| 光谱学与光谱分析 |
|
|
|
|
|
| Mahalanobis Distance Based Hyperspectral Characteristic Discrimination of Leaves of Different Desert Tree Species |
| LIN Hai-jun1, ZHANG Hui-fang2, GAO Ya-qi2*, LI Xia1, YANG Fan1, ZHOU Yan-fei1 |
1. College of Pratacultural and Environmental Science, Xinjiang Agricultural University, Urumqi 830052, China
2. Xinjiang Academy of Forestry Sciences Modern Forestry Institute, Urumqi 830000, China |
|
|
|
|
Abstract The hyperspectral reflectance of Populus euphratica, Tamarix hispida, Haloxylon ammodendron and Calligonum mongolicum in the lower reaches of Tarim River and Turpan Desert Botanical Garden was measured by using the HR-768 field-portable spectroradiometer. The method of continuum removal, first derivative reflectance and second derivative reflectance were used to deal with the original spectral data of four tree species. The method of Mahalanobis Distance was used to select the bands with significant differences in the original spectral data and transform spectral data to identify the different tree species. The progressive discrimination analyses were used to test the selective bands used to identify different tree species. The results showed that The Mahalanobis Distance method was an effective method in feature band extraction. The bands for identifying different tree species were most near-infrared bands. The recognition accuracy of four methods was 85%, 93.8%, 92.4% and 95.5% respectively. Spectrum transform could improve the recognition accuracy. The recognition accuracy of different research objects and different spectrum transform methods were different. The research provided evidence for desert tree species classification, monitoring biodiversity and the analysis of area in desert by using large scale remote sensing method.
|
|
Received: 2013-05-03
Accepted: 2013-10-14
|
|
|
|
Corresponding Authors:
GAO Ya-qi
E-mail: gyq611003@163.com
|
|
[1] TAN Bing-xiang(谭炳香). World Forestry Research(世界林业研究), 2003, 16(2): 33.
[2] Pontius J, Hallett R, Martin M. Remote Sensing of Environment, 2005, 97(2): 163.
[3] LIU Xiu-ying, LIN Hui, XIONG Jian-li, et al(刘秀英, 林 辉, 熊建利, 等). Remote Sensing Information(遥感信息), 2005, 4: 41.
[4] ZANG Zhuo, LIN Hui, YANG Min-hua(臧 卓, 林 辉, 杨敏华). Journal of Central South University of Forestry & Technology(中南林业科技大学学报), 2013, 33(1): 35.
[5] Kokaly R F, Clark R N. Remote Sensing of Environment, 1999, 67(3): 267.
[6] XIE Bo-cheng, XUE Xu-zhang, LIU Wei-dong, et al(谢伯承, 薛绪掌, 刘伟东, 等). Acta Pedologica Sinica(土壤学报), 2005, 42(1): 171.
[7] LI Ming-zhou(李明州). Spectrum Analysis Technology and Application(光谱分析技术和应用). Beijing: Science Press(北京: 科学出版社), 2006. 122, 163.
[8] TONG Qing-xi, ZHANG Bing, ZHENG Lan-fen(童庆禧, 张 兵, 郑兰芬). Hyperspectral Remote Sensing Principle, Technology and Application(高光谱遥感—原理, 技术与应用). Beijing: Higher Education Press(北京: 高等教育出版社), 2006. 158.
[9] De Maesschalck R D, Jouan-Rimbaud D L M. Chemometrics and Intelligent Laboratory Systems，2000, 50(1): 1.
[10] LIU Xiu-ying, ZANG Zhuo, SUN Hua, et al(刘秀英, 臧 卓, 孙 华, 等). Journal of Central South University of Forestry and Technology(中南林业科技大学学报), 2011, 31(11): 30.
[11] DING Li-xia, WANG Zhi-hui, GE Hong-li(丁丽霞, 王志辉, 葛宏立). Journal of Zhejiang Forestry College(浙江林学院学报), 2010, 27(6): 809. |
| [1] |
WANG Cai-ling1，ZHANG Jing1，WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
| [2] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
| [3] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
| [4] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
| [5] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
| [6] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
| [7] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
| [8] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
| [9] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
| [10] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
| [11] |
QIAN Rui1, XU Wei-heng2, 3 , 4*, HUANG Shao-dong2, WANG Lei-guang2, 3, 4, LU Ning2, OU Guang-long1. Tea Plantations Extraction Based on GF-5 Hyperspectral Remote Sensing
Imagery in the Mountainous Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3591-3598. |
| [12] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
| [13] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
| [14] |
SUN Lin1, BI Wei-hong1, LIU Tong1, WU Jia-qing1, ZHANG Bao-jun1, FU Guang-wei1, JIN Wa1, WANG Bing2, FU Xing-hu1*. Identification Algorithm of Green Algae Using Airborne Hyperspectral and Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3637-3643. |
| [15] |
TAO Jing-zhe1, 3, SONG De-rui1, 3, SONG Chuan-ming2, WANG Xiang-hai1, 2*. Multi-Band Remote Sensing Image Sharpening: A Survey[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2999-3008. |
|
|
|
|
