基于多时相MODIS数据的四川省森林植被类型信息提取  

Extracting Forest Vegetation Types from Multi-temporal MODIS Imagery in Sichuan Province

作　　者：杨存建[1] 周其林[2] 任小兰[1] 程武学[1] 王琴[1] 

机构地区：[1]四川师范大学西南土地资源评价与监测教育部重点实验室遥感与GIS中心,成都610068 [2]四川省遂宁市林业局,四川遂宁629000

出　　处：《自然资源学报》2014年第3期507-515,共9页Journal of Natural Resources

基　　金：国家973项目(2009CB421105,2007CB714401);国家自然科学基金项目(40771144);国家863项目(2009AA12Z140)

摘　　要：森林植被类型信息对于生态的保护、规划和建设具有重要的意义。论文针对单一时相遥感数据在提取森林植被类型信息方面的局限性,探讨了基于多时相MODIS遥感数据实现提取主要森林植被类型信息的方法。将四川省的森林植被划分为常绿落叶混交林、常绿阔叶林、常绿针叶林、落叶阔叶林、落叶针叶林5种类型。通过对其年内生长差异的分析,选取多时相(2005年1月9日、2月26日、4月22日、7月19日和10月23日)特征数据,利用光谱和时相特征知识建立了常绿林、落叶林和针叶林的提取模型;通过特征组合与逻辑判断,实现了5种植被类型信息的提取,提取精度总体达到84%,植被类型最低精度达到76%。研究表明,该方法可以节约大量的人力、物力和财力,在大范围的植被类型调查与监测方面具有较大的应用价值。该研究表明,四川省2005年的森林覆盖率为28.43%。各类型按所占百分比由高到低的排序为落叶阔叶林、常绿针叶林、常绿阔叶林、落叶针叶林和常绿落叶混交林。该数据对四川省森林植被的保护和利用具有重要的应用价值。Information of forest vegetation types is very important for ecological planning, protection and construction. In this paper, we discussed a method to extract vegetation types from multi-temporal MODIS imageries in order to overcome the limitation of single- temporal imagery in identifying vegetation types. The forest vegetation was classified into five types： the evergreen and deciduous mixed forest, the evergreen broadleaf forest, the evergreen coniferous forest, the deciduous broadleaf forest and the deciduous coniferous forest in Sichuan Province. The multi-temporal MODIS feature data were selected based on analyzing the growth difference of the vegetation types through a year. The multi-temporal Normalization Different Vegetation Index （NDVI） was calculated using the red band and near infrared band of MODIS images acquired on January 9, February 26, April 22, July 19 and October 23, which were respectively presented as NDVI（1-9）, NDVI（2-26）, NDVI（4-22）,NDVI（7-19） and NDVI（10-23）. The knowledge ＂NDVI（1-9） 〉 T1 and NDVI（10-23）〉 T2＂ for evergreen forest was discovered by multi-temporal image analysis, which was used to formulate model of extracting the evergreen feature of forest. The knowledge ＂NDVI（7-9）〉 T3, NDVI（2-26）〈 T4 and NDVI （4-22） 〉 T5＂ for deciduous forest was discovered, which was used to formulate model of extracting the deciduous feature of forest. The knowledge ＂NDVI（1-9） 〉 T6 and B2 〈 T7＂ for coniferous forest was discovered, which was used to create model of extracting coniferous forest. B2 is near infrared band of MODIS images acquired on January 9. The evergreen feature, deciduous feature and coniferous forest were obtained by using the models, multi-temporal NDVI and B2. The five vegetation types were obtained by judging and combining evergreen feature, deciduous feature and coniferous forest. The overall accuracy was about 84%. The lowest accuracy of vegetation type was 76%. It was shown that the method proposed he

关 键 词：MODIS数据 归一化植被指数 光谱特征 植被类型提取 

分 类 号：S718.5[农业科学—林学]