Photogrammetry for environmental monitoring: the use of drones and hydrological models for detection of soil contaminated by copper

Combining Interior Orientation Variables to Predict the Accuracy of Rpas–Sfm 3D Models

Remotely piloted aerial systems (RPAS) have been recognized as an effective low-cost tool to acquire photogrammetric data of low accessible areas reducing collection and processing time. Data processing techniques like structure from motion (SfM) and multiview stereo (MVS) techniques, can nowadays provide detailed 3D models with an accuracy comparable to the one generated by other conventional approaches. Accuracy of RPAS-based measures is strongly dependent on the type of adopted sensors. Nevertheless, up to now, no investigation was done about relationships between camera calibration parameters and final accuracy of measures. In this work, authors tried to fill this gap by exploring those dependencies with the aim of proposing a prediction function able to quantify the potential final error in respect of camera parameters. Predictive functions were estimated by combining multivariate and linear statistical techniques. Four photogrammetric RPAS acquisitions were considered, supported by ground surveys, to calibrate the predictive model while a further acquisition was used to test and validate it. Results are preliminary, but promising. The calibrated predictive functions relating camera internal orientation (I.O.) parameters with final accuracy of measures (root mean squared error) showed high reliability and accuracy.

Unmanned Aerial Vehicles for Debris Survey in Coastal Areas: Long-Term Monitoring Programme to Study Spatial and Temporal Accumulation of the Dynamics of Beached Marine Litter

Unmanned aerial vehicles (UAVs) are becoming increasingly accessible tools with widespread use as environmental monitoring systems. They can be used for anthropogenic marine debris survey, a recently growing research field. In fact, while the increasing efforts for offshore investigations lead to a considerable collection of data on this type of pollution in the open sea, there is still little knowledge of the materials deposited along the coasts and the mechanism that leads to their accumulation pattern. UAVs can be effective in bridging this gap by increasing the amount of data acquired to study coastal deposits, while also limiting the anthropogenic impact in protected areas. In this study, UAVs have been used to acquire geo-referenced RGB images in a selected zone of a protected marine area (the Migliarino, Massacciuccoli, and San Rossore park near Pisa, Italy), during a long-term (ten months) monitoring programme. A post processing system based on visual interpretation of the images allows the localization and identification of the anthropogenic marine debris within the scanned area, and the estimation of their spatial and temporal distribution in different zones of the beach. These results provide an opportunity to investigate the dynamics of accumulation over time, suggesting that our approach might be appropriate for monitoring and collecting such data in isolated, and especially in protected, areas with significant benefits for different types of stakeholders.

Mapping marine litter using UAS on a beach-dune system: a multidisciplinary approach

The amount of marine litter, mainly composed by plastic materials, has become a global environmental issue in coastal environments. Traditional monitoring programs are based on in-situ visual census, which require human effort and are time-demanding. Therefore, it is crucial to implement innovative mapping strategies to improve the environmental monitoring of marine litter on the coast. This work presents a procedure for an automated Unmanned Aerial System (UAS)-based marine litter mapping on a beach-dune system. A multidisciplinary framework, which comprises photogrammetry, geomorphology, machine learning and hydrodynamic modelling, was developed to process a block of UAS images. The work shows how each of these scientific methodologies can be complementary to improve and making more efficient the mapping of marine litter items with UAS on coastal environment. The very high-resolution orthophoto produced from UAS images was automatically screened by random forest machine learning method, in order to characterize the marine litter load on beach and dune areas, distinctively. The marine litter objects were identified with a F-test score of 75% when compared to manual procedure. The location of major marine litter loads within the monitored area was found related to beach slope and water level dynamics on the beach profiles, suggesting that UAS flight deployment and post-processing for beach litter mapping can be optimized based on these environmental parameters. The described UAS-based marine litter detection framework is intended to support scientists, engineers and decision makers aiming at monitoring marine and coastal pollution, with the additional aim of optimizing and automating beach clean-up operations.

