Frozen data? Polar research and fieldwork in a pandemic era

Polar Record ◽  
2021 ◽  
Vol 57 ◽  
Author(s):  
Sophie Duveau
Keyword(s):  
Remote Sensing ◽  
Data Acquisition ◽  
Decisive Role ◽  
Airborne Remote Sensing ◽  
Human Presence ◽  
Multiple Dimensions ◽  
Data Gaps ◽  
Field Campaigns

Abstract Drawing on an ethnographic survey in Svalbard before and during the coronavirus outbreak, this commentary reflects on the multiple dimensions of fieldwork highlighted by the pandemic. Firstly, the cancellation of many field campaigns has revealed the decisive role of personnel inhabiting scientific bases in the maintenance of scientific activities in Svalbard. Automatic and remote-controlled instruments are autonomous only in appearance as the crucial phases of data acquisition often call for human presence. Secondly, airborne remote sensing can be perceived as a response to fill data gaps. Although embedded in a long history, the use of remote sensed data has taken on a new meaning in the context of the pandemic. Finally, the fact that several researchers endeavour to go to the field whatever the travel conditions underlines a certain need of being in Svalbard as well as limitations of science performed remotely.

scholarly journals Integrating airborne remote sensing and field campaigns for ecology and Earth system science

2020 ◽  
Vol 11 (11) ◽  
pp. 1492-1508
Author(s):  
K. Dana Chadwick ◽  
Philip G. Brodrick ◽  
Kathleen Grant ◽  
Tristan Goulden ◽  
Amanda Henderson ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Earth System ◽  
Airborne Remote Sensing ◽  
Earth System Science ◽  
System Science ◽  
Field Campaigns

scholarly journals Applications of Unmanned Aerial Vehicles in Cryosphere: Latest Advances and Prospects

2020 ◽  
Vol 12 (6) ◽  
pp. 948 ◽  
Author(s):  
Clare Gaffey ◽  
Anshuman Bhardwaj
Keyword(s):  
Remote Sensing ◽  
Data Acquisition ◽  
Unmanned Aerial Vehicles ◽  
Regional Climate ◽  
Airborne Remote Sensing ◽  
Aerial Vehicles ◽  
Holistic Review ◽  
Sensing Platform ◽  
Sensing Platforms ◽  
Data Acquisition And Processing

Owing to usual logistic hardships related to field-based cryospheric research, remote sensing has played a significant role in understanding the frozen components of the Earth system. Conventional spaceborne or airborne remote sensing platforms have their own merits and limitations. Unmanned aerial vehicles (UAVs) have emerged as a viable and inexpensive option for studying the cryospheric components at unprecedented spatiotemporal resolutions. UAVs are adaptable to various cryospheric research needs in terms of providing flexibility with data acquisition windows, revisits, data/sensor types (multispectral, hyperspectral, microwave, thermal/night imaging, Light Detection and Ranging (LiDAR), and photogrammetric stereos), viewing angles, flying altitudes, and overlap dimensions. Thus, UAVs have the potential to act as a bridging remote sensing platform between spatially discrete in situ observations and spatially continuous but coarser and costlier spaceborne or conventional airborne remote sensing. In recent years, a number of studies using UAVs for cryospheric research have been published. However, a holistic review discussing the methodological advancements, hardware and software improvements, results, and future prospects of such cryospheric studies is completely missing. In the present scenario of rapidly changing global and regional climate, studying cryospheric changes using UAVs is bound to gain further momentum and future studies will benefit from a balanced review on this topic. Our review covers the most recent applications of UAVs within glaciology, snow, permafrost, and polar research to support the continued development of high-resolution investigations of cryosphere. We also analyze the UAV and sensor hardware, and data acquisition and processing software in terms of popularity for cryospheric applications and revisit the existing UAV flying regulations in cold regions of the world. The recent usage of UAVs outlined in 103 case studies provide expertise that future investigators should base decisions on.

