In this paper we present the generic modelling system SITE (SImulation of Terrestrial Environments), a software package to develop and apply models simulating regional land-use dynamics. The modelling system includes (i) a framework managing the model generics and (ii) code templates for the development of rule-based land-use and land-cover change (LUCC) models. SITE comprises built-in methods for e.g. map comparison, model optimization and environmental scenarios.

Land-use and land-cover change are important drivers of global environmental change, affecting the state of biodiversity, the global carbon cycle, and other aspects of the earth system. In this article we describe the development of the land-use model LandSHIFT, which aims to simulate land-use and land-cover change on the continental and global scale. The model is based on a “land-use systems” approach, which describes the interplay between anthropogenic and environmental system components as drivers of land-use change.

Fire regimes depend on climate, vegetation structure and human influences. Climate determines the water content in fuel and, in the longer term, the amount of biomass. Humans alter fire regimes through increased ignition frequency and by hindering the spread of fire through fire suppression and fuel fragmentation. Here, we present FIRE LADY (FIre REgime and LAndscape DYnamics), a spatially explicit fire regime model that takes into account daily weather data, topography, vegetation growth, fire behaviour, fire suppression and land use changes.

Although developments in remote sensing have greatly improved land cover mapping, the mixed pixel problem has not yet been fully addressed. Soft classification techniques have been introduced to address the problem, but they do not show the spatial location of the class proportions in a pixel. Subpixel mapping has been introduced to address the drawbacks of soft classifications. In this work, the feedforward backpropagating neural network (FFBPNN) was used for subpixel mapping.

Consistent, repeatable and broadly applicable land use, land cover data is needed across the Lake Superior basin to facilitate ecosystem condition assessment and trend analysis. Such a data set collected regularly through time could inform and focus field monitoring efforts, and help prioritize restoration and mitigation efforts. Unfortunately, few data sets exist that are bi-nationally consistent in time, classification method, or resolution.

Biological indicators can be used both to estimate ecological condition and to suggest plausible causes of ecosystem degradation across the U.S. Great Lakes coastal region. Here we use data on breeding bird, diatom, fish, invertebrate, and wetland plant communities to develop robust indicators of ecological condition of the U.S. Lake Superior coastal zone. Sites were selected as part of a larger, stratified random design for the entire U.S. Great Lakes coastal region, covering gradients of anthropogenic stress defined by over 200 stressor variables (e.g.

Coastal wetlands are important contributors to large-lake productivity and biodiversity and mediators of lake—watershed interactions. This study explores whether the size of the watershed in which coastal wetlands are embedded (a measure of strength of connection to the terrestrial landscape) influences their background condition and response to anthropogenic landuse.

China's Horquin area in the northern farming–pastoral transition zone is undergoing rapid land degradation and rangeland modification that is impacting far broader areas as the source of material for dust storms. Multi-temporal Landsat images of the Horquin core area were used to generate a time series of land use covering about a 30-year period, 1975–2003. We show that the physical environment in Horquin deteriorated between 1975 and 2000, although this situation was more controlled after 2000.

An optimized artificial immune network-based classification model, namely OPTINC, was developed for remote sensing-based land use/land cover (LULC) classification.

Boreal forests are a critical component of the global carbon cycle, and timely monitoring allows for assessing forest cover change and its impacts on carbon dynamics. Earth observation data sets are an important source of information that allow for systematic monitoring of the entire biome. Landsat imagery, provided free of charge by the USGS Center for Earth Resources Observation and Science (EROS) enable consistent and timely forest cover updates.

Methods for the estimation of forest growing stock volume (GSV) are a major topic of investigation in the remote sensing community. The boreal zone contains almost 30% of global forest by area but measurements of forest resources are often outdated. Although past and current spaceborne synthetic aperture radar (SAR) backscatter data are not optimal for forest-related studies, a multi-temporal combination of individual GSV estimates can improve the retrieval as compared to the single-image case.

Aim To study the importance of ecological and geographical factors in explaining arthropod species composition on islands. Location The Aeolian Islands, a volcanic archipelago in the central Mediterranean, near Sicily. Methods The influence of island area, age, distance to the mainland, distance to the nearest island and land cover categories on species composition of arthropod groups was analysed using canonical correspondence analysis (CCA).