Remote Sensing (RS)
Kolsoom Shokrilahizadeh; Hamed Naghavi; Morteza Ghobadi; Rahim Maleknia
Abstract
Extended Abstract
Introduction:
Urban green spaces constitute a pivotal component of urban ecosystems, offering a plethora of ecological benefits and services to cities. Augmenting these green patches within urban landscapes and establishing interconnected ecological networks therein represent viable ...
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Extended Abstract
Introduction:
Urban green spaces constitute a pivotal component of urban ecosystems, offering a plethora of ecological benefits and services to cities. Augmenting these green patches within urban landscapes and establishing interconnected ecological networks therein represent viable strategies to mitigate the adverse repercussions of inadequate urban development while bolstering urban environment resilience. In the past few decades, the landscape ecology paradigm has introduced innovative methodologies aimed at comprehending the intricacies of urban green space dynamics and how landscape configurations wield influence over the environmental processes within cities. This research, consequently, sets out with the intention of quantitatively assessing and dissecting the transformations transpiring within Khorramabad's urban green spaces. It does so by harnessing remote sensing data and leveraging landscape metrics to gain deeper insights into the urban landscape's evolution.
Materials & Methods:
The focus of this research centers on Khorramabad city, which serves as the capital of Lorestan province and holds the distinction of being the province's largest city in terms of both population and geographical expanse. Municipally zoned into three distinct regions, the study unfolds across two main phases. Initially, the endeavor involved the creation of comprehensive synoptic maps capturing Khorramabad city's green spaces. This process relied on satellite imagery, followed by a subsequent phase of scrutinizing these maps through the application of landscape metrics.
To execute this, satellite images from various sensors—namely TM, ETM+, and OLI on Landsat 5, 7, and 8 satellites—were harnessed for the years 1987, 2003, and 2019, respectively. These images underwent meticulous preprocessing, culminating in their classification using the maximum likelihood method within the ENVI software environment. To validate the accuracy of the resultant maps, an error matrix was employed. In order to model the quantitative alterations and patterns within Khorramabad's urban green spaces, landscape metrics were harnessed. Notably, the Fragstat software facilitated the analysis of selected landscape metrics, which encompassed four key measures: class area (CA), number of patches (NP), percent of landscape (PLAND), and mean Euclidean nearest neighbor distance (ENN-MN).
Results:
The analysis of spatial-temporal changes in Khorramabad city's green spaces reveals an evident declining trend in their overall pattern. The outcomes underscore a substantial reduction both in the quantity of green patches and the area they encompass, dwindling from 703.8 hectares in 1987 to 629.88 hectares in 2019. Additionally, the investigation into landscape metrics' composition and distribution underscores an absence of cohesive dispersion on the city-wide scale. Within Khorramabad city, regions 1 and 3 exhibit inadequate green space composition and distribution. The computed metric for Class Area (CA) reflects a decrease from 195.66 hectares in 1987 to 191.63 hectares in 2003, further diminishing to 170.145 hectares by 2019. Correspondingly, the metric for Number of Patches (NP) indicates the lowest count of patches (33) in 1987, which escalated to 122 patches in 2003, and ultimately reaching 183 patches by 2019. Moreover, Proportion of Landscape (PLAND) data highlights that regions 3 and 2 demonstrate the highest (19.45%) and lowest (7.18%) green area proportions, respectively. Notably, the PLAND metric underwent modification from 229.81 meters in 1987 to 88.47 meters in 2003, further diminishing to 78.65 meters in 2019. The findings underscore deficiencies in Khorramabad city's urban green spaces, indicating a lack of favorable conditions for their development.
Conclusion:
The research conducted an assessment of urban green spaces within the urban areas of Khorramabad, utilizing remote sensing data and landscape metrics. The findings indicated a consistent downward trend in the overall extent of green spaces in Khorramabad city over various years. The distribution of green patches within the city was deemed relatively inappropriate, lacking an optimal arrangement. To enhance the status of green spaces, there is a need to establish continuity between discrete green patches and smaller green areas. This study underscores the significance of prioritizing sustainable management for Khorramabad's urban green space, aiming to prevent its degradation. The study's limitation lies in its reliance on medium-resolution Landsat image data. Overcoming this constraint through the incorporation of high-resolution data holds promise, particularly for fragmented green spaces in urban areas.
