Automatic land-cover identification using remote-sensing images is essential for agricultural management and monitoring, which is an ongoing challenge. For permanent crops, which are of great importance economically and environmentally, it becomes even more challenging mainly due to the varying statistics of orchards such as the existence of different orchard types, different crown sizes even for the same type, different distances between orchards among various fields and overlapping crowns.

The objective of this article is to investigate whether it is possible to use Landsat data together with ancillary data and temporal context to accurately identify land covers found in the fallow areas of Montane Mainland Southeast Asia's (MMSEA's) difficult-to-map swidden landscapes. A rule-based non-parametric hybrid classification method that integrates knowledge about the vegetation regrowth patterns in these landscapes with analysis of Landsat imagery is developed. The method is applied to three upland districts of the Nghe An Province, Vietnam.

Moderate Resolution Imaging Spectroradiometer (MODIS) estimates of gross primary production (GPP) were validated using field-based estimates of net primary production from the Forest Inventory and Analysis (FIA) Program across the eastern USA. A total of 54 969 MODIS pixels and co-located FIA plots were analysed to validate MODIS GPP estimates. We used a data resolution of individual MODIS pixels and co-located FIA plots, and used detailed pixel- and plot-specific attributes by applying screening variables (SVs) to assess conditions under which MODIS GPP was most strongly validated.

With an ever expanding population, potential climate variability and an increasing demand for agriculture-based alternative fuels, accurate agricultural land-cover classification for specific crops and their spatial distributions are becoming critical to researchers, policymakers, land managers and farmers. It is important to ensure the sustainability of these and other land uses and to quantify the net impacts that certain management practices have on the environment.

We present an approach to regional environmental monitoring in the Northern Eurasian grain belt combining time series analysis of MODIS normalized difference vegetation index (NDVI) data over the period 2001-2008 and land cover change (LCC) analysis of the 2001 and 2008 MODIS Global Land Cover product (MCD12Q1). NDVI trends were overwhelmingly negative across the grain belt with statistically significant (p[Symbol: see text]0.05) positive trends covering only 1% of the land surface.

Estimation of land use during the Holocene is crucial to understand impacts of human activity on climate change in preindustrial period. Until now it is still a key issue to reconstruct amount and spatial distribution of prehistoric land use due to lack of data. Most reconstructions are simply extrapolations of population, cleared land amount per person and land suitability for agriculture. In this study, a new quantitative prehistoric land use model (PLUM) is developed based on semi-quantitative predictive models of archeological sites.

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.

A study on different types of bioindicator was carried out to detect river habitat quality in Central Italy. It was investigated the effectiveness of different bioindication models based on three biotic components: (i) vascular plants – hemeroby index for aquatic (Hw) and terrestrial species (Hₛ), and Ellenberg's soil nutrients indicator (N); (ii) aquatic macroinvertebrates – extended biotic index (EBI); (iii) soil microarthropods – index of soil biological quality (QBS-ar).

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.

Soil erosion is one of the major threats to the conservation of soil and water resources in the Danjiangkou Reservoir region (DRR), China. In order to describe the areas with high soil erosion risk (SER) and to develop adequate erosion prevention measures, SER in the DRR was assessed by integrating the CORINE model with GIS and RS.

Koa (Acacia koa) forests are found across broad environmental gradients in the Hawaiian Islands. Previous studies have identified important environmental factors controlling stand structure and productivity at the plot level, but these have not been applied at the landscape level because of small-scale spatial variability.

Landscape composition and habitat quality influence the abundance, population structure, and movements of animals. Understanding how an animal interacts with elements of the landscape helps predict its response to habitat loss and changes in land cover. We tested the hypothesis that the extent of movement depends on landscape composition in a threatened freshwater turtle, Emydoidea blandingii. We measured habitat composition at multiple spatial scales, ranging from the home range to the landscape scale.