A Comparative Analysis of the Legislation Evolution for Drone Use in OECD Countries

Drones have been employed for multiple uses, such as for military, surveillance, recreational, scientific, and research purposes. Their presence inside civil areas has necessitated the need to regulate their use. Towards this direction, many countries worldwide have issued national legislations, which vary on vehicle categorization according to the size, weight, flight altitude, purpose of use, and restrictions. In this study, we pursued the first comparative analysis of the Organization for Economic Co-operation and Development (OECD) countries’ national legislations, in order to explore the similarities and differences in drone use and recommend improvements and homogenization. Some of the examined countries issued legislation during recent years of drone application, while others amended their existing legislative framework in order to catch up with drone technology evolution. Although from the 35 OECD countries 22 belong to the European Union, we observed much diversity among national legal frameworks. The intensive use of drones has led to severe ethical dilemmas that policy makers will need to address in the near future. We conclude with a proposal regarding the basic legislation for different uses according to the criteria that have been developed so far, followed by limitations and restrictions.

The Design and Experimental Development of Air Scanning Using a Sniffer Quadcopter

This study presents a detailed analysis of an air monitoring development system using quadcopters. The data collecting method is based on gas dispersion investigation to pinpoint the gas source location and determine the gas concentration level. Due to its flexibility and low cost, a quadcopter was integrated with air monitoring sensors to collect the required data. The analysis started with the sensor placement on the quadcopter and their correlation with the generated vortex. The reliability and response time of the sensor used determine the duration of the data collection process. The dynamic nature of the environment makes the technique of air monitoring of topmost concern. The pattern method has been adapted to the data collection process in which area scanning was marked using a point of interest or grid point. The experiments were done by manipulating a carbon monoxide (CO) source, with data readings being made in two ways: point source with eight sampling points arranged in a square pattern, and non-point source with 24 sampling points in a grid pattern. The quadcopter collected data while in a hover state with 10 s sampling times at each point. The analysis of variance method (ANOVA) was also used as the statistical algorithm to analyze the vector of gas dispersion. In order to tackle the uncertainty of wind, a bivariate Gaussian kernel analysis was used to get an estimation of the gas source area. The result showed that the grid pattern measurement was useful in obtaining more accurate data of the gas source location and the gas concentration. The vortex field generated by the propeller was used to speed up the accumulation of the gas particles to the sensor. The dynamic nature of the wind caused the gas flow vector to change constantly. Thus, more sampling points were preferred, to improve the accuracy of the gas source location prediction.

UAS for Wetland Mapping and Hydrological Modeling

The miniaturization and affordable production of integrated microelectronics have improved in recent years, making unmanned aerial systems (UAS) accessible to consumers and igniting their interest. Researchers have proposed UAS-based solutions for almost any conceivable problem, but the greatest impact will likely be in applications that exploit the unique advantages of the technology: work in dangerous or difficult-to-access areas, high spatial resolution and/or frequent measurements of environmental phenomena, and deployment of novel sensing technology over small to moderate spatial scales. Examples of such applications may be the identification of wetland areas and use of high-resolution spatial data for hydrological modeling. However, because of the large—and growing—assortment of aircraft and sensors available on the market, an evolving regulatory environment, and limited practical guidance or examples of wetland mapping with UAS, it has been difficult to confidently devise or recommend UAS-based monitoring strategies for these applications. This paper provides a comprehensive review of UAS hardware, software, regulations, scientific applications, and data collection/post-processing procedures that are relevant for wetland monitoring and hydrological modeling.

