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).

There is vigorous debate about the potential for reforestation to offset losses in biodiversity associated with tropical deforestation, but a scarcity of good data. We quantified developmental trajectories following active restoration (replanting) of deforested pasture land to tropical Australian rainforest, using 20 different bird community indicators within chronosequences of multiple sites. Bird species composition in restored sites (1–24years old) was intermediate between that of reference sites in pasture and primary rainforest.

Tropical coastal wetlands form complex and dynamic ecosystems based on a mixture of vegetation, soil, and water components. Optical remotely sensed data have often been used to characterize and monitor these ecosystems, which are among the environments most threatened by climate change and anthropogenic activity worldwide.

Increasing the accuracy of thematic maps produced through the process of image classification has been a hot topic in remote sensing. For this aim, various strategies, classifiers, improvements, and their combinations have been suggested in the literature. Ensembles that combine the prediction of individual classifiers with weights based on the estimated prediction accuracies are strategies aiming to improve the classifier performances.

We examined the impacts of the Satsunai River Dam on the hydrology and development of riparian vegetation along the upper and lower reaches of the Satsunai River downstream from the dam. We estimated frequency curves of the flood discharge during the pre-dam (1976-1996) and post-dam (1997-2006) periods and simulated the flood frequency at sampling points within sites under pre-dam, post-dam and dam-removal (using the pre-dam flood discharge and post-dam cross-sections) scenarios. Changes in channel morphology and land cover were investigated by analyzing aerial photographs.

Based on 1-km land surface model geophysical predictions within the United States Southern Great Plains (Red-Arkansas River basin), an observing system simulation experiment (OSSE) is carried out to assess the impact of land surface heterogeneity, instrument error, and parameter uncertainty on soil moisture products derived from the National Aeronautics and Space Administration Hydrosphere State (Hydros) mission.

Binary images from one or more spectral bands have been used in many studies for land-cover change/no-change identification in diverse climatic conditions. Determination of appropriate threshold levels for change/no-change identification is a critical factor that influences change detection result accuracy. The most used method to determine the threshold values is based on the standard deviation (SD) from the mean, assuming the amount of change (due to increase or decrease in brightness values) to be symmetrically distributed on a standard normal curve, which is not always true.

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.