Coastal wetland vegetation classification with remotely sensed data has attracted increased attention but remains a challenge. This paper explored a hybrid approach on a Landsat Thematic Mapper (TM) image for classifying coastal wetland vegetation classes. Linear spectral mixture analysis was used to unmix the TM image into four fraction images, which were used for classifying major land covers with a thresholding technique. The spectral signatures of each land cover were extracted separately and then classified into clusters with the unsupervised classification method.

Accurate spatio-temporal predictions of land-cover are fundamentally important for assessing geomorphological and ecological patterns and processes. This study quantifies the epistemic uncertainty in the species distribution modeling, which is generated by spatio-temporal gaps between the biogeographical data, model selection and model complexity. Epistemic uncertainty is generally given by the sum of subjective and objective uncertainty. The subjective uncertainty generated by the modeler-choice in the manipulation of the environmental variables was analyzed.

In Central Asia, water is a particularly scarce and valuable good. In many ecosystems of this region, the vegetation development during the growing season is dependent on water provided by rainfall. With climate change, alterations of the seasonal distribution of precipitation patterns and a higher frequency of extreme events are expected. Vegetation dynamics are likely to respond to these changes and thus ecosystem services will be affected.

Development and agriculture are increasingly encroaching into riparian areas, with largely unknown effects on nearshore arthropods, which are important components of linked aquatic–terrestrial food webs.

This paper examines environmental disturbances related to energy development in the Orenburg region through a two-pronged approach. First, it ranks environmental performance per 25 administrative districts based on multidimensional clustering of 10 diagnostic indicators. Second, it examines land-use and land-cover (LULC) patterns in nine western administrative districts by classifying Landsat imagery and quantifying surface disturbance for the year 2001. Spatial analysis and descriptive statistics help identify the most disturbed administrative units.

Fire regimes depend on climate, vegetation structure and human influences. Climate determines the water content in fuel and, in the longer term, the amount of biomass. Humans alter fire regimes through increased ignition frequency and by hindering the spread of fire through fire suppression and fuel fragmentation. Here, we present FIRE LADY (FIre REgime and LAndscape DYnamics), a spatially explicit fire regime model that takes into account daily weather data, topography, vegetation growth, fire behaviour, fire suppression and land use changes.

Understanding the effects of land-cover alterations on ecosystem functioning has become a major challenge in ecological research during the last decade. This has stimulated a rapid growth in research investigating the links between land-cover change and biotic interactions, but to date no study has evaluated the progress towards achieving this scientific goal.

The present study was conducted using secondary database, remote sensing, geographical information system (GIS) and multivariate analysis tools in order to develop Multiple Linear Regression (MLR) models that could be able to predict level of water quality variables using compositional and spatial attributes of land cover in the river basins. The study encompasses 21 river basins with 32 000 Km² area, located in the Chugoku district in West Japan.

An accurate estimate of carbon fluxes associated with tropical deforestation from the last two decades is needed to balance the global carbon budget. Several studies have already estimated carbon emissions from tropical deforestation, but the estimates vary greatly and are difficult to compare due to differences in data sources, assumptions, and methodologies. In this paper, we review the different estimates and datasets, and the various challenges associated with comparing them and with accurately estimating carbon emissions from deforestation.

The relationship between land cover patterns and surface temperature was examined using random forest as well as simple linear regression for two urban sites in Denver, Colorado, USA. Among four land cover types of buildings, trees, grass, and roads and parking lots, only trees and roads and parking lots show significant spatial metrics affecting surface temperature using both the methods.

The breakdown of socialism caused massive socio-economic and institutional changes that led to substantial agricultural land abandonment. The goal of our study was to identify the determinants of agricultural land abandonment in post-Soviet Russia during the first decade of transition from a state-controlled economy to a market-driven economy (1990–2000). We analyzed the determinants of agricultural land abandonment for approximately 150,550km² of land area in the provinces (oblasts) of Kaluga, Rjazan, Smolensk, Tula and Vladimir in European Russia.

Environmental change can be monitored and assessed by analyzing changes in bird populations. This study employed multivariate factorial kriging (MFK) to determine the multiscale changes in the distribution of five commonly observed bird species to urban environmental change. Areas of metropolitan Taipei were analyzed to determine the extent and effect of land cover. A digital elevation model and normalized difference vegetation index were also constructed. Spatial patterns of variation in bird populations were analyzed by MFK at a regional scale (16 km) and local scale (2 km).