Regional-scale mapping of groundwater discharge zones using thermal satellite imagery

2013 ◽  
Vol 28 (23) ◽  
pp. 5662-5673 ◽  
Author(s):  
G. Z. Sass ◽  
I. F. Creed ◽  
J. Riddell ◽  
S. E. Bayley
Keyword(s):  
Satellite Imagery ◽  
Regional Scale ◽  
Groundwater Discharge

scholarly journals An estimate of ice wedge volume for a High Arctic polar desert environment, Fosheim Peninsula, Ellesmere Island

2018 ◽  
Vol 12 (11) ◽  
pp. 3589-3604 ◽  
Author(s):  
Claire Bernard-Grand'Maison ◽  
Wayne Pollard
Keyword(s):  
Satellite Imagery ◽  
Regional Scale ◽  
High Arctic ◽  
Ellesmere Island ◽  
The Arctic ◽  
Ground Ice ◽  
Polar Desert ◽  
Ice Volume ◽  
Carbon Release ◽  
Wedge Volume

Abstract. Quantifying ground-ice volume on a regional scale is necessary to assess the vulnerability of permafrost landscapes to thaw-induced disturbance like terrain subsidence and to quantify potential carbon release. Ice wedges (IWs) are a ubiquitous ground-ice landform in the Arctic. Their high spatial variability makes generalizing their potential role in landscape change problematic. IWs form polygonal networks that are visible on satellite imagery from surface troughs. This study provides a first approximation of IW ice volume for the Fosheim Peninsula, Ellesmere Island, a continuous permafrost area characterized by polar desert conditions and extensive ground ice. We perform basic GIS analyses on high-resolution satellite imagery to delineate IW troughs and estimate the associated IW ice volume using a 3-D subsurface model. We demonstrate the potential of two semi-automated IW trough delineation methods, one newly developed and one marginally used in previous studies, to increase the time efficiency of this process compared to manual delineation. Our methods yield acceptable IW ice volume estimates, validating the value of GIS to estimate IW volume on much larger scales. We estimate that IWs are potentially present on 50 % of the Fosheim Peninsula (∼3000 km2), where 3.81 % of the top 5.9 m of permafrost could be IW ice.

scholarly journals Regional scale crop mapping using multi-temporal satellite imagery

2015 ◽  
Vol XL-7/W3 ◽  
pp. 45-52 ◽  
Author(s):  
N. Kussul ◽  
S. Skakun ◽  
A. Shelestov ◽  
M. Lavreniuk ◽  
B. Yailymov ◽  
...  
Keyword(s):  
Neural Networks ◽  
Missing Data ◽  
Satellite Imagery ◽  
Missing Values ◽  
Spectral Band ◽  
Regional Scale ◽  
Test Site ◽  
Multilayer Perceptrons ◽  
Landsat 8 ◽  
Multi Temporal

One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this paper, a new approach to classification of multi-temporal optical satellite imagery with missing data due to clouds and shadows is proposed. First, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of satellite imagery. SOMs are trained for each spectral band separately using nonmissing values. Missing values are restored through a special procedure that substitutes input sample's missing components with neuron's weight coefficients. After missing data restoration, a supervised classification is performed for multi-temporal satellite images. An ensemble of neural networks, in particular multilayer perceptrons (MLPs), is proposed. Ensembling of neural networks is done by the technique of average committee, i.e. to calculate the average class probability over classifiers and select the class with the highest average posterior probability for the given input sample. The proposed approach is applied for regional scale crop classification using multi temporal Landsat-8 images for the JECAM test site in Ukraine in 2013. It is shown that ensemble of MLPs provides better performance than a single neural network in terms of overall classification accuracy, kappa coefficient, and producer's and user's accuracies for separate classes. The overall accuracy more than 85% is achieved. The obtained classification map is also validated through estimated crop areas and comparison to official statistics.

scholarly journals EVALUATION OF DIGITAL ELEVATION MODELS FOR GEOMORPHOMETRIC ANALYSES ON DIFFERENT SCALES FOR NORTHERN CHILE

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1229-1235 ◽  
Author(s):  
T. Kramm ◽  
D. Hoffmeister
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Thermal Emission ◽  
Digital Elevation Models ◽  
Regional Scale ◽  
Laser Altimeter ◽  
Absolute Deviation ◽  
Digital Elevation ◽  
Very High

