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.

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.

The spatio-temporal variation underlies and complicates studies related to earth surface processes. Subsequent to our previous studies reporting the temporal invariability of sediment concentration, this study further reports the spatial invariability of sediment concentration or the flow–sediment relationship in the hilly part of the Chinese Loess Plateau. The dense channel network dissects this area into numerous sub-watersheds, which are composed of entire slopes (a combination of the hill slope and the valley side slope).

Studies of land surface dynamics in heterogeneous landscapes often require remote sensing datawith high acquisition frequency and high spatial resolution. However, no single sensor meets this requirement. This study presents a new spatiotemporal data fusion method, the Flexible Spatiotemporal DAta Fusion (FSDAF) method, to generate synthesized frequent high spatial resolution images through blending two types of data, i.e., frequent coarse spatial resolution data, such as that from MODIS, and less frequent high spatial resolution data such as that from Landsat.

Coastal shorelines are naturally dynamic, shifting in response to coastal geomorphological processes. Globally, land use change associated with coastal urban development and growing human population pressures is accelerating coastal shoreline change. In southern Vietnam, coastal erosion currently is posing considerable risks to shoreline land use and coastal inhabitants.

Historic land‐cover/use change is important for studies on climate change, soil carbon, and biodiversity assessments. Available reconstructions focus on the net area difference between two time steps (net changes) instead of accounting for all area gains and losses (gross changes). This leads to a serious underestimation of land‐cover/use dynamics with impacts on the biogeochemical and environmental assessments based on these reconstructions.

Mesoamerican biodiversity is increasingly threatened by anthropogenic destruction of natural land cover. Habitat degradation and climate change are primary threats to specialized forest odonate species that are important model organisms for forest health and defining conservation units. The extreme niche specialization of Megaloprepus caerulatus, the world’s largest extant odonate, makes it well suited as an indicator for changing environmental conditions.

This paper explores which physical landscape components relate to subjective landscape dimensions. The ways in which people describe their surrounding cultural landscape was analyzed through an assessment of their representations of it. A special focus was placed on assessing the role of land cover as a means to communicate landscape meanings regarding a specific geographical region. The methodological framework was built on the basis of a questionnaire survey, multivariate statistical analysis and mapping approaches.