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.

The Chilan Mountain cypress forest, northeastern Taiwan, is the only one where the genus Chamaecyparis is situated in a subtropical region. The health of a forest ecosystem is closely tied to the evapotranspiration (ET) of water through forests. This study focused on estimating the ET of old-growth cypress in the Chilan Mountain area and investigated its spatial variability in different watershed divisions using remote sensing.

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.

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.