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.

Sub-pixel mapping of remotely sensed imagery is often performed by assuming that land cover is spatially dependent both within and between image pixels. Intra- and inter-pixel dependencies are two widely used approaches to represent different land-cover spatial dependencies at present. However, merely using intra- or inter-pixel dependence alone often fails to fully describe land-cover spatial dependence, making current sub-pixel mapping models defective.

In this study, land cover maps of Bozcaada district were developed using Landsat satellite images obtained in 2006, 2007 and 2008. In addition to original images (6 band Landsat TM), the new images constituted with image processing techniques were also used. A total of ten images were formed by supervised classification method using principal component analysis (PCA), normalized difference vegetation index (NDVI), and tasseled cap (TC) transformation methods.

Impervious surface mapping has become a recent concern in remote-sensing applications because of worldwide urban growth and the resultant environmental changes. However, many effective impervious surface mapping techniques developed for moderate-resolution imagery are not applicable to high spatial resolution imagery such as that of IKONOS and the Advanced Land Observing Satellite (ALOS) due to their limited number of spectral bands and the lack of middle-infrared bands.

Shoreline armoring is an ancient and globally used engineering strategy to prevent shoreline erosion along marine, estuarine, and freshwater coastlines. Armoring alters the land water interface and has the potential to affect nearshore submerged aquatic vegetation (SAV) by changing nearshore hydrology, morphology, water clarity, and sediment composition.

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.