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 (
There are two major sources of atmospheric aerosol: natural and human emissions, which is a system comprising atmospheric medium with mixed solid and liquid particles. The composition is complex and diverse, including various trace metals, inorganic oxides, sulfates, nitrates, and oxygen-containing organic compounds [
AOD is an important index of atmospheric turbidity [
At present, the research on the relationship between AOD and plant growth is mainly divided into two spatial scales: the sample scale and a large regional scale [
The purpose of this study is to find out the main influencing factors of aerosol distribution in exurban forest parks and to explore the differences in biomass growth of different forest types in aggregation areas with different concentrations. We tried to answer two questions: (1) in addition to human emission sources, what are the main ecological factors influencing exurban forest park AOD? and (2) do the AOD concentrations cause differences in biomass growth rates among different types of forests in the study area? The research results can provide guidance for improving the ecosystem service value of forest parks and provide reference for the further study of mechanisms.
We approached this research in the following steps: First, to create a database that integrates multiple types of GIS and remote sensing (RS) data (including Landsat 8 OLI images, Landsat 7-ETM+ images, FMPI, MODIS data, OpenStreetMap data, and Landscan data). Second, to preprocess the database. There were two substeps included: (1) replenishment of missing remote sensing pixel values, caused by cloud occlusion, through the Kriging interpolation which is a geostatistical method for predicting missing spatial information (AOD, MODIS data) [
Siming Mountain National Forest Park is in Ningbo City, Zhejiang Province, China (Figure
Map of study area. The inset shows the location in China.
Three types of data in two years, which included the year 2010 and 2017, were collected. The first type of data was the Landsat satellite remote sensing data for AOD retrieval with a spatial resolution of 30 m. To ensure representation of the data, we selected the two nearest meteorological stations in the China Meteorological Data Network (
Information about the GIS and RS data in this study.
Type | Data | Time |
---|---|---|
GIS | FMPI | 2010 |
2016 | ||
OSM | 2017 | |
LSP | 2017 | |
|
||
RS | Landsat 8 OLI |
April 2, 2017 |
August 24, 2017 | ||
October 27, 2017 | ||
January 15, 2018 | ||
|
||
RS | Landsat 7 ETM |
January 1, 2010 |
April 23, 2010 | ||
July 28, 2010 | ||
December 3, 2010 | ||
MODIS TPWV | The time of MODIS data set is the same as that of Landsat data set | |
MODIS LAI | ||
MODIS LST | ||
MODIS NDVI | ||
MOD04_3K |
The second type of data was the FMPI, which is obtained from in situ investigation and observation by the national forestry bureau and its affiliations in China. These data provide forest ground survey results on a regional scale. The attribute database contains information on forest patch area, diameter of breast height (DBH), height, canopy, tree density, tree composition, origin, forest management type, volume, forest resource distribution map, etc. The study area includes 20,452 and 53,980 irregular forest patches with the average of 6.499 ± 15.847 (mean ± SD) ha and 2.376 ± 6.784 ha in 2010 and 2017, respectively. The data selected for this research are (1) forest attribute data (age, DBH, dominant species, and species composition), (2) soil data (layer thickness and type), and (3) topographic data (altitude, slope degree, and slope direction). The values of the above data were calculated by averaging values of all units in the forest patches. The tree species of Siming Mountain National Forest Park mainly include
Forest types of the Siming Mountain National Forest Park in (a) 2010 and (b) 2017.
