation

Details

2-D Wavelet Decomposition of an image at Level 1

1 2

2 1 Keep one column out of two

Keep one row out of two

A H

V D

Level 1

Level 3

Level 2

A standard Haar wavelet decomposition with 3 levels of a commercial sample

A hypothetical image and its sub-images of the wavelet decomposition

Decomposition Procedures (Dyadic vs. Overcomplete)

Measures

Log Energy

Shannon’ s index

Angular Second Moment

Entropy

Ng

i

Ng

j

jiPASM1 1

2)),((

Ng

i

Ng

j

entropy jiPjiPSHANON1 1

)( )),(log(),(

Ng

i

Ng

j

entropy jipENERGY1 1

2

)( )),(log(

K

i

K

j

jiPjiPENT1 1

|),(|log*),(

ji

jic

jicjiP

,

2

2

),(

),(),(

NSF (Myint - Single PI) An Exploration of Frequency-based Multi-scale Multi-

decomposition Techniques for Effective Urban Mapping (2004-2006).

Normalization

(a) All sub-images

(b) First approximation and all sub-images

(c) Level wise

(d) Band wise

Band 1

Band 2

Band 3

Level 1 Level 2 Level 3

NSF (Myint - Single PI) Wavelet Analysis of High Spatial Resolution Imagery for

Urban Mapping Using Infinite Scale Decomposition Techniques (2012-2014).

New Classification Technique

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Wavelet-based Pattern Recognition for Crop Type Mapping Using Crop Phenology

Signal processing (1 D wavelets)

Spatial processing (2 D wavelets)

(OR) = (OR)

Both (3 D wavelets)

Input

Irrigation Districts – Phoenix AMA

INVESTIGATORS: (1) Soe W. Myint § (Principal Investigator) (2) Ronald R. Rindfuss € (Consultant) (3) Huei-Ping Huang £ (Co-Investigator) (4) Karen C. Seto ξ (Co-Investigator) (5) Ariane Middel Φ (Co-Investigator) (6) Anthony J. Brazel § (Collaborator) (7) Dan G. Blumberg ¥ (Collaborator) § School of Geographical Sciences and Urban Planning, Arizona State University € Department of Sociology and Carolina Population Center, UNC, and East-West Center, Honolulu £ School for Engineering of Matter, Transport, and Energy, Arizona State University ξ School of Forestry and Environmental Studies, Yale University Φ Decision Center for a Desert City, Global Institute of Sustainability, Arizona State University ¥ Earth and Planetary Image Facility, Ben-Gurion University of the Negev, Beer Sheva

NASA (Myint - PI) Understanding Impacts of Desert Urbanization on Climate and

Surrounding Environments to Foster Sustainable Cities Using Remote Sensing

and Numerical Modeling (2012-2015).

1. How does the oasis effect of cities in desert environments vary over space and time?

Research Questions

2. How do the internal features and spatial arrangements of land cover influence temperatures in desert cities and their surrounding environments?

3. What are the patterns and rate of change of land cover/land use (LCLU), including desert vegetation and agriculture within and around select desert cities, over time (1990-2010)?

4. What is the impact of the spatial arrangements of land cover and land fragmentation within and around desert cities on the surrounding environments?

5. How is urban heat island (UHI) related to desert city population as compared to other cities in different climate regions?

6. What is the relationship between population, surface temperatures and vegetation (trees/shrubs, grass) at various administrative boundary levels?

7. Are surface temperatures related to the pattern of demographic growth in different regions?

8. What are the typical changes in the meteorological state, including temperature, precipitation and wind pattern, associated with land-use changes due to urbanization?

9. How universal are the urban effects on regional climate?

10. What will the future precipitation, temperature and regional airflow patterns in arid and semiarid environments be around desert cities using the predicted land cover and land use change for 2030?

11. What adaptive options, alternate management opportunities and land-use planning scenarios are available through changes in manmade and natural landscapes for sustainable city development in desert environments?

Study Areas

Landsat TM data over the selected desert cities and surrounding regions displaying bands 4 (near infrared), 3 (red visible), and 2 (green visible)

in red, green, and blue respectively. Note: first row images were acquired around 1990 and second row images were acquired around 2010.

NOAA (Myint - PI) Evaluation of Drought Risks and its Impact on Agricultural Land

and Water Use to Support Adaptive Decision-making (2012-2015)

NSF (Myint - PI) Wavelet Analysis of High Spatial Resolution Imagery for Urban

Mapping Using Infinite Scale Decomposition Techniques (2012-2015)

NASA (Myint - PI) Understanding Impacts of Desert Urbanization on Climate and

Surrounding Environments to Foster Sustainable Cities Using Remote Sensing

and Numerical Modeling (2012-2015)