Agricultural implications of providing soil-based constraints on urban expansion: Land use forecasts to 2050.

Urbanization onto adjacent farmlands directly reduces the agricultural area available to meet the resource needs of a growing society. Soil conservation is a common objective in urban planning, but little focus has been placed on targeting soil value as a metric for conservation. This study assigns commodity and water storage values to the agricultural soils across all of the watersheds in Michigan's Lower Peninsula to evaluate how cities might respond to a soil conservation-based urbanization strategy. Land Transformation Model (LTM) simulations representing both traditional and soil conservation-based urbanization, are used to forecast urban area growth from 2010 to 2050 at five year intervals. The expansion of urban areas onto adjacent farmland is then evaluated to quantify the conservation effects of soil-based development. Results indicate that a soil-based protection strategy significantly conserves total farmland, especially more fertile soils within each soil type. In terms of revenue, ∼$88 million (in current dollars) would be conserved in 2050 using soil-based constraints, with the projected savings from 2011 to 2050 totaling more than $1.5 billion. Soil-based urbanization also increased urban density for each major metropolitan area. For example, there were 94,640 more acres directly adjacent to urban land by 2050 under traditional development compared to the soil-based urbanization strategy, indicating that urban sprawl was more tightly contained when including soil value as a metric to guide development. This study indicates that implementing a soil-based urbanization strategy would better satisfy future agricultural resource needs than traditional urban planning.

Transition index maps for urban growth simulation: application of artificial neural networks, weight of evidence and fuzzy multi-criteria evaluation.

Transition index maps (TIMs) are key products in urban growth simulation models. However, their operationalization is still conflicting. Our aim was to compare the prediction accuracy of three TIM-based spatially explicit land cover change (LCC) models in the mega city of Mumbai, India. These LCC models include two data-driven approaches, namely artificial neural networks (ANNs) and weight of evidence (WOE), and one knowledge-based approach which integrates an analytical hierarchical process with fuzzy membership functions (FAHP). Using the relative operating characteristics (ROC), the performance of these three LCC models were evaluated. The results showed 85%, 75%, and 73% accuracy for the ANN, FAHP, and WOE. The ANN was clearly superior compared to the other LCC models when simulating urban growth for the year 2010; hence, ANN was used to predict urban growth for 2020 and 2030. Projected urban growth maps were assessed using statistical measures, including figure of merit, average spatial distance deviation, producer accuracy, and overall accuracy. Based on our findings, we recomend ANNs as an and accurate method for simulating future patterns of urban growth.

Increases in the climate change adaption effectiveness and availability of vegetation across a coastal to desert climate gradient in metropolitan Los Angeles, CA, USA.

Urbanization has increased heat in the urban environment, with many consequences for human health and well-being. Managing climate change in part through increasing vegetation is desired by many cities to mitigate current and future heat related issues. However, little information is available on what influences the current effectiveness and availability of vegetation for local cooling. In this study, we identified the variation in the interacting relationships among vegetation (normalized difference vegetation index), socioeconomic status (neighborhood income), elevation and land surface temperature (LST) to identify how vegetation based surface cooling services change throughout the pronounced coastal to desert climate gradient of the Los Angeles, CA metropolitan region, a megacity of >18 million residents. A key challenge for understanding variation in vegetation as a climate change adaptation tool spanning neighborhood to megacity scales is developing new "big data" analytical tools. We used structural equation modeling (SEM) to quantify the interacting relationships among socio-economic status data obtained from government census data, elevation and new LST and vegetation data obtained from an airborne imaging campaign conducted in 2013 for the urban and suburban areas across a series of fifteen climate zones. Vegetation systematically increased in cooling effectiveness from 6.06 to 31.77 degrees with increasing distance from the coast. Vegetation and neighborhood income were positively correlated throughout all climate zones with a peak in the relationship occurring near 25km from the coast. Because of the interaction between these two relationships, we also found that higher income neighborhoods were cooler and that this effect peaked at about 30km from the coast. These results show the availability and effectiveness of vegetation on the local climate varies tremendously throughout the Los Angeles, CA metropolitan area. Further, using the more inland climate zones as future analogs for more coastal zones, suggests that in the warmer climate conditions projected for the region the effectiveness of vegetation for regional cooling may increase thus acting as a localized negative feedback mechanism.