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Remote Sensing for Climate Change Studies

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 August 2020) | Viewed by 21483

Special Issue Editors

East Coast Geospatial Consultants, Armidale, NSW 2350, Australia
Interests: environmental modelling; spatial ecology; climate change impacts; remote sensing; GIS; spatial modelling
Special Issues, Collections and Topics in MDPI journals
School of Agricultural, Earth and Environmental Sciences, University of Kwazulu-Natal, Durban, South Africa
Interests: remote sensing; land use; environment; vegetation; hyperspectral remote sensing; ecosystem ecology; spatial analysis; climate change impact analysist; vegetation mapping
Special Issues, Collections and Topics in MDPI journals
Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: radar image processing remote sensing and GIS applications GIS for engineers forecasting disaster hazard; stochastic analysis and modelling; natural hazards; environmental engineering modelling; geospatial information systems; photogrammetry and remote sensing; unmanned aerial vehicles (UAVs).
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate Change and its impacts on the environment is a current key topic of interest and discussion. Remote Sensing technology has the capacity to offer much to this field of research. Remote Sensing allows for the capture of images over large areas at a fraction of the cost and time and on a regular basis. A multitude of satellites provide global coverage at fine scales, with coverage as well as spatial and spectral resolutions improving with each launch. Such information can be used for climate monitoring and change detection studies, ranging from local to global extents. Some sensors have been launched recently and so have provided imagery for only a few decades. However, other systems have been recording data for many more years and so provide excellent opportunities to investigate long-term changes. As an example, the Vanguard-2 satellite has been recording solar irradiance and cloud reflection since its launch in 1959. Such data has become crucial for detecting longer-term changes in climate change studies.

This Special Issue calls for innovative methods and applications of Remote Sensing for climate-change-related studies. The range of topics includes but is not limited to:

  • glacial retreat;
  • changes in long-term snow cover;
  • land use changes and impacts on climate;
  • impacts of climate change on food production;
  • declining forest cover due to climate change;
  • declining forest cover and how it can affect climate through greenhouse gas (GHG) sequestration;
  • conversion of peatlands;
  • climate change impacts and temporal monitoring;
  • crop monitoring;
  • climate change impacting natural hazards;
  • coastal erosion analysis and prediction;
  • changes in mass wasting and mass movements;
  • multi-temporal high-resolution satellite images;
  • drought monitoring and assessment;
  • impact of climate change on natural resources;
  • soil carbon sequestration; and
  • climate impact on ecosystem services.

Professor Lalit Kumar
Prof. Onisimo Mutanga
Prof. Biswajeet Pradhan
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

  • climate change
  • snow cover
  • glacial retreat
  • land use change
  • natural hazard monitoring and management
  • natural resources
  • earth observation
  • coastal eorsion
  • drought assessment

Published Papers (4 papers)

