Using Spatial Uncertainty of Prior Measurements to Design Adaptive Sampling of Elevation Data

Field sampling can be a major expense for planning within-field management in precision agriculture. An efficient sampling strategy should address knowledge gaps, rather than exhaustively collect redundant data. Modification of existing schemes is possible by incorporating prior knowledge of spatial patterns within the field. In this study, spatial uncertainty of prior digital elevation model (DEM) estimates was used to locate adaptive re-survey regions in the field. An agricultural vehicle equipped with RTK-DGPS was driven across a 2.3 ha field area to measure the field elevation in a continuous fashion. A geostatistical simulation technique was used to simulate field DEMs using measurements with different pass intervals and to quantitatively assess the spatial uncertainty of the DEM estimates. The high-uncertainty regions for each DEM were classified using image segmentation methods, and an adaptive re-survey was performed on those regions. The addition of adaptive re-surveying substantially reduced the time required to resample and resulted in DEMs with lower error. For the widest sampling pass width, the RMSE of 0.46 m of the DEM produced from an initial coarse sampling survey was reduced to 0.25 m after an adaptive re-survey, which was close to that (0.22 m) of the DEM produced with an all-field re-survey. The estimated sampling time for the adaptive re-survey was less than 50% of that for all-field re-survey. These results indicate that spatial uncertainty models are useful in an adaptive sampling design to help reduce sampling cost while maintaining the accuracy of the measurements. The method is general and thus not limited to elevation data but can be extended to other spatially variable field data

Development and deployment of an improved Anopheles gambiae s.l. field surveillance by adaptive spatial sampling design

Introduction: Accurate assessments of vector occurrence and abundance, particularly in widespread vector-borne diseases such as malaria, are crucial for the efficient deployment of disease surveillance and control interventions. Although previous studies have explored the benefits of adaptive sampling for identifying disease hotspots (mostly through simulations), limited research has been conducted on field surveillance of malaria vectors. Methods: We developed and implemented an adaptive spatial sampling design in southwestern Benin, specifically targeting potential and uncertain Anopheles gambiae hotspots, a major malaria vector in sub-Saharan Africa. The first phase of our proposed design involved delineating ecological zones and employing a proportional lattice with close pairs sampling design to maximize spatial coverage, representativeness of ecological zones, and account for spatial dependence in mosquito counts. In the second phase, we employed a spatial adaptive sampling design focusing on high-risk areas with the greatest uncertainty. Results: The adaptive spatial sampling design resulted in a reduced sample size from the first phase, leading to improved predictions for both out-of-sample and training data. Collections of Anopheles gambiae in high-risk and low-uncertainty areas were nearly tripled compared to those in high-risk and high-uncertainty areas. However, the overall model uncertainty increased. Discussion: While the adaptive sampling design allowed for increased collections of Anopheles gambiae mosquitoes with a reduced sample size, it also led to a general increase in uncertainty, highlighting the potential trade-offs in multi-criteria adaptive sampling designs. It is imperative that future research focuses on understanding these trade-offs to expedite effective malaria control and elimination efforts

A new approach to adaptive monitoring

Invasive species are an increasing problem, costing several billion dollars to the global economy. Monitoring methods are needed to provide scientists the necessary information to control populations of invasive species. Adaptive monitoring means that, for each additional survey, the monitoring design is updated based on the information obtained during the previous surveys. In this thesis we introduce a new general framework for adaptive monitoring. This method focuses on increasing the detection rate of invasive species over consecutive surveys. Compared with the existing monitoring methods, the proposed algorithm aims to improve adaptive monitoring by the following three points: (1) The use of a spatially balanced sampling design to select a sample in each survey, (2) by using both spatial and ecological information to update the monitoring strategy, and (3) by incorporating an eradication strategy to an adaptive monitoring design. In Chapter 1, we emphasise the need for the development of adaptive monitoring methods for invasive species. In Chapter 2, we outline the algorithm of our proposed method for adaptive monitoring and discuss the use of spatially balanced sampling designs. In Chapter 3, several sampling designs are introduced and their use for (adaptive) monitoring is evaluated. We give special attention to a new spatially balanced sampling design, named Balanced Acceptance Sampling, and compare it with a selection of existing (spatially balanced) sampling designs such as Generalized Random Tessellation Stratified sampling. In Chapter 4, we illustrate how ecological information can be used to update the monitoring strategy, which is demonstrated using a case study on the Asian tiger mosquito. In addition, several practical issues with probability sampling are discussed. In Chapter 5, the Nearest Unit Tessellation methods are introduced. These methods can model the observed spatial information of the species to update the monitoring strategy. Finally, we demonstrate how to combine ecological and spatial information to adjust a monitoring strategy. Chapter 6 also explores the use of a method to incorporate an eradication strategy to an adaptive monitoring strategy, using the Great White Butterfly data

Surrogate modeling for fast experimental assessment of specific absorption rate

Experimental dosimetry of electromagnetic fields (EMFs) in biological tissue is important for validating numerical techniques, designing electromagnetic exposure systems, and compliance testing of wireless devices. Compliance standards specify a two-step procedure to determine the peak spatial-averaged SAR in 1 g or 10 g of tissue in which the measurement locations lie on a rectilinear grid (selected up-front). In this chapter, we show the potential of surrogate modeling techniques to significantly reduce the duration of experimental dosimetry of EMF by using a sequential design. A sequential design or adaptive sampling differs from a traditional design of experiments as data and models from previous iterations are used to optimally select new samples resulting in a more efficient distribution of samples as compared with the traditional design of experiments. Based on a data set of about 100 dosimetric measurements, we show that the adaptive sampling of surrogate modeling is suitable to speed up the determination of the peak SAR location in an area scan by up to 43 and 64% compared with the standardized area scan on a rectilinear grid (IEC 622090, IEEE Std 1528:2013) for the LOLA-Voronoi-error and the LOLA-max surrogate model, respectively

Adaption in Dynamic Contrast-Enhanced MRI

In breast DCE MRI, dynamic data are acquired to assess signal changes caused by contrast agent injection in order to classify lesions. Two approaches are used for data analysis. One is to fit a pharmacokinetic model, such as the Tofts model, to the data, providing physiological information. For accurate model fitting, fast sampling is needed. Another approach is to evaluate architectural features of the contrast agent distribution, for which high spatial resolution is indispensable. However, high temporal and spatial resolution are opposing aims and a compromise has to be found. A new area of research are adaptive schemes, which sample data at combined resolutions to yield both, accurate model fitting and high spatial resolution morphological information. In this work, adaptive sampling schemes were investigated with the objective to optimize fitting accuracy, whilst providing high spatial resolution images. First, optimal sampling design was applied to the Tofts model. By that it could be determined, based on an assumed parameter distribution, that time points during the onset and the initial fast kinetics, lasting for approximately two minutes, are most relevant for fitting. During this interval, fast sampling is required. Later time points during wash-out can be exploited for high spatial resolution images. To achieve fast sampling during the initial kinetics, data acquisition has to be accelerated. A common way to increase imaging speed is to use view-sharing methods, which omit certain k-space data and interpolate the missing data from neighboring time frames. In this work, based on phantom simulations, the influence of different view-sharing techniques during the initial kinetics on fitting accuracy was investigated. It was found that all view-sharing methods imposed characteristic systematic errors on the fitting results of Ktrans. The best fitting performance was achieved by the scheme ``modTRICKS'', which is a combination of the often used schemes keyhole and TRICKS. It is not known prior to imaging, when the contrast agent will arrive in the lesion or when the wash-out begins. Currently used adaptive sequences change resolutions a fixed time points. However, missing time points on the upslope may cause fitting errors and missing the signal peak may lead to a loss in morphological information. This problem was addressed with a new automatic resolution adaption (AURA) sequence. Acquired dynamic data were analyzed in real-time to find the onset and the beginning of the wash-out and consequently the temporal resolution was automatically adapted. Using a perfusion phantom it could be shown that AURA provides both, high fitting accuracy and reliably high spatial resolution images close to the signal peak. As alternative approach to AURA, a sequence which allows for retrospective resolution adaption, was assesses. Advantages are that adaption does not have to be a global process, and can be tailored regionally to local sampling requirements. This can be useful for heterogeneous lesions. For that, a 3D golden angle radial sequence was used, which acquires contrast information with each line and the golden angles allow arbitrary resolutions at arbitrary time points. Using a perfusion phantom, it could be shown that retrospective resolution adaption yields high fitting accuracy and relatively high spatial resolution maps

Adaption in Dynamic Contrast-Enhanced MRI

In breast DCE MRI, dynamic data are acquired to assess signal changes caused by contrast agent injection in order to classify lesions. Two approaches are used for data analysis. One is to fit a pharmacokinetic model, such as the Tofts model, to the data, providing physiological information. For accurate model fitting, fast sampling is needed. Another approach is to evaluate architectural features of the contrast agent distribution, for which high spatial resolution is indispensable. However, high temporal and spatial resolution are opposing aims and a compromise has to be found. A new area of research are adaptive schemes, which sample data at combined resolutions to yield both, accurate model fitting and high spatial resolution morphological information. In this work, adaptive sampling schemes were investigated with the objective to optimize fitting accuracy, whilst providing high spatial resolution images. First, optimal sampling design was applied to the Tofts model. By that it could be determined, based on an assumed parameter distribution, that time points during the onset and the initial fast kinetics, lasting for approximately two minutes, are most relevant for fitting. During this interval, fast sampling is required. Later time points during wash-out can be exploited for high spatial resolution images. To achieve fast sampling during the initial kinetics, data acquisition has to be accelerated. A common way to increase imaging speed is to use view-sharing methods, which omit certain k-space data and interpolate the missing data from neighboring time frames. In this work, based on phantom simulations, the influence of different view-sharing techniques during the initial kinetics on fitting accuracy was investigated. It was found that all view-sharing methods imposed characteristic systematic errors on the fitting results of Ktrans. The best fitting performance was achieved by the scheme ``modTRICKS'', which is a combination of the often used schemes keyhole and TRICKS. It is not known prior to imaging, when the contrast agent will arrive in the lesion or when the wash-out begins. Currently used adaptive sequences change resolutions a fixed time points. However, missing time points on the upslope may cause fitting errors and missing the signal peak may lead to a loss in morphological information. This problem was addressed with a new automatic resolution adaption (AURA) sequence. Acquired dynamic data were analyzed in real-time to find the onset and the beginning of the wash-out and consequently the temporal resolution was automatically adapted. Using a perfusion phantom it could be shown that AURA provides both, high fitting accuracy and reliably high spatial resolution images close to the signal peak. As alternative approach to AURA, a sequence which allows for retrospective resolution adaption, was assesses. Advantages are that adaption does not have to be a global process, and can be tailored regionally to local sampling requirements. This can be useful for heterogeneous lesions. For that, a 3D golden angle radial sequence was used, which acquires contrast information with each line and the golden angles allow arbitrary resolutions at arbitrary time points. Using a perfusion phantom, it could be shown that retrospective resolution adaption yields high fitting accuracy and relatively high spatial resolution maps

Function estimation with locally adaptive dynamic models

We present a nonparametric Bayesian method for fitting unsmooth and highly oscillating functions, which is based on a locally adaptive hierarchical extension of standard dynamic or state space models. The main idea is to introduce locally varying variances in the state equations and to add a further smoothness prior for this variance function. Estimation is fully Bayesian and carried out by recent MCMC techniques. The whole approach can be understood as an alternative to other nonparametric function estimators, such as local or penalized regression with variable bandwidth or smoothing parameter selection. Performance is illustrated with simulated data, including unsmooth examples constructed for wavelet shrinkage, and by an application to sales data. Although the approach is developed for classical Gaussian nonparametric regression, it can be extended to more complex regression problems