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Urban change (urbanization) has dominated land change science for several decades. However, few studies have focused on what many scholars call the urban densification process (i.e., urban intensity expansion) despite its importance to both planning and subsequent impacts to the environment and local economies. This paper documents past urban densification patterns and uses this information to predict future densification trends in southeastern Wisconsin (SEWI) by using a rich dataset from the United States and by adapting the well-known Land Transformation Model (LTM) for this purpose. Urban densification is a significant and progressive process that often accompanies urbanization more generally. The increasing proportion of lower density areas, rather than higher density areas, was the main characteristic of the urban densification in SEWI from 2001 to 2011. We believe that improving urban land use efficiency to maintain rational densification are effective means toward a sustainable urban landscape. Multiple goodness-of-fit metrics demonstrated that the reconfigured LTM performed relatively well to simulate urban densification patterns in 2006 and 2011, enabling us to forecast densification to 2016 and 2021. The predicted future urban densification patterns are likely to be characterized by higher densities continue to increase at the expense of lower densities. We argue that detailed categories of urban density and specific relevant predictor variables are indispensable for densification prediction. Our study provides researchers working in land change science with important insights into urban densification process modeling. The outcome of this model can help planners to identify the current trajectory of urban development, enabling them to take informed action to promote planning objectives, which could benefit sustainable urbanization definitely., Competing Interests: The authors have declared that no competing interests exist.

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., (Copyright © 2018. Published by Elsevier Ltd.)

Abstract Numerous machine learning-based land change models have been presented by researchers over the last two decades. To date, however, far less have simulated multiple land use classes and specific land use subclasses at the same time. In some areas of the world, it is important to simulate both of these dynamics to understand fully the drivers and consequences of land change. One important example is the process of urbanization in China, as urban policies have been developed that guide urban expansion, rural protections, and urban subclass development. This paper presents a new model integrating geographic information systems (GIS) with artificial neural networks (ANNs) to predict multiple transitions among land use types and urban subclasses in the Zhanggong District of Ganzhou city in China. We show that the model produces satisfactory goodness of fit values-based on location, quantity and spatial configuration-between simulated and observed land use maps for 2015. Our simulated future maps produced by the model for 2020 and 2025 demonstrate that transitions from farmland and forest to urban will remain the main pathway of urbanization although we predict that the rate will slow after 2025. The goals of urban planning should be aligned with land use planning according to "Combining multiple laws and regulations" in China. Taking into account the current and future land use transitions will enhance the accuracy and timeliness of land use policy making and urban land planning. For the sustainable land use in Zhanggong District, we argue for a strengthened regulation of the land market by government and believe that planning officials should guide the spatial distribution of land supply actively. Furthermore, improving the production, living and ecological functions of land resources are the key points to optimize urban land use functions and the allocation of land resources. We discuss how our model can be adapted to other areas to benefit land use management and urban planning in China. Highlights • GIS and ANNs were used to predict multiple land use transitions and urban subclasses. • A case study demonstrates that the model performs relatively well validated with several measures for goodness of fit. • The simulated maps suggest that farmland and forest are the most common candidates for urban expansion. • The methodology can be adapted to other study areas to benefit land use management and urban planning in China. [ABSTRACT FROM AUTHOR]

China faces the challenge of using limited farmland to feed more than 1.3 billion people. Accelerated urbanization has exacerbated this challenge by consuming a large quantity of high-quality farmland (HQF). It is therefore essential to assess the degree to which urban expansion has preferentially consumed HQF, and discern the mechanism behind this. We found urban areas in Beijing to expand at speeds of 48.97 km 2 /year, 21.89 km 2 /year, 62.30 km 2 /year and 20.32 km 2 /year during the periods 1986–1995, 1995–2000, 2000–2005 and 2005–2020, respectively. We developed an indicator of HQF consumption due to urban expansion, representing the ratio of HQF consumed to its proportion of overall farmland, and found its values were 2.21, 1.57, 1.99 and 1.10 for 1986–1995, 1995–2000, 2000–2005 and 2005–2020, respectively. Thus, although HQF has been overrepresented in the farmland consumed by Beijing's urbanization, this phenomenon has decreased over time. Centralized expansion has contributed greatly to consumption of HQF. Topography and distances to urban and water bodies determine the relative consumption of HQF in urbanization. [ABSTRACT FROM AUTHOR]

