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.

The impact on temperature of the urban growth in central Mexico from 1993 to 2009 and the sensitivity of forecast to change in land cover are studied using high resolution numerical simulations. The mesoscale atmospheric Weather Research and Forecasting model (WRF) uses Global Land Cover Characteristics (GLCC) data created from NOAA-AVHRR satellite images from 1992 and 1993.

Anthropogenic changes in land use and cover (LULC) in stream catchments can alter the composition of riparian plant communities, which can affect ecosystem functions of riparian areas and streams from local to landscape scales. We conducted a study to determine if woody plant species composition and abundance along headwater streams were correlated with categorical and continuous LULC and environmental variables along an urban-to-rural gradient.

During the last few decades, land use changes have largely affected the global warming process through emissions of CO₂. However, C sequestration in terrestrial ecosystems could contribute to the decrease of atmospheric CO₂rates. Although Mediterranean areas show a high potential for C sequestration, only a few studies have been carried out in these systems. In this study, we propose a methodology to assess the impact of land use and land cover change dynamics on soil organic C stocks at different depths.

Paired watersheds are used to develop a deciduous nutrient uptake stoichiometry. The watersheds are those of the House Rock Run and the Brubaker Run located in the Pennsylvania Appalachian Piedmont, USA. These two watersheds are nearly identical with respect to bedrock, regolith, climate, geomorphology, morphometry, baseflow hydrology, and type and successional stage of forest vegetation. They only differ by the percentage of deciduous forest cover, with House Rock Run having 59% and Brubaker Run having 76%.

We tested the possibility of using digital, color aerial ortho-photographs and monoscopic, panchromatic satellite images of comparable spatial and radiometric resolution, to map recent landslides in Italy and to update existing measures of landslide mobilization. In a 90-km² area in Umbria, central Apennines, rainfall resulted in abundant landslides in the period from September 2004 to June 2005. Analysis of the rainfall record determined the approximate dates of landslide occurrence and revealed that the slope failures occurred in response to moderately wet rainfall periods.

Satellite remote sensing has become a popular tool to analyze agricultural drought through terrestrial vegetation health conditions using the normalized difference vegetation index (NDVI). Drought monitoring techniques using remote sensing-based drought indices assume that vegetation conditions vary year-to-year due to prevailing weather conditions (e.g., precipitation and temperature), and current conditions are evaluated based on the deviation from the long-term statistics such as mean, minimum, or maximum.

Tidal salt marshes in the San Francisco Estuary region display heterogeneous vegetation patterns that influence wetland function and provide adequate habitat for native or endangered wildlife. In addition to analyzing the extent of vegetation, monitoring the dynamics of vegetation pattern within restoring wetlands can offer valuable information about the restoration process.

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.