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This paper discusses a project on the completion of a database of socio-economic indicators across the European Union for the years from 1990 onward at various spatial scales. Thus the database consists of various time series with a spatial component. As a substantial amount of the data was missing a method of imputation was required to complete the database. A Markov Chain Monte Carlo approach was opted for. We describe the Markov Chain Monte Carlo method in detail. Furthermore, we explain how we achieved spatial coherence between different time series and their observed and estimated data points.

The move towards services based on so-called ‘big data’ is already (and will increasingly) having a profound impact on almost every aspect of modern society. The environmental sector will be a major contributor to these changes, based on multi-modal information obtained from numerous sources. Critical to the creation of new services and related industries that will emerge from these trends will be access to high quality data that is essential for informed and accurate decision-making across agriculture and land use, optimised food production, management of water bodies, prediction of major climatic events (e.g. flooding, droughts) and organisation of their subsequent management. Two major sources of environmental information will be via remote sensing using satellite and flyovers (including drones), and in-situ distributed monitoring using sensor networks. In this paper, I will discuss the strengths and weaknesses of remote and in-situ sensing, and show how many of the weaknesses of each can be overcome by pooling the information from both sources, as this will enable the creation of a much more complete and robust picture of environmental status over time. Issues related to long-term in-situ chemical and biosensing will be discussed, along with the potential use of surrogate measurements (e.g. temperature) to track changes in the status of local chemistry/biology. *Corresponding Author: dermot.diamond@dcu.ie

Globally, the move towards services based on so-called ‘big data’ is, and will increasingly, profoundly affect the way in which every aspect of society functions. The the environmental sector will be a major contributor to these changes, based on multi-modal information obtained from numerous sources. Critical to the creation of new services and related industries that will emerge from these trends will be access to high quality data that is essential for informed and accurate decision-making across agriculture and land use, optimised food production, management of water bodies, prediction of climatic events (e.g. flooding, droughts) and organisation of their subsequent management. Two major sources of environmental information will be via remote sensing using satellite and flyovers (including drones), and in-situ distributed monitoring using sensor networks. In this paper, I will discuss the strengths and weaknesses of remote and in-situ sensing, and show how many of the weaknesses of each can be overcome by pooling the information from both sources, as this will enable the creation of a much more complete and robust picture of environmental status over time. Issues related to long-term in-situ chemical and biosensing will be discussed, along with the potential use of surrogate measurements to indicate the status of local chemistry/biology.

December 2015 and January 2016 saw extensive flooding along the banks of the River Shannon in the Republic of Ireland, with Limerick, Athlone and Portumna particularly badly aªected. The cost involved for infrastructure repair and clean-up could reach €100m, while the cost to businesses and livelihoods is as yet unknown.

Bathymetry is traditionally acquired using singlebeam or multibeam echosounders. This method produces accurate depth measurements along transects but is constrained by operating cost and an inability to survey in very shallow waters. Airborne Lidar is able to produce accurate bathymetric information over clear waters at depths up to 70m, but can be costly and is limited by a relatively coarse bathymetric sampling interval. Experience in Irish waters has resulted in very poor seabed detection along the east coast and limited penetration on the west coast. An efficient and cost-effective alternative is satellitederived bathymetry.

A challenging problem exists in the estimation of missing space-time data where the time series are relatively short, and the space series belong to a spatial hierarchy. An example is provided by the population estimates for regions belonging to the NUTS hierarchy which are available from the EUROSTAT data portal. The table demo_r_gind3 provides estimates of the population of NUTS0/1/2/3 regions at the 1st January 2000…2012 inclusive. Inspection of the table reveals that estimates are missing for 2000-2003 for two of the five NUTS3 regions in the NUTS2 region of Liège. There are other instances of missing data at NUTS3 where there are data for the corresponding higher level NUTS regions. The EUROSTAT table demo_r_d2jan provides estimates of the population on the 1st January for a longer time period, 1990…2012 inclusive, but these are only to NUTS2. Again, there is missing data. The question then arises as to whether it is possible to estimate the missing series. The NUTS2 values act as a constraint on the NUTS3 values – the total population of the NUTS3 regions should equal those of the corresponding NUTS2 regions. However, the relative shortness of the available series is a challenge if conventional methods of time series analysis are adopted. Furthermore, the imposition of the spatial constraints is both a check as well as a challenge.

