Reference data and accuracy assessments via error matrices build the foundation for measuring success of classifications. An error matrix is often based on the traditional holdout method that utilizes only one training/test dataset. If the training/test dataset does not fully represent the variability in a population, accuracy may be over – or under – estimated. Furthermore, reference data may be flawed by spatial errors or autocorrelation that may lead to overoptimistic results.

Central Asia has experienced drastic socio-economic, geopolitical, and ecological transitions within the last few decades. The USSR collapse in 1991 has led to widespread changes in land cover and land use due to economic and political transformations within the region. Management practices during and after the Soviet era have intensified ecological problems and demands on resources.

On 8 October 2005, a devastating earthquake struck northern Pakistan and several parts of Pakistani- and Indian-controlled Kashmir. The severely hit areas lie in close proximity to the most tectonically active region of the western Himalayas. The earthquake destroyed close to 400 000 houses and over 75 000 people lost their lives. The intensity of the earthquake was such that it triggered widespread landslides, which caused considerable destruction of the area's forests, and blocked the mountain roads and rivers.

The conversion of native habitats to pasture and other working lands, unbuilt lands modified by humans for production, is one of the greatest threats to biodiversity. While some human-dominated landscapes on continents support relatively high native biodiversity, this capacity is little studied in oceanic island systems characterized by high endemism and vulnerability to invasion.

Monitoring land cover over large areas on a yearly basis is challenging. The spatial and temporal consistency of an object-based change detection algorithm was tested through a multi-year application on the forest of Borneo, using SPOT-VEGETATION time series from 2000 to 2008. Continuous change thresholds allowed the tuning of the algorithm according to specific requirements in terms of omission and commission errors.

Land cover is considered to have significant influence on the hydrologic response of a river basin. In this study, we assessed how changes in land cover over time affected flood behaviour from 1988 to 2005, in the Upper Ping River Basin, northern Thailand. We correlated the types of land cover with rainfall–runoff behaviour for smaller and larger flood events taking place during this period. To quantify land cover, nine Landsat 5 TM images taken during the dry season (January or February) were obtained and processed to examine inter-annual land cover changes.

Understanding the rates and causes of land-use change is crucial in identifying solutions, especially in sensitive landscapes and ecosystems, as well as in places undergoing rapid political, socioeconomic or ecological change. Despite considerable concern at the rate of transformation and degradation of the biodiversity-rich Albany Thicket biome in South Africa, most knowledge is gleaned from private commercial lands and state conservation areas.

BACKGROUND: The toxic effects of ultrafine particles (UFP) are a public health concern. However, epidemiological studies on the long term effects of UFP are limited due to lacking exposure models. Given the high spatial variation of UFP, the assignment of exposure levels in epidemiological studies requires a fine spatial scale. The aim of this study was to assess the performance of a short-term measurement protocol used at a large number of locations to derive a land use regression (LUR) model of the spatial variation of UFP in Girona, Spain.

The trace metal (TM) content in arable soils has been monitored across a region of France characterised by a large proportion of calcareous soils. Within this particular geological context, the objectives were to first determine the natural levels of trace metals in the soils and secondly, to assess which sites were significantly contaminated. Because no universal contamination assessment method is currently available, four different methods were applied and compared in order to facilitate the best diagnosis of contamination.

Remote-sensing technology provides a powerful means for land use/land cover (LU/LC) monitoring at global and regional scales. However, it is more efficient and effective to combine remote-sensing measurements with a geographic information system (GIS) database and expert knowledge for change updating than to use remote-sensing technology alone. In this article, these different sources of information are integrated in the proposed framework, which is able to provide rapid updating of LU/LC information.

In automatic/semiautomatic mapping of land use/cover using very high resolution remote-sensing imagery, the major challenge is that a single class of land use contains ground targets with varied spectral values, textures, geometries and spatial features. Here we present an object-oriented strategy for automatic/semiautomatic classifications of land use/cover using very high resolution remote-sensing data. The strategy consists of character detecting, object positioning and coarse classification, then refining the classification result step by step.

Soils with pedogenic carbonate cover about 30% (3.44 x 10⁶ km²) of China, mainly across its arid and semiarid regions in the Northwest. Based on the second national soil survey (1979-1992), total soil inorganic carbon (SIC) storage in China was estimated to be 53.3±6.3 PgC (1 Pg=10¹⁵ g) to the depth investigated to 2 m. Soil inorganic carbon storages were 4.6, 10.6, 11.1, and 20.8 Pg for the depth ranges of 0-0.1, 0.1-0.3, 0.3-0.5, and 0.5-1 m, respectively. Stocks for 0.1, 0.3, 0.5, and 1 m of depth accounted for 8.7%, 28.7%, 49.6%, and 88.9% of total SIC, respectively.