共查询到20条相似文献，搜索用时 15 毫秒
1.
通过模型对区域土地利用/覆盖变化（LUCC）进行分析已经成为了当前全球的研究主要内容之一。元胞自动机（CA）模型是一种通过定义局部的简单的计算规则来模拟和表示整个系统中复杂现象的时空动态模型,其＂自下而上＂的研究思路,强大的复杂计算功能及高度动态,使得它在模拟空间复杂系统的时空动态演变方面具有很强的能力。CA模型通过与其他模型相结合,在综合考虑各种限制因素和转换规则的前提下,通过反复迭代综合空间分析与非空间分析,模拟土地利用变化情景,在国内外已经形成了较为成熟的研究模型。本文首先提出了CA模型在土地利用变化中应用的背景及其特点;然后,分析了CA模型的构成原理以及在国内外的应用进展与现状;最后,详细阐述了CA模型在土地利用变化中的发展趋势及今后研究工作中应注意的问题。 相似文献
2.
Modelling coastal land use change by incorporating spatial autocorrelation into cellular automata models 总被引：1，自引：0，他引：1
This paper presents a spatial autoregressive (SAR) method-based cellular automata (termed SAR-CA) model to simulate coastal land use change, by incorporating spatial autocorrelation into transition rules. The model captures the spatial relationships between explained and explanatory variables and then integrates them into CA transition rules. A conventional CA model (LogCA) based on logistic regression (LR) was studied as a comparison. These two CA models were applied to simulate urban land use change of coastal regions in Ningbo of China from 2000 to 2015. Compared to the LR method, the SAR model yielded smaller accumulated residuals that showed a random distribution in fitting the CA transition rules. The better-fitting SAR model performed well in simulating urban land use change and scored an overall accuracy of 85.3%, improving on the LogCA model by 3.6%. Landscape metrics showed that the pattern generated by the SAR-CA model has less difference with the observed pattern. 相似文献
3.
Cellular automata (CA) have proven to be very effective for simulating and predicting the spatio-temporal evolution of complex geographical phenomena. Traditional methods generally pose problems in determining the structure and parameters of CA for a large, complex region or a long-term simulation. This study presents a self-adaptive CA model integrated with an artificial immune system to discover dynamic transition rules automatically. The model’s parameters are allowed to be self-modified with the application of multi-temporal remote sensing images: that is, the CA can adapt itself to the changed and complex environment. Therefore, urban dynamic evolution rules over time can be efficiently retrieved by using this integrated model. The proposed AIS-based CA model was then used to simulate the rural-urban land conversion of Guangzhou city, located in the core of China’s Pearl River Delta. The initial urban land was directly classified from TM satellite image in the year 1990. Urban land in the years 1995, 2000, 2005, 2009 and 2012 was correspondingly used as the observed data to calibrate the model’s parameters. With the quantitative index figure of merit (FoM) and pattern similarity, the comparison was further performed between the AIS-based model and a Logistic CA model. The results indicate that the AIS-based CA model can perform better and with higher precision in simulating urban evolution, and the simulated spatial pattern is closer to the actual development situation. 相似文献
4.
Integrating cellular automata,artificial neural network,and fuzzy set theory to simulate threatened orchards: application to Maragheh,Iran 总被引：1，自引：0，他引：1
Urbanization processes challenge the growth of orchards in many cities in Iran. In Maragheh, orchards are crucial ecological, economical, and tourist sources. To explore orchards threatened by urban expansion, this study first aims to develop a new model by coupling cellular automata (CA) and artificial neural network with fuzzy set theory (CA–ANN–Fuzzy). While fuzzy set theory captures the uncertainty associated with transition rules, the ANN considers spatial and temporal nonlinearities of the driving forces underlying the urban growth processes. Second, the CA–ANN–Fuzzy model is compared with two existing approaches, namely a basic CA and a CA coupled with an ANN (CA–ANN). Third, we quantify the amount of orchard loss during the last three decades as well as for the upcoming years up to 2025. Results show that CA–ANN–Fuzzy with 83% kappa coefficient performs significantly better than conventional CA (with 51% kappa coefficient) and CA–ANN (with 79% kappa coefficient) models in simulating orchard loss. The historical data shows a considerable loss of 26% during the last three decades, while the CA–ANN–Fuzzy simulation reveals a considerable future loss of 7% of Maragheh’s orchards in 2025 due to urbanization. These areas require special attention and must be protected by the local government and decision-makers. 相似文献
5.
