Direct applications of remote sensing thermal infrared (TIR) data in landscape ecological research are rare due to limitations in the sensors, calibration, and difficulty in interpretation. Currently there is a general lack of methodology for examining the relationship between land surface temperatures (LST) derived from TIR data and landscape patterns extracted from optical sensors. A separation of landscapes into values directly related to their scale and signature is a key step.

The Queensland Koala Phascolarctos cinereus and possum harvests were regulated from 1906-1927 and 1906-1936, respectively. Before that, there was an uncontrolled harvest. Historical data from the harvest period were analysed to gain information on P. cinereus and possum (mainly Trichosurus vulpecula) population ecology and status. P. cinereusi> numbers peaked in southern Queensland around the turn of the century or in the first decade of the 20th century.

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.

The economic value of pasture improvement in a farming system in a Mediterranean-type environment is assessed using farm modelling. Bioeconomic modelling combined with sensitivity analysis is used to explore how new annual legume species may impact on farm profit and land use. If introduced on all suitable soils the new pastures lead to a 26% increase in farm profit with an additional 12% of farm area being switched into pasture to support more livestock. However, stocking rates decline slightly, the enterprise mix becomes less diversified and several rotational changes are required.

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.

The Large-scale Biosphere-Atmosphere Experiment in Amazonia (LBA) is a multinational, interdisciplinary research program led by Brazil. Ecological studies in LBA focus on how tropical forest conversion, regrowth, and selective logging influence carbon storage, nutrient dynamics, trace gas fluxes, and the prospect for sustainable land use in the Amazon region. Early results from ecological studies within LBA emphasize the variability within the vast Amazon region and the profound effects that land-use and land-cover changes are having on that landscape.

Functional indicators are being increasingly used to assess waterway health but their responses to pressure in non-wadeable rivers have not been widely documented or applied in modern survey designs that provide unbiased estimates of extent. This study tests the response of river metabolism and loss in cotton strip tensile strength across a land use pressure gradient in non-wadeable rivers of northern New Zealand, and reports extent estimates for river metabolism and decomposition rates.

PURPOSE: The peri-urban region to the south east of Madrid contains a mixture of housing, manufacturing industry and farming, some of which disperse metals, in particular cadmium, copper, lead, and zinc, into the soil. We have mapped the concentrations of these elements and identified the major influences on their distributions. MATERIAL AND METHODS: We sampled the topsoil at 125 sites across 1,050� km² of peri-urban land to the south east of the city on two grids, one nested inside the other. At each site, we measured the current contents of the four trace elements in the soil.

Humans have fundamentally altered global patterns of biodiversity and ecosystem processes. Surprisingly, existing systems for representing these global patterns, including biome classifications, either ignore humans altogether or simplify human influence into, at most, four categories. Here, we present the first characterization of terrestrial biomes based on global patterns of sustained, direct human interaction with ecosystems. Eighteen “anthropogenic biomes” were identified through empirical analysis of global population, land use, and land cover.

Despite being recognized as a key baseline dataset for many applications, especially those relating to biogeochemical cycles, land cover products in their current form are limiting. Typically they lack the thematic detail necessary for driving the models that depend upon them. This study has demonstrated the ability to produce a highly detailed (both spatially and thematically) land cover/land use dataset over Russia – by combining existing datasets into a hybrid information system.