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Mineralization in the Earth's crust can be regarded as a singular process resulting in large amounts of mass accumulation and element enrichment over short time or space scales. The elemental concentrations modeled by fractals and multifractals show self-similarity and scale-invariant properties. We take the view that fluid-pressure variations in response to earthquakes or fault rupture are primarily responsible for changes in solubility and trigger transient physical and chemical variations in ore-forming fluids that enhance the mineralization process. Based on this general concept, we investigated mineral precipitation processes driven by rapid fluid pressure reductions by coupling mineralization to a cellular automaton model to reveal the nonlinear mechanism of the orogenic gold mineralization process using simulation. In the model, fluid pressure can increase to the rock failure condition, which was set as lithostatic pressure at a depth of 10 km (270 MPa), due to either porosity reduction or dehydration reactions. Rapid drops in pressure resulting from fault rupture or local hydrofracture may induce repeated gold precipitation. The geochemical patterns generated by the model evolve from depletion to enrichment patterns, and from spatially random to spatially clustered structures quantified by multifractal models and geostatistics. Results show how metal elements self-organize to form high metal concentration patterns displaying self-similarity and scale-invariance. These transitions are attributed to the growth and coalescence of sub-networks with different fluid pressures up to the percolation threshold, resulting in a wide range of fluid pressure reductions and gold precipitation in the form of clusters. The results suggest that cyclic evolution of fluid pressure and its effects on gold precipitation systems can effectively mimic the repeated mineralization superposition process, and generate complex geochemical patterns characterized by a multifractal model. The nonlinear behavior exhibits scale-invariance and self-organized critical threshold, where mineral phase separations result from fluid pressure reductions associated with fault failure.

Although gross primary productivity (GPP) is estimated with remote sensing over large regions of Earth, urban areas are usually excluded due to the lack of light use efficiency (LUE) parameters for urban vegetation and the spatial heterogeneity of urban land cover. Here, we estimated midsummer GPP, both within and among vegetation and land-use types, across the Minneapolis-Saint Paul, Minnesota metropolitan region. We derived LUE parameters for urban vegetation types using estimates of GPP from tree sap flow and eddy covariance CO2 flux observations, and from fraction of absorbed photosynthetically active radiation based on 2 m resolution WorldView-2 satellite imagery. Mean GPP per unit land area (including vegetation, impervious surfaces, and soil) was 2.64 g C m−2 d−1, and was 4.45 g C m−2 d−1 per unit vegetated area. Mapped GPP estimates were within 11.4% of estimates from independent tall tower eddy covariance measurements. Turf grass GPP had a larger coefficient of variation (0.18) than other vegetation classes (~0.10). Vegetation composition was largely consistent across the study area. Excluding golf courses, mean land-use GPP for the total study area varied more by percent vegetation cover (R2 = 0.98, p < 0.001) than by variability within vegetation classes (R2 = 0.21, p = 0.19). Urban GPP in general was less than half that of natural forests and grasslands in the same climate zone.

Many countries have been constructing modern ground transportation projects. This raises questions about the impacts of such projects on development of impervious surfaces, yet there have been few attempts to systematically analyze these impacts. This paper attempts to narrow this information gap using the Hangzhou Bay Bridge project, China, as an exploratory case study. Using remotely sensed data, we developed a framework based on statistical techniques, wavelet multi-resolution analysis and Theil-Sen slope analysis to measure the changes in impervious surfaces. The derived changes were then linked to the bridge project with respect to socio-economic factors and land use development activities. The findings highlight that the analytical framework could reliably quantify the area, pattern and form of new urban area and urban intensification. Change detection analysis showed that urban area, GDP and the length of highways increased moderately in the pre-Hangzhou Bay Bridge period (1995–2002) while all of these variables increased more substantially during (2002–2009) and after (2009–2013) the bridge construction. The results indicate that the development of impervious surfaces due to new urban area came at the expense of permeable surfaces in the urban fringe and within rural regions, while urban intensification occurred mainly in the form of the redevelopment of older structures to modern high-rise buildings within existing urban regions. In the context of improved transportation infrastructure, our findings suggest that new urban area and urban intensification can be attributed to consecutive events which act like a chain reaction: construction of improved transportation projects, their impacts on land use development policies, effects of both systems on socio-economic variables, and finally all these changes influence new urban area and urban intensification. However, more research is needed to better understand this sequential process and to examine the broader applicability of the concept in other developing regions.

