Quantifying the Influences of Driving Factors on Vegetation EVI Changes Using Structural Equation Model: A Case Study in Anhui Province, China
"> Figure 1
<p>Elevation map of the Anhui Province study area. Note: Blue characters represent northern Anhui, red characters represent central Anhui and purple characters represent southern Anhui.</p> "> Figure 2
<p>Conceptual structural equation model. Note: Rectangles represent observed variables, and ellipses represent latent variables. AaT_change = annual average temperature change; AP_change = annual precipitation change; Lu_change = land use change; Ntl_change = nighttime light change.</p> "> Figure 3
<p>Temporal variations in annual maximum EVI mean during 2000–2020 in Anhui Province. (<b>a</b>) The entire Anhui Province and (<b>b</b>) Northern, (<b>c</b>) Central and (<b>d</b>) Southern Anhui.</p> "> Figure 4
<p>Spatial distribution of average EVI in Anhui Province during 2000–2020.</p> "> Figure 5
<p>Spatial distribution of changes in EVI in Anhui Province during 2000–2020. (<b>a</b>) Slope of variation in EVI. (<b>b</b>) Spatial variation trend of EVI.</p> "> Figure 6
<p>Stability of variations in EVI in Anhui Province during 2000–2020.</p> "> Figure 7
<p>Sustainability of variations in EVI in Anhui Province during 2000–2020. (<b>a</b>) Hurst exponents of variation in the EVI. (<b>b</b>) Sustainability classification of variation in the EVI.</p> "> Figure 8
<p>Land use in Anhui Province in (<b>a</b>) 2000 and (<b>b</b>) 2020.</p> "> Figure 9
<p>Superposition analysis of land-use change and Slope of EVI variation in Anhui Province. (<b>a</b>) Areas of EVI increase and (<b>b</b>) decrease.</p> "> Figure 10
<p>EVI changes of different land use types transferred in Anhui Province from 2000 to 2020 on pixel scale.</p> "> Figure 11
<p>SEM of the relationship between EVI change and its drivers in Anhui Province. Note: AaT_change = annual average temperature change; AP_change = annual precipitation change; Lu_change = Land use change; Ntl_change = nighttime light change. Green lines represent positive effects; red lines represent negative effects. Solid lines represent significant paths. *** Significant at the 99% level.</p> "> Figure 12
<p>SEM of the relationship between EVI change and its drivers in (<b>a</b>) Northern, (<b>b</b>) Central and (<b>c</b>) Southern Anhui Province. Note: AaT_change = annual average temperature change; AP_change = annual precipitation change; Lu_change = Land use change; Ntl_change = nighttime light change. Green lines represent positive effects; red lines represent negative effects. Solid lines represent significant paths. *** Significant at the 99% level; ** Significant at the 95% level; * Significant at the 90% level.</p> "> Figure 13
<p>Pearson correlations of EVI variation and its potential drivers in (<b>a</b>) Anhui Province and (<b>b</b>) Northern, (<b>c</b>) Central and (<b>d</b>) Southern Anhui. Note: AaT_change = annual average temperature change; AP_change = annual precipitation change; Lu_change = Land use change; Ntl_change = nighttime light change.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Acquisition and Processing
2.2.1. EVI Data
2.2.2. Topographic Factors
2.2.3. Human Activity Factors
2.2.4. Climate Factors
2.2.5. Data Processing
2.3. Methods
2.3.1. Univariate Linear Regression
2.3.2. Coefficient of Variation
2.3.3. Hurst Index
2.3.4. Structural Equation Model
3. Results
3.1. Spatial and Temporal Variations in EVI in Anhui Province
3.1.1. Interannual Variation in EVI
3.1.2. Spatial Distribution Characteristics of EVI and Areas of Transfer
3.1.3. Spatial Trend in EVI
3.1.4. Stability of EVI Variations
3.1.5. Sustainability of EVI Variations
3.2. Superposition of EVI Variation and Land-Use Change in Anhui Province
3.3. SEM of EVI Variation in Anhui Province
3.4. SEM of EVI Variations in Northern, Central and Southern Anhui
4. Discussion
4.1. Spatial and Temporal Variations in the EVI
4.2. Drivers of EVI Variation
4.2.1. Topographic Factors
4.2.2. Climate Factors
4.2.3. Human Activity Factors
4.3. Advantages of SEM
4.4. Limitations of This Study
5. Conclusions
- (1)
- Temporally, the EVI of Anhui Province showed a trend of a fluctuating increase at a rate of 0.0181·10a−1 between 2000 and 2020.
