A novel approach to image radiometric normalization for change detection is presented. The approach referred to as stratified relative radiometric normalization (SRRN) uses a time-series of imagery to stratify the landscape for localized radiometric normalization. The goal is to improve the detection accuracy of abrupt land cover changes (human-induced, natural disaster, etc.) while decreasing false detection of natural vegetation changes that are not of interest. These vegetation changes may be associated with such phenomena as phenology, growth and stress (e.g.

The random forest (RF) classifier is a relatively new machine learning algorithm that can handle data sets with large numbers and types of variables. Multi-scale object-based image analysis (MOBIA) can generate dozens, and sometimes hundreds, of variables used to classify earth observation (EO) imagery. In this study, a MOBIA approach is used to classify the land cover in an area undergoing intensive agricultural development. The information derived from the elevation data and imagery from two EO satellites are classified using the RF algorithm.

In this study we analyzed the large scale spatial patterns of river pH, alkalinity, and CO₂ partial pressure (PCO₂) in North America and their relation to river catchment properties. The goal was to set up empirical equations which can predict these hydrochemical properties for non-monitored river stretches from geodata of e.g. terrain attributes, lithology, soils, land cover and climate. For an extensive dataset of 1120 river water sampling locations average values of river water pH, alkalinity and PCO₂ were calculated.

This paper introduces this special issue of Aquatic Sciences. It outlines a multi-scale, hierarchical framework for developing process-based understanding of catchment to reach hydromorphology that can aid design and delivery of sustainable river management solutions. The framework was developed within the REFORM (REstoring rivers FOR effective catchment Management) project, funded by the European Union’s FP7 Programme. Specific aspects of this ‘REFORM framework’ and some applications are presented in other papers in this special issue.

This research presents an analysis of river responses to flow regulation and other disturbances over time. The study was conducted in the Porma and Curueño rivers, using the hierarchical multi-scale process-based framework developed within the European REFORM Project.

This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification.

In much of the United States and elsewhere, urbanization continues to transform landscapes. In central Texas, anthropogenic conversion of land is due in part to a rapidly growing population in the Austin and San Antonio metro areas and the subsequent infrastructure and resources needed to support that growth. Protected natural areas adjacent to urbanized landscapes are often intended to mitigate the impact of land development by serving as wildlife habitat.

Forest productivity is strongly affected by seasonal weather patterns and by natural or anthropogenic disturbances. However weather effects on forest productivity are not currently represented in inventory-based models such as CBM-CFS3 used in national forest C accounting programs. To evaluate different approaches to modelling these effects, a model intercomparison was conducted among CBM-CFS3 and four process models (ecosys, CN-CLASS, Can-IBIS and 3PG) over a 2500ha landscape in the Oyster River (OR) area of British Columbia, Canada.

This research selected water soil erosion indicators (land cover, vegetation cover, slope) to assess the risk of soil erosion, ARCMAP GIS ver.9.0 environments and ERDAS ver.9.0 were used to manage and process satellite images and thematic tabular data. Landsat TM images in 2003 were used to produce land/cover maps of the study area based on visual interpreting method and derived vegetation cover maps, and the relief map at the scale of 1:50,000 to calculate the slope gradient maps.

Support vector machine (SVM) was applied for land-cover characterization using MODIS time-series data. Classification performance was examined with respect to training sample size, sample variability, and landscape homogeneity (purity). The results were compared to two conventional nonparametric image classification algorithms: multilayer perceptron neural networks (NN) and classification and regression trees (CART).

AIM: Anthropogenic environmental change is having a major impact on biodiversity. By identifying traits that correlate with changes in species range, comparative studies can shed light on the mechanisms driving this change; but such studies will be more useful for conservation if they have true predictive power, i.e. if their trait‐based models can be transferred to new regions. We aim to examine the ability of trait‐based models to predict changes in plant distribution across seven geographic regions that varied in terms of land cover and species composition.

This research examined the spatial and temporal patterns of error in time-series classified maps as a first step to creating a model to propagate error in post-classification change analysis. Two Landsat images were acquired for Pittsfield Township, MI, USA, classified, and overlaid to produce a map of change. Error variables were created for the classified maps. Hypotheses were proposed describing the spatial and temporal structures of error in the classified maps, and evaluated using geostatistics and point pattern analysis.