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21 pages, 9237 KiB  
Article
Climatology of Planetary Boundary Layer Height-Controlling Meteorological Parameters Over the Korean Peninsula
Remote Sens. 2020, 12(16), 2571; https://doi.org/10.3390/rs12162571 - 10 Aug 2020
Cited by 23 | Viewed by 3517
Abstract
Planetary boundary layer (PBL) height plays a significant role in climate modeling, weather forecasting, air quality prediction, and pollution transport processes. This study examined the climatology of PBL-associated meteorological parameters over the Korean peninsula and surrounding sea using data from the ERA5 dataset [...] Read more.
Planetary boundary layer (PBL) height plays a significant role in climate modeling, weather forecasting, air quality prediction, and pollution transport processes. This study examined the climatology of PBL-associated meteorological parameters over the Korean peninsula and surrounding sea using data from the ERA5 dataset produced by the European Centre for Medium-range Weather Forecasts (ECMWF). The data covered the period from 2008 to 2017. The bulk Richardson number methodology was used to determine the PBL height (PBLH). The PBLH obtained from the ERA5 data agreed well with that derived from sounding and Global Positioning System Radio Occultation datasets. Significant diurnal and seasonal variability in PBLH was observed. The PBLH increases from morning to late afternoon, decreases in the evening, and is lowest at night. It is high in the summer, lower in spring and autumn, and lowest in winter. The variability of the PBLH with respect to temperature, relative humidity, surface pressure, wind speed, lower tropospheric stability, soil moisture, and surface fluxes was also examined. The growth of the PBLH was high in the spring and in southern regions due to the low soil moisture content of the surface. A high PBLH pattern is evident in high-elevation regions. Increasing trends of the surface temperature and accordingly PBLH were observed from 2008 to 2017. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change Studies)
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21 pages, 5311 KiB  
Article
A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data
Remote Sens. 2020, 12(9), 1373; https://doi.org/10.3390/rs12091373 - 26 Apr 2020
Cited by 31 | Viewed by 5267
Abstract
Flash floods induced by torrential rainfalls are considered one of the most dangerous natural hazards, due to their sudden occurrence and high magnitudes, which may cause huge damage to people and properties. This study proposed a novel modeling approach for spatial prediction of [...] Read more.
Flash floods induced by torrential rainfalls are considered one of the most dangerous natural hazards, due to their sudden occurrence and high magnitudes, which may cause huge damage to people and properties. This study proposed a novel modeling approach for spatial prediction of flash floods based on the tree intelligence-based CHAID (Chi-square Automatic Interaction Detector)random subspace, optimized by biogeography-based optimization (the CHAID-RS-BBO model), using remote sensing and geospatial data. In this proposed approach, a forest of tree intelligence was constructed through the random subspace ensemble, and, then, the swarm intelligence was employed to train and optimize the model. The Luc Yen district, located in the northwest mountainous area of Vietnam, was selected as a case study. For this circumstance, a flood inventory map with 1866 polygons for the district was prepared based on Sentinel-1 synthetic aperture radar (SAR) imagery and field surveys with handheld GPS. Then, a geospatial database with ten influencing variables (land use/land cover, soil type, lithology, river density, rainfall, topographic wetness index, elevation, slope, curvature, and aspect) was prepared. Using the inventory map and the ten explanatory variables, the CHAID-RS-BBO model was trained and verified. Various statistical metrics were used to assess the prediction capability of the proposed model. The results show that the proposed CHAID-RS-BBO model yielded the highest predictive performance, with an overall accuracy of 90% in predicting flash floods, and outperformed benchmarks (i.e., the CHAID, the J48-DT, the logistic regression, and the multilayer perception neural network (MLP-NN) models). We conclude that the proposed method can accurately estimate the spatial prediction of flash floods in tropical storm areas. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change Studies)
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27 pages, 9877 KiB  
Article
Assessing the Changes in the Moisture/Dryness of Water Cavity Surfaces in Imlili Sebkha in Southwestern Morocco by Using Machine Learning Classification in Google Earth Engine
Remote Sens. 2020, 12(1), 131; https://doi.org/10.3390/rs12010131 - 01 Jan 2020
Cited by 5 | Viewed by 5371
Abstract
Imlili Sebkha is a stable and flat depression in southern Morocco that is more than 10 km long and almost 3 km wide. This region is mainly sandy, but its northern part holds permanent water pockets that contain fauna and flora despite their [...] Read more.
Imlili Sebkha is a stable and flat depression in southern Morocco that is more than 10 km long and almost 3 km wide. This region is mainly sandy, but its northern part holds permanent water pockets that contain fauna and flora despite their hypersaline water. Google Earth Engine (GEE) has revolutionized land monitoring analysis by allowing the use of satellite imagery and other datasets via cloud computing technology and server-side JavaScript programming. This work highlights the potential application of GEE in processing large amounts of satellite Earth Observation (EO) Big Data for the free, long-term, and wide spatio-temporal wet/dry permanent salt water cavities and moisture monitoring of Imlili Sebkha. Optical and radar images were used to understand the functions of Imlili Sebkha in discovering underground hydrological networks. The main objective of this work was to investigate and evaluate the complementarity of optical Landsat, Sentinel-2 data, and Sentinel-1 radar data in such a desert environment. Results show that radar images are not only well suited in studying desertic areas but also in mapping the water cavities in desert wetland zones. The sensitivity of these images to the variations in the slope of the topographic surface facilitated the geological and geomorphological analyses of desert zones and helped reveal the hydrological functions of Imlili Sebkha in discovering buried underground networks. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change Studies)
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11 pages, 2259 KiB  
Letter
Exceptionally High 2018 Equilibrium Line Altitude on Taku Glacier, Alaska
Remote Sens. 2019, 11(20), 2378; https://doi.org/10.3390/rs11202378 - 14 Oct 2019
Cited by 11 | Viewed by 6152
Abstract
The Juneau Icefield Research Program (JIRP) has been examining the glaciers of the Juneau Icefield since 1946. The height of the transient snowline (TSL) at the end of the summer represents the annual equilibrium line altitude (ELA) for the glacier, where ablation equals [...] Read more.
The Juneau Icefield Research Program (JIRP) has been examining the glaciers of the Juneau Icefield since 1946. The height of the transient snowline (TSL) at the end of the summer represents the annual equilibrium line altitude (ELA) for the glacier, where ablation equals accumulation. On Taku Glacier the ELA has been observed annually from 1946 to 2018. Since 1998 multiple annual observations of the TSL in satellite imagery identify both the migration rate of the TSL and ELA. The mean ELA has risen 85 ± 10 m from the 1946–1985 period to the 1986–2018 period. In 2018 the TSL was observed at: 900 m on 5 July; 975 m on 21 July; 1075 m on 30 July; 1400 m on 16 September; and 1425 m on 1 October. This is the first time since 1946 that the TSL has reached or exceeded 1250 m on Taku Glacier. The 500 m TSL rise from 5 July to 30 July, 8.0. md−1, is the fastest rate of rise observed. This combined with the observed balance gradient in this region yields an ablation rate of 40–43 mmd−1, nearly double the average ablation rate. On 22 July a snow pit was completed at 1405 m with 0.93 m w.e. (water equivalent), that subsequently lost all snow cover, prior to 16 September. This is one of eight snow pits completed in July providing field data to verify the ablation rate. The result of the record ELA and rapid ablation is the largest negative annual balance of Taku Glacier since records began in 1946. Full article
(This article belongs to the Special Issue Remote Sensing for Climate Change Studies)
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