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287 Research products

  • Rural Digital Europe
  • 2013-2022
  • Doctoral thesis
  • NL

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    Authors: van Iersel, W.K.;

    Natural lowland rivers are dynamic environments with a high ecological value. However, 90% of the European and North-American river floodplains are in a degraded state. The functions of floodplains are strongly determined by land cover and they often compete for space in narrowed floodplains. Integrated river management (IRM) tries to take care of floodplains in such way that land cover is optimized for multiple functions. For IRM, monitoring is essential to capture the dynamics, to evaluate changes, and to document the state of floodplains over time. The main objective of this thesis was to establish remote-sensing methods for the monitoring of floodplain land cover over multiple spatial and temporal scales. Several remote-sensing based solutions have been developed for the monitoring of land-cover dynamics in river floodplains and tested in floodplains of the lower Rhine. The phenological change of floodplain vegetation over the course of one year was studied using temporal profiles of its height and greenness. Using multitemporal UAV images, vegetation height was determined with an accuracy similar to much more expensive airborne LiDAR data. Multitemporal elevation models yielded meaningful profiles of greenness and vegetation height over time, which enabled discriminating the different land-cover types. The same dataset combined with a powerful machine learning model (Random Forest) yielded unprecedented high classification accuracies for floodplain vegetation (> 90%), even for similar vegetation types such as grassland and herbaceous vegetation. This method is a practical and highly accurate solution for monitoring areas of a few square kilometres. For large-scale monitoring of floodplains, the same method is recommended, but with data from airborne platforms covering larger extents. Land-cover change over the course of five years was studied for a 100-km river section using satellite images. Using an object-based approach, a sequential deviation of a land-cover object from its class mean was used to detect land-cover change. For most classes the method was unsuccessful (accuracy 75%. The developed method has important advantages, such as high observation frequency, independence of repeated land-cover classification, and fast processing. At sub-daily frequency, it was assessed how accurate water temperature in a floodplain side channel can be documented from thermal UAV maps. The associated habitat suitability for native and alien fish assemblages was estimated based on the produced temperature maps. Water surface temperatures were mapped four times during a hot summer day with an overall RMSE of 0.53 oC. During the day, temperatures in the side channel increased rapidly to values detrimental for many fish species. The study showed that thermal imagery from UAVs is an efficient and accurate information source to monitor spatiotemporal patterns of thermal habitat suitability. The presently available range of spaceborne and airborne platforms and sensors offers great opportunities to collect information on land-cover change across a range of spatial and temporal scales. This may advance our management of floodplains and help us recovering and protecting these rich ecosystems and the benefits they provide us.

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    Authors: Ghaffarian, S.;
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    Doctoral thesis . 2020
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      Doctoral thesis . 2020
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    Authors: Pinto Rebelo, J.L. (author);

    Robots are particularly well suited for executing tasks that take place in locations which are too dangerous or inaccessible to human operators. For robot manipulators to execute complex activities in unknown, unstructured environments, despite the recent increases in computation power, human input is still required for task planning and execution. Most of the existing bilateral teleoperation systems, which make use of commercially available master devices to control industrial slave manipulators, show three main limitations: instability on contact with stiff environments, reduced force-feedback performance to the operator and limited master workspaces. It is the main goal of the research presented in this thesis to achieve high transparency and time-delay robustness in bilateral teleoperation using dissimilar multi-dof master-slave devices, in particular making use of impedance-type masters to command impedance-controlled slave manipulators. This research focuses on tasks which a human operator could manually execute if physically present in the remote environment. This implies that there should be no force scaling and the motion remains within the limits of the human operator arm. It is also assumed that a high level of transparency should be provided to the operator to enable the execution of the required tasks in teleoperation. Currently, modern communication devices and the Internet allow connections throughout the world with round-trip communication delays in the range of hundreds of milliseconds. Throughout this work, communication delay values smaller or equal to 250 ms, for which direct bilateral teleoperation is the most usable, are considered. Under these premises, the research approach followed on this thesis is divided in three main parts. These parts are: (1) Effect of different parameters on system stability and performance for a system with impedance-type master commanding an impedance-controlled slave (2) Robust stability methods for 4-channel architecture under time-delay (3) Propose hardware/software architectures for multi-dof teleoperation BioMechanical Engineering Mechanical, Maritime and Materials Engineering

