3D Models of the Cultural Heritage

UAS-based surveys and structure from motion (SfM) can lead to extraordinary and realistic 3D models to preserve our cultural heritage.

In our recent applications, our members are developing new strategies to build extremely detailed point clouds using UAS and portable cameras. In the following, we provided some examples developed within HARMONIOUS partnership cooperation:

Planning the future of Harmonious

The Department of Topography and Cartography of the Technical University of Madrid hosted our work group meeting of COST Action – HARMONIOUS from 27 up to the 30 of October.

During this meeting the WG1 finalized the Glossary of terms used for UAS-based applications considering the three macro categories : platform and equipment, software and outputs.

GLOSSARY

1 Category: Platforms and Equipment 

  • Global Navigation Satellite System (GNSS) is a constellation of satellites used for positioning a receiver on the ground.
  • GALILEO is the GNSS European solution used to determine the ground position of an object.  
  • GPS is the most common GNSS based on the reception of signals from about 24 orbiting satellites by the USA, used to determine the ground position of an object. This global and accurate system allows users to know their exact location, velocity, and time 24 hours per day, anywhere in the world.    
  • Light Detection and Ranging (LiDAR) is based on laser pulses to locate the acquired point cloud in a 3D remote sensing. LiDAR data products are often managed within a gridded or raster data format.
  • Multispectral imaging captures image data within specific wavelength ranges across the electromagnetic spectrum.  The used spectral regions are often at least partially outside the visible spectral range, covering parts of the infrared and ultraviolet region. For example, a multi-spectral imager may provide wavelength channels for near-UV, red, green, blue, near-infrared, mid-infrared and far-infrared light – sometimes even thermal radiation.
  • Near Infrared (NIR) is a subset of the infrared band that is just outside the range of what humans can see. Applied to cameras, NIR cameras cover the wavelength range of 900 to 1700 nm, a range that is best suited for absorption and radiation characteristics analyses.
  • Noise    is an irregular fluctuation that accompanies a transmitted electrical signal but is not part of it and tends to obscure it. The main sources of noise can be divided into two main categories: the physical noise, linked to physics constraints like the corpuscular nature of light, and the hardware noise, linked to mechanical issues in the camera.
  • Optical Camera is a photographic device aimed to form and record an image of an object. An optical camera sensor is an imager that collects visible light (400~700nm).
  • Payload is the weight a drone or unmanned aerial vehicle (UAV) can carry on board. It is usually counted outside of the weight of the drone itself and includes anything additional to the drone – such as extra cameras, sensors, or packages for delivery.
  • Pixel size of an image identifies the spatial resolution and it is dependent on the sensor capabilities. It provides a measure of the image resolution, which is higher with finer grids, where the degree of recognizable details increases.
  • RGB Camera is equipped with a standard Complementary Metal Oxide Semiconductor (CMOS) sensor through which the colourful images of persons and objects are acquired. In a CMOS sensor, the charge from the photosensitive pixel is converted to a voltage at the pixel site and the signal is multiplied by row and column to multiple on chip Digital-to-Analog Converters (DACs). In a RGB camera, the acquisition of static photos is commonly expressed in megapixels that define the amount of pixels in a singular photo. While, the acquisition of videos is usually expressed with terms such as Full HD or Ultra HD.        
  • Thermal Camera is a non-contact temperature measurement sensor. All objects (above absolute zero) emit infrared energy as a function of their temperature. The vibration of atoms and molecules generates infrared energy. The higher the temperature of an object, the faster its molecules and atoms move. This movement is emitted as infrared radiation, which our eyes cannot see but our skin can feel (as heat). Thermal imaging uses special infrared camera sensors to illuminate a spectrum of light invisible to the naked eye. Thermal energy is invisible to the naked eye and works in different ways; it can be emitted, absorbed, or reflected. Infrared cannot see through objects but can detect differences in radiated thermal energy between materials. This is known as thermal bridging or heat transfer. 
  • Unmanned Aerial System (UAS) is a remotely controlled professional system integrating several technological components (e.g., navigation system, gyroscope, and sensors) in order to perform spatial observations.
  • Unmanned Aerial Vehicle (UAV) is a remotely controlled vehicle able to perform several operations and observations.

