Monitoring urban expansion and land use/land cover changes of Shanghai metropolitan area during the transitional economy (1979�C2009) in China

This study explored the spatio-temporal dynamics and evolution of land use/cover changes and urban expansion in Shanghai metropolitan area, China, during the transitional economy period (1979?C2009) using multi-temporal satellite images and geographic information systems (GIS). A maximum likelihood supervised classification algorithm was employed to extract information from four landsat images, with the post-classification change detection technique and GIS-based spatial analysis methods used to detect land-use and land-cover (LULC) changes. The overall Kappa indices of land use/cover change maps ranged from 0.79 to 0.89. Results indicated that urbanization has accelerated at an unprecedented scale and rate during the study period, leading to a considerable reduction in the area of farmland and green land. Findings further revealed that water bodies and bare land increased, obviously due to large-scale coastal development after 2000. The direction of urban expansion was along a north-south axis from 1979 to 2000, but after 2000 this growth changed to spread from both the existing urban area and along transport routes in all directions. Urban expansion and subsequent LULC changes in Shanghai have largely been driven by policy reform, population growth, and economic development. Rapid urban expansion through clearing of vegetation has led to a wide range of eco-environmental degradation.     

Assessing Development Pressure in the Chesapeake Bay Watershed: An Evaluation of Two Land-Use Change Models

Natural resource lands in the Chesapeake Bay watershed are increasingly susceptible to conversion into developed land uses, particularly as the demand for residential development grows. We assessed development pressure in the Baltimore-Washington, DC region, one of the major urban and suburban centers in the watershed. We explored the utility of two modeling approaches for forecasting future development trends and patterns by comparing results from a cellular automata model, SLEUTH (slope, land use, excluded land, urban extent, transportation), and a supply/demand/allocation model, the Western Futures Model. SLEUTH can be classified as a land-cover change model and produces projections on the basis of historic trends of changes in the extent and patterns of developed land and future land protection scenarios. The Western Futures Model derives forecasts from historic trends in housing units, a U.S. Census variable, and exogenously supplied future population projections. Each approach has strengths and weaknesses, and combining the two has advantages and limitations.     

Assessment of transport performance index for urban transport development strategies — Incorporating residents' preferences

The performance of urban transport depends on a variety of factors related to metropolitan structure; in particular, the patterns of commuting, roads and public transport (PT) systems. To evaluate urban transport planning efforts, there is a need for a metric expressing the aggregate performance of the city's transport systems which should relate to residents' preferences. The existing metrics have typically focused on a measure to express the proximity of job locations to residences. A Transport Performance Index (TPI) is proposed in which the total cost of transportation system (operational and environmental costs) is divided by willingness to pay (WTP) for transport plus the willingness to accept (WTA) the environmental effects on residents. Transport operational as well as the environmental costs are derived from a simulation of all transport systems, to particular designs of spatial development. Willingness to pay for transport and willingness to accept the environmental effects are derived from surveys among residents. Simulations were modelled of Surabaya's spatial structure and public transport expansion. The results indicate that the current TPI is high, which will double by 2030. With a hypothetical polycentric city structure and adjusted job housing balance, a lower index occurs because of the improvements in urban transport performance. A low index means that the residents obtain much benefit from the alternative proposed. This illustrates the importance of residents' preferences in urban spatial planning in order to achieve efficient urban transport. Applying the index suggests that city authorities should provide fair and equitable public transport systems for suburban residents in the effort to control the phenomenon of urban sprawl. This index is certainly a good tool and prospective benchmark for measuring sustainability in relation to urban development.     

