119 research outputs found

    Development and Evaluation of Unmanned Aerial Vehicles for High Throughput Phenotyping of Field-based Wheat Trials.

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    Growing demands for increased global yields are driving researchers to develop improved crops, capable of securing higher yields in the face of significant challenges including climate change and competition for resources. However, abilities to measure favourable physical characteristics (phenotypes) of key crops in response to these challenges is limited. For crop breeders and researchers, current abilities to phenotype field-based experiments with sufficient precision, resolution and throughput is restricting any meaningful advances in crop development. This PhD thesis presents work focused on the development and evaluation of Unmanned Aerial Vehicles (UAVs) in combination with remote sensing technologies as a solution for improved phenotyping of field-based crop experiments. Chapter 2 presents first, a review of specific target phenotypic traits within the categories of crop morphology and spectral reflectance, together with critical review of current standard measurement protocols. After reviewing phenotypic traits, focus turns to UAVs and UAV specific technologies suitable for the application of crop phenotyping, including critical evaluation of both the strengths and current limitations associated with UAV methods and technologies, highlighting specific areas for improvement. Chapter 3 presents a published paper successfully developing and evaluating Structure from Motion photogrammetry for accurate (R2 ≥ 0.93, RMSE ≤ 0.077m, and Bias ≤ -0.064m) and temporally consistent 3D reconstructions of wheat plot heights. The superior throughput achieved further facilitated measures of crop growth rate through the season; whilst very high spatial resolutions highlighted both the inter- and intra-plot variability in crop heights, something unachievable with the traditional manual ruler methods. Chapter 4 presents published work developing and evaluating modified Commercial ‘Off the Shelf’ (COTS) cameras for obtaining radiometrically calibrated imagery of canopy spectral reflectance. Specifically, development focussed on improving application of these cameras under variable illumination conditions, via application of camera exposure, vignetting, and irradiance corrections. Validation of UAV derived Normalised Difference Vegetation Index (NDVI) against a ground spectrometer from the COTS cameras (0.94 ≤ R2 ≥ 0.88) indicated successful calibration and correction of the cameras. The higher spatial resolution obtained from the COTS cameras, facilitated the assessment of the impact of background soil reflectance on derived mean Normalised Difference Vegetation Index (NDVI) measures of experimental plots, highlighting the impact of incomplete canopy on derived indices. Chapter 5 utilises the developed methods and cameras from Chapter 4 to assess the impact of nitrogen fertiliser application on the formation and senescence dynamics of canopy traits over multiple growing seasons. Quantification of changes in canopy reflectance, via NDVI, through three select trends in the wheat growth cycle were used to assess any impact of nitrogen on these periods of growth. Results showed consistent impact of zero nitrogen application on crop canopies within all three development phases. Additional results found statistically significant positive correlations between quantified phases and harvest metrics (e.g. final yield), with greatest correlations occurring within the second (Full Canopy) and third (Senescence) phases. Chapter 6 focuses on evaluation of the financial costs and throughput associated with UAVs; with specific focus on comparison to conventional methods in a real-world phenotyping scenario. A ‘cost throughput’ analysis based on real-world experiments at Rothamsted Research, provided quantitative assessment demonstrating both the financial savings (£4.11 per plot savings) and superior throughput obtained (229% faster) from implementing a UAV based phenotyping strategy to long term phenotyping of field-based experiments. Overall the methods and tools developed in this PhD thesis demonstrate UAVs combined with appropriate remote sensing tools can replicate and even surpass the precision, accuracy, cost and throughput of current strategies

    Radiometric calibration of ‘Commercial off the shelf’ cameras for UAV-based high-resolution temporal phenotyping of reflectance and NDVI

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    Vegetation indices, such as the Normalised Difference Vegetation Index (NDVI), are 13 common metrics used for measuring traits of interest in crop phenotyping. However traditional 14 measurements of these indices are often influenced by multiple confounding factors such as canopy 15 cover and reflectance of underlying soil, visible in canopy gaps. Digital cameras mounted to 16 Unmanned Aerial Vehicles offer the spatial resolution to investigate these confounding factors, 17 however incomplete methods for radiometric calibration into reflectance units limits how the data 18 can be applied to phenotyping. In this study, we assess the applicability of very high spatial 19 resolution (1cm) UAV-based imagery taken with commercial off the shelf (COTS) digital cameras 20 for both deriving calibrated reflectance imagery, and isolating vegetation canopy reflectance from 21 that of the underlying soil. We present new methods for successfully normalising the imagery for 22 exposure and solar irradiance effects, generating multispectral (RGB-NIR) orthomosaics of our 23 target field based wheat crop trial. Validation against measurements from a ground spectrometer 24 showed good results for reflectance (R2 ≥ 0.6) and NDVI (R2 ≥ 0.88). Application of imagery collected 25 through the growing season and masked using the Excess Green Red index was used to assess the 26 impact of canopy cover on NDVI measurements. Results showed the impact of canopy cover 27 artificially reducing plot NDVI values in the early season, where canopy development is low

