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 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 Strong degradation of palsas and peat plateaus in northern Norway during the last 60 years

2017 ◽  
Vol 11 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Amund F. Borge ◽  
Sebastian Westermann ◽  
Ingvild Solheim ◽  
Bernd Etzelmüller
Keyword(s):  
Snow Depth ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Moderate Increase ◽  
Permafrost Degradation ◽  
Northern Norway ◽  
Important Pathway ◽  
The 1950S ◽  
Lateral Erosion ◽  
Peat Plateaus

Abstract. Palsas and peat plateaus are permafrost landforms occurring in subarctic mires which constitute sensitive ecosystems with strong significance for vegetation, wildlife, hydrology and carbon cycle. Firstly, we have systematically mapped the occurrence of palsas and peat plateaus in the northernmost county of Norway (Finnmark, ∼ 50 000 km2) by manual interpretation of aerial images from 2005 to 2014 at a spatial resolution of 250 m. At this resolution, mires and wetlands with palsas or peat plateaus occur in about 850 km2 of Finnmark, with the actual palsas and peat plateaus underlain by permafrost covering a surface area of approximately 110 km2. Secondly, we have quantified the lateral changes of the extent of palsas and peat plateaus for four study areas located along a NW–SE transect through Finnmark by utilizing repeat aerial imagery from the 1950s to the 2010s. The results of the lateral changes reveal a total decrease of 33–71 % in the areal extent of palsas and peat plateaus during the study period, with the largest lateral change rates observed in the last decade. However, the results indicate that degradation of palsas and peat plateaus in northern Norway has been a consistent process during the second half of the 20th century and possibly even earlier. Significant rates of areal change are observed in all investigated time periods since the 1950s, and thermokarst landforms observed on aerial images from the 1950s suggest that lateral degradation was already an ongoing process at this time. The results of this study show that lateral erosion of palsas and peat plateaus is an important pathway for permafrost degradation in the sporadic permafrost zone in northern Scandinavia. While the environmental factors governing the rate of erosion are not yet fully understood, we note a moderate increase in air temperature, precipitation and snow depth during the last few decades in the region.

scholarly journals Operational and experimental snow observation systems in the upper Rofental: data from 2017–2020

2021 ◽  
Author(s):  
Michael Warscher ◽  
Thomas Marke ◽  
Ulrich Strasser
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Liquid Water Content ◽  
Data Sets ◽  
Data Set ◽  
Creative Commons ◽  
Snow Drift ◽  
Original Dataset

Abstract. According to the living data process in ESSD, this publication presents extensions of a comprehensive hydrometeorological and glaciological data set for several research sites in the Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria). Whereas the original dataset has been published in a first original version in 2018 (https://doi.org/10.5194/essd-10-151-2018), the new time series presented here originate from meteorological and snow-hydrological recordings that have been collected from 2017 to 2020. Some data sets represent continuations of time series at existing locations, others come from new installations complementing the scientific monitoring infrastructure in the research catchment. Main extensions are a fully equipped automatic weather and snow monitoring station, as well as extensive additional installations to enable continuous observation of snow cover properties. Installed at three high Alpine locations in the catchment, these include automatic measurements of snow depth, snow water equivalent, volumetric solid and liquid water content, snow density, layered snow temperature profiles, and snow surface temperature. One station is extended by a particular arrangement of two snow depth and water equivalent recording devices to observe and quantify wind-driven snow redistribution. They are installed at nearby wind-exposed and sheltered locations and are complemented by an acoustic-based snow drift sensor. The data sets represent a unique time series of high-altitude mountain snow and meteorology observations. We present three years of data for temperature, precipitation, humidity, wind speed, and radiation fluxes from three meteorological stations. The continuous snow measurements are explored by combined analyses of meteorological and snow data to show typical seasonal snow cover characteristics. The potential of the snow drift observations are demonstrated with examples of measured wind speeds, snow drift rates and redistributed snow amounts in December 2019 when a tragic avalanche accident occurred in the vicinity of the station. All new data sets are provided to the scientific community according to the Creative Commons Attribution License by means of the PANGAEA repository (https://www.pangaea.de/?q=%40ref104365).

