A numerical research is carried out to investigate the regional impact of extreme wind-induced dust transport in the central-northern part of Mexico. In boreal winter, strong wind soil erosion processes occur in the arid zones of the Mexican highlands, as a consequence of land use change and land cover change. The effect of land use change and land cover change has consequences in the atmospheric circulation, by altering the balance in solar radiation, albedo, soil moisture and texture, aerodynamic roughness and other surface properties.

After summarizing experience of various countries in the field of landscape monitoring, it appears that the real changes of landscape have to be observed in a large scale (not smaller than 1:10,000) in order to avoid generalization of small landscape elements.

A major challenge in modeling the carbon dynamics of vegetation communities is the proper parameterization and calibration of eco-physiological variables that are critical determinants of the ecosystem process-based model behavior. In this study, we improved and calibrated a biochemical process-based WxBGC model by using in situ AmeriFlux eddy covariance tower observations. We simulated carbon dynamics of fire-dominated forests at tower sites and upscaled the tower site-based simulations to regional scale for the New Jersey Pinelands using LANDSAT-ETM land cover and DAYMET climate data.

Airborne Laser Swath Mapping instrument technology and subsequent algorithm advances have made it possible over the last few years to map the Earth's surface and land cover at unprecedented resolution. The ability of Airborne Laser Swath Mapping technology to densely sample ground elevations beneath forest canopies is particularly important because forested watersheds have traditionally been difficult to study with remote sensing techniques.

A standard methodology is needed to recognize potentially suitable areas for sustainable bioenergy crop production. This facilitates better identification of promising crops and cropping systems, logistical and economic studies, and work needed to meet regulatory criteria. A possible approach is built upon three layers of internationally available spatial data: (1) degrading and abandoned areas, (2) potentially suitable land cover classes, (3) exclusion zones such as nature reserves and areas of high biodiversity.

Persistent organic pollutants (POPs) impact upon human and animal health and the wider environment. It is important to determine where POPs are found and the spatial pattern of POP variation. The concentrations of 90 molecules which are members of four families of POPs and two families of herbicides were measured within a region of Northern France as part of the French National Soil Monitoring Network (RMQS: Réseau de Mesures de la Qualité des Sols).

The objective of this article is to develop and test a methodology capable of using medium spatial resolution satellite imagery to improve forest-area statistics derived from ground sampling. The methodology builds on the evidence that multitemporal Normalized Difference Vegetation Index (NDVI) images bring significant information on the spatial distribution of forest surfaces. Consequently, Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI images are potentially useful to improve forest-area assessment based on ground data.

Land cover identification and monitoring agricultural resources using remote sensing imagery are of great significance for agricultural management and subsidies. Particularly, permanent crops are important in terms of economy (mainly rural development) and environmental protection.

In support to the Remote Sensing Survey of the global Forest Resource Assessment 2010, the TREES-3 project has processed more than 12,000 Landsat TM and ETM+ data subsets systematically distributed over the tropics. The project aims at deriving area estimates of tropical forest cover change for the periods 1990–2000–2005.

For EU-Mediterranean olive groves (Olea europaea), mapped in CORINE, classes of olive farming intensities were derived from the outcome of a multi-temporal remote sensing vegetation dynamics analysis. The management intensity classes were developed in view of a differentiated accounting of olive groves when delineating High Nature Value Farmland areas (HNV) at pan-European level. The remote sensing input data used was the Green Vegetation Fraction (GVF), derived in 10-day intervals from a long-term time series of NOAA AVHRR data.

A geoinformatics-based land suitability assessment approach was used to examine the prospects for precision farming in the Savanna River basin in Nigeria. The procedure involved the overlay analysis of land use, land cover characteristics interpreted on Landsat Thematic Mapper (TM) imagery and physico-chemical soil properties and nutrient attributes in line with FAO suitability guidelines and models. The empirical analysis revealed areas of high, moderate and marginal suitability for the cultivation of maize, yam, cassava and oil palm in the basin.

Insular Southeast Asia experienced the highest level of deforestation among all humid tropical regions of the world during the 1990s. Owing to the exceptionally high biodiversity in Southeast Asian forest ecosystems and the immense amount of carbon stored in forested peatlands, deforestation in this region has the potential to cause serious global consequences. In this study, we analysed deforestation rates in insular Southeast Asia between 2000 and 2010 utilizing a pair of 250 m spatial resolution land cover maps produced with regional methodology and classification scheme.