Monitoring opencast mine restorations using Unmanned Aerial System (UAS) imagery

Open-pit mine is still an unavoidable activity but can become unsustainable without the restoration of degraded sites. Monitoring the restoration after extractive activities is a legal requirement for mine companies and public administrations in many countries, involving financial provisions for environmental liabilities. The objective of this contribution is to present a rigorous, low-cost and easy-to-use application of Unmanned Aerial Systems (UAS) for supporting opencast mining and restoration monitoring, complementing the inspections with very high (<10 cm) spatial resolution multispectral imagery, and improving any restoration documentation with detailed land cover maps. The potential of UAS as a tool to control restoration works is presented in a calcareous quarry that has undergone different post-mining restoration actions in the last 20 years, representing 4 reclaimed stages. We used a small (<2 kg) drone equipped with a multispectral sensor, along with field spectroradiometer measurements that were used to radiometrically correct the UAS sensor data. Imagery was processed with photogrammetric and Remote Sensing and Geographical Information Systems software, resulting in spectral information, vegetation and soil indices, structural information and land cover maps. Spectral data and land cover classification, which were validated through ground-truth plots, aided in the detection and quantification of mine waste dumping, bare soil and other land cover extension. Moreover, plant formations and vegetation development were evaluated, allowing a quantitative, but at the same time visual and intuitive comparison with the surrounding reference systems. The protocol resulting from this research constitutes a pipeline solution intended for the implementation by public administrations and privates companies for precisely evaluating restoration dynamics in an expedient manner at a very affordable budget. Furthermore, the proposed solution prevents subjective interpretations by providing objective data, which integrate new technologies at the service of scientists, environmental managers and decision makers.

Assessing climate change associated sea-level rise impacts on sea turtle nesting beaches using drones, photogrammetry and a novel GPS system

Climate change associated sea-level rise (SLR) is expected to have profound impacts on coastal areas, affecting many species, including sea turtles which depend on these habitats for egg incubation. Being able to accurately model beach topography using digital terrain models (DTMs) is therefore crucial to project SLR impacts and develop effective conservation strategies. Traditional survey methods are typically low-cost with low accuracy or high-cost with high accuracy. We present a novel combination of drone-based photogrammetry and a low-cost and portable real-time kinematic (RTK) GPS to create DTMs which are highly accurate (<10 cm error) and visually realistic. This methodology is ideal for surveying coastal sites, can be broadly applied to other species and habitats, and is a relevant tool in supporting the development of Specially Protected Areas. Here, we applied this method as a case-study to project three SLR scenarios (0.48, 0.63 and 1.20 m) and assess the future vulnerability and viability of a key nesting habitat for sympatric loggerhead (Caretta caretta) and green turtle (Chelonia mydas) at a key rookery in the Mediterranean. We combined the DTM with 5 years of nest survey data describing location and clutch depth, to identify (a) regions with highest nest densities, (b) nest elevation by species and beach, and (c) estimated proportion of nests inundated under each SLR scenario. On average, green turtles nested at higher elevations than loggerheads (1.8 m vs. 1.32 m, respectively). However, because green turtles dig deeper nests than loggerheads (0.76 m vs. 0.50 m, respectively), these were at similar risk of inundation. For a SLR of 1.2 m, we estimated a loss of 67.3% for loggerhead turtle nests and 59.1% for green turtle nests. Existing natural and artificial barriers may affect the ability of these nesting habitats to remain suitable for nesting through beach migration.

Surgical and Medical Applications of Drones: A Comprehensive Review

Background:

Drones have the ability to gather real time data cost effectively, to deliver payloads and have initiated the rapid evolution of many industrial, commercial, and recreational applications. Unfortunately, there has been a slower expansion in the field of medicine. This article provides a comprehensive review of current and future drone applications in medicine, in hopes of empowering and inspiring more aggressive investigation.

Database:

A literature search was performed by EBSCO (Elton B. Stephens Company) Discovery Service, searching the phrases “drones,” “UAV,” “unmanned aerial vehicles,” “UAS,” and “unmanned aerial systems.” A second search was used to identify sources that contained “drone” in the subject or title and “medicine” in any of the text, yielding 60,260 results. After screening for irrelevant material, 1296 sources remained applicable. Major themes and number of sources were as follows: 116 public health and medical surveillance, 8 telemedicine, and 78 medical transport systems.

