Groundwater is a vital non-renewable resource that is being over exploited at an alarming
rate. In Saudi Arabia, the majority of groundwater is used for agricultural activities. As
such, the mapping of irrigated lands is a crucial step for managing available water
resources. Even though traditional in-field mapping is effective, it is expensive, physically
demanding, and spatially restricted. The use of remote sensing combined with advanced
computational approaches provide a potential solution to this scale problem. However,
when attempted at large scales, traditional computing tends to have significant
processing and storage limitations. To address the scalability challenge, this project
explores open-source cloud-based resources to map and quantify center-pivot irrigation
fields on a national scale. This is achieved by first applying a land cover classification using
Random Forest which is a machine learning approach, and then implementing a circle
detection algorithm. While the analysis represents a preliminary exploration of these
emerging cloud-based techniques, there is clear potential for broad application to many
problems in the Earth and environmental sciences.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/668981 |
| Date | 04 1900 |
| Creators | Alwahas, Areej |
| Contributors | McCabe, Matthew, Biological and Environmental Sciences and Engineering (BESE) Division, Johansen, Kasper, Picioreanu, Cristian, Schuster, Gerard T. |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | 2021-04-26, At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis became available to the public after the expiration of the embargo on 2021-04-26. |
Page generated in 0.0027 seconds