    On the Use of Unmanned Aerial Systems for Environmental Monitoring

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    Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small- and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air- and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challengespublishersversionPeer reviewe

    Investigating the Potential of UAV-Based Low-Cost Camera Imagery for Measuring Biophysical Variables in Maize

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    The potential for improved crop productivity is readily investigated in agronomic field experiments. Frequent measurements of biophysical crop variables are necessary to allow for confident statements on crop performance. Commonly, in-field measurements are tedious, labour-intensive, costly and spatially selective and therefore pose a challenge in field experiments. With the versatile, flexible employment of the platform and the high spatial and temporal resolution of the sensor data, Unmanned Aerial Vehicle (UAV)-based remote sensing offers the possibility to derive variables quickly, contactless and at low cost. This thesis examined if UAV-borne modified low-cost camera imagery allowed for remote estimation of the crop variables green leaf area index (gLAI) and radiation use efficiency (RUE) in a maize field trial under different management influences. For this, a field experiment was established at the university's research station Campus Klein-Altendorf southwest of Bonn in the years 2015 and 2016. In four treatments (two levels of nitrogen fertilisation and two levels of plant density) with five repetitions each, leaf growth of maize plants was supposed to occur differently. gLAI and biomass was measured destructively, UAV-based data was acquired in 14-day intervals over the entire experiment. Three studies were conducted and submitted for peer-review in international journals. In study I, three selected spectral vegetation indices (NDVI, GNDVI, 3BSI) were related to the gLAI measurements. Differing but definite relationships per treatment factor were found. gLAI estimation using the two-band indices (NDVI, GNDVI) yielded good results up to gLAI values of 3. The 3-bands approach (3BSI) did not provide improved accuracies. Comparing gLAI results to the spectral vegetation indices, it was determined that sole reliance on these was insufficient to draw the right conclusions on the impact of management factors on leaf area development in maize canopies. Study II evaluated parametric and non-parametric regression methods on their capability to estimate gLAI in maize, relying on UAV-based low-cost camera imagery with non-plants pixels (i.e. shaded and illuminated soil background) a) included in and b) excluded from the analysis. With regard to the parametric regression methods, all possible band combinations for a selected number of two- and three-band formulations as well as different fitting functions were tested. With regard to non-parametric methods, six regression algorithms (Random Forests Regression, Support Vector Regression, Relevance Vector Machines, Gaussian Process Regression, Kernel Regularized Least Squares, Extreme Learning Machine) were tested. It was found that all non-parametric methods performed better than the parametric methods, and that kernel-based algorithms outperformed the other tested algorithms. Excluding non-plant pixels from the analysis deteriorated models' performances. When using parametric regression methods, signal saturation occurred at gLAI values of about 3, and at values around 4 when employing non-parametric methods. Study III investigated if a) UAV-based low-cost camera imagery allowed estimating RUEs in different experimental plots where maize was cultivated in the growing season of 2016, b) those values were different from the ones previously reported in literature and c) there was a difference between RUEtotal and RUEgreen. Fractional cover and canopy reflectance was determined based on the RS imagery. Our study showed that RUEtotal ranges between 4.05 and 4.59, and RUEgreen between 4.11 and 4.65. These values were higher than those published in other research articles, but not outside the range of plausibility. The difference between RUEtotal and RUEgreen was minimal, possibly due to prolonged canopy greenness induced by the stay-green trait of the cultivar grown. In conclusion, UAV-based low-cost camera imagery allows for estimation of plant variables within a range of limitations

    Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

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    Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers

    GEOSPATIAL-BASED ENVIRONMENTAL MODELLING FOR COASTAL DUNE ZONE MANAGEMENT

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    Tomaintain biodiversity and ecological functionof coastal dune areas, itis important that practical and effective environmentalmanagemental strategies are developed. Advances in geospatial technologies offer a potentially very useful source of data for studies in this environment. This research project aimto developgeospatialdata-basedenvironmentalmodellingforcoastaldunecomplexestocontributetoeffectiveconservationstrategieswithparticularreferencetotheBuckroneydunecomplexinCo.Wicklow,Ireland.Theprojectconducteda general comparison ofdifferent geospatial data collection methodsfor topographic modelling of the Buckroney dune complex. These data collection methodsincludedsmall-scale survey data from aerial photogrammetry, optical satellite imagery, radar and LiDAR data, and ground-based, large-scale survey data from Total Station(TS), Real Time Kinematic (RTK) Global Positioning System(GPS), terrestrial laser scanners (TLS) and Unmanned Aircraft Systems (UAS).The results identifiedthe advantages and disadvantages of the respective technologies and demonstrated thatspatial data from high-end methods based on LiDAR, TLS and UAS technologiesenabled high-resolution and high-accuracy 3D datasetto be gathered quickly and relatively easily for the Buckroney dune complex. Analysis of the 3D topographic modelling based on LiDAR, TLS and UAS technologieshighlighted the efficacy of UAS technology, in particular,for 3D topographicmodellingof the study site.Theproject then exploredthe application of a UAS-mounted multispectral sensor for 3D vegetation mappingof the site. The Sequoia multispectral sensorused in this researchhas green, red, red-edge and near-infrared(NIR)wavebands, and a normal RGB sensor. The outcomesincludedan orthomosiac model, a 3D surface model and multispectral imageryof the study site. Nineclassification strategies were usedto examine the efficacyof UAS-IVmounted multispectral data for vegetation mapping. These strategies involved different band combinations based on the three multispectral bands from the RGB sensor, the four multispectral bands from the multispectral sensor and sixwidely used vegetation indices. There were 235 sample areas (1 m × 1 m) used for anaccuracy assessment of the classification of thevegetation mapping. The results showed vegetation type classification accuracies ranging from 52% to 75%. The resultdemonstrated that the addition of UAS-mounted multispectral data improvedthe classification accuracy of coastal vegetation mapping of the Buckroney dune complex

    Investigating the potential for detecting Oak Decline using Unmanned Aerial Vehicle (UAV) Remote Sensing

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    This PhD project develops methods for the assessment of forest condition utilising modern remote sensing technologies, in particular optical imagery from unmanned aerial systems and with Structure from Motion photogrammetry. The research focuses on health threats to the UK’s native oak trees, specifically, Chronic Oak Decline (COD) and Acute Oak Decline (AOD). The data requirements and methods to identify these complex diseases are investigatedusing RGB and multispectral imagery with very high spatial resolution, as well as crown textural information. These image data are produced photogrammetrically from multitemporal unmanned aerial vehicle (UAV) flights, collected during different seasons to assess the influence of phenology on the ability to detect oak decline. Particular attention is given to the identification of declined oak health within the context of semi-natural forests and heterogenous stands. Semi-natural forest environments pose challenges regarding naturally occurring variability. The studies investigate the potential and practical implications of UAV remote sensing approaches for detection of oak decline under these conditions. COD is studied at Speculation Cannop, a section in the Forest of Dean, dominated by 200-year-old oaks, where decline symptoms have been present for the last decade. Monks Wood, a semi-natural woodland in Cambridgeshire, is the study site for AOD, where trees exhibit active decline symptoms. Field surveys at these sites are designed and carried out to produce highly-accurate differential GNSS positional information of symptomatic and control oak trees. This allows the UAV data to be related to COD or AOD symptoms and the validation of model predictions. Random Forest modelling is used to determine the explanatory value of remote sensing-derived metrics to distinguish trees affected by COD or AOD from control trees. Spectral and textural variables are extracted from the remote sensing data using an object-based approach, adopting circular plots around crown centres at individual tree level. Furthermore, acquired UAV imagery is applied to generate a species distribution map, improving on the number of detectable species and spatial resolution from a previous classification using multispectral data from a piloted aircraft. In the production of the map, parameters relevant for classification accuracy, and identification of oak in particular, are assessed. The effect of plot size, sample size and data combinations are studied. With optimised parameters for species classification, the updated species map is subsequently employed to perform a wall-to-wall prediction of individual oak tree condition, evaluating the potential of a full inventory detection of declined health. UAV-acquired data showed potential for discrimination of control trees and declined trees, in the case of COD and AOD. The greatest potential for detecting declined oak condition was demonstrated with narrowband multispectral imagery. Broadband RGB imagery was determined to be unsuitable for a robust distinction between declined and control trees. The greatest explanatory power was found in remotely-sensed spectra related to photosynthetic activity, indicated by the high feature importance of nearinfrared spectra and the vegetation indices NDRE and NDVI. High feature importance was also produced by texture metrics, that describe structural variations within the crown. The findings indicate that the remotely sensed explanatory variables hold significant information regarding changes in leaf chemistry and crown morphology that relate to chlorosis, defoliation and dieback occurring in the course of the decline. In the case of COD, a distinction of symptomatic from control trees was achieved with 75 % accuracy. Models developed for AOD detection yielded AUC scores up to 0.98,when validated on independent sample data. Classification of oak presence was achieved with a User’s accuracy of 97 % and the produced species map generated 95 % overall accuracy across the eight species within the study area in the north-east of Monks Wood. Despite these encouraging results, it was shown that the generalisation of models is unfeasible at this stage and many challenges remain. A wall-to-wall prediction of decline status confirmed the inability to generalise, yielding unrealistic results, with a high number of declined trees predicted. Identified weaknesses of the developed models indicate complexity related to the natural variability of heterogenous forests combined with the diverse symptoms of oak decline. Specific to the presented studies, additional limitations were attributed to limited ground truth, consequent overfitting,the binary classification of oak health status and uncertainty in UAV-acquired reflectance values. Suggestions for future work are given and involve the extension of field sampling with a non-binary dependent variable to reflect the severity of oak decline induced stress. Further technical research on the quality and reliability of UAV remote sensing data is also required

