The Spatiotemporal Pattern of the Aerosol Optical Depth (AOD) on the Canopies of Various Forest Types in the Exurban National Park: A Case in Ningbo City, Eastern China

Abstract

The spatiotemporal distribution pattern of the aerosol optical depth (AOD) is influenced by many environmental factors, such as meteorological condition changes, atmospheric pollution, and topographic changes. Understanding the relationship between the vegetation land cover and the AOD would favor the improvement of forest ecosystem services. This quantitative research integrated remote sensing and ground survey data and used spatial statistical methods to explore the drivers that influence the AOD of the exurban national forest park and analyze the differences between various forest types. The driver analysis was carried out in the hot (Z ≥ 1.64) and cold (Z ≤ −1.64) spots of AOD in 2010 and 2017. Our results showed that (1) the forest type was proved to be the main factor contributing to the AOD pattern and (2) from 2010 to 2017, the average growth rate of broad-leaved forest, coniferous forest, bamboo, and shrub in hot spots was significantly higher than that in cold spots, while there was no significant difference in the mixed forest. The average growth rate of biomass densities of bamboo, coniferous forest, and mixed forest were higher than that of the shrub and broad-leaved forest. These findings provided the guidance for the rational allocation of tree species to increase the biomass and improve the ecosystem service values of forest parks.

Effects of aerosol particles on precipitation and cloud parameters over East Africa-Ethiopia using MODIS satellite data: Part 01

“…All aerosols can also undergo further chemical changes, referred to as 'aging effects'. They have numerous potential feedback processes that are still poorly understood, making them an essential component of the atmospheric hydrological cycle and its radiation budget (Gaffney et al, 2006;Wild and Liepert, 2010;Behera, 2016;Chi et al, 2019).…”

Spatial heterogeneity and driving factors of aerosol in Western China: Analysis on multiangle implementation of atmospheric correction–aerosol optical depth in Xinjiang over 2001–2019

“…The contribution of crop straw burning to atmospheric aerosols can be as high as 70–90% in Northeast China (Yu et al ., 2019). At the regional forest level, vegetation type is a major factor that influences AOD (Chi et al ., 2019). The preceding studies provide many insights to increase our understanding of the aerosol pollution factors in terms of meteorology, topography, and anthropogenic activity.…”

Handling Missing Data in Large-Scale MODIS AOD Products Using a Two-Step Model

“…However, uncertainties in cloud detection, limitations of the AOD inversion algorithm, and sensor degradation are the three main factors that cause a partial loss of the AOD local data retrieved by satellites [23][24][25]. For example, the shortcomings of the DT algorithm and DB algorithm for AOD detection in bright areas, the errors of cloud detection in some heavily polluted areas and the degradation of other sensors directly affect the detection of dark pixels in low angle areas, which leads to the loss of AOD data in some areas [26,27]. A study of the Yangtze River Delta in China found that the missing rate of MOD AOD reached 89.6% between 2014 and 2017 [28].…”