Patrick Bogaert | UCLouvain (University of Louvain) (original) (raw)

Papers by Patrick Bogaert

Remote Sensing, 2020

Intertidal bars are common features on meso-and macro-tidal sandy beaches with low to moderate wa... more Intertidal bars are common features on meso-and macro-tidal sandy beaches with low to moderate wave energy environments. Understanding their morphodynamics is, hence, crucial for enhancing our knowledge on beach processes which is beneficial for coastal management. However, most studies have been limited by assessing bar systems two-dimensionally and typically over the short-term. Morphology and dynamics of an intertidal bar system in a macro-tidal environment have been investigated using bi-annual LiDAR topographic surveys over a period of seven years and along 3.2 km at Groenendijk beach (Belgium). The detected bars demonstrate that a morphology of an intertidal bar is permanently on the beach. However, these individual features are dynamic and highly mobile over the course of half a year. The mean height and width of the bars were 1.1 and 82 m, respectively. The highest, steepest, and asymmetric features were found on the upper beach, while they were least developed in the lower ...

Geoderma, 2017

Real-time monitoring of soil salinity based on field samples and laboratory analyses is a costly ... more Real-time monitoring of soil salinity based on field samples and laboratory analyses is a costly and time demanding procedure, so that sound methods that could reduce the burden by making use of cheaper data would be a step towards a more sustainable salinity hazards monitoring system on the long run. Typically, this involves replacing presumably error-free laboratory salinity measurements with indirect measurements that are however affected by various source of uncertainties, and these uncertainties need to be accounted for in order to avoid compromising the quality of the final results. More specifically, in a spatiotemporal prediction framework where salinity maps need to be drawn repeatedly at various time instants and where salinity values need to be compared over time for agricultural areas that are prone to salinity hazards, it is of major importance to process these uncertainties in a sound way, as failing to do so would impair our ability to detect salinity changes at an early stage for taking preventive actions. The aim of this paper is to propose a filtered kriging framework that allows the user to rely on cheap field sampled electrical conductivity (EC) measurements, that cannot however be assumed as error-free. Field EC measurements need to be calibrated from laboratory measurements and the corresponding calibration errors cannot be neglected. Moreover, when sampling is repeated over time, positioning errors are quite common and can adversely impact the results due to the inclusion of an extra variability source. It is shown how these uncertainties can be quantified and successfully processed afterwards for improving both the reliability of the spatial predictions and temporal comparisons of soil salinity. The idea is to rely on a same general optimal linear predictor that can be easily adapted to get rid of these unwanted effects. The procedure is illustrated by using a rich data set of EC measurements that cover a time span of seven years in the western part of Urmia Lake, northwest Iran. From these data, it is shown how calibration errors can be considered as spatially independent and zero-mean Gaussian distributed, while laboratory measurements exhibit a clear spatial structure but are also affected by a not inconsiderable spatial nugget effect, which is in turn impacting the errors for field EC measurements due to the positioning errors. By relying on a linear optimal predictor that reduces here to filtered kriging with measurement errors, it is shown that filtering out these two random effect components clearly improves the quality of the 1

Journal of Water Resource and Protection, 2012

Groundwater contamination by nitrate within an unconfined sandy aquifer was mapped using a Bayesi... more Groundwater contamination by nitrate within an unconfined sandy aquifer was mapped using a Bayesian Data Fusion (BDF) framework. Groundwater monitoring data was therefore combined with a statistical groundwater contamination model. In a first step, nitrate concentrations, measured at 99 monitoring stations irregularly distributed within the study area, were spatialized using ordinary kriging. Secondly, a statistical regression tree model of nitrate contamination in groundwater was constructed using land use, depth to the water table, altitude and slope as predictor variables. This allowed the construction of a regression tree based contamination map. In a third step, BDF was used to combine optimally the kriged nitrate contamination map with the regression tree based model into one single map, thereby weighing the kriged and regression tree based contamination maps in terms of their estimation uncertainty. It is shown that BDF allows integrating different sources of information about contamination in a final map, allowing quantifying the expected value and variance of the nitrate contamination estimation. It is also shown that the uncertainty in the final map is smaller than the uncertainty from the kriged or regression tree based contamination map.

Geoderma, 2015

Evidences exist that the spatial variability of soil organic carbon (SOC) in cropland is partiall... more Evidences exist that the spatial variability of soil organic carbon (SOC) in cropland is partially controlled by environmental or human factors acting on a field basis (e.g., agricultural management, landuse history, landscape structure). However, few studies have quantified the relative importance of the fields-related variability at the regional scale. Recent airborne hyperspectral imagery methods provide SOC estimates at high resolution and over large surfaces. They may be used to quantify and explain the spatial variation of SOC. In this study we used a SOC hyperspectral image over Luxembourg to separate SOC variation in three components: the effect of the texture class (as defined by a texture map), the effect of fields (as defined by a cadastral map) and spatially dependent residuals. The relative variance of these components and the spatial structure of the residuals were rigorously assessed by restricted maximum likelihood (REML). Results indicated that 65.7 ± 0.3% of the variance of SOC in the study area was explained by texture classes. The intensity of the field effect was largely dependent on the location. In some sub-areas of homogeneous texture class, no significant effect could be observed while in others, field explained up to 68.8 ± 12.0% of the variance. In contrast with other methods like ANOVA, the method developed here measures the variation related to spatial units (soil map units or fields) while taking explicitly into account spatial dependencies. As soon as the boundaries of these spatial units and a spatially extensive (or at least very dense) knowledge of a soil property over a region of interest are available, it allows rigorously determining if the soil property depends on these spatial units. In the present application, findings pointed out the importance of considering fields-related variability in SOC modeling and mapping studies.

2007 International Workshop on the Analysis of Multi-temporal Remote Sensing Images, 2007

ABSTRACT Change detection based on satellite remote sensing relies on the comparison of multispec... more ABSTRACT Change detection based on satellite remote sensing relies on the comparison of multispectral reflectance acquired at different dates. A major problem in forest change detection is to separate the signal change related to forest conversion from other sources of noise such as the ever-changing state of a forest stand from the regrowth to the mature age. This research aims at characterizing spectral reflectance over the whole forest succession in order to quantify the reflectance dynamics and to identify the most appropriate spectral signal thanks to a robust object-based change detection approach. From a large sample of spruce stands, spectral trajectories of forest cycle were derived for green, red and NIR reflectances and the NDVI derived from SPOT-HRVIR images. These trajectories were found typical for spruce stands and consistent between satellite images. Based on the object-based change detection method using image differencing, the combination of all spectral bands was proved more efficient to detect changes and more discriminant to distinguish them than any single spectral band or NDVI. Two forest changes, namely the clearing and the regrowth, have been distinguished based on multivariate analysis for different temporal resolutions. Finally, tills study emphasized the need of a better characterization of changes of interest for defining appropriate forest monitoring systems.

Water Resources Research, 2008

Water table elevations are usually sampled in space using piezometric measurements that are unfor... more Water table elevations are usually sampled in space using piezometric measurements that are unfortunately expensive to obtain and are thus scarce over space. Most of the time, piezometric data are sparsely distributed over large areas, thus providing limited direct information about the level of the corresponding water table. As a consequence, there is a real need for approaches that are able at the same time to (i) provide spatial predictions at unsampled locations and (ii) enable the user to account for all potentially available secondary information sources that are in some way related to water table elevations. In this paper, a recently developed Bayesian Data Fusion framework (BDF) is applied to the problem of water table spatial mapping. After a brief presentation of the underlying theory, specific assumptions are made and discussed in order to account for a digital elevation model as well as for the geometry of a corresponding river network. Based on a data set for the Dijle basin in the north part of Belgium, the suggested model is then implemented and results are compared to those of standard techniques like ordinary kriging and cokriging. Respective accuracies and precisions of these estimators are finally evaluated using a "leave-one-out" cross-validation procedure. Though the BDF methodology was illustrated here for the integration of only two secondary information sources (namely a digital elevation model and the geometry of a river network), the method can be applied for incorporating an arbitrary number of secondary information sources, thus opening new avenues for the important topic of data integration in a spatial mapping context.

Journal of Applied Geophysics, 2012

... non-invasive determination of shallow subsurface hydrogeophysical properties at the field sca... more ... non-invasive determination of shallow subsurface hydrogeophysical properties at the field scale through the ... Therefore, quantifying the relative difference between the near-infrared reflectance 'peak' and the ... Logarithmic scale 10800 ohm.m. Data collected on the 4th April 2005 ...

Geographical Research, 2006

Deforestation still nowadays occurs at an alarming rate in tropical regions. Forest monitoring is... more Deforestation still nowadays occurs at an alarming rate in tropical regions. Forest monitoring is required to delineate the extents of deforested areas based on high resolution satellite images (SPOT). But classical change detection techniques have failed to detect small clearing spread over the landscape as occurring in African forests. Developed initially for temperate forests, the automated object-based change detection method using segmentation and statistical algorithm was extended to tropical regions. This approach consists in three phases: (1) multidate segmentation and object signature computation, (2) forest/non-forest classification and (3) forest change detection. First, the multidate image was partitioned into objects using segmentation and several summary statistics were derived from the within-object reflectance differences. Second, a automated forest/non-forest classification was applied on the first image to define the initial forest mask. Finally, focused on these regions, the forest change detection algorithm detected deforestation thanks to a statistical test using a multivariate iterative trimming procedure. Tested over a protected area located at the eastern border of the Democratic Republic of Congo, this method produced a deforestation map with an overall accuracy of 84 % as assessed by an independent aerial survey. Given its efficiency to detect complex forest changes and its automated character, this method is seen as adequate operational tool for tropical forest monitoring.

Remote Sensing in …, 2004

ABSTRACT: Future Remote Sensing data will include hyperspectral data more and more. This study fr... more ABSTRACT: Future Remote Sensing data will include hyperspectral data more and more. This study frames in the preliminary studies to investigate the possibilities of these types of data. In this framework experimen-tal flights were organised by VITO (Flemish Institute for ...

Advances in Water Resources, 2000

The objective of this paper is to show that the structure of the spatiotemporal continuum has imp... more The objective of this paper is to show that the structure of the spatiotemporal continuum has important implications in practical stochastic hydrology (e.g., geostatistical analysis of hydrologic sites) and is not merely an abstract mathematical concept. We propose that the concept of physical geometry as a spatiotemporal continuum with properties that are empirically de®ned is important in hydrologic analyses, and that the elements of the spatiotemporal geometry (e.g., coordinate system and space/time metric) should be selected based on the physical properties of the hydrologic processes. We investigate the concept of space/time distance (metric) in various physical spaces, and its implications for hydrologic modeling. More speci®cally, we demonstrate that physical geometry plays a crucial role in the determination of appropriate spatiotemporal covariance models, and it can aect the results of geostatistical operations involved in spatiotemporal hydrologic mapping.

Geoderma, 2011

Full-waveform inversions were applied to retrieve surface, two-layered and continuous soil moistu... more Full-waveform inversions were applied to retrieve surface, two-layered and continuous soil moisture profiles from ground penetrating radar (GPR) data acquired in an 11-ha agricultural field situated in the loess belt area in central Belgium. The radar system consisted of a vector network analyzer combined with an off-ground horn antenna operating in the frequency range 200-2000 MHz. The GPR system was computer controlled and synchronized with a differential GPS for real-time data acquisition. Several inversion strategies were also tested using numerical experiments, which in particular demonstrated the potentiality to reconstruct simplified two-layered configurations from more complex, continuous dielectric profiles as prevalent in the environment. The surface soil moisture map obtained assuming a onelayered model showed a global moisture pattern mainly explained by the topography while local moisture patterns indicate a line effect. Two-layered and profile inversions provided consistent estimates with respect to each other and field observations, showing significant moisture increases with depth. However, some discrepancies were observed between the measured and modeled GPR data in the higher frequency ranges, mainly due to surface roughness effects which are not accounted for. The proposed GPR method and inversion strategies show great promise for high-resolution, realtime mapping of soil moisture at the field scale.

Journal of Hydrology, 2012

Ground penetrating radar (GPR) is an efficient method for soil moisture mapping at the field scal... more Ground penetrating radar (GPR) is an efficient method for soil moisture mapping at the field scale, bridging the scale gap between small-scale invasive sensors and large-scale remote sensing instruments. Nevertheless, commonly-used GPR approaches for soil moisture characterization suffer from several limitations and the determination of the uncertainties in GPR soil moisture sensing has been poorly addressed. Herein, we used an advanced proximal GPR method based on full-waveform inversion of ultra-wideband radar data for mapping soil moisture and uncertainties in the soil moisture maps were evaluated by three different methods. First, GPRderived soil moisture uncertainties were computed from the GPR data inversion, according to measurements and modeling errors and to the sensitivity of the electromagnetic model to soil moisture. Second, the reproducibility of the soil moisture mapping was evaluated. Third, GPR-derived soil moisture was compared with ground-truth measurements (soil core sampling). The proposed GPR method appeared to be highly precise and accurate, with spatially averaged GPR inversion uncertainty of 0.0039 m 3 m −3 , a repetition uncertainty of 0.0169 m 3 m −3 and an uncertainty of 0.0233 m 3 m −3 when compared with ground-truth measurements. These uncertainties were mapped and appeared to be related to some local model inadequacies and to small-scale variability of soil moisture. In a soil moisture mapping framework, the interpolation was found to be the determinant source of the observed uncertainties. The proposed GPR method was proven to be largely reliable in terms of accuracy and precision and appeared to be highly efficient for soil moisture

Arid Land Research and Management, 2013

ABSTRACT In this study it is shown how kriging with measurement errors (KME) is useful as opposed... more ABSTRACT In this study it is shown how kriging with measurement errors (KME) is useful as opposed to more conventional kriging methods. The goal of the study was to properly account for field measured soil electrical conductivity (EC) as soft data for the spatial prediction of soil salinity. Samplings were done in autumn 2009 (first dataset), spring and autumn 2010 (second and third datasets) around Uromieh Lake, northwest of Iran. The salinity was measured both in the field and laboratory for the first and second datasets. The first dataset was used for error measurements from which an error variance can be estimated. The measured errors were then used for characterizing probabilistic type soft data using the second dataset. The KME with only soft data (SKME), KME with both soft and hard data (HSKME) and ordinary kriging methods were compared. Validation criteria, mean error (ME) and mean squared error (MSE) were used for comparing the methods. Finally, the SKME method was applied as a way of improving the salinity prediction for the third dataset where only field measured soil salinity data were available. Comparing different kriging methods, Ordinary Kriging (OK) showed the best results among the comparing methods with ME and MSE equal to −0.12 and 0.55 respectively. SKME with ME equal to −0.13 was slightly different from OK and SKME with ME equal to −0.24 resulted in more bias predictions among others. KME method has shown to be useful for soil salinity monitoring and can effectively reduce sampling time.

Forest monitoring requires more automated systems to analyse the large amount of remote sensing d... more Forest monitoring requires more automated systems to analyse the large amount of remote sensing data. A new method of change detection is proposed for identifying forest land cover change using high spatial resolution satellite images. Combining the advantages of image segmentation, image differencing and stochastic analysis of the multispectral signal, this OB-Reflectance method is object-based and statistically driven. From a multidate image, a single segmentation using region-merging technique delineates multidate objects characterised by their reflectance differences statistics. Objects considered as outliers from multitemporal point of view are successfully discriminated thanks to a statistical procedure, i.e., the iterative trimming. Based on a chi-square test of hypothesis, abnormal values of reflectance differences statistics are identified and the corresponding objects are labelled as change. The object-based method performances were assessed using two sources of reference ...

geoENV II — Geostatistics for Environmental Applications

Statistical estimation and modeling of the variogram are essential steps in the analysis of spati... more Statistical estimation and modeling of the variogram are essential steps in the analysis of spatial variability in geostatistics. In spite of the frequent use of the methodof-moment variogram estimator, a precise quantification of the uncertainty of this estimator is rarely provided. In many cases, variogram selection and modeling are made on a somewhat subjective basis, without taking into account the distributional properties of these estimators. The variability of the variogram can be very high when few data are involved in its computations. Most of the approaches used to characterize the variogram variability are based on simulations, which are lenghty to obtain and provide approximate results. Exact theoretical results can however be obtained under some hypotheses. The first part of this paper provides the theoretical developments for the distribution of the method-of-moment variogram estimator and the least-squares variogram estimator. It is shown that, using an analytic approach based on the properties of characteristic functions in the frequency domain, the complete probability density functions of the method-of-moment variogram estimator can be obtained for each class of distance, as well as the covariance matrix of these estimators for different classes of distance. Using a Taylor expansion truncated to the first degree, the covariance matrix of the parameter estimators for the variogram model can also be obtained. In the second part, the usefulness of these developments is illustrated with a practical case study. It is shown how the theoretical results can be used for obtaining confidence intervals and hypothesis testing. A detailed methodology for the selection of a variogram model is proposed and applied to concentration mesurements in the springs of the Dyle watershed, Belgium.

Quantitative Geology and Geostatistics

We present an integrated modeling procedure for near-field ground-penetrating radar (GPR) and ele... more We present an integrated modeling procedure for near-field ground-penetrating radar (GPR) and electromagnetic induction (EMI) for the non-invasive determination of the constitutive properties of planar layered media in a high-resolution digital soil mapping framework. We validated the approach using GPR and EMI instruments set up using vector network analyzer technology, though the proposed methods also apply to conventional instruments. The antennas are modeled using a set of infinitesimal dipoles and characteristics, frequency-dependent, global reflection and transmission coefficients. The GPR and EMI antennas were calibrated using measurements at different heights over water. The electromagnetic models were then successfully validated using measurements acquired over water subject to different salinity levels. Finally, GPR and EMI data fusion strategies were investigated for resolving non-uniqueness issues that are inherent to multilayered media reconstruction