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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 22521

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Special Issue Editors

Prof. Dr. Quazi K. Hassan

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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; (iii) modelling issues related to boreal environment
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Dr. Ashraf Dewan

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Guest Editor

School of Earth and Planetary Sciences, Spatial Sciences Discipline, Curtin University, Perth, Australia
Interests: climate risk assessment; urban environment; health geography; disaster and emergency management; human–environment interaction
Special Issues, Collections and Topics in MDPI journals

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 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 2700 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 (6 papers)

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Research

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21 pages, 1911 KiB

Open AccessArticle

Remote Sensing of Local Warming Trend in Alberta, Canada during 2001–2020, and Its Relationship with Large-Scale Atmospheric Circulations

by Quazi K. Hassan, Ifeanyi R. Ejiagha, M. Razu Ahmed, Anil Gupta, Elena Rangelova and Ashraf Dewan

Remote Sens. 2021, 13(17), 3441; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173441 - 30 Aug 2021

Cited by 24 | Viewed by 3268

Abstract

Here, the objective was to study the local warming trend and its driving factors in the natural subregions of Alberta using a remote-sensing approach. We applied the Mann–Kendall test and Sen’s slope estimator on the day and nighttime MODIS LST time-series images to map and quantify the extent and magnitude of monthly and annual warming trends in the 21 natural subregions of Alberta. We also performed a correlation analysis of LST anomalies (both day and nighttime) of the subregions with the anomalies of the teleconnection patterns, i.e., Pacific North American (PNA), Pacific decadal oscillation (PDO), Arctic oscillation (AO), and sea surface temperature (SST, Niño 3.4 region) indices, to identify the relationship. May was the month that showed the most significant warming trends for both day and night during 2001–2020 in most of the subregions in the Rocky Mountains and Boreal Forest. Subregions of Grassland and Parkland in southern and southeastern parts of Alberta showed trends of cooling during daytime in July and August and a small magnitude of warming in June and August at night. We also found a significant cooling trend in November for both day and night. We identified from the correlation analysis that the PNA pattern had the most influence in the subregions during February to April and October to December for 2001–2020; however, none of the atmospheric oscillations showed any significant relationship with the significant warming/cooling months. Full article

(This article belongs to the Special Issue Environmental Modelling and Remote Sensing)

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25 pages, 8898 KiB

Open AccessEditor’s ChoiceArticle

Utilizing the Available Open-Source Remotely Sensed Data in Assessing the Wildfire Ignition and Spread Capacities of Vegetated Surfaces in Romania

by Artan Hysa, Velibor Spalevic, Branislav Dudic, Sanda Roșca, Alban Kuriqi, Ștefan Bilașco and Paul Sestras

Remote Sens. 2021, 13(14), 2737; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142737 - 12 Jul 2021

Cited by 16 | Viewed by 3543

Abstract

We bring a practical and comprehensive GIS-based framework to utilize freely available remotely sensed datasets to assess wildfire ignition probability and spreading capacities of vegetated landscapes. The study area consists of the country-level scale of the Romanian territory, characterized by a diversity of vegetated landscapes threatened by climate change. We utilize the Wildfire Ignition Probability/Wildfire Spreading Capacity Index (WIPI/WSCI). WIPI/WSCI models rely on a multi-criteria data mining procedure assessing the study area’s social, environmental, geophysical, and fuel properties based on open access remotely sensed data. We utilized the Receiver Operating Characteristic (ROC) analysis to weigh each indexing criterion’s impact factor and assess the model’s overall sensitivity. Introducing ROC analysis at an earlier stage of the workflow elevated the final Area Under the Curve (AUC) of WIPI from 0.705 to 0.778 and WSCI from 0.586 to 0.802. The modeling results enable discussion on the vulnerability of protected areas and the exposure of man-made structures to wildfire risk. Our study shows that within the wildland–urban interface of Bucharest’s metropolitan area, there is a remarkable building stock of healthcare, residential and educational functions, which are significantly exposed and vulnerable to wildfire spreading risk. Full article

(This article belongs to the Special Issue Environmental Modelling and Remote Sensing)

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21 pages, 8436 KiB

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

by Charles P.-A. Bourque, Philippe Gachon, Benjamin R. MacLellan and James I. MacLellan

Remote Sens. 2020, 12(7), 1177; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071177 - 07 Apr 2020

Cited by 6 | Viewed by 2703

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, 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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21 pages, 2456 KiB

Open AccessArticle

Performance Assessment of Sub-Daily and Daily Precipitation Estimates Derived from GPM and GSMaP Products over an Arid Environment

by Mohamed Shawky, Adel Moussa, Quazi K Hassan and Naser El-Sheimy

Remote Sens. 2019, 11(23), 2840; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11232840 - 29 Nov 2019

Cited by 24 | Viewed by 3535

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 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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16 pages, 7447 KiB

Open AccessEditor’s ChoiceArticle

Introducing a New Remote Sensing-Based Model for Forecasting Forest Fire Danger Conditions at a Four-Day Scale

by M. Razu Ahmed, Quazi K. Hassan, Masoud Abdollahi and Anil Gupta

Remote Sens. 2019, 11(18), 2101; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11182101 - 09 Sep 2019

Cited by 22 | Viewed by 3910

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 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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15 pages, 1077 KiB

Open AccessTechnical Note

Unmanned Aerial Vehicle (UAV)-Based Remote Sensing for Early-Stage Detection of Ganoderma

by Parisa Ahmadi, Shattri Mansor, Babak Farjad and Ebrahim Ghaderpour

Remote Sens. 2022, 14(5), 1239; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051239 - 03 Mar 2022

Cited by 24 | Viewed by 3024

Abstract

Early detection of Basal Stem Rot (BSR) disease in oil palms is an important plantation management activity in Southeast Asia. Practical approaches for the best strategic approach toward the treatment of this disease that originated from Ganoderma Boninense require information about the status of infection. In spite of the availability of conventional methods to detect this disease, they are difficult to be used in plantation areas that are commonly large in terms of planting hectarage; therefore, there is an interest for a quick and delicate technique to facilitate the detection and monitoring of Ganoderma in its early stage. The main goal of this paper is to evaluate the use of remote sensing technique for the rapid detection of Ganoderma-infected oil palms using Unmanned Aerial Vehicle (UAV) imagery integrated with an Artificial Neural Network (ANN) model. Principally, we sought for the most representative mean and standard deviation values from green, red, and near-infrared bands, as well as the best palm circle radius, threshold limit, and the number of hidden neurons for different Ganoderma severity levels. With the obtained modified infrared UAV images at 0.026 m spatial resolution, early BSR infected oil palms were most satisfactorily detected with mean and standard deviation derived from a circle radius of 35 pixels of band green and near-infrared, 1/8 threshold limit, and ANN network by 219 hidden neurons, where the total classification accuracies achieved for training and testing the dataset were 97.52% and 72.73%, respectively. The results from this study signified the utilization of an affordable digital camera and UAV platforms in oil palm plantation, predominantly in disease management. The UAV images integrated with the Levenberg–Marquardt training algorithm illustrated its great potential as an aerial surveillance tool to detect early Ganoderma-infected oil palms in vast plantation areas in a rapid and inexpensive manner. Full article

(This article belongs to the Special Issue Environmental Modelling and Remote Sensing)

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