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.

The Netherlands is a country primarily developed on coastal zones. Coastal zones are very important, since they are a source of valuable living and non-living resources for its population, and they also act as a natural protection against floods. Notably, the natural dynamics driven by the sea and the man-made actions on the land change them continuously. Consequently, the need for systematic coastal protection and management arises, for which coastal monitoring is of paramount necessity. The emergence of remote sensing in this field has been proven to have many advantages, especially due to the large coverage that it offers in a time- and cost-efficient way. Thus, the subject of this study is one of these remote sensing techniques, specifically the UAV Laser Scanning. UAV Laser Scanning has been introduced in the field of coastal monitoring quite recently, yet it has already proven to be a useful tool, outperforming the other methods used for the same purpose in a number of aspects. Its high accuracy and resolution coupled with flexibility and time efficiency make it a very promising technique. It works on the same principle as all the other laser scanning techniques. Its system components are a platform (drone), a laser scanner, a GNSS receiver and an Inertial Measurement Unit (IMU). The aerial system used in this study Alpha AL3-32, by Phoenix LiDAR Systems, in combination with a DJI Spreading Wings S1000 Octocopter. One of the main aims of this study was to investigate the error budget of that system and its impact on the positioning accuracy. The sources of error in such a system are its three main components as well as possible misalignments between these components, that cause bore-sight and lever-arm errors, coordinate system transformations and temporal interpolation issues. Herein, an error model was used for the determination of the quantitative impact of them on the positioning accuracy. Using the uncertainties of the instruments that Alpha AL3-32 consists of, the a priori positioning accuracy of the system was estimated equal to 2 cm. In addition, its dependency on the flight parameters was proven. The a posteriori positioning accuracy was derived by investigating a small flat dataset of the UAV Laser Scanning point cloud and was estimated to be 2 cm as well. From the same dataset, the precision was also computed as being equal to 4 cm. When it comes to the products of this technique, one of the main methods to analyse the morphological changes in coastal zones is by comparing Digital Terrain Models (DTMs) from different epochs. Thus, the generation of accurate DTMs was the other important objective of this study. The standard procedure includes preparation of the dataset, noise removal and above ground analysis. After the ground-only dataset was derived, it was converted into a grid using interpolation, namely the Moving Average or Moving Planes method. The grid size of the DTM can go down to 5 cm. In addition, the RMSE between the UAV Laser Scanning only-ground point cloud and the derived DTM was computed equal to 4 cm, while the DTM accuracy in height was estimated equal to 8 cm by using an empirical formula based on the point density and the slope of the terrain. Considering the 3D coordinate accuracy that can be achieved with such a system as well as the DTM quality, UAV Laser Scanning was found to be an appropriate technique for coastal monitoring, presenting important advantages over other competing methods. It can reduce the time and cost of surveys at the coastal zones and at the same time maintain high resolution with its high point density.

Water scarcity and drought (WS-D) are growing concerns throughout Europe and especially in Southern Regions. This study focuses on agriculture on Sicily, Italy. It explores the way(s) European policies to tackle WS-D are actively performed by the actors in the Sicilian water and agricultural sectors, in order to uncover potential disparities between the European policies on paper and how they are shaped in the practice. To do so, a qualitative research approach was used. Actors were retraced in the field, outlining the physical and political landscapes that they have shaped and in which they act; their interpretation of WS-D and their behavior towards such phenomena were continuously under study. While the policies on paper present various measures to tackle WS-D, the performed policies (in practice) are different and the use of available measures is often limited. The involved actors identified tools and measures within the Water Framework Directive and Rural Development Programme, available to tackle WS-D issues. However these are not fully implemented and their embedding presents complexities. Access to measures set under the RDP seems hampered for farmers. Not only because of bureaucratic and financial constraints, more so because of ideas of farming size, excluding small and many medium farms. Low level of information and participation also limits the involvement of farmers in the regional water-agricultural governance. Parallel to it, complex water governance, financial constraints and social acceptance issues give rise to barriers in the implementation of policy instruments. Behind this, the interpretations of WS-D phenomena result to be different according different stakeholders.