It is necessary to estimate the area of afforestation and deforestation in New Zealand, since 1990, to meet reporting obligations under the Kyoto Protocol. We describe a method for national mapping of forest change that achieves high accuracy, but only requires moderate effort. A national coverage of satellite imagery is standardised, classified (automatically) for land cover, and then compared with an existing 1990 land-use map to identify polygons (>1ha) of possible forest change. Each one of these possible change polygons is checked by operators for actual or spurious change.

Native animals are affected differently by urbanization. Some species respond favorably and thrive in human-dominated landscapes, but others are extirpated. Raptors are often sensitive to changes in land cover and prey abundance. We therefore used a combination of broadcast surveys and incidental observations while spot-mapping to evaluate the influences of these two variables on the presence of raptors at 21 sites from 2004–2008 along an urban-to-wildland gradient in western Washington, U.S.A.

The coyote (Canis latrans) has dramatically expanded its range to include the cities and suburbs of the western US and those of the Eastern Seaboard. Highly adaptable, this newcomer’s success causes conflicts with residents, necessitating research to understand the distribution of coyotes in urban landscapes. Citizen science can be a powerful approach toward this aim.

Delmarva Bays are depressional wetlands that are elliptical in shape with sandy rims, and occur mainly in the central portion of the Delmarva Peninsula within the Mid- Atlantic United States. Situated in a region with abundant agriculture, Delmarva Bays and other wetlands may enhance water quality by reducing nutrient and sediment levels in local waters. A significant portion have been drained and converted from forested wetlands to agriculture.

Mass movements are major hazards that threaten natural and human environments. In Lebanon, the occurrence of mass movements increased by almost 60% between 1956 and 2008. Forest fire has emerged as an additional hazard: it destroyed over 25% of Lebanon’s forests in a period less than 40 years. This paper investigates the potential effect of forest fire on the occurrence of mass movements in the Damour and Nahr Ibrahim watersheds of Lebanon. Mass movement and forest fire inventory maps were produced through remote sensing using aerial and satellite images.

The suitable spectral mode in remote sensing is often desirable to facilitate the inversion of ecological environment and landscape. This paper put forward an optimizing model based on variable precision rough sets (VPRS) for the land cover discrimination in wetland inventory. In the case study of Lake Baiyangdian which has important ecological functions to the northern China, this model is established successfully according to the domain-experts knowledge. The procedure is as follows.

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).

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.