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Recent Developments in Remote Sensing for Physical Geography

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 16624

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Dr. Caleb Pan

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Guest Editor

Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT 59812, USA
Interests: global snow cover trends; mid and high latitude cryospheric processes; remote sensing of snow and ice; arctic and mountain ecosystems

Mr. Brendan Hoover

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Guest Editor

Computational Earth Science (EES-16), Los Alamos National Laboratory; Department of Geography and the Environment, University of Texas at Austin, Austin, TX 78712, USA
Interests: computational movement analysis, machine learning for remote sensing, biogeography, carbon capture and storage, and GIScience

Dr. Nathaniel Robinson

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Guest Editor

Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT 59812, USA
Interests: remote sensing; vegetation productivity; rangeland management; biodiversity conservation
Special Issues, Collections and Topics in MDPI journals

Ms. Amelia Eisenhart

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Guest Editor

Digital Landscapes Laboratory, Department of Geography & the Environment, The University of Texas at Austin, Austin, TX 78712, USA
Interests: vegetation phenology, flood detection, land use land cover change at the wetland/dryland interface, savanna ecology

Special Issue Information

Dear Colleagues,

Remote sensing is a linchpin in physical geography, affording us the ability to observe changes in our landscape across both time and space. Fortunately, as the diversity of remote sensing platforms and data continues to increase, our computational capacity is also increasing, allowing for an exciting evolution of novel ways to use and interpret remote sensing data in physical geography. We are pleased to announce a Special Issue of Remote Sensing on ‘New Remote Sensing Developments and Applications in Physical Geography’ highlighted at the 2019 American Association of Geographers Annual Meeting. We invite submissions of papers that will be/were presented at the 2019 AAG Annual Meeting with a focus on, but not limited to, the following topics:

-The use of emerging multi-source and multi-scale remote sensing techniques to improve the retrieval of environmental properties and land surface parameters.

-Long-term monitoring of dynamic environmental change (i.e. climatology, snow cover, habitat, lentic and lotic systems, and vegetation) from microwave and/or optical sensors.

-Impacts of spatial and temporal resolution on the identification of ecosystem processes from physical landscape patterns

-Use of cloud computing and machine learning techniques to advance the analysis of large remote sensing datasets as well as more critical analyses of these methods.

-Novel applications of remotely-sensed data products in geophysical or biophysical systems modeling

Dr. Caleb Pan
Mr. Brendan Hoover
Dr. Nathaniel Robinson
Ms. Amelia Eisenhart
Guest Editors

Related References

Pan C G, Kirchner P, Kimball J S, Kim Y and Du J 2018 Rain-on-snow events in Alaska, and their frequency and distribution from satellite observations Environ. Res. Lett.

Robinson N P, Allred B W, Jones M O, Moreno A, Kimball J S, Naugle D E, Erickson T A and Richardson A D 2017 A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI ) Product for the Conterminous United States Remote Sens. 9 1–14

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

Physical geography
Multi-scalar imaging techniques
Environmental change
Land surface parameters
AAG

Published Papers (5 papers)

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Research

21 pages, 10211 KiB

Open AccessArticle

Modelling and Terrestrial Laser Scanning Methodology (2009–2018) on Debris Cones in Temperate High Mountains

by José Juan de Sanjosé-Blasco, Mariló López-González, Estrella Alonso-Pérez and Enrique Serrano

Remote Sens. 2020, 12(4), 632; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040632 - 14 Feb 2020

Cited by 2 | Viewed by 2369

Abstract

Debris cones are a very common landform in temperate high mountains. They are the most representative examples of the periglacial and nival processes. This work studies the dynamic behavior of two debris cones (Cone A and Cone B) in the Picos de Europa, in the north of the Iberian Peninsula. Their evolution was measured uninterruptedly throughout each August for 10 years (2009–2018) using the Terrestrial Laser Scanning (TLS) technique. The observations and calculations of the two debris cones were treated independently, but both showed the same behavior. Therefore, if these results are extrapolated to other debris cones in similar environments (temperate high mountain), they should show behavior similar to that of the two debris cones analyzed. Material falls onto the cones from the walls, and transfer of sediments follows linear trajectories according to the maximum slope. In order to understand the linear evolution of the two debris cones, profiles were created along the maximum slope lines of the Digital Elevation Model (DEM) of 2009, and these profile lines were extrapolated to the remaining years of measurement. In order to determine volumetric surface behavior in the DEMs, each year for the period 2009–2018 was compared. In addition, the statistical predictive value for position (Z) in year 2018 was calculated for the same planimetric position (X,Y) throughout the profiles of maximum slopes. To do so, the real field data from 2009–2017 were interpolated and used to form a sample of curves. These curves are interpreted as the realization of a functional random variable that can be predicted using statistical techniques. The predictive curve obtained was compared with the 2018 field data. The results of both coordinates (Z), the real field data, and the statistical data are coherent within the margin of error of the data collection. Full article

