Land cover conversion intensively occurred in the Tibetan Plateau of China during the past decades. However, responses of soil properties and soil microbial activities to land cover conversion under different land cover types have not been fully understood. The objective was to assess the effects of land cover conversion on soil C and N stocks and soil microbial properties of topsoil of an alpine meadow in the Tibetan Plateau. Soil cores of surface soil (0–20 cm) were collected from three adjacent land cover types: native alpine meadow, artificial grassland and mound-shaped denuded land.

Vegetation models are essential tools for projecting large-scale land-cover response to changing climate, which is expected to alter the distribution of biomes and individual species. A large-scale bioclimatic envelope model (RuBCliM) and an individual species based gap model (UVAFME) are used to simulate the Russian forests under current and future climate for two greenhouse gas emissions scenarios. Results for current conditions are compared between models and assessed against two independent maps of Russian forest biomes and dominant tree species.

Climate change is affecting species distribution, composition of biological communities, and species traits. Despite the growing body of knowledge on the reaction of species to climate change, the potentially delayed response of species is still severely understudied. In this paper we modelled the time needed by ground‐living invertebrates to effectively react to habitat modification induced by climate change in relation to dispersal abilities.

Land-use and land-cover change profoundly affect human well-being and, therefore, have become a major topic for society. A thorough understanding of past and present processes transforming landscapes is essential for guiding future developments toward the sustained provision of the ecosystem services humans critically depend upon. Drawing on the driving forces and resilience frameworks, we identify possible variables and patterns of land change, connecting them to empirical findings in three case study areas in the Swabian Alb region, southwestern Germany.

A framework for preparation of an early warning system was developed for Maryland, using a GIS database and a collective overlay of maps that highlight highway slopes susceptible to soil slides or slope failures in advance through spatial and statistical analysis. Data for existing soil slope failures was collected from geotechnical reports and field visits. A total of 48 slope failures were recorded and analyzed. Six factors, including event precipitation, geological formation, land cover, slope history, slope angle, and elevation were considered to affect highway soil slope stability.

The paper provides a coherent pattern identification analysis of the impacts of coastal land use and land cover (LULC) on evapotranspiration (ET) under the impact of seawater intrusion. The study applied the Landsat satellite data to characterize the LULC at Laizhou Bay, Shandong Province, China. Then, the ET and heat fluxes were estimated using the surface energy balance algorithm for land model with two-time phase thermal infrared band images and regional surface parameters.

Since China's economic reform in the late 1970s, Shanghai, the country's largest and most modern city, has experienced rapid expansion and urbanization. Here, we explore its land‐use and land‐cover changes, focusing on the impacts of the urbanization process on air and water quality, local climate, and biodiversity. Over the past 30 years, Shanghai's urban area and green land (eg urban parks, street trees, lawns) have increased dramatically, at the expense of cropland.

Widespread and increasing urbanization has resulted in the need to assess, monitor, and understand its effects on stream water quality. Identifying relations between stream ecological condition and urban intensity indicators such as impervious surface provides important, but insufficient information to effectively address planning and management needs in such areas.

Water resource conservation is of utmost importance, especially for agriculture in developing countries. Frequent occurrences of water shortage have driven more social efforts in researching on water resources spatial distribution, as the land cover changes recently have shown positive influences. For the purpose of efficient water resources management, hydrological processes under different types of land covers and soil textures are supposed to be accurately analyzed and evaluated.

The confusion matrix is the standard way for reporting the accuracy of land cover and other information classified from remote-sensing imagery. This letter describes a geographically weighted method for generating spatially distributed measures of accuracy (overall, user and producer accuracies) from a logistic geographically weighted regression. A kernel-based approach defines the data and weights that are used to calculate the accuracies at each location in the study area.

In this study, a terrain classification algorithm is presented that was derived from various properties of the returned full waveform signals collected from the Ice, Cloud and land elevation Satellite (ICESat) mission. Such an algorithm would be beneficial for current and future studies of the cryosphere, particularly Greenland and Antarctica, by helping to identify changes in the large scale surface properties over time. The algorithm developed was validated over a test region in the Dry Valleys of Antarctica, where the terrain is well known and regularly monitored.

Lidar and orthophotograph-derived land cover are combined with in situ vegetation measurements to assess habitat characteristics typifying four species of butterflies with differing habitat preferences across a large spatial extent (∼30,000 ha) in northern Idaho, USA. Lidar data are employed to characterize both vegetation structure and topography, whereas digital orthophotographs and in situ vegetation measurements are employed to quantify surrounding land use and larval host plant cover, respectively.