Landscape connectivity is of paramount importance not only in maintaining both functionality and resilience of social-ecological systems, but also in providing Ecosystem Services (ESs). Despite recent efforts, several issues still need to be faced to fully integrate ESs evaluation relating to landscape connectivity into effective plans and environmental assessment procedures. Choosing an appropriate scale of analysis is one of the most problematic topics, since it can profoundly affect the evaluation of single patch contribution to global connectivity.

Changes in the coastal landscape of Southern Sinaloa (Mexico), between 2000 and 2010, were analyzed to relate spatial variations in wetlands extent with the provision and economic value of the ecosystem services (ES). Remote sensing techniques applied to Landsat TM imagery were used to evaluate land use/land cover changes while the value transfer method was used to assess the value of ES by land cover category. Five wetland types and other four land covers were found as representative of the coastal landscape.

The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote-sensing applications. In fact, one of the most common applications of remote-sensing images is the extraction of land-cover information for digital image base maps by means of classification techniques. The aim of the study was to compare the potential classification accuracy provided by pan-sharpened orthoimages from both GeoEye-1 and WorldView-2 (WV2) VHR satellites over urban environments.

Pest control that results from the activity of naturally occurring parasitoids is an important service that could help reduce pesticide use. We analyzed parasitism in codling moth diapausing larvae from a total of 122 apple orchards in southeastern France during five consecutive years (2006–2010) in relation to the agronomic and land cover characteristics at both the local and landscape levels. Three species of hymenoptera parasitoids were observed, including two primary (Braconidae and Ichneumonidae) and one hyperparasitoid (Perilampidae) wasps.

The ability to spatially quantify changes in the landscape and create land-cover maps is one of the most powerful uses of remote sensing. Recent advances in object-based image analysis (OBIA) have also improved classification techniques for developing land-cover maps. However, when using an OBIA technique, collecting ground data to label reference units may not be straightforward, since these segments generally contain a variable number of pixels as well as a variety of pixel values, which may reflect variation in land-cover composition.

Large‐scale monitoring of avian species is often conducted by observers driving rural roads and highways. Surveys along roads allow for easy and reliable access over time and the ability to survey large geographic areas in a relatively short period of time. However, if the landscape directly adjacent to these roadside transects is not representative of the greater landscape inhabited by the bird species, then estimates of abundance along the route may not be accurate for the greater landscape.

Measurements of land-cover changes suggest that such shifts may alter atmospheric concentrations of greenhouse gases (GHGs). However, owing to the lack of large-scale GHG data, a quantitative description of the relationships between land-cover changes and GHG concentrations does not exist on a regional scale. The Greenhouse Gases Observing Satellite (GOSAT) launched by Japan on 23 January 2009 can be of use in investigating this issue.

Although spatial scale is important for understanding ecological processes and guiding conservation planning, studies combining a range of scales are rare. Habitat suitability modelling has been used traditionally to study broad-scale patterns of species distribution but can also be applied to address conservation needs at finer scales. We studied the ability of presence-only species distribution modelling to predict patterns of habitat selection at broad and fine spatial scales for one of the rarest mammals in the UK, the grey long-eared bat (Plecotus austriacus).

The West African savannas are subject to changes in fire regimes related to land use intensification, which may infer significant biological modifications. We investigated the efficiency of MODIS fire products to account for the variability of fire regimes in relation to changes in land cover and savanna vegetation. The specificity and complementarities of both MODIS active fire (MOD14A2 and MYD14A2) and burnt area (MCD45A1) products were assessed in order to characterize fire regimes and to relate them with land cover.

Land cover is a key parameter in geosciences and a linkage between many aspects of the physical and human environments. Savannas belong to the biomes where land cover mapping with remote sensing faces the most difficulties and several studies already addressed the challenging definition of savanna land cover classes. With the aim to standardize ongoing mapping activities, the Land Cover Classification System (LCCS) was initiated in 1993.

In this study, a two-step classification procedure was used for classifying urban land cover. First, a hierarchy of seven image segmentations of different scales was created for an urban scene, and preliminary classifications were performed for each of the segmentations using a classification algorithm that provides the probability that a segment belongs to a land-cover class in addition to the class assignment. A higher probability for the assigned class indicates that a segment is more likely to have been classified correctly.

A number of land-cover products, both global and regional, have been produced and more are forthcoming. Assessing their accuracy would be greatly facilitated by a global validation database of reference sites that allows for comparative assessments of uncertainty for multiple land-cover data sets. We propose a stratified random sampling design for collecting reference data.