The third type of data was obtained from open-access resources. It consisted of daily Moderate Resolution Imaging Spectroradiometer (MODIS), total precipitable water vapor (TPWV), daily leaf area index (LAI), daily land surface temperature (LST), 16-day normalised difference vegetation index (NDVI), MODIS Terra Aerosol 5-Min L2 Swath 3 km (MOD04_3K) and Landscan population (LSP) data (
The atmospheric top radiation captured by satellite sensors is the result of the interaction of electromagnetic waves and the earth’s atmospheric system. Atmospheric aerosol remote sensing, which is sensitive to aerosol scattering, depends on the characteristics of the short wavelength of visible light combined with Second Simulation of the Satellite Signal in the Solar Spectrum, 6 S atmosphere transmission model, to realise the inversion [
In this study, we used ENVI software and IDL language to calculate AOD values at 550 nm. The first step was preprocessing of the Landsat images, including masking, radiation calibration, geometric correction, and the calculation of the apparent reflectance (ρ_TOA) of the atmosphere. The second step was inversion of the AOD by using the dark pixel method because of the high vegetation cover of the study area. The detailed processes include four substeps [
Two mean AOD spatial distribution maps were mapped based on the calculation of the average of each pixel of four AOD images in 2010 and 2017, respectively. The Getis-Ord Gi
The Geodetector is a statistical tool for detecting spatially stratified heterogeneity and revealing the contribution of factors to the heterogeneity [
The independent sample
To ensure that the analysis of biomass growth rate is not affected by seedling plantation or forest fire, we selected forest patches with biomass growth from 2010 to 2017. Moreover, we also screened the forest patches with the same forest type in both years in order to reduce statistical errors.
The biomass is an important indicator to evaluate the quality of forest park because it is a fundamental factor in evaluating plant growth. The Volume-Biomass Compatibility Model (VBCM) was used to estimate individual tree biomass by tree height, the DBH, and the coefficients [
Coefficients of different dominant tree species in the VBCM.
Tree species (dominant species) |
|
|
|
|
---|---|---|---|---|
Coniferous |
|
0.0811 | 1.6942 | 0.8472 |
Bamboo |
|
66.9197 | 2.5500 | 0.0437 |
Broad-leaved |
|
0.2993 | 1.8530 | 0.2774 |
Shrub |
|
0.1510 | 2.0170 | 0.0000 |
As shown in Figure
Verification results of Landsat AOD and MOD04_3K data. (a) 2010. (b) 2017. The color bars represent the counts of points. The red solid line represents the regression line.
From 2010 to 2017, the annual average AOD decreased from 0.567 to 0.292 with a change in tree species distribution. Specifically, from the proportion of tree species distribution aspect, the area proportional to broad-leaved forest increased greatly (from 24.49% to 42.58%). The area proportion of nonforest land, coniferous forest, mixed coniferous, and broad-leaved forest decreased significantly (from 21.38%, 15.57%, and 9.06% to 16.2%, 6.29%, and 4.14%, respectively), while bamboo (from 23.01% to 23.90%) and shrub (from 6.49 to 6.89%) showed a slight increase. The aggregation degree of AOD distribution decreased in terms of Moran’s I, which was 0.348 and 0.177 in the two years, respectively (Table
Results of Moran’s I.
Year | 2010 | 2017 |
---|---|---|
Moran’s I | 0.348 |
0.177 |
|
98.157 | 52.983 |
Pattern | Aggregated | Aggregated |
Note.
The average AOD of the cold and hot spots area in 2010 was significantly different from that in 2017 (Figure
Cold and hot spots of the AOD spatial distribution maps in (a) 2010 and (b) 2017. The regions with 1–5 indicate great changes in hot and cold spots.
The 10,178 forest patches of cold spots and the 10,581 forest patches of hot spots were selected based on the screening principle in the overlapped regions of cold and hot spots in 2010 and 2017. The area proportion of the various forest types in cold and hot spots area varied dramatically (Table
Area proportion of different forest types in the overlapped cold and hot spots in 2010 and 2017.
% | Coniferous forest | Broad-leaved forest | Shrub | Nonforest | Bamboo | Con and bro mixed forest | Total |
---|---|---|---|---|---|---|---|
Cold spots | 3.80 | 50.50 | 3.00 | 1.80 | 39.80 | 1.10 | 100.00 |
Hot spots | 9.09 | 32.22 | 10.30 | 14.95 | 24.75 | 9.70 | 100.00 |
Con and bro mixed forest = coniferous and broad-leaved mixed forest.
