Human activity such as the development of slope land around watersheds has dramatically affected the ecological environment in Taiwan. This situation has been aggravated by heavy precipitation from typhoons in the summertime. The results include serious soil erosion and mass movement in the Shih-men Reservoir watershed.

Context Primates are one of the most charismatic and widely studied vertebrate groups. However, the study of new world primates in green patches within urban areas has been neglected. Such primates have been viewed as a source of human–animal conflict; however, their ecological importance to urban ecosystems and their role in human well being is poorly understood. Aims To increase understanding of both ecological and socioeconomical factors affecting the distribution, density and group sizes of urban marmosets in a large Brazilian city (Belo Horizonte).

Automatic land-cover identification using remote-sensing images is essential for agricultural management and monitoring, which is an ongoing challenge. For permanent crops, which are of great importance economically and environmentally, it becomes even more challenging mainly due to the varying statistics of orchards such as the existence of different orchard types, different crown sizes even for the same type, different distances between orchards among various fields and overlapping crowns.

Context Reptiles, especially snakes, can cause a fear reaction in the public and are, therefore, a good model to examine human–wildlife conflicts. Human city dwellers often respond to the presence of snakes or other reptiles by calling out the responsible agency for animal control, which has to mediate the situation. Aims To determine how the temporal and spatial occurrence of human–reptile conflicts were associated with environmental conditions and socio-economic factors in a large Brazilian city (Belo Horizonte).

The objective of this article is to investigate whether it is possible to use Landsat data together with ancillary data and temporal context to accurately identify land covers found in the fallow areas of Montane Mainland Southeast Asia's (MMSEA's) difficult-to-map swidden landscapes. A rule-based non-parametric hybrid classification method that integrates knowledge about the vegetation regrowth patterns in these landscapes with analysis of Landsat imagery is developed. The method is applied to three upland districts of the Nghe An Province, Vietnam.

The past two decades saw rapid and massive urbanization and industrialization in China. Despite much research has been reportedly done at local and regional scales, little has been reported on the trajectories, patterns, and drivers of these two intertwining processes at the national level. This is mainly due to the fact that until recently, high resolution spatial data of land use and land cover change were not available at national level. The research reported in this paper aimed to fill this knowledge gap.

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.