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    Natural lowland rivers are dynamic environments with a high ecological value. However, 90% of the European and North-American river floodplains are in a degraded state. The functions of floodplains are strongly determined by land cover and they often compete for space in narrowed floodplains. Integrated river management (IRM) tries to take care of floodplains in such way that land cover is optimized for multiple functions. For IRM, monitoring is essential to capture the dynamics, to evaluate changes, and to document the state of floodplains over time. The main objective of this thesis was to establish remote-sensing methods for the monitoring of floodplain land cover over multiple spatial and temporal scales. Several remote-sensing based solutions have been developed for the monitoring of land-cover dynamics in river floodplains and tested in floodplains of the lower Rhine. The phenological change of floodplain vegetation over the course of one year was studied using temporal profiles of its height and greenness. Using multitemporal UAV images, vegetation height was determined with an accuracy similar to much more expensive airborne LiDAR data. Multitemporal elevation models yielded meaningful profiles of greenness and vegetation height over time, which enabled discriminating the different land-cover types. The same dataset combined with a powerful machine learning model (Random Forest) yielded unprecedented high classification accuracies for floodplain vegetation (> 90%), even for similar vegetation types such as grassland and herbaceous vegetation. This method is a practical and highly accurate solution for monitoring areas of a few square kilometres. For large-scale monitoring of floodplains, the same method is recommended, but with data from airborne platforms covering larger extents. Land-cover change over the course of five years was studied for a 100-km river section using satellite images. Using an object-based approach, a sequential deviation of a land-cover object from its class mean was used to detect land-cover change. For most classes the method was unsuccessful (accuracy 75%. The developed method has important advantages, such as high observation frequency, independence of repeated land-cover classification, and fast processing. At sub-daily frequency, it was assessed how accurate water temperature in a floodplain side channel can be documented from thermal UAV maps. The associated habitat suitability for native and alien fish assemblages was estimated based on the produced temperature maps. Water surface temperatures were mapped four times during a hot summer day with an overall RMSE of 0.53 oC. During the day, temperatures in the side channel increased rapidly to values detrimental for many fish species. The study showed that thermal imagery from UAVs is an efficient and accurate information source to monitor spatiotemporal patterns of thermal habitat suitability. The presently available range of spaceborne and airborne platforms and sensors offers great opportunities to collect information on land-cover change across a range of spatial and temporal scales. This may advance our management of floodplains and help us recovering and protecting these rich ecosystems and the benefits they provide us.

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    https://hdl.handle.net/1874/38...
    Doctoral thesis . 2020
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      https://hdl.handle.net/1874/38...
      Doctoral thesis . 2020
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    Authors: Knist, C.L.;