The forest type played the most important role in both cold and hot spots during the two years. In the year of 2010, temperature (0.0301 and 0.0159) and NDVI (0.0257 and 0.0354) ranked second and third in both cold and hot spots. In the year of 2017, the second and third important factors were the Landscan population density (0.0189) and the OSM road length (0.0195) in hot spots and the NDVI (0.0204) and Landscan population density (0.0201) in cold spots. In the two-year average, the followers were the OSM road length (0.0234) and NDVI (0.0158) in the hot spots and the NDVI (0.0273) and slope (0.0064) in the cold spots. The forest type and other factors were classified into two clusters through the hierarchical cluster analysis (Table
The results of different factors through FD and cluster analysis in 2010, 2017 and the two periods’ average.
Factors | 2010 | 2017 | Two periods’ average | Hierarchical cluster | |||
---|---|---|---|---|---|---|---|
Hot spots | Cold spots | Hot spots | Cold spots | Hot spots | Cold spots | ||
TSY | 0.1248 |
0.1196 |
0.1389 |
0.1450 |
0.1569 |
0.1735 |
1 |
NDVI | 0.0257 |
0.0354 |
0.0146 |
0.0204 |
0.0158 |
0.0273 |
2 |
LST | 0.0301 |
0.0159 |
0.0101 |
0.0095 |
0.0094 |
0.0057 |
2 |
TPWV | 0.0009 |
0.0008 |
0.0011 |
0.0012 |
0.0005 |
0.0027 |
2 |
ST | 0.0099 |
0.0121 |
0.0115 |
0.0141 |
0.0093 |
0.0051 |
2 |
SLT | 0.0085 |
0.0099 |
0.0054 |
0.0021 |
0.0056 |
0.0027 |
2 |
AT | 0.0114 |
0.0089 |
0.0021 |
0.0012 |
0.0078 |
0.007 |
2 |
SL | 0.0088 |
0.0066 |
0.0025 |
0.0045 |
0.0098 |
0.0058 |
2 |
SD | 0.0027 |
0.0056 |
0.0077 |
0.0096 |
0.0025 |
0.0064 |
2 |
LSP | 0.0012 |
0.0098 |
0.0189 |
0.0201 |
0.0029 |
0.0003 |
2 |
OSM | 0.0012 |
0.0083 |
0.0195 |
0.0055 |
0.0234 |
0.0017 |
2 |
Note.
Analysis of samples from the broad-leaved forest, shrub, coniferous and broad-leaved mixed forest, coniferous forest, and bamboo were in the number of 385, 214, 31, 196, and 63 patches in the hot spots and 596, 88, 22, 191, and 239 patches in the cold spots, respectively.
In 2010, there was no significant difference between the biomass densities of different forest types in the hot and cold spots. The average biomass densities of the broad-leaved forest, mixed forest, coniferous forest, bamboo, and shrub in the hot spots were 142.82 t/ha, 126.57 t/ha, 102.11 t/ha, 51.82 t/ha, and 38.25 t/ha, and in the cold spots, 143.18 t/ha, 128.14 t/ha, 100.68 t/ha, 51.53 t/ha, and 36.26 t/ha, respectively.
However, in 2017, the biomass density differences were significant between the hot and cold spots (
Comparing the growth rate of biomass density in different forest types from 2010 to 2017 (Figure
Biomass and biomass growth rates of different forest types in 2010 and 2017 (age and SD in 2010). The different letters indicate significant differences in the biomass increase rate between forest types at
The exploration of the impact factors of aerosol spatial distribution and the influences of AOD on the canopies of different forest types in the exurbs of cities encourage forest managers to improve the ecosystem service level. This study used multisource data, which included high-resolution remote sensing images and detailed ground investigation data to discover if the type of tree species is the one of the main factors affecting the AOD concentration difference in Siming Mountain Forest Park. We observed the impacts of AOD concentration on the growth of different tree species in the forest park, which supplemented the evidence of the relationship between AOD spatial distribution and the forest on a regional scale.