<p><strong>Abstract.</strong> The resolution and accuracy of digital elevation models (DEMs) have direct influence on further geoscientific computations like landform classifications and hydrologic modelling results. Thus, it is crucial to analyse the accuracy of DEMs to select the most suitable elevation model regarding aim, accuracy and scale of the study. Nowadays several worldwide DEMs are available, as well as DEMs covering regional or local extents. In this study a variety of globally available elevation models were evaluated for an area of about 190,000&amp;thinsp;km<sup>2</sup>. Data from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m, Shuttle Radar Topography Mission (SRTM) 30&amp;thinsp;m and 90&amp;thinsp;m, Advanced Land Observing Satellite (ALOS) World 3D 30&amp;thinsp;m and TanDEM-X WorldDEM&amp;trade; &amp;ndash; 12&amp;thinsp;m and 90&amp;thinsp;m resolution were obtained. Additionally, several very high resolution DEM data were derived from stereo satellite imagery from SPOT 6/7 and Pléiades for smaller areas of about 100&amp;ndash;400&amp;thinsp;km<sup>2</sup> for each dataset. All datasets were evaluated with height points of the Geoscience Laser Altimeter System (GLAS) instrument aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite on a regional scale and with nine very high resolution elevation models from UAV-based photogrammetry on a very large scale. For all datasets the root mean square error (RMSE) and normalized median absolute deviation (NMAD) was calculated. Furthermore, the association of errors to specific terrain was conducted by assigning these errors to landforms from the topographic position index (TPI), topographic roughness index (TRI) and slope. For all datasets with a global availability the results show the highest overall accuracies for the TanDEM-X 12&amp;thinsp;m (RMSE: 2.3&amp;thinsp;m, NMAD: 0.8&amp;thinsp;m). The lowest accuracies were detected for the 30&amp;thinsp;m ASTER GDEM v3 (RMSE: 8.9&amp;thinsp;m, NMAD: 7.1&amp;thinsp;m). Depending on the landscape the accuracies are higher for all DEMs in flat landscapes and the errors rise significantly in rougher terrain. Local scale DEMs derived from stereo satellite imagery show a varying overall accuracy, mainly depending on the topography covered by the scene.</p>

Integration of Satellite Imagery and Forest Inventory in Mapping Dominant and Associated Species at a Regional Scale

2009 ◽  
Vol 44 (2) ◽  
pp. 312-323 ◽  
Author(s):  
Yangjian Zhang ◽  
Hong S. He ◽  
William D. Dijak ◽  
Jian Yang ◽  
Stephen R. Shifley ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Forest Inventory ◽  
Regional Scale ◽  
Associated Species

Königshafen Submarine Groundwater Discharge Network (KiSNet)

2020 ◽  
Author(s):  
Ulf Mallast ◽  
Hannelore Waska ◽  
Nils Moosdorf
Keyword(s):  
Temporal Variability ◽  
Submarine Groundwater Discharge ◽  
Chemical Constituents ◽  
Regional Scale ◽  
Groundwater Discharge ◽  
Interdisciplinary Approach ◽  
Biological Indicators ◽  
Spatial And Temporal Variability ◽  
Flux Data ◽  
Submarine Groundwater

&lt;p&gt;Submarine groundwater discharge (SGD) as a pathway for water and chemical constituents between land and ocean is a rather young topic. For a long time it has been neglected by the scientific community and coastal managers. However, it has increasingly attracted attention since the turn of the millennium. Yet, SGD is mostly investigated either by terrestrial or marine disciplines although a broader, interdisciplinary approach would benefit SGD research. Moreover, so far reported SGD flux data at local to regional scale are a) hardly comparable as, to our best knowledge, only a few, mostly isolated studies directly compared available SGD methods in a quantitative fashion and b) flux data contain large uncertainties, either because they were up-scaled from local discrete (point) measurements to regional scales or because they were derived from modelling/ budgeting of regional or even global matter fluxes despite the known high spatial and temporal variability.&amp;#160;&lt;/p&gt;&lt;p&gt;In order to pave the way for a more standardized and interdisciplinary SGD research that would reduce inherited measurement/ extrapolation uncertainties, the K&amp;#246;nigshafen Submarine Groundwater Discharge Network (KiSNet)&amp;#160; seeks to contribute through three concrete aims:&amp;#160;&amp;#160;&lt;/p&gt;&lt;ol&gt;&lt;li&gt;forming an interdisciplinary group of SGD experts to initiate and intensify collaborative ties across disciplines&lt;/li&gt; &lt;li&gt;improving individual methodologies by groundtruthing through interdisciplinary intercomparison, which includes a focus on spatial and temporal variability, and&lt;/li&gt; &lt;li&gt;providing a method catalogue which outlines optimal combinations for qualitative and quantitative SGD investigations that may serve as basis for future standardized SGD research.&lt;/li&gt; &lt;/ol&gt;&lt;p&gt;The network will convene at the bay of K&amp;#246;nigshafen on Sylt, Germany, during two different points in time. Each time, all members of the network will apply qualitative (remote sensing, marine and terrestrial ground-based geophysics, biological indicators and socio-scientific methods) and quantitative (seepage meters, temperature rods, natural tracers, numerical simulation) methods from terrestrial and marine disciplines to investigate SGD synchronously and provide a robust basis to tackle above mentioned aims.&amp;#160;&lt;/p&gt;&lt;p&gt;Here, we will outline exact procedures, methods and anticipated results the network will produce and provide an overview on future actions the network anticipates.&lt;/p&gt;

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