The first size reduction stage in open pit hard rock mining is blasting and is fundamental for mineral resource extraction as it enables transportation of the rock. Consistent frag-mentation results are preferable as it can ease the loading, hauling and crushing stages. Fragmentation analysis assists in identifying areas where similar blast results appear when comparable drill and blast designs are used. Subsequently, drill and blast domains can be defined for the Leveäniemi mine to ease the drill and blast design process.

Measuring fragmentation has been continuously researched over the past years. Image analysis methods were developed as it minimised disruption to production and provided a reasonable indirect estimation of particle sizes. So far, research was focussed on 2D image analysis. However, by adding a 3rd dimension, some limitations can be overcome that were experienced when using only two dimensions. Therefore, the potential of 3D image analysis of blasted rock, loaded in haul trucks, is of interest. By evaluating truck loads, the muck pile is better represented in comparison to measuring a whole muck pile. This is one of the few studies done until now on 3D image analysis in open pit mining, analysing material in loaded trucks.

A 3D image analysis field test was executed to measure blast-induced fragmentation in a production environment at the Leveäniemi mine. The aim was to gain a clear understand-ing of the factors contributing to an optimal blast result and to establish blast domains. The test setup consisted of an image acquisition system, photographing truck loads from above using two cameras that were triggered by a laser. RFID truck markers were com-bined with Minestar data to identify the origin of the truck. Truck loads were analysed using software developed by LKAB and 3GSM, constructing 3D models and automatically delineating particles to analyse fragmentation. No pre- or post-processing of the images or delineation results have been done. Drill and blast, and muck pile shape parameters were acquired as well.

The results show x50 particle sizes ranging from 5 to 56cm and x80 ranging from 20 to 150cm. Care should be taken when interpreting these results due to the limited amount of data analysed and bias in the measurements and software. Fines are underestimated and correct particle delineation occurred on average in 42% of each load. The limited amount of data resulted from the practical problems arising during data acquisition. Con-tinuous data acquisition of images was not achieved.

Correlation of fragmentation to drill and blast, or muck pile shape parameters was not achieved due to the challenges faced. 3D image analysis of truck loads proved to have potential but requires many modifications and developments to the system and software to achieve continuous data acquisition. A focus on image quality and the practicalities of the system is recommended. Continuous data acquisition is required when using frag-mentation analysis for establishing blast domains.

Comparable and repetitive measurements are the main prerequisites for choosing a method to analyse fragmentation for the purpose of defining blast domains. Hence, other methods like using drones to analyse whole muck piles after blasting should be consid-ered, though it would statistically be less representative. For the Leveäniemi mine, it is recommended to achieve geotechnical domains including joint spacing and orientation before a follow-up study is started on blast domains. Additionally, the development of this method should be finalised, or an alternative is recommended.

Eutrophication processes in coastal waters are becoming more prominent as a result of high nutrient discharges from intensive agriculture and increased urban waste. These processes can be devastating for local ecosystems and lead to dissolved oxygen depletion, which applies considerable stress on aquatic organisms. For ecosystems to preserve their status, stop and reverse the negative effects of eutrophication, regular estimation of corresponding indicators has to take place. In this direction, mostly process-driven models have been used, but the presented project argues that freely available remote sensing data can also provide useful insights for the oxygen saturation of the water. The proposed methodology uses Sea Surface Temperature and Chlorophyll-a estimations from AQUA and ENVISAT satellite sensors for the period 2003-2011 to predict the dissolved oxygen content in the Dutch coastal waters. It does so by implementing various Machine Learning models, namely Random Forest, Artificial Neural Network and Gradient Boosting Regressors, with the latter demonstrating the best results. After extensive data pre-processing, the results show that dissolved oxygen can be predicted with an average Root-Mean-Squared error of 0.8 g/m3. Important steps towards a lower error include the use of gap-filled variables and their decomposition into their temporal components as inputs for the model. Furthermore, the effect of the Sea Surface Temperature on the dissolved oxygen is documented through its contribution in the estimation of the latter’s seasonal variability, while the estimation of the maximum dissolved oxygen values is attributed to Chlorophyll-a. Further feature engineering and model development can possibly improve the estimation of the minimum dissolved oxygen values in the coast and the overall prediction in more complex intertidal areas, like the Wadden Sea.

Geodesists use multiple methods to monitor surface deformation. This gives the opportunity to integrate complementary data for better interpretation of deformation processes. The integration of data has to deal with: (1) spatio-temporal differences in sampling methodologies, meaning that observed objects are not the same and thus observed processes may stem from different sources; and (2) physical differences in the methodologies leading to different kinds of coordinate reference systems with possibly different datums. This makes integration of data challenging.In this thesis, we address one of the fundamental roots to this integration problem, by developing co-located reference points for multiple geodetic monitoring methods. This will ensure that, for multiple geodetic monitoring methods, one common deformation process is observed. This eliminates interpolation between observations. I show the main requirements for an Integrating Geodetic Reference Station (IGRS), present a design for a fully functional IGRS and describe its performance. Furthermore, the protocols for deployment and operational use are given. Benchmarks for InSAR, GNSS, levelling, LiDAR, and gravimetry are present on the IGRS. From field tests, it is found that presented radar reflectors have a 1-sigma measurement precision of <0.5 mm in the Line-of-Sight of the radar. This includes both the structural stability as the influence due to clutter. Furthermore, it is shown that the GNSS antenna has mm precise performance, similar to standard monitoring GNSS set-ups.The developed IGRS can be deployed to create a local datum connection between datasets. Ideally IGRSs, either the design presented in this thesis or similar, are deployed where integration, validation and calibration of data, especially InSAR data, is needed.

