Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based Land Transformation Model: an integrated ML-CA-LTM modeling framework

Cellular automata (CA) and artificial neural networks (ANNs) have been used by researchers over the last three decades to simulate land-use change (LUC). While conventional CA and ANN models assign a cell to only one land-use class, in reality, a cell may belong to several land-use classes simultaneously. The recently developed multi-label (ML) concept overcomes this limitation in land change science. Although the ML concept is a new paradigm with nonexclusive classes and has shown considerable merit in several applications, few studies in land change science have applied it. In addition, determining transition rules in conventional CA is difficult when the number of drivers is large. Since CA has been shown as a potential model to consider neighborhood effects and ANN has been shown effective in determining CA transition rules, we integrated both CA with an ANN model to overcome limitations of each tool. In this study, we specifically extended the ANN-based Land Transformation Model (LTM) with both a CA-based model and the ML concept to create an integrated ML-CA-LTM modeling framework. We also compared, using standard evaluation measures, differences between the proposed integrated model with a conventional CA-based LTM model (called the ml-CA-LTM). Parameterization was made using a learning and testing procedure common in machine learning. Results showed that the modified LUC model, ML-CA-LTM, produced consistently better goodness of fit calibration values compared to the ml-CA-LTM. The outcome of this modified model can be used by managers and decision makers for improved urban planning.     

A grey wolf optimizer–cellular automata integrated model for urban growth simulation and optimization

This article proposes a grey wolf optimizer (GWO) and cellular automata (CA) integrated model for the simulation and spatial optimization of urban growth. A new grey wolf‐inspired approach is put forward to determine the urban growth rules of CA cells by using the GWO algorithm, which is suitable for solving optimization problems. The inspiration for GWO comes from the social leadership of wolf groups, as well as their hunting behavior. The GWO‐optimized urban growth rules for CA describe the relationship between the spatial variables and the urban land‐use status for each cell in the formation of “if–then.” The GWO algorithm and CA model are then integrated as the GWO–CA model for urban growth simulation and optimization. By taking Nanjing City as an example, the simulation accuracy in terms of urban cells is 86.6%, and the kappa coefficient is 0.715, indicating that the GWO algorithm is efficient at obtaining urban growth rules from spatial variables. The validation of the GWO–CA model also illustrates that it performs well in terms of the simulation and spatial optimization of urban growth, and can further contribute to urban planning and management.     

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.     

城市扩展元胞自动机多结构卷积神经网络模型

传统的城市扩展元胞自动机(CA)模型是基于单个元胞的变量信息挖掘来构建转换规则的。针对这一问题,本文基于多结构卷积神经网络提出从区域特征出发且顾及区域多尺度特征挖掘转换规则的城市扩展元胞自动机模型(MSCNN-CA),并以武汉主城区和上海浦东新区为例,模拟了两个试验区2005—2015年期间城市扩展过程。模型验证表明:与逻辑回归和神经网络相比,本文构建的3个单一结构的卷积神经网络元胞自动机(CNN-CA)模型在4个指标(Kappa系数、FoM(figure of merit)值、命中率(h)和错误率(m))上都有不同程度的提高。特别是FoM指数,在武汉主城区提高了23.3%~29.4%,在上海浦东新区提高了20.3%~28.5%。此外,MSCNN-CA模型与3个单一结构的CNN-CA模型相比,在各个指标上也有所改善,FoM指数在武汉主城区提高了0.8%~4.8%,上海浦东新区提高了2.8%~7.8%。两个试验区的模拟结果表明:相比传统CA模型,基于多结构卷积神经网络的城市扩展元胞自动机模型(MSCNN-CA)能够有效提高城市扩展模拟的精度,更真实地反映城市扩展空间演变过程。相比单结构的卷积神经网络CA模型,多结构卷积神经网络CA模型的稳定性和模拟结果准确性有所提升。     

An Artificial-Neural-Network-based,Constrained CA Model for Simulating Urban Growth

Insufficient research has been done on integrating artificial-neural-network-based cellular automata (CA) models and constrained CA models, even though both types have been studied for several years. In this paper, a constrained CA model based on an artificial neural network (ANN) was developed to simulate and forecast urban growth. Neural networks can learn from available urban land-use geospatial data and thus deal with redundancy, inaccuracy, and noise during the CA parameter calibration. In the ANN-Urban-CA model we used, a two-layer Back-Propagation (BP) neural network has been integrated into a CA model to seek suitable parameter values that match the historical data. Each cell's probability of urban transformation is determined by the neural network during simulation. A macro-scale socio-economic model was run together with the CA model to estimate demand for urban space in each period in the future. The total number of new urban cells generated by the CA model was constrained, taking such exogenous demands as population forecasts into account. Beijing urban growth between 1980 and 2000 was simulated using this model, and long-term (2001–2015) growth was forecast based on multiple socio-economic scenarios. The ANN-Urban-CA model was found capable of simulating and forecasting the complex and non-linear spatial-temporal process of urban growth in a reasonably short time, with less subjective uncertainty.     

