Urban environmental and land cover change analysis using the scatter plot, kernel, and neural network methods

Jamali, Ali Akbar; Kalkhajeh, Reza Ghorbani

doi:10.1007/s12517-019-4258-7

Cited by 19 publications

(6 citation statements)

References 39 publications

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“…While previous studies were successful in predicting future LULC change, their results were based on discrete patches of LULC change which promotes a piecemeal approach to LULC change management. Other studies have successfully used spatially explicit models based on different sets of environmental factors to predict the probability of occurrence (susceptibility) of LULC change [24,25,53]. However, one of the shortcomings of these studies is that they could not deduce the spread pathways of LULC change and strategic pinch points for intervention.…”

Section: Discussionmentioning

confidence: 99%

“…Despite its tremendous potential to provide spatially explicit predictions on spread pathways that are relevant to sustainable conservation, the application of connectivity analysis in LULC studies remains scanty. Previous studies have been able to predict the distribution and extent of LULC change in TDBs using spatially explicit models that stochastically forecast locations of LULC change patches based on either potential-transition maps that indicate the likelihood of a LULC transition or potential-occurrence maps that indicate the spatial susceptibility of land-cover types [21][22][23][24][25]. Their outputs were mainly patch-based projections of LULC change and the probability of LULC change maps.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Connectivity and Spread Pathways of Land Use/Cover Change in a Transboundary Basin Based on the Circuit Theory

2022

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Understanding the spatial spread pathways and connectivity of Land Use/Cover (LULC) change within basins is critical to natural resources management. However, existing studies approach LULC change as distinct patches but ignore the connectivity between them. It is crucial to investigate approaches that can detect the spread pathways of LULC change to aid natural resource management and decision-making. This study aims to evaluate the utility of the Circuit Theory to detect the spread and connectivity of LULC change within the Okavango basin. Patches of LULC change sites that were derived from change detection of LULC based on the Deep Neural Network (DNN) for the period between 2004 and 2020 were used. The changed sites were categorized based on the nature of the change of the classes, namely Category A (natural classes to artificial classes), Category B (artificial classes to natural classes), and Category C (natural classes to natural classes). In order to generate the resistance layer; an ensemble of machine learning algorithms was first calibrated with social-ecological drivers of LULC change and centroids of LULC change patches to determine the susceptibility of the landscape to LULC change. An inverse function was then applied to the susceptibility layer to derive the resistance layer. In order to analyze the connectivity and potential spread pathways of LULC change, the Circuit Theory (CT) model was built for each LULC change category. The CT model was calibrated using the resistance layer and patches of LULC change in Circuitscape 4.0. The corridor validation index was used to evaluate the performance of CT modeling. The use of the CT model calibrated with a resistance layer (derived from susceptibility modeling) successfully established the spread pathways and connectivity of LULC change for all the categories (validation index > 0.60). Novel maps of LULC change spread pathways in the Okavango basin were generated. The spread pathways were found to be concentrated in the northwestern, central, and southern parts of the basin for Category A transitions. As for category B transitions, the spread pathways were mainly concentrated in the northeastern and southern parts of the basin and along the major rivers. While for Category C transitions were found to be spreading from the central towards the southern parts, mainly in areas associated with semi-arid climatic conditions. A total of 186 pinch points (Category A: 57, Category B: 71, Category C: 58) were detected. The pinch points can guide targeted management LULC change through the setting up of conservation areas, forest restoration projects, drought monitoring, and invasive species control programs. This study provides a new decision-making method for targeted LULC change management in transboundary basins. The findings of this study provide insights into underlying processes driving the spread of LULC change and enhanced indicators for the evaluation of LULC spread in complex environments. Such information is crucial to inform land use planning, monitoring, and sustainable natural resource management, particularly water resources.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting Connectivity and Spread Pathways of Land Use/Cover Change in a Transboundary Basin Based on the Circuit Theory

2022

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“…Visual image interpretation was used for land use/land cover and geomorphological classification. Initially, a false color composite (FCC) of infrared:red:green (IR:R:G) as RGB was used to understand different land use/land cover and geomorphological classes [26]. It must be noted the vegetation class has maximum spectral reflectance in the infrared band (IR), and as such, it has been used as a standard spectral band for mapping vegetation classes.…”

Section: Methodsmentioning

confidence: 99%

Assessment of the Impact of Urbanization on Geoenvironmental Settings Using Geospatial Techniques: A Study of Panchkula District, Haryana

et al. 2022

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Urbanization is an unavoidable process of social and economic growth in modern times. However, the speed with which urbanization is taking place produces complex environmental changes. It has affected the surface albedo and roughness of the soil, thereby modulating hydrological and ecological systems, which in turn has affected regional and local climate systems. In developing countries of South Asia, rampant and unplanned urbanization has created a complex system of adverse environmental scenarios. Similar is the case in India. The state of the urban environment across India is degrading so quickly that the long-term sustainability of its cities is endangered. Many metropolitan cities in India are witnessing the harmful impacts of urbanization on their land ecology. In this context, remote sensing and geographic information system (GIS) based assessments provide a comprehensive and effective analysis of the rate and the impact of urbanization. The present study focuses on understanding the spatiotemporal characteristics of urban growth and its implications on the geomorphology of the Panchkula District, Haryana, one of the fastest-growing urban centers in India. The study links the changes in land use/land cover (LULC) with the changing geomorphology of the study area using satellite remote sensing and GIS. The results showed that between 1980 and 2020, agricultural (+73.71%), built-up (+84.66%), and forest (+4.07%) classes of land increased in contrast to that of the fallow land (−76.80%) and riverbed (−50.86%) classes that have decreased in spatial extents. It has been observed that the hill geomorphological class had decreased in the area owing to conversion to industrial and built-up activities. Assessment of the environmental quality of cities involves multiple disciplines that call for a significant amount of scientific evaluation and strong decision making, and the present study shall lay down the baseline analysis of the impact of changing LULC on the geomorphological setup of the selected urban center.

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“…Worldwide, academia has mainly employed a variety of automatic land classification approaches in their studies to extract the timely spatial data of land use land cover change (LULCC) considering remote sensing (i.e., Landsat, etc.) images, such as; maximum likelihood classification (MLC) [ 49 , 54 , [55] , [56] , [57] , [58] , 59 , 60 ], minimum distance classification ( ) [ 61 , 62 ], random forest (RF) [ 24 , 50 , 63 ], support vector machine (SVM) [ [64] , [65] , [66] ], multi-layer perception neural network (MLP NN) [ 62 , 67 ], and object-oriented classification [ [68] , [69] , [70] ] methods, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Examining the informal urban growth trends in a Port city

Li,

Khan,

Sahito

et al. 2023

Heliyon

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Urban environmental and land cover change analysis using the scatter plot, kernel, and neural network methods

Cited by 19 publications

References 39 publications

Detecting Connectivity and Spread Pathways of Land Use/Cover Change in a Transboundary Basin Based on the Circuit Theory

Detecting Connectivity and Spread Pathways of Land Use/Cover Change in a Transboundary Basin Based on the Circuit Theory

Assessment of the Impact of Urbanization on Geoenvironmental Settings Using Geospatial Techniques: A Study of Panchkula District, Haryana

Examining the informal urban growth trends in a Port city

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