Measuring the effect of an ongoing urbanization process on biodiversity conservation suitability index: integrating scenario-based urban growth modelling with Conservation Assessment and Prioritization System (CAPS)

The present study adopts an integrative modelling methodology, which combines the strengths of the SLEUTH model and the Conservation Assessment and Prioritization System (CAPS) method. By developing a scenario-based geographic information system simulation environment for Hashtpar City, Iran, the manageability of the landscape under each urban growth scenario is analysed. In addition, the CAPS approach was used for biodiversity conservation suitability mapping. The SLEUTH model was implemented to generate predictive urban layers of the years 2020, 2030, 2040 and 2050 for each scenario (dynamic factors for conservation suitability mapping). Accordingly, conservation suitability surface of the area is updated for each time point and under each urban development storyline. Two-way analysis of variance and Duncan’s new multiple range tests were employed to compare the functionality of the three scenarios. Based on results, the managed urban growth scenario depicted better results for manageability of the landscape and less negative impact on conservation suitability values.     

Interference of Human Impacts in Urban Growth Modelling with Transition Rules of Cellular Automata, GIS and Multi-Temporal Satellite Imagery: A Case Study of Maraghe, Iran

Urban growth is the result of physical and human impacts. In this study Cellular Automata (CA) has been used to analyze physical suitability and human forces in urban growth modelling of Maraghe. The multi-temporal satellite imagery, physical suitability and human impacts Layers have been applied to the modelling. In order to evaluate the accuracy of the image classification methods, Fuzzy ARTMAP is compared with Maximum Likelihood Classification (MLC) and Minimum Distance Classification (MDC) methods. The image classification results showed an overall accuracy of 93 %. Therefore, it is employed for classification of multi-temporal satellite imagery. In order to weight physical suitability and human impacts layers or geographical transition rules in the modelling, regression analysis, the correlation coefficient, trial-and-error method and visual comparison used. The statistical methods are presented to validate neighbourhood scales in the urban growth modelling. The calibration of the model is in fact to the estimate value of the physical suitability and human impacts layer (combinatory layer of demand for urban land and the government facilities) in the modelling. The results obtained from the model calibration showed that human impacts have the highest influence in the urban growth among other factors. Also a small neighbourhood scale (25:5?×?5 cells) is more realistic in the modeling. The accuracy of final validation is 83 % and the final scenario is based on this validation. A fuzzy CA has been used in urban growth modeling of Maraghe. The final scenario shows that Maraghe will growth on the east side, where the land demand for built up area and government facilities plays the significant role.     

Assessing the visual impacts of new urban features: coupling visibility analysis with 3D city modelling

Abstract

Land use changes associated with urbanization often have negative impacts on scenic beauty. In this paper, we explore and compare the visual impact of two different scenarios of urban growth including historical urban growth (HUG) and aesthetically sound urban growth (AUG) with two different categories of height for buildings in the city of Gorgan, Iran. This was done by viewshed and landscape metric analysis of different viewpoints and 3D representation of each scenario. The results show that with the AUG scenario, viewsheds are less impacted by new developments than the HUG scenario in all the viewpoints. It can be concluded that building locations can considerably affect the landscape visibility while building height does not impact to the same degree as location. The results of this research, as a Spatial Decision Support System, would help the managers for better understanding of different patterns of urbanization and its effect on landscape view.     

Study on forecast scenarios for simulation of future urban growth in Shenyang City based on SLEUTH model

The SLEUTH urban growth model was used to simulate future urban growth patterns and to explore potential environ-mental impacts of urban development under different conditions of development in Shenyang City, China. The SLEUTH model was calibrated with historical data (1988-2004) extracted from a time series of TM satellite images, and the future growth was pro-jected out to 2030 assuming three different policy scenarios: (1) current trends scenario (Scenario CT), (2) regional policy and ur-ban planning sce...     