Spatial Validation of Land Use Change Models Using Multiple Assessment Techniques: A Case Study of Transition Potential Models

Using land use and cover change (LUCC) models for the urban growth planning, environmental assessment, and decision-making needs the establishment of an appropriate level of confidence in their performance. The objective of this research is to explore the importance of using multiple assessment techniques in order to fairly evaluate the performance of land use models. An application is conducted by using the Land Change Modeler for Ecological Sustainability (LCM) which is an empirical and transition potential model. LCM is applied to model the agricultural to developed areas transition in Rennes metropolitan area (France). The land demand is estimated using the Markov Chain model; whereas, the transition potential map is implemented using the Multi-Layer Perceptron Neural Network (MLP) method based on historical changes and driving variables. The model performance is assessed based on a variety of the most commonly used validation techniques. At the study area level, the correctness and disagreement analysis shows that LCM performs better at predicting the amount than the allocation of developed areas. Additionally, landscape metrics reveal that LCM tends to predict a fragmented urban form, which seems evident because of the large number of the individual urban patches. At the municipality level, the error budget analysis shows that the model performance, which varies highly between different subareas, needs to be improved. Moreover, the cross-tabulation between the transition potential map and both the observed and the predicted agricultural to developed areas transitions reveals that the order of the transition potential values does not perfectly fit the observed change; whereas, the predicted change is not solely limited to areas with high potential.     

Scenario Analysis for the San Pedro River, Analyzing Hydrological Consequences of a Future Environment

Studies of future management and policy options based on different assumptions provide a mechanism to examine possible outcomes and especially their likely benefits and consequences. The San Pedro River in Arizona and Sonora, Mexico is an area that has undergone rapid changes in land use and cover, and subsequently is facing keen environmental crises related to water resources. It is the location of a number of studies that have dealt with change analysis, watershed condition, and most recently, alternative futures analysis. The previous work has dealt primarily with resources of habitat, visual quality, and groundwater related to urban development patterns and preferences. In the present study, previously defined future scenarios, in the form of land-use/land-cover grids, were examined relative to their impact on surface-water conditions (e.g., surface runoff and sediment yield). These hydrological outputs were estimated for the baseline year of 2000 and predicted twenty years in the future as a demonstration of how new geographic information system-based hydrologic modeling tools can be used to evaluate the spatial impacts of urban growth patterns on surface-water hydrology.     

Land use patterns and urbanization in the holy city of Varanasi, India: a scenario

Rapid urbanization and increasing land use changes due to population and economic growth in selected landscapes is being witnessed of late in India and other developing countries. The cities are expanding in all directions resulting in large-scale urban sprawl and changes in urban land use. The spatial pattern of such changes is clearly noticed on the urban fringes or city peripheral rural areas than in the city center. In fact, this is reflected in changing urban land use patterns. There is an urgent need to accurately describe land use changes for planning and sustainable management. In the recent times, remote sensing is gaining importance as vital tool in the analysis and integration of spatial data. This study intends to estimate land use pattern in a planned and unplanned urban setup and also to analyze the impact of change in land use pattern in the Varanasi urban environment. The results indicate that the planned urban setup had a higher tree cover to that of unplanned area in the Varanasi City, although a considerable disparity existed within the planned urban setups. The results emphasize the need to critically review concepts of urban planning and give more consideration to the preservation and management of urban tree cover/greenspace.     

基于形态空间格局分析方法和最小累积阻力模型的北京市平原区造林工程生态格局分析

为了科学评估北京市平原区造林工程的生态影响，利用高分卫星数据获取2018—2020年北京市平原区造林面积、分布及变化信息，分析了造林工程的空间格局特征，并采用形态空间格局分析(MSPA)方法及最小累积阻力(MCR)模型，构建了北京市平原区生态网络，通过情景模拟分析造林工程对生态源地、生态廊道产生的影响。结果显示，2018—2020年北京市平原区造林工程主要沿西北东南方向实施，实现了对原有林地资源分布不均衡问题的解决。热点建设区域为北部的延庆及东南部的通州、大兴，呈现由城市边界逐步向市中心蔓延的发展趋势。在不考虑其他因素对土地利用的影响的前提下，造林工程的实施使平原区林地面积增长了9.8%。通过情景模拟发现，造林工程的实施使生态网络向连通、完整、均衡方向发展。其中，生态源地面积增长了131.0 km²,主要生态源地连通性提升了41.1%,生态廊道的分布由主要分布在西北部山区向西北、东南两个方向均有密集分布转变。综上，造林工程的生态环境效应显著，造林工程的实施对生态环境质量改善具有重要意义。     

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.     