    Accurate ground-based remotely sensed microphysical and optical properties of liquid water clouds are essential references to validate satellite-observed cloud properties and to improve cloud parameterizations in weather and climate models. This requires the evaluation of algorithms for retrieval of cloud microphysical and optical properties using ground-based remote sensing observations, because there are large differences between the cloud property retrievals of various algorithms due to the differences in the applied retrieval theories, assumptions, retrieval inputs and constraints. This thesis focuses on three commonly used vertical cloud models for the parameterization of the in-cloud vertical structure in cloud property retrieval schemes. The objective is to explore the impact of the vertical cloud models on the computations of microphysical and optical properties of liquid water clouds and to evaluate their uncertainties. This information can help to improve current liquid water cloud property retrieval schemes and to increase the accuracy of the obtained cloud physical properties. A comparison of three algorithms with different vertical cloud models for the retrieval of liquid water cloud microphysical and optical properties is performed. In the first algorithm, the vertical structure of the cloud is parameterized as being vertically homogeneous (Vertically Uniform, VU). In the second algorithm, the used vertical cloud model originates from an adiabatic model (Scaled Adiabatic Stratified, SAS) and the third algorithm relies on a vertical model, which considers the impact of cloud top entrainment mixing processes on the cloud microphysical properties (Homogenous-Mixing, HM). All three algorithms use observations of the cloud radar reflectivity, the microwave radiometer obtained liquid water path (LWP) and the cloud geometrical thickness from lidar and cloud radar. They require a priori assumptions on the cloud droplet size distribution (DSD). Hence, the gamma function is used to parametrize the DSDs and possible values for the gamma DSD shape parameter are evaluated from reanalyzed in-situ observations. All three algorithms investigated here retrieve vertical profiles of the liquid water content (LWC), the droplet concentration, the effective radius, the visible optical extinction and the visible optical depth. The differences between the cloud property retrievals of each algorithm are explained on the basis of remote sensing observations that appear to be typical for low-level water clouds. The results of the VU cloud model per se lack detailed information on the vertical distribution of the cloud property retrievals. Under adiabatic conditions, the retrievals of the SAS and the HM models are equivalent, while the vertical distributions of the LWC, the effective radius and the optical extinction differ substantially under non-adiabatic conditions, especially at the cloud boundaries. The droplet concentrations of the SAS and HM models are very close to each other for both conditions. The model of uniform cloud properties yields values of the droplet concentration that are 25% lower than those from the models of non-uniform cloud properties. Interestingly, the differences between the cloud microphysical properties lead to very similar values of the retrieved visible optical depths. Sensitivity and error analyses suggest that the droplet concentration retrieval is generally most strongly affected by errors in the radar reflectivity and the LWP, while the retrievals of the effective radius are most robust in all three models. The retrievals of the optical depth and the effective radius are less affected by the variations in the DSD shape parameter as compared to the impact of the errors in observations. In contrast, the droplet concentration is more sensitive to changes in the gamma DSD shape parameter. Consequently the DSD shape parameter should be known a priori with reasonable precision. In order to evaluate the validity of the cloud property retrievals, the three algorithms are applied to synthetic surface remote sensing observations of a modeled liquid water cloud layer. The retrievals are compared with the physical properties of the modeled cloud layer as a function of the cloud height. Applying the algorithms to the best estimate “observations” and the assumed value for the DSD shape parameter leads to consistent HM model cloud property retrievals. In turn, significant overestimations of the SAS model LWC (50%) and the effective radius (10%) occur at cloud top where the SAS model retrieves the maximum values in the profiles. In all layers below the cloud top, the SAS cloud model retrievals of the LWC and the effective radius are very close to the modeled ones, because the true properties are increasing nearly adiabatically. As expected, the differences in the LWC and the effective radius profiles are largest upon the application of the VU model, which significantly overestimates both properties in the lower levels and underestimates them in the upper height levels. The very simple assumption that all cloud properties are uniformly distributed leads to a significant underestimation of the droplet concentration by about 20%. The SAS model droplet concentration is only slightly overestimated by 7%. Nevertheless, all cloud model retrievals of the optical depth agree well with those of the modeled cloud layer. To evaluate the performance of the cloud property retrievals obtained from real remote sensing observations, a broadband shortwave (SW) radiation closure analysis is performed for a selected water cloud case study. The SW fluxes at the surface calculated on the basis of the cloud properties of VU, SAS and HM models agree well with the surface radiation observations. The mean difference between the simulated and the measured SW fluxes is 2 W/m2 to 5 W/m2 with a standard deviation of 13W/m2. The uncertainty in the simulated fluxes can be explained by the uncertainty in the LWC and the effective radius due to errors in the LWP, the reflectivity and the assumption on the gamma DSD shape parameter. The three presented retrieval methods provide reliable cloud optical depth values for the selected water cloud case study. The different vertical distributions of the LWC and the effective radius, as well as differences in the droplet concentration, have a minor effect on simulating SW fluxes, because they lead to similar values of the optical depth. The present work shows that the liquid water cloud property retrievals obtained from the remote sensing observations depend on the model that is used to describe the vertical cloud structure. It shows that systematic deviations between the microphysical cloud properties of the VU, SAS and HM cloud models exist, especially regarding the droplet concentration, the LWC, the effective radius and the optical extinction at the cloud boundaries. The cloud microphysical properties estimated using the HM model parametrization show the best performance. The SAS cloud model can represent the vertically resolved microphysical properties well if they are very close to being adiabatic. Clearly, there are significant deviations in the cloud microphysics from the clouds that are parameterized as being vertically homogeneous (VU model). The different combinations of the microphysical properties in the three models lead to almost equivalent VU, SAS and HM optical depth retrievals, which agree well with the values of the modeled liquid water cloud. They are all able to reproduce the surface shortwave broadband radiative flux. However, by modeling clouds as being vertically homogeneous, sufficient accuracy in both the microphysical and the optical property retrievals cannot be achieved. Geoscience and Remote Sensing Civil Engineering and Geosciences

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    https://doi.org/10.4233/uuid:2...
    Doctoral thesis . 2014
    Data sources: Datacite
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      https://doi.org/10.4233/uuid:2...
      Doctoral thesis . 2014
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    Authors: Roosjen, Peter P.J.;

    Optical remote sensing enables the estimation of crop parameters based on reflected light through empirical-statistical methods or inversion of radiative transfer models. Natural surfaces, however, reflect light anisotropically, which means that the intensity of reflected light depends on the viewing and illumination geometry. Therefore, reflectance anisotropy can be considered as an unwanted effect since it may lead to inaccuracies in parameter estimations. However, it can also be considered as information source due to its unique response to the optical and structural properties of the observed surface. In the past, reflectance anisotropy was studied by multi-angular reflectance measurements from space-borne or ground-based sensors. In this research, the opportunities of Unmanned Aerial Vehicles (UAVs) to collect multi-angular measurements were explored. The main results of this research show that multi-angular measurements can be done with UAVs and that the reflectance anisotropy signal can be used to improve the retrieval of crop parameters.