Measuring the relationships between landscape aesthetics suitability and spatial patterns of urbanized lands: an informed modelling framework for developing urban growth scenarios

This study develops an informed modelling approach that follows a bottom-up planning strategy to define plausible urban growth scenarios. In this case, landscape aesthetics suitability of the area was first generated using multi-criteria evaluation method. Then, a buffer zone of 1 km was considered to extract the average values of aesthetics suitability scores surrounding urban patches with medium physical size (10–30 hectares). The averaged values were considered as the dependent variable. In the next step, landscape metrics of these urban patches, as explanatory variables, were also computed to measure compositional and configuration-based attributes of urban clusters. Bivariate associations (Pearson correlation analysis) and statistical relationships (linear regression algorithm) between landscape metrics and their associated aesthetics values were measured and modelled. According to the results, both composition and configuration values are significantly correlated to the dependent variable in which configuration-based attributes depicted a stronger explanatory power.     

Manifestation of Remote Sensing Data in Modeling Urban Sprawl Using the SLEUTH Model and Brute Force Calibration: A Case Study of Sana’a City, Yemen

Sana’a the metropolitan capital of Yemen, has experienced rapid spatial growth and uncontrolled development for decades. In the absence of a means to forecast and predict urban growth trends, planning and urban policy decisions have been found wanting. In this study the SLEUTH (Slope, landuse, exclusion, urban extent, transportation and hillshade) model which has been widely and successfully applied in developed countries, has been applied to predict the spatial urban sprawl pattern from 2004–2020 in Sana’a. This was to provide the necessary forecast for better planning and decision making. The model performed well as per the calibration coefficient values. The results showed that there will a 29 % increase in spatial urban sprawl growth during the modeling period. Growth of the sprawl will be mainly at the edges of the urban boundary, there will also be a wide area of scattered urban clusters. Factors that will have major influence on spatial expansion of the city will be diffusion, natural and internal growth, slope (that will hinder spread) and transportation (along which most of the urban sprawl will occur). The study also provides an insight into how the SLEUTH model performs in a poorly planned urban environment as compared to the planned and controlled environment where it has been applied.     

Urban growth monitoring and projection using remote sensing and geographic information systems: a case study in the Twin Cities Metropolitan Area,Minnesota

This study investigates urban growth dynamics from regional to local scales in the Twin Cities Metropolitan Area and demonstrates how metropolitan growth can be driven by policies. Urban change from 1975 to 2006 was detected using Landsat imagery. Future growth in 2030 was modelled based on two scenarios with or without regional development policies incorporated. City- or township-level growth was examined by a zonal analysis. Results show urban grew 126,700 ha from 1975 to 2006. The Markov-Cellular Automata model projected at least another 67,000 ha of urban growth from 2006 to 2030. When regional development policies were incorporated, homogeneous and compact growth patterns were predicted along the urban periphery; however, actual land supplies within the cities along the urban edge are facing challenges to accommodate the projected growth as large portions of suitable lands are located outside of the 2030 Municipal Urban Service Area boundary.     

Feasible conversion degree of dryland to paddy field in Jinxian County,Jiangxi province,China

The purpose of this paper was to evaluate the feasibility of conversion from dryland to paddy field in Jinxian County under the water resources constraint and dryland suitability condition. We constructed a water resources balance model to evaluate irrigation needs and a dryland suitability model that coupled various spatial data layers. Our research showed that under the water resources constraint, the amount of conversion from dryland to paddy field was 26,971.69 ha and the feasible conversion degree of dryland to paddy field was 0.84, while under the dryland suitability condition, it was 23,262.74 ha and 0.72, respectively. According to the principle of maximum constraint, we conclude that the feasible conversion degree of dryland to paddy field was 0.72. This research can provide an objective and scientific basis for carrying out a programme of farmland conversion in counties of China as well as similar areas worldwide.     

Mapping 3D visibility in an urban street environment from mobile LiDAR point clouds

ABSTRACT

Visibility determination is a key requirement in a wide range of national and urban applications, such as national security, landscape management, and urban design. Mobile LiDAR point clouds can depict the urban built environment with a high level of details and accuracy. However, few three-dimensional visibility approaches have been developed for the street-level point-cloud data. Accordingly, an approach based on mobile LiDAR point clouds has been developed to map the three-dimensional visibility at the street level. The method consists of five steps: voxelization of point-cloud data, construction of lines-of-sight, construction of sectors of sight, construction of three-dimensional visible space, and calculation of volume index. The proposed approach is able to automatically measure the volume of visible space and openness at any viewpoint along a street. This approach has been applied to three study areas. The results indicated that the proposed approach enables accurate simulation of visible space as well as high-resolution (1 m × 1 m) mapping of the visible volume index. The proposed approach can make a contribution to the improvement of urban planning and design processes that aim at developing more sustainable built environments.     

