A comparison of tools and techniques for stabilising UAS imagery for surface flow observations

This research presents an investigation of different strategies and tools for digital image stabilisation for image velocimetry purposes. Basic aspects of image stabilisation and transformation are presented, and their applicability is discussed in terms of image velocimetry. Seven free-to-use open-source tools (six community-developed and one off-the-shelf) are described and compared according to their stabilisation accuracy, robustness in different flight and ground conditions, computational complexity, ease of use, and other capabilities. A novel approach for fast stabilisation accuracy analysis is also developed, presented, and applied to the stabilised image sequences. Based on the obtained results, some general guidelines for choosing a suitable tool for specific image velocimetry tasks have been obtained. This research also aims to provide a basis for further development or improvement of digital image stabilisation tools, as well as for the analyses of stabilisation impact on image velocimetry results.

How to cite: Robert Ljubičić, Dariia Strelnikova, Matthew T. Perks, Anette Eltner, Salvador Peña-Haro, Alonso Pizarro, Silvano Fortunato Dal Sasso, Ulf Scherling, Pietro Vuono, and Salvatore Manfreda, A comparison of tools and techniques for stabilising UAS imagery for surface flow observations, Hydrology and Earth System Sciences, 2021.

VISION: VIdeo StabilisatION using automatic features selection

This project presents the codes and example of the use of one of the algorithms (FAST) used in the automatic feature selection part of the manuscript entitled “A comparison of tools and techniques for stabilizing UAS imagery for surface flow observations”. The “StabilisationFunction.m” is a Matlab function aiming at stabilising videos for image velocimetry analyses in rivers. It is a command-line function without GUI at the moment. An example of how to call the stabilisation function is also provided in the file “ExampleScript.m”. All the codes were written in Matlab R2020a.

CODE: https://doi.org/10.17605/OSF.IO/HBRF2

How to cite: Pizarro, A., S.F. Dal Sasso, S. Manfreda, VISION: VIdeo StabilisatION using automatic features selection, DOI 10.17605/OSF.IO/HBRF2, 2021.

HARMONIOUS deliverables of 2020

This year, COST Action – HARMONIOUS members produced a quite impressive number of results working online. Imagine what we could do without restrictions!
See the following list:
1. Use of UAVs with the simplified “triangle” technique https://lnkd.in/dFQftqY
2. Identifying the optimal spatial distribution of tracers https://lnkd.in/dyEcmzq
3. A geostatistical approach to map near-surface soil moisture https://lnkd.in/dymZzHB
4. Refining image-velocimetry performances for streamflow monitoring https://lnkd.in/dyQzvyc
5. Metrics for the quantification of seeding characteristics https://lnkd.in/gvMBe4c
6. Harmonisation of image velocimetry techniques for river surface velocity observations https://lnkd.in/d-ygHpY
7. An integrative information aqueduct to close the gaps in water observations https://lnkd.in/dfTHZcG
8. Practical guidance for UAS-based environmental mapping https://lnkd.in/dAAuFmf
9. Long-term soil moisture observations over Tibetan Plateau https://lnkd.in/dguKMCE
10. Image velocimetry techniques under low flow conditions https://lnkd.in/dGRwY9Y

#hydrology #environmentalmonitoring #remotesensing #UAS #rivermonitoring

Call for Papers on Advances in Hydrological Monitoring with UASs

We are promoting a new research topic entitled Advances in Hydrological Monitoring with Unmanned Aerial Systems in Frontiers in Remote Sensing.


Abstract Submission by March 2021
Manuscript Submission by July 2021

This Research Topic, we would like to promote research which explores the contribution that UASs can provide on hydrological observations, understanding of hydraulic and hydrological processes and development of modelling approaches. More specifically, topics of interests are the following:

• Development of new sensors and Unmanned Aerial System configurations devoted to hydrological monitoring;
• Definition of guidelines of the best-practices to improve the overall quality of the final products promoting a consistent use of UASs in hydrology;
• Development of new algorithms able to exploit high resolution observations;
• Development of new methodologies to fill the gap between satellite observation and field data;
• Coupled application of hydrological models exploiting Unmanned Aerial System observations; and
• Linking the Unmanned Aerial System monitoring of hydrological processes to its novel applications in agricultural management, water resources management, early warning systems etc.

Keywords: UAS, Environmental Monitoring, Hydrology, Rivers, Vegetation.