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    Other literature type . Doctoral thesis . 2017
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    Doctoral thesis . Thesis . 2017 . Peer-reviewed
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      Other literature type . Doctoral thesis . 2017
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      Doctoral thesis . Thesis . 2017 . Peer-reviewed
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    Authors: Tijmons, S. (author);

    Many types of drones have emerged over the last decade and new applications in various sectors are announced almost on a daily basis. In scientific literature, small drones are called Micro Air Vehicles (MAVs). Especially very small MAVs will play a significant role in indoor applications, since their small size allows them to navigate in narrow, cluttered environments. At the same time, many indoor applications will benefit from MAVs becoming fully autonomous. That will allow these vehicles to operate in areas that cannot be accessed by humans for various reasons. Control & Simulation

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    https://doi.org/10.4233/uuid:7...
    Doctoral thesis . 2017
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      https://doi.org/10.4233/uuid:7...
      Doctoral thesis . 2017
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    Authors: Haalboom, A.F.;

    This history studies how the domains of public health and agriculture have negotiated control over livestock-associated zoonoses in the Netherlands during the twentieth century, and how the disciplines of veterinary medicine and medicine have related to one another in this broader context. Four case-studies of dealings with particular livestock-associated zoonoses in a particular period form the body of the thesis: bovine tuberculosis (1898-1956), influenza (1918-1957), salmonellosis (1951-1978) and bovine spongiform encephalopathy (1988-2001). These examples of livestock-associated zoonoses have been studied using a wide variety of primary sources, like archival documents, scientific journals, newspapers, and interviews.

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    Authors: Rutten, M.M. (author);

