Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review

Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earth’s surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earth’s surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future

Autonomous surveillance for biosecurity

The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats. We discuss the spatial and temporal attributes of autonomous surveillance technologies and map them to three broad categories of biosecurity threat: (i) vector-borne diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a broad range of opportunities to serve biosecurity needs through autonomous surveillance.Comment: 26 pages, Trends in Biotechnology, 3 March 2015, ISSN 0167-7799, http://dx.doi.org/10.1016/j.tibtech.2015.01.003. (http://www.sciencedirect.com/science/article/pii/S0167779915000190

Geosensors to Support Crop Production: Current Applications and User Requirements

Sensor technology, which benefits from high temporal measuring resolution, real-time data transfer and high spatial resolution of sensor data that shows in-field variations, has the potential to provide added value for crop production. The present paper explores how sensors and sensor networks have been utilised in the crop production process and what their added-value and the main bottlenecks are from the perspective of users. The focus is on sensor based applications and on requirements that users pose for them. Literature and two use cases were reviewed and applications were classified according to the crop production process: sensing of growth conditions, fertilising, irrigation, plant protection, harvesting and fleet control. The potential of sensor technology was widely acknowledged along the crop production chain. Users of the sensors require easy-to-use and reliable applications that are actionable in crop production at reasonable costs. The challenges are to develop sensor technology, data interoperability and management tools as well as data and measurement services in a way that requirements can be met, and potential benefits and added value can be realized in the farms in terms of higher yields, improved quality of yields, decreased input costs and production risks, and less work time and load

Images from unmanned aircraft systems for surveying aquatic and riparian vegetation

Aquatic and riparian vegetation in lakes, streams, and wetlands has important ecological and regulatory functions and should be monitored to detect ecosystem changes. Field surveys are often tedious and in countries with numerous lakes and streams a nationwide assessment is difficult to achieve. Remote sensing with unmanned aircraft systems (UASs) provides aerial images with high spatial resolution and offers a potential data source for detailed vegetation surveys. The overall objective of this thesis was to evaluate the potential of sub-decimetre resolution true-colour digital images acquired with a UAS for surveying non-submerged (i.e., floating-leaved and emergent) aquatic and riparian vegetation at a high level of thematic detail. At two streams and three lakes in northern Sweden we applied several image analysis methods: Visual interpretation, manual mapping, manual mapping in combination with GPS-based field surveys, and automated object-based image analysis and classification of both 2D images and 3D point data. The UAS-images allowed for high taxonomic resolution, mostly at the species level, with high taxa identification accuracy (>80%) also in mixed-taxa stands. UAS-images in combination with ground-based vegetation surveys allowed for the extrapolation of field sampling results, like biomass measurement, to areas larger than the sampled sites. In automatically produced vegetation maps some fine-scale information detectable with visual interpretation was lost, but time-efficiency increased which is important when larger areas need to be covered. Based on spectral and textural features and height data the automated classification accuracy of non-submerged aquatic vegetation was ~80% for all test sites at the growth-form level and for four out of five test sites at the dominant-taxon level. The results indicate good potential of UAS-images for operative mapping and monitoring of aquatic, riparian, and wetland vegetation. More case studies are needed to fully assess the added value of UAS-technology in terms of invested labour and costs compared to other survey methods. Especially the rapid technical development of multi- and hyperspectral lightweight sensors needs to be taken into account

Automated identification of river hydromorphological features using UAV high resolution aerial imagery

European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management

Spectral Identification of Wild Rice (Zizania palustris L.) Using Indigenous Knowledge and Landsat Multispectral Data

