A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations

While the availability and affordability of unmanned aerial systems (UASs) has led to the rapid development of remote sensing applications in hydrology and hydrometry, uncertainties related to such measurements must be quantified and mitigated. The physical instability of the UAS platform inevitably induces motion in the acquired videos and can have a significant impact on the accuracy of camera-based measurements, such as velocimetry. A common practice in data preprocessing is compensation of platform-induced motion by means of digital image stabilisation (DIS) methods, which use the visual information from the captured videos – in the form of static features – to first estimate and then compensate for such motion. Most existing stabilisation approaches rely either on customised tools developed in-house, based on different algorithms, or on general purpose commercial software. Intercomparison of different stabilisation tools for UAS remote sensing purposes that could serve as a basis for selecting a particular tool in given conditions has not been found in the literature. In this paper, we have attempted to summarise and describe several freely available DIS tools applicable to UAS velocimetry. A total of seven tools – six aimed specifically at velocimetry and one general purpose software – were investigated in terms of their (1) stabilisation accuracy in various conditions, (2) robustness, (3) computational complexity, and (4) user experience, using three case study videos with different flight and ground conditions. In an attempt to adequately quantify the accuracy of the stabilisation using different tools, we have also presented a comparison metric based on root mean squared differences (RMSDs) of inter-frame pixel intensities for selected static features. The most apparent differences between the investigated tools have been found with regards to the method for identifying static features in videos, i.e. manual selection of features or automatic. State-of-the-art methods which rely on automatic selection of features require fewer user-provided parameters and are able to select a significantly higher number of potentially static features (by several orders of magnitude) when compared to the methods which require manual identification of such features. This allows the former to achieve a higher stabilisation accuracy, but manual feature selection methods have demonstrated lower computational complexity and better robustness in complex field conditions. While this paper does not intend to identify the optimal stabilisation tool for UAS-based velocimetry purposes, it does aim to shed light on details of implementation, which can help engineers and researchers choose the tool suitable for their needs and specific field conditions. Additionally, the RMSD comparison metric presented in this paper can be used in order to measure the velocity estimation uncertainty induced by UAS motion.

How to cite: Ljubičić, R., Strelnikova, D., Perks, M. T., Eltner, A., Peña-Haro, S., Pizarro, A., Dal Sasso, S. F., Scherling, U., Vuono, P., and Manfreda, S.: A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations, Hydrol. Earth Syst. Sci., 25, 5105–5132, https://doi.org/10.5194/hess-25-5105-2021, 2021. [pdf]

Characterizing vegetation complexity with unmanned aerial systems (UAS) – A framework and synthesis

Ecosystem complexity is among the important drivers of biodiversity and ecosystem functioning, and unmanned aerial systems (UASs) are becoming an important tool for characterizing vegetation patterns and processes. The variety of UASs applications is immense, and so are the procedures to process UASs data described in the literature. Optimizing the workflow is still a matter of discussion. Here, we present a comprehensive synthesis aiming to identify common rules that shape workflows applied in UAS-based studies facing complexity in ecosystems. Analysing the studies, we found similarities irrespective of the ecosystem, according to the character of the property addressed, such as species composition (biodiversity), ecosystem structure (stand volume/complexity), plant status (phenology and stress levels), and dynamics (disturbances and regeneration). We propose a general framework allowing to design UAS-based vegetation surveys according to its purpose and the component of ecosystem complexity addressed. We support the framework by detailed schemes as well as examples of best practices of UAS studies covering each of the vegetation properties (i.e. composition, structure, status and dynamics) and related applications. For an efficient UAS survey, the following points are crucial: knowledge of the phenomenon, choice of platform, sensor, resolution (temporal, spatial and spectral), model and classification algorithm according to the phenomenon, as well as careful interpretation of the results. The simpler the procedure, the more robust, repeatable, applicable and cost effective it is. Therefore, the proper design can minimize the efforts while maximizing the quality of the results.

