Spatial variation is well known to exist in water quality parameters of the Great Lakes nearshore; however, strong patterns for extended reaches also have been observed and found to be robust across seasonal time frames. Less is known about robustness of inter-annual variation within parameters for water quality in the nearshore.

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.

To conserve and manage wetland resources, it is important to inventory and monitor wetlands and their adjacent uplands. Satellite remote sensing has several advantages for monitoring wetland resources, especially for large geographic areas. This review summarizes the literature on satellite remote sensing of wetlands, including what classification techniques were most successful in identifying wetlands and separating them from other land cover types. All types of wetlands have been studied with satellite remote sensing.

We suggest a classificatory approach for land cover analysis that integrates fuzzy set theory with permutation techniques. It represents a non parametric alternative and/or a complement of traditional multivariate statistics when data are scarce, missing, burdened with high degree of uncertainty and originated from different sources and/or times. According to this approach, the Operational Geographic Units (OGUs) in which landscape is subdivided and sampled are classified with hierarchical clustering methods.

The increased integration of ecosystem service concepts into natural resource management places renewed emphasis on prediction and mapping of fish biomass as a major provisioning service of rivers. The goals of this study were to predict and map patterns of fish biomass as a proxy for the availability of catchable fish for anglers in rivers and to identify the strongest landscape constraints on fish productivity.

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.