Special Issue "Applications of Remote Sensing and GIS Integration in Natural Resources and Environmental Science"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 28 February 2023 | Viewed by 1006

Special Issue Editors

Dr. Jian Yang
E-Mail Website
Guest Editor
Department of Forestry and Natural Resources, University of Kentucky, 730 Rose Street, Lexington, KY 40546, USA
Interests: forest landscape ecology; disturbance ecology; ecosystem modeling; land use and land cover change; ecosystem services; remote sensing and GIS; spatial statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing (RS) and geographic information systems (GIS) often work hand in hand to map, analyze, and disseminate spatial information. As a science of obtaining information from a distance, RS extracts spatially explicit attributes about the Earth’s land and water surfaces using images acquired from aircraft or satellites. Such RS-derived geospatial attributes can be integrated into a GIS framework to (1) map spatial patterns of the characteristics of interest, (2) identify the relationships of RS-derived Earth surface attributes to GIS-derived landscape features, (3) determine how the Earth’s surface characteristics change over time, and (4) estimate new characteristics or emergent properties from the existing remote sensing products. In essence, RS provides invaluable spatial data, often in raster format, to the GIS for further geoprocessing. Vice versa, many critical analyses of remotely sensed data such as geometric registration, radiometric correction, image classification, and change detection can benefit from the use of ancillary GIS data (often in vector format) and geoprocessing procedures (e.g., masking, overlay, and proximity analysis). The integration of RS/GIS has been successfully applied in many fields related to natural resources and environmental science, including agriculture, forestry, land use, biological conversation, ecological restoration, and natural hazard management. With the recent advances in computing innovation, artificial intelligence, and big data science, the integration of remote sensing and GIS is approaching a new phase that will further enhance the analysis of spatial data from various sources.

In this Special Issue, we would like to invite you to submit original research showcasing the innovative use of integrating remote sensing and GIS to solve complex research questions closely related to natural resources and environmental sciences. Comprehensive reviews of this subject are also welcome. Potential topics include but are not limited to the following:

  • State-of-the-art geospatial techniques integrating remote sensing and GIS;
  • Original methods or tools developed to seamlessly integrate remote sensing and GIS in the applications of natural resources and environmental science;
  • Comprehensive use of multifaceted geoprocessing tools and GIS data to enhance remote sensing image processing operations;
  • Novel GIS analysis of recently developed remote sensing data to assess natural resources and environmental conditions.

You may choose our Joint Special Issue in Land.

Dr. Jian Yang
Dr. Le Yu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Integration of remote sensing and GIS
  • Geoprocessing of remote sensing data
  • Natural resource mapping
  • Remote sensing of environment
  • Land surface processes
  • Landscape approach
  • Ecosystem modeling
  • Spatial analysis
  • System integration

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Published Papers (1 paper)

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Estimating the Carbon Emissions of Remotely Sensed Energy-Intensive Industries Using VIIRS Thermal Anomaly-Derived Industrial Heat Sources and Auxiliary Data
Remote Sens. 2022, 14(12), 2901; https://doi.org/10.3390/rs14122901 - 17 Jun 2022
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The energy-intensive industrial sector (EIIS) occupies a majority of global CO2 emissions, but spatially monitoring the spatiotemporal dynamics of these emissions remains challenging. In this study, we used the Chinese province with the largest carbon emissions, Shandong Province, as an example to [...] Read more.
The energy-intensive industrial sector (EIIS) occupies a majority of global CO2 emissions, but spatially monitoring the spatiotemporal dynamics of these emissions remains challenging. In this study, we used the Chinese province with the largest carbon emissions, Shandong Province, as an example to investigate the capacity of remotely sensed thermal anomaly products to identify annual industrial heat source (IHS) patterns at a 1 km resolution and estimated the carbon emissions of these sources using auxiliary datasets and the boosting regression tree (BRT) model. The IHS identification accuracy was evaluated based on two IHS references and further attributed according to corporate inventory data. We followed a bottom-up approach to estimate carbon emissions for each IHS object and conducted model fitting using the explanatory strength of the annual population density, nighttime light (NTL), and relevant thermal characteristic information derived from the Visible Infrared Imaging Radiometer Suite (VIIRS). We generated a time series of IHS distributions from 2012 to 2020 containing a total of over 3700 IHS pixels exhibiting better alignment with the reference data than that obtained in previous work. The results indicated that the identified IHSs mostly belonged to the EIIS, such as energy-related industries (e.g., thermal power plants) and heavy manufacturing industries (e.g., chemistry and cement plants), that primarily use coal and coke as fuel sources. The BRT model exhibited a good performance, explaining 61.9% of the variance in the inventory-based carbon emissions and possessing an index of agreement (IOA) of 0.83, suggesting a feasible goodness of fit of the model when simulating carbon emissions. Explanatory variables such as the population density, thermal power radiation, NTL, and remotely sensed thermal anomaly durations were found to be important factors for improving carbon emissions modeling. The method proposed in this study is useful to aid management agencies and policymakers in tracking the carbon footprint of the EIIS and regulating high-emission corporations to achieve carbon neutrality. Full article
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