地理科学进展 ›› 2012, Vol. 31 ›› Issue (7): 911-920.doi: 10.11820/dlkxjz.2012.07.010

• 植被与土壤 • 上一篇    下一篇

基于优化GeoCA模型的土壤侵蚀时空变化模拟——以福建省长汀县为例

林晨1,2, 周生路2, 吴绍华2   

  1. 1. 中国科学院南京地理与湖泊研究所, 南京210008;
    2. 南京大学地理与海洋科学学院, 南京210093
  • 收稿日期:2011-10-01 修回日期:2012-02-01 出版日期:2012-07-25 发布日期:2012-07-25
  • 通讯作者: 周生路(1968-),教授,博士生导师,主要从事土地资源与环境研究。Email:zhousl@nju.edu.cn E-mail:zhousl@nju.edu.cn
  • 作者简介:林晨(1984-),男,江苏南京人,博士生,主要从事遥感技术在土地利用与资源环境中的应用研究。Email:dreamlive_9@163.com
  • 基金资助:

    “十一五”国家科技支撑计划重点项目(2009BADC6B)。

Simulation of Spatial-temporal Evolution of Soil Erosion Based on Optimized GeoCA: A Case Study in Changting County, Fujian Province

LIN Chen1,2, ZHOU Shenglu2, WU Shaohua2   

  1. 1. Nanjing Institute of Geography&Limnology, Chinese Academy of Sciences, Nanjing 210008, China;
    2. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, China
  • Received:2011-10-01 Revised:2012-02-01 Online:2012-07-25 Published:2012-07-25

摘要: 土壤侵蚀动态监测是侵蚀研究体系中的重要内容, 目前研究主要集中在强度评价以及趋势预测两方面, 其中, 地理元胞自动机(Geo-Cellular Automata, GeoCA)是最常用的土壤侵蚀趋势模拟模型, 但是现有研究主要基于邻域计算规则, 强度模型的数学意义, 而忽视了元胞自身发生变化的可能性, 不能完全体现土壤侵蚀演变的复杂性, 这些问题不仅降低了模拟的精度, 而且使得趋势预测与侵蚀评价两者间的融合性降低, 不利于完整的研究体系形成。本研究认为土壤侵蚀时空变化趋势是其自身侵蚀状态、自然条件以及邻域转换规则共同作用决定, 从而设计元胞侵蚀强度指数算法、元胞侵蚀强度函数以及元胞邻域转换函数, 对传统GeoCA进行了优化, 并在GIS与遥感技术支撑下, 以福建省长汀县为研究区, 设计了上述算法的整合模型, 对福建省长汀县30 年来土壤侵蚀时空演替过程进行了测算与分析。研究结果显示: 长汀县土壤侵蚀最严重的地区主要分布在以河田镇为中心的汀中地区, 20 世纪90 年代以来, 土壤侵蚀治理工作成效显著, 改善趋势明显加快, 至2000 年以后有所放缓。预计至2020 年, 土壤侵蚀区的比重将从1990 年的约40%下降至约20%。通过比较, 整合算法的精度达到72.7%, 高于单纯的侵蚀强度评价算法以及传统GeoCA模型, 证明优化GeoCA不仅是进行土壤侵蚀时空演变模拟研究的有效手段, 更为土壤侵蚀这一复杂地理系统的元胞表述规则研究进一步深入提供了思路。

关键词: 长汀县, 地理元胞自动机, 时空演变, 土壤侵蚀, 优化

Abstract: Monitoring the soil erosion dynamically is an important part in soil erosion research. The studies mainly reflected in two aspects: The first is the monitoring of erosion intensity, and the second is the simulation of soil erosion tendency. However, the studies are mainly based on neighborhood transition rules and the mathematical meaning is emphasized, while the transition possibility of the cell itself has been ignored to a certain degree, which cannot fully reflect the complexity of soil erosion evolution. These shortages not only reduce the simulation accuracy, but also decrease the integration of erosion intensity assessment and erosion tendency simulation, which are not conducive to the formation of a complete research system. This study suggested that the spatial and temporal evolution of soil erosion is determined by its own status of soil erosion, natural conditions and neighborhood transformation rules, so the cellular algorithms of erosion intensity index, cellular erosion intensity functions and cellular neighborhood transition function were designed and the traditional GeoCA was optimized. Finally, the integration model of optimized GeoCA was designed and calculated with the support of GIS and remote sensing technologies, and the spatial and temporal soil erosion transition tendency was analyzed in Changting County, Fujian Province. The results showed that there are characteristics of concentration and contiguity in erosion distribution, and the highest erosion intensity is observed in central Changting, and the soil erosion has been mitigated in the past 30 years, especially in the 1990s, Soil erosion control has achieved remarkable results, and the improving tendency was clearly accelerating, but slow down since 2000. It is expected that by the year 2020, the proportion of soil erosion area would decrease from 40% to about 20%. By comparison, the accuracy of optimized integration model can reach 72.7%, higher than the erosion intensity assessment algorithm and traditional GeoCA model, which proves that the optimized model of GeoCA is not only an effective means to simulate soil erosion, but also provides reference for further study of GeoCA.

Key words: Changting County, GeoCA, optimization, soil erosion, spatial and temporal evolution