The current generation of Mobile Mapping Systems (MMSs) capture increasingly larger amounts of data in a short time frame. Due to the relative novelty of this technology there is no concrete understanding of the point density that different scanner confgurations and scanner hardware settings will exhibit on objects at specific distances. Depending on the project requirements, obtaining the required point density impacts on survey time, processing time, data storage and is the underlying limit of automated algorithms. Insufficient knowledge of the factors in uencing MMS point density means that defning point density in project specifications is a complicated process. The objectives of this thesis are to calculate point density, to assess MMS laser scanner configuration and hardware settings and to benchmark a selection of MMSs in terms of their point density. The calculation methods involve a combination of algorithms applying 3D surface normals and 2D geometric formulae and outputs profile angle, profile spacing, point spacing and point density. Each of these elements are a major factor in calculating point density on arbitrary objects, such as road signs, poles or buildings - all important features in asset management surveys. These algorithms are combined in a system called the Mobile Mapping Point Density Calculator (MIMIC). MIMIC is then applied in a series of tests identifying the recommended MMS laser scanner configuration and scanner hardware settings for near side infrastructure. The in uence that the scanner orientation and location on the MMS has on point density is quantified, resulting in a recommended MMS laser scanner configuration. A series of benchmarking tests assess the performance of one commercial and two theoretical MMSs in terms of their point density. The recommended configuration identified in the previous tests allows a low specification MMS to increase its performance in relation to a higher specification MMS. The benchmarking tests also highlight that a high pulse repetition rate is preferable to a high mirror frequency for maximising point density. The findings in this thesis enable a MMS to be configured to maximise point density for specific targets. Researchers can utilise MIMIC to tailor their automated algorithm's point density requirements for specific targets.

The large volumes of point cloud data collected by a Mobile Mapping System(MMS) equipped with a laser scanner have attracted the attention of the research community, primarily towards developing automated algorithms to help when processing this data. This has resulted in insufficient attention being paid to quantifying the capabilities of these systems, and due to the relative youth of this technology there is no concrete understanding of the point density that different hardware configurations and operating parameters will exhibit on objects at specific distances. Obtaining the required point density for a project impacts on survey time, processing time, data storage and is the underlying limit of automated algorithms. Lack of understanding of these systems makes defining point density in project specifications a complicated process. We are in the process of developing a method for determining the quantitative resolution of point clouds collected by a MMS with respect to known objects at specified distances. We have previously demonstrated the effect that scanner orientation in one axis, scanner configuration and scanner operating speed have on scan profiles. We have also focused on the effect on scan profiles of the combined vertical and horizontal rotations of the scanner (dual-axis rotations) and also incorporated point spacing for planar surfaces at different scanner mirror speeds, pulse repetition rates and field of view as a function of range into our model. The subject of this paper is to investigate the effect that a dual-axis scanner rotation has on profile spacing and to design a theoretical system to calculate the angular change on profiles exhibited on horizontal and vertical surfaces for different system configurations. The second goal of the research presented in this paper is to include in our calculations a method for incorporating surfaces that are not parallel to the direction of travel or that are not perfectly vertical, such as walls facing away from the road or sloped surfaces. Profile angle impacts on profile spacing and is a major factor in calculating point density on arbitrary objects, such as road signs, poles or buildings, all important features in asset management surveys. A number of tests were designed to investigate these issues and the results show that these tests have justified our methods, but it has been made apparent that vehicle dynamics play a larger role than anticipated.

When a laser scanner is mounted on a moving platform and combined with a GNSS receiver and inertial navigation system, it is capable of producing millions of geo-referenced points which can then be used to create near-3D models. The development of processing algorithms for these point clouds has largely been the focus of the research community to date. However, given an arbitrary known static object positioned at a specific distance away from a mobile mapping system (MMS) the resolution of the resulting point cloud that will describe that object is unknown. This is the underlying limit of point cloud processing algorithms. We are in the process of developing a method for determining the quantitative resolution of point clouds collected by a MMS with respect to known objects at specified distances. Our previous work has demonstrated our initial investigations into the effect that a scanner position, configuration and operating speed has on scan lines - both in profile spacing and scan line orientation at varying vehicle speeds. This paper focuses on the combined effect on profiles of both vertical and horizontal rotations of the scanner, explores in greater detail the effect on scan line orientation caused by vehicle motion and also incorporates point spacing as a function of range into our model. As with our previous work, we will develop a system to calculate this information and then verify our equations and analysis by comparing our simulated data to the point cloud data collected by our XP-1 mobile mapping system.

We present the GLIMPSE system that provides a framework for storage, management, accessibility and integration of 3D LiDAR data acquired from multiple platforms. We detail a point cloud retrieval approach, which provides spatially optimised access to point cloud data from the system for a particular geographic area based on user specifications. We tested our point cloud retrieval approach to facilitate the extraction of road medians from large volumes of ALS data stored in the GLIMPSE system. The integrated use of a geospatial database, the GLIMPSE system and the point cloud retrieval approach improved the efficiency of road median extraction. Refereed/Peer-reviewed