Special Issue "Environmental Modelling and Remote Sensing"

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

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editors

Prof. Dr. Quazi K. Hassan
Website
Guest Editor
Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr. N.W., Calgary, AB T2N 1N4, Canada
Interests: optical/thermal remote sensing in: (i) forecasting and monitoring of natural hazards/disasters, such as forest fire, drought, and flooding; (ii) comprehending the dynamics of natural resources, such as forestry, agriculture, and water; and (iii) modelling issues related to boreal environment
Special Issues and Collections in MDPI journals
Dr. Ashraf Dewan
Website
Guest Editor

Special Issue Information

Dear Colleagues,

Environmental modelling can be described as a simplified form of a real system that enhances our knowledge of how a system operates. Such models represent the functioning of various processes of the environment, such as: processes related to atmosphere, hydrology, and land surface, among others. In fact, environmental models may span a wide spectrum of geographic (i.e., from local to regional to global-levels) and temporal (i.e., diurnal to monthly to annual to decadal-levels) scale. They often integrates various aspects of the environment that can be described upon employing various types of models, such as process-driven, empirical or data-driven, deterministic, stochastic, etc.

In fact, the comprehension of environmental issues is critical for ensuring our existence on the earth’s  surface and environmental sustainability. Here, the purpose is to gather remote sensing/earth observation scientist/researcher(s) related to this topic, aiming to highlight ongoing research investigations and new applications in the field. In this framework, the editors of this special issue would like to invite both applied and theoretical research contributions, and submissions of original works furthering knowledge concerned with any aspect of the use of remote sensing and/or big data in the field of geospatial analysis in modelling environmental issues. Note that these manuscripts must be not only unpublished, but also not under consideration for potential publication elsewhere. In addition, the manuscripts must employ one of the following remote sensing data types, such as optical, thermal, hyperspectral, active and passive microwave ones acquired by either airborne or space-borne remote sensing platforms in dealing with environmental issues.

The topics of interest include, but not limited to the following set of natural resources and hazards/disaster modelling:

  • Agriculture,
  • Forestry,
  • Water,
  • Forest fire,
  • Drought,
  • Flooding,
  • Volcano,
  • Local/regional warming,
  • Urban environment, and
  • Environmental pollution.

Prof. Quazi K. Hassan
Dr. Ashraf Dewan
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 papers will be 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 2200 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.

Keywords

  • Agriculture
  • Forestry
  • Water
  • Forest fire
  • Drought
  • Flooding
  • Volcano
  • Local/regional warming
  • Urban environment
  • Environmental pollution

Published Papers (3 papers)

