Monitoring Snow Depth by GNSS Reflectometry in Built-up Areas: A Case Study for Wettzell, Germany

Author(s):  
Sibylle Vey ◽  
Andreas Guntner ◽  
Jens Wickert ◽  
Theresa Blume ◽  
Heiko Thoss ◽  
...  
2020 ◽  
Author(s):  
Jennifer M. Jacobs ◽  
Adam G. Hunsaker ◽  
Franklin B. Sullivan ◽  
Michael Palace ◽  
Elizabeth A. Burakowski ◽  
...  

Abstract. Shallow snowpack conditions, which occur throughout the year in many regions as well as during accumulation and ablation periods in all regions, are important in water resources, agriculture, ecosystems, and winter recreation. Terrestrial and airborne (manned and unmanned) laser scanning and structure from motion (SfM) techniques have emerged as viable methods to map snow depths. Lidar on an unmanned aerial vehicle is also a potential method to observe field and slope scale variations of shallow snowpacks. This paper describes an unmanned aerial lidar system, which uses commercially available components, for snow depth mapping on the landscape scale. The system was assessed in a mixed deciduous and coniferous forest and open field for a shallow snowpack (


Author(s):  
Julien Meloche ◽  
Alexandre Langlois ◽  
Nick Rutter ◽  
Don McLennan ◽  
Alain Royer ◽  
...  

Increased surface temperatures (0.7℃ per decade) in the Arctic affects polar ecosystems by reducing the extent and duration of annual snow cover. Monitoring of these important ecosystems needs detailed information on snow cover properties (depth and density) at resolutions (< 100 m) that influence ecological habitats and permafrost thaw. As arctic snow is strongly influenced by vegetation, an ecotype map at 10 m resolution was added to a method with the Random Forest (RF) algorithm previously developed for alpine environments and applied here over an arctic landscape for the first time. The topographic parameters used in the RF algorithm were Topographic Position Index (TPI) and up-wind slope index (Sx), which were estimated from the freely available Arctic DEM at 2 m resolution. Ecotypes with taller vegetation with moister soils were found to have deeper snow because of the trapping effect. Using feature importance with RF, snow depth distributions were predicted from topographic and ecosystem parameters with a root mean square error = 8 cm (23%) (R² = 0.79) at 10 m resolution for an arctic watershed (1 500 km²) in western Nunavut, Canada.


2016 ◽  
Vol 8 (1) ◽  
pp. 63 ◽  
Author(s):  
Shuanggen Jin ◽  
Xiaodong Qian ◽  
Hakan Kutoglu
Keyword(s):  

2003 ◽  
Vol 34 (5) ◽  
pp. 427-448 ◽  
Author(s):  
Wolf-Dietrich Marchand ◽  
Ånund Killingtveit ◽  
Peter Wilén ◽  
Per Wikström

Snow depth measurements with the help of georadar (Ground Penetrating Radar, GPR) were investigated for almost two decades in Scandinavia. For the first tests in the early 1980s, results were of poor quality. Later, data quality improved when different systems were developed. In Norway, emphasis was put on ground-based snow measurements of scientific character; few attempts for operational use were undertaken. In Sweden, airborne, operational snow measurements with georadar were performed since 1986. A helicopter, flying near to the ground, was used as platform. The objective of the presented study was to compare results from ground-based and airborne snow measurements. The radar control units used were comparable, but the antenna configuration and frequencies differed. Also radar data interpretation and the conversion of radar signal travel time into snow depth values varied. Measurements were made at common snow courses. The comparison showed in general high correlation between radar results from both methods. Differing results were found for shallow snow and bare areas. Here, the ground-based method indicated zero or close-to-zero snow depth, whereas the airborne method rarely detected zero snow depth. This phenomenon seemed to be connected to the bigger footprint size of the airborne radar and to the different radar data interpretation methods. On average, the airborne measurements indicated shallower snow depths than ground-based measurements, 4% less in open terrain and 7% less in forest. Comparing snow depth as grid cell values, the best agreement, less than 1% difference, was obtained for the 10 m resolution.


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