    The boundary that separates the earth from the atmosphere is a crucial zone of study for meteorology and hydrology. Here, solar energy is partitioned into sensible heat which drives atmospheric circulation, latent heat needed for evaporation from the soil and transpiration of vegetation, and soil heat which warms the subsurface. Precipitation is partitioned into interception that evaporates directly into the atmosphere, surface runoff that discharges quickly into water courses and infiltration which resides longer in the subsurface. Soil moisture influences all these processes and is therefore considered a key variable in land-atmosphere interaction. In order to obtain a better understanding of the heat and water balance of topsoil, observations are key, but challenging with in situ point sensors. Recent rapid developments in remote sensing have tremendously increased our ability to observe the boundary between soil and atmosphere. Retrieving state variables such as soil temperature and moisture from remote sensing is far from trivial: detected signals originate not only from the soil, but also from the atmosphere and vegetation, the depth of the detection is a function of the soil moisture itself, and pixels are large and heterogeneous. Field validation is difficult, because of scale disparity between in situ point sensors and remote sensing pixels. Still, given the limitations, remote sensing provides an opportunity to improve understanding of heat and moisture transfer in the topsoil. The central question of this research is: What can be learnt from (remote sensing) observations about the heat and moisture balance of the topsoil? First a cross validation of different soil moisture products based on remote sensing was performed to investigate similarities and differences between these products. The differences were significant and could be attributed to differences in land use and vegetation, but not fully explained. This illustrated that retrieval algorithms for soil moisture are far from converged. One prerequisite for improving retrieval algorithms is ground truth, ground observations at scales relevant for remote sensing. Second, a field technique was developed that can potentially be used for bridging the observation gap between point sensors and remote sensing pixels. This technique uses Distributed Temperature Sensing (DTS) over horizontal extents up to kilometers to infer soil moisture at this intermediate scale. Propagation of variations in atmospheric temperature and radiation with depth is a function of soil moisture. By using DTS observations at three depths, it is possible to infer soil moisture, assuming that heat conduction is the dominant heat transfer mechanism. The heat diffusion equation is inverted to obtain estimates of soil heat diffusivity and soil moisture. Since this technique relies on observations of the passive thermal response of the soil to atmospheric temperature and radiation variations, this technique is called passive SoilDTS in contrast to active soil DTS, which relies on active heat pulses. The feasibility of passive SoilDTS for soil moisture estimation was asserted in a field experiment conducted in Monster in the Netherlands. The analysis of the experimental results of this feasibility study pointed out a number of technical and modeling issues that needed to be investigated further in order for passive SoilDTS to be used for soil moisture estimation and scaling. Some soil moisture estimates were not reasonable due to uncertainties in cable depths and heat transfer mechanisms. To separate the technical issue of cable depth from the modeling challenges, the same methodology used to infer soil moisture from passive SoilDTS was applied to profile data of temperature and soil moisture obtained with point sensors. The depth of these point sensors could be determined with far greater accuracy than the cable depth. Analysis of the point observations challenged the common assumption that conduction is the dominant heat transfer mechanism in soil. Evaporation seemed to play a dominant role in heat transfer on dry days. Yet evaporation rates found were higher than would be expected if mass diffusion would be the dominant transfer mechanism of water vapor. Vapor diffusion appeared to be enhanced. Enhancement of vapor diffusion is a long-studied phenomenon, subject to debate on the explanations of underlying mechanism. In an extensive literature review on vapor enhancement in soils, the plausibility of various mechanisms was assessed. We reviewed mechanisms based on (combinations of) diffusive, viscous, buoyant, capillary and external pressure forces including: thermodiffusion, dispersion, Stefan’s flow, Knudsen diffusion, liquid island effect, hydraulic lift, free convection, double diffusive convection and forced convection. The analysis of the order of magnitude of the mechanisms based on first principles clearly distinguishes between plausible and implausible mechanisms. Thermodiffusion, Stefan’s flow, Knudsen effects, liquid islands do not significantly contribute to enhanced evaporation. Double diffusive convection seemed unlikely due to lack of experimental evidence, but could not be completely excluded from the list of potential mechanisms. Hydraulic lift, the mechanism that small capillaries lift liquid water to the surface where it evaporates, does significantly contribute to enhanced evaporation from soils, also from dryer soils. The experimental evidence for and the theoretical underpinnings of this mechanism are convincing. However, we sought mechanisms that both explain enhanced evaporation and steep temperature gradients in the soil during the daytime. These often observed gradients consist of a sharp decrease of temperature with a depth up to the depth of the evaporation front. Hydraulic lift cannot explain this because the evaporation front is located at the surface. One remaining mechanism is forced convection due to atmospheric pressure fluctuations, also referred to as wind pumping. Wind pumping causes displacement and flow velocities too small for significant convective and too small for significant dispersive transport, when steady state dispersion formulations are used. However, experiments do indicate significant dispersive transport that can be explained by dispersion under unsteady flow conditions. Forced convection due to pressure fluctuations seems to be the only mechanism that can explain both enhanced evaporation and the steep temperature gradients. We investigated under which conditions wind pumping can enhance water vapor transfer from the soil to the atmosphere and which mechanisms are responsible for this enhancement in a modeling study. Previous models of wind pumping relied on enhanced transfer due to enhanced mixing described with empirical macroscopic dispersion coefficients with weak physical foundations. We searched for better understanding of physical mechanisms driving enhanced mixing. With combination of order of magnitude analysis, phenomenological, empirical and analytical models, mechanisms were investigated. A model for surface pressure fluctuations was coupled with a pressure diffusion model, a pore flow velocity model and a dispersion model. Based on this coupled model, we propose that the enhancement is caused by mixing at the pore level due to flow instabilities. Fast pressure fluctuations at the soil-air interface make vortices in the soil unstable. Instabilities arise when the timescale of the pressure fluctuations is close to the timescale of viscous dissipation which is related to the pore size. In this case, vortices in the soil cannot increase, decrease and turn direction, in phase with the pressure fluctuations and instabilities occur in the form of ejections. The ejections of vortices enhance mixing and transport. Timescales of wind induced pressure fluctuations and pore sizes are such that this mechanism is considered likely in soils. Further research is needed to prove this mechanism and quantify it. The developed model is a hypothesis and should be tested with numerical and laboratory experiments. For estimating the effect of this vapor enhancement on the soil heat budget, a coupled heat and moisture transfer model should be developed. Such a model could also shed light on the relative importance of hydraulic lift and wind pumping for evaporation rates. Perhaps, because the topsoil forms the boundary between land and atmosphere, but also between two disciplines meteorology and hydrology, there are still many questions that remain about heat and moisture transfer in the upper few centimeters of the soil. Remote sensing soil moisture retrievals force the scientific community to revisit our understanding of the topsoil. As a result, remote sensing presents not only a challenge for ground validation, but also an opportunity for hydrological and meteorological model improvement. Observation is the beginning of most learning. Water Management Civil Engineering and Geosciences

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    Authors: Hiremath, S.;