(This article belongs to the Special Issue Recent Developments in Remote Sensing for Physical Geography)

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23 pages, 6068 KiB

Open AccessArticle

A Long-Term Passive Microwave Snowoff Record for the Alaska Region 1988–2016

by Caleb G. Pan, Peter B. Kirchner, John S. Kimball and Jinyang Du

Remote Sens. 2020, 12(1), 153; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12010153 - 02 Jan 2020

Cited by 10 | Viewed by 3139

Abstract

Snowoff (SO) date—defined as the last day of observed seasonal snow cover—is an important governor of ecologic and hydrologic processes across Alaska and Arctic-Boreal landscapes; however, our understanding and capacity for the monitoring of spatial and temporal variability in the SO date is still lacking. In this study, we present a 6.25 km spatially gridded passive microwave (PMW) SO data record, complimenting current Alaskan SO records from Moderate Resolution Imaging Spectrometer (MODIS) and Landsat, but extending the SO record an additional 13 years. The PMW SO record was validated against in situ snow depth observations and showed favorable accuracy (0.66–0.92 mean correlations; 2–10 day mean absolute errors) for the major climate regions of Alaska. The PMW SO results were also within 10 days of finer spatial scale SO observational records, including Interactive Multisensor Snow and Ice Mapping System (IMS), MODIS, and Landsat, for a majority (75%) of Alaska. However, the PMW record showed a general SO delay at higher elevations and across the Alaska North Slope, and earlier SO in the Alaska interior and southwest regions relative to the other SO records. Overall, we assign an uncertainty +/−11 days to the PMW SO. The PMW SO record benefits from the near-daily temporal fidelity of underlying brightness temperature (Tb) observations and reveals a mean regional trend in earlier SO timing (−0.39 days yr⁻¹), while significant (p < 0.1) SO trend areas encompassed 11% of the Alaska domain and ranged from −0.11 days yr⁻¹ to −1.31 days yr⁻¹ over the 29-year satellite record. The observed SO dates also showed anomalous early SO dates during markedly warm years. Our results clarify the pattern and rate of SO changes across Alaska, which are interactive with global warming and contributing to widespread permafrost degradation, changes in regional hydrology, ecosystems, and associated services. Our results also provide a robust means for SO monitoring from satellite PMW observations with similar precision as more traditional and finer scale observations. Full article

(This article belongs to the Special Issue Recent Developments in Remote Sensing for Physical Geography)

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23 pages, 6616 KiB

Open AccessArticle

On the Interest of Optical Remote Sensing for Seasonal Snowmelt Parameterization, Applied to the Everest Region (Nepal)

by Benjamin Bouchard, Judith Eeckman, Jean-Pierre Dedieu, François Delclaux, Pierre Chevallier, Simon Gascoin and Yves Arnaud

Remote Sens. 2019, 11(22), 2598; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11222598 - 06 Nov 2019

Cited by 5 | Viewed by 2933

Abstract

In the central part of the Hindu Kush Himalayan region, snowmelt is one of the main inputs that ensures the availability of surface water outside the monsoon period. A common approach for snowpack modeling is based on the degree day factor (DDF) method to represent the snowmelt rate. However, the important seasonal variability of the snow processes is usually not represented when using a DDF method, which can lead to large uncertainties for snowpack simulation. The SPOT-VGT and the MODIS-Terra sensors provide valuable information for snow detection over several years. The aim of this work was to use those data to parametrize the seasonal variability of the snow processes in the hydrological distributed snow model (HDSM), based on a DDF method. The satellite products were corrected and combined in order to implement a database of 8 day snow cover area (SCA) maps over the northern part of the Dudh Koshi watershed (Nepal) for the period 1998–2017. A revisited version of the snow module of the HDSM model was implemented so as to split it into two parameterizations depending on the seasonality. Corrected 8 day SCA maps retrieved from MODIS-Terra were used to calibrate the seasonal parameterization, through a stochastic method, over the period of study (2013–2016). The results demonstrate that the seasonal parameterization reduces the error in the simulated SCA and increases the correlation with the MODIS SCA. The two-set version of the model improved the yearly RMSE from 5.9% to 7.7% depending on the basin, compared to the one-set version. The correlation between the model and MODIS passes from 0.73 to 0.79 in winter for the larger basin, Phakding. This study shows that the use of a remote sensing product can improve the parameterization of the seasonal dynamics of snow processes in a model based on a DDF method. Full article

