光谱学与光谱分析 |
|
|
|
|
|
Researches on the Spatial Distribution of Digital Elevation Model and Particulate Matter Around the Central Metropolitan Correlation Based on Hyperspectral Ring |
MAO Hai-ying |
Specialized Forces College of the Chinese Armed Police Force, Beijing 102202, China |
|
|
Abstract Increased concentration of air respirable particulate matter associated with a number of combination factors. Spatial dispersion is also correlated with elevation DEM. In order to study the fog haze pollution associated with digital elevation model of spatial relations, this paper used the capital area ring within 100 km as the research scope, partitioning different length scale grid according to the rectangular grid method in the study area, obtaining visible light image data and hyperspectral image data by using unmanned aerial vehicle (UAV) for the extraction and integration air pollution factor and elevation factor within the scope of this study. GS+ software of kriging interpolation method was used to research the spatial correlation of variable data extraction; the MODIS remote sensing image data combined with field survey were used to analyze nonlinear regression of the terrain and environmental data.With the Calculation of variation effects of the particulate matter in the air and the spatial of the elevation factor under different grid scale ring of capital region, an optimization model of spatial correlation between them was established. Then the relation between the concentration of PM10 and height was determined. The biggest influence distance of elevation DEM associated with particulate matter API is 14.74 km. DEM space since the correlation of waning with the increase of the distance between sample points, which is also an important innovation of this paper. This result shows that the spatial correlation between the elevation DEM and environment conforms to the statistical spherical Gaussian model, correlation coefficient R2 were over 90%, which model fittings good. This study provides a certain theoretical and practical guidance for the control of air pollution index in the future as the change of height to select different tree species for afforestation.
|
Received: 2016-01-06
Accepted: 2016-05-15
|
|
Corresponding Authors:
MAO Hai-ying
E-mail: rubymm@126.com
|
|
[1] ZHU Chuan-feng, ZHAO He-ping(朱传凤, 赵和平). Gansu Environmental Study and Monitoring(甘肃环境研究与监测),1998, 2: 14. [2] YU Tong(虞 统). Urban Management and Technology(城市管理与科技), 2000, 1(2): 23. [3] LIAN Feng-bao(连凤宝). Popular Standardization(大众标准化),2001, 4: 39. [4] YANG Jie, WANG Guo-ping(杨 洁, 王国平). Environmental Reasearch and Monitoring(环境研究与监测), 2005,2: 7. [5] QING Yi-wei, YANG Lin(庆易微, 杨 林). Journal of Qinghai University(青海大学学报),2008, 26(4): 25. [6] TONG Nai-xing, ZHAO Jing-ming, ZHANG Peng, et al(佟乃兴, 赵晶明, 张 鹏,等). Chemical Engineering Management(化工管理), 2013, 8: 143. [7] LI Xiao-hui, YUAN Feng, JIA Cai, et al(李晓晖, 袁 峰, 贾 蔡,等). Science of Surveying and Mapping(测绘科学), 2012, 37(3): 88. [8] LIU Jin-zhao, MA Jie(刘今朝, 马 杰). Agricultural Engineering(农业工程), 2013, 3(2): 50. [9] LIU Xiao-lin, LI Wen-feng, YANG Lin-nan(刘晓林, 李文峰, 杨林楠). Chinese J. of Soil Science(土壤通报), 2012, 43(6): 1432. [10] WAN Li(万 丽). Statistics and Decision Making(统计与决策), 2006, (4): 26. [12] WANG Yan-ni, XIE Jin-mei, GUO Xiang(王艳妮, 谢金梅, 郭 祥). Software Guide(软件导刊), 2008, 12(7): 36. [13] TIAN Li, ZHANG Xiao-pan, YUAN Yan-bin(田 力, 张晓盼, 袁艳斌). Science of Surveying and Mapping(测绘科学), 2014, 39(2): 110. [14] Anatoly A Gitelson, Yoram J Kaufman, Robert Stark, et al. Remote Sensing of Environment, 2002, 80(1): 76. [15] Boyd D S, Foody G M, Ripple W J. Applied Geography, 2002, 22: 375. |
[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. |
|
|
|
|