scholarly journals Interpretation of the Chemical and Physical Time-Series Retrieved from Sentik Glacier, Ladakh Himalaya, India

1984 ◽  
Vol 30 (104) ◽  
pp. 66-76 ◽  
Author(s):  
Paul A. Mayewski ◽  
W. Berry Lyons ◽  
N. Ahmad ◽  
Gordon Smith ◽  
M. Pourchet
Keyword(s):  
Time Series ◽  
Chemical Species ◽  
Data Sets ◽  
Reactive Iron ◽  
Physical Time ◽  
Ladakh Himalaya ◽  
Data Density ◽  
Mass Circulation ◽  
The Himalaya ◽  
Analysis Of Time Series

AbstractSpectral analysis of time series of a c. 17 ± 0.3 year core, calibrated for total ß activity recovered from Sentik Glacier (4908m) Ladakh, Himalaya, yields several recognizable periodicities including subannual, annual, and multi-annual. The time-series, include both chemical data (chloride, sodium, reactive iron, reactive silicate, reactive phosphate, ammonium, δD, δ(18O) and pH) and physical data (density, debris and ice-band locations, and microparticles in size grades 0.50 to 12.70 μm). Source areas for chemical species investigated and general air-mass circulation defined from chemical and physical time-series are discussed to demonstrate the potential of such studies in the development of paleometeorological data sets from remote high-alpine glacierized sites such as the Himalaya.

scholarly journals An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

2021 ◽  
Author(s):  
Cong Gao ◽  
Ping Yang ◽  
Yanping Chen ◽  
Zhongmin Wang ◽  
Yue Wang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Estimation Method ◽  
Feature Representation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Sets ◽  
Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

scholarly journals IMG2nDSM: Height Estimation from Single Airborne RGB Images with Deep Learning

2021 ◽  
Vol 13 (12) ◽  
pp. 2417
Author(s):  
Savvas Karatsiolis ◽  
Andreas Kamilaris ◽  
Ian Cole
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Surface Model ◽  
Large Area ◽  
Digital Terrain ◽  
Terrain Models ◽  
Architectural Features ◽  
Rgb Images

Estimating the height of buildings and vegetation in single aerial images is a challenging problem. A task-focused Deep Learning (DL) model that combines architectural features from successful DL models (U-NET and Residual Networks) and learns the mapping from a single aerial imagery to a normalized Digital Surface Model (nDSM) was proposed. The model was trained on aerial images whose corresponding DSM and Digital Terrain Models (DTM) were available and was then used to infer the nDSM of images with no elevation information. The model was evaluated with a dataset covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest LiDAR dataset. The results suggest that the proposed DL architecture is suitable for the task and surpasses other state-of-the-art DL approaches by a large margin.

Local Feature Performance Evaluation for Structure-from-Motion and Multi-View Stereo Using Simulated City-Scale Aerial Imagery

2020 ◽  
pp. 1-1
Author(s):  
Ke Gao ◽  
Hadi Ali Akbarpour ◽  
Joshua Fraser ◽  
Koundinya Nouduri ◽  
Filiz Bunyak ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Structure From Motion ◽  
Local Feature ◽  
Aerial Imagery

scholarly journals A Hypothesis Test for the Goodness-of-Fit of the Marginal Distribution of a Time Series with Application to Stablecoin Data

2021 ◽  
Vol 5 (1) ◽  
pp. 10
Author(s):  
Mark Levene
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Marginal Distribution ◽  
Hypothesis Test ◽  
Data Sets ◽  
Test Statistic ◽  
Sample Test ◽  
Kolmogorov Smirnov ◽  
Heavy Tailed ◽  
Jensen Shannon Divergence

A bootstrap-based hypothesis test of the goodness-of-fit for the marginal distribution of a time series is presented. Two metrics, the empirical survival Jensen–Shannon divergence (ESJS) and the Kolmogorov–Smirnov two-sample test statistic (KS2), are compared on four data sets—three stablecoin time series and a Bitcoin time series. We demonstrate that, after applying first-order differencing, all the data sets fit heavy-tailed α-stable distributions with 1<α<2 at the 95% confidence level. Moreover, ESJS is more powerful than KS2 on these data sets, since the widths of the derived confidence intervals for KS2 are, proportionately, much larger than those of ESJS.

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