Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery

Abstract

In this research work, a multi-index-based support vector machine (SVM) classification approach has been proposed to determine the complex and morphologically heterogeneous land cover/use (LCU) patterns of cities, with a special focus on separating bare lands and built-up regions, using Istanbul, Turkey as the main study region, and Ankara and Konya (in Turkey) as the independent test regions. The multi-index approach was constructed using three-band combinations of spectral indices, where each index represents one of the three major land cover categories, green areas, water bodies, and built-up regions. Additionally, a shortwave infrared-based index, the Normalized Difference Tillage Index (NDTI), was proposed as an alternative to existing built-up indices. All possible index combinations and the original ten-band Sentinel-2A image were classified with the SVM algorithm, to map seven LCU classes, and an accuracy assessment was performed to determine the multi-index combination that provided the highest performance. The SVM classification results revealed that the multi-index combination of the normalized difference tillage index (NDTI), the red-edge-based normalized vegetation index (NDVIre), and the modified normalized difference water index (MNDWI) improved the mapping accuracy of the heterogeneous urban areas and provided an effective separation of bare land from built-up areas. This combination showed an outstanding overall performance with a 93% accuracy and a 0.91 kappa value for all LCU classes. The results of the test regions provided similar findings and the same index combination clearly outperformed the other approaches, with 92% accuracy and a 0.90 kappa value for Ankara, and an 84% accuracy and a 0.79 kappa value for Konya. The multi-index combination of the normalized difference built-up index (NDBI), the NDVIre, and the MNDWI, ranked second in the assessment, with similar accuracies to that of the ten-band image classification.

Multitemporal Analysis of Soil Sealing and Land Use Changes Linked to Urban Expansion of Salamanca (Spain) Using Landsat Images and Soil Carbon Management as a Mitigating Tool for Climate Change

“…Multitemporal evaluation has been widely used to study changes in land cover, especially using Landsat image analyses. Recently, some studies have incorporated higher resolution Sentinel images for researching the most recent changes, as more accurate results can be obtained [91,92]. In this work, we maintain Landsat Regarding the model, we find a part that analyzes the evolution of coverage, and another that evaluates past impacts, and that also serves to restrict future urbanization.…”

Analyzing Temporal Changes in an Urbanized Area Using Densely Staked Image Classification and Multinomial Logistic Regression (MLR) Technique

“…The swelling population density and outspreading urban sprawls, majorly in metropolitan municipalities, leads to intensifying demand of natural resources like water, energy, land surface thereby further results to deforestation, deserti cation and rigorous loss in agricultural lands. These land parcel variations mutually contribute in changing global environment and near-surface temperatures (Osgouei et al, 2019). Urbanization and changing life patterns during last three decades necessitated urban planners to refurbish an effective methodology and estimate the spatial extent of urbanization.…”

“…Accuracy in urban area identi cation and modeling is of substantial interest to the municipal authorities for applications on urban planning such as resource allocation, management and distribution, facility provision and promotional policies (Jat et al, 2008). Non-parametric techniques like machine learning classi cation, decision tree algorithms and knowledge-based classi ers are used extensively to classify Landsat Imagery (Osgouei et al, 2019). Analysis and prediction modelling of impervious area using classi cation techniques consume high computational power and time.…”

“…Typically, the accuracy to classify satellite images for 3 major classes (vegetation, water and urban) is higher (over 85%) but while identifying more number of features, it becomes di cult and time consuming (Herold et al, 2003;Jia et al, 2014;Li et al, 2014;. Out of various machine learning algorithms, support vector machines (SVM) and maximum likelihood classi cation (MLC) have been found as the most effective techniques (Osgouei et al, 2019). Various image enhancement procedures like principal component analysis (PCA), independent component analysis (ICA) or multiple marginal sher analysis are used to eradicate the dependency of bands and correlation between them.…”

Automated Built-Up Extraction Index: A New Technique for Mapping Surface Built-Up Areas Using LANDSAT 8 OLI Imagery

“…Generally, in previous studies for mapping built-up areas usually coarse-resolution satellite imagery such as moderate resolution imaging spectroradiometer (MODIS) [18,19] and defense meteorological program / operational line-scan system (DMSP/OLS) [20], medium-resolution satellite imagery such as Landsat [21] and Sentinel-2 [22], and high-resolution satellite imagery such as Quick Bird [23] and WorldView [24] have been used. Landsat imagery has the potential to provide a historical record for mapping built-up areas, as Landsat has been acquiring images since the early 1980s.…”