Conclusion:

Drones are used for surveillance of disaster sites and areas with biological hazards, as well as in epidemiology for research and tracking disease spread. Telecommunication drones are being used for diagnosis and treatment, perioperative evaluation, and telementoring in remote areas. Drones have the potential to be reliable medical delivery platforms for microbiological and laboratory samples, pharmaceuticals, vaccines, emergency medical equipment, and patient transport. Government agencies have placed drone use on the national agenda. The next steps include aggressive research initiatives in the areas of safety, industry expansion, increased public awareness, and participation.

Comparative assessment of autochthonous bacterial and fungal communities and microbial biomarkers of polluted agricultural soils of the Terra dei Fuochi

Organic and inorganic xenobiotic compounds can affect the potential ecological function of the soil, altering its biodiversity. Therefore, the response of microbial communities to environmental pollution is a critical issue in soil ecology. Here, a high-throughput sequencing approach was used to investigate the indigenous bacterial and fungal community structure as well as the impact of pollutants on their diversity and richness in contaminated and noncontaminated soils of a National Interest Priority Site of Campania Region (Italy) called "Terra dei Fuochi". The microbial populations shifted in the polluted soils via their mechanism of adaptation to contamination, establishing a new balance among prokaryotic and eukaryotic populations. Statistical analyses showed that the indigenous microbial communities were most strongly affected by contamination rather than by site of origin. Overabundant taxa and Actinobacteria were identified as sensitive biomarkers for assessing soil pollution and could provide general information on the health of the environment. This study has important implications for microbial ecology in contaminated environments, increasing our knowledge of the capacity of natural ecosystems to develop microbiota adapted to polluted soil in sites with high agricultural potential and providing a possible approach for modeling pollution indicators for bioremediation purposes.

Drone Mission Definition and Implementation for Automated Infrastructure Inspection Using Airborne Sensors

This paper describes a Mission Definition System and the automated flight process it enables to implement measurement plans for discrete infrastructure inspections using aerial platforms, and specifically multi-rotor drones. The mission definition aims at improving planning efficiency with respect to state-of-the-art waypoint-based techniques, using high-level mission definition primitives and linking them with realistic flight models to simulate the inspection in advance. It also provides flight scripts and measurement plans which can be executed by commercial drones. Its user interfaces facilitate mission definition, pre-flight 3D synthetic mission visualisation and flight evaluation. Results are delivered for a set of representative infrastructure inspection flights, showing the accuracy of the flight prediction tools in actual operations using automated flight control.

Emerging technologies to measure neighborhood conditions in public health: implications for interventions and next steps

Adverse neighborhood conditions play an important role beyond individual characteristics. There is increasing interest in identifying specific characteristics of the social and built environments adversely affecting health outcomes. Most research has assessed aspects of such exposures via self-reported instruments or census data. Potential threats in the local environment may be subject to short-term changes that can only be measured with more nimble technology. The advent of new technologies may offer new opportunities to obtain geospatial data about neighborhoods that may circumvent the limitations of traditional data sources. This overview describes the utility, validity and reliability of selected emerging technologies to measure neighborhood conditions for public health applications. It also describes next steps for future research and opportunities for interventions. The paper presents an overview of the literature on measurement of the built and social environment in public health (Google Street View, webcams, crowdsourcing, remote sensing, social media, unmanned aerial vehicles, and lifespace) and location-based interventions. Emerging technologies such as Google Street View, social media, drones, webcams, and crowdsourcing may serve as effective and inexpensive tools to measure the ever-changing environment. Georeferenced social media responses may help identify where to target intervention activities, but also to passively evaluate their effectiveness. Future studies should measure exposure across key time points during the life-course as part of the exposome paradigm and integrate various types of data sources to measure environmental contexts. By harnessing these technologies, public health research can not only monitor populations and the environment, but intervene using novel strategies to improve the public health.