Global knowledge about the spatial distribution of species is orders of magnitude coarser in resolution than other geographically-structured environmental datasets such as topography or land cover. Yet such knowledge is crucial in deciphering ecological and evolutionary processes and in managing global change. In this review, we propose a conceptual and cyber-infrastructure framework for refining species distributional knowledge that is novel in its ability to mobilize and integrate diverse types of data such that their collective strengths overcome individual weaknesses.

This paper describes the spatiotemporal changes pertaining to land use land cover (LULC) and the driving forces behind these changes in Doodhganga watershed of Jhelum Basin. An integrated approach utilizing remote sensing and geographic information system (GIS) was used to extract information pertaining to LULC change. Multi-date LULC maps were generated by analyzing remotely sensed images of three dates which include LandSat TM 1992, LandSat ETM+ 2001 and IRS LISS-III 2005.

Classifying multi-temporal image data to produce thematic maps and quantify land cover changes is one of the most common applications of remote sensing. Mapping land cover changes at the regional level is essential for a wide range of applications including land use planning, decision making, land cover database generation, and as a source of information for sustainable management of natural resources.

Three quarters of the global human population will live in coastal areas in the coming decades and will continue to develop these areas as population density increases. Anthropogenic stressors from this coastal development may lead to fragmented habitats, altered food webs, changes in sediment characteristics, and loss of near-shore vegetated habitats. Seagrass systems are important vegetated estuarine habitats that are vulnerable to anthropogenic stressors, but provide valuable ecosystem functions.

Spatial indices measure the geometric arrangement of land use and land cover classes at various scales and are computationally adaptive with wavelet transform coefficients. Decision rules built on permutations of three spatial indices – energy, log energy and Shannon's diversity – are used to improve the accuracy of multi-resolution hierarchical wavelet-based classifications. Comparisons are made with classification results derived from other texture measures, as well as with classification results calculated from more conventional per-pixel techniques.

Many environmental patterns that may have profound effects on wildlife communities occur at the landscape scale, e.g. habitat fragmentation, human demography and distribution of various resources. In order to understand how alterations of such patterns could influence e.g. wildlife species occurrences and community composition, it is important to first study these relationships empirically and at the appropriate scale. We surveyed the wildlife community in a boreal ecosystem in central Sweden using pellet group counts, while walking ‘‘wildlife triangles’’.

The ground reference data obtained to assess map accuracy can be simultaneously used to estimate area (extent). This dual-purpose use of ground reference data is examined for the special case of a two-class map of ‘change’ and ‘no change’. To assess the accuracy of a change map, stratified sampling is often implemented with a disproportionately larger sample size allocated to the map change stratum. But this allocation targeting user's accuracy of change is not necessarily effective for the competing objective of estimating the area of change.

Irrigation along the Nile River has resulted in dramatic changes in the biophysical environment of Upper Egypt. In this study we used a combination of Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m Normalized Difference Vegetation Index data and Landsat imagery to identify areas that changed from 2001 to 2008 as a result of irrigation and water-level fluctuations in the Nile River and nearby waterbodies.

The method of surface modeling of land cover scenarios (SMLCS) has been improved to simulate the scenarios of land cover in the karst areas of southwestern China. On the basis of the observation monthly climatic data collected from 782 weather stations of China during the period from 1981 to 2010, the climatic scenarios data of RCP26, RCP45 and RCP85 scenarios released by CMIP5, and the land cover current data of China in 2010, the land cover scenarios of southwestern China were respectively simulated. The average total accuracy and Kappa index of SMLCS are 90.25 and 87.96 %, respectively.

In this study we (1) examined the applicability of the widely available CORINE land cover map of Europe in predicting several components of the richness of forest breeding bird community, and (2) analysed how different ecologically meaningful species groups respond to the differences in landscape composition and how these differences are reflected in the relationships between total species richness and richness of these species groups at the 2.5 × 2.5 km2 scale.

OTALEX II is the Territorial and Environmental Observatory of Alentejo (Portugal) and Extremadura (Spain), co-financed by POCTEP, developed with the cross-border collaboration of several Portuguese and Spanish bodies. It is composed of a Spatial Data Infrastructure (SDI) created in 2007, SDI-OTALEX (www.ideotalex.eu), which is an exchange platform for geographic information and Webmapping services among project partners.

The European hare (Lepus europaeus) has declined throughout its native range but invaded numerous regions where it has negatively impacted native wildlife. In southern Sweden, it replaces the native mountain hare (L. timidus) through competition and hybridisation. We investigated temporal change in the invasive range of the European hare in Ireland, and compared its habitat use with the endemic Irish hare (L. timidus hibernicus). The range of the European hare was three times larger and its core range twice as large in 2012–2013 than in 2005.