A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree,Markov chain and cellular automata models in GIS

Urban development is a continuous and dynamic spatio-temporal phenomenon associated with economic developments and growing populations. To understand urban expansion, it is important to establish models that can simulate urbanization process and its deriving factors behaviours, monitor deriving forces interactions and predict spatio-temporally probable future urban growth patterns explicitly. In this research, therefore, we presented a hybrid model that integrates the chi-squared automatic integration detection decision tree (CHAID-DT), Markov chain (MC) and cellular automata (CA) models to analyse, simulate and predict future urban expansions in Tripoli, Libya in 2020 and 2025. First, CHAID-DT model was applied to investigate the contributions of urban factors to the expansion process, to explore their interactions and to provide future urban probability map; second, MC model was employed to estimate the future demand of urban land; third, CA model was used to allocate estimated urban land quantity on the probability map to present future projected land use map. Three satellite images of the study area were obtained from the periods of 1984, 2002 and 2010 to extract land use maps and urban expansion data. We validated the model with two methods, namely, receiver operating characteristic and the kappa statistic index of agreement. Results confirmed that the proposed hybrid model could be employed in urban expansion modelling. The applied hybrid model overcame the individual shortcomings of each model and explicitly described urban expansion dynamics, as well as the spatio-temporal patterns involved.     

Geospatial scenario based modelling of urban and agricultural intrusions in Ramsar wetland Deepor Beel in Northeast India using a multi-layer perceptron neural network

In recent decades, the world has experienced unprecedented urban growth which endangers the green environment in and around urban areas. In this work, an artificial neural network (ANN) based model is developed to predict future impacts of urban and agricultural expansion on the uplands of Deepor Beel, a Ramsar wetland in the city area of Guwahati, Assam, India, by 2025 and 2035 respectively. Simulations were carried out for three different transition rates as determined from the changes during 2001–2011, namely simple extrapolation, Markov Chain (MC), and system dynamic (SD) modelling, using projected population growth, which were further investigated based on three different zoning policies. The first zoning policy employed no restriction while the second conversion restriction zoning policy restricted urban-agricultural expansion in the Guwahati Municipal Development Authority (GMDA) proposed green belt, extending to a third zoning policy providing wetland restoration in the proposed green belt. The prediction maps were found to be greatly influenced by the transition rates and the allowed transitions from one class to another within each sub-model. The model outputs were compared with GMDA land demand as proposed for 2025 whereby the land demand as produced by MC was found to best match the projected demand. Regarding the conservation of Deepor Beel, the Landscape Development Intensity (LDI) Index revealed that wetland restoration zoning policies may reduce the impact of urban growth on a local scale, but none of the zoning policies was found to minimize the impact on a broader base. The results from this study may assist the planning and reviewing of land use allocation within Guwahati city to secure ecological sustainability of the wetlands.     

Model of Land Suitability Evaluation Based on Computational Intelligence

A novel model of land suitability evaluation is built based on computational intelligence (CI). A fuzzy neural network (FNN) is constructed by the integration of fuzzy logic and artificial neural network (ANN). The structure and process of this network is clear. Fuzzy rules (knowledge) are expressed in the model explicitly, and can be self-adjusted by learning from samples. Genetic algorithm (GA) is employed as the learning algorithm to train the network, and makes the training of the model efficient. This model is a self-learning and self-adaptive system with a rule set revised by training.     

基于计算智能的土地适宜性评价模型

将计算智能理论引入土地评价领域，构建了一个全新的土地适宜性评价模型。首先基于模糊逻辑和人工神经网络构造了一个模糊神经网络模型，然后采用改进的遗传算法进行训练，能够快速收敛到最优解，对初始的规则库进行修正，形成了一个自学习、自适应的评价系统。     

Modelling of land use land cover change using earth observation data-sets of Tons River Basin,Madhya Pradesh,India

Abstract

An integrated Markov Chain and Cellular Automata modelling (CA MARKOV), multicriteria evaluation techniques have been applied to produce transition probability. The unsupervised method was employed to classify the satellite images of year 1985, 1995, 2005 and 2015 to meet the magnitude of LULC change. Results showing the spatial pattern of the sub-basin is largely influenced by the biophysical and socio-economic drivers leading to growth of agricultural lands and built-up area in the basin. Simulated plausible future LULC changes for 2025 which is based on a CA MARKOV that integrates Markovian transition probabilities computed from satellite-derived LULC maps and a CA contiguity spatial filter (5 × 5). Further, the fragmentation analysis was performed to check the fragmentation scenario in the year 2025. The result for year 2025 with reasonably good accuracy will be useful to the planners, policy- and decision-makers.     