SIOS’s airborne remote sensing campaigns in Svalbard 

2021 ◽  
Author(s):  
Shridhar Jawak ◽  
Agnar Sivertsen ◽  
Veijo Pohjola ◽  
Małgorzata Błaszczyk ◽  
Jack Kohler ◽  
...  
Keyword(s):  
Remote Sensing ◽  
3D Models ◽  
Hyperspectral Data ◽  
Aerial Images ◽  
Airborne Remote Sensing ◽  
Aerial Photos ◽  
Travel Restrictions ◽  
Field Campaigns ◽  
Local Company ◽  
Hyperspectral Sensor

<p>Svalbard Integrated Arctic Earth Observing System (SIOS) is an international collaboration of 24 research institutions from 9 countries studying the environment and climate in and around Svalbard. The global pandemic of Coronavirus disease (Covid-19) has affected the Svalbard research in a number of ways due to nationwide lockdown in many countries, strict travel restrictions in Svalbard, and quarantine regulations. Many field campaigns to Svalbard were cancelled in 2020 and campaigns in 2021 are still uncertain. In response to this challenge, we conducted practical activities to support the Svalbard science community in filling gaps in scientific observations. One of our activities involved conducting airborne remote sensing campaigns in Svalbard to support scientific projects. In 2020, SIOS supported 10 scientific projects by conducting 25 hours of aircraft and unmanned aerial vehicle (UAV)-based data collection in Svalbard. This is one of the finest ways to fill the data gap in the current situation as it is practically possible to conduct field campaigns using airborne platforms in spite of travel restrictions. We are using the aerial camera and hyperspectral sensor installed onboard the Dornier DO228 aircraft operated by the local company Lufttransport to acquire aerial images and hyperspectral data from various locations in Svalbard. The hyperspectral sensor image the ground in 186 spectral bands covering the range 400-1000 nm. Hyperspectral data can be used to map and characterise earth, ice and ocean surface features, such as minerals, vegetation, glaciers and snow cover, colour and pollutants. Further, it can be used to make 3D models of the terrain as well as searching for the presence of animals (e.g. counting seals). In addition, aerial photos are particularly useful tool to follow the seasonal dynamic changes and extent in sea ice cover, tracking icebergs, ocean productivity (Chlorophyll a) and river runoff (turbidity). Data collected from the SIOS funded airborne missions will not only help to fill a few of the data gaps resulting from the lockdown but also will be used by glaciologists, biologists, hydrologists, and other Earth system scientists to understand the state of the environment of Svalbard during these times. In 2021, we are continuing this activity by conducting more airborne campaigns in Svalbard. In this presentation, we will specifically focus on the overview of projects supported by airborne remote sensing campaigns.</p>

scholarly journals A Framework for Actual Evapotranspiration Assessment and Projection Based on Meteorological, Vegetation and Hydrological Remote Sensing Products

2021 ◽  
Vol 13 (18) ◽  
pp. 3643
Author(s):  
Yuan Liu ◽  
Qimeng Yue ◽  
Qianyang Wang ◽  
Jingshan Yu ◽  
Yuexin Zheng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Dynamic Assessment ◽  
Remote Sensing Data ◽  
Decisive Role ◽  
Actual Evapotranspiration ◽  
Support Vector ◽  
Optimal Output ◽  
Pixel Scale ◽  
Boruta Algorithm

As the most direct indicator of drought, the dynamic assessment and prediction of actual evapotranspiration (AET) is crucial to regional water resources management. This research aims to develop a framework for the regional AET evaluation and prediction based on multiple machine learning methods and multi-source remote sensing data, which combines Boruta algorithm, Random Forest (RF), and Support Vector Regression (SVR) models, employing datasets from CRU, GLDAS, MODIS, GRACE (-FO), and CMIP6, covering meteorological, vegetation, and hydrological variables. To verify the framework, it is applied to grids of South America (SA) as a case. The results meticulously demonstrate the tendency of AET and identify the decisive role of T, P, and NDVI on AET in SA. Regarding the projection, RF has better performance in different input strategies in SA. According to the accuracy of RF and SVR on the pixel scale, the AET prediction dataset is generated by integrating the optimal results of the two models. By using multiple parameter inputs and two models to jointly obtain the optimal output, the results become more reasonable and accurate. The framework can systematically and comprehensively evaluate and forecast AET; although prediction products generated in SA cannot calibrate relevant parameters, it provides a quite valuable reference for regional drought warning and water allocating.

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