Estimating the extent of near-surface permafrost using remote sensing, Mackenzie Delta, Northwest Territories

2009 ◽  
Vol 20 (2) ◽  
pp. 141-153 ◽  
Author(s):  
T-N. Nguyen ◽  
C. R. Burn ◽  
D. J. King ◽  
S. L. Smith
Keyword(s):  
Remote Sensing ◽  
Northwest Territories ◽  
Mackenzie Delta ◽  
Near Surface

Geochemistry of the active layer and near-surface permafrost, Mackenzie delta region, Northwest Territories, Canada

2005 ◽  
Vol 42 (1) ◽  
pp. 37-48 ◽  
Author(s):  
S V Kokelj ◽  
C R Burn
Keyword(s):  
Northwest Territories ◽  
Active Layer ◽  
Permafrost Soil ◽  
Mackenzie Delta ◽  
Delta Region ◽  
Ion Content ◽  
Near Surface ◽  
Fine Grained ◽  
Permafrost Table ◽  
Soluble Ion

The soluble ion content of the active layer and near-surface permafrost was determined at 41 sites in the Mackenzie delta region, Northwest Territories, Canada. In delta soils, Ca2+ and Mg2+ are the dominant soluble cations, but the quantity and relative abundance of Na+ increase with proximity to the Beaufort Sea coast. Soils beneath frequently flooded surfaces are ion rich in comparison with ground above the level of decadal flooding. Within a terrain type, near-surface permafrost soil solute concentrations are similar between paired cores spaced <1 m apart, but at greater distances (cores spaced 3–13 m apart), solute concentrations are significantly different. Comparatively low soil solute concentrations in old upland surfaces near Inuvik may be a result of progressive removal of soluble materials from the active layer and permafrost during periods of deeper thaw. In sandy silt alluvium, solutes excluded during downward freezing may accumulate at the base of the active layer and be sequestered by a rising permafrost table. At sites with finer grained clayey silts, the correspondence between zones of ice and cation enrichment indicates coupled movement of water and solutes during freeze-back of the active layer and development of aggradational ice. Solute enrichment of near-surface permafrost is greatest at fine-grained ice-rich alluvial sites, where mean concentrations in permafrost are up to 7.5 times greater than those in the active layer.

scholarly journals Aerosol Characterization during the Summer 2017 Huge Fire Event on Mount Vesuvius (Italy) by Remote Sensing and In Situ Observations

2021 ◽  
Vol 13 (10) ◽  
pp. 2001
Author(s):  
Antonella Boselli ◽  
Alessia Sannino ◽  
Mariagrazia D’Emilio ◽  
Xuan Wang ◽  
Salvatore Amoruso
Keyword(s):  
Remote Sensing ◽  
Biomass Burning ◽  
Ground Level ◽  
Large Area ◽  
Fire Event ◽  
Near Surface ◽  
Mean Values ◽  
Microphysical Properties ◽  
Observational Site ◽  
Biomass Burning Aerosol

During the summer of 2017, multiple huge fires occurred on Mount Vesuvius (Italy), dispersing a large quantity of ash in the surrounding area ensuing the burning of tens of hectares of Mediterranean scrub. The fires affected a very large area of the Vesuvius National Park and the smoke was driven by winds towards the city of Naples, causing daily peak values of particulate matter (PM) concentrations at ground level higher than the limit of the EU air quality directive. The smoke plume spreading over the area of Naples in this period was characterized by active (lidar) and passive (sun photometer) remote sensing as well as near-surface (optical particle counter) observational techniques. The measurements allowed us to follow both the PM variation at ground level and the vertical profile of fresh biomass burning aerosol as well as to analyze the optical and microphysical properties. The results evidenced the presence of a layer of fine mode aerosol with large mean values of optical depth (AOD > 0.25) and Ångstrom exponent (γ > 1.5) above the observational site. Moreover, the lidar ratio and aerosol linear depolarization obtained from the lidar observations were about 40 sr and 4%, respectively, consistent with the presence of biomass burning aerosol in the atmosphere.

Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska

2021 ◽  
Author(s):  
Thomas Douglas ◽  
Caiyun Zhang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Snow Depth ◽  
Spatial Scales ◽  
Critical Role ◽  
Winter Season ◽  
Thermal Conditions ◽  
Near Surface ◽  
Lidar Measurements ◽  
Remote Sensing Methods

The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.

Regional characterisation of soil properties by combining soil science and hyperspectral and thermal remote sensing: A technical overview of lab and field remote sensing methods

2021 ◽  
Author(s):  
Richard Mommertz ◽  
Lars Konen ◽  
Martin Schodlok
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Regional Scale ◽  
Arable Land ◽  
Soil Mapping ◽  
Soil Science ◽  
Near Surface ◽  
Soil Parameter ◽  
The Impact ◽  
Parameter Retrieval

&lt;p&gt;Soil is one of the world&amp;#8217;s most important natural resources for human livelihood as it provides food and clean water. Therefore, its preservation is of huge importance. For this purpose, a proficient regional database on soil properties is needed. The project &amp;#8220;ReCharBo&amp;#8221; (Regional Characterisation of Soil Properties) has the objective to combine remote sensing, geophysical and pedological methods to determine soil characteristics on a regional scale. Its aim is to characterise soils non-invasive, time and cost efficient and with a minimal number of soil samples to calibrate the measurements. Konen et al. (2021) give detailed information on the research concept and first field results in a presentation in the session &amp;#8220;SSS10.3 Digital Soil Mapping and Assessment&amp;#8221;. Hyperspectral remote sensing is a powerful and well known technique to characterise near surface soil properties. Depending on the sensor technology and the data quality, a wide variety of soil properties can be derived with remotely sensed data (Chabrillat et al. 2019, Stenberg et al. 2010). The project aims to investigate the effects of up and downscaling, namely which detail of information is preserved on a regional scale and how a change in scales affects the analysis algorithms and the possibility to retrieve valid soil parameter information. Thus, e.g. laboratory and field spectroscopy are applied to gain information of samples and fieldspots, respectively. Various UAV-based sensors, e.g. thermal &amp; hyperspectral sensors, are applied to study soil properties of arable land in different study areas at field scale. Finally, airborne (helicopter) hyperspectral data will cover the regional scale. Additionally forthcoming spaceborne hyperspectral satellite data (e.g. Prisma, EnMAP, Sentinel-CHIME) are a promising outlook to gain detailed regional soil information. In this context it will be discussed how the multisensor data acquisition is best managed to optimise soil parameter retrieval. Sensor specific properties regarding time and date of acquisition as well as weather/atmospheric conditions are outlined. The presentation addresses and discusses the impact of a multisensor and multiscale remote sensing data collection regarding the results on soil parameter retrieval.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;References&lt;/p&gt;&lt;p&gt;Chabrillat, S., Ben-Dor, E. Cierniewski, J., Gomez, C., Schmid, T. &amp; van Wesemael, B. (2019): Imaging Spectroscopy for Soil Mapping and Monitoring. Surveys in Geophysics 40:361&amp;#8211;399. https://doi.org/10.1007/s10712-019-09524-0&lt;/p&gt;&lt;p&gt;Stenberg, B., Viscarra Rossel, R. A., Mounem Mouazen, A. &amp; Wetterlind, J. (2010): Visible and Near Infrared Spectroscopy in Soil Science. In: Donald L. Sparks (editor): Advances in Agronomy. Vol. 107. Academic Press:163-215. http://dx.doi.org/10.1016/S0065-2113(10)07005-7&lt;/p&gt;

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