This contribution refers to a companion modelling approach applied to the study of interactions between social and environmental dynamics in a small protected island, part of a biosphere reserve. This approach leads to a role-playing game designed by a pluridisciplinary workgroup (researchers and reserve manager), used as management support and as a tool to help people regarding the stakes of sustainable development. For several years, Ushant Island (Brittany, France) was in the process of social and environmental restructuration due to land-use and land-cover changes.

Capsule Differences in arable land-use intensity and land use changes in lowland and higher-altitude regions of the Czech Republic affected long-term population trends of farmland birds. Aims To describe changes in arable land-use intensity and land use in lowlands and at higher altitudes from 1982 to 2000 focusing on changes around 1990 when agricultural intensity declined and to test how these changes affected farmland bird populations.

Traditional regression techniques such as ordinary least squares (OLS) are often unable to accurately model spatially varying data and may ignore or hide local variations in model coefficients. A relatively new technique, geographically weighted regression (GWR) has been shown to greatly improve model performance compared to OLS in terms of higher R 2 and lower corrected Akaike information criterion (AICC).

The changes in land use/land cover (LULC) play a major role in the study of various aspects of environmental issues. Land use is the results of various socioeconomic activities taking place at various urban and regional setups. In this paper, landscape dynamic characteristics are investigated by using remote sensing and geographic information system in mountainous watershed of Abha, Saudi Arabia. Land use classes were mapped and assessed from a time series of maps of year 2000–2010. The LULC transformations were also analyzed according to elevation and slope.

Within the framework of IWRM, a major concern in the humid tropics is the effects of ‘global warming' on the storm rainfall-runoff hydrology of both forests and converted forest lands. Further how such effects need to be incorporated within adaptive, forest-water-land management. But since the mid- 20th century, dramatic changes in land- use (LU) and land cover (LC) have also occurred which have led to rapid rates of deforestation and an expansion of land--forest degradation.

Although land cover and meteorological conditions are known to impact mercury (Hg) deposition processes, few studies have addressed how changes in forest cover and shifting climatic conditions will impact the Hg cycle. The purpose of this study was to examine the effects of forest type (hardwood vs. conifer) and meteorological variation on atmospheric Hg deposition in two forest stands in Huntington Wildlife Forest in upstate New York, USA.

Cloud forest streams are diverse ecosystems that provide valuable services (i.e., drinking water), but are threatened by anthropogenic activities on the watershed. We assessed the relationships between forest cover in the catchment, riparian vegetation, water chemistry, channel geomorphology, and the diversity of aquatic insects (taxonomic and functional diversity) in streams running through cloud forest in the upper part of La Antigua watershed, Mexico.

This study aims to evaluate different methods of integrating optical and multipolarized radar data for land use and land cover (LULC) mapping in an agricultural frontier region in the Central Brazilian Amazon, which requires continuous monitoring due to the increasing human intervention. The evaluation is performed using different sets of fused and combined data.

This paper examines the newly constructed geographically scaled economic output measure, Gross Cell Product (GCP), of Australia and New Zealand to quantify the impacts of climate change in the region. The paper discusses advantages of using the GCP instead of the Gross Domestic Product. The paper reveals that the GCP falls sharply as temperature increases in the region. A 1°C increase in temperature would decrease the productivity with an elasticity of −2.4. A 1 per cent decrease in precipitation would decrease productivity with an elasticity of −2.3.

We used boosted regression trees (BRT) to model stream biological condition as measured by benthic macroinvertebrate taxonomic completeness, the ratio of observed to expected (O/E) taxa. Models were developed with and without exclusion of rare taxa at a site. BRT models are robust, requiring few assumptions compared with traditional modeling techniques such as multiple linear regression.

This article presents a sufficient comparison of two types of advanced non-parametric classifiers implemented in remote sensing for land cover classification. A SPOT-5 HRG image of Yanqing County, Beijing, China, was used, in which agriculture and forest dominate land use. Artificial neural networks (ANNs), including the adaptive backpropagation (ABP) algorithm, Levenberg–Marquardt (LM) algorithm, Quasi-Newton (QN) algorithm and radial basis function (RBF) were carefully tested.

In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, longterm time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land cover change. Nevertheless, on a worldwide basis the number of meteorological networks and stations measuring soil moisture, in particular on a continuous basis, is still limited and the data they provide lack standardization of technique and protocol.