2 Software 

  • Aero-triangulation is the method most frequently applied to the photogrammetry to determine the X, Y, and Z ground coordinates of individual points based on photo coordinate measurements. The purpose of aero-triangulation is to increase the density of a geodetic network in order to provide images with an exhaustive number of control points for topographic mapping. Deliverables from aero-triangulation may be three-dimensional or planimetric, depending on the number of point coordinates determined.
  • Checkpoints are Ground Control Points (GCPs) used to validate the relative and absolute accuracy of the geo-localization of maps. The checkpoints are not used for processing. Instead, they are used to calculate the error of the map by comparing the known measured locations of the checkpoints to the coordinates of the checkpoints shown on the map.
  • Flight Type refers to the flight mission mode (manual or autonomous). In the manual mode, a pilot manages the UAS during the flight. The autonomous mission is programmed to react to various types of events, in a preset and direct way by means of special sensors. This makes UAS flight predictable and subject to intervention by a remote pilot, only if necessary.
  • Flight Time is a measurement of the total time needed to complete a mission, from the first to the last image taken during a flight. Flight time can be used to characterize the wind impacts on flight performance of UAS.    
  • Ground Control Points (GCPs) are user defined and priorly determined tie points within the mapping polygon used in the process of indirectly georeferencing UAS images. Such tie points can be permanent or portable markers with or without georeferenced data.
  • Masking is the procedure of excluding some part of the scene from image analysis. For instance, clouds, trees, bushes and their shadows should not be considered in further processing, such as in vegetation studies for the evaluation of crop vegetation indices.        
  • Orthorectification is a process of linearly scaling the image pixel size to real-world distances. This is achieved by accounting for the impacts of camera perspective and relative height above the sensed object. The objective is the reprojection of the original image, which could be captured from oblique viewing angles looking at unlevelled terrain, into an image plane to generate a distortion-free photo. 
  • Point Cloud is a collection of data points in a three-dimensional plane. Each point contains several measurements, including its coordinates along the X, Y, and Z-axes, and sometimes additional data such as a color value, which is stored in RGB format, and luminance value, which determines how bright the point is.
  • Radiometric Calibration is a process that allows the transformation of the intensities or digital numbers (DN) of multiple images in order to describe an area and detect relative changes of the landscape, removing anomalies due to atmospheric factors or illumination conditions. 
  • Structure from Motion (SfM) is the process of reconstructing a three-dimensional model from the projections derived from a series of images taken from different viewpoints. Camera orientation and scene geometry are reconstructed simultaneously through the automatic identification of matching features in multiple images.        
  • Tie Point is a point in a digital image or aerial photograph that can be found in the same location in an adjacent image or aerial photograph. A tie point is a feature that can be clearly identified in two or more images and selected as a reference point and whose ground coordinates are not known. The ground coordinates of Tie Points are computed during block triangulation. So, Tie points represent matches between key points detected on two (or more) different images and represent the link between images to get 3D relative positioning.
  • Precision is a description of random errors in the 2D/3D representations.
  • Quality Assessment is an estimation of the statistical geometric and radiometric errors of the final products obtained using ground true data.           

UAS-based Outputs

  • 2D Model is a bidimensional representation of the earth that contains 2 coordinates X and Y.
  • 3D Model is a mathematical or virtual representation of a three dimensional object.
  • 2.5D Model (Pseudo 3D Model) is a three-dimensional representation that uses X, Y coordinates, which are associated to a single elevation value in order to relate different points.
  • Digital Elevation Model (DEM) or Digital Height Model (DHM) is a gridded image describing the altitude of the earth excluding all other objects artificial or natural.    
  • Digital Surface Model (DSM) is a gridded image describing the altitude of the earth including all other objects artificial or natural. For instance, the DSM provides information about dimensions of buildings and forests.    
  • Digital Terrain Model (DTM) is a vector or raster dataset consisting of a virtual representation of the land environment in the mapping polygon. In a DTM the height of the point belongs to the bare ground.
  • Orthophoto is an aerial or terrestrial photograph that has been geometrically corrected to make the scale of the photograph uniform and use it as a map. Since each pixel of the orthophoto has a X and Y, it can be overlapped to other orthophotos, and it can be used to measure true distances of features within the photograph.        
  • Orthomosaic    is a high resolution image made by the combination of many orthophotos. It is a single, radiometrically corrected image that offers a photorealistic representation of an area that can produce surveyor-grade measurements of topography, infrastructure, and buildings.    
  • Feature Identification is a vector information computed from images using artificial intelligence algorithms in order to identify objects (roads, buildings, bridges, etc.) automatically. 
  • Point Cloud is a set of data points in space representing a three-dimensional object. Each point position has its set of Cartesian coordinates (X, Y, Z). It can be generated from overlapping images or LiDAR sensors.
  • Point Cloud Classification is the output of an algorithm that classifies the points of a cloud by computing a set of geometric and radiometric attributes.
  • Image Segmentation is a process that detects the features of an image clearly distinguishable based on the image texture and color.
  • Triangulated Irregular Network (TIN) is a pseudo three-dimensional representation obtained from the  relations in a point cloud using triangles.   
  • Vegetation Indices (VIs) are combinations of surface reflectance at two or more wavelengths designed to highlight a particular property of vegetation. VIs are designed to maximize sensitivity to the vegetation characteristics while minimizing confounding factors such as soil background reflectance, directional, or atmospheric effects. VIs can be found in the scientific literature under different forms such as NDVI, EVI, SAVI, etc.                
  • Aerial photograph is an image taken from an air-borne (i.e., UAS) platform using a precision camera. From aerial photographs, it is possible to derive qualitative information of the depicted areas, such as land use/land cover, topographical forms, soil types, etc. 
  • Terrestrial photograph is an image taken from the earth surface using a camera with an orientation that in most cases is not Nadiral.               

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.

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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.