Extending large-scale forest inventories to assess urban forests

Urban areas are continuously expanding today, extending their influence on an increasingly large proportion of woods and trees located in or nearby urban and urbanizing areas, the so-called urban forests. Although these forests have the potential for significantly improving the quality the urban environment and the well-being of the urban population, data to quantify the extent and characteristics of urban forests are still lacking or fragmentary on a large scale. In this regard, an expansion of the domain of multipurpose forest inventories like National Forest Inventories (NFIs) towards urban forests would be required. To this end, it would be convenient to exploit the same sampling scheme applied in NFIs to assess the basic features of urban forests. This paper considers approximately unbiased estimators of abundance and coverage of urban forests, together with estimators of the corresponding variances, which can be achieved from the first phase of most large-scale forest inventories. A simulation study is carried out in order to check the performance of the considered estimators under various situations involving the spatial distribution of the urban forests over the study area. An application is worked out on the data from the Italian NFI.     

Fitting Data to Linear Combinations of Covariograms

We pursue a regression model for spatially-indexed data whose spatial correlation is determined by a linear combination of simple covariograms. The main interest lies in the estimation of the spatial parameters. As several common techniques appear ineffective for this setting, an algorithm is proposed to obtain parameter estimates and is assessed through simulation. It is found to provide greater stability than other methods of estimation. We discuss the influence of parametrization and site location on the efficacy of the estimation algorithms, and develop some guidelines as to the placement of sampling sites to improve the algorithm's performance. This revised version was published online in July 2006 with corrections to the Cover Date.     

Temporal and spatial change detecting (1998–2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images

Land use/land cover (LULC) has a profound impact on economy, society and environment, especially in rapid developing areas. Rapid and prompt monitoring and predicting of LULC’s change are crucial and significant. Currently, integration of Geographical Information System (GIS) and Remote Sensing (RS) methods is one of the most important methods for detecting LULC’s change, which includes image processing (such as geometrical-rectifying, supervised-classification, etc.), change detection (post-classification), GIS-based spatial analysis, Markov chain and a Cellular Automata (CA) models, etc. The core corridor of Pearl River Delta was selected for studying LULC’s change in this paper by using the above methods for the reason that the area contributed 78.31% (1998)–81.4% (2003) of Gross Domestic Product (GDP) to the whole Pearl River Delta (PRD). The temporal and spatial LULC’s changes from 1998 to 2003 were detected by RS data. At the same time, urban expansion levels in the next 5 and 10 years were predicted temporally and spatially by using Markov chain and a simple Cellular Automata model respectively. Finally, urban expansion and farmland loss were discussed against the background of China’s urban expansion and cropland loss during 1990–2000. The result showed: (1) the rate of urban expansion was up to 8.91% during 1998–2003 from 169,078.32 to 184,146.48 ha; (2) the rate of farmland loss was 5.94% from 312,069.06 to 293,539.95 ha; (3) a lot of farmland converted to urban or development area, and more forest and grass field converted to farmland accordingly; (4) the spatial predicting result of urban expansion showed that urban area was enlarged ulteriorly compared with the previous results, and the directions of expansion is along the existing urban area and transportation lines.     