This study aims to support sustainable urban and environmental planning by using urban growth simulation models, in which environmental quality is employed as one of the inputs. We proposed an extended SLEUTH urban growth model (UGM) for the regions threatened by environmental quality degradation caused by uncontrolled urban expansion. In this model, habitat quality is assessed by the InVEST model and is used to represent environmental quality, which is utilized in urban growth simulation. The habitat quality map is used to replace the slope layer as input for the SLEUTH model's urban growth simulation for cities where relatively flat topography makes this layer of minimal explanatory value. The extended SLEUTH UGM was calibrated using data for Changzhou city, China in 1990, 2000, 2010, and 2014. The best value of the Optimal SLEUTH Metric (OSM) was calculated for both the standard SLEUTH UGM and the extended SLEUTH UGM independently. The OSM value for the latter model was much higher than that of the former model, which indicated that the extended model provided a better explanation of urban growth in the study area. The calibrated extended SLEUTH UGM was applied to predict growth in Changzhou city from 2014 to 2030. The result showed that the urban area is expected to expand about 626 km2 by 2030. Comparison with the prediction result by using standard SLEUTH UGM showed that the area with high habitat quality could be reserved and the urban expansion could be limited by using our model. The findings demonstrate that the extended SLEUTH UGM could be a valuable tool for sustainable urban and environmental planning and management in developing regions where environmental protection should be considered as one of the major land-use objectives in their rapid urbanization process.

In Sub-Saharan Africa rapid urban growth combined with rising poverty is creating diverse urban environments, the nature of which are not adequately captured by a simple urban-rural dichotomy. This paper proposes an alternative classification scheme for urban mapping based on a gradient approach for the southern portion of the West African country of Ghana. Landsat Enhanced Thematic Mapper Plus (ETM+) and European Remote Sensing Satellite-2 (ERS-2) synthetic aperture radar (SAR) imagery are used to generate a pattern based definition of the urban context. Spectral mixture analysis (SMA) is used to classify a Landsat scene into Built, Vegetation and Other land covers. Landscape metrics are estimated for Built and Vegetation land covers for a 450 meter uniform grid covering the study area. A measure of texture is extracted from the SAR imagery and classified as Built/Non-built. SMA based measures of Built and Vegetation fragmentation are combined with SAR texture based Built/Non-built maps through a decision tree classifier to generate a nine class urban context map capturing the transition from unsettled land at one end of the gradient to the compact urban core at the other end. Training and testing of the decision tree classifier was done using very high spatial resolution reference imagery from Google Earth. An overall classification agreement of 77% was determined for the nine-class urban context map, with user's accuracy (commission errors) being lower than producer's accuracy (omission errors). Nine urban contexts were classified and then compared with data from the 2000 Census of Ghana. Results suggest that the urban classes appropriately differentiate areas along the urban gradient.

The forecasting slope deformation potential is required to evaluate slope safety during open-pit mining, allowing us to formulate and promote effective emergency strategies in advance to prevent slope failure disasters. Although fractal models have been used to predict slope deformation, such limitations as low prediction accuracy, poor stability and the requirement for large amounts of data must be overcome. This article proposes an improved fractal model to forecast mine slope deformation using the grey system theory. The GM (1, 1) model is used in the improved fractal model to optimize the fitting function of the fractal dimension because of its high computational efficiency and strong fitting ability. Data sequences spanning 13 days from 11 global positioning system monitoring stations in the Jinduicheng open-pit mine in Shaanxi Province, China, were applied to forecast the slope deformation. The results from both the traditional fractal model and the improved fractal model can accurately forecast the slope deformation value fairly close to the actual field monitoring value, but the latter can make a more accurate prediction than the former. There is a significant relationship between the prediction accuracy and the data sequence dispersion. Further analysis revealed that our improved fractal model is more capable of resisting the volatility existing in the data sequences than the traditional fractal model. These findings assist in understanding the applicability of prediction models and the deformation trends of open-pit mine slopes.