Dynamic Land Cover Mapping of Urbanized Cities with Landsat 8 Multi-temporal Images: Comparative Evaluation of Classification Algorithms and Dimension Reduction Methods

Abstract

Uncontrolled and continuous urbanization is an important problem in the metropolitan cities of developing countries. Urbanization progress that occurs due to population expansion and migration results in important changes in the land cover characteristics of a city. These changes mostly affect natural habitats and the ecosystem in a negative manner. Hence, urbanization-related changes should be monitored regularly, and land cover maps should be updated to reflect the current situation. This research presents a comparative evaluation of two classification algorithms, pixel-based support vector machine (SVM) classification and decision-tree-oriented geographic object-based image analysis (GEOBIA) classification, in producing a dynamic land cover map of the Istanbul metropolitan city in Turkey between 2013 and 2017 using Landsat 8 Operational Land Imager (OLI) multi-temporal satellite images. Additionally, the efficiencies of the two data dimension reduction methods are evaluated as part of this research. For dimension reduction, built-up index (BUI) and principal component analysis (PCA) data were calculated for five images during the mentioned period, and the classification algorithms were applied on data stacks for each dimension reduction method. The classification results indicate that the GEOBIA classification of the BUI data set provided the highest accuracy, with a 91.60% overall accuracy and 0.91 kappa value. This combination was followed by the GEOBIA classification of the PCA data set, which highlights the overall efficiency of the GEOBIA over the SVM method. On the other hand, the BUI data set provided more reliable and consistent results for urban expansion classes due to representing physical responses of the surface when compared to the data set of the PCA, which is a spectral transformation method.

Geospatial assessment of land use and land cover dynamics in the mid-zone of Ghana

“…The resultant maps from these processes rapidly become outmoded with the passage of time due the dynamic changes occurring in the world. Remote Sensing techniques on the other hand provides accurate LULC maps and monitoring changes at relatively less labour intensive, regular intervals of time and can be continuously updated (Mani and Varghese 2018;Alganci 2019). Despite the spatial and spectral heterogeneity challenges of agricultural lands (tree cash crops like cocoa, palm, oranges to perennial crops such as maize, rice, plantain) in Africa and urban environments, remote sensing remains the preferred choice as the suitable source of reliable information about the multiple facets of LULC (Pandey et al 2019;Orynbaikyzy et al 2019).…”

Detection and mapping of land use land cover with Support vector machines SVM-based change monitoring using Landsat and Sentinel-2 data. The case of Quseir, Red Sea

“…These parameters include: (i) generic parameters of the algorithm (Gamma parameter and The penalty parameter) and (ii) software specific parameters of the classification process (Pyramid levels, Pyramid reclassification threshold, and Classification probability threshold). In their works, Alganci, 2019, Petropoulos et al, 2010, Petropoulos et al, 2011, and Srivastava et al, 2012 described the use of the suitable values for these parameters when using SVM. Gamma parameter is calculated as the inverse of the number of image bands.…”

Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation

“…To better understand ecological patterns, it is also expanded to the species-level mapping of vegetation [19]. Other research, like References [20,21], presented a comparative evaluation of the pixel-based method and the object-based; especially, Reference [21] compared the pixel-based support vector machine (SVM) classification and decision-tree-oriented geographic object-based image analysis (GEOBIA) classification, which indicated that the GEOBIA classification provided the highest accuracy. Besides, work, like Reference [22], discussed the idea and method of geographic ontology modeling based on object-oriented remote sensing technology in detail.…”