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.

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.

Mangroves in Kenya provide a wide range of valuable services to coastal communities despite their relatively small total area. Studies at single sites show reductions in extent and quality caused by extraction for fuel wood and timber and clearance for alternative land use including saltpans, aquaculture, and tourism. Such studies suggest that Kenyan mangroves are likely to conform to the general global trend of declining area but there are no reliable recent estimates of either total mangrove extent or trends in coverage for the country.

The mean shift (MS) algorithm is based on a statistical approach to the clustering problem. Specifically, the method is a variant of density estimation. We revisit in this article the MS paradigm and its use for clustering of remotely sensed images. Specifically, we investigate further the classification accuracy of remotely sensed images as a function of various MS parameters, such as the variant used, kernel type, dimensionality, kernel bandwidth, etc.

The Soil and Water Assessment Tool (SWAT) has been used for assessing the impact of land cover and land management changes on water resources for a wide range of scales and environmental conditions across the globe. However, originally designed for temperate regions, SWAT must be critically examined for its appropriate use in tropical watersheds. One major concern is the simulation of perennial tropical vegetation due to the absence of dormancy.