    Autonomous operation of robotic systems in an agricultural environment is a difficult task due to the inherent uncertainty in the environment. The robot is in a dynamic, non-deterministic and semi-structured environment with many sources of noise and a high degree of uncertainty. A novel approach dealing with uncertainty is by means of probabilistic methods. This PhD thesis studies the efficacy of probabilistic methods for autonomous robot applications in agriculture focusing on two agricultural tasks namely automatic detection of weed in a grassland and autonomous navigation of a robot in a Maize field. In automatic weed detection we look at the detection of a common weed called Rumex obtusifolius (Rumex). The suitability of image analysis for the task is examined, various existing methods are scrutinized and new probabilistic methods are proposed for robust detection of Rumex using a monocular camera in real-time. For autonomous navigation in a Maize field, probabilistic methods are developed for row following using a camera as well as a laser scanner. New sensor models are proposed to characterize the noisy measurements which are used in the navigation method for tracking the position of the robot and the plant rows. Through extensive field experiments we show that the proposed probabilistic methods are robust to varying operating conditions and conclude that probabilistic methods are essential for autonomous operation of robotic systems in an agricultural environment.

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    Other literature type . Doctoral thesis . 2013
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      Other literature type . Doctoral thesis . 2013
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    Authors: van Iersel, W.K.;

    Natural lowland rivers are dynamic environments with a high ecological value. However, 90% of the European and North-American river floodplains are in a degraded state. The functions of floodplains are strongly determined by land cover and they often compete for space in narrowed floodplains. Integrated river management (IRM) tries to take care of floodplains in such way that land cover is optimized for multiple functions. For IRM, monitoring is essential to capture the dynamics, to evaluate changes, and to document the state of floodplains over time. The main objective of this thesis was to establish remote-sensing methods for the monitoring of floodplain land cover over multiple spatial and temporal scales. Several remote-sensing based solutions have been developed for the monitoring of land-cover dynamics in river floodplains and tested in floodplains of the lower Rhine. The phenological change of floodplain vegetation over the course of one year was studied using temporal profiles of its height and greenness. Using multitemporal UAV images, vegetation height was determined with an accuracy similar to much more expensive airborne LiDAR data. Multitemporal elevation models yielded meaningful profiles of greenness and vegetation height over time, which enabled discriminating the different land-cover types. The same dataset combined with a powerful machine learning model (Random Forest) yielded unprecedented high classification accuracies for floodplain vegetation (> 90%), even for similar vegetation types such as grassland and herbaceous vegetation. This method is a practical and highly accurate solution for monitoring areas of a few square kilometres. For large-scale monitoring of floodplains, the same method is recommended, but with data from airborne platforms covering larger extents. Land-cover change over the course of five years was studied for a 100-km river section using satellite images. Using an object-based approach, a sequential deviation of a land-cover object from its class mean was used to detect land-cover change. For most classes the method was unsuccessful (accuracy 75%. The developed method has important advantages, such as high observation frequency, independence of repeated land-cover classification, and fast processing. At sub-daily frequency, it was assessed how accurate water temperature in a floodplain side channel can be documented from thermal UAV maps. The associated habitat suitability for native and alien fish assemblages was estimated based on the produced temperature maps. Water surface temperatures were mapped four times during a hot summer day with an overall RMSE of 0.53 oC. During the day, temperatures in the side channel increased rapidly to values detrimental for many fish species. The study showed that thermal imagery from UAVs is an efficient and accurate information source to monitor spatiotemporal patterns of thermal habitat suitability. The presently available range of spaceborne and airborne platforms and sensors offers great opportunities to collect information on land-cover change across a range of spatial and temporal scales. This may advance our management of floodplains and help us recovering and protecting these rich ecosystems and the benefits they provide us.

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    Authors: Ghaffarian, S.;
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    Doctoral thesis . 2020
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      Doctoral thesis . 2020
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    Authors: Pinto Rebelo, J.L. (author);

    Robots are particularly well suited for executing tasks that take place in locations which are too dangerous or inaccessible to human operators. For robot manipulators to execute complex activities in unknown, unstructured environments, despite the recent increases in computation power, human input is still required for task planning and execution. Most of the existing bilateral teleoperation systems, which make use of commercially available master devices to control industrial slave manipulators, show three main limitations: instability on contact with stiff environments, reduced force-feedback performance to the operator and limited master workspaces. It is the main goal of the research presented in this thesis to achieve high transparency and time-delay robustness in bilateral teleoperation using dissimilar multi-dof master-slave devices, in particular making use of impedance-type masters to command impedance-controlled slave manipulators. This research focuses on tasks which a human operator could manually execute if physically present in the remote environment. This implies that there should be no force scaling and the motion remains within the limits of the human operator arm. It is also assumed that a high level of transparency should be provided to the operator to enable the execution of the required tasks in teleoperation. Currently, modern communication devices and the Internet allow connections throughout the world with round-trip communication delays in the range of hundreds of milliseconds. Throughout this work, communication delay values smaller or equal to 250 ms, for which direct bilateral teleoperation is the most usable, are considered. Under these premises, the research approach followed on this thesis is divided in three main parts. These parts are: (1) Effect of different parameters on system stability and performance for a system with impedance-type master commanding an impedance-controlled slave (2) Robust stability methods for 4-channel architecture under time-delay (3) Propose hardware/software architectures for multi-dof teleoperation BioMechanical Engineering Mechanical, Maritime and Materials Engineering

