We investigated the role of land use/land cover and agriculture practices on stream dissolved organic carbon (DOC) dynamics in the Willow Slough watershed (WSW) from 2006 to 2008. The 415km² watershed in the northern Central Valley, California is covered by 31% of native vegetation and the remaining 69% of agricultural fields (primarily alfalfa, tomatoes, and rice). Stream discharge and weekly DOC concentrations were measured at eight nested subwatersheds to estimate the DOC loads and yields (loads/area) using the USGS developed stream load estimation model, LOADEST.

Various spectral data preprocessing approaches have been used to improve endmember extraction for urban landscape decomposition, yet little is known of their comparative adequacy for impervious surface mapping. This study tested four commonly used spectral data treatment strategies for endmember derivation, including original spectra, image fusion via principal component analysis, spectral normalization, and the minimum noise fraction (MNF) transformation.

Based on the fact that streamwater quality reflects landscape conditions, the objectives of this study were: to investigate nitrogen (N), carbon (C), and major ion concentrations in six streams crossing minimally disturbed Atlantic Forest areas, with similar geomorphological characteristics; to determine N and C fluxes in one of these pristine streams (Indaiá); and assess the impact of human activity on the biogeochemistry of two other streams in the same region, crossing urbanized areas.

Optimization techniques are often used in remote sensing retrieval of surface or atmospheric parameters. Nevertheless, different algorithms may exhibit different performances for the same optimization problem. Comparison of some classic optimization approaches in this article aims to select the best method for retrieving aerosol opacity, or even for other parameters, from remotely sensed data. Eight frequently used optimization algorithms were evaluated using both simulated data and actual AATSR (advanced along track scanning radiometer) data.

Priorities for the management of lowland rural floodplains in many parts of Europe have changed from a focus on agricultural production towards multi-functional landscapes that provide a range of environmental, social and economic benefits to society. This paper uses an ecosystem services framework to explore alternative management scenarios with different objectives (production, biodiversity, floodwater storage, agri-environment and income) for two rural floodplains in England. Each scenario featured different types of land cover and hydrological management.

The assessment of freshwater resources in a drainage basin is not only dependent on its hydrologic parameters but also on the socio-economic system driving development in the watershed area; the socio-economic aspect, that is often neglected in hydrologic studies, is one of the novelties of this study. The aim of this paper is twofold: (1) presenting an integrated working methodology and (2) studying a local case of a North African watershed where scarce field data are available.

A remote sensing and geographic information system-based study has been carried out to map areas susceptible to landslides using three statistical models, frequency ratio (FR), logistic regression (LR), and fuzzy logic at the central Zab basin in the mountainsides in the southwest West Azerbaijan province in Iran. Ten factors such as slope, aspect, elevation, lithology, normalized difference vegetation index (NDVI), land cover, precipitation, distance to fault, distance to drainage, and distance to road were considered. Landsat ETM⁺images were used for NDVI and land cover maps.

In northern New Mexico, complex interactions among climate, land use, and the associated reduction of surface fire in forest and savanna communities facilitated the expansion of piñon-juniper woodlands. Because increasing tree cover can outcompete herbaceous vegetation (e.g., forbs and grasses), woodland expansion resulted in reduced herbaceous cover and increased soil exposure, leading to increased runoff and erosion.

We report an investigation aimed at improving the understanding of the relationships between hydromorphology and land cover, and in particular aimed at identifying the spatial scale on which land cover patterns best account for the hydromorphology at a stream reach. This investigation was carried out in the Adour-Garonne basin. Several key findings emerged from the use of a new modeling procedure called “Random Forests”. Firstly, we established a typology of sites showing an upstream/downstream gradient structured by geographical descriptors and catchment hydromorphological features.

Studies of the impact of human activity on vegetation dynamics of the Sahelian belt of Africa have been recently re-invigorated by new scientific findings that highlighted the primary role of climate in the drought crises of the 1970s–1980s. Time series of satellite observations revealed a re-greening of the Sahelian belt that indicates no noteworthy human effect on vegetation dynamics at sub continental scale from the 1980s to late 1990s.

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).

Land cover maps obtained from classification of remotely sensed imagery provide valuable information in numerous environmental monitoring and modeling tasks. However, many uncertainties and errors can directly or indirectly affect the quality of derived maps. This work focuses on one key aspect of the supervised classification process of remotely sensed imagery: the quality of the reference dataset used to develop a classifier. More specifically, the representative power of the reference dataset is assessed by contrasting it with the full dataset (e.g. entire image) needing classification.