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Research

Open AccessArticle
Projected Wind Impact on Abies balsamea (Balsam fir)-Dominated Stands in New Brunswick (Canada) Based on Remote Sensing and Regional Modelling of Climate and Tree Species Distribution
Remote Sens. 2020, 12(7), 1177; https://doi.org/10.3390/rs12071177 - 07 Apr 2020
Abstract
The paper describes the development of predictive equations of windthrow for five tree species based on remote sensing of wind-affected stands in southwestern New Brunswick (NB). The data characterises forest conditions before, during and after the passing of extratropical cyclone Arthur, July 4–5, [...] Read more.
The paper describes the development of predictive equations of windthrow for five tree species based on remote sensing of wind-affected stands in southwestern New Brunswick (NB). The data characterises forest conditions before, during and after the passing of extratropical cyclone Arthur, July 4–5, 2014. The five-variable logistic function developed for balsam fir (bF) was validated against remote-sensing-acquired windthrow data for bF-stands affected by the Christmas Mountains windthrow event of November 7, 1994. In general, the prediction of windthrow in the area agreed fairly well with the windthrow sites identified by photogrammetry. The occurrence of windthrow in the Christmas Mountains was prominent in areas with shallow soils and prone to localised accelerations in mean and turbulent airflow. The windthrow function for bF was subsequently used to examine the future impact of windthrow under two climate scenarios (RCP’s 4.5 and 8.5) and species response to local changes anticipated with global climate change, particularly with respect to growing degree-days and soil moisture. Under climate change, future windthrow in bF stands (2006–2100) is projected to be modified as the species withdraws from the high-elevation areas and NB as a whole, as the climate progressively warms and precipitation increases, causing the growing environment of bF to deteriorate. Full article
(This article belongs to the Special Issue Environmental Modelling and Remote Sensing)
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Open AccessArticle
Performance Assessment of Sub-Daily and Daily Precipitation Estimates Derived from GPM and GSMaP Products over an Arid Environment
Remote Sens. 2019, 11(23), 2840; https://doi.org/10.3390/rs11232840 - 29 Nov 2019
Cited by 2
Abstract
Precipitation is a critical variable for comprehending various climate-related research, such as water resources management, flash flood monitoring and forecasting, climatic analyses, and hydrogeological studies, etc. Here, our objective was to evaluate the rainfall estimates obtained from Global Precipitation Mission (GPM), and Global [...] Read more.
Precipitation is a critical variable for comprehending various climate-related research, such as water resources management, flash flood monitoring and forecasting, climatic analyses, and hydrogeological studies, etc. Here, our objective was to evaluate the rainfall estimates obtained from Global Precipitation Mission (GPM), and Global Satellite Mapping of Precipitation (GSMaP) constellation over an arid environment like the Sultanate of Oman that is characterized by a complex topography and extremely variable rainfall patterns. Global Satellite-based Precipitation Estimates (GSPEs) can provide wide coverage and high spatial and temporal resolutions, but evaluating their accuracy is a mandatory step before involving them in different hydrological applications. In this paper, the reliability of the Integrated Multi-satellitE Retrievals for the GPM (IMERG) V04 and GSMaP V06 products were evaluated using the reference in-situ rain gauges at sub-daily (e.g., 6, 12, and 18 h) and daily time scales during the period of March 2014–December 2016. A set of continuous difference statistical indices (e.g., mean absolute difference, root mean square error, mean difference, and unconditional bias), and categorical metrics (e.g., probability of detection, critical success index, false alarm ratio, and frequency bias index) were used to evaluate recorded precipitation occurrences. The results showed that the five GSPEs could generally delineate the spatial and temporal patterns of rainfall while they might have over- and under-estimations of in-situ gauge measurements. The overall quality of the GSMaP runs was superior to the IMERG products; however, it also encountered an exaggeration in case of light rain and an underestimation for heavy rain. The effects of the gauge calibration algorithm (GCA) used in the final IMERG (IMERG-F) were investigated by comparison with early and late runs. The IMERG-F V04 product did not show a significant improvement over the early (i.e., after 4 h of rainfall observations) and late (i.e., after 12 h of rainfall observations) products. The results indicated that GCA could not reduce the missed precipitation records considerably. Full article
(This article belongs to the Special Issue Environmental Modelling and Remote Sensing)
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Open AccessEditor’s ChoiceArticle
Introducing a New Remote Sensing-Based Model for Forecasting Forest Fire Danger Conditions at a Four-Day Scale
Remote Sens. 2019, 11(18), 2101; https://doi.org/10.3390/rs11182101 - 09 Sep 2019
Cited by 7
Abstract
Forest fires are natural disasters that create a significant risk to the communities living in the vicinity of forested landscape. To minimize the risk of forest fires for the resilience of such urban communities and forested ecosystems, we proposed a new remote sensing-based [...] Read more.
Forest fires are natural disasters that create a significant risk to the communities living in the vicinity of forested landscape. To minimize the risk of forest fires for the resilience of such urban communities and forested ecosystems, we proposed a new remote sensing-based medium-term (i.e., four-day) forest fire danger forecasting system (FFDFS) based on an existing framework, and applied the system over the forested regions in the northern Alberta, Canada. Hence, we first employed moderate resolution imaging spectroradiometer (MODIS)-derived daily land surface temperature (Ts) and surface reflectance products along with the annual land cover to generate three four-day composite for Ts, normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) at 500 m spatial resolution for the next four days over the forest-dominant regions. Upon generating these four-day composites, we calculated the variable-specific mean values to determine variable-specific fire danger maps with two danger classes (i.e., high and low). Then, by assuming the cloud-contaminated pixels as the low fire danger areas, we combined these three danger maps to generate a four-day fire danger map with four danger classes (i.e., low, moderate, high, and very high) over our study area of interest, which was further enhanced by incorporation of a human-caused static fire danger map. Finally, the four-day scale fire danger maps were evaluated using observed/ground-based forest fire occurrences during the 2015–2017 fire seasons. The results revealed that our proposed system was able to detect about 75% of the fire events in the top two danger classes (i.e., high and very high). The system was also able to predict the 2016 Horse River wildfire, the worst fire event in Albertian and Canadian history, with about 67% agreement. The higher accuracy outputs from our proposed model indicated that it could be implemented in the operational management, which would be very useful for lessening the adverse impact of such fire events. Full article
(This article belongs to the Special Issue Environmental Modelling and Remote Sensing)
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