Landsat-7 ETM+ (SLC-off) multispectral satellite imagery was tested to identify and delineate natural stands of wild rice (Zizania palustris L.) from other aquatic vegetation growing on area lakes of the Leech Lake Native American reservation in northern Minnesota. Leech Lake is located within the Mississippi River Headwaters drainage ecosystem and contains some of the largest natural stands of wild rice in the country. Local indigenous knowledge; in this case, the knowledge of Ojibwe tribal elders who have traditionally harvested wild rice by canoe for centuries, was utilized to build training data polygons for a supervised classification. By testing several supervised classification algorithms, it was hypothesized that wild rice could be delineated from other aquatic vegetation, but the coarse (30 m X 30 m) spatial resolution of Landat-7 ETM+ multispectral imagery (bands 1-5) would be a limiting factor. Masking upland areas using a 5-category ISODATA Boolean mask improved the classification results of the aquatic emergent vegetation. Maximum likelihood classification yielded a 79.03% accuracy (kappa = 0.6747) and a minimum distance to means classification yielded a 51.61% accuracy (kappa = 0.2092). It was also discovered that by adding band 7 to the stack, the accuracy of the maximum likelihood classifier dropped to 43.55% accuracy (kappa = 0.1891); therefore, band 7 was omitted from the study. The use of local indigenous knowledge, which includes personal observations and recollection of past harvest years, in conjunction with satellite remote sensing data demonstrated a more precise methodology for identifying culturally important resources on tribal lands. It is recommended that higher spatial resolution imagery be used in conjunction with local indigenous knowledge to identify and delineate species-specific landcover categories such as wild rice. This unique methodology has great potential in many remote regions of the world where indigenous peoples still subsist from the land

IPM2.0: PRECISION AGRICULTURE FOR SMALL-SCALE CROP PRODUCTION

In order to manage pests impacting New England crop production integrated pest management (IPM) practices should be reevaluated or updated regularly to ensure that effective control of crop pests is being achieved. Three fungal taxa, Colletotrichum gloeosporioides, C. acutatum, and Glomerella cingulata, are currently associated with bitter-rot of apple (Malus domestica), with C. acutatum typically being the dominant species found in the northeastern United States. However, a recent phylogenetic study demonstrated that both C. gloeosporioides and C. acutatum are species complexes with over 10 distinct species being recovered from apple between the two studies. Based on this recent information, the objectives of this study were 1) to complete a phylogenetic analysis to determine species diversity and distribution of Colletotrichum isolates associated with bitter-rot and Glomerella leaf spot in the northeastern United States and 2) to evaluate the sensitivity of these isolates to several commercially used fungicides. A multi-gene phylogenetic analysis was completed using ITS, GADPH and BT gene sequences in order to determine which species and how many species of Colletotrichum were infecting apples in the northeastern U.S. The results of this study demonstrated that C. fioriniae is the primary pathogen causing both bitter rot and Glomerella leaf spot in the northeastern U.S. A second experiment was conducted in order to update management practices for apple scab, caused by the ascomycete Venturia inaequalis. The objective of this project was to evaluate the ability of RIMpro, an apple scab warning system, to control apple scab in New England apple orchards in addition to evaluating the performance of potassium bicarbonate + sulfur as a low-cost alternative spray material for the control of apple scab suitable for organic apple production. Use of RIMpro allowed for the reduction in the total number of spray applications made during the primary scab season by two sprays in 2013 and one spray in 2014 (28% and 25% reductions, respectively). Also, the potassium bicarbonate + sulfur treatment was shown to provide the same level of control as Captan. Finally, disease outbreaks, insect infestation, nutrient deficiencies, and weather variation constantly threaten to diminish annual yields and profits in orchard crop production systems. Automated crop inspection with an unmanned aerial vehicle (UAV) can allow growers to regularly survey crops and detect areas affected by disease or stress and lead to more efficient targeted applications of pesticides, water and fertilizer. The overall goal of this project was to develop a low cost aerial imaging platform coupling imaging sensors with UAVs to be used for monitoring crop health. Following completion of this research, we have identified a useful tool for agricultural and ecological applications

Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure

Feature Papers of Drones - Volume II

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 24–41 are focused on drone applications, but emphasize two types: firstly, those related to agriculture and forestry (articles 24–35) where the number of applications of drones dominates all other possible applications. These articles review the latest research and future directions for precision agriculture, vegetation monitoring, change monitoring, forestry management, and forest fires. Secondly, articles 36–41 addresses the water and marine application of drones for ecological and conservation-related applications with emphasis on the monitoring of water resources and habitat monitoring. Finally, articles 42–54 looks at just a few of the huge variety of potential applications of civil drones from different points of view, including the following: the social acceptance of drone operations in urban areas or their influential factors; 3D reconstruction applications; sensor technologies to either improve the performance of existing applications or to open up new working areas; and machine and deep learning development