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.

This study aimed at clarifying the relationship between the dynamics of land use/land cover (LULC) changes and decline in the groundwater levels, and specifying an LULC category strongly affecting such decline in a Quaternary sedimentary basin. Groundwater level data recorded at 26 observation wells for a 14-year period in the Kumamoto Plain, central Kyushu, southwest Japan, were used for the analysis. The general trends of LULC were detected by a satellite image classification technique and surface spline method, which highlighted the decreases in groundwater-recharge materials.

Inland waters transport large amounts of dissolved organic matter (DOM) from terrestrial environments to the oceans, but DOM also reacts en route, with substantial water column losses by mineralization and sedimentation. For DOM transformations along the aquatic continuum, lakes play an important role as they retain waters in the landscape allowing for more time to alter DOM. We know DOM losses are significant at the global scale, yet little is known about how the reactivity of DOM varies across landscapes and climates. DOM reactivity is inherently linked to its chemical composition.

Urbanization is becoming increasingly important in terms of climate change and ecosystem functionality worldwide. We are only beginning to understand how the processes of urbanization influence ecosystem dynamics and how peri-urban environments contribute to climate change. Brisbane in South East Queensland (SEQ) currently has the most extensive urban sprawl of all Australian cities. This leads to substantial land use changes in urban and peri-urban environments and the subsequent gaseous emissions from soils are to date neglected for IPCC climate change estimations.

Policy-makers are paying increasing attention to ecosystem services, given improved understanding that they underpin human well-being, and following their integration within the Aichi Targets of the Convention on Biological Diversity. Decision-makers need information on trends in biodiversity and ecosystem services but tools for assessing the latter are often expensive, technically demanding and ignore the local context.

This work investigated the effects of land cover and land-use change (LUC) on the ability of a soil to store carbon (C) and reduce carbon dioxide (CO₂) emissions, in a Mediterranean area. Using a paired-site approach, we estimated the effect of land-cover change on the C stock from 1972 to 2008 in a natural reserve (Grotta di Santa Ninfa) in western Sicily. We selected 15 paired sites representative of five LUCs.

We predict fine-scale species richness patterns at large spatial extents by linking a systematic sample of vascular plants with a multitude of independent environmental descriptors. Switzerland, covering 41,244 km² in central Europe. Vascular plant species data were collected along transects of 2500-m length within 1-km² quadrats on a systematic national grid (n = 354), using a standardized assessment method. Generalized linear models (GLM) were used to correlate species richness of vascular plants per transect (SRt) with three sets of variables: topography, environment and land cover.