Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2016, Vol. 28 Issue (2) : 126-131     DOI: 10.6046/gtzyyg.2016.02.20
Technology Application |
Particulate matter indices derived from MODIS data for indicating urban air pollution
HE Junliang1, ZHANG Shuyuan1, LI Jia2, ZHA Yong3
1. Department of Resources and Environment, Shijiazhuang University, Shijiazhuang 050035, China;
2. College of Tourism and Geographical Sciences, Yunnan Normal University, Kunming 650500, China;
3. Key Laboratory of Ministry of Education for Virtual Geographic Environment, Nanjing Normal University, Nanjing 210046, China
Download: PDF(1712 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

According to the variation of MODIS apparent reflectance caused by aerosol scattering and absorption, spectral indices for urban particulate pollution were constructed, which include difference vegetation index(DVI), normalized difference haze index (NDHI), normalized difference build-up index (NDBI) and difference build-up index (DBI). Relations between the indices and particle concentrations (PM10) measured by the Shijiazhuang Environmental Monitoring Station were discussed. Coefficient analysis indicates that there is negative correlation between the particle concentrations and the spectral indices except NDHI. The MODIS DBI is linearly related to PM10. The estimating model of PM10 based on several indices makes it easier to quickly monitor and evaluate atmospheric particulate pollution in urban area.

Keywords Landsat8      remote sensing satellite      data preprocessing      data decompression     
:  X87  
  TP79  
Issue Date: 14 April 2016
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
ZHU Jia
Cite this article:   
ZHU Jia. Particulate matter indices derived from MODIS data for indicating urban air pollution[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 126-131.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.02.20     OR     https://www.gtzyyg.com/EN/Y2016/V28/I2/126

[1] 王桥,厉青,陈良富,等.大气环境卫星遥感技术及其应用[M].北京:科学出版社,2010:1-2. Wang Q,Li Q,Chen L F,et al.The Technology and Applications of the Satellite Remote Sensing in Atmospheric Environment[M].Beijing:Sciences Press,2010:1-2.

[2] Koelemeijer R B A,Homan C D,Matthijsen J.Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe[J].Atmospheric Environment,2006,40(27):5304-5315.

[3] Engel-Cox J A,Holloman C H,Coutant B W,et al.Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality[J].Atmospheric Environment,2004,38(16):2495-2509.

[4] 李成才,毛节泰,刘启汉,等.利用MODIS光学厚度遥感产品研究北京及周边地区的大气污染[J].大气科学,2003,27(5):869-880. Li C C,Mao J T,Liu Q H,et al.Research on the air pollution in Beijing and its surroundings with MODIS AOD Products[J].Chinese Journal of Atmospheric Sciences,2003,27(5):869-880.

[5] Wang Q,Zha Y,Gao J,et al.Estimation of atmospheric particulate matter based on MODIS haze optimized transformation[J].International Journal of Remote Sensing,2013,34(5):1855-1865.

[6] 曹进,曾光明,石林,等.基于RS和GIS的长沙城市热岛效应与TSP污染耦合关系[J].生态环境,2007,16(1):12-17. Cao J,Zeng G M,Shi L,et al.Coupling relationship between the urban Heat-Island and the total suspended particles pollution in Changsha City based on remote sensing and GIS[J].Ecology and Environment,2007,16(1):12-17.

[7] 余梓木,周红妹,郑有飞.基于遥感和GIS的城市颗粒物污染分布研究[J].自然灾害学报,2004,13(3):58-64. Yu Z M,Zhou H M,Zheng Y F.Study on distribution of urban particle pollution by remote sensing and GIS[J].Journal of Natural Disasters,2004,13(3):58-64.

[8] 唐明,赵文吉,赵文慧.基于SPOT影像的可吸入颗粒物遥感反演[J].国土资源遥感,2011,23(1):62-65.doi:10.6046/gtzyyg.2011.01.12. Tang M,Zhao W J,Zhao W H.The retrieval of inhalable particulate matter based on SPOT images[J].Remote Sensing for Land and Resources,2011,23(1):62-65.doi:10.6046/gtzyyg.2011.01.12.

[9] 王艳慧,肖瑶.北京市PM5.0可吸入颗粒物反演及其时空分布分析[J].环境科学,2014,35(2):428-435. Wang Y H,Xiao Y.Inversion and spatial-temporal distribution analysis on PM5.0 inhalable particulate in Beijing[J].Environmental Science,2014,35(2):428-435.

[10] Zha Y,Gao J,Jiang J J,et al.Normalized difference haze index:A new spectral index for monitoring urban air pollution[J].International Journal of Remote Sensing,2012,33(1):309-321.

[11] 黄晓园,周汝良,罗辉.MODIS影像条带噪声去除邻域插值法研究[J].地理空间信息,2008,6(1):101-103. Huang X Y,Zhou R L,Luo H.Method for removing the stripe noises with neighboring-region interpolation algorithm in MODIS images[J].Geospatial Information,2008,6(1):101-103.

