Mountain regions meet an increasing demand for pleasant landscapes, offering many cultural ecosystem services to both their residents and tourists. As a result of global change, land managers and policy makers are faced with changes to this landscape and need efficient evaluation techniques to assess cultural ecosystem services. This study provides a spatially explicit modelling approach to estimating aesthetic landscape values by relating spatial landscape patterns to human perceptions via a photo-based survey.

We have investigated multi-temporal polarimetric synthetic aperture radar (SAR) data for urban land-cover classification using an object-based support vector machine (SVM) in combinations of rules. Six-date RADARSAT-2 high-resolution polarimetric SAR data in both ascending and descending passes were acquired in the rural–urban fringe of the Greater Toronto Area during the summer of 2008.

The classification of remotely sensed images such as aerial photographs or satellite sensor images for deriving ecosystem-related maps (e.g., land cover, land use, vegetation, soil) is generally based on clustering of spatial entities within a spectral space. In most cases, Boolean logic is applied in order to map landscape patterns. One major concern is that this implies an ability to divide the gradual variability of the Earth's surface into a finite number of discrete non-overlapping classes, which are considered to be exhaustively defined and mutually exclusive.

Land cover changes around Lake Mutirikwi in 1984–2011 were mapped from Landsat images using traditional image classification methods including the maximum likelihood classifier algorithm. The possibility of mapping the coverage and abundance of surface floating aquatic weeds was also tested. Landsat images from 1984, 1995, 2001 and 2011 were used to compute a normalised difference vegetation index (NDVI), which was then used as a proxy for indicating areas infested by surface floating aquatic weeds.

Sub-pixel mapping is a process to provide the spatial distributions of land cover classes with finer spatial resolution than the size of a remotely sensed image pixel. Traditional Markov random field-based sub-pixel mapping (MRF_SPM) adopts a fixed smoothing parameter estimated based on the entire image to balance the spatial and spectral energies. However, the spectra of the remotely sensed pixels are always spatially variable.

Understanding the linkages between the biogeophysical and socio-economic processes that operate at different spatial and temporal scales is important for land cover change mitigation. This study analysed several factors that explained the forest-shrubland conversions, grassland conversions and cropland expansions in Lake Nakuru drainage basin and Eastern Mau forest reserve in Kenya from 1985 to 2011.

The spatio-temporal distribution of land cover provides fundamental data for global climate and environmental change research. In recent decades, five global land cover maps have been produced based on remote sensing data sources and methodologies. Related research have shown that the availability and quality of the first four global land cover datasets are poor at the regional or the continental scale for a variety of reasons. There is still no consensus on the accuracy of the latest global land cover map.

Super-resolution mapping (SRM) is a recently developed research task in the field of remotely sensed information processing. It provides the ability to obtain land-cover maps at a finer scale using relatively low-resolution images. Existing algorithms based on indicator geostatistics and downscaling cokriging offer an SRM approach using spatial structure models derived from real data. In this article, a novel SRM method is developed based on a sequentially produced with local indicator variogram (SLIV) SRM model.

We studied the influence of food abundance, land cover and disturbance on European wildcat (Felis silvestris silvestris Schreber, 1777) presence in Scotland. Wildcat records were collected using camera trapping, and prey data were assessed through linear transects and small mammal trapping. Surveys were carried out in three study areas in northeast Scotland. Wildcat occupancy was best predicted by a combination of food and land cover variables.

We present here a method to integrate geologic, topographic, and land-cover data in a geographic information system to provide a fine-scale, spatially explicit prediction of sediment yield to support management applications. The method is fundamentally qualitative but can be quantified using preexisting sediment-yield data, where available, to verify predictions using other independent data sets.

The assessment of the value of ecosystem services is a valuable tool for biodiversity conservation that can facilitate better environmental policy decision-making and land management, and can help land managers develop interventions to compensate for biodiversity loss at the patch level. Previous studies have suggested that it is appropriate to assess the value of biodiversity for conservation planning by considering both the condition of the landscape and the spatial configuration of adjacent land uses that can be reflected as a proximity effect.

Immense land-cover changes in Iceland over the last millennium have encompassed birch (Betula pubescencs) woodland depletion and extensive soil erosion; few studies have focused on spatial distribution change of birch woodland in Iceland over centuries.