Improved image processing and geographic information system techniques for improved water resources management
Improved image processing and geographic information system techniques for improved water resources management
The objective of the thesis was to use satellite imagery and geographic information systems (GIS) for real time water resources management. The thesis aims to: 1) improve the classification of crops from satellite images to an accuracy that is acceptable for crop water allocation, 2) improve the methodology for estimating crop water demand from satellite images, 3) estimate real time field crop coefficient factor (Kc) for crop cotton from satellite images, and 4) build a GIS model based on the real time information obtained from satellite data to estimate the crop water demand, predict the irrigation water needs and water use efficiency within the whole irrigation scheme and the command areas down to the secondary level of irrigation canals.
The developed methodology was applied on to two large irrigated massifs in the Aral Sea basin in southern Kazakhstan. The satellite images were used to identify the irrigated land and the areas of the different crop. A new multi-layered classification methodology was developed to identify the crop/land use classes achieving a 94% accuracy, which is more than adequate for crop water allocation. In Chardara irrigation scheme, the classification resulted in a total cropped area of 52516 ha being identified which is 92% of the officially stated cropped area; cotton represents the highest area (32591 ha) of about 77% of the irrigated land, mixed crops cover 8176 ha, which include watermelon, tomatoes, and gardens, and finally alfalfa and rice cover 1505 ha 244 ha respectively. The classification showed a huge area of natural vegetation and rough pasture (47066 ha) which consumes more than the cropped area.
The crop evapotranspiration (Et) is conventionally calculated form the meteorological data using CropWat method (i.e. modified FAO Penman-Monteith formula), and is also estimated directly from the satellite data using the SEBAL method of Bastiaanssen et al (1998) and Bastiaanssen (2000). The latter method has been modified in this research. The advantages and disadvantages of both methodologies are discussed. The estimates of Kc in Kazakhstan showed that the FAO-56 Kc values are not applicable for short (60-70 cm) early maturing cotton in Kazakhstan and the most applicable Kc is the local crop coefficient of Danilchenko (1972) which are typically 12% less than the FAO recommended Kc values.
An Arcview GIS model is developed to spatially compare the water demand in the head and tail of the irrigation scheme.
University of Southampton
Abou El-Magd, Islam Hamza
0230fe36-7aa9-4a29-9c57-e9036585a94f
2004
Abou El-Magd, Islam Hamza
0230fe36-7aa9-4a29-9c57-e9036585a94f
Abou El-Magd, Islam Hamza
(2004)
Improved image processing and geographic information system techniques for improved water resources management.
University of Southampton, Doctoral Thesis.
Record type:
Thesis
(Doctoral)
Abstract
The objective of the thesis was to use satellite imagery and geographic information systems (GIS) for real time water resources management. The thesis aims to: 1) improve the classification of crops from satellite images to an accuracy that is acceptable for crop water allocation, 2) improve the methodology for estimating crop water demand from satellite images, 3) estimate real time field crop coefficient factor (Kc) for crop cotton from satellite images, and 4) build a GIS model based on the real time information obtained from satellite data to estimate the crop water demand, predict the irrigation water needs and water use efficiency within the whole irrigation scheme and the command areas down to the secondary level of irrigation canals.
The developed methodology was applied on to two large irrigated massifs in the Aral Sea basin in southern Kazakhstan. The satellite images were used to identify the irrigated land and the areas of the different crop. A new multi-layered classification methodology was developed to identify the crop/land use classes achieving a 94% accuracy, which is more than adequate for crop water allocation. In Chardara irrigation scheme, the classification resulted in a total cropped area of 52516 ha being identified which is 92% of the officially stated cropped area; cotton represents the highest area (32591 ha) of about 77% of the irrigated land, mixed crops cover 8176 ha, which include watermelon, tomatoes, and gardens, and finally alfalfa and rice cover 1505 ha 244 ha respectively. The classification showed a huge area of natural vegetation and rough pasture (47066 ha) which consumes more than the cropped area.
The crop evapotranspiration (Et) is conventionally calculated form the meteorological data using CropWat method (i.e. modified FAO Penman-Monteith formula), and is also estimated directly from the satellite data using the SEBAL method of Bastiaanssen et al (1998) and Bastiaanssen (2000). The latter method has been modified in this research. The advantages and disadvantages of both methodologies are discussed. The estimates of Kc in Kazakhstan showed that the FAO-56 Kc values are not applicable for short (60-70 cm) early maturing cotton in Kazakhstan and the most applicable Kc is the local crop coefficient of Danilchenko (1972) which are typically 12% less than the FAO recommended Kc values.
An Arcview GIS model is developed to spatially compare the water demand in the head and tail of the irrigation scheme.
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Published date: 2004
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Local EPrints ID: 465392
URI: http://eprints.soton.ac.uk/id/eprint/465392
PURE UUID: c8fdf3a1-0ac8-4127-a80c-1bbc082bef26
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Date deposited: 05 Jul 2022 00:42
Last modified: 23 Jul 2022 01:14
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Author:
Islam Hamza Abou El-Magd
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