Iberian dehesa and montado are paradigmatic high nature value (HNV) agroforestry systems in Europe. Nevertheless their conservation status is uncertain as a consequence of their typological variety, different intensity of management practices on the ground, and other ongoing processes challenging their long-term sustainability. The existing broad gradients of dehesa and montado types impose difficulties in estimating not only their distribution and extent, but also their condition, since probably not all these agroforestry systems should readily be considered as HNV.

Cucumber (Cucumis sativus L.) is among the plants highly dependent on insectmediated pollination, but little is known about its unmanaged pollinators. Both domestic and wild bee populations in central Wisconsin pickling cucumber fields were assessed using a combination of pan trapping and floral observations before and during bloom. Together with land cover analyses extending 2,000 m from field centers, the relationship of land cover components and bee abundance and diversity were examined.

Montado decline has been reported since the end of the nineteenth century in southern Portugal and increased markedly during the 1980s. Consensual reports in the literature suggest that this decline is due to a number of factors, such as environmental constraints, forest diseases, inappropriate management, and socioeconomic issues. An assessment on the pattern of montado distribution was conducted to reveal how the extent of land management, environmental variables, and spatial factors contributed to montado area loss in southern Portugal from 1990 to 2006.

The transition of land between forest and grassland has important implications for greenhouse gas emissions and removals. In this paper, we use New Zealand as a case study to comprehensively assess, compare and quantify the net climate change impact of shifting land use between temperate forest and grassland. Forests store large amounts of carbon in their biomass, whereas grasslands contain relatively little biomass carbon. These biomass changes tend to dominate the carbon balance under land-use change.

Future land-use changes are predicted to be influenced by both climate-driven environmental changes and concomitant changes in local economic conditions. Assessing the impact of climate change on ecosystems, and the goods and services that they provide, therefore requires an understanding of the dynamic link between land-cover, ecosystem services and economic-driven land-use decisions. The economic land allocation model (ALUAM) simulates the competition between forest and a range of agricultural land-uses to estimate land-use conversions in a spatially explicit manner at high resolution.

The dynamics, degradation, and conservation of forest ecosystems are matters of prime concerns worldwide at the present. Proper planning and management of a forest area are essentially needed to protect it from the grasp of burgeoning pressure of urban-industrial sprawl. Establishment of eco-sensitive zones (ESZs), which act as buffer areas around the core forests, is one of the key approaches towards achieving this goal.

Urbanization is one phenomena that drives land use pattern change. Persistent rapid urbanization is associated with depletion of natural resources and worsening conditions in the urban environment. Monitoring urban development is, therefore, an absolute necessity in order to assure sustainable cities in the future. The main objective of this paper is to develop and apply an urban growth potential model incorporating spatial metrics. The model has been tested in Jinan City, China. Firstly, two satellite images (1989 and 2004 SPOT) were used to extract the land-cover.

The UAE has witnessed rapid urban development and economic growth in recent years. With its ambitious vision to become one of the advanced nations by 2021, planners and policy-makers need to know the most likely direction of future urban development. In this study, remotely sensed imagery coupled with cellular automata models were used to predict land cover in Al Ain, the second largest city in the Emirate of Abu Dhabi. Markov and cellular automata models were used for 1992 and 2006 to predict land cover in 2012.

Land cover changes affect local surface energy balances by changing the amount of solar energy reflected, the magnitude and duration over which absorbed energy is released as heat, and the amount of energy that is diverted to non-heating fluxes through evaporation. However, such local influences often are only crudely included in climate modeling exercises, if at all. A better understanding of local land conversion dynamics can serve to inform inputs for climate models and increase the role for land use planning in climate management policy.

This paper proposes an automatic framework for land cover classification. In majority of published work by various researchers so far, most of the methods need manually mark the label of land cover types. In the proposed framework, all the information, like land cover types and their features, is defined as prior knowledge achieved from land use maps, topographic data, texture data, vegetation’s growth cycle and field data.

Lake Chad in Africa experienced severe droughts in the 1970s and 1980s and overexploitations of water resulting in a decline of water level in the Lake and surrounding rivers. Such droughts and overexploitation of water caused a significant change of land use and water management practices over the last 50� years. Understanding the change of land use and land cover is, therefore, crucial to understand disturbance of the water cycle around the Lake.

CO₂ consumption by chemical weathering is an integral part of the boundless carbon cycle, whose spatial patterns and controlling factors on continental scale are still not fully understood. A dataset of 338 river catchments throughout North America was used to empirically identify predictors of bicarbonate fluxes by chemical weathering and interpret the underlying controlling factors. Detailed analysis of major ion ratios enables distinction of the contributions of silicate and carbonate weathering and thus quantifying CO₂ consumption.