|
|
|
|
|
|
| Effect of Leaf Dust Retention on Spectral Characteristics of Euonymus japonicus and Its Dust Retention Prediction |
| ZHU Ji-you1, YU Qiang1*, LIU Xiao-xi2, YU Yang3, YAO Jiang-ming4, SU Kai1, NIU Teng1, ZHU Hua5, ZHU Qiu-yu5 |
1. Forestry College, Beijing Forestry University, Beijing 100083，China
2. Foreign Languages College, Beijing Forestry University, Beijing 100083，China
3. Beijing Institute of Spacecraft Environment Engineering, Beijing 100094，China
4. Forestry College, Guangxi University, Nanning 530005, China
5. Department of Testing, Guangxi Medical College, Nanning 530012, China |
|
|
|
|
Abstract Atmospheric particulate pollution has become one of the serious urban environmental problems on a global scale. In order to explore the influence of leaf surface dust on the spectral characteristics of the leaves, a prediction model of leaf surface dross based on high-spectrum data was established. In order to explore the influence of leaf surface dust on the spectral characteristics of the leaves, a prediction model of leaf surface dross based on high-spectrum data was established. Our study focused on the common greening tree species (Euonymus japonicus) in Beijing, and collected 720 leaf samples in high, medium and low dust pollution gradient environments, and then used the ASDF Fildsoec Handheld spectrometer to obtain hyperspectral data. The results showed that the spectral reflection peaks were at 560 and 900 nm, respectively, and the absorption valleys were in the range of 400~500, 600~700 and 1 000~1 050 nm. The leaf reflectivity with or without dust retention showed different rules in different bands. The spectral reflectances in the range of 400~760 and 760~1 100 nm were as follows: dust-retaining leaves>dust-removing leaves, dust-retaining leavesR2，which were y=-1.18x2+0.542 4x+0.991 7, y=-7.67x2+3.692 4x+0.371 4, respectively. The regression model was validated by using the predicted samples, and the R2 reached 0.987 7 and 0.887 3, respectively. The fitting effect was good, indicating that the two prediction models can effectively estimate the dust retention of the leaf of Euonymus japonicus.
|
|
Received: 2019-01-18
Accepted: 2019-05-20
|
|
|
|
Corresponding Authors:
YU Qiang
E-mail: yuqiang@bjfu.edu.cn
|
|
[1] Merico E, Donateo A, Gambaro A, et al. Atmospheric Environment, 2016, 139: 1.
[2] XIONG Xin-zhu, TAO Shuang-cheng, GAO Shuo-han, et al(熊新竹, 陶双成, 高硕晗, 等). Ecology and Environmental Sciences(生态环境学报), 2017, 26(7): 1167.
[3] LI Xing-ru, BAI Yu, CHEN Xi, et al(李杏茹, 白 羽, 陈 曦, 等). Environmental Chemistry(环境化学), 2018, 37(11): 2397.
[4] LIU Chen-ming, ZHANG Zhi-qiang, CHEN Li-xin, et al(刘辰明，张志强，陈立欣，等). Acta EcologicaSinica(生态学报), 2018, 38(7): 2353.
[5] Chen L X, Liu C M, Zou R，et al. Environmental Pollution, 2016, 208(A): 198.
[6] LI Yan-mei, CHEN Qi-bo, WANG Shao-jun, et al(李艳梅, 陈奇伯, 王邵军, 等). Scientia Silvae Sinicae(林业科学)，2018, 54(5): 18.
[7] HU Shu, XIAO Xin, JIA Han-shuai, et al(胡 舒, 肖 昕, 贾含帅, 等). Chinese Agricultural Science Bulletin(中国农学通报), 2012, 28(16): 95.
[8] DAI Si-di, MA Ke-ming, BAO Le, et al(戴斯迪, 马克明, 宝 乐, 等). Acta Scientiae Circumstantiae(环境科学学报), 2013, 33(1): 154.
[9] MA Yuan, JIA Yu-long, WANG Cheng, et al(马 远, 贾雨龙, 王 成, 等). Forest Research(林业科学研究), 2018, 31(5): 110.
[10] LI Wei-tao, WU Jian, CHEN Tai-sheng, et al(李伟涛, 吴 见, 陈泰生, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(2): 180.
[11] Yan X, Shi W Z, Zhao W J, et al. Science of the Total Environment, 2015, (506-507): 604.
[12] CHENG Xiao-yun, LUO Chun-wang, LIU Qi-jing, et al(程小云, 罗春旺, 刘琪璟, 等). Environmental Science & Technology(环境科学与技术), 2017, 40(5): 1. |
| [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] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
| [5] |
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. |
| [6] |
HAO Zi-yuan1, YANG Wei1*, LI Hao1, YU Hao1, LI Min-zan1, 2. Study on Prediction Models for Leaf Area Index of Multiple Crops Based on Multi-Source Information and Deep Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3862-3870. |
| [7] |
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. |
| [8] |
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. |
| [9] |
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. |
| [10] |
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. |
| [11] |
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. |
| [12] |
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. |
| [13] |
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. |
| [14] |
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. |
| [15] |
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. |
|
|
|
|
