地理研究 ›› 2009, Vol. 28 ›› Issue (6): 1713-1721.doi: 10.11821/yj2009060027

• 地球信息科学 • 上一篇    下一篇

盐城湿地类型演化预测分析

张怀清1, 唐晓旭2, 刘锐3, 周金星4, 凌成星1   

  1. 1. 中国林业科学研究院资源信息研究所,北京 100091;
    2. 北京市测绘设计研究院,北京 100038;
    3. 北京师范大学地理学与遥感科学学院,北京100875;
    4. 中国林业科学研究院林业研究所,北京 100091
  • 收稿日期:2009-02-18 修回日期:2009-06-15 出版日期:2009-11-25 发布日期:2009-11-25
  • 作者简介:张怀清(1973-),男,湖南宁乡人,博士,副研究员。主要从事林业可视化模拟技术与湿地资源监测技术研究。E-mail:zhang@caf.ac.cn
  • 基金资助:

    国家"十一五"科技支撑项目资助(2006BAD23B03,2006BAC08B03)

Study on prediction models of wetland types in Yancheng

ZHANG Huai-qing1, TANG Xiao-xu2, LIU Rui3, ZHOU Jin-xing4, LING Cheng-xing1   

  1. 1. Research Institute of Forestry Resource Information Techniques, Chinese Academy of Forestry (CAF), Beijing 100091, China;
    2. Beijing Institute of Surveying and Mapping, Beijing 100038, China;
    3. School of Geography, Beijing Normal University, Beijing 100875, China;
    4. Research Institute of Forestry, CAF, Beijing 100091, China
  • Received:2009-02-18 Revised:2009-06-15 Online:2009-11-25 Published:2009-11-25
  • Supported by:

    国家"十一五"科技支撑项目资助(2006BAD23B03,2006BAC08B03)

摘要:

以盐城湿地主要分布的三个县市:射阳、大丰和东台作为研究区,通过1988~2006年每隔6年的4期TM遥感影像,开展盐城湿地类型的动态变化分析,根据遥感影像解译结果,利用基于可拓物元模型的湿地演化元胞自动机(CA)模型和马尔可夫模型从空间和数据上进行湿地类型的变化预测。分析结果表明:(1)将基于可拓物元CA模型模拟预测结果与遥感分类结果进行逐个像元比较,计算得出相似程度达到 70%,因此基于可拓物元CA预测模型引入到湿地类型演化预测中是可行的。(2)基于可拓物元CA模型与马尔科夫模型的数值预测结果具有较高的相似度,表明研究区在2012年养殖场将成为盐城湿地最主要的湿地利用类型,预测结果分别达到了750.06km2和721.96km2,同时,耕地、居民用地、养殖场、米草面积总体呈增长趋势,滩地、芦苇、碱蓬和盐田呈剧烈减少趋势,米草逐渐占据优势物种,大面积的滩涂开发是盐城湿地类型变化最主要的原因。

关键词: 遥感监测, 湿地类型, 预测模型, 盐城

Abstract:

This paper takes three main wetland distributing counties of Dongtai, Dafeng and Sheyang of Yancheng as study areas. With the processing of four-period (every six years from 1988 to 2006) remote sensing (RS) images, a dynamic change analysis of the Yancheng wetland types was illuminated at first. Then, according to the results of the RS images interpretation, the change prediction of the wetland types was analyzed by using cellular automata (CA) model based on extension matter-element model and Markov model. The results are shown as follows: (1) It is a feasible method in wetland types prediction according to the comparability (70%) after the comparison between the results calculated by CA model based on extension matter-element model and the remote sensing classification. (2) The results of CA model based on extension matter-element model is greatly consistent with the results of the results of Markov model, that is, the aquaculture farm is the main wetland type covering areas of 750.06 km2 and 740.20 km2 in 2006, respectively. Generally, the area of cropland, residential land, aquaculture farm, Spartina patens and seepweed has an increasing tendency, while the area of mudflat, reed and brine pan tends to decrease sharply. Spartina patens will become the dominant species gradually due to its evolution trend. The most important reason for these changes is the current policy of large-scale coastal exploitation.

Key words: remote sensing monitoring, wetland types, prediction model, Yancheng