Monitoring urban growth and detecting land-cover changes on the Istanbul metropolitan area

Istanbul is the most populated city of Turkey with a population of around 10.58 M (2000) living on around 5,750 km². In 1980, the population was only 4.7 M and then it has been more than doubled in only two decades. The population has been increasing as a result of mass immigration. An urbanization process continues and it causes serious increases in urban areas while decreasing the amount of green areas. This rapid, uncontrolled, and illegal urbanization accompanied by insufficient infrastructure has caused degradation of forest and barren lands in the metropolitan area, especially through the last two decades. The watershed basins inside the metropolitan area and the transportation network have accelerated the land-cover changes, which have negative impacts on water quality of the basins. Monitoring urban growth and land cover change will enable better management of this complex urban area by the Greater Istanbul Metropolitan Municipality (GIMM). A temporal assessment of land-cover changes of Istanbul has been documented in this study. The study mainly focuses on the acquisition and analysis of Landsat TM and Landsat GeoCover LC satellite images reflecting the significant land-cover changes between the years of 1990 and 2005. Raster data were converted to vector data and used in Geographic Information Systems (GIS). A database was created for Istanbul metropolitan area to plan, manage, and utilize statistical attribute data covering population, water, forest, industry, and topographic position. Consequently an overlay analysis was carried out and land use/cover changes through years have been detected for the case study area. The capability of Landsat images in determining the alterations in the macro form of the city are also discussed.     

The response of hydrophobic organics and potential toxicity in streams to urbanization of watersheds in six metropolitan areas of the United States

Semipermeable membrane devices (SPMDs) were deployed in streams along a gradient of urban land-use intensity in and around six metropolitan areas: Atlanta, Georgia; Raleigh-Durham, North Carolina; and Denver-Fort Collins, Colorado, in 2003; and Dallas-Fort Worth, Texas; Milwaukee-Green Bay, Wisconsin; and Portland, Oregon, in 2004 to examine relations between percent urban land cover in watersheds and the occurrence, concentrations, and potential toxicity of hydrophobic compounds. Of the 142 endpoints measured in SPMD dialysates, 30 were significantly (alpha = 0.05) related to the percent of urban land cover in the watersheds in at least one metropolitan area. These 30 endpoints included the aggregated measures of the total number of compounds detected and relative toxicity (Microtox(R) and P450RGS assays), in addition to the concentrations of 27 individual hydrophobic compounds. The number of compounds detected, P450RGS assay values, and the concentrations of pyrogenic polycyclic aromatic hydrocarbons (PAHs) were significantly related to percent urban land cover in all six metropolitan areas. Pentachloroanisole, the most frequently detected compound, was significantly related to urban land cover in all metropolitan areas except Dallas-Fort Worth. Petrogenic PAHs and dibenzofurans were positively related to percent urban land cover in Atlanta, Raleigh-Durham, Denver, and Milwaukee-Green Bay. Results for other endpoints were much more variable. The number of endpoints significantly related to urban land cover ranged from 6 in Portland to 21 Raleigh-Durham. Based on differences in the number and suite of endpoints related to urban intensity, these results provide evidence of differences in factors governing source strength, transport, and/or fate of hydrophobic compounds in the six metropolitan areas studied. The most consistent and significant results were that bioavailable, aryl hydrocarbon receptor agonists increase in streams as basins become urbanized. Potential toxicity mediated by this metabolic pathway is indicated as an important factor in the response of aquatic biota to urbanization.     

Multipoint source method in air pollution modelling of cold start emission

The paper presents a new method of air pollution modelling on a micro scale. For estimation of concentration of car exhaust pollutants, each car is treated as an instantaneous moving emission source. This approach enables us to model time and spatial changes of emission, especially during cold and cool start of an engine. These stages of engine work are a source of significant pollution concentration in urban areas. In this work, two models are proposed: one for the estimation of emission after cold start of the engine and another for the prediction of pollutant concentration. The first model (defined for individual exhaust gas pollutants) enables us to calculate the emission as a function of time after the cold or cool start, ambient temperature and average speed of motion. This model uses the HBEFA database. The second mathematical model is developed in order to calculate the pollutant dispersion and concentrations. The finite volume method is applied to discretise the set of partial differential equations describing wind flow and pollutant dispersion in the domain considered. Models presented in this paper can be called short-term models on a small spatial scale. The results of numerical simulation of pollutant emission and dispersion are also presented.     

