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.

Biodiversity conservation is a primary function of protected areas. However, protected areas also attract people, and therefore, land use has intensified at the boundaries of these lands globally. In the USA, since the 1970s, housing growth at the boundaries (

China is the most populated country in the world with slightly more than half of the population is still living in rural areas. In the past couple of decades, rapid urbanization and industrialization have significantly changed the land use/land cover (LULC) pattern in rural areas, particularly those around the big cities in eastern China. Shandong Peninsula, a traditional agriculture area, also has witnessed rapid urbanization and industrialization.

Human‐induced land cover change threatens species diversity and ecosystem services. The rapid pace of current change makes predicting species’ declines imperative, but leaves little time for thorough study of all species. One solution is to make generalizations about species’ vulnerability to urbanization based on traits common among studied species in decline. To date, most generalizations about traits associated with species’ declines in response to urbanization are based on presence or abundance, or detailed studies of a few species.

A software application for automatic descriptive feature extraction from image-objects, FETEX 2.0, is presented and described in this paper. The input data include a multispectral high resolution digital image and a vector file in shapefile format containing the polygons or objects, usually extracted from a geospatial database.

Land-cover classification based on multi-temporal satellite images for scenarios where parts of the data are missing due to, for example, clouds, snow or sensor failure has received little attention in the remote-sensing literature. The goal of this article is to introduce support vector machine (SVM) methods capable of handling missing data in land-cover classification.

Characterizations of land-cover dynamics are among the most important applications of Earth observation data, providing insights into management, policy and science. Recent progress in remote sensing and associated digital image processing offers unprecedented opportunities to detect changes in land cover more accurately over increasingly large areas, with diminishing costs and processing time.

Most studies on the reflectance properties of the Earth's surface are addressed estimating the bidirectional reflectance distribution function (BRDF) of high spatial resolution and high spectral resolution satellite measurements. This article assesses the development of broadband (BB) BRDFs from radiances corresponding to large footprints classified according to the International Geosphere-Biosphere Programme (IGBP) land-cover classification.

In-depth statistical analysis of forest transition between land-cover types over time can reveal the dominant signals of landscape transformation, which are needed in order to develop appropriate land management strategies. We applied a recently developed methodology to analyse the transition matrix of six land-cover classes, derived from 1986 and 2002 Landsat images of an area of 15 675 km² in southern Burkina Faso. Results show that most landscape transformations followed a systematic process.