How to cite: Müllerová J. , X. Gago, M. Bučas,J. Company, J. Estrany, J. Fortesa, S. Manfreda, A. Michez, M. Mokroš, G. Paulus, E. Tiškus, M. A. Tsiafouli, R. Kent, Characterizing vegetation complexity with unmanned aerial systems (UAS) – A framework and synthesis, Ecological Indicators, Volume 131, November 2021, 108156. [pdf]

Recent Advancements and Perspectives in UAS-Based Image Velocimetry

Videos acquired from Unmanned Aerial Systems (UAS) allow for monitoring river systems at high spatial and temporal resolutions providing unprecedented datasets for hydrological and hydraulic applications. The cost-effectiveness of these measurement methods stimulated the diffusion of image-based frameworks and approaches at scientific and operational levels. Moreover, their application in different environmental contexts gives us the opportunity to explore their reliability, potentialities and limitations, and future perspectives and developments. This paper analyses the recent progress on this topic, with a special focus on the main challenges to foster future research studies.

How to cite: Dal Sasso, S.F.; Pizarro, A.; Manfreda, S. Recent Advancements and Perspectives in UAS-Based Image VelocimetryDrones5, 81, 2021. [pdf]

Mapping Water Infiltration Rate Using Ground and UAV Hyperspectral Data: A Case Study of Alento, Italy

Water infiltration rate (WIR) into the soil profile was investigated through a comprehensive study harnessing spectral information of the soil surface. As soil spectroscopy provides invaluable information on soil attributes, and as WIR is a soil surface-dependent property, field spectroscopy may model WIR better than traditional laboratory spectral measurements. This is because sampling for the latter disrupts the soil-surface status. A field soil spectral library (FSSL), consisting of 114 samples with different textures from six different sites over the Mediterranean basin, combined with traditional laboratory spectral measurements, was created. Next, partial least squares regression analysis was conducted on the spectral and WIR data in different soil texture groups, showing better performance of the field spectral observations compared to traditional laboratory spectroscopy. Moreover, several quantitative spectral properties were lost due to the sampling procedure, and separating the samples according to texture gave higher accuracies. Although the visible near-infrared–shortwave infrared (VNIR–SWIR) spectral region provided better accuracy, we resampled the spectral data to the resolution of a Cubert hyperspectral sensor (VNIR). This hyperspectral sensor was then assembled on an unmanned aerial vehicle (UAV) to apply one selected spectral-based model to the UAV data and map the WIR in a semi-vegetated area within the Alento catchment, Italy. Comprehensive spectral and WIR ground-truth measurements were carried out simultaneously with the UAV–Cubert sensor flight. The results were satisfactorily validated on the ground using field samples, followed by a spatial uncertainty analysis, concluding that the UAV with hyperspectral remote sensing can be used to map soil surface-related soil properties.

How to cite: Francos, N.; Romano, N.; Nasta, P.; Zeng, Y.; Szabó, B.; Manfreda, S.; Ciraolo, G.; Mészáros, J.; Zhuang, R.; Su, B.; Ben-Dor, E.  Mapping Water Infiltration Rate Using Ground and UAV Hyperspectral Data: a Case Study of AlentoItalyRemote Sensing13, 2606, (doi: 10.3390/rs13132606) 2021. [pdf]

Special Issue on RS entitled Global Gridded Soil Information Based on Machine Learning

This Special Issue is dedicated to machine learning-based methods in:•proximal and digital global mapping of soil properties (e.g., basic, hydraulic, thermal, functional, ecosystem services);•computing systems/algorithms/approaches using Earth observation data to derive global gridded soil datasets;•preprocessing Earth observation data to feed into global soil mapping;•data-intensive computing methods for incorporating Earth observation data for predictive soil mapping;•optimizing temporal resolution to globally track the changes of soil properties;•uncertainty assessment of the derived gridded soil information;•other related topics.

A 40% discount can be granted to papers received from this conference/project on the basis that the manuscript is accepted for publication following the peer review process.

