| 光谱学与光谱分析 |
|
|
|
|
|
| Retrieving Dustfall Distribution in Beijing City Based on Ground Spectral Data and Remote Sensing |
| WANG Hao-fei1,2, FANG Na3, YAN Xing4, CHEN Fan-tao1,2, XIONG Qiu-lin1,2, ZHAO Wen-ji1,2* |
1. Key Laboratory of 3D Information Acquisition and Application of Ministry of Education, Capital Normal University, Beijing 100048, China
2. Beijng Key Laboratory of Resource Environment and Geographic Information System, Capital Normal University, Beijing 100048, China
3. Chengdu University of Technology, College of Tourism and Urban-Rural Planning, Chengdu 610059, China
4. Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hong Kong, China |
|
|
|
|
Abstract Dust-fall distribution of vegetation leaves can indicate the degree of air pollution; therefore the analysis of spatial characteristics of urban vegetation dust-fall has important practical significance for making more effective air pollution control policy. Based on the data of weight of dust, spectral reflectance and leaf area of Euonymus japonicus, Sophora japonica, poplar and davidiana collected in the main area of Beijing city, we compared the curve of spectrum of four plants “dust leaves” to “clean leaves”; the correlation analysis between leaf spectral reflectance ratio (Dust/Clean) of narrow band and satellite band was processed with the weight of dust-fall respectively, with application of four plants leaf data. Then, we built the regression model of the satellite band reflectance and NDVI with dustfall weight respectively, and we used the best model to retrieve the dust-fall distribution of vegetation coverage area in Beijing city, furthermore, we obtained the dust distribution of the whole Beijing city through interpolation. Finally, we carried out the rationality verification of the result by the land cover and land use of the high dust region, as well as the average concentration of PM10. The results showed that, dust leaves had an obviously lower reflectance than clean leaves in 780~1 300 nm which belonged to near-infrared bands; therewas a higher correlation between narrow band reflectance and dust-fall weight in 520~620 and 1 390~1 600 nm, up to -0.626; the coefficients of determination (R2) of inversion models were respectively 0.446 and 0.465,which were constructed by green band and NDVI of Landsat8 with dust-fall weight. Using the model established with NDVI to retrieving the dust-fall distribution of Beijing city, the results demonstrate that the distribution of dust-fall is high in north and low in south, high in east and low in west, high in downtown and low in the suburbs. This study offers a low-cost and effective method for investigating dust-fall distribution in urban area, and provides data support to analysis sources of pollution for the environmental protection department.
|
|
Received: 2016-01-23
Accepted: 2016-05-10
|
|
|
|
Corresponding Authors:
ZHAO Wen-ji
E-mail: zhwenji1215@163.com
|
|
[1] Yang F M， Ye B M，He K B，et al. Total Environ.， 2005, 343：221.
[2] He K B，Yang F M，Ma Y L，et al. Atmos. Environ.， 2001, 35：4959.
[3] Ram S S, Kumar R V, Chaudhuri P, et al. Ecol. Indic., 2014，36: 334.
[4] Shu J, Dearing J A, Morse A P, et al. Atmos Environ., 2000; 35: 2615.
[5] Bilal M, Nichol J E, Chan P W. Remote Sens. Environ., 2014，153: 50.
[6] LUO Na-na，ZHAO Wen-ji，YAN Xing(罗娜娜，赵文吉，晏 星). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(), 2715.
[7] Yan X, Shi W Z, Zhao W J, et al. Spectrosc. Lett., 2014a, 47: 536.
[8] Chudnovsky A, Ben-Dor E, Saaroin H. Adv. Geosci., 2007, 1: 51.
[9] Ong C, Cudahy T, Caccetta M, et al. Geosci. Remote Sens. Lett., 2001, 1: 296.
[10] ZHANG Xi-ping, ZHANG Qi-xiang(郑西平, 张启翔). Landscape Plants(中国园林)，2011, 5: 81.
[11] NIU Ya-fei, XIE Li-bo, LIU Chun-feng(牛亚菲, 谢丽波, 刘春凤). Geographical Research(地理研究)，2005, 2: 283.
[12] Wen Q H, Yang S H. Environ. Monit. Assess., 2006, 117: 463.
[13] Yan Xing, Shi Wenzhong, Zhao Wenji, et al. Science of the Total Environment, 2015，506-507：604.
[14] TANG Xin-ming, LIU Hao, LI Jing, et al(唐新明, 刘 浩, 李 京, 等). China Environmental Science(中国环境科学), 2015, 35(9)：2561.
[15] TANG Ming(唐 明). Master Dissertation. Capital Normal University, 2011.
[16] WANG Yan-hui, XIAO Yao(王艳慧，肖 瑶). Environmental Science(环境科学), 2014, 2: 428.
[17] Bealey W J, McDonald A G, Nemitz E, et al. Journal of Environmental Management, 2007, 85: 44. |
| [1] |
LIANG Ye-heng1, DENG Ru-ru1, 2*, LIANG Yu-jie1, LIU Yong-ming3, WU Yi4, YUAN Yu-heng5, AI Xian-jun6. Spectral Characteristics of Sediment Reflectance Under the Background of Heavy Metal Polluted Water and Analysis of Its Contribution to
Water-Leaving Reflectance[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 111-117. |
| [2] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
| [3] |
ZHU Wen-jing1, 2，FENG Zhan-kang1, 2，DAI Shi-yuan1, 2，ZHANG Ping-ping3，JI Wen4，WANG Ai-chen1, 2，WEI Xin-hua1, 2*. Multi-Feature Fusion Detection of Wheat Lodging Information Based on UAV Multispectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 197-206. |
| [4] |
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. |
| [5] |
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. |
| [6] |
LIANG Shou-zhen1, SUI Xue-yan1, WANG Meng1, WANG Fei1, HAN Dong-rui1, WANG Guo-liang1, LI Hong-zhong2, MA Wan-dong3. The Influence of Anthocyanin on Plant Optical Properties and Remote Sensing Estimation at the Scale of Leaf[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 275-282. |
| [7] |
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. |
| [8] |
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. |
| [9] |
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. |
| [10] |
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. |
| [11] |
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. |
| [12] |
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. |
| [13] |
LI Si-yuan, JIAO Jian-nan, WANG Chi*. Specular Reflection Removal Method Based on Polarization Spectrum
Fusion and Its Application in Vegetation Health Monitoring[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3607-3614. |
| [14] |
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. |
| [15] |
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. |
|
|
|
|