基于ANN-FES的石漠化综合治理智能决策支持系统设计及应用研究

对人工智能技术的两个分支神经网络(ANN)与模糊专家系统(FES)各自的基本工作原理进行了阐述,分析了基于神经网络和模糊专家系统集成的混合系统功能框架,并介绍了系统结构及知识表示、知识获取、知识简化和推理机制等方面的基本方法.在此基础上,以都安石漠化综合治理智能决策为例,提出了基于神经网络和专家系统集成的石漠化智能决策系统的结构.人工神经网络实现石漠化预警分析,预测得到的石漠化危险性指数,最后得出专家系统所需的预警度(无警、轻警、中警和重警).模糊专家系统的推理机通过对神经网络得到的初步数据和其他测量的数据处理,实现系统的综合诊断,最后由治理模式系统确定采用哪种综合治理模式.该方法融合了神经网络自适应学习能力强和模糊专家系统知识表达明确的优点,简化了神经网络学习数据获取及模糊推理规则建立的过程.     

MonoLoop:CA城市模型状态转换规则获取的一种方法

状态转换规则是元胞自动机(Cellular Automata,CA)的核心,如何获取并建立CA的状态转换规则是构建CA模型的关键。邻域作用是CA能够模拟复杂物理现象的核心驱动力,而在已有的用于城市增长模拟的CA城市模型中,因为邻域作用在模拟的过程中为时间动态的变量,其系数很难通过常用的Logistic回归方法识别,致使已有的CA城市模型的状态转换规则中,往往仅通过Logistic回归获取邻域作用之外的空间变量的模型参数,而邻域作用的参数通常采用主观赋值的方法。本文提出了CA城市模型的多指标评价(Multi-Criteria Evalua-tion,MCE)形式状态转换规则获取的一种新方法 MonoLoop,并针对北京市域1976～2006年的城市增长开展了该方法的实验。基于这种方法,一方面利用历史数据可以建立更为客观的状态转换规则;另一方面也可以大大降低模型参数识别的时间。     

Automatic urban road extraction using airborne laser scanning/altimetry and high resolution satellite data

Automatic road extraction from remotely sensed images has been an active research in urban area during last few decades. But such study becomes difficult in urban environment due to mix of natural and man-made features. This research explores methodology for semiautomatic extraction of urban roads. An integrated approach of airborne laser scanning (ALS) altimetry and high-resolution data has been used to extract road and differentiate them from flyovers. Object oriented fuzzy rule based approach classifies roads from high resolution satellite images. Complete road network is extracted with the combination of ALS and high-resolution data. The results show that an integration of LiDAR data and IKONOS data gives better accuracy for automatic road extraction. The method was applied on urban area of Amsterdam, The Netherlands.     

Modelling for prediction of global deforestation based on the growth of human population

Deforestation due to ever-increasing activities of the growing human population has been an issue of major concern for the global environment. It has been especially serious in the last several decades in the developing countries. A population-deforestation model has been developed by the authors to relate the population density with the cumulative forest loss, which is defined and computed as the total forest loss until 1990 since prior to human civilisation. NOAA-AVHRR-based land cover map and the FAO forest statistics have been used for 1990 land cover. A simulated land cover map, based on climatic data, is used for computing the natural land cover before the human impacts. With the 1990 land cover map as base and using the projected population growth, predictions are then made for deforestation until 2025 and 2050 in both spatial and statistical forms.     

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.     

Full polarimetric PALSAR-based land cover monitoring in Cambodia for implementation of REDD policies

Abstract

Forest cover monitoring plays an important role in the implementation of climate change mitigation policies such as Kyoto protocol and Reducing Emissions from Deforestation and Forest Degradation (REDD). In this study, we have monitored land cover using the PALSAR (Phased Array type L-band Synthetic Aperture Radar) full polarimetric data based on incoherent target decomposition. Supervised classification technique has been applied on Cloude–Pottier decomposition, Freeman–Durden three component, and Yamaguchi four component decomposition for accurate mapping of different types of land cover classes. Based on confusion matrix derived from the predicted and defined pixels, the evergreen and sparsely deciduous forests have shown high producer's accuracy by Freeman–Durden three component and Yamaguchi four component classifications. The overall accuracy of Maximum Likelihood Classification by Yamaguchi four component is 94.1% with 0.93 kappa coefficient as compared to the 90.3% with 0.88 kappa coefficient by Freeman–Durden three component and 89.7% with 0.88 kappa coefficient by Cloude–Pottier decomposition. High accuracy of classification in a forested area using full polarimetric PALSAR data may have been because of high penetration of L-band SAR. The content of this study could be useful for the forest cover mapping during cloudy days needed for proper implementation of REDD policies in Cambodia.     