This paper compares one global parametric land use change model, the artificial neural network – based Land Transformation Model, with two local non-parametric models: a classification and regression tree and multivariate adaptive regression spline model. We parameterized these three models with identical data from different regions of the world; one region undergoing extensive agricultural expansion (East Africa), another region where forests are re-growing (Muskegon River Watershed in the United States), and a third region where urbanization is prominent (South-Eastern Wisconsin in the United States). Independent training data and testing data were used to train and calibrate each model, respectively. Comparisons of simulated maps from the three kinds of land use change patterns were made using conventional goodness-of-fit metrics in land use change science. The results of temporal and spatial comparison of the data mining models show that the artificial neural network outperformed all other models in a short-time interval (East Africa; 5 years) and for coarse resolution data (East Africa; 1 km); however, the three data mining models obtained similar accuracies in a long-time interval (Muskegon River Watershed; 20 years) and for fine resolution data with large numbers of cells (Muskegon River Watershed; 30 m). Furthermore, the results showed that the probability of agriculture gain was more likely in locations closer to towns and large cities in East Africa, urbanization was more likely in locations closer to roads and urban areas in South-Eastern Wisconsin and the probability of forest gain was more likely in locations closer to the forest and shrub land cover and farther away from roads in Muskegon River Watershed. [ABSTRACT FROM AUTHOR]

Abstract: Decreases in both quantity and quality of cultivated land in China have drawn close attention recently due to the threat to food security. China has implemented a set of cultivated land balance (CLB) programs since the late 1990s, aiming to maintain the quantity and quality of cultivated land across the country. We assessed the outcomes of CLB policy in terms of both quantity balance and quality balance. In particular, we evaluated the effects of CLB policy on potential land productivity (PLP) of cultivated land. During 1999–2008, a total of 21,011 km2 of cultivated land was lost due to urbanization and economic development while 27,677 km2 of cultivated land was gained by land exploitation, consolidation and rehabilitation. Thus, the quantity balance aimed for by CLB was achieved. In contrast, quality balance was not met due to both the loss of highly productive cultivated land from urban expansion and economic development and a flawed approach to adding newly cultivated land. In particular, China has typically relied on adding cultivated land by exploitation instead of consolidation, which would add higher productivity land. Therefore, the PLP of the added cultivated land has been rather poor. Nevertheless, the average PLP did increase slightly during 1999–2008, but this was despite CLB rather than because of it. The main cause of the PLP increase was actually a grain-for-green policy that induced considerable reduction in cultivation of low productivity cultivated land. [Copyright &y& Elsevier]

Cities have shown remarkable growth due to attraction, economic, social and facilities centralization in the past few decades. Population and urban expansion especially in developing countries, led to lack of resources, land use change from appropriate agricultural land to urban land use and marginalization. Under these circumstances, land use activity is a major issue and challenge for town and country planners. Different approaches have been attempted in urban expansion modelling. Artificial Neural network (ANN) models are among knowledge-based models which have been used for urban growth modelling. ANNs are powerful tools that use a machine learning approach to quantify and model complex behaviour and patterns. In this research, ANN and logistic regression have been employed for interpreting urban growth modelling. Our case study is Sanandaj city and we used Landsat TM and ETM+ imageries acquired at 2000 and 2006. The dataset used includes distance to main roads, distance to the residence region, elevation, slope, and distance to green space. Percent Area Match (PAM) obtained from modelling of these changes with ANN is equal to 90.47% and the accuracy achieved for urban growth modelling with Logistic Regression (LR) is equal to 88.91%. Percent Correct Match (PCM) and Figure of Merit for ANN method were 91.33% and 59.07% and then for LR were 90.84% and 57.07%, respectively. [ABSTRACT FROM AUTHOR]

