Land use change has a major impact on goods and services that our environment supplies for society. While detailed ecological or biophysical field studies are needed to quantify the exact amount of ecosystem service supply at local scales, such a monitoring might be unfeasible at the regional scale. Since field scale monitoring schemes for ecosystem services or ecosystem functioning are missing, proxy based indicators can help to assess the historic development of ecosystem services or ecosystem functioning at the regional scale.

Pixel-based and object-oriented processing of Chinese HJ-1-A satellite imagery (resolution 30 m) acquired on 23 July 2009 were utilized for classification of a study area in Budapest, Hungary. The pixel-based method (maximum likelihood classifier for pixel-level method (MLCPL)) and two object-oriented methods (maximum likelihood classifier for object-level method (MLCOL) and a hybrid method combining image segmentation with the use of a maximum likelihood classifier at the pixel level (MLCPL)) were compared. An extension of the watershed segmentation method was used in this article.

Land use change and landscape patterns have a large effect on land productivity and ecosystem biodiversity. Based on geographical information system technology and remote sensing data related to land use and land cover of Jiangsu and Zhejiang provinces and Shanghai (Jiang-Zhe-Hu area), we analyzed patterns of landscape change and predicted land use dynamics using the CA-MARKOV model. We also analyzed the conversion rate and area among landscape classes using the CA-Markov model.

In this article, a new index, the visible and shortwave infrared drought index (VSDI), is proposed for monitoring both soil and vegetation moisture using optical spectral bands. VSDI is defined as , where ρ represents the reflectance of shortwave infrared (SWIR) red and blue channels, respectively. VSDI is theoretically based on the difference between moisture-sensitive bands (SWIR and red) and moisture reference band (blue), and is expected to be efficient for agricultural drought monitoring over different land-cover types during the plant-growing season.

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.

Preserving large tracts of natural habitats is essential to maintain biodiversity. Nevertheless, even large areas may still suffer from less “visible” impacts such as loss of ecological processes. Because mapping ecological processes over large scales is not practical, an alternative is to map surrogate species that are key for those processes.

Fine particulate matter (PM₂.₅) is the major air pollutant in Beijing, posing serious threats to human health. Land use regression (LUR) has been widely used in predicting spatiotemporal variation of ambient air-pollutant concentrations, though restricted to the European and North American context. We aimed to estimate spatiotemporal variations of PM₂.₅by building separate LUR models in Beijing. Hourly routine PM₂.₅measurements were collected at 35 sites from 4th March 2013 to 5th March 2014.

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.