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Special Issue: Recent Advances in Remote Sensing for Sustainable Environment

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Closed

The environmental stress from unsustainable socioeconomic development is one of the major growing concerns for the Earth’s ecosystem. There are many factors are responsible for this such as population growth, climate variability, urbanization, pollution and industrialization. In order to address this issue, Governments worldwide very much interested in the assessment and possible controls for sustainable environment. Remote sensing provides a cost-effective solution to the mapping and analysis of ecological and environmental interactions and their impact on the Earth’s surface and its changes over time which further allows the effective decision-making at various temporal and spatial scales to bridge gaps among disciplines and achieve sustainable development. Therefore, the focus of this Special issue (SI) is on the recent advances in the algorithms and applications of remote sensing in sustainable environment. Inviting research papers for a Special issue on recent advances in remote sensing for a sustainable environment can highlight the most recent research and trends in the field, bring together a collection of high-quality research papers, stimulate discussion and collaboration within the research community, increase the visibility and impact of ongoing research, and support the journal’s mission and goals.

This Special issue welcomes research or review manuscripts on all aspects related to the following topics (but not limited):

Urban environment

Pollution risk assessments and controls

Marine and coastal biodiversity and ecosystems

Agriculture and functional traits

Water resources vulnerability

Natural disaster management

Climate change and its impact

Crop yield forecasting and crop phenology analysis

New satellite sensor for environmental monitoring

Water quality and pollutant concentrations

Forest fire monitoring and management

Land productivity and vegetation trend analysis

Land use/land cover change

Machine learning and deep learning for environmental applications

Editors

  • Vishakha Sood Vishakha Sood , 

    Vishakha Sood

    Department of Civil Engineering, Indian Institute of Technology, Ropar, Punjab, India (vishakha.sood@ieee.org)

  • Reet Kamal Tiwari Reet Kamal Tiwari , 

    Reet Kamal Tiwari

    Department of Civil Engineering, Indian Institute of Technology (IIT), Ropar, Punjab, India (reetkamal@iitrpr.ac.in)

  • Sartajvir Singh Sartajvir Singh , 

    Sartajvir Singh

    University Institute of Engineering, Chandigarh University, Mohali Punjab, India, (sartajvir.e15199@cumail.in)

  • Surya Prakash Tiwari Surya Prakash Tiwari  &

    Surya Prakash Tiwari

    Applied Research Center for Environment and Marine Studies, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia (Surya.tiwari@kfupm.edu.sa)

  • Akshar Tripathi Akshar Tripathi

    Akshar Tripathi

    Department of Civil and Environmental Engineering, Indian Institute of Technology (IIT), Patna, India (akshar@iitp.ac.in)

Articles (27 in this collection)