Incorporating sprawl and adjacency measures in land‐use forecasting model: A case study of Collin County,TX

Sprawl measures have largely been neglected in land‐use forecasting models. The current approach for land‐use allocation using optimization mostly utilizes objective functions and constraints that are non‐spatial in nature. Application of spatial constraints could take care of the contiguity and compactness of land uses and can be utilized to address urban sprawl. Because a land‐use model is used as an input to transportation modeling, a better spatial allocation strategy for more compact land‐use projections will promote better transportation planning and sustainable development. This study formulates a scenario‐based approach to normative modeling of urban sprawl. In doing so, it seeks to improve the land‐use projections by employing a spatial optimization model with contiguity and compactness consideration. This study incorporates urban sprawl measures based on smart growth principles together with a mixed‐use factor, and adjacency consideration of nearby land uses. The objective function used in the study maximizes net suitability based on imposed constraints. These constraints are based on smart growth principles that enhance walkability in neighborhoods, promote better health for residents, and encourage mixed‐use development. The formulated model has been applied to Collin County, TX, a fast‐developing suburban county located to the north of the Dallas–Fort Worth metroplex. The suitability of land cells indicates the probability of conversion, which is calculated using spatial discrete choice analysis with Moran eigenvector spatial filtering for vacant cells at a resolution of 150 × 150 m employing factors of the built environment, and socioeconomic and demographic characteristics. This study demonstrates how spatial proximity between land uses, which has been ignored to date, can be used to control sprawl, resulting in better mixing of different land uses based on constraints imposed in a spatial optimization problem.     

Geosimulation of urban growth and demographic decline in the Ruhr: a case study for 2025 using the artificial intelligence of cells and agents

The Ruhr is an “old acquaintance” in the discourse of urban decline in old industrialized cities. The agglomeration has to struggle with archetypical problems of former monofunctional manufacturing cities. Surprisingly, the image of a shrinking city has to be refuted if you shift the focus from socioeconomic wealth to its morphological extension. Thus, it is the objective of this study to meet the challenge of modeling urban sprawl and demographic decline by combining two artificial intelligent solutions: The popular urban cellular automaton SLEUTH simulates urban growth using four simple but effective growth rules. In order to improve its performance, SLEUTH has been modified among others by combining it with a robust probability map based on support vector machines. Additionally, a complex multi-agent system is developed to simulate residential mobility in a shrinking city agglomeration: residential mobility and the housing market of shrinking city systems focuses on the dynamic of interregional housing markets implying the development of potential dwelling areas. The multi-agent system comprises the simulation of population patterns, housing prices, and housing demand in shrinking city agglomerations. Both models are calibrated and validated regarding their localization and quantification performance. Subsequently, the urban landscape configuration and composition of the Ruhr 2025 are simulated. A simple spatial join is used to combine the results serving as valuable inputs for future regional planning in the context of multifarious demographic change and preceding urban growth.     

Multi-source Classification Using Artificial Neural Network in a Rugged Terrain

Abstract

This study advocates the use of GIS and remote sensing technologies to establish urban evolution maps and assess the impact of urbanization on agricultural areas over the last three decades. The target area is the city of Béni‐Mellal, located in central Morocco. The methodology adopted makes use of panchromatic SPOT images to survey the urban areas during the 1980s and 1990s. Available topographic maps provided the information for the 1970s. Maps and statistics of land use and urban growth for Béni Mellal were established after manually classifying images on a per-polygon basis and digitizing topographic maps using GIS capabilities. The results show an increase in dense urban area by 980.7 ha from the 1970s to the 1990s. This increase occurred at the expense of forests (24.7 ha), plantations (752.3 ha), rangeland (113.4 ha), non‐irrigated land (69.7 ha), and irrigated land (20.6 ha). During this period, scattered urban areas, predominantly suburbs, increased by 755.9 ha to the detriment of forests (14.9 ha), plantations (109.8 ha), rangeland (138.9 ha), non‐irrigated land(400.5 ha), and irrigated land (91.9 ha). These cartographic and statistic results are efficient decision‐making tools for protecting agricultural land and planning urban and suburban areas.     

