Water samples were collected for determination of dissolved trace metals in 56 sampling sites throughout the upper Han River, China. Multivariate statistical analyses including correlation analysis, stepwise multiple linear regression models, and principal component and factor analysis (PCA/FA) were employed to examine the land use influences on trace metals, and a receptor model of factor analysis-multiple linear regression (FA-MLR) was used for source identification/apportionment of anthropogenic heavy metals in the surface water of the River.

Human activities are radically changing natural land cover and increasing the delivery of soil, organic compounds, nutrients, toxic agrochemicals and other contaminants to aquatic ecosystems. The eutrophication of streams, rivers, lakes, reservoirs and costal zones is one of the most important consequences of human activities. In this study we assessed the trophic status of 28 wadeable stream reaches of the Santa Lucía basin, an important economic region of Uruguay. We developed a Trophic State Index of Benthic Invertebrates (TSI-BI), the first of its kind for South American lotic systems.

Retrieval of soil moisture content using the vertical and horizontal polarizations of multiple frequency bands on microwave sensors can provide an estimate of vegetation water content (VWC). Another approach is to use foliar-water indices based on the absorption at shortwave-infrared wavelengths by liquid water in the leaves to determine canopy water content, which is then related to VWC.

The suitable spectral mode in remote sensing is often desirable to facilitate the inversion of ecological environment and landscape. This paper put forward an optimizing model based on variable precision rough sets (VPRS) for the land cover discrimination in wetland inventory. In the case study of Lake Baiyangdian which has important ecological functions to the northern China, this model is established successfully according to the domain-experts knowledge. The procedure is as follows.

Forest productivity is strongly affected by seasonal weather patterns and by natural or anthropogenic disturbances. However weather effects on forest productivity are not currently represented in inventory-based models such as CBM-CFS3 used in national forest C accounting programs. To evaluate different approaches to modelling these effects, a model intercomparison was conducted among CBM-CFS3 and four process models (ecosys, CN-CLASS, Can-IBIS and 3PG) over a 2500ha landscape in the Oyster River (OR) area of British Columbia, Canada.

A novel approach to image radiometric normalization for change detection is presented. The approach referred to as stratified relative radiometric normalization (SRRN) uses a time-series of imagery to stratify the landscape for localized radiometric normalization. The goal is to improve the detection accuracy of abrupt land cover changes (human-induced, natural disaster, etc.) while decreasing false detection of natural vegetation changes that are not of interest. These vegetation changes may be associated with such phenomena as phenology, growth and stress (e.g.

This research examined the spatial and temporal patterns of error in time-series classified maps as a first step to creating a model to propagate error in post-classification change analysis. Two Landsat images were acquired for Pittsfield Township, MI, USA, classified, and overlaid to produce a map of change. Error variables were created for the classified maps. Hypotheses were proposed describing the spatial and temporal structures of error in the classified maps, and evaluated using geostatistics and point pattern analysis.

Rice (Oryza sativa L.) agriculture is estimated to cover 161millionha of land on Earth, with 10% grown in temperate regions. Currently there are strong concerns about surface water nutrient pollution, and the purpose of this study was to determine the impacts of temperate rice cultivation on nutrient dynamics at the small watershed scale. Over the course of the 2008 growing season (May through September), bi-weekly grab samples were collected from outlets of 11 agricultural subwatersheds in California.

The impact of climate change on hydrological processes and nutrient input is one of the major uncertainties in projecting future global warming. The Soil and Water Assessment Tool (SWAT) model is applied to simulate the effects of temperature on the hydrology and sediment and nutrient load in the lower Pearl River Basin, South China. Calibration and validation results for SWAT showed that the Yamen estuary is appropriate for simulating the impacts of temperature change on both hydrological processes and nutrient input.

The German Remote Sensing Data Center operates the Biosphere Energy Transfer Hydrology Model, a process model that estimates the net primary productivity of agricultural areas. The model is driven by remote sensing data and meteorological data. Remotely sensed datasets including a time series of the leaf area index, which describes vegetation condition, and a land cover classification, which provides information about land use, are needed. Currently leaf area indices and land cover data derived from the sensor vegetation are used.

In this study we (1) examined the applicability of the widely available CORINE land cover map of Europe in predicting several components of the richness of forest breeding bird community, and (2) analysed how different ecologically meaningful species groups respond to the differences in landscape composition and how these differences are reflected in the relationships between total species richness and richness of these species groups at the 2.5 × 2.5 km2 scale.

Reference data and accuracy assessments via error matrices build the foundation for measuring success of classifications. An error matrix is often based on the traditional holdout method that utilizes only one training/test dataset. If the training/test dataset does not fully represent the variability in a population, accuracy may be over – or under – estimated. Furthermore, reference data may be flawed by spatial errors or autocorrelation that may lead to overoptimistic results.