MEASURING LAND USE CHANGES AND QUANTIFYING URBAN EXPANSION USING REMOTE SENSING AND GIS TECHNIQUES – A CASE STUDY OF QOM

Rapid urban sprawl necessitates solid urban planning tactics, which requires assessing and quantifying the sustainable or unsustainable encroachment of urban settings towards the urban periphery. Like many cities in developing countries, Iranian cities have witnessed tremendous changes in recent decades. The process of urbanization following economic and social developments has caused the intractable and unrestrained growth of cities with a national and regional role. Remote sensing and geospatial information system technology give urban planners the appropriate and pertinent information they need to guarantee the sustainable management of urban environments. This study explored the changes in Qom metropolis, Iran during four time periods, from 1985, 2000, 2010, and 2021, and assessed how the city expanded using Shannon's entropy model and the Urban Expansion Intensity Index. In this study, Landsat satellite images from the selected years were employed, and three land use/land cover classification types, including agricultural, built-up, and others, were derived using the maximum likelihood classification approach. The relative Shannon’s entropy result for the study years (1985, 2000, 2010, and 2021) are 0.66, 0.68, 0.69, and 0.86 respectively, which demonstrate a dispersed expansion pattern, with the maximum value in 2021. Also, the Urban Expansion Intensity Index, with the values 0.33, 0.33, and 0.51 for three periods (1985–2000, 2000–2010, 2010–2021), indicates that the city's expansion rate was low-speed throughout the chosen periods, despite having reached its peak between 2010 and 2021

Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance

Cellular automata (CA) is a powerful tool for modeling the evolution of macroscopic scale phenomena as it couples time, space, and variables together while remaining in a simplified form. However, such application has remained challenging in forest insect epidemics due to the highly dynamic nature of insect behavior. Recent advances in temporal trajectory-based image analysis offer an alternative way to obtain high-frequency model calibration data. In this study, we propose an insect-CA modeling framework that integrates cellular automata, remote sensing, and Geographic Information System to understand the insect ecological processes, and tested it with measured data of mountain pine beetle (MPB) in the Rocky Mountains. The overall accuracy of the predicted MPB mortality pattern in the test years ranged from 88% to 94%, which illuminates its effectiveness in modeling forest insect dynamics. We further conducted sensitivity analysis to examine responses of model performance to various parameter settings. In our case, the ensemble random forest algorithm outperforms the traditional linear regression in constructing the suitability surface. Small neighborhood size is more effective in simulating the MPB movement behavior, indicating that short-distance is the dominating dispersal mode of MPB. The introduction of a stochastic perturbation component did not improve the model performance after testing a broad range of randomness degree, reflecting a relative compact dispersal pattern rather than isolated outbreaks. We conclude that CA with remote sensing observation is useful for landscape insect movement analyses;however, consideration of several key parameters is critical in the modeling process and should be more thoroughly investigated in future work

Urbogeosystemic Approach to Agglomeration Study within the Urban Remote Sensing Frameworks

The spatial arrangement of human activity within urban areas is normally provided by areal management, and its effective provision is a complicated problem. The current urban development causes a number of problems and urgent challenges, which can be met and resolved exclusively on the basis of innovative scientific and technological advances. The main research objective of this chapter is to represent the authors’ theoretic concept of the urban geographical system combined with the original Urban Remote Sensing approach based on the advanced technique of airborne LiDAR (Light Detection And Ranging) data processing. The authors attempted to prove that the presented concept could contribute to an understanding of the urban agglomeration as an urbanized spatial entity. The chapter explains in what way the urbanistic environment is a quasi-rasterized 3D model of actual city space, and the urbogeosystem (UGS) is a quasi-vector 3D model of the hierarchical formalized aggregate of UGS elementary functional units–buildings, both can efficiently simulate and visualize an urbanized area. Web-based geoinformation software for LiDAR data processing with the objectives of urban studies has been introduced together with its key functionalities. The population estimation use case has been examined in detail within the presented approach frameworks

Integrated modeling in urban hydrology: reviewing the role of monitoring technology in overcoming the issue of ‘big data’ requirements

Increasingly, the application of models in urban hydrology has undergone a shift toward integrated structures that recognize the interconnected nature of the urban landscape and both the natural and engineered water cycles. Improvements in computational processing during the past few decades have enabled the application of multiple, connected model structures that link previously disparate systems together, incorporating feedbacks and connections. Many applications of integrated models look to assess the impacts of environmental change on physical dynamics and quality of landscapes. Whilst these integrated structures provide a more robust representation of natural dynamics, they often place considerable data requirements on the user, whereby data are required at contrasting spatial and temporal scales which can often transcend multiple disciplines. Concomitantly, our ability to observe complex, natural phenomena at contrasting scales has improved considerably with the advent of increasingly novel monitoring technologies. This has provided a pathway for reducing model uncertainty and improving our confidence in modeled outputs by implementing suitable monitoring regimes. This commentary assesses how component models of an exemplar integrated model have advanced over the past few decades, with a critical focus on the role of monitoring technologies that have enabled better identification of the key physical process. This reduces the uncertainty of processes at contrasting spatial and temporal scales, through a better characterization of feedbacks which then enhances the utility of integrated model applications