Quantifying urban land cover change between 2001 and 2006 in the Gulf of Mexico region

We estimated urbanization rates (2001–2006) in the Gulf of Mexico region using the National Land Cover Database (NLCD) 2001 and 2006 impervious surface products. An improved method was used to update the NLCD impervious surface product in 2006 and associated land cover transition between 2001 and 2006. Our estimation reveals that impervious surface increased 416 km² with a growth rate of 5.8% between 2001 and 2006. Approximately 1110.1 km² of non-urban lands were converted into urban land, resulting in a 3.2% increase in the region. Hay/pasture, woody wetland, and evergreen forest represented the three most common land cover classes that transitioned to urban. Among these land cover transitions, more than 50% of the urbanization occurred within 50 km of the coast. Our analysis shows that the close-to-coast land cover transition trend, especially within 10 km off the coast, potentially imposes substantial long-term impacts on regional landscape and ecological conditions.     

Spatiotemporal changes of urban impervious surface area and land surface temperature in Beijing from 1990 to 2014

This study examined changes in urban expansion and land surface temperature in Beijing between 1990 and 2014 using multitemporal TM, ETM+, and OLI images, and evaluated the relationship between percent impervious surface area (%ISA) and relative mean annual surface temperature (RMAST). From 1990 to 2001, both internal land transformation and outward expansion were observed. In the central urban area, the high-density urban areas decreased by almost 7 km², while the moderate- and high-density urban land areas increased by 250 and 90 km², respectively, outside of the third ring road. From 2001 to 2014, high-density urban areas between the fifth and sixth ring roads experienced the greatest increase by more than 210 km², and RMAST generally increased with %ISA. During 1990–2001 and 2001–2014, RMAST increased by more than 1.5 K between the south third and fifth ring roads, and %ISA increased by more than 50% outside of the fifth ring road. These trends in urban expansion and RMAST over the last two decades in Beijing can provide useful information for urban planning decisions.     

Evaluation of machine learning models for rice dry biomass estimation and mapping using quad-source optical imagery

ABSTRACT

Several machine learning regression models have been advanced for the estimation of crop biophysical parameters with optical satellite imagery. However, literature on the comparative performances of such models is still limited in range and scope, especially under multiple data sources, despite the potential of multi-source imagery to improving crop monitoring in cloudy areas. To fill in this knowledge gap, this study explored the synergistic use of Landsat-8, Sentinel-2A, China’s environment and disaster monitoring and forecasting satellites (HJ-1 A and B) and Gaofen-1 (GF-1) data to evaluate four machine learning regression models that include Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Gradient Boosting Decision Tree (GBDT), for rice dry biomass estimation and mapping. Taking a major rice cultivation area in southeast China as case study during the 2016 and 2017 growing seasons, a cross-calibrated time series of the Enhanced Vegetation Index (EVI) was obtained from the quad-source optical imagery and on which the aforementioned models were applied, respectively. Results indicate that in the before rice heading scenario, the most accurate dry biomass estimates were obtained by the GBDT model (R² of 0.82 and RMSE of 191.8 g/m²) followed by the RF model (R² of 0.79 and RMSE of 197.8 g/m²). After heading, the k-NN model performed best (R² of 0.43 and RMSE of 452.1 g/m²) followed by the RF model (R² of 0.42 and RMSE of 464.7 g/m²). Whist the k-NN model performed least in the before heading scenario, SVM performed least in the after heading scenario. These findings may suggest that machine learning regression models based on an ensemble of decision trees (RF and GBDT) are more suitable for the estimation of rice dry biomass, at least with optical satellite imagery. Studies that would extend the evaluation of these machine learning models, to other parameters like leaf area index, and to microwave imagery, are hereby recommended.     