(This article belongs to the Special Issue Recent Developments in Remote Sensing for Physical Geography)

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20 pages, 7308 KiB

Open AccessArticle

Role of Surface Melt and Icing Events in Livestock Mortality across Mongolia’s Semi-Arid Landscape

by Caleb G. Pan, John S. Kimball, Munkhdavaa Munkhjargal, Nathaniel P. Robinson, Erik Tijdeman, Lucas Menzel and Peter B. Kirchner

Remote Sens. 2019, 11(20), 2392; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11202392 - 16 Oct 2019

Cited by 7 | Viewed by 3884

Abstract

Livestock production is a socioeconomic linchpin in Mongolia and is affected by large-scale livestock die-offs. Colloquially known as dzuds, these die-offs are driven by anomalous climatic events, including extreme cold temperatures, extended snow cover duration (SCD) and drought. As average temperatures across Mongolia have increased at roughly twice the global rate, we hypothesized that increasing cold season surface melt including soil freeze/thaw (FT), snowmelt, and icing events associated with regional warming have become increasingly important drivers of dzud events as they can reduce pasture productivity and inhibit access to grazing. Here, we use daily brightness temperature (T_b) observations to identify anomalous surface melt and icing events across Mongolia from 2003–2016 and their contribution to dzuds relative to other climatic drivers, including winter temperatures, SCD, and drought. We find a positive relationship between surface melt and icing events and livestock mortality during the fall in southern Mongolia and during the spring in the central and western regions. Further, anomalous seasonal surface melt and icing events explain 17–34% of the total variance in annual livestock mortality, with cold temperatures as the leading contributor of dzuds (20–37%). Summer drought showed the greatest explanatory power (43%) but overall had less statistically significant relationships relative to winter temperatures. Our results indicate that surface melt and icing events will become an increasingly important driver of dzuds as annual temperatures and livestock populations are projected to increase in Mongolia. Full article

(This article belongs to the Special Issue Recent Developments in Remote Sensing for Physical Geography)

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21 pages, 6304 KiB

Open AccessArticle

Deriving Regional Snow Line Dynamics during the Ablation Seasons 1984–2018 in European Mountains

by Zhongyang Hu, Andreas J. Dietz and Claudia Kuenzer

Remote Sens. 2019, 11(8), 933; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11080933 - 17 Apr 2019

Cited by 81 | Viewed by 3686

Abstract

Snowmelt in the mid-latitude European mountains is undergoing significant spatiotemporal changes. Regional snow line elevation (RSLE) is an appropriate indicator for assessing snow cover variations in mountain areas. To derive regional snow line dynamics during the ablation seasons 1984–2018, the present study unprecedentedly introduced a readily applicable framework. The framework constitutes four steps: atmospheric and topographic correction, snow classification, RSLE retrieval, and regional snow line retreat curve (RSLRC) derivation. The developed framework has been successfully applied to 8641 satellite images acquired by Landsat, ASTER, and Sentinel-2. The results of the intra-annual regional snow line variations show that: (1) regional snow lines in the Alpine catchments preserve the longest; (2) RSLEs are lower in the northern Pyrenees than in the southern part; (3) regional snow lines persist the shortest in the Carpathian catchments; and (4) during the end of the ablation season 2018, intermediate snowfall events in the catchments Adda, Tagliamento, and Uzh are observed. In terms of the long-term inter-annual variations, significantly accelerating snow line recession is detected in the northern Pyrenean catchment Ariege. In the Alpine catchment Alpenrhein and Drac, RSLRCs are shifting towards lower accumulated air-temperature (AT) significantly, with the magnitude of −3.77 °C·a⁻¹ (Alpenrhein) and −3.99 °C·a⁻¹ (Drac). Full article

(This article belongs to the Special Issue Recent Developments in Remote Sensing for Physical Geography)

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