土地变更调查是国土资源管理的重要基础性工作。本文结合广东省多年来积极探索建立的适应自身需求的变更调查工作机制,开展年度变更调查实践,总结项目组织实施、技术方法等方面的经验,特别是对重点变更类别提出了具体操作办法,对今后进一步高效、精确地开展土地调查具有一定的借鉴意义。 相似文献
6.
《International Journal of Digital Earth》2013,6(3):223-238
Abstract The paper explores a framework combining remote sensing and GIS-cellular automata (CA) concepts aimed at improving the modeling of unauthorized land use sprawl. Remote sensing data have been used in urban modeling and analysis, the use of high-resolution remote sensing data in assessing unauthorized development is quite unexplored. This work has demonstrated systematic combination utilization of geospatial analyses tools to acquire a new level of information to enable urban modeling and sprawl analysis in assisting urban sustainable management. In this study, Kuantan city, Malaysia was selected in simulation of the unauthorized land use with CA concept for a period of 15 years (2000–2015), with main input time-series land use observation from 1995 to 2005. The 2000 and 2005 land use input was also used as calibrated and test assessment of the simulation. The results show excellent agreement between in-situ changes of the unauthorized land use classes and the corresponding simulated classes within the same periods. In conclusion, CA model can lead to new levels of understanding of how urban areas grow and change as in view of digital earth aspiration. 相似文献
7.
《制图学和地理信息科学》2013,40(4):369-380
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. 相似文献
8.
Urban land-use change is the result of coupling interaction between planning and environment systems. The aim of our study was to construct an effective model to show how the urban land-use changes under the planning–environment interaction system with multi-hierarchy and major function oriented zoning. Combining the Cellular automata (CA) model with logistic regression model, the proposed multi-hierarchal vector CA model (MH-VCA3) was constructed by mining multi-hierarchal land-use transition rules under the planning–environment interaction system. Taking Jiangyin City (China) as an example, we compared the simulated result of the proposed model to those of the well-accepted Logistic CA and traditional multi-level CA models to demonstrate the effectiveness of the consideration of top-down decomposition constraint and bottom-up updating. Furthermore, by simulating the land-use changes under different population regionalization scenarios, we found that in order to form the spatial pattern of “agglomeration in the north and ecology in the south,” the planned population growth at the global hierarchal level should be allocated to the district units according to the law of Central district > Chengxi district > Chengdong district > Chengnan district > Chengdongnan district. The proposed model is expected to provide scientific support for the formulation of urban planning schemes in the future. 相似文献
9.
Enhancing a GIS Cellular Automata Model of Land Use Change: Bayesian Networks, Influence Diagrams and Causality 总被引：1，自引：0，他引：1
Cellular Automata (CA) models at present do not adequately take into account the relationship and interactions between variables. However, land use change is influenced by multiple variables and their relationships. The objective of this study is to develop a novel CA model within a geographic information system (GIS) that consists of Bayesian Network (BN) and Influence Diagram (ID) sub‐models. Further, the proposed model is intended to simplify the definition of parameter values, transition rules and model structure. Multiple GIS layers provide inputs and the CA defines the transition rules by running the two sub‐models. In the BN sub‐model, land use drivers are encoded with conditional probabilities extracted from historical data to represent inter‐dependencies between the drivers. Using the ID sub‐model, the decision of changing from one land use state to another is made based on utility theory. The model was applied to simulate future land use changes in the Greater Vancouver Regional District (GVRD), Canada from 2001 to 2031. The results indicate that the model is able to detect spatio‐temporal drivers and generate various scenarios of land use change making it a useful tool for exploring complex planning scenarios. 相似文献
10.
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. 相似文献
11.
Hossein Shafizadeh-Moghadam Ali Asghari Mohammad Taleai Marco Helbich Amin Tayyebi 《地理信息系统科学与遥感》2017,54(5):639-656
This study evaluates the effects of cellular automata (CA) with different neighborhood sizes on the predictive performance of the Land Transformation Model (LTM). Landsat images were used to extract urban footprints and the driving forces behind urban growth seen for the metropolitan areas of Tehran and Isfahan in Iran. LTM, which uses a back-propagation neural network, was applied to investigate the relationships between urban growth and the associated drivers, and to create the transition probability map. To simulate urban growth, the following two approaches were implemented: (a) the LTM using a top-down approach for cell allocation grounding on the highest values in the transition probability map and (b) a CA with varying spatial neighborhood sizes. The results show that using the LTM-CA approach increases the accuracy of the simulated land use maps when compared with the use of the LTM top-down approach. In particular, the LTM-CA with a 7 × 7 neighborhood size performed well and improved the accuracy. The level of agreement between simulated and actual urban growth increased from 58% to 61% for Tehran and from 39% to 43% for Isfahan. In conclusion, even though the LTM-CA outperforms the LTM with a top-down approach, more studies have to be carried out within other geographical settings to better evaluate the effect of CA on the allocation phase of the urban growth simulation. 相似文献
12.
