High Spatial Resolution Hyperspectral Mapping for Forest Ecosystem at Tree Species Level

スポンサーリンク

概要

• 論文の詳細を見る

• Integrated management of forest ecosystems requires an accurate and all-sided mastery of the forest information, of which forest ecosystem cover especially at tree species level is the most basic and important component. The study investigated and demonstrated the mapping potential of the forest ecosystem at tree species level from high spatial resolution hyperspectral images. The mapping performances of eight conventional classification methods including Maximum Likelihood (ML), Mahalanobis Distance (MaD), Minimum Distance (MD), Parallelepiped (P), Binary Encoding (BE), Spectral Angle Mapper (SAM), Spectral Information Divergence (SID) and Support Vector Machine (SVM), were verified based on two noise treatments (noise fraction and noise removal) and three leaf growth periods (tender leaf period, young leaf period and adult leaf period). It could be confirmed that noise removal obviously contributed to improving the classification agreement and young leaf period was most suitable for mapping forest ecosystem at tree species level from high spatial resolution hyperspectral images. ML, P, BE and SID were not considered appropriate according to good results with overall accuracy and kappa coefficient exceeding 85% and 0.80 respectively. Though MD also produced a very high classification agreement, it could not cover up its poor potential to identify tree species by spectral features. Even if SVML, SVMP, SVMR and SVMS performed the stablest and could generate good results across three periods, the best result was obtained by SAM. Except that the difference was significant between MD and SVMS at the 5% significance level in tender leaf period, the comparative tests did not provide more proof to show the significant difference between the methods considered appropriate.

著者

• Sakai Kenshi

  Faculty of Agriculture, Tokyo University of Agriculture and Technology

• Hoshino Yoshinobu

  Faculty of Agriculture, Tokyo University of Agriculture and Technology

• Shen Gang

  Faculty of Agriculture, Tokyo University of Agriculture and Technology

関連論文

• Potential Application of Color and Hyperspectral Images for Estimation of Weight and Ripeness of Oil Palm (Elaeis guineensis Jacq. var. tenera)
• High Spatial Resolution Hyperspectral Mapping for Forest Ecosystem at Tree Species Level

スポンサーリンク