scholarly journals Mapping snow depth and volume at the alpine watershed scale from aerial imagery using Structure from Motion

2021 ◽  
Author(s):  
Joachim Meyer ◽  
McKenzie Skiles ◽  
Jeffrey Deems ◽  
Kat Boremann ◽  
David Shean
Keyword(s):  
Structure From Motion ◽  
Snow Depth ◽  
Large Scale ◽  
Aerial Images ◽  
Data Sets ◽  
Watershed Scale ◽  
Multiple Resolutions ◽  
Snow Water ◽  
Snow Distribution ◽  
Alpine Watershed

Abstract. Time series mapping of water held as snow in the mountains at global scales is an unsolved challenge to date. In a few locations, lidar-based airborne campaigns have been used to provide valuable data sets that capture snow distribution in near real-time over multiple seasons. Here, an alternative method is presented to map snow depth and quantify snow volume using aerial images and Structure from Motion (SfM) photogrammetry over an alpine watershed (300 km2). The results were compared at multiple resolutions to the lidar-derived snow depth measurements from the Airborne Snow Observatory (ASO), collected simultaneously. Where snow was mapped by both ASO and SfM, the depths compared well, with a mean difference between −0.02 m and 0.03 m, NMAD of 0.22 m, and close snow volume agreement (+/−5 %). ASO mapped a larger snow area relative to SfM, with SfM missing ~14 % of total snow volume as a result. Analyzing the differences shows that challenges for SfM photogrammetry remain in vegetated areas, over shallow snow (< 1 m), and slope angles over 50 degrees. Our results indicate that capturing large scale snow depth and volume with airborne images and photogrammetry could be an additional viable resource for understanding and monitoring snow water resources in certain environments.

scholarly journals Mapping snow depth and volume at the alpine watershed scale from aerial imagery using Structure from Motion

2021 ◽  
Author(s):  
Joachim Meyer ◽  
S. McKenzie Skiles ◽  
Jeffrey Deems ◽  
Kat Bormann ◽  
David Shean
Keyword(s):  
Time Series ◽  
Structure From Motion ◽  
Snow Depth ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Data Sets ◽  
Watershed Scale ◽  
Valuable Data ◽  
Snow Distribution ◽  
Alpine Watershed

Abstract. Time series mapping of water held as snow in the mountains at global scales is an unsolved challenge to date. In a few locations, lidar-based airborne campaigns have been used to provide valuable data sets that capture snow distribution in near real-time over multiple seasons. Here, an alternative method is presented to map snow depth and quantify snow volume using aerial images and Structure from Motion (SfM) photogrammetry over an alpine watershed (300 km2). The results were compared to the lidar-derived snow depth measurements from the Airborne Snow Observatory, collected simultaneously. Where snow was mapped by both ASO and SfM, the depths compared well, with a mean difference of 0.01 m, NMAD of 0.22 m, and snow volume agreement (difference 1.26 %). ASO though, mapped a larger snow area relative to SfM, with SfM missing ~14 % of total snow volume as a result. Analyzing the SfM reconstruction errors shows that challenges for photogrammetry remain in vegetated areas, over shallow snow (

scholarly journals Boundary-Aware Refined Network for Automatic Building Extraction in Very High-Resolution Urban Aerial Images

2021 ◽  
Vol 13 (4) ◽  
pp. 692
Author(s):  
Yuwei Jin ◽  
Wenbo Xu ◽  
Ce Zhang ◽  
Xin Luo ◽  
Haitao Jia
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Aerial Images ◽  
Data Sets ◽  
Building Extraction ◽  
Visual Interpretation ◽  
Urban Scenes ◽  
Multi Scale ◽  
Spatial Pyramid Pooling ◽  
Very High

Convolutional Neural Networks (CNNs), such as U-Net, have shown competitive performance in the automatic extraction of buildings from Very High-Resolution (VHR) aerial images. However, due to the unstable multi-scale context aggregation, the insufficient combination of multi-level features and the lack of consideration of the semantic boundary, most existing CNNs produce incomplete segmentation for large-scale buildings and result in predictions with huge uncertainty at building boundaries. This paper presents a novel network with a special boundary-aware loss embedded, called the Boundary-Aware Refined Network (BARNet), to address the gap above. The unique properties of the proposed BARNet are the gated-attention refined fusion unit, the denser atrous spatial pyramid pooling module, and the boundary-aware loss. The performance of the BARNet is tested on two popular data sets that include various urban scenes and diverse patterns of buildings. Experimental results demonstrate that the proposed method outperforms several state-of-the-art approaches in both visual interpretation and quantitative evaluations.