- (2)
- The EVI in Anhui Province showed a spatial distribution pattern of being high in the north and south and low in the middle. The spatial trend in EVI was dominated by improvement, with 64.2% of the regions having significant improvements in EVI. The fluctuation in EVI variation in most regions of the province was extremely low and low. High fluctuations occurred in urban areas. After 2020, the EVI is likely to decrease, so the government should strengthen relevant vegetation greening and protection measures.
- (3)
- Among the areas where EVI increased, 10.8% of the areas was transferred from “other” land use to farmland, mainly in the northern and central Anhui plain areas. Some 6.6% were transferred from “other” land use to woodland, mainly in the mountainous area of central and southern Anhui. Among the regions with reduced EVI, 13.7% was transferred from farmland to construction land, mainly in Hefei, Fuyang, Bozhou, Huaibei, and the Wanjiang River urban belt. Therefore, the government needs to pay special attention to the coordinated development of accelerated urbanization and ecological environmental protection.
- (4)
- The SEM showed that human activity changes (mainly nighttime light change) were the main cause of EVI decreases in Anhui Province. Except for northern Anhui, central and southern Anhui were affected by the complexity of the topography. In addition, the EVI variations in Anhui Province were less influenced by annual average temperature change, and the influence of annual precipitation change showed that northern and central Anhui were higher than southern Anhui.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Dataset | Temporal /Spatial Resolution | Period | Data Sources | |
---|---|---|---|---|
EVI | 16 d/250 m | 2000–2020 | MOD13Q1.006 Terra Vegetation Indices 16-Day Global 250 m (https://earthengine.google.com, accessed on 26 January 2022) | |
Topographic factors | Elevation | 30 m | 2000 | SRTM DEM (https://earthexplorer.usgs.gov, accessed on 26 January 2022) |
Slope | 30 m | 2000 | Derived from SRTM DEM | |
Aspect | ||||
Human activity factors | Populationdensity | 1 km | 2000–2020 | World population density map (https://www.worldpop.org, accessed on 26 January 2022) |
Nighttime light | 500 m | 2000–2018 | An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data (https://doi.org/10.7910/DVN/YGIVCD accessed on 26 January 2022) | |
Land use | 1 km | 2000/2020 | Anhui land-use datasets (http://www.resdc.cn, accessed on 26 January 2022) | |
Climate factors | Annual average temperature | Monthly /1 km | 2000–2020 | Daily surface climate data for China (V3.0) (http://data.cma.cn, accessed on 26 January 2022) |
Annual precipitation | Monthly /1 km | 2000–2020 | Daily surface climate data for China (V3.0) (http://data.cma.cn, accessed on 26 January 2022) |
Significance | 0 < H < 0.5 | H = 0.5 | 0.5 < H < 1 |
---|---|---|---|
Significant degradation | Significant improvement | Uncertain | Significant degradation |
Degradation | Improvement | Degradation | |
Basically stable | Basically stable | Basically stable | |
Improvement | Degradation | Improvement | |
Significant improvement | Significant degradation | Significant improvement |
