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

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.

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.