The effect of 19 environmental variables on species richness of macrophytes was studied in 39 Slovak streams. The studied streams were poor in species; in total, 88 macrophyte taxa were found and the average number of macrophytes per sampling site was 4, ranging from 0 to15. The most frequently occurring macrophytes were filamentous algae (occurrence at 38.6% of sampling sites), followed by Rhynchostegium riparioides (28.4%) and Phalaris arundinacea (19.3%).

Riparian forests are increasingly threatened by urban expansion and land use change worldwide. This study examined the relationships between landscape characteristics and woody plant diversity, structure, and composition of small order riparian corridors along an urban-rural land use gradient in the Georgia Piedmont, US. Riparian plant diversity, structure, and composition were related to landscape metrics and land use. Species richness was negatively associated with impervious surfaces and landscape diversity, and positively associated with forest cover and largest forest patch index.

The objective of this paper was the development of a methodology for the classification of digital aerial images, which, with the aid of object-based classification and the Normalized Difference Vegetation Index (NDVI), can quantify agricultural areas, by using algorithms of expert classification, with the aim of improving the final results of thematic classifications.

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.

1. Arctic vegetation tends to be spatially heterogeneous and can have large areas of mixed ‘transition zone’ vegetation between stands dominated by a single or few species. If plant photosynthesis and growth within these transition zones differs significantly from main vegetation stands, and if transition zones are not considered when extrapolating stand‐level findings to larger scales in space, then transition zones will provide considerable error to landscape‐level estimates of gross primary productivity (GPP).

Land cover changes affect ecological landscape spatial pattern, and evolving landscape patterns inevitably cause an evolution in ecosystem functionality. Various ecological landscape variables, such as biological productivity (plant biomass and stock capacity), soil nutrients (organic matter and N content) and water source conservation capacity are identified as landscape function characteristics. A quantitative method and digital model for analyzing evolving landscape functionality in the headwaters areas of the Yangtze River, China were devised.

Understanding changes in soil quality resulting from land use and land management changes is important to design sustainable land management plans or interventions. This study evaluated the influence of land use and land cover (LULC) on key soil quality indicators (SQIs) within a small watershed (Jedeb) in the Blue Nile Basin of Ethiopia. Factor analysis based on principal component analysis (PCA) was used to determine different SQIs.

Image segmentation is a basic and important procedure in object-based classification of remote-sensing data. This study presents an approach to multi-scale optimal segmentation (OS), given that single-scale segmentation may not be the most suitable approach to map a variety of land-cover types characterized by various spatial structures; it objectively measures the appropriate segmentation scale for each object at various scales and projects them onto a single layer. A 1.8 m spatial resolution Worldview-2 image was used to perform successive multi-scale segmentations.

Information about changes in, and causes of, land use/land cover (LULC) is crucial for land use resource planning. We investigated the processes involved in LULC change (LUCC) in the Dongjiang Watershed, in Southern China, over a 15-year period to gain a better understanding of the causes of the main types of LUCC. Using a depth transition matrix and redundancy analysis (RDA), the major types and causes of LUCC for each LULC type over the past 15 years were identified. LUCC exhibited obvious net change, relatively low persistence, and high swap change.

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.

Change detection with remotely sensed imagery plays an important role in land cover mapping, process analysis and dynamic information services. Euclidean distance, correlation and other mathematic metrics between spectral curves have been used to calculate change magnitude in most change detection methods. However, many pseudo changes would also be detected because of inter-class spectral variance, which remains a significant challenge for operational remote sensing applications.

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.