Siming Mountain Forest Park is located near the east coast of China, with a high forest coverage rate far from the urban center. The variation of AOD values may be caused by the combined action of moisture and particulate matter. In order to minimize the influence of water vapor, we selected eight remote sensing images taken during good and stable weather conditions between 10 : 00 and 11 : 00 am to avoid the condensation of water vapor caused by low temperature in the morning, although the average value of AOD in the study area is low. The AOD is the integral of the extinction coefficient of the medium in the vertical direction, which reflects the comprehensive effect of atmospheric scattering and surface reflection. We cannot distinguish the aerosol types. Based on the analysis of aerosol sources in four forest areas in Sichuan Province, China, it was found that the aerosols were mainly from local primary emission sources such as crust dust and biomass combustion, followed by the long distance transmission of aged organic aerosols (aged OOA) [
The results of the Geodetector and cluster analysis in the cold and hot spots of this study in two years showed that the forest type was the main factor affecting AOD distribution in the exurban forest park, followed by NDVI and temperature. In the cold spots, more than half of the area was broad-leaved forest, while the percentage of broad-leaved forest decreased and the proportion of nonforest land area augmented in the hot spots. The result is similar to that of Deng et al. [
From the perspective of the impact of aerosols on plants, the concentration threshold reversed the impact of aerosols on photosynthesis above which the net radiation (direct + scattering) effect of atmospheric aerosols would reduce the photosynthetic absorption of plants [
There are two limitations in this study. First, the change of AOD is continuous with time. Landsat remote sensing satellite image data (a scene every 16 days) only obtains instantaneous information of the environment. Although the average value of four specific periods throughout the year was used as the research object in this study, the representativeness of inversion results still needs to be considered. At present, ground-based remote sensing and monitoring technologies represent the distribution of sample points and have the ability to obtain series time resolution data. Statistical analyses or machine learning algorithms could be used to obtain the relationships between the sample plot and the surface data. The scale expansion analysis based on multisource data fusion technology could be a solution to explore the mechanism of the aerosol characteristics in forest regions affected by the microclimate. Second, the research area was in the developed area of southeastern China. During the period of economic development from 2010 to 2013, many local residents engaged in the planting of flower and tree seedlings for economic benefits, resulting in a large amount of deforestation. From 2013 to 2017, the local government invested 150 million Yuan to restore 226,637 ha of forest area in order to restore the forest ecological service level. Large areas of land were affected by human disturbance in a short time, which may affect the formation of aerosols in the air. Although rigorous screening criteria were set up to ensure the accuracy of forest type classification, the flow feature of aerosols cannot exclude the influence of other disturbances.
In addition, this study has additional advantages. High-resolution AOD inversion data and detailed comprehensive ground forest resource survey data not only accurately depicted the spatial heterogeneity of the research objects but also explored the impact of different forest attributes on spatial heterogeneity. On the basis of integrating multisource data, the effect of diffusion radiation fertilization on regional forest park scale was verified, and the relationship between forest type and AOD concentration was discussed.
This study overcame the low resolution of AOD remote sensing products and the unavailability problem of the forest vegetation vertical structure information from the optical remote sensing data by the use of high-resolution Landsat and ground survey data of forest resources. We analyzed the AOD spatial-temporal distribution pattern and its main influencing factors and explored the relationship of different types of forest canopies at the regional scale. It was found that the AOD distribution in the study area was clustered, and the forest type was one of the main impact factors. 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 not a significant difference in mixed forests. The average growth rate of biomass in the cold and hot spots was higher in bamboo, coniferous forest, and mixed forest than in shrub and broad-leaved forest. In summary, at the regional forest park scale, the vegetation type had the closest interaction with AOD. The research results provide the guidance for the rational allocation of tree species to improve biomass and ecosystem service value of the exurban forest park.
The meteorological observation data were supplied by the National Meteorological Information Center (
The authors declare that there are no conflicts of interest.
This work was supported by the National Key Research Program of China (2016YFC0502704), National Natural Science Foundation of China (31670645, 31470578, 31200363, 41801182, 41771462, and 41807502), National Social Science Fund of China (17ZDA058), Fujian Provincial Department of S&T Project (2016T3032, 2016T3037, 2018T3018), Key Program of the Chinese Academy of Sciences (KFZDSW-324) and Ningbo Public Welfare Project (2009c10056).