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FOREST RESOURCES WANAGEMENT ›› 2021, Vol. 0 ›› Issue (4): 94-103.doi: 10.13466/j.cnki.lyzygl.2021.04.013

• Scientific Research • Previous Articles     Next Articles

Correlation Analysis of Fine Particulate Pollutants and Land Cover Landscape Pattern in Beijing

MA Bolun(), WANG Lei(), HUA Yongchun   

  1. Inner Mongolia Agricultural University,Hohhot 010019,China
  • Received:2021-06-29 Revised:2021-07-07 Online:2021-08-28 Published:2021-09-26
  • Contact: WANG Lei E-mail:864835709@qq.com;1602173685@qq.com

Abstract:

Air pollution is a key environmental issue nowadays.It is of great significance to understand the interaction between different land cover landscape patterns and air fine particulate pollutants for improving urban ecological environment.Aerosol optical depth(AOD) is the premise of the generation of fine particulate pollutants in the air.The AOD data measured by the AERONET ground monitoring points in Beijing and the surface fine particulate pollutants data were used for fitting analysis,and then the AOD of four seasons in Beijing was retrieved by using MODIS data.The Landsat8 image was processed to obtain the landscape type index of Beijing in 2018,and the correlation analysis was conducted with four seasons AOD data.The results showed that:1) fine particulate pollutants were negatively correlated with LPI(maximum patch index),ED(boundary density),COHESION(patch connectivity) and AI(patch aggregation),but positively correlated with PD(patch fragmentation) and LSI(landscape shape index);2) The annual forest and grassland were the core types with significant negative correlation with fine particulate pollutants,while the correlation between farmland and water body was significantly related to seasonal changes;3) Multiple linear regression analysis was used to get the regular model of forest,grassland,farmland,water and fine particulate pollutants in four seasons,which further proved that landscape index could be used to estimate the mass concentration of regional fine particulate pollutants.

Key words: remote sensing, aerosol, fine particulate pollutants, landscape pattern index

CLC Number: