scholarly journals Assessments of Available Riverine Hydrokinetic Energy: A Review

2021 ◽  
Author(s):  
Katelyn Kirby ◽  
Sean Ferguson ◽  
Colin Rennie ◽  
Ioan NISTOR ◽  
Julien Cousineau
Keyword(s):  
Remote Sensing ◽  
Uncertainty Analysis ◽  
Curve Fitting ◽  
Satellite Remote Sensing ◽  
Regional Studies ◽  
New Techniques ◽  
Hydrokinetic Energy ◽  
Remote Sensing Methods

Methods of estimating riverine hydrokinetic (HK) power for localized and regional studies are reviewed, evaluated, and compared. It was found that localized HK studies were not entirely consistent, with the most common discrepancies being discharge variability characterization, uncertainty analysis, and the amount of data used to derive the results. The issues associated with localized assessments were amplified for regional assessments. Regional HK assessments were less common, the methods were less consistent across studies, and the amount and type of data available varied widely across regions. New techniques and technologies, developed in Canada and globally, were evaluated for their usefulness to improve regional HK assessments. Emphasis was put on satellite remote sensing methods to estimate discharge and channel dimensions, as well as regionalized curve fitting to estimate channel roughness. The review of new techniques suggests that accuracy of the results is dependent on the amount and quality of the data available.

scholarly journals Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

2014 ◽  
Vol 11 (16) ◽  
pp. 4305-4320 ◽  
Author(s):  
S. T. Klosterman ◽  
K. Hufkens ◽  
J. M. Gray ◽  
E. Melaas ◽  
O. Sonnentag ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Curve Fitting ◽  
Satellite Remote Sensing ◽  
Vegetation Index ◽  
Deciduous Forest ◽  
Spatial Scales ◽  
Visual Assessment ◽  
Statistical Uncertainty ◽  
Near Surface

Abstract. Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of fall, can be derived from sensor-based time series, but must be interpreted in terms of biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate deciduous forest sites throughout eastern North America, and to perform digital image analysis for time-series-based estimation of phenophase transition dates. We then compare these results to remote sensing metrics of phenophase transition dates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit that uses a generalized sigmoid function to estimate phenology dates, and we quantify the statistical uncertainty of phenophase transition dates estimated using this method. Results show that the generalized sigmoid provides estimates of dates with less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than satellite remote sensing metrics of phenology, and that dates derived from the remotely sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time-series estimates for the start of spring are found to closely match estimates derived from visual assessment of leaf-out, as well as satellite remote-sensing-derived estimates of the start of spring. However late spring and fall phenology metrics exhibit larger differences between near-surface and remote scales. Differences in late spring phenology between near-surface and remote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.

scholarly journals Performance Analysis of Different Filters for Digital Image Processing

2021 ◽  
Vol 12 (2) ◽  
pp. 2572-2576
Author(s):  
Ch. Kavya , Et. al.
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Pattern Recognition ◽  
Digital Image Processing ◽  
Digital Image ◽  
High Frequency ◽  
Satellite Remote Sensing ◽  
Low Frequency ◽  
Linear Filters

Digital image processing is one of the drastically growing areas used in various real- time industries like medical, satellite, remote sensing, and pattern recognition. The output of the image processing depends on the quality of the image. Filters are used to modify the images, such as removing the noise and smoothing the images. It is essential to suppress the high- frequency values in the image for smoothening and improving the low-frequency values to enhance the image of strengthening else it doesn't provide good output. This paper discussed various filters and their functionalities concerning digital image processing. Here linear, as well as non-linear filters, are presented. It is easy to decide about the better filter for improving the image processing output from the discussion.

Identification of Potentially Unmanageable Ice Features

2017 ◽  
Author(s):  
Svetlana Shafrova ◽  
Dmitri Matskevitch ◽  
Curtis Holub ◽  
Ted Kokkinis
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Near Field ◽  
Remote Sensing Data ◽  
Data Interpretation ◽  
Dual Frequency ◽  
Sensing Technology ◽  
Field Reconnaissance ◽  
Remote Sensing Systems ◽  
Remote Sensing Methods

Satellite remote sensing technology plays an important role in ice monitoring and characterization in support of ice management operations for Arctic floating drilling that previously have been described by industry to include three stages: (1) far-field reconnaissance for potentially unmanageable ice features (2) mid-field verification of ice breakability and (3) near-field ice floe size reduction. The paper discusses the application of satellite remote sensing methods for identification of Potentially Unmanageable Ice Features (PUIF) as well as challenges associated with satellite data interpretation and feature tracking. Examples of PUIF identification using both publicly and commercially available satellite imagery and other remote sensing data collected during the Oden Arctic Technology Research Cruise 2015 (OATRC 2015) are presented and the challenges with the PUIF detection and monitoring are discussed. In addition, airborne remote sensing systems for PUIF identification, both existing (such as Electromagnetic Induction (EMI)) and under development (such as dual frequency radar, multi-band synthetic aperture radar), are discussed and their capabilities contrasted and compared to satellite-based methods. Furthermore, potential ways of optimally combining airborne and satellite remote sensing are proposed.

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