2000 | 2020 | |||||
---|---|---|---|---|---|---|
[0,0.2] | [0.2,0.4] | [0.4,0.6] | [0.6,0.8] | [0.8,1] | Total | |
[0,0.2] | 117.31 (0.09%) | 270.88 (0.20%) | 48.13 (0.03%) | 11.5 (0.01%) | 1 (~0.00%) | 448.82 (0.33%) |
[0.2,0.4] | 166 (0.12%) | 1785.38 (1.33%) | 1982.44 (1.47%) | 328.38 (0.24%) | 16.25 (0.01%) | 4278.45 (3.17%) |
[0.4,0.6] | 201 (0.15%) | 3774.13 (2.80%) | 33,035.88 (24.52%) | 35,755.25 (26.54%) | 1820.88 (1.35%) | 74,587.14 (55.36%) |
[0.6,0.8] | 70.5 (0.05%) | 1097.75 (0.81%) | 9495.13 (7.05%) | 38,039.44 (28.23%) | 5529.44 (4.11%) | 54,232.26 (40.25%) |
[0.8,1] | 4.44 (~0.00%) | 32.13 (0.02%) | 212.38 (0.16%) | 774 (0.58%) | 172.31 (0.13%) | 1195.26 (0.89%) |
Total | 559.25 (0.41%) | 6960.27 (5.16%) | 44,773.96 (33.23%) | 74,908.57 (55.60%) | 7539.88 (5.60%) | 134,741.93 (100%) |
2000 | 2020 | ||||||
---|---|---|---|---|---|---|---|
Farmland | Woodland | Grassland | Waterbodies | Construction Land | Unused Land | Total | |
Farmland | 61,135 (43.76%) | 5062 (3.62%) | 1233 (0.88%) | 2189 (1.57%) | 11,191 (8.01%) | 18 (0.01%) | 80,828 (57.85%) |
Woodland | 4878 (3.49%) | 24,193 (17.32%) | 2350 (1.68%) | 249 (0.18%) | 441 (0.32%) | 2 (~0.00%) | 32,113 (22.99%) |
Grassland | 1238 (0.89%) | 2306 (1.65%) | 4478 (3.21%) | 123 (0.09%) | 173 (0.12%) | 3 (~0.00%) | 8321 (5.96%) |
Waterbodies | 2030 (1.45%) | 225 (0.16%) | 147 (0.11%) | 4537 (3.25%) | 306 (0.22%) | 0 (0.00%) | 7245 (5.19%) |
Construction land | 8085 (5.79%) | 209 (0.15%) | 100 (0.07%) | 252 (0.18%) | 2546 (1.82%) | 2 (~0.00%) | 11,194 (8.01%) |
Unused land | 3 (~0.00%) | 2 (~0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 5 (~0.00%) |
Total | 77,369 (55.38%) | 31,997 (22.90%) | 8308 (5.95%) | 7350 (5.27%) | 14657 (10.49%) | 25 (0.01%) | 139,706 (100%) |
Fitting Optimization Index | Adaptation Standard | Evaluation Values for Each Region | ||
---|---|---|---|---|
Northern Anhui | Central Anhui | Southern Anhui | ||
CFI | >0.90 | 1.00 | 0.98 | 0.99 |
GFI | >0.90 | 1.00 | 0.99 | 0.99 |
IFI | >0.90 | 1.00 | 0.98 | 0.99 |
RMSEA | <0.08 | 0.01 | 0.07 | 0.06 |
SRMR | <0.05 | 0.01 | 0.02 | 0.03 |
Driving Factors | Northern Anhui | Central Anhui | Southern Anhui |
---|---|---|---|
Topography | 0 | 0.22 | 0.27 |
AaT_change | −0.06 | 0.05 | −0.08 |
AP_change | 0.21 | −0.26 | 0.04 |
Human activity change | −0.62 | −0.49 | −0.60 |
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Gu, Z.; Zhang, Z.; Yang, J.; Wang, L. Quantifying the Influences of Driving Factors on Vegetation EVI Changes Using Structural Equation Model: A Case Study in Anhui Province, China. Remote Sens. 2022, 14, 4203. https://doi.org/10.3390/rs14174203
Gu Z, Zhang Z, Yang J, Wang L. Quantifying the Influences of Driving Factors on Vegetation EVI Changes Using Structural Equation Model: A Case Study in Anhui Province, China. Remote Sensing. 2022; 14(17):4203. https://doi.org/10.3390/rs14174203
Chicago/Turabian StyleGu, Zhengnan, Zhen Zhang, Junhua Yang, and Leilei Wang. 2022. "Quantifying the Influences of Driving Factors on Vegetation EVI Changes Using Structural Equation Model: A Case Study in Anhui Province, China" Remote Sensing 14, no. 17: 4203. https://doi.org/10.3390/rs14174203