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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Evaluation of an airborne thermal scanner (8-12 µm) as an irrigation scheduling tool for cotton (Gossypium hirsutum)

Malouf, Christopher P., n/a January 1996 (has links)
Water is Australia's most precious natural resource. The quality, quantity and availability of this resource is the single factor most limiting agricultural development and sustainability in this country. Since the development of Australia's cotton industry in the 1960's, and the expanding areas of irrigated crop, there has been an increasing demand placed on the limited water resources of the country. Consequently, the cotton industry has been the target of protest from conservation groups, residents of rural townships and others farmers engaged in competing rural sectors. Therefore, cotton farmers need to develop best practice in terms of water use efficiency. Not only does this make good ecological sense but also good economic sense. Traditional methods of irrigation scheduling have proven to be subjective and haphazard. Recently developed methods, while providing more quantitative techniques, do not give a synoptic view of a field's or region's crop moisture status. The main objective of this project was to evaluate an airborne thermal scanner (8-12 µm) as practical tool for monitoring the water requirements of an irrigated cotton crop. The thermal scanner was mounted below a light aircraft and imagery was collected over Field 86 , Togo Station, north-west NSW during the summer of 1990/91. The field was divided into nine treatments for the purpose of this project. Three irrigation regimes (early, normal and late) with three repetitions were applied to the nine treatments. A total of fourteen images were selected for analysis. These images were grouped into sets of AM images, PM images as well as diurnal groupings which were interpreted for three separate dates during the growing season. Ground based measurements of infrared crop surface and soil temperature, soil moisture deficit, leaf area index (LAI) and the Crop Water Stress Index (CWSI) were collected to calibrate the airborne imagery. Imagery was in the first instance visually interpreted to determine what information could be gained from this technique. Patterns on the imagery were related to diurnal variations in soil and crop temperatures. This investigation revealed a number of soil related phenomena inherent to the field which were influencing the airborne detected temperatures. While this technique showed variability across the field, the interpretation was somewhat subjective. Temperature values were extracted from the imagery in order to conduct an analysis of variance (ANOVA) between the airborne and ground measurements of infrared crop surface temperature. In summary, this analysis did not show a strong relationship between the airborne and ground based measurements. A number of contributing factors have been proposed as the reason for this variation in the two datasets. Pearson's correlation analysis was applied to the AM (r = 0.65) and PM (r = 0.32) groups of airborne and ground temperatures. Airborne derived calculations of the CWSI were compared to ground based measurements for the AM group of flights. These derived values were only acceptable in instances where the ANOVA results had shown them to approximate the ground based measurements. While airborne thermal imagery provides a useful tool for determining general variations in temperatures across a field, there are many additional factors, the most dominant being the thermal characteristics of the background soil, which influence the detected temperatures. This technique does not provide the precise quantitative information required to accurately determine across-field measurement of the CWSI.

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