Very high spatial resolution optical and radar imagery in tracking water level fluctuations of a small inland reservoir

Simon, Ricardo Nouailhetas; Tormos, T.; Danis, P.A.

doi:10.1016/j.jag.2014.12.007

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…Remotely sensed imagery can also be used to directly map and evaluate functional surface-water connections, including spatial extent and frequency, timing, and changes in hydrologic connectivity over time (Sass and Creed 2008;Lang et al 2012;Huang et al 2014;. While moderate resolution sources of imagery, such as Landsat, are effective to identify connections that occur through the expansion of larger features, such as lakes (Vanderhoof and Alexander 2016), finer resolution sources of imagery (e.g., lidar, Worldview-2) can be used to identify changes in the spatial extent of surface water for smaller wetlands and facilitate the identification of narrower connections (Huang et al 2011;Simon et al 2015;Vanderhoof, Distler, et al 2017).…”

Section: Quantifying Connectivitymentioning

confidence: 99%

Connectivity of Streams and Wetlands to Downstream Waters: An Integrated Systems Framework

Leibowitz

Wigington

Schofield

et al. 2018

J American Water Resour Assoc

147

123

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Interest in connectivity has increased in the aquatic sciences, partly because of its relevance to the Clean Water Act. This paper has two objectives: (1) provide a framework to understand hydrological, chemical, and biological connectivity, focusing on how headwater streams and wetlands connect to and contribute to rivers; and (2) review methods to quantify hydrological and chemical connectivity. Streams and wetlands affect river structure and function by altering material and biological fluxes to the river; this depends on two factors: (1) functions within streams and wetlands that affect material fluxes; and (2) connectivity (or isolation) from streams and wetlands to rivers that allows (or prevents) material transport between systems. Connectivity can be described in terms of frequency, magnitude, duration, timing, and rate of change. It results from physical characteristics of a system, e.g., climate, soils, geology, topography, and the spatial distribution of aquatic components. Biological connectivity is also affected by traits and behavior of the biota. Connectivity can be altered by human impacts, often in complex ways. Because of variability in these factors, connectivity is not constant but varies over time and space. Connectivity can be quantified with field-based methods, modeling, and remote sensing. Further studies using these methods are needed to classify and quantify connectivity of aquatic ecosystems and to understand how impacts affect connectivity.

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Section: Quantifying Connectivitymentioning

confidence: 99%

Connectivity of Streams and Wetlands to Downstream Waters: An Integrated Systems Framework

Leibowitz

Wigington

Schofield

et al. 2018

J American Water Resour Assoc

147

123

View full text Add to dashboard Cite

show abstract

“…The multi-satellite methodology helps to avoid the noise [31,32]. In addition to optical remote sensing data, radar and microwave remote sensing data are partially suitable for “water” extraction [33,34]. Some optical remote sensing images with much cloudiness can be replaced by radar data during the same period.…”

Section: Discussionmentioning

confidence: 99%

Continuous Dynamics Monitoring of Multi-Lake Water Extent Using a Spatial and Temporal Adaptive Fusion Method Based on Two Sets of MODIS Products

Rao

Lou

Jiang

et al. 2019

Sensors

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Due to the widespread presence of noise, such as clouds and cloud shadows, continuous, high spatiotemporal-resolution dynamic monitoring of lake water extents is still limited using remote sensing data. This study aims to take an approach to mapping continuous time series of highly-accurate lake water extents. Four lakes from diverse regions of China were selected as cases. In order to reduce the impact of noise and ensure high spatial and temporal resolution of the final results, two sets of MODIS products (including MOD09A1 and MOD13Q1) are used to extract water bodies. This approach mainly comprises preliminary classification, post processing and data fusion. The preliminary classification used the Random Forest (RF) classifier to efficiently and automatically obtain the initial classification results. Post-processing is implemented to repair the classification results affected by noise as much as possible. The processed results of the two sets of products are fused by using the Homologous Data-Based Spatial and Temporal Adaptive Fusion Method (HDSTAFM), which reduces the effect of noise and also improve the temporal and spatial resolution for the final water results. We determined the accuracy using Landsat-based water results, and the values of overall accuracy (OA), user’s accuracy (UA), producer’s accuracy (PA), and kappa coefficients (KC) are mostly greater than 0.9. Good correlation was achieved for a time series of water area and altimetry data, obtained by multiple satellites, and also for water-level data selected from hydrological stations.

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“…In addition to data collection from gauge stations, remote sensing technologies such as optical imagery and radar imagery are also widely used for measuring the wide-area water level and scope for defining a flooded area [ 17 , 18 , 19 , 20 , 21 ]. Remote altimetry technology can be used to continuously measure the water level variation within a large area and can thus be used to extensively monitor an entire flood event [ 22 , 23 , 24 ]. Another advantage of this technology is that it remotely acquires data of absolute water elevations, which is helpful for the integration of flood management and for environmental science research.…”

Section: Introductionmentioning

confidence: 99%

Visual Sensing for Urban Flood Monitoring

Lin

et al. 2015

Sensors

127

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With the increasing climatic extremes, the frequency and severity of urban flood events have intensified worldwide. In this study, image-based automated monitoring of flood formation and analyses of water level fluctuation were proposed as value-added intelligent sensing applications to turn a passive monitoring camera into a visual sensor. Combined with the proposed visual sensing method, traditional hydrological monitoring cameras have the ability to sense and analyze the local situation of flood events. This can solve the current problem that image-based flood monitoring heavily relies on continuous manned monitoring. Conventional sensing networks can only offer one-dimensional physical parameters measured by gauge sensors, whereas visual sensors can acquire dynamic image information of monitored sites and provide disaster prevention agencies with actual field information for decision-making to relieve flood hazards. The visual sensing method established in this study provides spatiotemporal information that can be used for automated remote analysis for monitoring urban floods. This paper focuses on the determination of flood formation based on image-processing techniques. The experimental results suggest that the visual sensing approach may be a reliable way for determining the water fluctuation and measuring its elevation and flood intrusion with respect to real-world coordinates. The performance of the proposed method has been confirmed; it has the capability to monitor and analyze the flood status, and therefore, it can serve as an active flood warning system.

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Very high spatial resolution optical and radar imagery in tracking water level fluctuations of a small inland reservoir

Cited by 10 publications

References 15 publications

Connectivity of Streams and Wetlands to Downstream Waters: An Integrated Systems Framework

Connectivity of Streams and Wetlands to Downstream Waters: An Integrated Systems Framework

Continuous Dynamics Monitoring of Multi-Lake Water Extent Using a Spatial and Temporal Adaptive Fusion Method Based on Two Sets of MODIS Products

Visual Sensing for Urban Flood Monitoring

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