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.

The last decade has seen a remarkable increase in the number of modeling tools available to examine future land-use and land-cover (LULC) change. Integrated modeling frameworks, agent-based models, cellular automata approaches, and other modeling techniques have substantially improved the representation of complex LULC systems, with each method using a different strategy to address complexity. However, despite the development of new and better modeling tools, the use of these tools is limited for actual planning, decision-making, or policy-making purposes.

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.

Migration and population growth lead in coastal zones, especially in developing countries like Benin in Western Africa, to extreme land use pressure, causing ecological as well as land cover and land use changes, socio-economic modifications, and conflicts of interest and generational conflicts. To detect those fast-moving processes area-wide remains almost impossible in developing countries due to the lack of official statistics, often restricted remote sensing data, and limited financial resources.

Remote-sensing data for protected areas in northern Togo, obtained in three different years (2007, 2000, and 1987), were used to assess and map changes in land cover and land use for this drought prone zone. The normalized difference vegetation index (NDVI) was applied to the images to map changes in vegetation. An unsupervised classification, followed by classes recoding, filtering, identifications, area computing and post-classification process were applied to the composite of the three years of NDVI images.

An understanding of changes in Amazonian landscapes depends on documentation about alterations in land cover. This article highlights the efforts of the Large Scale Biosphere-Atmosphere Experiment in Amazônia (LBA) related to the topic. In particular, a longitudinal study has analyzed the social and biophysical dimensions of land use/land cover, using a multi-scalar georeferenced approach. The study areas represent a gradient of soil fertility in Amazônia and include distinct landscape mosaics, from the Amazon estuary and the Bragantina region to northeastern Rondônia.

Land degradation and global warming are currently highly active research topics. Land degradation can both change land cover and surface climate and significantly influence atmospheric circulation. Researches have verified that carbon dioxide (CO₂) and methane (CH₄) are major greenhouse gases (GHG) in the atmosphere and are directly affected by human activity. However, to date, there is no research on the spatial distribution of GHG concentrations and also no research on how land degradations affect GHG concentrations in arid and semi-arid regions.

Soil and water conservation measures including terracing, afforestation, construction of sediment‐trapping dams, and the ‘Grain for Green Program’ have been extensively implemented in the Yanhe River watershed, of the Loess Plateau, China, over the last six decades, and have resulted in large‐scale land use and land cover changes. This study examined the trends and shifts in streamflow regime over the period of 1953–2010 and relates them to changes in land use and soil and water conservation and to the climatic factors of precipitation and air temperature.