Camelot and Greenleaf Meadow, which have fully developed trees and shrubs and ... Costello, L., Matheny, N. & Clark, J. (1992) Estimating crop coefficients for ...
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Remote Sensing and Hydrology 2000 (Proceedings of a symposium held at Santa Fe, N e w Mexico, U S A , April 2000). I A H S Publ. no. 267, 2 0 0 1 .
Development of a CIS-based model to estimate landscape water demand at the urban/rural interface F A Y E K FARAG, C H R I S T O P H E R M. U. N E A L E Department ofBiological Utah 84322, USA
and Irrigation
Engineering,
Utah State University,
Logan,
e - m a i l : gisfavekf5jhotrnail.com
ROGER KJELGREN Plants,
Soils and Biometeorology
Department,
Utah State
University,
Logan
Utah 84322,
USA
Abstract In the arid west of the United States, there has been a redistribution of water from agriculture to municipal, industrial, recreational, and environ mental uses due to population growth. A growing municipality in search for new water resources usually leads to a rural-to-urban conversion of water uses, affecting irrigated agriculture. Multispectral airborne digital images at one-metre resolution were collected over agricultural and urban landscaped areas in the City of Layton, Utah. Spectral signatures were extracted from the image representing the classes of interest. Water demand in the urban areas was calculated as the volume of water consumed by each house for irrigated landscape areas. Comparison between the estimated volume of water used for irrigation purposes and the actual water use obtained from the water billing data was studied. This study demonstrated the ability to use high-resolution airborne imagery in a GIS environment to estimate landscape water demand for individual houses or subdivisions. This allows the water supplier to identify and target particular end users with water conserving measures. Key words
G I S analysis o f irrigated u r b a n l a n d s c a p e ; U t a h , U S A ; u r b a n l a n d s c a p e w a t e r
demand; urban study area
INTRODUCTION Competition for scarce water supplies in the western United States has increased due to urbanization and population growth. Water is being converted from agricultural uses to municipal, industrial, recreational and environmental uses, because of the increased economic value of land and water. Preliminary research results have shown great potential for using high resolution airborne multispectral imagery to obtain the landscaped areas in urban sectors (Farag et al, 1999). Landscaped areas can be combined with évapo transpiration (ET) data to estimate water use. This study demonstrates the capability of estimating landscape water demand for individual houses or groups of houses and neighbourhoods using high resolution remotely sensed input in a GIS environment.
METHODOLOGY Image processing Multispectral airborne digital images at one-metre resolution were collected over agricultural and urban landscaped areas in the city of Layton, Utah (USA) in August
Development of a GIS-based model of landscape water demand at the urban/rural interface
459
1998. The spectral images were registered into 3-band images, rectified and then mosaicked together into a large image mosaic covering the entire agricultural-urban area.
Image classification and GIS development Spectral signatures were extracted visually and iteratively from the Layton image to cover the agricultural and urban areas and to represent the classes of interest using the seed property method of ERDAS IMAGINE, resulting in ten basic classes shown in Fig. 1(a). GIS layers were obtained from the city of Layton containing lot boundary, street names, lot tax identification number, subdivisions, etc. The GIS layers were overlaid on top of the classified image and the analysis of areas has been conducted (Fig. 1(b)).
Fig. 1 (a) The GIS parcels overlaid on the classified image, (b) The selected subdivisions.
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Fayek Farag et al.
Water demand estimates in the urban area Water demand in the urban areas was calculated as the volume of water consumed by each house for irrigated areas of grass, trees and shrubs using the following relation:
V =YZK ^ET xA IE l
L
r
L
(1)
I
where V = total volume, A = average area of each landscape vegetation type for all lots, ET = the daily reference ET, KL = landscape water use coefficient for each vegetation type (Costello et al., 1992), £ ) = irrigation efficiency assumed to be (85%) for typical home sprinkler systems, x = number of days in a month, and y = number of crops. Weather data was obtained from the Kaysville weather station, located a few kilometres north of Layton. Nine subdivisions were selected from the classified image for comparison between estimated water volume used for irrigation and actual water use obtained from the water billing data. T
L
R
RESULTS AND CONCLUSIONS The average values for ET for the months studied are shown in Table 1. The average amount of baseline water use (calculated as 7000 gallons month' lof ; note: 1 gallon = 3.785 x 10~ m , and the average lot size is 1000 m ) was subtracted from the actual water use to obtain the volume of water used for irrigation. It was assumed that 4 0 % of trees and shrubs have grass beneath, thus increasing the estimated water use. The water use differences for all the subdivisions for the study period are shown in Table 2. R
1
3
3
1
2
Table 1 The ET values for each month (1998) using Penman-Monteith ET for a grass reference. r
Month 1
ET (mm day" ) r
r
June
July
4.7
5.5
August 5.0
September 3.6
October 2.2
Table 2 The difference between the actual and the estimated water use for the summer season. Subdivision
Difference between the actual and the estimated water use (gallons month"'): June July August September October Total
VaeView Summer-field Robins Park Park West Marshall Heights Jamestown Greenleaf Meadows Camelot Mala
450 814 859 260 25 790 186 459 186 409 147010 417 774 777 905 66 588
Season total
-79 091 694 963 9 243 -290 912 67 074 103 182 161 990 215 845 -77 908
252 100 797 649 19 585 7 445 141658 130 574 321 855 567 133 12 402
471 682 688 167 5 041 193 091 116 494 99 774 293 226 733 986 22 770
652 765 591 686 3 497 373 737 94 830 54 473 289 597 891 840 132 137
1 748 270 3 631 726 63 156 469 819 606 464 535 013 1 484 443 3 186 709 155 990 11 881 591
The results show that for the summer season subdivisions VaeView, Summerfield, Camelot and Greenleaf Meadow, which have fully developed trees and shrubs and
Development of a GIS-based model of landscape water demand at the urban/rural interface
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established lawns use more water for irrigation than the newer subdivision such as Park West. This study shows the potential for using high-resolution airborne imagery in a GIS environment to estimate landscape water demand for individual houses or subdivisions, thus allowing the water supplier to identify and target particular end users with water conservation measures.
REFERENCES Costello, L., Matheny, N . & Clark, J. ( 1 9 9 2 ) Estimating crop coefficients for landscape plantings. University of Agricultural
and Natural Resources
Leaflet
California
21493.
Farag, F., N e a l e , C. M . U. & Kjelgren, R. ( 1 9 9 9 ) Estimation of Agricultural and U r b a n L a n d s c a p e Vegetated Areas U s i n g A i r b o r n e Multispectral Digital Imagery and G I S . In: Proc. 17th biennial workshop on Color Photography and Videography in Resource Assessment ( M a y 1999, Reno, N e v a d a ) (ed. b y P. T. Tueller), 1 2 3 - 1 3 5 . A m . Soc. Photogram. & R e m o t e Sens, and D e p a r t m e n t of Environmental and R e s o u r c e Sciences, University of N e v a d a , Reno, Nevada, USA. N e a l e , C. M . U. & Crowther, B . G. ( 1 9 9 4 ) A n airborne multispectral video/radiometer r e m o t e sensing system: d e v e l o p m e n t and calibration. Remote Sens. Environ. 4 9 , 1 8 7 - 1 9 4 .