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    Natural lowland rivers are dynamic environments with a high ecological value. However, 90% of the European and North-American river floodplains are in a degraded state. The functions of floodplains are strongly determined by land cover and they often compete for space in narrowed floodplains. Integrated river management (IRM) tries to take care of floodplains in such way that land cover is optimized for multiple functions. For IRM, monitoring is essential to capture the dynamics, to evaluate changes, and to document the state of floodplains over time. The main objective of this thesis was to establish remote-sensing methods for the monitoring of floodplain land cover over multiple spatial and temporal scales. Several remote-sensing based solutions have been developed for the monitoring of land-cover dynamics in river floodplains and tested in floodplains of the lower Rhine. The phenological change of floodplain vegetation over the course of one year was studied using temporal profiles of its height and greenness. Using multitemporal UAV images, vegetation height was determined with an accuracy similar to much more expensive airborne LiDAR data. Multitemporal elevation models yielded meaningful profiles of greenness and vegetation height over time, which enabled discriminating the different land-cover types. The same dataset combined with a powerful machine learning model (Random Forest) yielded unprecedented high classification accuracies for floodplain vegetation (> 90%), even for similar vegetation types such as grassland and herbaceous vegetation. This method is a practical and highly accurate solution for monitoring areas of a few square kilometres. For large-scale monitoring of floodplains, the same method is recommended, but with data from airborne platforms covering larger extents. Land-cover change over the course of five years was studied for a 100-km river section using satellite images. Using an object-based approach, a sequential deviation of a land-cover object from its class mean was used to detect land-cover change. For most classes the method was unsuccessful (accuracy 75%. The developed method has important advantages, such as high observation frequency, independence of repeated land-cover classification, and fast processing. At sub-daily frequency, it was assessed how accurate water temperature in a floodplain side channel can be documented from thermal UAV maps. The associated habitat suitability for native and alien fish assemblages was estimated based on the produced temperature maps. Water surface temperatures were mapped four times during a hot summer day with an overall RMSE of 0.53 oC. During the day, temperatures in the side channel increased rapidly to values detrimental for many fish species. The study showed that thermal imagery from UAVs is an efficient and accurate information source to monitor spatiotemporal patterns of thermal habitat suitability. The presently available range of spaceborne and airborne platforms and sensors offers great opportunities to collect information on land-cover change across a range of spatial and temporal scales. This may advance our management of floodplains and help us recovering and protecting these rich ecosystems and the benefits they provide us.

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    https://hdl.handle.net/1874/38...
    Doctoral thesis . 2020
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      https://hdl.handle.net/1874/38...
      Doctoral thesis . 2020
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    Authors: Knist, C.L.;

