Digital land use data, generally derived by remote sensing and often organized in grid form, have become widely available for even the most remote areas of the globe. Here we investigate how to use land use data to measure three of the most characteristic aspects of urban sprawl: low density, low continuity of land use type (scatteredness), and low compactness of the shape of the city. For each of these categories we present multiple urban sprawl indicators. Some of these indicators have been used in the literature before, others we developed ourselves. For density measurements we illustrate how simple changes to common density indicators can improve their meaningfulness. With respect to scatteredness we show that the interpretation of entropy measures can be ambiguous. Using a variant on Moran's I index does a better job at measuring scatteredness. When it comes to measuring compactness, the grid structure of land use data can inflate the boundary of the measured area. We introduce new compactness indices that correct for this problem. To illustrate the discussed indices, we apply them to Graz, the second largest city in Austria, using data from the CORINE Land Cover (CLC) Project [1].

For detecting anomalies or interventions in the field of forest monitoring we propose an approach based on the spatial and temporal forecast of satellite time series data. For each pixel of the satellite image three different types of forecasts are provided, namely spatial, temporal and combined spatio-temporal forecast. Spatial forecast means that a clustering algorithm is used to group the time series data based on the features normalised difference vegetation index (NDVI) and the short-wave infrared band (SWIR). For estimation of the typical temporal trajectory of the NDVI and SWIR during the vegetation period of each spatial cluster, we apply several methods of functional data analysis including functional principal component analysis, and a novel form of random regression forests with online learning (streaming) capability. The temporal forecast is carried out by means of functional time series analysis and an autoregressive integrated moving average model. The combination of the temporal forecasts, which is based on the past of the considered pixel, and spatial forecasts, which is based on highly correlated pixels within one cluster and their past, is performed by functional data analysis, and a variant of random regression forests adapted to online learning capabilities. For evaluation of the methods, the approaches are applied to a study area in Germany for monitoring forest damages caused by wind-storm, and to a study area in Spain for monitoring forest fires.