Mapping and monitoring wetland ecosystems over large geographic areas based on remote sensing is challenging because of the spatial and spectral complexities of the inherent ecosystem dynamics. The main objective of this research was to develop and evaluate a new method for detecting and quantifying wetland changes in the Northern Gulf of Mexico (NGOM) region using multitemporal, multispectral, and multisensor remotely sensed data.

This paper presents the conceptual design and application of a new land-change modelling framework that represents geographical, sociological, economic, and ecological aspects of a land system. The framework provides an overarching design that can be extended into specific model implementations to evaluate how policy, land-management preferences, and land-market dynamics affect (and are affected by) land-use and land-cover change patterns and subsequent carbon storage and flux.

Natural wetland ecosystem plays an important role in global climate change due to its large amounts of stored carbon and nitrogen. The Sanjiang Plain, Northeast China, encompasses large area of natural freshwater marshy wetlands. However, the magnitude and temporal patterns of major greenhouse gases (GHGs: CO₂, CH₄ and N₂O) in this region remain far from certain. Here we used a process-based ecosystem model to examine GHGs fluxes and their underlying mechanisms in the marshland across the Sanjiang Plain over the period 1949–2008.

Landscape similarity search involves finding landscapes from among a large collection that are similar to a query landscape. An example of such collection is a large land cover map subdivided into a grid of smaller local landscapes, a query is a local landscape of interest, and the task is to find other local landscapes within a map which are perceptually similar to the query. Landscape search and the related task of pattern-based regionalization, requires a measure of similarity – a function which quantifies the level of likeness between two landscapes.

Many arid and semi‐arid landscapes around the world are affected by a shift from grassland to shrubland vegetation, presumably induced by climate warming, increasing atmospheric CO₂concentrations, and/or changing land use. This major change in vegetation cover is likely sustained by positive feedbacks with the physical environment. Recent research has focused on a feedback with microclimate, whereby cold intolerant shrubs increase the minimum nocturnal temperatures in their surroundings.

An illumination correction algorithm has been developed to improve the accuracy of forest change detection from Landsat-derived reflectance data. This algorithm is based on an empirical rotation model and was tested on Landsat image pairs over the Cherokee National Forest, Tennessee; Uinta–Wasatch–Cache National Forest, Utah; San Juan National Forest, Colorado; and Sinkyone Wilderness State Park, California. The illumination correction process successfully eliminated correlation between Landsat reflectance and illumination condition.

Aim We test the prediction that beta diversity (species turnover) and the decay of community similarity with distance depend on spatial resolution (grain). We also study whether patterns of beta diversity are related to variability in climate, land cover or geographic distance and how the independent effects of these variables depend on the spatial grain of the data. Location Europe, Great Britain, Finland and Catalonia. Methods We used data on European birds, plants, butterflies, amphibians and reptiles, and data on British plants, Catalonian birds and Finnish butterflies.

Boulengerula uluguruensis is a terrestrial caeciliid caecilian described from the Uluguru Mountains of Tanzania. We investigated the relative abundance of B. uluguruensis in agricultural and forested habitats at the beginning of the rainy season. This caecilian was found over a wide altitudinal range (450–1175 m a.s.l.), and in many land cover types (including natural forest, plantation forest and small scale agriculture), and different soil textures (including very compact soil). Based on quantitative and semi‐quantitative surveys, B.

The planning and management of urban forest has become increasingly important as a focus of urban environmental management. The objectives of this study were to analyze the landuse/land cover and to map functional zones of the urban forest in the upper catchment area of Addis Ababa. This study identifies five landuse/land cover types: (i) Eucalyptus–Juniperus dominated forest, (ii) mixed native forest, (iii) built-up areas, (iv) Eucalyptus plantation (v) crop/grazing lands.

Understanding the relationship between the distribution of organisms and their environment is important for regional conservation planning. While most herpetofaunal community studies address environmental influence on species richness, few studies describe environmental influence on species composition at geographic scales. Field surveys of herpetofauna were conducted during 1994-2000 in Tochigi Prefecture, which covers ca. 6400 km_ of mainland Japan and includes a wide range of elevation, annual mean temperature, and human population densities.

Tropical mountain regions are prone to landslide hazards. Given the current land pressure with increasing occupation of steep uplands, landslide hazards are expected to increase in the near future. Understanding the factors that control landslide hazards is therefore essential. Rare event logistic regression allows us to perform a robust detection of landslide controlling factors. This technique is here applied to the tropical Andes to evaluate the impact of dynamic land cover changes on landslide occurrences. Land cover change trajectories (i.e.

The distribution of invasive plants across a landscape is largely governed by disturbance invoking anthropogenic land-use practices and propagule pressure. However, spatial variability associated with anthropogenic disturbances and propagule pressure is seldom used to develop distribution models of invasive plants. This study makes use of large-scale survey data to develop a spatially explicit predictive model for the invasive wetland plant—purple loosestrife.