scholarly journals Measuring the Morphology and Dynamics of the Snake River by Remote Sensing

2011 ◽  
Vol 33 ◽  
pp. 3-15
Author(s):  
Carl Legleiter
Keyword(s):  
Remote Sensing ◽  
Image Data ◽  
River System ◽  
Field Work ◽  
Hyperspectral Data ◽  
Snake River ◽  
Central Component ◽  
Data Sets ◽  
Channel Form ◽  
And Behavior

The Snake River is a central component of Grand Teton National Park, and this dynamic fluvial system plays a key role in shaping the landscape and creating diverse aquatic and terrestrial habitat. The river’s complexity and propensity for change make effective characterization of this resource difficult, however, and conventional, ground-based methods are simply inadequate. Remote sensing provides an appealing alternative approach that could facilitate resource management while providing novel insight on the factors controlling channel form and behavior. In this study, we evaluate the potential to measure the morphology and dynamics of a large, complex river system such as the Snake using optical image data. Initially, we made use of existing, publicly available images and basic digital aerial photography acquired in August 2010. Analysis to date has focused on estimating flow depths from these data, and preliminary results indicate that remote bathymetric mapping is feasible but not highly accurate, with important constraints related to the limited radiometric resolution of these data sets. Additional, more sophisticated hyperspectral data are scheduled for collection in 2011, along with further field work.

scholarly journals Measuring the Morphology and Dynamics of the Snake River by Remote Sensing

2011 ◽  
Vol 34 ◽  
pp. 53-67
Author(s):  
Carl Legleiter ◽  
Brandon Overstreet
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Field Measurements ◽  
Optical Data ◽  
Snake River ◽  
Central Component ◽  
Data Sets ◽  
Data Set ◽  
Channel Changes ◽  
Multispectral Satellite Images

The Snake River is a central component of Grand Teton National Park, and this dynamic fluvial system plays a key role in shaping the landscape and maintaining a diversity of habitat conditions. The river’s inherent variability and propensity for change complicate effective characterization of this important resource, however; conventional, ground-based methods are not adequate for this purpose. Remote sensing provides an appealing alternative that could facilitate resource management while providing novel insight on factors influencing channel form and behavior. This study evaluates the potential for using optical data to measure the morphology and dynamics of a large, complex river such as the Snake. More specifically, we assessed the feasibility of estimating flow depth from multispectral satellite images acquired in September 2011. Our initial results indicate that reliable maps of river bathymetry can be produced from such data. We are also examining channel changes associated with a prolonged period of high flow during the 2011 snowmelt runoff season by comparing these satellite images with digital aerial photography from August 2010. An extensive field data set on flow velocities provides some hydraulic context for the observed morphodynamics. More sophisticated hyperspectral and LiDAR data sets are scheduled for collection in 2012, along with additional field measurements.

A Lightweight SE-YOLOv3 Network for Multi-Scale Object Detection in Remote Sensing Imagery

2021 ◽  
Author(s):  
Lifang Zhou ◽  
Guang Deng ◽  
Weisheng Li ◽  
Jianxun Mi ◽  
Bangjun Lei
Keyword(s):  
Remote Sensing ◽  
High Efficiency ◽  
Computational Cost ◽  
Image Data ◽  
Detection Accuracy ◽  
Data Sets ◽  
One Stage ◽  
Efficiency And Effectiveness ◽  
Spatial Pyramid Pooling ◽  
Feature Augmentation

Current state-of-the-art detectors achieved impressive performance in detection accuracy with the use of deep learning. However, most of such detectors cannot detect objects in real time due to heavy computational cost, which limits their wide application. Although some one-stage detectors are designed to accelerate the detection speed, it is still not satisfied for task in high-resolution remote sensing images. To address this problem, a lightweight one-stage approach based on YOLOv3 is proposed in this paper, which is named Squeeze-and-Excitation YOLOv3 (SE-YOLOv3). The proposed algorithm maintains high efficiency and effectiveness simultaneously. With an aim to reduce the number of parameters and increase the ability of feature description, two customized modules, lightweight feature extraction and attention-aware feature augmentation, are embedded by utilizing global information and suppressing redundancy features, respectively. To meet the scale invariance, a spatial pyramid pooling method is used to aggregate local features. The evaluation experiments on two remote sensing image data sets, DOTA and NWPU VHR-10, reveal that the proposed approach achieves more competitive detection effect with less computational consumption.

scholarly journals Towards Multiscale and Multisource Remote Sensing Mineral Exploration Using RPAS: A Case study in the Lofdal Carbonatite-Hosted REE Deposit, Namibia

2019 ◽  
Vol 11 (21) ◽  
pp. 2500 ◽  
Author(s):  
Booysen ◽  
Zimmermann ◽  
Lorenz ◽  
Gloaguen ◽  
Nex ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mineral Exploration ◽  
Field Work ◽  
Relevant Information ◽  
Hyperspectral Data ◽  
Multiscale Approach ◽  
Carbonatite Complex ◽  
Lithological Mapping ◽  
Aircraft System ◽  
Difficult Terrain

