Browsing by Author "Aksu G.A."

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Analysis of the association between image resolution and landscape metrics using multi-sensor LULC maps
(2023-01-01) Varol B.; Szabo S.; Topaloğlu R.H.; Aksu G.A.; Sertel E.
This study aims to investigate the changes in landscape metrics with varying spatial resolution from Sentinel-2 (10 m), SPOT 7 (1.5 m), Pleaides (0.5 m), and Worldview-4 (0.3 m) images. We implemented Geographic Object-Based Image Analysis (GEOBIA) techniques to all images to identify 21 land use and land cover (LULC) classes, which were then used to calculate several landscape metrics. We performed the Welch hypothesis testing on the class-level landscape metrics and applied Standardized Principal Component Analysis (PCA) with the correlation matrix to reveal the multivariate pattern of landscape metrics. Our results showed that 10 m and even the 1.5 m spatial resolutions cannot guarantee the identification of all LULC classes, and class areas change with varying spatial resolution (sometimes with 200% differences). Sentinel-2 images have some limitations, specifically from the landscape ecological planning perspective; on the other hand, Pleaides and Worldview-4 seem good alternatives to understand habitats’ viability and landscape isolation/connectivity.
Evaluation of Artificial Surface—Urban Ecosystem Relations by Using Analytical Hierarchy Process: The Urban Landscape of Istanbul
(2023-01-01) Aksu G.A.; Kırca S.
Especially in urban landscapes under intense urbanization pressure, artificial surfaces affect many components of the urban ecosystem and disrupt the flow of natural cycles. Due to the decrease in the continuity of the green system, population movement is interrupted, biodiversity decreases, the precipitation water infiltration capacity of soils and the transpiration rates decline as a result of sparse and interrupted vegetation cover, and the increase of impermeable surfaces trigger runoff rates and density. The artificial topography created by artificial surfaces, wind-shadow corridors, and urban heat island formations can be counted among further main adverse effects of the unplanned increase in artificial surfaces. Considering all these negative effects, relationships between the artificial surfaces and the green system were evaluated in our research. For this purpose, the criteria of surface cover type, surface flow direction, and slope were overlapped according to the weight ratios determined with the help of the Analytical Hierarchy Process (AHP). A map of priority areas, which enabled us to interpret the disruptions caused by artificial surfaces in the urban ecosystem, was produced. This map has been evaluated with a holistic perception to guide sustainable stormwater management and landscape planning and restoration and management processes related to the urban ecosystem. Artificial surfaces, which dominate the landscape with 62% surface cover in the research area, were assessed in terms of building blocks, transportation networks, and hardscapes, while suggestions were made for sustainable urban landscape planning.
High-resolution land use and land cover change analysis using GEOBIA and landscape metrics: A case of Istanbul, Turkey
(2022-01-01) Topaloğlu R.H.; Aksu G.A.; Ghale Y.A.G.; Sertel E.
Determination of the spatio-temporal distribution of Land use and Land cover (LU/LC) is important to understand the dynamics of urbanization, agricultural abandonment, and industrialization. This study aims to create multi-temporal high-resolution LU/LC maps and analyze thematically extensive LU/LC changes using Geographic Object-Based Image Analysis (GEOBIA) and Landscape Metrics for the selected study region in the Istanbul metropolitan city of Turkey. HR SPOT 6/7 images acquired in 2009, 2013, and 2019 were used as main Earth Observation data to create LU/LC maps. Open-source geospatial data were also integrated into classification to better identify some LU/LC classes to increase total classification accuracy. Overall classification accuracy of 2009, 2013, and 2019 dated LU/LC maps are 87.45%, 88.16%, 90.74% respectively. Principal Component Analysis (PCA) and Pearson correlation were used to selecting the landscape metrics and evaluate the results. PCA resulted in three principal components and the total variance was found as 87.3%.
Landscape Ecological Evaluation of Cultural Patterns for the Istanbul Urban Landscape
(2022-12-01) Aksu G.A.; Tağıl Ş.; Musaoğlu N.; Canatanoğlu E.S.; Uzun A.
With the widespread population growth in cities, anthropogenic influences inevitably lead to natural disturbances. The metropolitan area of Istanbul, with its rapid urbanization rate, has faced intense pressure regarding the sustainability of urban habitats. In this context, landscapes comprising patches affected by various disturbances and undergoing temporal changes must be analyzed, in order to assess city-related disturbances. In this study, the main objective was to understand how urbanization changed the function of the spatial distribution of the urban mosaic and, more specifically, its relationship with the size, shape, and connection among land-use classes. For this purpose, we took Besiktas, a district of Istanbul, as the study area. We evaluated the landscape pattern of the urban environment in two stages. First, we used medium-resolution satellite imagery to reveal the general interactions in the urbanization process. Landscape- and class-level landscape metrics were selected to quantify the landscape connectivity, and the distances between classes (green areas and artificial surfaces), patterns, and processes, using five satellite images representing a time span of 51 years (1963, 1984, 1997, 2005, and 2014). The general landscape structure was examined by looking at the temporal–spatial processes of artificial surface and green areas obtained from these medium-resolution satellite images. The trends in selected landscape-level metrics were specified and discussed through the use of a moving window analysis. We then used Pleiades high-resolution satellite imagery (2015) to analyze the landscape structure in more detail. This high-resolution base image allows us to recognize the possibility of classifying basic cultural landscape classes. The findings regarding the spatial arrangement of each class in the areas allocated to 14 cultural landscape classes were interpreted by associating them with the landscape functions. Finally, particulate matter (PM10) concentration data were collected and evaluated as an ecological indicator, in order to reveal the relationships between landscape structure and landscape function. In short, we first evaluated the whole landscape structure using medium-resolution data, followed by the classification of cultural landscapes using high-resolution satellite imagery, providing a time-effective—and, therefore, essential—auxiliary method for landscape evaluation. This two-stage evaluation method enables inferences to be made that can shed light on the landscape functions in an urban environment based on the landscape structure.