The use of cellular automata (CA) has for some time been considered among the most appropriate approaches for modeling land‐use changes. Each cell in a traditional CA model has a state that evolves according to transition rules, taking into consideration its own and its neighbors’ states and characteristics. Here, we present a multi‐label CA model in which a cell may simultaneously have more than one state. The model uses a multi‐label learning method—a multi‐label support vector machine, Rank‐SVM—to define the transition rules. The model was used with a multi‐label land‐use dataset for Luxembourg, built from vector‐based land‐use data using a method presented here. The proposed multi‐label CA model showed promising performance in terms of its ability to capture and model the details and complexities of changes in land‐use patterns. Applied to historical land use data, the proposed model estimated the land use change with an accuracy of 87.2% exact matching and 98.84% when including cells with a misclassification of a single label, which is comparably better than a classical multi‐class model that achieved 83.6%. The multi‐label cellular automata outperformed a model combining CA and artificial neural networks. All model goodness‐of‐fit comparisons were quantified using various performance metrics for predictive models. 相似文献
13.
随着愈加快速的土地资源开发,土地利用时空变化研究已全面启动,本文结合ArcObjects组件、Access数据库和VC 工具开发了一个土地利用时空分析系统,实现了土地利用空间数据建库、地图查询分析、土地利用时空模型解算等功能。为土地利用时空研究提供功能集成的数据运算与图形处理平台,也为土地利用规划的制定及其修编等工作提供了技术支持。 相似文献
14.
Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules 总被引：2，自引：0，他引：2
The dynamic relationships between land use change and its driving forces vary spatially and can be identified by geographically weighted regression (GWR). We present a novel cellular automata (GWR-CA) model that incorporates GWR-derived spatially varying relationships to simulate land use change. Our GWR-CA model is characterized by spatially nonstationary transition rules that fully address local interactions in land use change. More importantly, each driving factor in our GWR model contains effects that both promote and resist land use change. We applied GWR-CA to simulate rapid land use change in Suzhou City on the Yangtze River Delta from 2000 to 2015. The GWR coefficients were visualized to highlight their spatial patterns and local variation, which are closely associated with their effects on land use change. The transition rules indicate low land conversion potential in the city’s center and outer suburbs, but higher land conversion potential in the inner near suburbs along the belt expressway. Residual statistics show that GWR fits the input data better than logistic regression (LR). Compared with an LR-based CA model, GWR-CA improves overall accuracy by 4.1% and captures 5.5% more urban growth, suggesting that GWR-CA may be superior in modeling land use change. Our results demonstrate that the GWR-CA model is effective in capturing spatially varying land transition rules to produce more realistic results, and is suitable for simulating land use change and urban expansion in rapidly urbanizing regions. 相似文献
15.
This paper combines participatory activities (PA) with remote sensing analysis into an integrated methodology to describe and explain land-cover changes. A remote watershed on Mindanao (Philippines) is used to showcase the approach, which hypothesizes that the accuracy of expert knowledge gained from remote sensing techniques can be further enhanced by inputs from vernacular knowledge when attempting to understand complex land mosaics and past land-use changes. Six participatory sessions based on focus-group discussions were conducted. These were enhanced by community-based land-use mapping, resulting in a final total of 21 participatory land-use maps (PLUMs) co-produced by a sample of stakeholders with different sociocultural and ecological perspectives. In parallel, seven satellite images (Landsat MSS, Landsat TM, Landsat ETM+, and SPOT4) were classified following standard techniques and provided snapshots for the years 1976, 1996, and 2010. Local knowledge and collective memory contributed to define and qualify relevant land-use classes. This also provided information about what had caused the land-use changes in the past. Results show that combining PA with remote-sensing analysis provides a unique understanding of land-cover change because the two methods complement and validate one another. Substantive qualitative information regarding the chronology of land-cover change was obtained in a short amount of time across an area poorly covered by scientific literature. The remote sensing techniques contributed to test and to quantify verbal reports of land-use and land-cover change by stakeholders. We conclude that the method is particularly relevant to data-poor areas or conflict zones where rapid reconnaissance work is the only available option. It provides a preliminary but accurate baseline for capturing land changes and for reporting their causes and consequences. A discussion of the main challenges encountered (i.e. how to combine different systems of knowledge), and options for further methodological improvements, are also provided. 相似文献
16.