Traditional exploration techniques usually rely on extensive field work supported by geophysical ground surveying. However, this approach can be limited by several factors such as field accessibility, financial cost, area size, climate, and public disapproval. We recommend the use of multiscale hyperspectral remote sensing to mitigate the disadvantages of traditional exploration techniques. The proposed workflow analyzes a possible target at different levels of spatial detail. This method is particularly beneficial in inaccessible and remote areas with little infrastructure, because it allows for a systematic, dense and generally noninvasive surveying. After a satellite regional reconnaissance, a target is characterized in more detail by plane-based hyperspectral mapping. Subsequently, Remotely Piloted Aircraft System (RPAS)-mounted hyperspectral sensors are deployed on selected regions of interest to provide a higher level of spatial detail. All hyperspectral data are corrected for radiometric and geometric distortions. End-member modeling and classification techniques are used for rapid and accurate lithological mapping. Validation is performed via field spectroscopy and portable XRF as well as laboratory geochemical and spectral analyses. The resulting spectral data products quickly provide relevant information on outcropping lithologies for the field teams. We show that the multiscale approach allows defining the promising areas that are further refined using RPAS-based hyperspectral imaging. We further argue that the addition of RPAS-based hyperspectral data can improve the detail of field mapping in mineral exploration, by bridging the resolution gap between airplane- and ground-based data. RPAS-based measurements can supplement and direct geological observation rapidly in the field and therefore allow better integration with in situ ground investigations. We demonstrate the efficiency of the proposed approach at the Lofdal Carbonatite Complex in Namibia, which has been previously subjected to rare earth elements exploration. The deposit is located in a remote environment and characterized by difficult terrain which limits ground surveys.

scholarly journals An Overview of Hyperspectral Remote Sensing and its applications in various Disciplines

2016 ◽  
Vol 5 (2) ◽  
pp. 85 ◽  
Author(s):  
Alpana Shukla ◽  
Rajsi Kot
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Spatial Information ◽  
Coastal Zone Management ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Data Sets ◽  
Imaging Data ◽  
Advantages And Disadvantages

<div><p><em>Recent advances in remote sensing and geographic information has opened new directions for the development of hyperspectral sensors. Hyperspectral remote sensing, also known as imaging spectroscopy is a new technology. Hyperspectral imaging is currently being investigated by researchers and scientists for the detection and identification of vegetation, minerals, different objects and background.</em><em> Hyperspectral remote sensing combines imaging and spectroscopy in a single system which often includes large data sets and requires new processing methods. Hyperspectral data sets are generally made of about 100 to 200 spectral bands of relatively narrow bandwidths (5-10 nm), whereas, multispectral data sets are usually composed of about 5 to 10 bands of relatively large bandwidths (70-400 nm). Hyperspectral imagery is collected as a data cube with spatial information collected in the X-Y plane, and spectral information represented in the Z-direction. </em><em>Hyperspectral remote sensing is applicable in many different disciplines. It was originally developed for mining and geology; it has now spread into fields such as agriculture and forestry, ecology, coastal zone management, geology and mineral exploration. This paper presents an overview of hyperspectral imaging, data exploration and analysis, applications in various disciplines, advantages and disadvantages and future aspects of the technique.</em></p></div>

scholarly journals Ecological Study of River Otters in Grand Teton National Park

1982 ◽  
Vol 6 ◽  
pp. 46-51
Author(s):  
Joseph Hall
Keyword(s):  
National Park ◽  
Ecological Study ◽  
Distribution Density ◽  
Activity Patterns ◽  
Field Work ◽  
Snake River ◽  
Grand Teton National Park ◽  
River Otters ◽  
And Behavior

The primary goals of the study are to obtain information on the distribution, density, movements, activity patterns and behavior of otters on the Snake River from Jackson Lake Dam to Moose. Most observations were concentrated along the 3.5 mile section from the dam to and including the Oxbow. This report covers field work during the periods of March 7-17 and August 11 - September 25. No otters were seen in March but they were observed for 28 hours during 88 hours afield in August and September. Additionally, Mr. John Turner of Triang1e-X Float Trips and Mr. Dick Barker of Barker-Ewing Float Trips were most cooperative in obtaining sightings of otters made on the river by their parties between Deadman Bar #2 and Moose.

scholarly journals Measuring the Morphology and Dynamics of the Snake River by Remote Sensing

2014 ◽  
Vol 37 ◽  
pp. 12-20
Author(s):  
Carl Legleiter ◽  
Brandon Overstreet
Keyword(s):  
Remote Sensing ◽  
Water Surface ◽  
Management Strategies ◽  
River System ◽  
Sediment Supply ◽  
Sediment Flux ◽  
Snake River ◽  
Central Feature ◽  
Flow Management ◽  
Extensive Field

