Tracking Forest and Open Area Effects on Snow Accumulation by Unmanned Aerial Vehicle Photogrammetry

Lendzioch, Theodora; Langhammer, Jakub; Jeníček, Michal

doi:10.5194/isprsarchives-xli-b1-917-2016

Cited by 12 publications

(10 citation statements)

References 9 publications

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“…In addition, airborne laser scanning (ALS) and terrestrial laser scanning (TLS) technologies have been applied as the preferred methods to obtain HS data [3,[26][27][28][29][30][31][32][33]. Moreover, tachymetry [28], ground-penetrating radar (GPR) [34,35], and time-lapse photography [36,37] have been used.The use of unmanned aerial system (UAS) technology in snow and avalanche studies has been recently reported in the literature [10,19,33,35,[38][39][40][41][42][43][44]. While the first studies on the use of a UAS in HS mapping investigated its potential and limitations by using manual HS probing for accuracy assessment, more recent studies have used time series of a UAS and compared it with other techniques, such as airborne sensors, including the ADS100 [45], TLS [33,44], and tri-stereoscopic Pléiades satellite images [46].…”

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confidence: 99%

“…The use of unmanned aerial system (UAS) technology in snow and avalanche studies has been recently reported in the literature [10,19,33,35,[38][39][40][41][42][43][44]. While the first studies on the use of a UAS in HS mapping investigated its potential and limitations by using manual HS probing for accuracy assessment, more recent studies have used time series of a UAS and compared it with other techniques, such as airborne sensors, including the ADS100 [45], TLS [33,44], and tri-stereoscopic Pléiades satellite images [46].…”

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confidence: 99%

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Monitoring of Snow Cover Ablation Using Very High Spatial Resolution Remote Sensing Datasets

Eker

Bühler²,

Schlögl³

et al. 2019

Remote Sensing

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This study tested the potential of a short time series of very high spatial resolution (cm to dm) remote sensing datasets obtained from unmanned aerial system (UAS)-based photogrammetry and terrestrial laser scanning (TLS) to monitor snow cover ablation in the upper Dischma valley (Davos, Switzerland). Five flight missions (for UAS) and five scans (for TLS) were carried out simultaneously: Four during the snow-covered period (9, 10, 11, and 27 May 2016) and one during the snow-free period (24 June 2016 for UAS and 31 May 2016 for TLS). The changes in both the areal extent of the snow cover and the snow depth (HS) were assessed together in the same case study. The areal extent of the snow cover was estimated from both UAS- and TLS-based orthophotos by classifying pixels as snow-covered and snow-free based on a threshold value applied to the blue band information of the orthophotos. Also, the usage possibility of TLS-based orthophotos for mapping snow cover was investigated in this study. The UAS-based orthophotos provided higher overall classification accuracy (97%) than the TLS-based orthophotos (86%) and allowed for mapping snow cover in larger areas than the ones from TLS scans by preventing the occurrence of gaps in the orthophotos. The UAS-based HS were evaluated and compared to TLS-based HS. Initially, the CANUPO (CAractérisation de NUages de POints) binary classification method, a proposed approach for improving the quality of models to obtain more accurate HS values, was applied to the TLS 3D raw point clouds. In this study, the use of additional artificial ground control points (GCPs) was also proposed to improve the quality of UAS-based digital elevation models (DEMs). The UAS-based HS values were mapped with an error of around 0.1 m during the time series. Most pixels representing change in the HS derived from the UAS data were consistent with the TLS data. The time series used in this study allowed for testing of the significance of the data acquisition interval in the monitoring of snow ablation. Accordingly, this study concluded that both the UAS- and TLS-based high-resolution DSMs were biased in detecting change in HS, particularly for short time spans, such as a few days, where only a few centimeters in HS change occur. On the other hand, UAS proved to be a valuable tool for monitoring snow ablation if longer time intervals are chosen.

