@ The University of Oklahoma

Month: August 2022

We are organizing a Special Issue “Using Remote Sensing and GIS Technique/Methods to Address Current Urbanization Issues” in Remote Sensing

We are running a Special Issue entitled “Using Remote Sensing and GIS Methods to Study Current Urbanization Issues” with the journal Remote Sensing (IF: 5.349, ISSN 2072-4292). This special issue belongs to the section “Urban Remote Sensing“. The guest editors for this issue are Dr. Zutao Ouyang from Stanford University, Dr. Chenghao Wang from the University of Oklahoma, and Dr. Peilei Fan from Michigan State University. The submission deadline is April 30, 2023.

Further details on this Special Issue and how to submit can be found here: https://www.mdpi.com/journal/remotesensing/special_issues/Y602B3CNT6.

The increase in the number of people living in urban areas, the proliferation of megacities, and the pervasive expansion of per-urban areas are some of the most challenging transformations in the 21st century. The complexity of urbanization imposes intertwined social, economic, and environmental impacts. While urbanization can achieve social and economic benefits, such as improved education, job opportunities, and healthcare, it also brings numerous negative ecological and social consequences, such as increasing the cost of living and social and economic inequality, deforestation, loss of natural habitat and biodiversity, soil, air, and water pollution, increased emission of greenhouse gases, heat island effect, and increased risk of disease. Therefore, it is imperative to create a sustainable urban environment that reconciles the conflicts between human and natural systems and reduces the negative impacts of the urbanization process. Remote sensing techniques could provide a “unique view” of the urban landscape. When combined with GIS-based spatial analysis, it can serve as a powerful tool to study processes and patterns of urbanization, drivers and impacts of urbanization, and the coupled human and natural systems embedded in urban ecosystems.

The main objective of this Special Issue shall be to provide a scientific forum for advancing the successful implementation of remote sensing (RS) technologies and geographic information system (GIS)-based methods towards urbanization issues and the peri-urban environment and to foster informed debates among scientists and stakeholders on the environmental issues prevalent therein, relating these to city growth dynamics.

This Special Issue will provide readers in the fields of GIS, remote sensing, Earth science, environmental science, and computer science with theoretical and practical advances in urbanization-related research. Topics of research articles, or reviews, submitted to this Special Issue include, but are not limited to:

  • Integration of remote sensing data for urban environmental analysis.
  • Novel remote sensing applications (new sensors, new methodology, etc.) in urban ecology and sustainability.
  • Tracking urban growth and land use change with remote sensing technologies and GIS tools.
  • Remote sensing and GIS analysis informing/supporting urban and peri-urban governance and planning.
  • Landscape ecological analysis.
  • Urban growth and fringe development.
  • Water, river, and lake monitoring in and surrounding urban areas.
  • Relations between urban growth and climate change.
  • Social and environmental justice issues relevant to urban residents.
  • Impacts and mitigation of urban heat.

New paper on causal urban climate network published in Journal of Environmental Management

Our new paper, “Detecting the causal influence of thermal environments among climate regions in the United States“, is published in Journal of Environmental Management (IF: 8.910). This paper is from the collaboration with the Urban Environment Research Group at Arizona State University (ASU). The first author, Xueli Yang, is a Ph.D. candidate at ASU. Congratulations to Xueli!

The Share Link to download a copy of our paper is https://authors.elsevier.com/c/1fepj14Z6tlDl~ (valid until Oct 15, 2022).

Authors: Xueli Yang, Zhi-Hua Wang, Chenghao Wang, and Ying-Cheng Lai

Abstract: The quantification of cross-regional interactions for the atmospheric transport processes is of crucial importance to improve the predictive capacity of climatic and environmental system modeling. The dynamic interactions in these complex systems are often nonlinear and non-separable, making conventional approaches of causal inference, such as statistical correlation or Granger causality, infeasible or ineffective. In this study, we applied an advanced approach, based on the convergent cross mapping algorithm, to detect and quantify the causal influence among different climate regions in the contiguous U.S. in response to temperature perturbations using the long-term (1901–2018) climatology of near surface air temperature record. Our results show that the directed causal network constructed by convergent cross mapping algorithm, enables us to distinguish the causal links from spurious ones rendered by statistical correlation. We also find that the Ohio Valley region, as an atmospheric convergent zone, acts as the regional gateway and mediator to the long-term thermal environments in the U.S. In addition, the temporal evolution of dynamic causality of temperature exhibits superposition of periodicities at various time scales, highlighting the impact of prominent low frequency climate variabilities such as El Niño–Southern Oscillation. The proposed method in this work will help to promote novel system-based and data-driven framework in studying the integrated environmental system dynamics.

DOI: https://doi.org/10.1016/j.jenvman.2022.116001

Fig. 3. Comparison of the correlation and causality networks for the nine CONUS climate regions: (a) the matrix of connectivity determined by undirected statistical correlation, (b) the matrix of connectivity determined by directed causality, and (c) graphic representation of causal and spurious links resulted from (a) and (b), with a threshold strength of 0.5. Cells with dashed boxes in (a) and (b) represent causally (above the threshold) connected pairs. The gray dashed lines represent the spurious link between different regions, and lines with an arrow the directed causal influence with strength denoted by different colors (the same scale as in (b)).

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