Website

Interview on SpeCtrum

Can you tell us how you started working on using UASs for environmental monitoring? What was your motivation, and what did you find the most interesting in this research field? What are the knowledge gaps and major challenges in this research field?

I have always been interested in spatial patterns of natural ecosystems. Nature is able to create an incredible diversity of elements that have been inspiring for all of us. The driving processes that produce such patterns are open questions stimulating many of my studies. In this context, UAS offers the opportunity to explore such patterns at a level of detail that was unimaginable a few years ago. Therefore, I envisaged the possibility to use this tool to tackle my research questions in the field of hydrological and ecohydrological science.

Can you share with us any current specific project, activity, or initiative that you are particularly excited about?

I’m particularly proud to be the Chair of the COST Action “Harmonization of UAS techniques for agricultural and natural ecosystems monitoring – HARMONIOUS”, which includes more than 100 scientists from 36 countries. The HARMONIOUS Action is one of the biggest Actions funded by COST Organization (https://www.cost.eu) focusing on the development of guidelines for the use of UAS applied for hydrological monitoring. Members of the HARMONIOUS Action are now focusing on the preparation of a book edited by Elsevier providing more detailed guidelines for UAS applications in hydrology, which will be one of the main deliverables of the project.

More details about the project activities can be found on the web-page

What are some of the areas of research you’d like to see tackled over the next ten years?

UAS offers the opportunity of acquiring high-resolution data for monitoring environmental processes, bridging the gap between traditional field studies and satellite remote sensing [An important paper in this context is https://doi.org/10.3390/rs10040641]. Their versatility, adaptability, and flexibility may allow the implementation of new strategies to support the validation of satellite products, which are systematically adopted in a series of operational weather and hydrological models. This may help to develop an integrated global monitoring system of higher accuracy and precision.

Can you share with us your perspectives and experiences on how UAS remote sensing has changed the way the world addresses environmental monitoring and conservation agendas? What do you think is the role of remote sensing and geospatial information science in achieving a sustainable environment?

With the evolution of drone technologies over the last decade, UAS became an inexpensive way of mapping environmental processes for forestry planning, tracking landslides, river monitoring and precision agriculture. Environmental agencies and civil protection are increasingly adopting UAS- photogrammetry, but there are an enormous number of additional information that may be retrieved by UAS (e.g., stream flow, morphological evolution, soil moisture, state of vegetation, among others). It is our responsibility to simplify the use of UASs and make their products accessible to anyone.

What are some of the biggest challenges you face (or have you faced) as a scientist in your field? Are there any common misconceptions about this area of research?

It is common to underestimate the complexity associated with the use of these tools. UAS requires a large number of competencies and knowledge that should be implemented in clear protocols in order to transform the huge amount of data acquired to useful information. Therefore, one challenge is represented by the standardization of procedures adopted for UAS surveys in different operating configurations and environmental conditions. In this context, the members of the HARMONIOUS COST Action have published some preliminary studies to support this process [see the manuscript].

Finally, what are you most passionate about? What is your advice to students and young professionals who are pursuing research on UAS remote sensing and environmental protection, and nature conservation? Which areas in this research field remain understudied and should be considered for future research?

I believe that UAS remote sensing will evolve in the coming years, offering new monitoring opportunities. One of the main limitations that we are encountering right now in the description of hydrological processes is represented by the limited extent of UAS imagery. There is a pressing need to extend the limits of surveyed areas in order to have intercomparison between UAS and satellite data. This may help to define downscaling procedures for the estimation of environmental variables at high resolution and over large scales. This will be possible with the use of long range UAS or with swarms of drones which will be fundamental for future advances in remote sensing.

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: Ljubičić, R., D. Strelnikova, M. T. Perks, A. Eltner, S. Peña-Haro, A. Pizarro, S. F. Dal Sasso, U. Scherling, P. Vuono, and S. Manfreda, A comparison of tools and techniques for stabilising UAS imagery for surface flow observations, Hydrology and Earth System Sciences, 2021. [pdf]

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