Moderate Resolution Imaging Spectroradiometer (MODIS) estimates of gross primary production (GPP) were validated using field-based estimates of net primary production from the Forest Inventory and Analysis (FIA) Program across the eastern USA. A total of 54 969 MODIS pixels and co-located FIA plots were analysed to validate MODIS GPP estimates. We used a data resolution of individual MODIS pixels and co-located FIA plots, and used detailed pixel- and plot-specific attributes by applying screening variables (SVs) to assess conditions under which MODIS GPP was most strongly validated.

We present an approach to regional environmental monitoring in the Northern Eurasian grain belt combining time series analysis of MODIS normalized difference vegetation index (NDVI) data over the period 2001-2008 and land cover change (LCC) analysis of the 2001 and 2008 MODIS Global Land Cover product (MCD12Q1). NDVI trends were overwhelmingly negative across the grain belt with statistically significant (p[Symbol: see text]0.05) positive trends covering only 1% of the land surface.

With an ever expanding population, potential climate variability and an increasing demand for agriculture-based alternative fuels, accurate agricultural land-cover classification for specific crops and their spatial distributions are becoming critical to researchers, policymakers, land managers and farmers. It is important to ensure the sustainability of these and other land uses and to quantify the net impacts that certain management practices have on the environment.

Fecal pollution enters coastal waters through multiple routes, many of which originate from land-based activities. Runoff from pervious and impervious land surfaces transports pollutants from land to sea and can cause impairment of coastal ocean waters. To understand how land use practices and water characteristics influence concentrations of fecal indicator bacteria (FIB) and pathogens in natural waters, fourteen coastal streams, rivers, and tidal lagoons, surrounded by variable land use and animal densities, were sampled every six weeks over two years (2008 & 2009).

Estimation of land use during the Holocene is crucial to understand impacts of human activity on climate change in preindustrial period. Until now it is still a key issue to reconstruct amount and spatial distribution of prehistoric land use due to lack of data. Most reconstructions are simply extrapolations of population, cleared land amount per person and land suitability for agriculture. In this study, a new quantitative prehistoric land use model (PLUM) is developed based on semi-quantitative predictive models of archeological sites.

Dams strongly impair the fluvial environment by altering downstream flows. We analysed riverscapes downstream of three dams and hypothesized that different dam types in rivers with diverse history of land-use and land cover (LULC) change have significant riparian cover differences at diverse biogeomorphic units (banks, riverbanks, islands). We performed a temporal comparison using pre-dam (1965) and post-dam (2013) high-resolution airborne imagery. A new approach was devised to correct the spatial offset between historical and contemporary imagery.

Steppe, an important belt to protect North China from dust storms, is vulnerable and has been degraded in recent decades because of climatic change and heavy grazing. In order to improve steppe management, this study presents a framework for the monitoring of grazing intensity in Xilingol steppe of middle Inner Mongolia, northern China, by integrating the CENTURY ecosystem model-based simulation and remotely sensed MODIS data-based inversion.

The paper gives an overview on the trends in the usage of landscape metrics as indicators for: land use changes, habitat functions (biodiversity, habitats), landscape regulating functions (fire control, microclimate control, etc.), and information functions (landscape aesthetics). We reviewed papers published in international peer-reviewed journals that are indexed by the Institute of Science Information (ISI) Web of Knowledge from 2000 to 2010. The terms “landscape metrics”, “landscape indexes” and “landscape indices” were searched.

Water resources are influenced by various factors such as weather, topography, geology, and environment. Therefore, there are many difficulties in evaluating and analyzing water resources for the future under climate change. In this paper, we consider climate, land cover and water demand as the most critical factors affecting change in future water resources. We subsequently introduce the procedures and methods employed to quantitatively evaluate the influence of each factor on the change in future water resources.

Mapping and monitoring wetland ecosystems over large geographic areas based on remote sensing is challenging because of the spatial and spectral complexities of the inherent ecosystem dynamics. The main objective of this research was to develop and evaluate a new method for detecting and quantifying wetland changes in the Northern Gulf of Mexico (NGOM) region using multitemporal, multispectral, and multisensor remotely sensed data.

This paper presents the conceptual design and application of a new land-change modelling framework that represents geographical, sociological, economic, and ecological aspects of a land system. The framework provides an overarching design that can be extended into specific model implementations to evaluate how policy, land-management preferences, and land-market dynamics affect (and are affected by) land-use and land-cover change patterns and subsequent carbon storage and flux.

Natural wetland ecosystem plays an important role in global climate change due to its large amounts of stored carbon and nitrogen. The Sanjiang Plain, Northeast China, encompasses large area of natural freshwater marshy wetlands. However, the magnitude and temporal patterns of major greenhouse gases (GHGs: CO₂, CH₄ and N₂O) in this region remain far from certain. Here we used a process-based ecosystem model to examine GHGs fluxes and their underlying mechanisms in the marshland across the Sanjiang Plain over the period 1949–2008.