A new locally-adaptive image classification method LAGMA (Locally-Adaptive Global Mapping Algorithm) has been developed to meet requirements of land cover mapping over large areas using remote-sensing data. The LAGMA involves the grid-based supervised image classification using classes’ features estimated locally in classified pixels’ surrounding from spatially distributed reference data.

Potential data sets for landcover classification, such as Landsat (or pre-processed data such as the National Land Cover Dataset (NLCD)), are often too coarse for fine-scale research needs or are cost-prohibitive (Quickbird, Ikonos and Geoeye). Repeated attempts at classifying high spatial resolution data, National Agricultural Imagery Program (NAIP) imagery, based on traditional techniques, such as a maximum likelihood supervised classification, have failed to produce a product with sufficient accuracy.

AIM: The geographical expansion of white‐winged doves (Zenaida asiatica) in North America has attracted the attention of biologists and sportsmen because of their recreational and aesthetic value; however, data on factors driving the spatial spread of this species are lacking. We examined spatial and temporal patterns of range expansion for white‐winged doves along the northern edge of their geographical range from 1979 to 2007 and used a dynamic occupancy model to estimate when and where doves would be found along an expansion gradient. LOCATION: Southern half of the USA.

Quantifying the amount of forest and change in the amount of forest are key to ensure that appropriate management practices and policies are in place to maintain the array of ecosystem services provided by forests. There are a range of analytical techniques and data available to estimate these forest parameters, however, not all ‘forest’ is the same and various components of change have been presented. Forest as defined by use and forest as defined by cover are different, although it is common for scientists and policy makers to infer one from the other.

Despite the global trend in urbanization, little is known about patterns of biodiversity or provisioning of ecosystem services in urban areas. Bee communities and the pollination services they provide are important in cities, both for small-scale urban agriculture and native gardens. To better understand this important ecological issue, we examined bee communities, their response to novel floral resources, and their potential to provide pollination services in 25 neighborhoods across Chicago, IL (USA).

Soil erosion due to accelerating runoff in various land cover types pose a serious threat to the long term sustainability of the fragile Himalayan landscape characterized by subsistence farming. Delimitation of high runoff zones, fostering soil erosion in the agricultural dominated watersheds is thus a necessity for watershed managers, NGO’s, urban planners, policy makers, and municipal administrations. The approach is practical, SWAT is a straightforward modeling system using GIS information. The BMP is also a very practical approach to mitigation of runoff accumulation on sub basin.

This paper reports on an exercise in obtaining landform information from a Digital Elevation Model (DEM) using Geographic Information Systems (GIS), and relating land cover information from satellite data to the landform classes and soil formation. The Beypazari area (northern Turkey) was chosen because of its varied landforms, land use and land cover. Soil samples, collected from forty-four soil pits were analysed and classified. Most of the soils were classified as Entisol and Inceptisol. In general, the effect of parent material on soil formation was dominated by landform.

By increased rural-urban migration in many African countries, the assessment of changes in catchment hydrologic responses due to urbanization is critical for water resource planning and management. This paper assesses hydrological impacts of urbanization on two medium-sized Zimbabwean catchments (Mukuvisi and Marimba) for which changes in land cover by urbanization were determined through Landsat Thematic Mapper (TM) images for the years 1986, 1994 and 2008. Impact assessments were done through hydrological modeling by a topographically driven rainfall-runoff model (TOPMODEL).

Study region: Increasing demographic pressure and economic development in the Mekong Basin result in greater dependency on river water resources and increased vulnerability to streamflow variations.Study focus: Improved knowledge of flow variability is therefore paramount, especially in remote catchments, rarely gauged, and inhabited by vulnerable populations. We present simple multivariate power-law relationships for estimating streamflow metrics in ungauged areas, from easily obtained catchment characteristics.