Aim Human land use activities are creating a biodiversity crisis. However, it is not clear how species respond to different intensities of land use at a global scale. We used spatial analyses to determine the relationships between human settlements, croplands, rangelands and forests of varying intensities and the endangerment status of mammal species across the globe. In doing so, we sought to assess the role of different land use and land management strategies in global biodiversity conservation. Location Global. Methods A GIS database with distribution ranges for 4734 Red- Listed and non-threatened terrestrial mammal species was obtained from the IUCN and combined with the global anthropogenic biomes dataset, which uses population density class (dense, residential, populated and remote) as a proxy for land use intensity. Ordered logistic regression was used to predict the effect of each land use intensity class on the probability of having a more endangered IUCN status for all mammal species together and for species within major mammal groups separately. Results The probability of having a more endangered status generally increased for mammal species as the proportion of urban settlements, mosaic and cropped 'villages', and residential and populated croplands within their distribution ranges increased. In contrast, endangerment probability of mammals decreased with proportion of irrigated, pastoral and rainfed 'villages', remote croplands and all rangeland classes. Endangerment probability also decreased with proportion of wildlands and remote forests, but increased with proportion of populated forests for several mammal groups. Main conclusions Urbanization and high-intensity crop production pose a significant threat to mammals worldwide. In contrast, rangelands, low-intensity croplands and certain low-intensity human settlements may in fact help maintain mammal diversity at global scales. While the area of wild habitat available to species is also highly important for biodiversity conservation, forested lands that are disturbed or over utilized by humans do not benefit and may even endanger species. [ABSTRACT FROM AUTHOR]

Abstract: This study examines the role of urban spatial development on hydrologic response at both catchment (sub-grid) and river basin (between grid) scales in central Indiana. At the catchment scale, effective impervious area (EIA) was estimated using high density urban area and the patch size of low density urban area. The impact of urban spatial arrangement on floods was investigated using the estimated EIA for the entire White River basin, Indiana, as input to the Variable Infiltration Capacity (VIC) hydrology model with urban representation. A geographic information systems (GIS)-based routing scheme was introduced to the VIC model to route the total runoff. Seven theoretical scenarios were developed with equal urban area distributed in different isochrone zones. For return periods up to and including 100-year, the flood peaks at the basin outlet are largest if the travel time from urban cells is approximate to the mode of all pixels’ travel time (plus the time for runoff rate to reach its maximum in a storm event) of the basin. Flood peaks tend to be smaller and arrive earlier with urbanization if development occurs in areas with shorter travel time, so the largest impacts of urbanization are not necessarily seen immediately downstream of the development. This research demonstrated that the spatial pattern of urban development can affect the hydrologic regime by influencing the hydrologic connectivity of urban area at a catchment scale, while at the river basin scale it is the travel time of urban location that controls flood patterns. [Copyright &y& Elsevier]

Abstract: Land use/cover maps derived from aerial photography for five areas in the Upper Great Lake States were analyzed for rates and patterns of change using our framework for spatial–temporal analysis. The Detroit (1978–2000), Chicago (1990–2000), Milwaukee (1963–2000), and Twin Cities (1990–1997) Metropolitan Areas, along with the Muskegon River Watershed (1978–1998), were examined for five spatial levels (global, regional, zonal, landscape, and patch) and two temporal rates (referred to as first and second order) of change. We found that all regions gained considerable amounts of urban, lost land in agriculture, and either gained (MRW) or lost (all others) forest land. Urban doubled in most areas over 20 years, even in study areas where agricultural land was not initially prevalent. The amount of land in shrubland was substantial in many of these areas, at times exceeding the amount of land in subclasses of urban (e.g., commercial). The transitional shrubland cover type was involved most commonly in land use transitions (“into” and “out of”) throughout the five study regions. In general, ratios of agricultural loss to residential gain varied between 1.04 and 2.72. The amount of fragmentation of land use/cover varied over time and across 5km buffer zones but increased substantially over the study periods. On average, the rate of urban growth exceeded population growth rates by a factor of four. Urbanization and fragmentation are clear characteristics of land use change across the Upper Great Lakes States, and our analysis framework allowed for a rigorous comparison across regions both spatially and temporally. [Copyright &y& Elsevier]

The article presents a study on the impact of regional land cover/land use and projected climatic changes on hydrologic cycles in upper Midwest U.S. It says that land cover data were utilized to drive variable infiltration capacity (VIC) model using observed meteorological forcing data and projected land cover data for 2030, driven through downscaled and bias-corrected projected future climate forcing. Results show an increased baseflow and annual surface runoff, and reduced evapotranspiration.