Simulating urban growth in a megalopolitan area using a patch‐based cellular automata

Although traditional cellular automata (CA)‐based models can effectively simulate urban land‐use changes, they typically ignore the spatial evolution of urban patches, due to their use of cell‐based simulation strategies. This research proposes a new patch‐based CA model to incorporate a spatial constraint based on the growth patterns of urban patches into the conventional CA model for reducing the uncertainty of the distribution of simulated new urban patches. In this model, the growth pattern of urban patches is first estimated using a developed indicator that is based on the local variations in existing urban patches. The urban growth is then simulated by integrating the estimated growth pattern and land suitability using a pattern‐calibrated method. In this method, the pattern of new urban patches is gradually calibrated toward the dominant growth pattern through the steps of the CA model. The proposed model is applied to simulate urban growth in the Tehran megalopolitan area during 2000–2006–2012. The results from this model were compared with two common models: cell‐based CA and logistic‐patch CA. The proposed model yields a degree of patch‐level agreement that is 23.4 and 7.5% higher than those of these pre‐existing models, respectively. This reveals that the patch‐based CA model simulates actual development patterns much better than the two other models.     

Simulating urban growth processes by integrating cellular automata model and artificial optimization in Binhai New Area of Tianjin,China

This study presents an optimized algorithm into the cellular automata (CA) models for urban growth simulation in Binhai New Area of Tianjin, China. The optimized CA model by particle swarm optimization (PSO) was compared with the logistic-based cellular automata (LOGIT-CA) model to see the effects of the simulation. The study evaluated the stochastic disturbance in the development of urban growth using the Monte Carlo method; the coefficient d determined the state of urban growth. The validation was conducted by both cross-tabulation test and structural measurements. The results showed that the simulations of PSO-CA were better than LOGIT-CA model, indicating an improvement in the spatio-temporal simulation of urban growth and land use changes in study area. Since the simulations reached their best values when the coefficient was between 1 and 2, the urban growth in the study area was in the period of conversion from spontaneous growth to edge-expansion and infilling growth.     

Spatial modelling of site suitability assessment for hospitals using geographical information system-based multicriteria approach at Qazvin city,Iran

Due to the population growth and continuous migration of people from rural areas to urban areas, it is important to identify the suitable locations for future development in order to find suitable sites for various kinds of facilities such as schools, hospital and fire stations for new and existing urban areas. Site suitability modelling is a complex process involving various kinds of objectives and issues. Such a complex process includes spatial analysis, use of several decision support tools such as high-spatial resolution remotely sensed data, geographical information system (GIS) and multi criteria analysis (MCA) such as analytical hierarchy process (AHP), and in some cases, prediction techniques like cellular automata (CA) or artificial neural networks (ANN). This paper presents a comparison between the results of AHP and the ordinary least square (OLS) evaluation model, based on various criteria, to select suitable sites for new hospitals in Qazvin city, Iran. Based on the obtained results, proximity to populated areas (0.3) and distance to air polluted areas (0.23–0.26) were the two highest important criteria with high weight value. The results show that these two techniques not only have similarity in size (in m²) for each suitability class but they also have similarity in spatial distribution of each class in the entire study area. Based on calculations of both techniques, 1–2%, 25%, 40–43%, 16–20% and 14% of study areas are assigned as ‘not suitable', ‘less suitable', ‘moderately suitable', ‘suitable' and ‘most suitable' areas for construction of new hospitals. Results revealed that a 75% similarity was found in the distribution of suitability classes in Qazvin city using both techniques. Nineteen per cent (19%) of the study area are assigned as ‘suitable' and ‘most suitable' by both methods, so these areas can be considered as safe or secure areas for clinical purposes. Moreover, almost all (99.8%) suitable areas are located in district 3, because of its higher population, less numbers of existing hospitals and large numbers of barren land plots of acceptable size.     

Fluidity of land use patterns over the last century in and around the Baroda Urban Complex,Gujarat state

The fluidity of land-use patterns over the last century in and around the Baroda Urban Complex has been worked out using Survey of India topographic maps (1876–78, 1959–60) and SPOT satellite imagery (1988). The most striking feature of this study was the alarming loss of non-built up areas comprising agricultural land to urban sprawl. In 1876–78, non-built up land constituted 701.30 sq. km out of a total of 714 sq. km whereas in 1988, it was reduced to 625.27 sq. km. This urban growth pattern would not be conducive for sustainable development.