scholarly journals The Effect of Snow Distribution on Gamma-Ray Survey of Snow Cover

1985 ◽  
Vol 6 ◽  
pp. 211-214
Author(s):  
Morten Johnsrud
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Gamma Ray ◽  
Snow Water Equivalent ◽  
Ground Surface ◽  
Snow Water ◽  
Snow Distribution ◽  
One Year ◽  
Source Of Error ◽  
Small Elements

Topography and snowdrifts may cause large variations in the snow cover as well as in snow depth from one year to another. A simple model is developed to study the influence of different snow distributions and the importance of this as a source of error. The ground surface “seen” from the detector will appear as a disc that can be divided into a number of small elements where it is possible to place the wanted snow distribution. Calculations of the gamma radiation field with different snow distributions show how small changes in the snow cover and distribution will influence the measurements as a function of the average snow water equivalent.

Arctic snow distribution patterns at the watershed scale

2014 ◽  
Vol 46 (4) ◽  
pp. 507-520 ◽  
Author(s):  
Joel W. Homan ◽  
Douglas L. Kane
Keyword(s):  
Large Scale ◽  
Snow Water Equivalent ◽  
Regional Scale ◽  
Distribution Patterns ◽  
Local Scale ◽  
Small Scale ◽  
Watershed Scale ◽  
Quality Of Data ◽  
Weather Patterns ◽  
Snow Distribution

Watershed-scale hydrologic models require good estimates of spatially distributed snow water equivalent (SWE) at winter's end. Snow on the ground in arctic environments is susceptible to significant wind redistribution, which results in heterogeneous snowpacks. The scarcity and quality of data collected by snow gauges provides a poor indicator of actual snowpack distribution. Snow distribution patterns are similar from year to year because they are largely controlled by the interaction of topography, vegetation, and consistent weather patterns. Consequently, shallow and deep areas of snow tend to be spatially predetermined, resulting in depth (or SWE) differences that may vary as a whole, but not relative to each other. Our aim was to identify snowpack distribution patterns and establish their stability in time and space at a watershed scale. Snow patterns were established by: (1) using numerous field surveys from end-of-winter field campaigns; and (2) differentiating snowpacks that characterize small-scale anomalies (local scale) from snowpacks that represent a large-scale area (regional scale). We concluded that basic snow survey site descriptions could be used to separate survey locations into regional and local-scale representative sites. Removing local-scale influences provides a more accurate representation of the regional snowpack, which will aid in forecasting snowmelt runoff events.

scholarly journals The Effect of Snow Distribution on Gamma-Ray Survey of Snow Cover

1985 ◽  
Vol 6 ◽  
pp. 211-214
Author(s):  
Morten Johnsrud
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Gamma Ray ◽  
Snow Water Equivalent ◽  
Ground Surface ◽  
Snow Water ◽  
Snow Distribution ◽  
One Year ◽  
Source Of Error ◽  
Small Elements

Topography and snowdrifts may cause large variations in the snow cover as well as in snow depth from one year to another. A simple model is developed to study the influence of different snow distributions and the importance of this as a source of error. The ground surface “seen” from the detector will appear as a disc that can be divided into a number of small elements where it is possible to place the wanted snow distribution. Calculations of the gamma radiation field with different snow distributions show how small changes in the snow cover and distribution will influence the measurements as a function of the average snow water equivalent.

scholarly journals Snow depth variability in the Northern Hemisphere mountains observed from space

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Hans Lievens ◽  
Matthias Demuzere ◽  
Hans-Peter Marshall ◽  
Rolf H. Reichle ◽  
Ludovic Brucker ◽  
...  
Keyword(s):  
Water Resources ◽  
Sierra Nevada ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Large Scale ◽  
Reanalysis Data ◽  
European Alps ◽  
Mountain Ranges ◽  
Detection Approach ◽  
Snow Water

Abstract Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

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