Identifying Irrigated Areas in the Columbia River Basin
Paige
1
Pruisner ,
Dr. Jennifer
2
Adam ,
Kirti
2
Rajagopalan
1. Civil, Environmental, and Architectural Engineering, University of Colorado at Boulder
2. Civil and Environmental Engineering, Washington State University
Introduction
Bioearth (http://www.cereo.wsu.edu/bioearth/) is a team
effort between several groups to create a regional-scale
earth systems model of the interactions between carbon,
nitrogen, and water at the land/atmosphere interface to
provide information for natural and agricultural resource
management. Mapping the extent of irrigation provides
insight into the amount of evaporation and transpiration
in an area, which contributes to understanding hydrology
at the land/atmosphere interface. The goals of this project
are to (1) evaluate currently available irrigation extent
maps, (2) create a composite map of irrigated areas in the
Columbia River Basin, and (3) document the uncertainties
involved.
Agreement Maps and Data
Ozdogan and
Gutman
Misalignment
Red = 0, non-irrigated agreement
Orange = 1, WSDA irrigated, data set non-irrigated
Blue = 2, data set irrigated, WSDA non-irrigated
Green = 3, irrigated agreement
•
Doll and Siebert
Ozdogan and Gutman
•
0% threshold
•
•
25% threshold
Visually judged to not align
with WSDA data in all
areas, even when displayed
in consistent datum
Data sets were created
independently
Error possibly due to
sampling or recording
errors
Error can result when the
angle of the remote sensing
device is not accounted for
in the data
Conclusions and Discussion
50% threshold
Doll and Siebert
•
•
Original data not primarily based on remote sensing
information
Sources of Error
•
Figure 1: Pivot Irrigation in Washington
Photo Credit: Biofuels Cropping System Research and Extension Project, Department of Crop and
Soil Sciences, Washington State University
(http://css.wsu.edu/biofuels/projects/region3/index.html)
Data Processing and Agreement
Analysis
•
Acquire data from Ozdogan and Gutman (2008) and
Doll and Siebert (2005) as well as an irrigation extent
map created by the Washington State Department of
Agriculture (WSDA).
•
•
•
•
•
Obtain projection information, if absent, to align data.
Implement irrigation thresholds.
•
•
•
•
•
•
•
Doll and Siebert data measure percentage of cell
equipped for irrigation.
Ozdogan and Gutman data measure fraction of
irrigated area per cell.
WSDA data are binary.
Set three thresholds for irrigation, 0%, 25%, 50%
If a cell is more than 50% irrigated, it is labeled
as irrigated, etc.
Create a numerical attribute code for irrigation.
Convert data to raster file type. Specify cell size so that
data match.
Use Snap to Raster to ensure cell alignment.
Clip files to WSDA data extent.
Use weighted sum to calculate agreement.
0% threshold
Value
Count
Doll and Siebert
25% threshold
Value
Count
50% threshold
Value
Count
0
1
2
3
Sum
Agree
99
5
290
118
512
217
0
1
2
3
Sum
Agree
329
63
60
60
512
389
0
1
2
3
Sum
Agree
363
97
26
26
512
389
% Agree
42.38%
% Agree
75.98%
% Agree
75.98%
Differ
295
Differ
123
Differ
123
% Differ
57.62%
% Differ
24.02%
% Differ
24.02%
•
Large pixel size shows less detail, leads to more
inaccuracy
Original data measured percentage of cell equipped
for irrigation, not necessarily areas of active
irrigation
Ozdogan and Gutman
•
Other Sources of Error
•
Data collected during different years
•
•
•
MODIS (Moderate Resolution Imaging
Spectroradiometer) in 2001
NASS (United States Department of
Agriculture National Agricultural Statistics
Service) in 2002
Irrigation potential based on climate, which
varies annually
Based on percentage of cells in agreement, the Doll and
Siebert threshold that agrees most strongly with the
WSDA data set is both the 25% or 50% thresholds.
Areas for Future Work
•
•
Explore other methods of data alignment to reduce error in comparison with Ozdogan and Gutman data set
Make further comparisons between independent data sets and WSDA irrigation data (Thenkabail et al., 2008)
References
Ozodogan, M.; Gutman, G. A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the contiental US. Remote Sens. Environ. 2008, 112, 3520-3537
Thenkabail, P.S.; Biradar, C.M.; Noojipady, P. Dheeravath, V., Li, Y.J.; Velpuri, M.; Reddy, G.P.O.; Cai, X.L.; Gumma, M.; Turral, H.; Vithanage, J.; Schull, M; and Dutta, R. (2008). A Global Irrigated Area Map (GIAM) Using Remote
Sensing at the End of the Last Millenium. International Water Management Institute. pp. 63.
Siebert, S.; P. Doll; J. Hoogeveen; J.-M. Faures; K. Frenken, and S. Feick (2005), Development and validation of the global map of irrigation areas, Hydrol. Earth Syst. Sci. Disc., 2, pp. 1299-1327
Acknowledgements
I would like to thank the National Science Foundation for funding this Research Experience for Undergraduates at Washington State University. This work was supported by the National Science Foundation’s REU program under
grant number 0754990.
I would like to thank Dr. Adam and Kirti Rajagopalan for advising me as an undergraduate summer researcher.
I would like to thank the graduate students and postdocs who assisted me with my project and welcomed me into their office for the summer, especially Elizabeth Allen and Kiran Chinnayakanahalli.