Simulation and optimization of pedestrian evacuation in high-density urban areas for effectiveness improvement

Rapid urbanization has made many cities in China enter an era that increment and stock develop in parallel. Urban central area presents the characteristics of complex spatial structure and high functional complexity, resulting in the huge disaster risks and severe security issues of evacuation. This paper proposes a method of improving emergency evacuation effectiveness under the restriction of limited available land resources in high-density areas, which is combined with urban renewal planning, aiming at optimizing the layout of shelters and evacuation passageway by cyclic evacuation simulation, evaluation and optimization with Anylogic software and facility location tool based on ArcGIS. Taking the renewal planning of Jintang Road Area in Tianjin, a typical high-density urban area, as a case study. The results showed that: 1) The evacuation space system of Jintang Road Area exists potential danger that severe congestion occurred in the simulation process and the congested section spread outward with three shelters as the center. Which lasted for several hours. The rate of successful asylum in 5 min and 30 min was 13% and 34%; 2) The matching degree between spatial layout of emergency shelters and population distribution is low, which is reflected in the utilization rate of shelter: the rate of shelter No.1 is as high as 826%, while that of No 4 and No 5 is 48% and 11%, and also causes unreasonable distribution of people flow in evacuation passages during the evacuation process; 3) The research method of integrating simulation, facility location and renewal planning is an effective way to improve the effectiveness of pedestrian evacuation in high-density urban areas. After the improvement, the four indexes have been effectively improved.     

Modeling the spatial pattern of farmland using GIS and multiple logistic regression: a case study of Maotiao River Basin, Guizhou Province, China

Land use change is an important topic in the field of global environmental change and sustainable development. Land use change modeling has attracted substantial attention because it helps researchers understand the mechanisms of land use change and assists regulatory bodies in formulating relevant policies. Maotiao River Basin is located in the province of Guizhou, China, which has a developed agricultural industry in the karst mountain areas. This paper selected biophysical and social–economic factors as independent variables, and constructed a multiple logistic regression of farmland spatial distribution probability by random sampling. Then, by using GIS technology and integrating the 2000 data, this study predicted the farmland spatial pattern. When the predicted map was compared with the actual farmland map for 2000, we noted that 71% of the simulation is in accordance with the 2000 farmland pattern. The result satisfactorily proves the reasonability and applicability of our model.     

Performance assessment of geospatial simulation models of land-use change—a landscape metric-based approach

Performance evaluation is a critical step when developing land-use and cover change (LUCC) models. The present study proposes a spatially explicit model performance evaluation method, adopting a landscape metric-based approach. To quantify GEOMOD model performance, a set of composition- and configuration-based landscape metrics including number of patches, edge density, mean Euclidean nearest neighbor distance, largest patch index, class area, landscape shape index, and splitting index were employed. The model takes advantage of three decision rules including neighborhood effect, persistence of change direction, and urbanization suitability values. According to the results, while class area, largest patch index, and splitting indices demonstrated insignificant differences between spatial pattern of ground truth and simulated layers, there was a considerable inconsistency between simulation results and real dataset in terms of the remaining metrics. Specifically, simulation outputs were simplistic and the model tended to underestimate number of developed patches by producing a more compact landscape. Landscape-metric-based performance evaluation produces more detailed information (compared to conventional indices such as the Kappa index and overall accuracy) on the model’s behavior in replicating spatial heterogeneity features of a landscape such as frequency, fragmentation, isolation, and density. Finally, as the main characteristic of the proposed method, landscape metrics employ the maximum potential of observed and simulated layers for a performance evaluation procedure, provide a basis for more robust interpretation of a calibration process, and also deepen modeler insight into the main strengths and pitfalls of a specific land-use change model when simulating a spatiotemporal phenomenon.