    Using Unmanned Aerial Vehicles in Postfire Vegetation Survey Campaigns through Large and Heterogeneous Areas: Opportunities and Challenges

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    17 p.This study evaluated the opportunities and challenges of using drones to obtain multispectral orthomosaics at ultra-high resolution that could be useful for monitoring large and heterogeneous burned areas. We conducted a survey using an octocopter equipped with a Parrot SEQUOIA multispectral camera in a 3000 ha framework located within the perimeter of a megafire in Spain. We assessed the quality of both the camera raw imagery and the multispectral orthomosaic obtained, as well as the required processing capability. Additionally, we compared the spatial information provided by the drone orthomosaic at ultra-high spatial resolution with another image provided by theWorldView-2 satellite at high spatial resolution. The drone raw imagery presented some anomalies, such as horizontal banding noise and non-homogeneous radiometry. Camera locations showed a lack of synchrony of the single frequency GPS receiver. The georeferencing process based on ground control points achieved an error lower than 30 cm in X-Y and lower than 55 cm in Z. The drone orthomosaic provided more information in terms of spatial variability in heterogeneous burned areas in comparison with theWorldView-2 satellite imagery. The drone orthomosaic could constitute a viable alternative for the evaluation of post-fire vegetation regeneration in large and heterogeneous burned areasS

    Multispectral Remote Sensing and Spatiotemporal Mapping of the Environment and Natural Disasters Using Small UAS

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    This dissertation focuses on development of new methods for multispectral remote sensing, measurement, and mapping of the environment and natural disasters using small Unmanned Aircraft Systems (UAS). Small UAS equipped with multispectral cameras such as true color (RGB), near infrared (NIR), and thermal can gather important information about the environment before, during, and after a disaster without risking pilots or operators. Additionally, small UAS are generally inexpensive, easy to handle, and can detect features at small spatiotemporal scales that are not visible in manned aircraft or satellite imagery. Four important problems in UAS remote sensing and disaster data representation are focused in this dissertation. First, key considerations for the development of UAS disaster sensing systems are provided, followed by detailed descriptions of the KHawk system and representative environment and disaster data sets. Second, a new method is proposed and demonstrated for accurate mapping and measurement of grass fire evolution using multitemporal thermal orthomosaics collected by a fixed-wing UAS flying at low altitudes. Third, a low-cost and effective solution is further developed for spatiotemporal representation and measurement of grass fire evolution using time-labeled UAS NIR orthomosaics and a novel Intensity Variance Thresholding (IVT) method is proposed for grass fire front extraction to support fire spread metrics measurement of fire front location and rate of spread (ROS). A UAS grass fire observation data set is also presented including thermal and NIR orthomosaics and supporting weather and fuel data. Fourth, a new Satellite-based Cross Calibration (SCC) method is proposed for surface reflectance estimation of UAS images in digital numbers (DN) using free and open calibrated satellite reflectance data. This also serves as a solid foundation for data-enabled multiscale remote sensing and large scale environmental observations. Finally, the main conclusions and future research considerations are summarized
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