Accurate identification of urban land use is essential for many applications. However, as physical surfaces of land-cover types are not necessarily related to their use and economic function, differentiating among thematically detailed urban land uses (single-family residential, multi-family residential, commercial, industrial, etc.) using remotely sensed imagery is a challenging task, particularly over large areas.

The purpose of this study was to investigate the spatial distribution of annual soil loss in the Danjiangkou Reservoir Region, China. Soil erosion was estimated by integrating the universal soil loss equation (USLE) model with GIS. The main factors affecting soil erosion including rainfall erosivity, soil erodibility, slope length and steepness, cover and management factor, and conservation supporting practice factor were determined from precipitation data, soil sample analysis, digital elevation model, land use and land cover, and empirical assessment, respectively.

The wild boar is the target species selected for developing a GIS model of potential habitat for big game species, mainly using many GIS layers and kilometric abundance indices (KAI). We identify and weight environmental factors that determine the suitability for wild boar populations in a Mediterranean region, highly influenced by urban and agro-forestry activities. Marina Baja region (Spain) is selected to make a regional analysis. In the GIS modelling process, a suitability value is assigned to each pixel, which represents the habitat preference of the species.

This study combined uncertainty analysis of carbon emissions with local stakeholders' perspectives to develop an effective Reducing Emission from Deforestation and Degradation (REDD+) scheme at the district level. Uncertainty of carbon emission estimates depends on scale while local stakeholders' views on plausible REDD+ schemes influence and limit transaction costs. The uncertainty analysis formed the basis for determining an appropriate scale for monitoring carbon emission estimates as performance measures for REDD+ incentives.

Water consumed through evapotranspiration (ET) impacts local and regional hydrologic regimes on various spatial and temporal scales. Estimating ET in the Great Plains is a prerequisite for effective regional water resource management of the Ogallala (High Plains) Aquifer, which supplies vital water resources in the form of irrigation for extensive agricultural production.

The active remote-sensing technique differential radar interferometry (D-InSAR) is a powerful method for detection and deformation monitoring of landslides. But the radar-specific imaging geometry causes specific spatial distortions in radar images (as e.g. the layover and shadowing effect), which have a negative impact on the suitability of these images for D-InSAR applications. To address this issue, we present a geographical information system (GIS) procedure to accurately predict the areas in which layover and shadowing will occur, before the area of interest is recorded by radar.

The cold regions hydrological model (CRHM) platform, a physically based hydrological model using a modular and object-oriented structure, has been applied for simulating the redistribution of snow by wind, snowmelt, infiltration, evapo-transpiration, soil moisture balance, surface depression storage and run-off routing. Land use and land cover classification is a preprocessing procedure to provide the required parameters for CRHM. Per-pixel-based and object-oriented classifications are the two major classification approaches currently in practice.

Extreme droughts, heat waves, frosts, precipitation, wind storms and other climate extremes may impact the structure, composition and functioning of terrestrial ecosystems, and thus carbon cycling and its feedbacks to the climate system. Yet, the interconnected avenues through which climate extremes drive ecological and physiological processes and alter the carbon balance are poorly understood. Here, we review the literature on carbon cycle relevant responses of ecosystems to extreme climatic events.

The effect of using spectral transform images as input data on segmentation quality and its potential effect on products generated by object-based image analysis are explored in the context of land cover classification in Accra, Ghana. Five image data transformations are compared to untransformed spectral bands in terms of their effect on segmentation quality and final product accuracy. The relationship between segmentation quality and product accuracy is also briefly explored.

A key driver of biodiversity loss is human landscape transformation. Change detection and trajectory analysis are frequently applied methods for studying landscape change. We studied to what degree habitat-specific change detection and trajectory analysis provide different information on landscape change compared to the analysis with land-cover statistics. Our research was carried out at two spatial scales (regional, 1800 km2, 360 random points; local, 23 km2, polygon-based maps) in the Kiskunság, Hungary.

In this letter, we present the methodology and accuracy assessment of a new regional 250-m spatial resolution land cover map of insular Southeast Asia. Nearly 500 daily Moderate Resolution Imaging Spectroradiometer images (acquired 2 January–3 July 2010) were used in the production of the map. Additionally, peatland maps, elevation information and Daichi-Advanced Land Observing Satellite mosaic data were utilized in the mapping process.