    Accurate ground-based remotely sensed microphysical and optical properties of liquid water clouds are essential references to validate satellite-observed cloud properties and to improve cloud parameterizations in weather and climate models. This requires the evaluation of algorithms for retrieval of cloud microphysical and optical properties using ground-based remote sensing observations, because there are large differences between the cloud property retrievals of various algorithms due to the differences in the applied retrieval theories, assumptions, retrieval inputs and constraints. This thesis focuses on three commonly used vertical cloud models for the parameterization of the in-cloud vertical structure in cloud property retrieval schemes. The objective is to explore the impact of the vertical cloud models on the computations of microphysical and optical properties of liquid water clouds and to evaluate their uncertainties. This information can help to improve current liquid water cloud property retrieval schemes and to increase the accuracy of the obtained cloud physical properties. A comparison of three algorithms with different vertical cloud models for the retrieval of liquid water cloud microphysical and optical properties is performed. In the first algorithm, the vertical structure of the cloud is parameterized as being vertically homogeneous (Vertically Uniform, VU). In the second algorithm, the used vertical cloud model originates from an adiabatic model (Scaled Adiabatic Stratified, SAS) and the third algorithm relies on a vertical model, which considers the impact of cloud top entrainment mixing processes on the cloud microphysical properties (Homogenous-Mixing, HM). All three algorithms use observations of the cloud radar reflectivity, the microwave radiometer obtained liquid water path (LWP) and the cloud geometrical thickness from lidar and cloud radar. They require a priori assumptions on the cloud droplet size distribution (DSD). Hence, the gamma function is used to parametrize the DSDs and possible values for the gamma DSD shape parameter are evaluated from reanalyzed in-situ observations. All three algorithms investigated here retrieve vertical profiles of the liquid water content (LWC), the droplet concentration, the effective radius, the visible optical extinction and the visible optical depth. The differences between the cloud property retrievals of each algorithm are explained on the basis of remote sensing observations that appear to be typical for low-level water clouds. The results of the VU cloud model per se lack detailed information on the vertical distribution of the cloud property retrievals. Under adiabatic conditions, the retrievals of the SAS and the HM models are equivalent, while the vertical distributions of the LWC, the effective radius and the optical extinction differ substantially under non-adiabatic conditions, especially at the cloud boundaries. The droplet concentrations of the SAS and HM models are very close to each other for both conditions. The model of uniform cloud properties yields values of the droplet concentration that are 25% lower than those from the models of non-uniform cloud properties. Interestingly, the differences between the cloud microphysical properties lead to very similar values of the retrieved visible optical depths. Sensitivity and error analyses suggest that the droplet concentration retrieval is generally most strongly affected by errors in the radar reflectivity and the LWP, while the retrievals of the effective radius are most robust in all three models. The retrievals of the optical depth and the effective radius are less affected by the variations in the DSD shape parameter as compared to the impact of the errors in observations. In contrast, the droplet concentration is more sensitive to changes in the gamma DSD shape parameter. Consequently the DSD shape parameter should be known a priori with reasonable precision. In order to evaluate the validity of the cloud property retrievals, the three algorithms are applied to synthetic surface remote sensing observations of a modeled liquid water cloud layer. The retrievals are compared with the physical properties of the modeled cloud layer as a function of the cloud height. Applying the algorithms to the best estimate “observations” and the assumed value for the DSD shape parameter leads to consistent HM model cloud property retrievals. In turn, significant overestimations of the SAS model LWC (50%) and the effective radius (10%) occur at cloud top where the SAS model retrieves the maximum values in the profiles. In all layers below the cloud top, the SAS cloud model retrievals of the LWC and the effective radius are very close to the modeled ones, because the true properties are increasing nearly adiabatically. As expected, the differences in the LWC and the effective radius profiles are largest upon the application of the VU model, which significantly overestimates both properties in the lower levels and underestimates them in the upper height levels. The very simple assumption that all cloud properties are uniformly distributed leads to a significant underestimation of the droplet concentration by about 20%. The SAS model droplet concentration is only slightly overestimated by 7%. Nevertheless, all cloud model retrievals of the optical depth agree well with those of the modeled cloud layer. To evaluate the performance of the cloud property retrievals obtained from real remote sensing observations, a broadband shortwave (SW) radiation closure analysis is performed for a selected water cloud case study. The SW fluxes at the surface calculated on the basis of the cloud properties of VU, SAS and HM models agree well with the surface radiation observations. The mean difference between the simulated and the measured SW fluxes is 2 W/m2 to 5 W/m2 with a standard deviation of 13W/m2. The uncertainty in the simulated fluxes can be explained by the uncertainty in the LWC and the effective radius due to errors in the LWP, the reflectivity and the assumption on the gamma DSD shape parameter. The three presented retrieval methods provide reliable cloud optical depth values for the selected water cloud case study. The different vertical distributions of the LWC and the effective radius, as well as differences in the droplet concentration, have a minor effect on simulating SW fluxes, because they lead to similar values of the optical depth. The present work shows that the liquid water cloud property retrievals obtained from the remote sensing observations depend on the model that is used to describe the vertical cloud structure. It shows that systematic deviations between the microphysical cloud properties of the VU, SAS and HM cloud models exist, especially regarding the droplet concentration, the LWC, the effective radius and the optical extinction at the cloud boundaries. The cloud microphysical properties estimated using the HM model parametrization show the best performance. The SAS cloud model can represent the vertically resolved microphysical properties well if they are very close to being adiabatic. Clearly, there are significant deviations in the cloud microphysics from the clouds that are parameterized as being vertically homogeneous (VU model). The different combinations of the microphysical properties in the three models lead to almost equivalent VU, SAS and HM optical depth retrievals, which agree well with the values of the modeled liquid water cloud. They are all able to reproduce the surface shortwave broadband radiative flux. However, by modeling clouds as being vertically homogeneous, sufficient accuracy in both the microphysical and the optical property retrievals cannot be achieved. Geoscience and Remote Sensing Civil Engineering and Geosciences

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    https://doi.org/10.4233/uuid:2...
    Doctoral thesis . 2014
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      https://doi.org/10.4233/uuid:2...
      Doctoral thesis . 2014
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