Satellite altimetry is an important technology used to measure sea level with high spatial and temporal resolution. Sentinel-3, a Copernicus satellite mission, offers three types of variables captured simultaneously for the first time; sea level (SSH), sea surface temperature (SST) and ocean colour (OC) variables. Sea level is measured with SAR altimetry, a technique that considerably increases spatial resolution compared to other means of observation. Altimetry measures sea level across a line that coincides with the satellite ground track, whereas SST and OC are measured on a grid. What we lack are sea level observations in-between ground tracks that would better resolve meso-scale variability. This thesis is focused on two objectives, considering previous work that has indicated associations between those variables. The first objective was to investigate the correlations among SSH, SST and OC, while the second objective was to assess to what extent inter-track sea level can be estimated using SST and OC as predictors in machine learning algorithms. Daily Sentinel-3 data over a period of eleven months were pre-processed and brought into a form that allowed for computation of metrics such as auto- and cross-correlations in the along-track direction. The focus was on the spatial scales that would enable to detect meso-scale features, such as eddies. With respect to the inter-track sea level estimation two paths were followed. In the first path, Random Forest (RF) and Multilayer Perceptron (MLP) were applied using the complete 11-month dataset as input. Moreover, RF was applied on input data that belong to each separate day. In the second path, 1D Convolutional Neural Network (CNN) was used on the complete 11-month dataset, which inherently honors the spatial dependency of the variables in contrast to the first path. Generally, the correlations between the variables were found to exist in the meso-scale but were not always strong and they depend on several other factors, such as meteorological conditions, scales included in the analysis and techniques used. All three techniques -RF, MLP and 1D CNN- that were applied on the complete 11-month dataset gave poor results. On the contrary, when RF was applied on the per-day data gave promising results that are reliable mostly in the vicinity of the ground track, although they are not based on one single global model. The results from this project suggest that there must be more research on the correlation analysis of Sentinel-3 data. It can be improved by using additional or similar techniques, such as localized cross-correlation metrics on various spatial scales. With respect to the inter-track sea level estimation, far more investigation is needed. However, there are indications that a machine learning data-driven approach could potentially work to some extent. Sentinel-3 data will become more abundant in the next years which will assist data science algorithms such as CNNs which require huge datasets.

In this study, deep neural networks are employed to act as a surrogate model between the Meteo France land surface model and Advanced SCATterometer (ASCAT) satellite observations. This provides a measurement operator for the assimilation of ASCAT satellite derivations into this model. Currently, TU Wien uses ASCAT measurements to retrieve soil moisture from backscatter. Next to backscatter signal, two additional vegetation parameters are extracted from the TUWien SoilMoisture Retrieval Approach. These parameters are slope and curvature and describe the second order Taylor polynomial, which explains the incidence dependency of backscatter. A recent study showed that slope and curvature could contain valuable information about vegetation water dynamics and biomass phenology. The new explored relationship gives an unique opportunity to relate land surface variables with these observation parameters. This is significant as it could be used to create a climatological data set of high quality and temporal consistency. The surrogate model avoids the need to use a Radiative TransferModel (RTM) to relate the land surface model to the ASCAT observations. RTM’s require complex input variables such as size, shape, height, thickness and orientation of the canopy but also the dielectric properties. Additionally, RTM’s are not based on the actual output of the land surface model (LSM), as the LSMs simulate vegetation parameters such as leaf area index, soilmoisture, gross primary production, temperature and respiration. Thismakes RTMs less suitable to act as a measurement operator. The suggested method to simulate ASCAT observations are deep neural networks. Deep neural networks are able to capture every highly non-linear relationship by using only the outputs from the LSMs. For this study a regular feed forward deep neural network is used to simulate the backscatter signal of the ASCAT instrument, whereas slope and curvature are simulated by a deep convolutional neural network. The results show that the deep neural network is able to simulate the seasonal and inter-seasonal variation of backscatter. Concerning slope the model was capable to capture the seasonal trends and some of the interseasonal variations. Curvature shows the worst model performance, as the model is only able to capture the timing of the seasonal changes but not the right magnitudes. In general, the performance depends on the variation of the observation and land surface data. This suggests that the model structure needs to be adapted according to the complexity of the investigated grid point. A black-box interpretation model, called DeepSHAP, is used to extract the most important features for each observation simulation. This feature importance allows a physical interpretation of the ASCAT observations. The most relevant feature for backscatter is soilmoisture, which is consistent with previous research and gives confidence to the feature importance extraction method. The slope signal is mostly related to the gross primary production and therefore biomass assimilation. Curvature shows the highest correlation for LAI. Both results confirm the previous assumptions that curvature and slope are, respectively, related to structural and phenology changes. The results of this research are substantial as they allow to the first time to relate actual vegetation parameters to the slope and curvature signals. It additionally proves that deep neural networks are a possible choice to act as a surrogate model between ASCAT observations and a land surface model.