Comparative study to evaluate three ground-based optical remote sensing techniques under field conditions by a gas tracer experiment

2014 ◽  
Vol 72 (5) ◽  
pp. 1435-1441 ◽  
Author(s):  
Nils Reiche ◽  
Tanja Westerkamp ◽  
Steffen Lau ◽  
Helko Borsdorf ◽  
Peter Dietrich ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Comparative Study ◽  
Tracer Experiment ◽  
Field Conditions ◽  
Optical Remote Sensing ◽  
Remote Sensing Techniques ◽  
Gas Tracer

Optimal Detection and Estimation of Marine Oil Spills Through Coherent Pluralism

2013 ◽  
Vol 4 (1) ◽  
pp. 84-111
Author(s):  
Kufre Bassey ◽  
Polycarp Chigbu
Keyword(s):  
Remote Sensing ◽  
Environmental Science ◽  
Oil Spills ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Marine Oil ◽  
Detection And Estimation ◽  
Number Of False Alarms ◽  
Marine Oil Spills ◽  
Remote Sensing Techniques

An important area of environmental science involves the combination of information from diverse sources relating to a similar endpoint. Majority of optical remote sensing techniques used for marine oil spills detection have been reported lately of having high number of false alarms (oil slick look-a-likes) phenomena which give rise to signals which appear to be oil but are not. Suggestions for radar image as an operational tool has also been made. However, due to the inherent risk in these tools, this paper presents the possible research directions of combining statistical techniques with remote sensing in marine oil spill detection and estimation.

Estimating Biomass for New Zealand Pasture Using Optical Remote Sensing Techniques

1999 ◽  
Vol 14 (3) ◽  
pp. 89-94 ◽  
Author(s):  
M. M. Hanna ◽  
D. A. Steyn-Ross ◽  
Moira Steyn-Ross
Keyword(s):  
Remote Sensing ◽  
New Zealand ◽  
Optical Remote Sensing ◽  
Remote Sensing Techniques

Optical remote sensing techniques characterize the properties of atmospheric aerosols

2010 ◽  
Author(s):  
Russell Philbrick ◽  
Hans Hallen ◽  
Andrea Wyant ◽  
Tim Wright ◽  
Michelle Snyder
Keyword(s):  
Remote Sensing ◽  
Atmospheric Aerosols ◽  
Optical Remote Sensing ◽  
Remote Sensing Techniques

scholarly journals Monitoring the Recent Activity of Landslides in the Mailuu-Suu Valley (Kyrgyzstan) Using Radar and Optical Remote Sensing Techniques

Geosciences ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 164
Author(s):  
Valentine Piroton ◽  
Romy Schlögel ◽  
Christian Barbier ◽  
Hans-Balder Havenith
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mining Area ◽  
Mass Movements ◽  
Optical Remote Sensing ◽  
Velocity Anomaly ◽  
In Situ Data ◽  
Remote Sensing Techniques ◽  
The Impact

Central Asian mountain regions are prone to multiple types of natural hazards, often causing damage due to the impact of mass movements. In spring 2017, Kyrgyzstan suffered significant losses from a massive landslide activation event, during which also two of the largest deep-seated mass movements of the former mining area of Mailuu-Suu—the Koytash and Tektonik landslides—were reactivated. This study consists of the use of optical and radar satellite data to highlight deformation zones and identify displacements prior to the collapse of Koytash and to the more superficial deformation on Tektonik. Especially for the first one, the comparison of Digital Elevation Models of 2011 and 2017 (respectively, satellite and unmanned aerial vehicle (UAV) imagery-based) highlights areas of depletion and accumulation, in the scarp and near the toe, respectively. The Differential Synthetic Aperture Radar Interferometry analysis identified slow displacements during the months preceding the reactivation in April 2017, indicating the long-term sliding activity of Koytash and Tektonik. This was confirmed by the computation of deformation time series, showing a positive velocity anomaly on the upper part of both landslides. Furthermore, the analysis of the Normalized Difference Vegetation Index revealed land cover changes associated with the sliding process between June 2016 and October 2017. In addition, in situ data from a local meteorological station highlighted the important contribution of precipitation as a trigger of the collapse. The multidirectional approach used in this study demonstrated the efficiency of applying multiple remote sensing techniques, combined with a meteorological analysis, to identify triggering factors and monitor the activity of landslides.

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