Functional indicators are being increasingly used to assess waterway health but their responses to pressure in non-wadeable rivers have not been widely documented or applied in modern survey designs that provide unbiased estimates of extent. This study tests the response of river metabolism and loss in cotton strip tensile strength across a land use pressure gradient in non-wadeable rivers of northern New Zealand, and reports extent estimates for river metabolism and decomposition rates.

Green spaces considered natural remnants in urban environments play a key role in promoting environmental quality and forming a sustainable city. Nowadays, development of urban spaces has taken a hasty growth. In the meanwhile, the natural green spaces are severely damaged. The current study aims at investigating the trend of changes in green space of Tabriz City, the second large industrial city in Iran, on the basis of landscape ecology principles. In order to prepare the land-cover maps, Landsat TM and IRS LISS-III of the years 1989 and 2006 were applied.

We developed a global, 30-m resolution dataset of percent tree cover by rescaling the 250-m MOderate-resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Fields (VCF) Tree Cover layer using circa- 2000 and 2005 Landsat images, incorporating the MODIS Cropland Layer to improve accuracy in agricultural areas. Resulting Landsat-based estimates maintained consistency with the MODIS VCF in both epochs (RMSE =8.6% in 2000 and 11.9% in 2005), but showed improved accuracy in agricultural areas and increased discrimination of small forest patches.

Precise soil quality assessment is critical for designing sustainable agriculture policies, restoring degraded soils, carbon (C) modeling, and improving environmental quality. Although the consequences of soil quality reduction are generally recognized, the spatial extent of soil degradation is difficult to determine, because no universal equation or soil quality prediction model exists that fits all ecoregions. Furthermore, existing soil organic C (SOC) models generate estimates with uncertainties that may exceed 50%.

Atmospheric correction of high spatial resolution (10–30 m pixel sizes) satellite imagery for use in large-area land-cover monitoring is difficult due to the lack of aerosol optical depth (AOD) estimates made coincident with image acquisition. We present a methodology to determine the upper and lower bounds of AOD estimates that allow the subsequent calculation of a biophysical variable of interest to a pre-determined precision. Knowledge of that range can be used to identify an appropriate method for estimating AOD.

Numerous land-cover change detection techniques have been developed with varying opinions about their appropriateness and success. Decisions on the selection of the most suitable change detection method is often influenced by the study region landscape complexity and the type of data used for analysis. For different climatic areas, the method that suits best the seasonal land-cover change identification remains uncertain.

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.

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.

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.

Earth observation based monitoring of change in vegetation phenology and productivity is an important and widely used approach to quantify degradation of ecosystems due to climatic or human influences. Most satellite based studies apply linear or polynomial regression methods for trend detections. In this paper it is argued that natural systems hardly react to human or natural influences in a linear or a polynomial manner. At shorter time-scales of few decades natural systems fluctuate to a certain extent in a non-systematic manner without necessarily changing equilibrium.

In the anthropogenic fire-disturbed ecosystem of the San Francisco Valley in the Andes of southeastern Ecuador, dense stands of an aggressive invasive weed, the southern bracken fern (Pteridium arachnoideum and Pteridium caudatum), dominate the landscape. To secure sustainable land management in the region, a comprehensive understanding of bracken spatial-distribution patterns and life cycle dynamics is crucial.

During the last decade, the use of the normalized difference vegetation index (NDVI) for drought monitoring applications has drawn many criticisms, mainly because a number of drivers such as land-cover/land-use change, pest infestation, and flooding may depress the NDVI, further causing false drought identification. In this study, the impacts of land-cover change on the NDVI-derived satellite drought indicator, the vegetation condition index (VCI), are presented.