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Special Issue "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: 30 June 2019

Special Issue Editor

Guest Editor
Prof. Salvatore Grimaldi

Department of Innovation in Biology, Agri-food and Forest systems (DIBAF), Tuscia University, Viterbo, Italy
Website | E-Mail
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

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (1 paper)

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Research

Open AccessFeature PaperArticle
Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-Based Filtering for Surface Streamflow Observations
Remote Sens. 2018, 10(12), 2010; https://doi.org/10.3390/rs10122010
Received: 25 October 2018 / Revised: 5 December 2018 / Accepted: 9 December 2018 / Published: 11 December 2018
Cited by 2 | PDF Full-text (6599 KB) | HTML Full-text | XML Full-text
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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