Retrieval of Land-Use/Land Cover Change (LUCC) Maps and Urban Expansion Dynamics of Hyderabad, Pakistan via Landsat Datasets and Support Vector Machine Framework

Abstract

Land-use/land cover change (LUCC) is an important problem in developing and under-developing countries with regard to global climatic changes and urban morphological distribution. Since the 1900s, urbanization has become an underlying cause of LUCC, and more than 55% of the world’s population resides in cities. The speedy growth, development and expansion of urban centers, rapid inhabitant’s growth, land insufficiency, the necessity for more manufacture, advancement of technologies remain among the several drivers of LUCC around the globe at present. In this study, the urban expansion or sprawl, together with spatial dynamics of Hyderabad, Pakistan over the last four decades were investigated and reviewed, based on remotely sensed Landsat images from 1979 to 2020. In particular, radiometric and atmospheric corrections were applied to these raw images, then the Gaussian-based Radial Basis Function (RBF) kernel was used for training, within the 10-fold support vector machine (SVM) supervised classification framework. After spatial LUCC maps were retrieved, different metrics like Producer’s Accuracy (PA), User’s Accuracy (UA) and KAPPA coefficient (KC) were adopted for spatial accuracy assessment to ensure the reliability of the proposed satellite-based retrieval mechanism. Landsat-derived results showed that there was an increase in the amount of built-up area and a decrease in vegetation and agricultural lands. Built-up area in 1979 only covered 30.69% of the total area, while it has increased and reached 65.04% after four decades. In contrast, continuous reduction of agricultural land, vegetation, waterbody, and barren land was observed. Overall, throughout the four-decade period, the portions of agricultural land, vegetation, waterbody, and barren land have decreased by 13.74%, 46.41%, 49.64% and 85.27%, respectively. These remotely observed changes highlight and symbolize the spatial characteristics of “rural to urban transition” and socioeconomic development within a modernized city, Hyderabad, which open new windows for detecting potential land-use changes and laying down feasible future urban development and planning strategies.

Spatial Pattern Simulation of Land Use Based on FLUS Model under Ecological Protection: A Case Study of Hengyang City

“…In addition to the traditional mathematical and computer models, artificial intelligence algorithms are an advanced and novel research method. A consistent, comprehensive, and continuous monitoring framework, remotely sensed data sets, and a relevant statistical machine learning algorithm can effectively govern the changes in land-use patterns and play an important role in smart city governance [59], e.g., the scene simulation of GA can be based on historical data of land use to predict the development of different land-use conditions, to suggest how to optimize the number of land resources and space allocation in a particular scenario [77]. Therefore, various artificial intelligence algorithms can be extended to the research field of urbanization.…”

“…Therefore, various artificial intelligence algorithms can be extended to the research field of urbanization. The sustainable development of land use affects biodiversity, food security, carbon storage, and other issues [59]. Taking these influencing factors as variables in various algorithms will also be a research focus in the future.…”

“…In addition, there are computer algorithms such as the Artificial Neural Network (ANN) algorithm [50,51], ant colony (ACO) algorithm [52,53], genetic (GA) algorithm [54][55][56], simulated annealing (SA) algorithm [57], particle swarm optimization (PSO) algorithm [58], and so on. Land use/land cover change (LUCC) is an important issue in urban form distribution [59]. Different LUCC models have different functional mechanisms and have specific characteristics in the evolution of urban spatial patterns.…”

Geospatial Analysis of Land Use/Cover Change and Land Surface Temperature for Landscape Risk Pattern Change Evaluation of Baghdad City, Iraq, Using CA–Markov and ANN Models

“…Numerous techniques and software, such as ArcGIS, ENVI, ERDAS, and QGIS, have been used for the LUCC pre-processing to ensure the correct interpretation of the temporal dynamics and geometric compatibility with other sources [45]. In this study, the image classification and interpretation used the combined classification method based on ERDAS IMAGINE 2015 and ArcGIS 10.8.…”

Simulating Urban Expansion Based on Ecological Security Pattern—A Case Study of Hangzhou, China

“…Under the influence of the long-term policies of prioritizing economic development, a large amount of non-construction land has been converted into construction land, especially in urban areas. With the rapid expansion of construction land, a large area of ecological lands, such as agricultural land, vegetation, and water, has shrunk drastically [ 1 ]. In recent years, more and more scholars have noticed the negative ecological impact caused by urban expansion [ 2 ]: the expansion of built-up areas has led to the heat island effect [ 3 , 4 ], and there also exists a long-run equilibrium relationship between environmental pollution and urban expansion [ 5 ].…”