Snow cover monitoring with images from digital camera systems

Salvatori, Rosamaria; Plini, Paolo; Giusto, Marco; Valt, Mauro; Salzano, Roberto; Montagnoli, Mauro; Cagnati, Anselmo; Crepaz, Giuseppe; Sigismondi, Daniele

doi:10.5721/itjrs201143211

Cited by 46 publications

(85 citation statements)

References 6 publications

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“…Hence, the optimization of the camera target point offset t and an enlarged parameter range for the camera target point (T t ) is necessary. and an algorithm developed by Salvatori et al (2011). This algorithm is a threshold based procedure, which automatically analyses the blue band DN frequency histogram and sets the snow threshold.…”

Section: Snow Classification In Partially Shadow Affected Photographsmentioning

confidence: 99%

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PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.1)

2016

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Abstract. Terrestrial photography combined with the recently presented Photo Rectification And ClassificaTIon SoftwarE (PRACTISE V.1.0) has proven to be a valuable source to derive snow cover maps in a high temporal and spatial resolution. The areal coverage of the used digital photographs is however strongly limited. Satellite images on the other hand can cover larger areas but do show uncertainties with respect to the accurate detection of the snow covered area. This is especially the fact if user defined thresholds are needed, e.g. in case of the frequently used normalizeddifference snow index (NDSI). The definition of this value is often not adequately defined by either a general value from literature or over the impression of the user, but not by reproducible independent information. PRACTISE V.2.1 addresses this important aspect and shows additional improvements. The Matlab-based software is now able to automatically process and detect snow cover in satellite images. A simultaneously captured camera-derived snow cover map is in this case utilized as in situ information for calibrating the NDSI threshold value. Moreover, an additional automatic snow cover classification, specifically developed to classify shadow-affected photographs, was included. The improved software was tested for photographs and Landsat 7 Enhanced Thematic Mapper (ETM+) as well as Landsat 8 Operational Land Imager (OLI) scenes in the Zugspitze massif (Germany). The results show that using terrestrial photography in combination with satellite imagery can lead to an objective, reproducible, and user-independent derivation of the NDSI threshold and the resulting snow cover map. The presented method is not limited to the sensor system or the threshold used in here but offers manifold application options for other scientific branches.

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Section: Snow Classification In Partially Shadow Affected Photographsmentioning

confidence: 99%

“…In a first step the algorithm of Salvatori et al (2011) described in Härer et al (2013) is used for classifying snow at sunny locations. Snow cover detected in this step is illustrated in red in Fig.…”

Section: Snow Classification In Partially Shadow Affected Photographsmentioning

confidence: 99%

PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.1)

2016

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“…Most approaches (e.g. [24,22]) rely on cameras designed and positioned ad hoc by researchers, and are not applicable to usergenerated images created in uncontrolled conditions. Porzi et al [18] propose an app for mountain peak detection; the contribution focuses on the time efficient peak identification and does not address mobile AR requirements, such as realtime response, asynchronous dynamics of the algorithms and uncertain internet connection.…”

Section: Related Workmentioning

confidence: 99%

“…Image analysis in mountain regions is a well investigated area, with applications that support environmental studies on climate change and tourism [7]. Mountain image analysis research focuses on peak identification in public photographs [2,1] and the problem of segmenting the portion of the photograph corresponding to a certain mountain in snow covered areas [24,22]. A prominent application field of mountain image analysis is snow information extraction.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Outdoor Mobile Augmented Reality and Its Application to Mountain Peak Detection

Fedorov

Frajberg

Fraternali

2016

Lecture Notes in Computer Science

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Abstract. Outdoor augmented reality applications project information of interest onto views of the world in real-time. Their core challenge is recognizing the meaningful objects present in the current view and retrieving and overlaying pertinent information onto such objects. In this paper we report on the development of a framework for mobile outdoor augmented reality application, applied to the overlay of peak information onto views of mountain landscapes. The resulting app operates by estimating the virtual panorama visible from the viewpoint of the user, using an online Digital Terrain Model (DEM), and by matching such panorama to the actual image framed by the camera. When a good match is found, meta-data from the DEM (e.g, peak name, altitude, distance) are projected in real time onto the view. The application, besides providing a nice experience to the user, can be employed to crowdsource the collection of annotated mountain images for environmental applications.

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“…Besides vegetation monitoring, time lapse images have been used in snow cover monitoring. Salvatori et al (2011) developed a method for analysing snow cover changes based on the thresholding of blue channel histograms. The method has been recently tested also in northern boreal Finland .…”

Section: Introductionmentioning

confidence: 99%

Webcam network and image database for studies of phenological changes of vegetation and snow cover in Finland, image time series from 2014 to 2016

Peltoniemi

Aurela

Böttcher

et al. 2018

Earth Syst. Sci. Data

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Abstract. In recent years, monitoring of the status of ecosystems using low-cost web (IP) or time lapse cameras has received wide interest. With broad spatial coverage and high temporal resolution, networked cameras can provide information about snow cover and vegetation status, serve as ground truths to Earth observations and be useful for gap-filling of cloudy areas in Earth observation time series. Networked cameras can also play an important role in supplementing laborious phenological field surveys and citizen science projects, which also suffer from observer-dependent observation bias. We established a network of digital surveillance cameras for automated monitoring of phenological activity of vegetation and snow cover in the boreal ecosystems of Finland. Cameras were mounted at 14 sites, each site having 1-3 cameras. Here, we document the network, basic camera information and access to images in the permanent data repository (http://www.zenodo.org/communities/phenology_camera/). Individual DOI-referenced image time series consist of half-hourly images collected between 2014 and 2016 (https://doi.org/10.5281/zenodo.1066862). Additionally, we present an example of a colour index time series derived from images from two contrasting sites.

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Snow cover monitoring with images from digital camera systems

Cited by 46 publications

References 6 publications

PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.1)

PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.1)

A Framework for Outdoor Mobile Augmented Reality and Its Application to Mountain Peak Detection

Webcam network and image database for studies of phenological changes of vegetation and snow cover in Finland, image time series from 2014 to 2016

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