[12] Jensen J R.遥感数字影像处理导论[M].陈晓玲,龚威,李平湘,等译.3版.北京:机械工业出版社,2007. Jensen J R.Introductory Digital Image Processing:A Remote Sensing Perspective[M].Chen X L,Gong W,Li P X,et al,trans.3rd ed.Beijing:Machine Press,2007.

[13] 唐明.北京城区可吸入颗粒物分布与土地覆盖类型的关系研究[D].北京:首都师范大学,2011. Tang M.The Correlation between the Particulate Matter Distribution and Land Cover Types in Beijing[D].Beijing:Capital Normal University,2011.

[14] 查勇,倪绍祥,杨山.一种利用TM图像自动提取城镇用地信息的有效方法[J].遥感学报,2003,7(1):37-40. Zha Y,Ni S X,Yang S.An effective approach to automatically extract urban land-use from TM imagery[J].Journal of Remote Sensing,2003,7(1):37-40.

[1] QIU Yifan, CHAI Dengfeng. A deep learning method for Landsat image cloud detection without manually labeled data[J]. Remote Sensing for Land & Resources, 2021, 33(1): 102-107.
[2] CAI Yaotong, LIU Shutong, LIN Hui, ZHANG Meng. Extraction of paddy rice based on convolutional neural network using multi-source remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(4): 97-104.
[3] WANG Lin, XIE Hongbo, WEN Guangchao, YANG Yunhang. A study on water information extraction method of cyanobacteria lake based on Landsat8[J]. Remote Sensing for Land & Resources, 2020, 32(4): 130-136.
[4] Haigang SHI, Chunli LIANG, Jianyong ZHANG, Chunlei ZHANG, Xu CHENG. Remote sensing survey of the influence of coastline changes on the thermal discharge in the vicinity of Tianwan Nuclear Power Station[J]. Remote Sensing for Land & Resources, 2020, 32(2): 196-203.
[5] Chang LIU, Kang YANG, Liang CHENG, Manchun LI, Ziyan GUO. Comparison of Landsat8 impervious surface extraction methods[J]. Remote Sensing for Land & Resources, 2019, 31(3): 148-156.
[6] Dazhao WANG, Simeng WANG, Chang HUANG. Comparison of Sentinel-2 imagery with Landsat8 imagery for surface water extraction using four common water indexes[J]. Remote Sensing for Land & Resources, 2019, 31(3): 157-165.
[7] Wenya LIU, Ruru DENG, Yeheng LIANG, Yi WU, Yongming LIU. Retrieval of chlorophyll-a concentration in Chaohu based on radiative transfer model[J]. Remote Sensing for Land & Resources, 2019, 31(2): 102-110.
[8] Junnan XIONG, Wei LI, Weiming CHENG, Chunkun FAN, Jin LI, Yunliang ZHAO. Spatial variability and influencing factors of LST in plateau area: Exemplified by Sangzhuzi District[J]. Remote Sensing for Land & Resources, 2019, 31(2): 164-171.
[9] Guifen SUN, Xianlin QIN, Shuchao LIU, Xiaotong LI, Xiaozhong CHEN, Xiangqing ZHONG. Potential analysis of typical vegetation index for identifying burned area[J]. Remote Sensing for Land & Resources, 2019, 31(1): 204-211.
[10] Jing LI, Qiangqiang SUN, Ping ZHANG, Danfeng SUN, Li WEN, Xianwen LI. A study of auxiliary monitoring in iron and steel plant based on multi-temporal thermal infrared remote sensing[J]. Remote Sensing for Land & Resources, 2019, 31(1): 220-228.
[11] Yueru WANG, Pengpeng HAN, Shujing GUAN, Yu HAN, Lin YI, Tinggang ZHOU, Jinsong CHEN. Information extraction of Dracaena sanderiana planting area based on Landsat8 OLI data[J]. Remote Sensing for Land & Resources, 2019, 31(1): 133-140.
[12] Haiyang PANG, Xiangsheng KONG, Lili WANG, Yonggang QIAN. A study of the extraction of snow cover using nonlinear ENDSI model[J]. Remote Sensing for Land & Resources, 2018, 30(1): 63-71.
[13] Yali ZHANG, Tashpolat·Teyibai, Ardak·Kelimu, Dong ZHANG, Ilyas·Nuermaimaiti, Fei ZHANG. Estimation model of soil salinization based on Landsat8 OLI image spectrum[J]. Remote Sensing for Land & Resources, 2018, 30(1): 87-94.
[14] Hanyue CHEN, Li ZHU, Jiaguo LI, Xieyu FAN. A comparison of two mono-window algorithms for retrieving sea surface temperature from Landsat8 data in coastal water of Hongyan River nuclear power station[J]. Remote Sensing for Land & Resources, 2018, 30(1): 45-53.
[15] ZHANG Chengcai, LUO Weiran, DOU Xiaonan, WANG Jinxin. Research on the method of using Landsat8 data to improve FCD model[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 33-38.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech