This study investigates pollutant dispersion from residential wood burning in a neighborhood in Utrecht, Netherlands, employing the Dutch Atmospheric Large-Eddy Simulation (DALES) model under various atmospheric conditions.
Residential wood combustion is a major source of urban air pollution, especially during winter months. This research aims to quantify the distribution and distance traveled by pollutants from their release source. By using DALES, detailed analyses of atmospheric variables and pollutant concentration fields are conducted, providing valuable insights into how atmospheric stability influences pollutant spread.
The results show that atmospheric stability significantly affects pollutant dispersion. Higher pollutant concentrations were generally observed near the surface under stable and very stable conditions as compared to neutral conditions, due to restricted vertical motions that limit the vertical dispersion of pollutants. Additionally, under stable conditions, pollutant concentrations remained elevated farther from the source, affecting residents who do not live close to the emission source. The study also compared the performance of DALES with commonly used Gaussian plume models (GPMs) to evaluate their performance in urban environments and under different atmospheric conditions. Three schemes that provide the dispersion parameters for the GPMs are tested to determine their accuracy in representing the DALES results. The comparison reveals that, while GPMs offer a general overview of pollutant distribution, they often fail to accurately capture concentration decay rates or the spatial extent of the plume.
The study concludes that further research should investigate the impact of atmospheric stability on air pollutant dispersion in urban environments. It also highlights the limitations of Gaussian Plume Models (GPMs), which often simplify processes occurring in the boundary layer. In urban settings, where the urban geometry plays a significant role in pollutant dispersion, DALES proves to be more effective than GPMs, which cannot accurately capture the effects of buildings on the plume.

Local Climate Zone (LCZ) classification plays a crucial role in understanding and managing urban environments, particularly through the lens of Land Surface Temperature (LST) behavior. This study investigates the effectiveness of a Convolutional Neural Network (CNN) with U-net architecture for classifying urban LCZs using spatio-temporal thermal imagery.

The research addresses several key sub-questions, including the creation of a representable training dataset, optimizing hyperparameters for the U-net model, and assessing the impact of temporal factors on classification performance. An unsupervised clustering approach was adopted to label the training data, utilizing the Iterative Self-Organizing Data Analysis Technique (ISODATA) algorithm on stacks of thermal images to generate clusters based on thermal behavior, which were then manually refined and validated. Through experimentation, optimal hyperparameters were identified: a learning rate of 0.001, a patch size of 64, and the SparseCategoricalCrossentropy loss function. The study also highlights the significant influence of temporal factors, when using daytime and Spring/Summer thermal images for training and testing the model better classification outcomes were obtained compared to nighttime and Autumn/Winter images.

The research contributes to the LCZ classification by incorporating both spatial and temporal dimensions of LST patterns, providing valuable insights for urban planning. The findings demonstrate that a CNN with U-net architecture is highly suitable for classifying urban LCZs, particularly when the dataset captures diverse seasonal and extreme conditions. This approach offers a robust and adaptable framework for urban environmental monitoring and planning. This thesis has explored the utilization of a new source for LCZ classification, providing a useful starting position for further enhancement of standardized LCZ classification. Future work is therefore recommended to focus on integrating additional geospatial data sources, refining classification categories, and integrating the standardized LCZ classification system by Stewart and Oke [2012].

Water management is an integral part of Dutch history, driven by the continuous need to reduce flood risk. Because a large area of the country is located below Normaal Amsterdams Peil (NAP), there is an ongoing challenge to safely discharge all the water to the sea. Therefore, flood safety policy has become crucial to protect the Netherlands from natural hazards. An essential part of this strategy involves the Waardegedreven Onderhoudscontract Uiterwaarden (WOCU) Rijntakken project, which is responsible for managing the floodplains adjacent to the Rijntakken within the Netherlands.

The current lack of efficiency and effectiveness regarding change inspection in the large and sometimes inaccessible areas of the floodplain requires the use of remote sensing change detection to move toward a data-driven maintenance process, in particular, by using point cloud data. This is nowadays a widely used data source in a variety of fields to capture elevations and in this way extract valuable information from terrains. Despite its usage in a variety of applications, the data is often underused since the data is frequently processed directly to other data formats. This research therefore aims to reveal the potential of explorative point clouds in floodplain maintenance.

Light Detection and Ranging (LiDAR)- and multispectral data were acquired at two moments, one before and one after the summer, with a time interval of 45 days. Subsequently, these acquired datasets evolved into an explorative point cloud by adding attributes, including vegetation health, also known as Normalized Difference Vegetation Index (NDVI), and the distance between these two point clouds, the cloud-to-cloud distance. This explorative point cloud with the integrated additional information was visualised to several disciplines involved in the WOCU project. This was done in Three Dimensional (3D) by using Virtual Reality (VR). This collaborative approach revealed the potential use cases of the Red, Green, Blue (RGB), cloud-to-cloud distance, and NDVI point clouds highlighting the potential of explorative point clouds.

Potential use cases that were found are; highly detailed area modeling, vegetation overgrowth monitoring, bank erosion detection, flora status assessment, monitoring of vegetation types, digital inspection of remote sites, participation medium, and identification of atrophied ground patches. Attributes added to point clouds enhanced insights. Especially the RGB point cloud sparked excitement due to its realistic appearance. The Cloud-to-Cloud Distance (C2CD) attribute showed potential, especially for erosion detection. However, due to the short timeframe between measurements, it could not be detected. The NDVI attribute was perceived as less interesting.

The use of explorative point clouds, generated from raw LiDAR point cloud data, offers potential uses and insights for floodplain maintenance. The interdisciplinary value of explorative point clouds was clearly visible. This thesis emphasizes that underused raw LiDAR data, by making it explorative, can act as a valuable resource.

A personal weather station (PWS) is a set of weather measuring instruments that is owned and operated by an individual, club, association, or business. Even tho these stations might be less advanced as the professional stations, the amount of amateur stations that are active is much higher. With these PWS networks in full effect and still growing, the potential to use PWS data for weather forecasting or research purposes has gained popularity in the scientific field for its potential to ”fill data gaps”. However, since the stations are managed by “amateurs”, the “usefulness” and “value” of these stations are under questions. In this thesis, we tried to solve this by proposing a new way of assessing the “Value” of an amateur weather station in a citizen network. We utilized bots to extract all information about stations in the Netherlands and for the first time did a national analysis of PWS networks. Significant progress has been made in developing a framework to assess the value of the Dutch PWS network. The research process involved overcoming numerous obstacles, which have provided valuable insights and lessons for the future development of a more refined PWS network assessment system.

Antarctica plays a crucial role in global climate change research, but current models only extend back to the satellite era from the 1990s to the present, leaving significant gaps in our understanding of the continent’s past and covering only limited locations. Historical aerial surveys, which may extend back to the 1930s and cover more locations, offer an opportunity to overcome these limitations. However, creating models from historical imagery poses a significant challenge. This study presents a workflow for automatically extracting camera altitude information from historical imagery and doing preliminary geolocalization by computing the footprints of the imagery based on the extracted altitude information. The workflow was applied to two flight lines in the TMA Single Frames dataset, and the results were evaluated using a scoring system and by comparing the photos with their footprints. This work serves as a preliminary stage in geolocating and georeferencing historical imagery, laying the foundation for future model-building with these photographs.

Icebergs drifting through the Southern Ocean release fresh water and nutrients. This has local impacts on surrounding ecosystems and sea ice formation. On a global scale, salinity patterns and ocean circulation are affected. In addition,
tudying icebergs as a proxy for ice shelves in a warming climate can help predict future climate impacts and sea level rise. Furthermore, drifting icebergs can pose a threat to ship navigation and offshore projects. In the past, icebergs have been tracked mostly manually, a time-consuming and labour-intensive task. The most widely used data source for this is Synthetic Aperture Radar (SAR), as icebergs often have a much higher backscatter than their surroundings. A few attempts have been made to automatically track icebergs, but these methods do not allow tracking of icebergs that are only partially visible in a satellite image. In this study, a new method is proposed based on partial contour recognition using the contours’ curvature, a technique derived from the matching of ancient pottery fragments. Since the automatic tracking of multiple icebergs requires a large amount of data and computational resources, the web-based environment of Google Earth Engine is used. The new method, called the Contour Curvature (CC) method, is based on three main steps. (1) Detection of icebergs using Simple Non-Iterative Clustering (SNIC) in combination with a threshold function. (2) The icebergs targets are filtered using an area and solidity filter. (3) Among the remaining targets, the best match is selected by comparing the curvature function of the contour with the reference iceberg. The performance of the algorithm is tested by automatically tracking 15 icebergs and comparing
the results to the existing Centroid Distance Histogram (CDH) method. The overall performance of the CC method can be attributed in large part to the inclusion of the area and the solidity filter, with the latter serving as an overall shape filter. For small icebergs (< 10 km2), both the CC and CDH method perform poorly, due to the abundance of icebergs in this range. For medium to large icebergs (10 to 1000 km2), the methods show similar performance with one method occasionally outperforming the other method. For large icebergs (> 1000 km2), the CC method performs better. Since these icebergs are often only partially visible, this leads to strong deviations in the histogram used in the CDH method, making this method less suitable for these situations. Since the CC method allows for partial contour recognition, these icebergs can still be identified. Furthermore, due to the wide variety of backscatter conditions, the detection method occasionally fails to distinguish icebergs from their surroundings.

New mining and exploration projects have revived across Europe with an increasing demand for critical raw materials driven by the energy transition and unstable political conditions. Hyperspectral imaging proves to be an excellent tool for quick and efficient non-invasive mineral exploration. By mounting hyperspectral- and LiDAR sensors on drones and other robots, inaccessible and dangerous areas can safely be mapped for geological information. With increasingly deeper and more hazardous underground mines, a robot-aided
hyperspectral mineral exploration method in underground environments is needed. This study configures a Cubert X20P hyperspectral camera (VNIR) and VLP16 LiDAR on the versatile Boston Dynamics Spot robot, creating a multi-sensor robotic platform for data acquisition in underground mining environments. A data workflow is proposed and applied to the granite greisen rocks of the Zinnwald/Cinnovec mine (Germany). Combining hyperspectral and geometric data provides unique 3D hypercloud results interpreted for mineral
and structural features. Hyperspectral analysis successfully identifies iron and clay minerals along with multiple vein and fault structures. A remote mineral exploration method in underground mines significantly improves safety by keeping the operator away from hazardous areas. The proposed platform and workflow show potential to contribute to underground mineral exploration, especially if future improvements in data quality and autonomous capabilities are made.

Absolute accuracy plays a crucial role in most geospatial applications. Particularly in the case of mobile mapping systems (MMS) where large amounts of data are collected, it comprises a key factor that can sometimes be dangerously underestimated. Within the scope of mobile laser scanning, the accuracy of the measurements is mainly ruled by the quality of the navigation solution. Commonly, as for the MMS that Fugro employs to capture 3D measurements from railway surroundings, called RILA, the trajectory of these mobile systems is determined by integrating data from positioning sensors, such as GNSS and IMU. Due to their complementary characteristics, the accuracy and robustness of the navigation solution can generally be considered sufficient. Nevertheless, in certain challenging environments where GNSS positioning conditions are limited, the trajectory results attained may not yet be accurate enough. Therefore, this study presents an alternative procedure to enhance the navigation solution of this MMS, making use of the point cloud data already acquired and georeferenced. Multiple runs or passes of the mapping system over the same problematic area take place at different times and some might possess satisfactory outcomes. Hence, after employing this dataset as reference, the quality of the others could be improved by means of point cloud matching methods. Next, the computed registration values are also applied to the trajectory, accordingly. Results over the area of interest selected show that applying Iterative Closest Point (ICP) using just a few feature points can already enhance the results. ICP iteratively finds the optimal rotation and translation to match two point clouds, minimising the differences between them. Furthermore, trajectory accuracies increase with the implementation of ICP utilising all points, without leading to extra processing times. However, GNSS/INS errors are not constant throughout time and space, which means that the application of a single rigid transformation may not be adequate. For that reason, an interpolated ICP-based method has been introduced and tested. An increasing number of point cloud sections was considered and among all, the solution with a relatively high number of sections performed better. This algorithm comprised a division of the point cloud data into 1000 contiguous sections or slices along the trajectory, each approximately 60 cm wide. The RMSE of the resulting absolute trajectory error and its standard deviation were greatly improved, with their values decaying from 41.1 cm and 18.1 cm to just 4.1 cm and 2.4 cm, respectively. In addition, provided a good reference dataset is available, the method proposed can be executed completely automatically and independently of the type of data captured and environment conditions. Even though these results might not yet reach the desired accuracy of around 1 cm to correctly georeference RILA’s LiDAR data, it has been proven that this method has the potential to yield much more accurate, consistent and reliable outcomes. Further research should be directed at generating an accurate reference dataset when none is initially available, by collectively and statistically combining the results of multiple surveys.

Satellite data, such as optical and Synthetic Aperture Radar imagery, can provide information about the location and level of destruction caused by natural hazards. This information is essential to optimise the rescue mission logistics by humanitarian aid organisations and save people in need. Currently, many Automatic Damage Assessment (ADA) methods exist, designed explicitly for one data type with corresponding spatial resolution. However, the weather and satellite coverage conditions can hinder rapid and complete data acquisitions after large events. Therefore, it is important to identify the limits and capabilities of novel methodologies testing various data availability scenarios and adjusting them to become robust and widely deployable.
In this research, the Convolutional Neural Network Caladrius of 510 ­ an organisation of the Red Cross Netherlands is selected to perform experiments. Initially, the model was designed to input high-­resolution imagery and based on the Siamese Architecture, including two Inception­-V3 modules fol­lowed by three connected layers. The multiple experiments are based on single­, dual­, and cross­mode scenarios, representing data characteristics with varying resolutions, satellite sources and observation sensor types. The xBD dataset provides pre- and post-­event high­-resolution optical imagery of numer­ous disasters with corresponding validated damage labels of the included buildings. Subsequently, this dataset is replicated in three down­sampled versions and using Sentinel-­2 1C and Sentinel­-1 GRD data. With the use of the Macro F1-­score and the Cohen’s Kappa coefficient the performances are compared and the predictions’ reliability is determined in operational situations.
The results indicate that a lower resolution of the input data has a negative effect on the correct classi­fied buildings. A linear relation does not express the loss in performance, as most damage propertiesare captured between 0.5­ and 2.5-meter. Consequently, this implies that the Sentinel 10­-meter res­olution datasets provide little recognisable features. The Sentinel­-2 1C experiment outperforms the Sentinel­-1 GRD, which equals the output of a random classifier. However, no final conclusion is drawn between the true prediction rate of the model compared to the input data type; optical and SAR im­agery due to the non-­optimal experiment circumstances and limited included datasets. Furthermore,the results from the dual-­mode mapping showcase the importance of identical data characteristics be­tween train and test datasets. Conversely, with the use of the cross-­mode experiments, it is found not essential to match the pre-­ and post­-event resolution imagery. This latter is very promising for the Red Cross and creates flexibility to construct datasets quickly after the disaster has struck.

Sea level rise causes more difficulties for coastal maintenance. Although hard structures were built in the past to prevent for flooding, nature based solutions are preferred more recently. For the application of nature based solutions, a better understanding of the coastal processes is desired. Dry sand is transported towards the dunes by aeolian processes, whose bedform development is understudied while knowledge on this is important. One of the most visually clear bedforms are organised dry sand over a moist beach, referred to as sand strips. In order to improve the knowledge on sand strips, this study focusses on sand strip properties and occurring environmental conditions during their presence at the Noordwijk beach, using data of a terrestrial laser scanning (TLS) device.

Sand strips are detected with the Fourier transform. Since surface moisture can be derived from the reflectance intensity of the TLS-data, and due to the different moisture content of the sand strips compared to the surrounding beach, the Fourier transform is applied on the reflectance intensity. Sand strips are detected based on the energy in the variance density spectrum for a wavenumber-range corresponding to sand strips. The detected sand strips were oriented alongshore to oblique-alongshore with a mean wavelength and height of 13.2 m and 4.0 cm respectively, which is in correspondence with similar sand strip-related studies.

According to sand samples of the beach the grain size varies in transverse direction of the sand strips, comparable with the grain size variation of aeolian sand ripples. The coarser grains were located at the crest and the finer grains at the lee-side. Additionally, the samples also showed a significant difference in gravimetric moisture content between the sand strips and surrounding beach, as expected due to the reflectance-based detection. At the sand strips, the maximum moisture content was 5.3%, while the minimum determined moisture content at the surrounding beach was 6.0%. The mean values were equal to 2.6% and 9.4% for the sand strips and surrounding beach respectively.

In addition, sand strips mainly occurred during (almost) alongshore wind events with a wind velocity in excess of 8 m/s. However, the threshold wind velocity for sand strip formation is determined at 10 m/s. Due to the significant height difference that can remain present during precipitation events, these events are not necessarily restrictive factors for sand strip development, although the reflectance intensity suggests different. Furthermore, sand strips mostly formed during falling tide and they were mostly destroyed during rising tide.

The one life cycle of the sand strips that is analysed showed dynamic sand strip behaviour. The migration rate of the sand strips varied over the width of the beach, causing more inland oriented sand strips. This dynamic behaviour cannot be related to weather conditions since they remained constant, however it could be related to topographic steering caused by the dune. Nevertheless, the results of the dynamic properties are only indicative and encourage further study of dynamic sand strip properties.

The lack of insight regarding the stability of many dolmen in the Netherlands is problematic. In 2019, a capstone fell down dolmen D14, greatly damaging this cultural heritage. In order to decide on whether it would be wise to reconstruct this dolmen, it is useful to understand the stability of potential reconstruction scenarios without having to perform invasive tests that could damage the structure. To this end, this research has focused on the application of non-destructive, digital methods to analyze the stability of dolmen D14. Laser scanners were used to obtain point cloud data from the zone around the dislocated capstone, with which 3D meshes were created. Furthermore, tests were conducted to estimate the relevant parameters to quantify the shear strength of the rock contact areas. This was done in the field using the Equotip and Barton’s comb and by performing tilt tests and Golder shear box tests in the laboratory. For the rock parameters, a basic friction angle of 33° was found with tilt tests and a residual friction angle of 31.2° and 30.5° was found for the Golder shear box on a flat sample. The meshes and rock parameters were used as input to conduct stability analyses in 3DEC. Two different models were analyzed, one based on the rock configuration as it was in 2019 (Model 1) and the second model with a rock configuration as it was in 1925 (Model 2). In the stability analysis, it was found that Model 1 is much more unstable compared to Model 2. The minimum pushing force for instability of the structure was 2 kN for Model 1 and 94 kN for Model 2, for the conditions under research. Furthermore, it was found that the number of cycles used in the analysis, mesh coarseness and rock joint friction angle all have a significant impact on the model results. All in all, this research has demonstrated the potential for using digital and non-destructive methods to analyze the stability of megalithic structures.

Since the behaviour of damage formation on steep slopes varies from mild slopes, a comprehensive definition of damage needs to be established for different situations. Knowledge about the transition of rock stability between steep and mild slopes is lacking. This study presents a reliable and efficient design method. The goal of this study is to describe the stability of rock slopes (1:6 - 1:10). Insight into governing processes and quantified damage characteristics are gained by analyzing wave attack in the transition zone of mild and steep slopes. The method is based on physical model test series with deep water wave conditions and perpendicular wave attack, which were performed to understand the stability of slope 1:6. The slopes were measured with stereophotogrammetry and processed in a 3D model with Agisoft Metashape software. Before wave attack, an initial profile is retrieved which can be compared with cumulative damaged profiles. Automatically detectable Ground Control Points are used as reference to assign scaling information and location recognition. The quantity and location of normative damage on mild slopes are identified for six damage parameters, based on different relations regarding erosion area and depth. The tests results have led to a number of key findings. Damage formations are developed by dominant downward (bar profile) or upward transport (berm profile). In general, a thicker layer and a milder slope give more upward transport. Besides the change in transportation direction, no significant differences in stability numbers were observed for various layer thicknesses with similar wave characteristics. For an increasing slope angle, the influence on stability significantly decreases between various wave steepnesses, due to the impact of a higher amount of more damaging plunging waves. The definition of initial, intermediate and failure damage limits are specified and the test profiles are matched per limit. Initial and intermediate damage are found at a constant value of _3_.3 for a varying layer thickness and slope angle based on the definition and matching profile analysis. Failure of the slope is defined by the event in which the impermeable layer or filter layer becomes visible, which occurs for a layer thickness of 2.53=50 at a damage level of _3_.3 = 1.5. Research at 1:6 slope shows that a higher failure limit is observed for layer thickness 53=50. Due to the lack of test results of other slopes (1:8 - 1:10), specific values cannot yet be indicated for the range 1:6 - 1:10. The lack of testing is also due to limits of the flume and wave machine. In order to remain at least 13=50 over the slope, an acceptable damage limit for a design with a layer thickness of 2.53=50 is found at _3_.3 = 0.9. For a layer thickness of 53=50, an acceptable value of _3_.3 = 1.5 holds a remaining layer of 2.53=50 due to the increase of bed mobility for higher damage numbers. The design formula of mild slopes is based on _3_.3 because this gives the lowest bias error, variability and measurement errors. The design formula for mild slopes is used to estimate stability, nominal rock diameter and acceptable damage for conditions within the applicability range of performed tests. This new method for mild slope design indicates that a higher allowable stability can be used compared to the previously used (extrapolated) method of Van der Meer (1988). A displacement is not necessarily linked to erosion due to alternating upward and downward transport on mild slopes. It can be discussed to what extent a rock, after displacement, contributes to the stability of the design. This can be investigated by the ratio of mobility and erosion over the entire slope. This study has shown that the damage parameters, based on displacements, are larger than the width-averaged erosion parameters. This appears to be a useful ratio for distinguishing mild slopes from steep slopes. In the latter these parameters are considered equal. Requirements for mild slope stability can also be imposed on the basis of the ratio between mobility and erosion as described in this research.

Bathymetric surveying of the Netherlands Continental Shelf (NCS) is taken care of, by the Dutch Hydrographic Office and Rijkswaterstaat. The survey frequencies vary from location to location depending on a set of factors defined by the authorities. These are the minimum depth, draught (the difference between seafloor depth and ship’s keel), shipping intensity, human interventions and seafloor dynamics that are taking place in an area. The project is about developing a methodology using the available time-series of bathymetric data to automatically make a well-informed decision on the survey frequency of critical areas of the NCS. This study aims to contribute to the decision-making by performing a risk-based analysis using a probabilistic approach. The main objectives are to estimate and map the risk that the seafloor is changing significantly. Risk severity should be measured and the probability of risk's consequence to occur should be estimated and mapped in temporal and spatial scale indicating if possible, the optimal surveying frequency. Firstly, the accuracy, quality and reliability of the prediction values were defined and computed. This was a necessary step to realize how powerful are the predictions used as an input dataset for the risk assessment of the seafloor to change significantly. As a step further, the risk's impact and probability to occur should be determined. Hence, it was necessary to define quantities related to seabed dynamics that express the evolution of the seafloor. These are mentioned as risk indicators and are the Relative Depth Change (RDC), the Rate of Change (RoC) and the direction of change. The RoC was used for probability estimation and the rest of it as informative variables for the analysis. Finally, an adjustment of the method used here with the Netherlands Hydrographic Office (NLHO) standards was done and conclusions were made regarding the survey frequency of the chosen study areas. The analysis was applied for two study areas: West IJmuiden and West Rotterdam, as these areas according to the NLHO are highly prioritised (should be resurveyed every 2 years) and thus, they are critical areas of the NCS. The method applied and the results can be used as an informative tool in designing a successful, reliable and cost-effective bathymetric survey plan of the NCS.

Subsidence can be observed at various locations in the Netherlands. While it can be due to both shallow and deep subsurface processes, the shallow subsidence is mainly a result from compaction, oxidation and/or groundwater drainage. Moreover, for grasslands on organic soils, in particular pastures on drained peat soils, the vertical position of the ground level is subject to significant temporal variability. Temporal scales of the vertical motion are expected to vary between days and centuries, and spatial scales between millimeters and kilometers. Unfortunately, performing precise, reliable, and representative geodetic measurements of shallow subsidence processes is very difficult for soils, as fixed benchmarks do not exist. Here we propose an insitu measurement procedure for grass-covered soils using laser scanning, both terrestrial and airborne, using a vertically fixed reference platform. We show that for terrestrial laser scanning (TLS) it is possible to detect changes in the vertical position of soils with a systematic error of up to 2.6 mm and a standard deviation of 0.4 mm. Given a predefined level of significance of 훼 =0.05 (confidence level of 95%) and a detectability power of 훾 =90%, we achieve a minimal detectable vertical deformation (MDD) of 21.1 mm in our study area. We show that the results are influenced by the grass density and length, the incidence angle of the laser beam, as well as other settings of the laser scanner. We find that the parameter settings of the method for estimating soil surface, and subsequently the subsidence, has an influence on the results and related statistics. For airborne laser scanning (ALS), using a precisely leveled reference platform, we find that the quality of elevation estimates is still limited, requiring further considerations on the design of data acquisition surveys and the reference platforms. Our results demonstrate the ability of laser scanning technology for investigating shallow subsurface motion of grass-covered soils relative to a benchmark on a local scale. Based on the quality assessment, the detection of vertical ground level change is better understood in terms of time and probability. In future research, the factors affecting terrestrial laser scanning technology to accurately identify soils affected by vegetation, environment, and device condition, should be further studied.

Until now, an extensive design method for mild slopes has not been available. The aim of this thesis is to understand the stability of rock on mild slopes under wave attack for impermeable cores in order to optimize designs. Physical model tests have been executed to study this stability for a 1:8 slope. Mossinkoff (2019) executed physical model tests for a 1:10 slope and a re-analysis of these tests is included in this thesis as well. Damage caused by entrained rocks is quantified by damage parameters using stereophotogrammetry and coloured rocks in strips. In this study, the influence of several hydraulic and structural parameters on damage parameters has been investigated for mild slopes. A positive correlation has been found between the significant wave height and the damage parameters. Besides this, based on the analysis it can be concluded that the wave steepness and damage parameters are negatively correlated. An increase in layer thickness of the rocks does not seem to increase the stability of rock on mild slopes. However, the slope angle does have an effect on the stability as a milder slope is associated with less damage. Another conclusion is that more damage continues to be observed even after 15 000 waves. Based on the results of the physical model tests, a design formula is developed for mild slopes to be able to increase efficiency in designs of coastal structures for these mild slopes. This study also provides evidence that rocks on mild slopes have different characteristics of damage and damage development compared to steep slopes. The largest share of entrained rocks transport in upward direction and rocks on mild slopes seem to be more mobile compared to steep slopes. This suggested that it might be more efficient to study the moment when the filter layer or the core becomes visible instead of the static stability of rock within the armour layer itself.

Remote retrieval of Normalized Difference Vegetation Index (NDVI) over the Earth’s surface is a critical component of monitoring the surface processes of our planet. NDVI is a widely used and useful indicator of vegetation health and quantity ­ however its retrieval using satellite data is hindered by the frequent presence of clouds in the Earth’s atmosphere. Zeng et al. (2016) developed a novel technique that estimates a surface's Bidirectional Reflectance Distribution Function (BRDF) with a Ross­Li­Maignan (RLM) BRDF model from a set of observations. This method, the Changing­Weight Iterative (CWI) method, uses iterative a posteriori estimation of observation errors to reduce the impact of cloud-contaminated measurements in the sample. Its performance was compared to two conventional methods, ordinary­least squares (OLS) and Li­Gao BRDF-fitting. The three different BRDF­fitting methods were compared in a numerical experiment. 6,000 surface types covering a broad range of surface types were modeled using the canopy radiative transfer model PROSAIL. For each surface, sets of pseudo-observations of the surface’s red and NIR band reflectance were generated using realistic sun­target view geometries from the MODIS and MERSI satellite sensors. The effects of cloud­contamination were simulated by adding different numbers of cloud­contaminated observation to the sample, with varying degrees of contamination. The RLM BRDF model was fitted to these samples using the three different methods to estimate the BRDF model parameters. These were subsequently used to calculate a NDVI composite value. Each method’s estimate was compared to a reference ­value generated by PROSAIL. Results for the 6,000 surfaces confirmed that the CWI method is more noise­resistant than OLS and Li­Gao in situations with many observations (i.e. a large sample), and resulted in estimates that more closely matched the reference value from PROSAIL, compared to the conventional Li­Gao and OLS methods. In scenarios of low­cloud contamination, all three methods failed to detect and significantly suppress the impact of noisy observations, which was expected from existing literature. For a large­sized sample of 13 pseudo­observations studied for the validation site Mongu, Zambia, the CWI method was observed to have a very accurate performance, for up to 5 contaminated observations in the sample. With smaller sized samples of 8 and 10 for two other validation sites, it was found that the RMSE of the CWI method would suddenly increase approximately tenfold when the number of contaminated observations increased beyond 2 and 3, respectively. After these ’tipping points’, the Li­Gao method was more accurate and outperformed CWI. The CWI method therefore performed promisingly when given a large enough sample size, and in these cases it was more accurate than the conventional Li-Gao and OLS methods. However, when it fails to correctly identify noisy observations, its accuracy could decrease suddenly, which should be taken into consideration for operational use. Since the results of the experiment were averaged over 6,000 different sampling points of the PROSAIL model's parameter space, it is suggested that the conclusions apply to a wide range of surface types found all over the Earth.