The Scops Owl Otus scops (L., 1758) is a species of European concern, which suffered a noticeable decrease in distribution in the last decades, and changes in agricultural practices have been proposed as a major threat for this owl. We studied the habitat preference of the Scops Owl by assessing the habitat occupancy of 401 territories distributed in a large area in northwest Italy, with a special focus on 98 territories located in a high-density area (Monferrato).

Environmental fragility models are important decision tools for policy makers as they help quantify environmental sensitivity and understand the relationship between human activities and environmental quality. The objective of this study was to evaluate three different environmental fragility models within the Brazilian rainforest region and to use the results to develop environmental zone classes. Two rural river basins located in Ibiuna, Sao Paulo state, Brazil, were studied.

This work presents a new four-tier hierarchical change-point algorithm designed to detect land-cover change from satellite data. We tested the algorithm using Global Inventory Modelling and Mapping Studies (GIMMS) data for eastern Africa. Using a unique sequence of four statistical change-point detection methods, we identified significant increases or decreases in normalized difference vegetation index (NDVI), estimated the approximate time of change, and characterized the likely forms of change (i.e. linear trend, abrupt mean and/or variability change, and hockey-stick shaped change).

Sub-pixel mapping is a technology to obtain the spatial distribution information of land cover within mixed pixels. In this letter, two kinds of edge-directed interpolation algorithms are proposed. These algorithms are applied to coarse spatial resolution images to obtain a high-resolution image with probability information. Then hard classification on a per sub-pixel basis is implemented to achieve sub-pixel mapping. Both methods are demonstrated with three synthetic images.

Providing land cover spatio-temporal information and geo-computing through web service is a new challenge for supporting global change research, earth system simulation and many other societal benefit areas. This requires an integrated knowledge representation and web implementation of static land cover and change information, as well as the related operations for geo-computing. The temporal logic relations among land cover snapshots and increments were examined with a matrix-based three-step analysis.

Optical Earth Observation data with moderate spatial resolutions, typically MODIS (Moderate Resolution Imaging Spectroradiometer), are of particular value to environmental applications due to their high temporal and spectral resolutions. Time-series of MODIS data capture dynamic phenomena of vegetation and its environment, and are considered as one of the most effective data sources for land cover mapping at a regional and national level.

One of the potential applications of polarimetric Synthetic Aperture Radar (SAR) data is the classification of land cover, such as forest canopies, vegetation, sea ice types, and urban areas. In contrast to single or dual polarized SAR systems, full polarimetric SAR systems provide more information about the physical and geometrical properties of the imaged area. This paper proposes a new Bayes risk function which can be minimized to obtain a Likelihood Ratio (LR) for the supervised classification of polarimetric SAR data.

This study aims to evaluate different methods of integrating optical and multipolarized radar data for land use and land cover (LULC) mapping in an agricultural frontier region in the Central Brazilian Amazon, which requires continuous monitoring due to the increasing human intervention. The evaluation is performed using different sets of fused and combined data.

Uses of high spatial resolution data obtained from satellite-based sensors include creating land cover maps, deriving large-scale quantitative assessments such as vegetation indices, and visually assessing an area for qualitative information only assessable from large-scale digital data. One of the more popular uses of high spatial resolution data is to use the image as a base map for on-screen digitizing spatially dependent vector products.

Areal interpolation is used to transfer attribute data between geographically incongruous zonal systems. Remotely sensed land cover data are widely used in intelligent areal interpolation methods to solve this problem. This article examines the usefulness of different publicly available remotely sensed land cover data sets as ancillary data used in conjunction with different areal interpolation methods.

Detailed urban land-cover maps are essential information for sustainable planning. Land-cover maps assist planners in designing strategies for the optimisation of urban ecosystem services and climate change adaptation. In this study, the statistical software R was applied to land cover analysis for the Catania metropolitan area in Sicily, Italy. Six land cover classes were extracted from high-resolution orthophotos. Five different classification algorithms were compared. Texture and contextual layers were tested in different combinations as ancillary data.

This contribution refers to a companion modelling approach applied to the study of interactions between social and environmental dynamics in a small protected island, part of a biosphere reserve. This approach leads to a role-playing game designed by a pluridisciplinary workgroup (researchers and reserve manager), used as management support and as a tool to help people regarding the stakes of sustainable development. For several years, Ushant Island (Brittany, France) was in the process of social and environmental restructuration due to land-use and land-cover changes.