AUTHOR=Zhou Jingchun , Feng Zhanyong , Li Yiping , Wang Jinliang , Meng Xiangrui , Liu Yuan , Qiu Shaobo TITLE=Identification of dominant tree species based on Resource-1 02D hyperspectral image data JOURNAL=Frontiers in Earth Science VOLUME=12 YEAR=2024 URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2024.1418865 DOI=10.3389/feart.2024.1418865 ISSN=2296-6463 ABSTRACT=

Fine-grained classification of tree species by using high-spectral image data has garnered considerable attention from scholars. In this study, through field measurements from Maguan County, Wenshan Prefecture, Yunnan Province, China, high-spectral image data from the Chinese Resource-1 02D satellite were used as the data source. Various analyses were conducted on the original image’s spectral curve, the spectral curve after envelope removal, the spectral curve after first-order differential transformation, and the spectral curve after second-order differential transformation. A spectral angle mapping classification method was employed to classify and identify four dominant tree species in Maguan County, and the accuracy of the classification results was validated using a confusion matrix. Results indicate that the highest accuracy in tree species classification was achieved when first-order differential transformation and envelope removal were used for the spectral curve; the overall accuracy exceeded 95%, and the kappa value was approximately 0.95. The classification results for the spectral curve after second-order differential transformation were the lowest, with an overall accuracy of 81.69% and a kappa value of 0.76. This research demonstrates that applying first-order differential transformation or envelope removal in combination with spectral angle mapping classification considerably reduces data processing time and improves tree species classification accuracy.