Land cover derived from remotely sensed products is an important input to a number of different global, regional and national scale applications including resource assessments and economic land use models. During the last decade three global land cover datasets have been created, i.e. the GLC-2000, MODIS and GlobCover, but comparison studies have shown that there are large spatial discrepancies between these three products. One of the reasons for these discrepancies is the lack of sufficient in-situ data for the development of these products.

The poor water condition in agricultural watersheds in Poland provides evidence of a major impact of this sector on water quality, especially with regard to nutrients. What is specific about Polish agriculture, especially in the Wielkopolska region, is a large area of arable land and a high concentration of point sources. The outflow from these sources is very changeable and depends strongly on weather conditions, in particular precipitation. Therefore, it is very difficult to measure the runoff from those sources.

There is a long tradition of river monitoring using taxonomy-based metrics to assess environmental quality in Europe via benthic macroinvertebrate communities. A promising alternative is the use of their species life-history traits. Both methods (taxonomy-based and trait-based), however, have relied on the time-consuming identification of taxa. River biotopes, (i.e.

Traditional regression techniques such as ordinary least squares (OLS) are often unable to accurately model spatially varying data and may ignore or hide local variations in model coefficients. A relatively new technique, geographically weighted regression (GWR) has been shown to greatly improve model performance compared to OLS in terms of higher R 2 and lower corrected Akaike information criterion (AICC).

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.

Human activity is one of the most important aerosol sources. Because the underlaying surface feature records most human activities, it is important to recognize the correlation between aerosol distribution and the underlaying surface.

The aim of this study is to extract landslide-related factors from remote-sensing data, such as Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery, and to examine their applicability to landslide susceptibility near Boun, Korea, using a geographic information system (GIS). Landslide was mapped from interpretation of aerial photographs and field surveying. Factors that influence landslide occurrence were extracted from ASTER imagery.

Manure is applied to cropland areas with varying surface cover to meet single-year or multiple-year crop nutrient requirements. The objectives of this field study were to: (1) examine runoff water quality characteristics following land application of manure to sites with and without wheat residue, (2) compare the water quality impacts of land application of manure to meet 0-, 1-, 2-, 4-, and 8-year P-based requirements for corn, and (3) evaluate the effects of varying runoff rates on runoff nutrient loads.

A set of landscape metrics is used to study the long-term environmental transformation of a typical coastal Mediterranean area from 1850 to 2005. Our figures show a dramatic environmental deterioration between 1950 and 2005. The main proximate drivers of this landscape degradation are the effects of urban sprawl on former agricultural areas located in the coastal plains, together with the abandonment and reforestation of hilly slopes intercepted by low-density residential areas, highways, and other linear infrastructures.

Game management is widely implemented in Spain, affecting more than 70 % of land cover. Management intensity may be linked to the financial aims of hunting estates, but no study of these aspects has been developed in Spain, where commercial hunting is common. Through interviews with game managers and field surveys, we quantified physical and economic traits, management techniques, and hunting methods in a sample of 59 small-game hunting estates located in south-central Spain (where red-legged partridge hunting has the highest socioeconomic importance in the country).

Faecal Coliform (FC) bacteria were used as Faecal Indicator Organisms (FIOs) for assessment of microbiological pollution risk in two large, mixed land use catchments in Scotland. FC counts varied spatially in relation to land use and human population and resulting trophic status. These were highest in catchments with a high cover of improved pasture (which was assumed to be a proxy for cattle and sheep grazing densities) and significant human populations. FC counts were lowest in oligotrophic upland areas, where domesticated animal populations were low.

Most of the current global land cover datasets and global scale land-use models use a classification of land cover based on the dominant land cover type within a distinct region or pixel. Such a classification disregards the diversity and intensity of human influence on land systems. In this paper we propose a novel way of classification and modeling land-use using a classification based on land-use systems (LUSs) that represent specific combinations of human-environment interactions. A cluster analysis was used to identify and map these LUSs.