Efficient management and conservation of natural habitats requires a thorough knowledge and sustained monitoring of their ecological quality. In recent years, several methods have been developed to assess the local conservation status in the field. These typically combine estimates of coarse-scale indicators, such as tree and grass encroachments, with very fine-scale indicators that require precise fieldwork, such as the number of key species present. We first tested whether coarse-scale field characteristics can provide information on fine-scale indicators.

The authors researched changes in specific habitat types in areas considered to be the most important in Croatia for protection of endangered bird fauna during the period 1990–2000. The analysis focused on four areas in the continental part of Croatia (Danube and Drava River alluvium, Pokupsko depression, Sava River basin, Upper Drava River basin) and four areas in the Mediterranean part (Neretva River estuary, NW part of North Dalmatia, Paško field, Lake Vrana). For the classified habitat types, changes in Corine land cover databases in 1990 and 2000 were analysed.

This study established the possibility of rapid evaluation of land cover structure and situation using as an example the Temelin Nuclear Power Plant (Czech Republic) emergency zone. The composition, surface representation and spatial distribution of crop species in the area of interest were assessed on the basis of satellite data analysis (Landsat 5 TM). The supervised classification method of Landsat data yielded 92% accuracy of classification into the land cover classes.

This paper addresses two important issues for large Mediterranean city regions: the differential impact of compact urban ‘growth’ and low-density ‘sprawl’ on land cover changes (LCCs), and their final effect on changing land cover relationships (LCRs). The urban expansion of Rome (Italy) during the last 50 years and the related LCCs were investigated as a paradigmatic example of compact versus dispersed urban development.

Urban and community forests play an important role in the overall carbon budget of the USA. Accurately quantifying carbon sequestration by these forests can provide insight for strategic planning to mitigate greenhouse gas effects on climate change. This study provides a new methodology to estimate net forest carbon sequestration (FCS) in urban and community lands of northern New England using ground based forest growth rates, housing density data, satellite derived land cover and tree canopy cover maps at the county level.

In pace with rapid urbanization, urban areas in many countries are undergoing huge changes. The large spectral variance and spatial heterogeneity within the ‘buildings’ land cover class, as well as the similar spectral properties between buildings and other urban structures, make building change detection a challenging problem. In this work, we propose a set of novel building change indices (BCIs) by combining morphological building index (MBI) and slow feature analysis (SFA) for building change detection from high-resolution imagery.

A Boosted Genetic Fuzzy Classifier (BGFC) is proposed in this paper, for land cover classification from multispectral images. The model comprises a set of fuzzy classification rules, which resemble the reasoning employed by humans. Fuzzy rules are generated in an iterative fashion, incrementally covering subspaces of the feature space, as directed by a boosting algorithm. Each rule is able to select the required features, further improving the interpretability of the obtained model.

Texture provides spatial features complementary to spectral information in land cover classification of high spatial resolution imagery. In texture classification, window size is an important factor influencing classification accuracy, but selecting the optimal window size is difficult. In this paper, we propose an optimized window size texture classification method which can solve the window size selection problem.

There are many factors controlling groundwater pollution and vulnerability. However, the factors’ weights are still not reasonably investigated. In order to assess groundwater quality and the controlling factors in semiarid region, 178 groundwater samples were collected and analyzed for salinity and nitrate content. New statistical techniques of prediction profiler and hierarchical cluster combined with geographic information systems (GIS) were used to assess the groundwater quality based on three categorical controlling factors; landuse/ land cover (LULC), soil texture, and aquifer type.

Over the past few decades, groundwater has become an essential commodity owing to increased demand as a result of growing population, industrialization, urbanization and so on. The water supply situation is expected to become more severe in the future because of continued unsustainable water use and projected change in hydrometeorological parameters due to climate change.

The angular effects of emissivity are ignored in current land surface temperature (LST) products. As a result, the directionality of these LST products limits their further application in many fields. Accurate correction of the angular problem of LST products requires explicit understanding of the angular effects of emissivity at the pixel scale. Currently, nearly ten years of global emissivity products of MODIS are available. However, the pixel-scale directionality of emissivity has never been analyzed.

Paired watersheds are used to develop a deciduous nutrient uptake stoichiometry. The watersheds are those of the House Rock Run and the Brubaker Run located in the Pennsylvania Appalachian Piedmont, USA. These two watersheds are nearly identical with respect to bedrock, regolith, climate, geomorphology, morphometry, baseflow hydrology, and type and successional stage of forest vegetation. They only differ by the percentage of deciduous forest cover, with House Rock Run having 59% and Brubaker Run having 76%.