Shifting cultivation systems have been blamed as the primary cause of tropical deforestation and are being transformed through various forms of conservation and development policies and through the emergence of new markets for cash crops. Here, we analyze the outcomes of different policies on land use/land cover change (LUCC) in a traditional, shifting cultivation landscape in the Atlantic Forest (Brazil), one of the world’s top biodiversity hotspots.

Landslides are a major geologic hazard with estimated tens of deaths and $1–2 billion in economic losses per year in the US alone. The island of Puerto Rico experiences one or two large events per year, often triggered in steeply sloped areas by prolonged and heavy rainfall. Identifying areas susceptible to landslides thus has great potential value for Puerto Rico and would allow better management of its territory. Landslide susceptibility zonation (LSZ) procedures identify areas prone to failure based on the characteristics of past events.

Land use change, natural disturbance, and climate change directly alter ecosystem productivity and carbon stock level. The estimation of ecosystem carbon dynamics depends on the quality of land cover change data and the effectiveness of the ecosystem models that represent the vegetation growth processes and disturbance effects. We used the Integrated Biosphere Simulator (IBIS) and a set of 30- to 60-m resolution fire and land cover change data to examine the carbon changes of California's forests, shrublands, and grasslands.

This study is a contribution to a model intercomparison experiment initiated during a workshop at the 2013 IAHS conference in Göteborg, Sweden. We present discharge simulations with the conceptual precipitation–runoff model COSERO in 11 basins located under different climates in Europe, Africa and Australia. All of the basins exhibit some form of non-stationary conditions, due, for example, to warming, droughts or land-cover change.

Habitat assessments for biodiversity conservation are often complicated by the lack of detailed knowledge of a study species’ distribution. As an alternative to resource-intensive field-based methods to obtain such information, remotely sensed products can be utilized in species distribution models to infer a species’ distribution and ecological needs. Here we demonstrate how to arbitrate among a variety of remotely sensed predictor variables to estimate the distribution and ecological needs of an endangered butterfly species occurring mainly in inaccessible areas.

The robustness of the physically-based, semi-distributed hydrological model ECOMAG with respect to changing (climatic or land-use) conditions was evaluated for two basins, considered within the modelling workshop held in the frame of the 2013 IAHS conference in Göteborg, Sweden. The first basin, the Garonne River basin, France, is characterized mostly by changes in climatic conditions, while the second, Obyån Creek, Sweden, was exposed to drastic land cover change due to deforestation.

The increasing interest in biodiversity conservation has led to the development of new approaches to facilitate ecologically based conservation policies and management plans. In this context, the development of effective methods for the classification of forest types constitutes a crucial issue as forests represent the most widespread vegetation structure and play a key role in ecosystem functioning. In this study a maximum entropy approach (Maxent) to forest type classification in a complex Mediterranean area, has been investigated.

Testing hydrological models under changing conditions is essential to evaluate their ability to cope with changing catchments and their suitability for impact studies. With this perspective in mind, a workshop dedicated to this issue was held at the 2013 General Assembly of the International Association of Hydrological Sciences (IAHS) in Göteborg, Sweden, in July 2013, during which the results of a common testing experiment were presented.

The integration of spectral, textural, and topographic information using a random forest classifier for land-cover mapping in the rugged Nujiang Grand Canyon was investigated in this study. Only a few land-cover categories were accurately discriminated using spectral information exclusively, with an overall accuracy of 0.56 and a kappa coefficient of 0.51.

Recent progress in very high spatial resolution imagery (VHSRI) has increased the availability of fine‐scale land cover data over extensive areas. This new spatial information might improve our understanding of how land cover affects stream ecosystems. Land cover information was investigated in whole catchments and riparian areas in Normandy (France) and related to stream macroinvertebrates at 155 sites. The first model was based on the land cover data obtained via moderate spatial resolution imagery (MSRI) at the catchment scale.

Examination of the diurnal variations in surface urban heat islands (UHIs) has been hindered by incompatible spatial and temporal resolutions of satellite data. In this study, a diurnal temperature cycle genetic algorithm (DTC-GA) approach was used to generate the hourly 1 km land-surface temperature (LST) by integrating multi-source satellite data. Diurnal variations of the UHI in ‘ideal’ weather conditions in the city of Beijing were examined. Results show that the DTC-GA approach was applicable for generating the hourly 1 km LSTs.

AIM: Remote sensing coupled with direct observation allows recent changes in vegetation to be investigated but, in order to extend our understanding of land‐cover change further back in time, different proxies for vegetation are required. The pseudobiomization (PBM) approach has been developed to transform fossil pollen data into land‐cover classes (LCCs) in order to reconstruct broad‐scale anthropogenic land‐use change through time.