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confidence: 99%

mentioning

confidence: 99%

Monitoring of Snow Cover Ablation Using Very High Spatial Resolution Remote Sensing Datasets

Eker

Bühler²,

Schlögl³

et al. 2019

Remote Sensing

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“…Three DoDs were created ( Figure 5 d and Figure 6 e,f ) based on standard photogrammetric procedure Usually, by subtracting the snow-free DSMs from the snow-covered DSMs, the snow depth values appeared negative in the maps, which were classified as outliers but not masked out, and depicted as zero values in the maps, for readability. This discrepancy has already been noted during photogrammetric surveys [ 27 , 28 , 40 ] and can be attributed to the effect of compressible vegetation and instrumental precision [ 28 , 66 ].…”

Section: Resultsmentioning

confidence: 92%

Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry

Lendzioch

Langhammer

Jeníček

2019

Sensors

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This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth estimation, based on a multi-temporal set of high-resolution digital surface models (DSMs) of snow-free and of snow-covered conditions, taken in a partially healthy to insect-induced Norway spruce forest and meadow coverage area within the Šumava National Park (Šumava NP) in the Czech Republic, was assessed over a winter season. The UAV-derived DSMs featured a resolution of 0.73–1.98 cm/pix. By subtracting the DSMs, the snow depth was determined and compared with manual snow probes taken at ground control point (GCP) positions, the root mean square error (RMSE) ranged between 0.08 m and 0.15 m. A comparative analysis of UAV-based snow depth with a denser network of arranged manual snow depth measurements yielded an RMSE between 0.16 m and 0.32 m. LAI assessment, crucial for correct interpretation of the snow depth distribution in forested areas, was based on downward-looking UAV images taken in the forest regime. To identify the canopy characteristics from downward-looking UAV images, the snow background was used instead of the sky fraction. Two conventional methods for the effective winter LAI retrieval, the LAI-2200 plant canopy analyzer, and digital hemispherical photography (DHP) were used as a reference. Apparent was the effect of canopy density and ground properties on the accuracy of DSMs assessment based on UAV imaging when compared to the field survey. The results of UAV-based LAI values provided estimates were comparable to values derived from the LAI-2200 plant canopy analyzer and DHP. Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring LAI was underestimated by using UAV photogrammetry method. Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics.

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“…Besides manual sampling design as used in this study a lot of promising results have been also reported using remote sensing approaches such as the use of unmanned aerial systems (UAV) (Lendzioch et al 2016;Nolan et al 2015), aerial or terrestrial laser scanning (Bühler et al 2015;Grünewald et al 2013;Revuelto et al 2016) and MODIS satellite data (Duchacek 2014;He et al 2014;Krajčí et al 2016). A camera placed on UAV platform was tested to monitor the snow depth characteristics in our study area, more specifically in the plot D with mixed vegetation (Lendzioch et al 2016).…”

Section: Snow Sampling Designmentioning

confidence: 98%

Chernozem. From concept to classification: a review

Vysloužilová¹,

Ertlen²,

Schwartz³

et al. 2016

Auc Geographica

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The knowledge of water volume stored in the snowpack and its spatial distribution is important to predict the snowmelt runoff. The objective of this study was to quantify the role of different forest types on the snowpack distribution at a plot scale during snow accumulation and snow ablation periods. Special interest was put in the role of the forest affected by the bark beetle (Ips typographus). We performed repeated detailed manual field survey at selected mountain plots with different canopy structure located at the same elevation and without influence of topography and wind on the snow distribution. A snow accumulation and ablation model was set up to simulate the snow water equivalent (SWE) in plots with different vegetation cover. The model was based on degree-day approach and accounts for snow interception in different forest types.The measured SWE in the plot with healthy forest was on average by 41% lower than in open area during snow accumulation period. The disturbed forest caused the SWE reduction by 22% compared to open area indicating increasing snow storage after forest defoliation. The snow ablation in healthy forest was by 32% slower compared to open area. On the contrary, the snow ablation in disturbed forest (due to the bark beetle) was on average only by 7% slower than in open area. The relative decrease in incoming solar radiation in the forest compared to open area was much bigger compared to the relative decrease in snowmelt rates. This indicated that the decrease in snowmelt rates cannot be explained only by the decrease in incoming solar radiation. The model simulated best in open area and slightly worse in healthy forest. The model showed faster snowmelt after forest defoliation which also resulted in earlier snow melt-out in the disturbed forest.

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Tracking Forest and Open Area Effects on Snow Accumulation by Unmanned Aerial Vehicle Photogrammetry

Cited by 12 publications

References 9 publications

Monitoring of Snow Cover Ablation Using Very High Spatial Resolution Remote Sensing Datasets

Monitoring of Snow Cover Ablation Using Very High Spatial Resolution Remote Sensing Datasets

Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry

Chernozem. From concept to classification: a review

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