The CORONA Atlas Project: Orthorectification of CORONA Satellite Imagery and Regional-Scale Archaeological Exploration in the Near East

2012 ◽  
pp. 33-43 ◽  
Author(s):  
Jesse Casana ◽  
Jackson Cothren
Keyword(s):  
Satellite Imagery ◽  
Near East ◽  
Regional Scale

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 Use of Satellite Imagery to Predict Corn Phenology at a Regional Scale

2019 ◽  
Vol 5 (6) ◽  
Author(s):  
L. Nieto ◽  
R. Schwalbert ◽  
I. A. Ciampitti
Keyword(s):  
Satellite Imagery ◽  
Regional Scale

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

Stereo analysis, DEM extraction and orthorectification of CORONA satellite imagery: archaeological applications from the Near East

Antiquity ◽  
2008 ◽  
Vol 82 (317) ◽  
pp. 732-749 ◽  
Author(s):  
Jesse Casana ◽  
Jackson Cothren
Keyword(s):  
Satellite Imagery ◽  
Near East ◽  
Three Dimensional ◽  
Archaeological Survey ◽  
Survey Tool ◽  
Original Shape ◽  
Stereo Analysis ◽  
Three Dimensional Images ◽  
New Procedures

CORONA satellite imagery, preserving an account of the earth's surface from 40 years ago, is a most important archaeological survey tool and we have often sung its praises. Here the authors use new procedures to extend the competence and revelations of CORONA even further. Stereo pairs derived from images taken from fore and aft of the satellite give three dimensional images of landscapes and even individual sites. Techniques of modelling and rectification restore the sites to their original shape without recourse to survey on the ground – in many cases no longer possible since the sites have been buried, inundated or erased. The ingenuity shown here indicates that results from CORONA are only going to get better.

Swords into Ploughshares: Archaeological Applications of CORONA Satellite Imagery in the Near East

2012 ◽  
Author(s):  
Jesse Casana ◽  
◽  
Jackson Cothren ◽  
Tuna Kalayci
Keyword(s):  
Satellite Imagery ◽  
Near East

Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale

2015 ◽  
Vol 17 (3) ◽  
pp. 332-348 ◽  
Author(s):  
Lin Yuan ◽  
Ruiliang Pu ◽  
Jingcheng Zhang ◽  
Jihua Wang ◽  
Hao Yang
Keyword(s):  
Powdery Mildew ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
High Spatial Resolution ◽  
Regional Scale

scholarly journals Evaluation of Approaches for Mapping Tidal Wetlands of the Chesapeake and Delaware Bays

2019 ◽  
Vol 11 (20) ◽  
pp. 2366
Author(s):  
Brian T. Lamb ◽  
Maria A. Tzortziou ◽  
Kyle C. McDonald
Keyword(s):  
Random Forest ◽  
Synthetic Aperture Radar ◽  
Satellite Imagery ◽  
Regional Scale ◽  
Google Earth ◽  
Synthetic Aperture ◽  
Tidal Wetlands ◽  
Study Sites ◽  
Aperture Radar ◽  
Emergent Wetlands

The spatial extent and vegetation characteristics of tidal wetlands and their change are among the biggest unknowns and largest sources of uncertainty in modeling ecosystem processes and services at the land-ocean interface. Using a combination of moderate-high spatial resolution (≤30 meters) optical and synthetic aperture radar (SAR) satellite imagery, we evaluated several approaches for mapping and characterization of wetlands of the Chesapeake and Delaware Bays. Sentinel-1A, Phased Array type L-band Synthetic Aperture Radar (PALSAR), PALSAR-2, Sentinel-2A, and Landsat 8 imagery were used to map wetlands, with an emphasis on mapping tidal marshes, inundation extents, and functional vegetation classes (persistent vs. non-persistent). We performed initial characterizations at three target wetlands study sites with distinct geomorphologies, hydrologic characteristics, and vegetation communities. We used findings from these target wetlands study sites to inform the selection of timeseries satellite imagery for a regional scale random forest-based classification of wetlands in the Chesapeake and Delaware Bays. Acquisition of satellite imagery, raster manipulations, and timeseries analyses were performed using Google Earth Engine. Random forest classifications were performed using the R programming language. In our regional scale classification, estuarine emergent wetlands were mapped with a producer’s accuracy greater than 88% and a user’s accuracy greater than 83%. Within target wetland sites, functional classes of vegetation were mapped with over 90% user’s and producer’s accuracy for all classes, and greater than 95% accuracy overall. The use of multitemporal SAR and multitemporal optical imagery discussed here provides a straightforward yet powerful approach for accurately mapping tidal freshwater wetlands through identification of non-persistent vegetation, as well as for mapping estuarine emergent wetlands, with direct applications to the improved management of coastal wetlands.

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