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.

Support vector machines (SVM) are nowadays receiving increasing attention in remote sensing applications although this technique is very sensitive to the parameters setting and training set definition. Self-training is an effective semisupervised method, which can reduce the effort needed to prepare the training set by training the model with a small number of labeled examples and an additional set of unlabeled examples. In this study, a novel semisupervised SVM model that uses self-training approach is proposed to address the problem of remote sensing land cover classification.

Anthropogenic forces that alter the physical landscape are known to cause significant soil erosion, which has negative impact on surface water bodies, such as rivers, lakes/reservoirs, and coastal zones, and thus sediment control has become one of the central aspects of catchment management planning. The revised universal soil loss equation empirical model, erosion pins, and isotopic sediment core analyses were used to evaluate watershed erosion, stream bank erosion, and reservoir sediment accumulation rates for Ni Reservoir, in central Virginia.

Labeling of connected components in an image or a raster of non-imagery data is a fundamental operation in fields of pattern recognition and machine intelligence. The bulk of effort devoted to designing efficient connected components labeling (CCL) algorithms concentrated on the domain of binary images where labeling is required for a computer to recognize objects.

Countryside Survey is a unique large scale long-term monitoring programme investigating stock and change of habitats, landscape features, vegetation, soil and freshwaters of Great Britain. Repeat field surveys combine policy and scientific objectives to provide evidence on how multiple aspects of the environment are changing over time, a key goal of international science in the face of profound human impacts on ecosystems.

SUMMARYThis study analyses spatio-temporal patterns of wildfires in Greece using a multidimensional statistical framework based on non-parametric correlations, principal component analysis, clustering and stepwise discriminant analysis. Specifically, we assess the frequency, seasonal profile, severity and land-use type of 135 178 wildfires which occurred between 2000–2012 in Greece, one of the countries most affected by fire in Europe.

The most obvious, although not exclusive, explanation for the increase of species richness with increasing sample area (the species-area relationship) is that species richness is ultimately linked to area-based increases in habitat heterogeneity. The aim of this paper is to examine the relative importance of area and habitat heterogeneity in determining species richness in nature reserves. Specifically, the work tests the hypothesis that species-area relationships are not positive if habitat heterogeneity does not increase with area.

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.