Agronomic intensification has transformed many agricultural landscapes into expansive monocultures with little natural habitat. A pervasive concern is that such landscape simplification results in an increase in insect pest pressure, and thus an increased need for insecticides. We tested this hypothesis across a range of cropping systems in the Midwestern United States, using remotely sensed land cover data, data from a national census of farm management practices, and data from a regional crop pest monitoring network.

This article presents a raster-based Geographic Information Systems (GIS) tool called CN-Idris that outputs runoff estimates from land use/land cover and hydrologic soil group maps based on the Natural Resources Conservation Service Curve Number method. The tool enables the user to select among three antecedent moisture conditions and two values of the initial abstraction (Iₐ) parameter, and it has the option to include spatial variation in rainfall input.

In this article we investigate if qualitative soil fertility datasets derived during participatory processes can be combined with a corresponding land use change model (i) to improve the understanding of the social-ecological complexity of land use change and (ii) to allow testing of alternative scenarios even in data-poor environments. To test this hypothesis, a participatory assessment approach was combined with the spatially explicit, soil fertility driven FALLOW (Forest, Agroforest, Low-value Landscape Or Wasteland?) model.

This study compared the spatial and temporal patterns of Culex tarsalis Coquillett and Aedes vexans Meigen populations and examined their relationships with land cover types and climatic variability in Sioux Falls, SD. Between 24 and 30 CDC CO2-baited light traps were set annually in Sioux Falls from May to September 2005–2008. Land cover data were acquired from the 2001 National Land Cover Dataset and the percentages of selected land cover types were calculated within a 600-m buffer zone around each trap. Meteorological information was summarized from local weather stations. Cx.

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote-sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images and different classification algorithms, maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA) and object-based classification (OBC), were explored.

This study investigates the impact of using different combinations of Moderate Resolution Imaging Spectroradiometer (MODIS) and ancillary datasets on overall and per-class classification accuracies for nine land cover types modified from the classification system of the International Geosphere Biosphere Programme (IGBP).

Improvement in remote sensing techniques in spatial/spectral resolution strengthens their applicability for urban environmental study. Unfortunately, high spatial resolution imagery also increases internal variability in land cover units and can cause a ‘salt-and-pepper’ effect, resulting in decreased accuracy using pixel-based classification results. Region-based classification techniques, using an image object (IO) rather than a pixel as a classification unit, appear to hold promise as a method for overcoming this problem.

Suburban areas continue to grow rapidly and are potentially an important land-use category for anthropogenic carbon-dioxide (CO₂) emissions. Here eddy covariance techniques are used to obtain ecosystem-scale measurements of CO₂ fluxes (FC) from a suburban area of Baltimore, Maryland, USA (2002–2006). These are among the first multi-year measurements of FC in a suburban area. The study area is characterized by low population density (1500inhabitants km⁻²) and abundant vegetation (67.4% vegetation land-cover).

Although spatial scale is important for understanding ecological processes and guiding conservation planning, studies combining a range of scales are rare. Habitat suitability modelling has been used traditionally to study broad-scale patterns of species distribution but can also be applied to address conservation needs at finer scales. We studied the ability of presence-only species distribution modelling to predict patterns of habitat selection at broad and fine spatial scales for one of the rarest mammals in the UK, the grey long-eared bat (Plecotus austriacus).

We examined the impacts of the Satsunai River Dam on the hydrology and development of riparian vegetation along the upper and lower reaches of the Satsunai River downstream from the dam. We estimated frequency curves of the flood discharge during the pre-dam (1976-1996) and post-dam (1997-2006) periods and simulated the flood frequency at sampling points within sites under pre-dam, post-dam and dam-removal (using the pre-dam flood discharge and post-dam cross-sections) scenarios. Changes in channel morphology and land cover were investigated by analyzing aerial photographs.

Monitoring natural resources in Alaskan national parks is challenging because of their remoteness, limited accessibility, and high sampling costs. We describe an iterative, three-phased process for developing sampling designs based on our efforts to establish a vegetation monitoring program in southwest Alaska. In the first phase, we defined a sampling frame based on land ownership and specific vegetated habitats within the park boundaries and used Path Distance analysis tools to create a GIS layer that delineated portions of each park that could be feasibly accessed for ground sampling.

Spatial data quality is a paramount concern in all geographical information systems (GIS) applications. Existing standards and guidelines for spatial data commonly assume the positional error is normally distributed. While non-normal behaviour of the error in digital elevation data has been observed in previous research, current guidelines for digital elevation data still assume that the errors for observations in open terrain are normally distributed.