Exploring the use of UAVs with the simplified ‘triangle’ technique for soil water content and evaporative fraction retrievals in a Mediterranean setting

Information acquired from Unmanned Aerial Vehicles (UAVs) is frequently used nowadays in a variety of disciplines and research fields. The present study explores for the first time the combined use of UAVs with a newly proposed technique for estimating evaporative fraction (EF) and surface soil moisture (SSM). The investigation is performed in a typical Mediterranean setting, a citrus field with flat topography divided in two plots with different irrigation schemes, in Sicily, Italy, at which ground data acquired during an extensive field campaign in July 2019. Reasonable estimates of both EF and surface wetness were produced, with patterns in agreement to vegetation cover fragmentation, topography, and other site-specific characteristics. Validation shows average error of 0.053 for EF and of 0.040 cm3 cm−3 for SSM. The results are comparable or better to those reported in analogous studies performed in similar areas. This implies that the investigated approach performs well under the semi-arid conditions characterizing the experimental set up. To our knowledge, this study represents the first evaluation of the combined use of the ‘simplified triangle’ with very high-resolution UAV imagery. As such, the findings are of significance regarding the potential future use of the ‘simplified triangle’ approach particularly with very fine resolution imagery such as that provided by UAV for mapping and monitoring EF and SSM in agricultural and natural ecosystems.

Figure: Maps of EF (a) and SSM (b) computed from the ‘simplified triangle’ implementation using the data retrieved with UAV.

How to cite: Petropoulos, G.P., A. Maltese, T. N. Carlson, G. Provenzano, A. Pavlides, G. Ciraolo, D. Hristopulos, F. Capodici, C. Chalkias, G. Dardanelli, S. Manfreda, Exploring the use of UAVs with the simplified “triangle” technique for Soil Water Content and Evaporative Fraction retrievals in a Mediterranean setting, International Journal of Remote Sensing, VOL. 42, NO. 5, 1623–1642, (doi: 10.1080/01431161.2020.1841319) 2021. [pdf]

Optimal spatial distribution of tracers for velocimetry applications

River monitoring is of particular interest as a society that faces increasingly complex water management issues. Emerging technologies have contributed to opening new avenues for improving our monitoring capabilities but have also generated new challenges for the harmonised use of devices and algorithms. In this context, optical-sensing techniques for stream surface flow velocities are strongly influenced by tracer characteristics such as seeding density and their spatial distribution. Therefore, a principal research goal is the identification of how these properties affect the accuracy of such methods. To this aim, numerical simulations were performed to consider different levels of tracer clustering, particle colour (in terms of greyscale intensity), seeding density, and background noise. Two widely used image-velocimetry algorithms were adopted: (i) particle-tracking velocimetry (PTV) and (ii) particle image velocimetry (PIV). A descriptor of the seeding characteristics (based on seeding density and tracer clustering) was introduced based on a newly developed metric called the Seeding Distribution Index (SDI). This index can be approximated and used in practice as SDI=ν0.1/(ρ/ρcν1), where νρ, and ρcν1 are the spatial-clustering level, the seeding density, and the reference seeding density at ν=1, respectively. A reduction in image-velocimetry errors was systematically observed for lower values of the SDI; therefore, the optimal frame window (i.e. a subset of the video image sequence) was defined as the one that minimises the SDI. In addition to numerical analyses, a field case study on the Basento river (located in southern Italy) was considered as a proof of concept of the proposed framework. Field results corroborated numerical findings, and error reductions of about 15.9 % and 16.1 % were calculated – using PTV and PIV, respectively – by employing the optimal frame window.

How to cite: Pizarro, A., S.F. Dal Sasso, M. Perks and S. Manfreda, Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow, Hydrology and Earth System Sciences, 24, 5173–5185, (10.5194/hess-24-5173-2020) 2020. [pdf]

Seeding metrics for error minimisation

River streamflow monitoring is currently facing a transformation due to the emerging of new innovative technologies. Fixed and mobile measuring systems are capable of quantifying surface flow velocities and discharges, relying on video acquisitions. This camera-gauging framework is sensitive to what the camera can “observe” but also to field circumstances such as challenging weather conditions, river background transparency, transiting seeding characteristics, among others. This short communication paper introduces the novel idea of optimising image velocimetry techniques selecting the most informative sequence of frames within the available video. The selection of the optimal frame window is based on two reasonable criteria: i) the maximisation of the number of frames, subject to ii) the minimisation of the recently introduced dimensionless seeding distribution index (SDI). SDI combines seeding characteristics such as seeding density and spatial clustering of tracers, which are used as a proxy to enhance the reliability of image velocimetry techniques. Two field case studies were considered as a proof-of-concept of the proposed framework, on which seeding metrics were estimated and averaged in time to select the proper application window. The selected frames were analysed using LSPIV to estimate the surface flow velocities and river discharge. Results highlighted that the proposed framework might lead to a significant error reduction. In particular, the computed discharge errors, at the optimal portion of the footage, were about 0.40% and 0.12% for each case study, respectively. These values were lower than those obtained, considering all frames available.

