基于多时相MODIS数据的四川省森林植被类型信息提取

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。

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

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 singletemporal 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_(7-19) > 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 here is labor-saving and cost-effective, which is of high application value in investigating and monitoring vegetation types in large extent. It was also shown that forest vegetation covered 28.43% of the total area of Sichuan Province in 2005. According to their percentage, vegetation types arranged in descending order were the deciduous broadleaf forest, the evergreen coniferous forest, the evergreen broadleaf forest, the deciduous coniferous forest and the evergreen and deciduous mixed forest, and the percentages were respectively 32%, 29%, 18%, 14% and 7%. The vegetation type data are of important value in protecting and utilizing the forest vegetation in Sichuan Province.