Object-based delineation of urban tree canopy: assessing change in Oklahoma City, 2006–2013

Ellis, Emily A.; Mathews, Adam J.

doi:10.1016/j.compenvurbsys.2018.08.006

Cited by 27 publications

(24 citation statements)

References 32 publications

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“…First, our analysis found a substantial change in terms of urban areas, including for housing and roads, encroaching into the rural environment, as has been observed for other urban settings across the globe [2,3,21,22]. From our analysis and the initial view of what changed in Riyadh, the areas that have been newly occupied by impervious surfaces may be viewed as unproductive 'desert' land.…”

Section: Discussionsupporting

confidence: 55%

“…Based on the first author's observations of the city and the data collection, it appears the OOC classification best matched the reality on the ground. Growth in tree-like vegetation areal coverage is generally not the norm in urban settings [2], except in older cities, but in many arid and semi-arid environments, growth in vegetation cover is plausible. In these settings, as Jensen [40] noted for other places, the advancement of housing may lead to people planting lawns once they have established a house.…”

Section: Discussionmentioning

confidence: 99%

“…The area under consideration covered the main municipal center of Riyadh (Figure 1), and for the purposes of this analysis, we considered the area that indicated the presence of impervious surfaces. 2 and was projected in WGS 1984, Zone 38. The SPOT 5 data used in this analysis were pan-sharpened by merging the multispectral data (10 m resolution) with the panchromatic data (2.5 m).…”

Section: Study Areamentioning

confidence: 99%

“…Change at various scales, and the implications for local-level processes, has attracted significant research interest in recent times [1,2]. Within urban settings, 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].…”

Section: Introductionmentioning

confidence: 99%

“…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]. Simultaneously, the scientific community has developed data analysis methods and tools that have allowed for the extraction of timely information on urban processes [2,[9][10][11][12][13][14][15][16][17][18][19][20]. In fact, the availability of data and methods for urban landscapes is now 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].…”

Section: Introductionmentioning

confidence: 99%

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Assessing Riyadh’s Urban Change Utilizing High-Resolution Imagery

Alghamdi

Cummings

2019

Land

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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. landscape [24]. Due to the physical and morphological conditions, economic state, population growth, political situation, policies, and social behaviors that vary from one region to another, predicting patterns of urban growth is complicated [25]. Nevertheless, significant progress has been made in understanding urban processes, including the importance of performing spatiotemporal analyses of urban growth to understand the characteristics and consequences of growth and how this is manifested from one location to another [8,21,[25][26][27][28][29][30][31]. One of the critical factors that have emerged from recent studies in urban processes and change is that in trying to understanding future growth within urban centers, it is critical to first understand and document the physical conditions within a city. Despite the significant progress made in understanding urban growth, our knowledge of urban change has not been uniform across all settings, a fact especially manifested in the states of the Middle East and North Africa.The urban centers of the Middle East and North Africa are undergoing rapid transformation, including significant growth in the youth population, and an urbanization trend that will result in large numbers of people settling in major cities [30]. Still, the analysis of the changes occurring within Middle Eastern cities remains incomplete. This is particularly critical as the territory of the Middle East and North Africa is 11.2 million km 2 -larger than the United States (9.8 million km 2 ), China (9.5 million km 2 ), and the European Union (4.3 million km 2 ). To date, only a few studies have focused on urban processes and change within the Middle East. Among the studies completed in the region was the work of ...

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Section: Discussionsupporting

confidence: 55%

Section: Discussionmentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%