Abstract
The implications of change on local processes have attracted significant research interest in recent times. In urban settings, green spaces and forests have attracted much attention. Here, we present an assessment of change within the predominantly desert Middle Eastern city of Riyadh, an understudied setting. We utilized high-resolution SPOT 5 data and two classification techniques—maximum likelihood classification and object-oriented classification—to study the changes in Riyadh between 2004 and 2014. Imagery classification was completed with training data obtained from the SPOT 5 dataset, and an accuracy assessment was completed through a combination of field surveys and an application developed in ESRI Survey 123 tool. The Survey 123 tool allowed residents of Riyadh to present their views on land cover for the 2004 and 2014 imagery. Our analysis showed that soil or ‘desert’ areas were converted to roads and buildings to accommodate for Riyadh’s rapidly growing population. The object-oriented classifier provided higher overall accuracy than the maximum likelihood classifier (74.71% and 73.79% vs. 92.36% and 90.77% for 2004 and 2014). Our work provides insights into the changes within a desert environment and establishes a foundation for understanding change in this understudied setting.
Highlights
Change at various scales, and the implications for local-level processes, has attracted significant research interest in recent times [1,2]
Riyadh was dominated by soil in 2004, according to the maximum likelihood classifier (MLC) and object-oriented classifier (OOC) classification methods (Table 3), and declined substantially by 2014
Unlike previous work that sought to understand urban green spaces and forests, our focus was on assessing changes within a desert environment
Summary
The implications for local-level processes, has attracted significant research interest in recent times [1,2]. Change has received a significant amount of attention over the past decade or so, with urban forests and green spaces the primary focus [3,4,5]. Our ability to keep track of change within urban settings has been enhanced by the rapid development of remote sensing technology and the associated high-resolution data they make available [6,7,8]. The availability of data and methods for urban landscapes is providing scholars with the ability to use spatially referenced data and analysis methods to predict urban change processes [21], and to respond to challenges related to environmental and ecological sustainability within urban centers [22]. While urban green spaces and forests have been widely covered, changes within desert environments have been underassessed
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