Abstract: The primary objective of this paper is to quantify the areal footprint of parking lots across a four state region including Illinois, Indiana, Michigan and Wisconsin located in the Upper Great Lakes Basin. Parking lots are virtually everywhere in urban and suburban environments, are often larger than the building footprints they serve yet they are largely unregulated on a regional scale. We focus on a method that estimates parking lot coverage over the four state region and use independent studies to estimate model parameters and validate our model. In total, we estimate that the states of Illinois, Indiana, Michigan and Wisconsin allocate 1260km2 of their land to parking lots, with the lower bound of the estimate at 976km2 and the upper bound at 1745km2. This accounts for approximately 4.97% of urban land use and over 43 million parking spaces whereas the number of adults over 18 years old in these four states amounted to just over 25 million. The estimated number of spaces per vehicle in each state ranges from 2.49 in Indiana to 2.95 in Michigan. Within the four states studied, states that had a higher proportion of their urban land devoted to parking lots were states where urban sprawl was more prevalent. [Copyright &y& Elsevier]

Abstract This study introduces a novel framework for land change simulation that combines the traditional Land Transformation Model (LTM) with data clustering tools for the purposes of conducting land change simulations of large areas (e.g., continental scale) and over multiple time steps. This framework, called "LTM-cluster", subsets massive land use datasets which are presented to the artificial neural network-based LTM. LTM-cluster uses the k -means clustering algorithm implemented within the Spark high-performance compute environment. To illustrate the framework, we use three case studies in the United States which vary in simulation extents, cell size, time intervals, number of inputs, and quantity of urban change. Findings indicate consistent and substantial improvements in accuracy performance for all three case studies compared to the traditional LTM model implemented without input clustering. Specifically, the percent correct match, the area under the operating characteristics curve, and the error rate improved on average of 9%, 11%, and 4%. These results confirm that LTM-cluster has high reliability when handling large datasets. Future studies should expand on the framework by exploring other clustering methods and algorithms. Highlights • We addressed the challenges of simulating land change across large regions and spans of time. • Our model, called LTM-Cluster, is a scalable modeling framework aimed to benefit research involving large datasets. • The Spark environment is used to reduce the burden of high computational time when handling a huge amount of data. • The implementation of LTM-Cluster is available free-of-charge. • Our results consistently showed significant performance improvements of the LTM-Cluster compared to the traditionally parameterized LTM. [ABSTRACT FROM AUTHOR]

Nutrient loading from the Maumee River watershed is a significant reason for the harmful algal blooms (HABs) problem in Lake Erie. The nutrient loading from urban areas needs to be reduced with the installation of green infrastructure (GI) practices. The Long-Term Hydrologic Impact Assessment-Low Impact Development 2.1 (L-THIA-LID 2.1) model was used to explore the influences of land use (LU) and climate change on water quantity and quality in Spy Run Creek watershed (SRCW) (part of Maumee River watershed), decide whether and where excess phosphorus loading existed, identify critical areas to understand where the greatest amount of runoff/pollutants originated, and optimally implement GI practices to obtain maximum environmental benefits with the lowest costs. Both LU/climate changes increased runoff/pollutants generated from the watershed. Areas with the highest runoff/pollutant amount per area, or critical areas, differed for various environmental concerns, land uses (LUs), and climates. Compared to optimization considering all areas, optimization conducted only in critical areas can provide similar cost-effective results with decreased computational time for low levels of runoff/pollutant reductions, but critical area optimization results were not as cost-effective for higher levels of runoff/pollutant reductions. Runoff/pollutants for 2011/2050 LUs/climates could be reduced to amounts of 2001 LU/climate by installation of GI practices with annual expenditures of $0.34 to $2.05 million. The optimization scenarios that were able to obtain the 2001 runoff level in 2011/2050, can also reduce all pollutants to 2001 levels in this watershed. [ABSTRACT FROM AUTHOR]