M. Heinl J. Walde G. Tappeiner U. Tappeiner 《International Journal of Applied Earth Observation and Geoinformation》2009
The study investigates the performance of image classifiers for landscape-scale land cover mapping and the relevance of ancillary data for the classification success in order to assess and to quantify the importance of these components in image classification. Specifically tested are the performance of maximum likelihood classification (MLC), artificial neural networks (ANN) and discriminant analysis (DA) based on Landsat7 ETM+ spectral data in combination with topographic measures and NDVI. ANN produced high accuracies of more than 75% also with limited input information, while MLC and DA produced comparable results only by incorporating ancillary data into the classification process. The superiority of ANN classification was less pronounced on the level of the single land cover classes. The use of ancillary data generally increased classification accuracy and showed a similar potential for increasing classification accuracy than the selection of the classifier. Therefore, a stronger focus on the development of appropriate and optimised sets of input variables is suggested. Also the definition and selection of land cover classes has shown to be crucial and not to be simply adaptable from existing land cover class schemes. A stronger research focus towards discriminating land cover classes by their typical spectral, topographic or seasonal properties is therefore suggested to advance image classification. 相似文献
17.
Soil erosion prediction based on land cover dynamics at the Semenyih watershed in Malaysia using LTM and USLE models 总被引：1，自引：0，他引：1
This study attempts to identify and forecast future land cover (LC) by using the Land Transformation Model (LTM), which considers pixel changes in the past and makes predictions using influential spatial features. LTM applies the Artificial Neural Networks algorithm) in conducting the analysis. In line with these objectives, two satellite images (Spot 5 acquired in 2004 and 2010) were classified using the Maximum Likelihood method for the change detection analysis. Consequently, LC maps from 2004 to 2010 with six classes (forest, agriculture, oil palm cultivations, open area, urban, and water bodies) were generated from the test area. A prediction was made on the actual soil erosion and the soil erosion rate using the Universal Soil Loss Equation (USLE) combined with remote sensing and GIS in the Semenyih watershed for 2004 and 2010 and projected to 2016. Actual and potential soil erosion maps from 2004 to 2010 and projected to 2016 were eventually generated. The results of the LC change detections indicated that three major changes were predicted from 2004 to 2016 (a period of 12 years): (1) forest cover and open area significantly decreased at rates of almost 30 and 8 km2, respectively; (2) cultivated land and oil palm have shown an increment in sizes at rates of 25.02 and 5.77 km2, respectively; and, (3) settlement and Urbanization has intensified also by almost 5 km2. Soil erosion risk analysis results also showed that the Semenyih basin exhibited an average annual soil erosion between 143.35 ton ha?1 year?1 in 2004 and 151 in 2010, followed by the expected 162.24 ton ha?1 year?1. These results indicated that Semenyih is prone to water erosion by 2016. The wide range of erosion classes were estimated at a very low level (0–1 t/ha/year) and mainly located on steep lands and forest areas. This study has shown that using both LTM and USLE in combination with remote sensing and GIS is a suitable method for forecasting LC and accurately measuring the amount of soil losses in the future. 相似文献
18.
土地利用变化模拟模型及应用研究进展 总被引：9，自引：0，他引：9
元胞自动机CA(Cellular Automata)和多智能体ABM(Agent-Based Model)模型是土地利用格局和演化模拟的主流方法,两者在模拟自然因素影响和人文驱动机制方面具有突出优势,为LUCC研究提供了重要的工具。当前,ABM无论在模型构建还是应用研究方面,CA和ABM均取得了显著进展。论文从数据基础、模拟尺度、CA转换规则挖掘、ABM行为规则定义、CA和ABM的耦合4个方面梳理土地利用模拟模型和方法的研究进展。并总结这些模型在虚拟城市模拟与理论验证、真实城市模拟与规划预测以及多类用地模拟与辅助决策等方面的应用。最后,总结土地利用模拟模型在精细模拟和全球变化研究方面存在的局限性,认为未来发展将主要集中于解决从2维模型向3维模型发展、大数据与规则精细挖掘以及大尺度模拟与知识迁移等问题。 相似文献
19.
基于人工神经元网络技术的土地利用/覆盖变化探测 总被引：6，自引：0，他引：6
针对现有的一些土地利用／覆盖变化探测方法存在的某些不足,提出了利用人工神经元网络（antificial neural network,ANN)进行土地利用／覆盖变化探测的方法,并对ANN网络的输出输出,网络结构和不同的网络模型进行了深入研究,充分利用已有的基础地理信息和高分辨率遥感影像辅助选取了ANN训练样本,试验结果表明,利用ANN总体上可提高土地利用／覆盖变化探测效率。 相似文献