The Snake River is a prominent, central feature of Grand Teton National Park, and this dynamic fluvial system maintains diverse habitats while actively shaping the landscape. Although the riparian corridor is relatively pristine, the Snake River is by no means free from anthropogenic influences: streamflows have been regulated since 1907 by Jackson Lake Dam. Among dam-controlled rivers in the western U.S., the Snake River is unique in that tributaries entering below the dam supply sufficient coarse bed material to produce a braided morphology. As a result of tributary inputs, sediment flux along the Snake River has been relatively unaffected by Jackson Lake Dam, but flow regulation has reduced the magnitude and altered the timing of streamflows. In this study we are coupling an annual image time series with extensive field surveys to document channel changes occurring on the Snake River. Our objective is to quantify how snowmelt runoff events and flow management strategies influence patterns of sediment transfer and storage throughout the river system, with a particular focus on tributary junctions. More specifically, we are using the image sequence to identify areas of erosion and deposition and hence infer the sediment flux associated with the observed changes in channel morphology. This analysis will improve our understanding of the river’s response to flow management and enable us to generate hypotheses as to how the system might adapt to future anthropogenic and/or climate-driven alterations in streamflow and sediment supply. In addition, our research on the Snake River involves an ongoing assessment of the potential to measure the morphology and dynamics of large, complex rivers via remote sensing. A new aspect of this investigation involves estimating flow velocities from hyperspectral images that capture the texture of the water surface. Extensive field measurements of velocity and water surface roughness are being used to develop this innovative approach and thus increase the amount of river information that can be inferred via remote sensing.

scholarly journals A database system for querying of river networks: facilitating monitoring and prediction applications

2021 ◽  
Author(s):  
Erik Bollen ◽  
Brianna R. Pagán ◽  
Bart Kuijpers ◽  
Stijn Van Hoey ◽  
Nele Desmet ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Resources ◽  
Real Time ◽  
River System ◽  
Sensor Data ◽  
Data Management System ◽  
Data Sets ◽  
River Systems ◽  
Real Time Processing ◽  
Data Driven Approach

Abstract The increasing availability of real-time in situ measurements and remote sensing observations have the potential to contribute to the optimization of water resources management. Global challenges such as climate change, intensive agriculture and urbanization put a high pressure on our water resources. Due to recent innovations in measuring both water quantity and quality, river systems can now be monitored in real time at an unprecedented spatial and temporal scale. To interpret the sensor measurements and remote sensing observations additional data for example on: the location of the measurement, upstream and downstream catchment characteristics, … are required. In this paper, we present a data management system to support flow-path related functionality for decision making and prediction modelling. Adding meta data sets and facilitating (near) real-time processing of sensor data questions are key concepts for the systems. The potential of the database framework for hydrological applications is demonstrated using different applications for the river system of Flanders. In one, the database framework is used to simulate the daily discharge for each segment within a catchment using a simple data-driven approach. The presented system is useful for numerous applications including pollution tracking, alerting and inter-sensor validation in river systems, or related networks.

scholarly journals Detection of kimberlitic rocks in West Greenland using airborne hyperspectral data: the HyperGreen 2002 project

2005 ◽  
Vol 7 ◽  
pp. 69-72 ◽  
Author(s):  
Tapani Tukiainen ◽  
Leif Thorning
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Field Work ◽  
Hyperspectral Data ◽  
West Greenland ◽  
Sensing Data ◽  
Important Target ◽  
Viable Method

Previous investigations by the Geological Survey of Denmark and Greenland (GEUS) and exploration companies have demonstrated that some of the kimberlites in West Greenland are diamond bearing, making the region an important target for diamond prospecting. High-resolution hyperspectral (HS) remote sensing data have been successfully used for the location of kimberlitic rocks, e.g. in Australia and Africa. However, its potential as a viable method for the mapping of kimberlite occurrences in Arctic glaciated terrain with high relief was previously unknown. In July–August 2002, GEUS conducted an airborne hyperspectral survey in central West Greenland (Fig. 1) using the commercially available HyMap hyperspectral scanner operated by HyVista Corporation, Australia. Data were processed in 2003, and in 2004 follow-up field work was carried out in the Kangerlussuaq region to test possible kimberlites indicated by the HS data (Fig. 1). The project wasfinanced by the Bureau of Minerals and Petroleum, Government of Greenland.

scholarly journals Machine Learning with Remote Sensing Image Data Sets

Informatica ◽  
2021 ◽  
Vol 45 (3) ◽  
Author(s):  
Biserka Petrovska ◽  
Tatjana Atanasova Pacemska ◽  
Natasa Stojkovik ◽  
Aleksandra Stojanova ◽  
Mirjana Kocaleva
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Image Data ◽  
Remote Sensing Image ◽  
Data Sets
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