Detection of damaged forest by tomicus piniperda using multiple types of remote sensing data

Xieqiong Dong; Xuezhi Feng; Shuhe Zhao; Xiaopeng Yang; Hongping Liu; Hong Xu; Yinghai Ke

doi:10.1117/12.578492

20 January 2005 Detection of damaged forest by tomicus piniperda using multiple types of remote sensing data

Xieqiong Dong, Xuezhi Feng, Shuhe Zhao, Xiaopeng Yang, Hongping Liu, Hong Xu, Yinghai Ke

Proceedings Volume 5655, Multispectral and Hyperspectral Remote Sensing Instruments and Applications II; (2005) https://doi.org/10.1117/12.578492
Event: Fourth International Asia-Pacific Environmental Remote Sensing Symposium 2004: Remote Sensing of the Atmosphere, Ocean, Environment, and Space, 2004, Honolulu, Hawai'i, United States

Abstract

This paper analyzed the damaged forest by tomicus piniperda using multiple types of remote sensing data such as TM, CBERS-1, AVHRR and MODIS data. It selected a typical region including heavy damaged and healthy forest. The region was located by GPS (Global Position System). Then the spectral features of the above remote sensing data (March, 2001) were given. It indicates that the values of healthy forest of TM NIR band (0.76-0.9 ) and SWIR band (1.55-1.75 ) are distinctly greater than those of damaged forest. The values of CBERS-1 NIR bands (0.77-0.89 ), AVHRR bands (0.725-1.0 ) and MODIS bands (0.841-0.876 ) behave in the same pattern with TM. Otherwise, the values of MODIS thermal bands (3.929-3.89 , 10.78-11.28 and 11.77-12.27 ) of damaged forest are distinctly greater than those of healthy forest. The AVHRR thermal bands are not so. Finally, two detection models were put forward according to the spectral changing characteristics. One was named Difference Rate (DR) model with NIR and VIR data, which applied for TM, CBERS-1, AVHRR and MODIS. DR is greater, the forest grow healthily. Basis on the typical sample, the different guidelines distinguished healthy and damaged forests are obtained. The other model was named Disaster Index (DI) model with thermal and NIR data, only suitable for MODIS. The guidelines of healthy and damaged forest are determined too. DI is greater the forest is stricken more badly. In conclusion, it will help monitoring and assessing the vermin occurrence and impact by remote sensing detection model.

Citation Download Citation

Xieqiong Dong, Xuezhi Feng, Shuhe Zhao, Xiaopeng Yang, Hongping Liu, Hong Xu, and Yinghai Ke "Detection of damaged forest by tomicus piniperda using multiple types of remote sensing data", Proc. SPIE 5655, Multispectral and Hyperspectral Remote Sensing Instruments and Applications II, (20 January 2005); https://doi.org/10.1117/12.578492

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