不同样本方案下遗传元胞自动机的土地利用模拟及景观评价

冯 永 玖 Feng, Yong-jiu, 刘艳 Liu, Yan and 韩震 Han, Zhen (2011) 不同样本方案下遗传元胞自动机的土地利用模拟及景观评价. 應用生態學報, 22 4: 957-963.

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Author 冯 永 玖 Feng, Yong-jiu
刘艳 Liu, Yan
韩震 Han, Zhen
Title 不同样本方案下遗传元胞自动机的土地利用模拟及景观评价
Translated title Land use simulation and landscape assessment using genetic algorithm based cellular automata under different sampling schemes
Language of Title chi
Journal name 應用生態學報   Check publisher's open access policy
Translated journal name Chinese Journal of Applied Ecology
Language of Journal Name chi
ISSN 1001-9332
Publication date 2011-04
Sub-type Article (original research)
Open Access Status
Volume 22
Issue 4
Start page 957
End page 963
Total pages 7
Place of publication Beijin, China
Publisher Kexue Chubanshe
Collection year 2012
Language chi
eng
Formatted abstract
利用元胞自動機(cellular automata,CA)模擬土地利用情景,有助于理解其變化機理,并為土地資源持續利用提供空間決策支持.本文基于生物進化過程的遺傳算法(genetic algorithm,GA)將CA參數編碼成為染色體,在模擬結果與真實結果差異值的引導下,通過選擇、雜交和變異算子使最優的染色體得以遺傳和保留,從而建立智能優化的元胞自動機模型.以浙江省嘉興市1992-2008年土地利用變化為例,分別利用6%(66個·km-2)和3%(33個·km-2)兩種樣本方案構建遺傳CA模型進行土地利用變化模擬,并通過混淆矩陣、Kappa系數和景觀指數對模擬結果進行評估.結果表明:遺傳CA模擬結果能在數量、位置和景觀格局上以超過80%的水平接近真實分類,且大樣本量構建的遺傳CA的模擬精度更高;2008年的模擬精度和景觀綜合指數低于2001年,表明遺傳CA的模擬精度和景觀綜合指數隨模擬時間而衰減.

Simulating land use change scenarios with cellular automata (CA) can help to the policy makers in understanding the mechanisms of land change, and support the spatial decision-making for the sustainable use of land resources. Genetic algorithm (GA), an intelligent approach originally conceived from the biological process of evolution, has the capability of minimizing the difference between simulated and observed land use patterns with optimum chromosomes (i. e. , feasible CA parameters) obtained through a set of selection, crossover, and mutation operations. In this paper, GA-based CA model was developed, and applied to simulate the land use change in Jiaxing City of Zhejiang Province in 1992-2008. This model was calibrated with 6% (66 samples·km-2 ) and 3% (33 samples·km-2) samplings, and the simulation results were evaluated based on confusion matrix, Kappa coefficient, and landscape metrics analysis. Over 80% of the land use features generated by the GA-based CA model matched the observed classification of land features geographically, and much higher simulation accuracy could be obtained with a larger sample. The simulation accuracy and the landscape metrics for 2001 were better than those for 2008, suggesting a tendency that the model's accuracy decreased over the simulating process.
Keyword Land use simulation
Cellular automata
Multi-samples
Genetic algorithm
Landscape assessment
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Abstract only in English.

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Geography, Planning and Environmental Management Publications
Official 2012 Collection
 
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Created: Thu, 14 Jul 2011, 10:57:51 EST by Helen Smith on behalf of School of Geography, Planning & Env Management