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Remote Sensing of Surface Runoff

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 7449

Special Issue Editor


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Guest Editor
Department of Innovation in Biology, Agri-food and Forest systems (DIBAF), Tuscia University, Viterbo, Italy
Interests: hydrological observations; tracers for surface hydrology; river velocity estimation; surface travel time estimation; large scale particle image velocimetry; image analysis for hydrological applications; rainfall measurements; time series analysis; long memory models; linear parametric models; multivariate distributions; copula function; hydrological modelling in ungauged basins; rainfall runoff models; GIS terrain analysis; DEM analysis; geomorphological unit hydrograph; flood mapping; design hydrograph
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Special Issue Information

Dear Colleagues,

Surface runoff includes a variety of hydrological processes crucial for understanding and modelling, among others, water resources management, flood formation, and erosion dynamic.

Recent innovation and advancement in sensors, computational power, and monitoring platforms are rewording the meaning of remote sensing that was previously limited to satellite observations. Nowadays, CubeSat systems, drones, radar technology, and image analysis are augmenting the remote sensing perspective and the field of surface runoff observations may greatly benefit from such multidisciplinary approaches.

The aim of this Special Issue is to collect contributions providing innovative surface runoff remote sensing applications at different spatial scales related, but not limited, to:

  • Hydrometric observation;
  • River velocity measurements;
  • Hillslope runoff velocity estimation;
  • Soil water content estimation;
  • Water stress estimation;
  • Floodplain and flood inundation observations.
  • Role of vegetation land cover and land use activities

Dr. Salvatore Grimaldi
Guest Editor

Manuscript Submission Information

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Keywords

  • Surface runoff phenomena
  • UAV – drones
  • Radar devices
  • Image analysis algorithms
  • Satellite hydrological products

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Published Papers (1 paper)

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Research

24 pages, 6599 KiB  
Article
Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-Based Filtering for Surface Streamflow Observations
by Flavia Tauro, Fabio Tosi, Stefano Mattoccia, Elena Toth, Rodolfo Piscopia and Salvatore Grimaldi
Remote Sens. 2018, 10(12), 2010; https://doi.org/10.3390/rs10122010 - 11 Dec 2018
Cited by 67 | Viewed by 6425
Abstract
Nonintrusive image-based methods have the potential to advance hydrological streamflow observations by providing spatially distributed data at high temporal resolution. Due to their simplicity, correlation-based approaches have until recent been preferred to alternative image-based approaches, such as optical flow, for camera-based surface flow [...] Read more.
Nonintrusive image-based methods have the potential to advance hydrological streamflow observations by providing spatially distributed data at high temporal resolution. Due to their simplicity, correlation-based approaches have until recent been preferred to alternative image-based approaches, such as optical flow, for camera-based surface flow velocity estimate. In this work, we introduce a novel optical flow scheme, optical tracking velocimetry (OTV), that entails automated feature detection, tracking through the differential sparse Lucas-Kanade algorithm, and then a posteriori filtering to retain only realistic trajectories that pertain to the transit of actual objects in the field of view. The method requires minimal input on the flow direction and camera orientation. Tested on two image data sets collected in diverse natural conditions, the approach proved suitable for rapid and accurate surface flow velocity estimations. Five different feature detectors were compared and the features from accelerated segment test (FAST) resulted in the best balance between the number of features identified and successfully tracked as well as computational efficiency. OTV was relatively insensitive to reduced image resolution but was impacted by acquisition frequencies lower than 7–8 Hz. Compared to traditional correlation-based techniques, OTV was less affected by noise and surface seeding. In addition, the scheme is foreseen to be applicable to real-time gauge-cam implementations. Full article
(This article belongs to the Special Issue Remote Sensing of Surface Runoff)
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