Forest Resources Monitoring for Zhuji CityBased on Thematic Mapper(TM) Imagery

Abstract

Abstract: It has a significant meaning to increase the efficiency of the national forest inventory using remote sensing technology.Maximum likelihood,back propagation neural network,and k-nearest neighbor were applied to monitor forest resources for the the Zhuji city.Classification results were compared with forest inventory data.Results showed that these three methods accurately estimated the total area of different forest types with accuracy between 77.53% and 83.18%.However,the accuracies of these three methods are low at town level with relative root mean square error(RMSEr) of 41.83%,44.91%,and 44.18% respectively.Except for shrub forest,these three classification methods are not significantly different(p>0.5).Some techniques,such as multi-source remote sensing image fusion,should be used to increase classification accuracy in future study.

Key words: maximum likelihood, back propagation neural network, k-nearest neighbor, forest resources monitoring, Zhuji City

CLC Number:

S771.8

CHEN Jun, QIU Baoyin. Forest Resources Monitoring for Zhuji CityBased on Thematic Mapper(TM) Imagery[J]. FOREST RESOURCES WANAGEMENT, 2011, 0(6): 104-109.

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