How to cite: Pizarro, A., S. F. Dal Sasso, S. Manfreda, Refining image‐velocimetry performances for streamflow monitoring: Seeding metrics to errors minimisation, Hydrological Processes, (doi: 10.1002/hyp.13919 ), 2020.

A Geostatistical Approach to Map Near-Surface Soil Moisture Through Hyperspatial Resolution Thermal Inertia

Thermal inertia has been applied to map soil water content exploiting remote sensing data in the short and long wave regions of the electromagnetic spectrum. Over the last years, optical and thermal cameras were sufficiently miniaturized to be loaded onboard of unmanned aerial systems (UASs), which provide unprecedented potentials to derive hyperspatial resolution thermal inertia for soil water content mapping. In this study, we apply a simplification of thermal inertia, the apparent thermal inertia (ATI), over pixels where underlying thermal inertia hypotheses are fulfilled (unshaded bare soil). Then, a kriging algorithm is used to spatialize the ATI to get a soil water content map. The proposed method was applied to an experimental area of the Alento River catchment, in southern Italy. Daytime radiometric optical multispectral and day and nighttime radiometric thermal images were acquired via a UAS, while in situ soil water content was measured through the thermo-gravimetric and time domain reflectometry (TDR) methods. The determination coefficient between ATI and soil water content measured over unshaded bare soil was 0.67 for the gravimetric method and 0.73 for the TDR. After interpolation, the correlation slightly decreased due to the introduction of measurements on vegetated or shadowed positions (r² = 0.59 for gravimetric method; r² = 0.65 for TDR). The proposed method shows promising results to map the soil water content even over vegetated or shadowed areas by exploiting hyperspatial resolution data and geostatistical analysis.

How to cite: Paruta, A., P. Nasta, G. Ciraolo, F. Capodici, S. Manfreda, N. Romano, E. Bendor, Y. Zeng, A. Maltese, S. F. Dal Sasso and R. Zhuang, A geostatistical approach to map near-surface soil moisture through hyper-spatial resolution thermal inertia, IEEE Transactions on Geoscience and Remote Sensing, (doi: 10.1109/TGRS.2020.3019200) 2020. [pdf]

Towards harmonisation of image velocimetry techniques for river surface velocity observations

Since the turn of the 21st century, image-based velocimetry techniques have become an increasingly popular approach for determining open-channel flow in a range of hydrological settings across Europe and beyond. Simultaneously, a range of large-scale image velocimetry algorithms have been developed that are equipped with differing image pre-processing and analytical capabilities. Yet in operational hydrometry, these techniques are utilised by few competent authorities. Therefore, imagery collected for image velocimetry analysis (along with reference data) is required both to enable inter-comparisons between these differing approaches and to test their overall efficacy. Through benchmarking exercises, it will be possible to assess which approaches are best suited for a range of fluvial settings, and to focus future software developments. Here we collate and describe datasets acquired from seven countries across Europe and North America, consisting of videos that have been subjected to a range of pre-processing and image velocimetry analyses (Perks et al.2020https://doi.org/10.4121/uuid:014d56f7-06dd-49ad-a48c-2282ab10428e). Reference data are available for 12 of the 13 case studies presented, enabling these data to be used for reference and accuracy assessment.

How to cite: Perks, M. T., Dal Sasso, S. F., Hauet, A., Jamieson, E., Le Coz, J., Pearce, S., Peña-Haro, S., Pizarro, A., Strelnikova, D., Tauro, F., Bomhof, J., Grimaldi, S., Goulet, A., Hortobágyi, B., Jodeau, M., Käfer, S., Ljubičić, R., Maddock, I., Mayr, P., Paulus, G., Pénard, L., Sinclair, L., and Manfreda, S.: Towards harmonisation of image velocimetry techniques for river surface velocity observations, Earth Syst. Sci. Data, 12, 1545–1559, https://doi.org/10.5194/essd-12-1545-2020, 2020. [pdf]