Sensing Sprawl: Towards the Monitoring of Urban Expansion Using Dempster‐Shafer Theory

Abstract

The outward expansion of cities in the United States has been a source of concern and policy debate for well over forty years. This sprawling urban landscape has been cited as a contributing factor behind the loss of open space, environmental damage and increased congestion. To better understand urban expansion, monitoring programs are required to facilitate the systematic observation of urban expansion, and to provide critical information in order to adjust urban development policies. Monitoring the urban landscape has been a major application focus of satellite remote sensing technologies. Yet, research has shown that the complexity of the urban landscape frustrates simple characterization of cumulative land cover processes such as sprawl. In this paper an approach to the remote detection and characterization of sprawl is introduced based on the use of Dempster‐Shafer Theory of Evidence. Functioning as a soft‐classification algorithm, Demptster‐Shafer Theory offers a unique solution to the mapping problem when evidence of class structure in underscored by uncertainty. Through the use of this technique it was possible to model uncertainty based on the concept of belief. This conceptualization was instrumental in deciphering the complexities of urban land cover arrangements and offered an alternative logic which enhanced delineation of subtle changes in land cover indicative of sprawl.     

Spatial Differences in Multi-Resolution Urban Automata Modeling

The last decade has seen a renaissance in spatial modeling. Increased computational power and the greater availability of spatial data have aided in the creation of new modeling techniques for studying and predicting the growth of cities and urban areas. Cellular automata is one modeling technique that has become widely used and cited in the literature; yet there are still some very basic questions that need to be answered with regards to the use of these models, specifically relating to the spatial resolution during calibration and how it can impact model forecasts. Using the SLEUTH urban growth model ( Clarke et al. 1997 ), urban growth for San Joaquin County (CA) is projected using three different spatial grains, based on four calibration routines, and the spatial differences between the model outputs are examined. Model outputs show that calibration at finer scaled data results in different parameter sets, and forecasting of urban growth in areas that was not captured through the use of more coarse data.     

Evaluating environmental equities of urban forest in terms of cooling services using ETM+ and Google data

The urban forest plays an important role in mitigating the urban heat island. However, the cooling effects of different types of urban forest are unclear. In addition, the fairness of the cooling effect of the urban forest has not been discussed. In this study, the land surface temperature (LST) of Changchun City, China was obtained from Landsat ETM+ data and then correlated with detailed urban forest information derived from the high-spatial-resolution Google Maps in order to determine the cooling intensity and cooling distance of different types of urban forest. In addition, the Gini coefficient was used to evaluate the equity of cooling services provided by the urban forest. The results indicated that (1) the total area of urban forest in Changchun City is 106.69 km² and is composed of attached forest (AF, 45.83 km²), road forest (RF, 23.87 km²), ecological public welfare forest (EF, 23.24 km²) and landscape forest (LF, 13.75 km²); (2) the cooling effect of different types of urban forest varies. The cooling intensity and cooling distance are 3.2 °C and 125 m (LF), 0.2 °C and 150 m (EF) and 0.6 °C and 5 m (AF), and RF had no cooling effect; and (3) the cooling effect of urban forest benefits approximately 760,000 people in Changchun City, and the Gini coefficient of the cooling services of urban forest was 0.29, indicating that the cooling service was reasonable. Therefore, we demonstrated that ETM+ and Google data are a convenient and affordable approach to study the LST on an urban scale, and the Gini coefficient could be a meaningful indicator to evaluate urban ecological services.     

Characterizing urban area dynamics in historic city of Kurukshetra,India, using remote sensing and spatial metric tools

Abstract

Developing countries like India are an urbanization hotspot with many upcoming towns and cities. Growth in small and medium sized towns and cities have been unnoticed and growing without appropriate urban planning. Utilizing the available medium resolution satellite data and geospatial platforms, the growth dynamics of Kurukshetra city was analysed over a period of 24 years. The study employed a combination of change detection technique and spatial metrics (six each of class and landscape levels) analysis to delineate the growth track of the city and its environs. A significant increase in urban built up (dense 237%; open 1038%) is seen majorly at the cost of open area (70%) and tree clad (58%). Phases of city’s aggregation and diffusion are observed using class and landscape level spatial metrics. Understanding and monitoring of land use changes in and around city limits using integrated spatial tools provide better decision making capability.