The paper presents a case study in which land use strategies to mitigate Climate Change effects are developed for a model in Saxony, Germany. In this region, the degree of freedom to respond to Climate Change with land-cover changes is very small. Based on a participatory process, an approach was developed to extend land-cover classes (e.g. forest, agriculture) by land management classes. In this paper, the focus is on the forest management classification.

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.

With the increasing availability of European and global forest maps, users are facing the difficult choice to select the most appropriate map for their purposes. Many of these maps are potential input datasets for forest-related applications for the European Union (EU), due to their spatial extent and harmonised approach at the European level. However, they possess different characteristics in terms of spatial detail or thematic accuracy. Little attention has been paid to the effect of these characteristics on simulation models and the resultant policy implications.

Over the past decades, the Gujarat state of India experienced intensive agricultural and industrial activities, fertilizer consumption and abstraction of groundwater, which in turn has degraded the ground water quality. Protection of aquifers from nitrate pollution is a matter of prime concern for the planners and decision-makers. The present study assessed the spatial and temporal variation of groundwater nitrate levels in areas with different land use/land cover activities for both pre- and post-monsoon period.

An improved Carnegie Ames Stanford Approach (CASA) model based on two kinds of remote sensing (RS) data, Landsat Enhanced Thematic Mapper Plus (ETM +) and Moderate Resolution Imaging Spectro-radiometer (MODIS), and climate variables were applied to estimate the Net Primary Productivity (NPP) of Xuzhou in June of each year from 2001 to 2010. The NPP of the study area decreased as the spatial scale increased. The average NPP of terrestrial vegetation in Xuzhou showed a decreasing trend in recent years, likely due to changes in climate and environment.

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.