Accepted Manuscript

Quantifying influences of natural factors on vegetation NDVI changes based on geographical detector in Sichuan, western China

Wenfu Peng, Tingting Kuang, Shuai Tao

PII: S0959-6526(19)31897-9 DOI: https://doi.org/10.1016/j.jclepro.2019.05.355 Reference: JCLP 17120

To appear in: Journal of Cleaner Production

Received Date: 28 September 2018 Revised Date: 16 May 2019 Accepted Date: 29 May 2019

Please cite this article as: Peng W, Kuang T, Tao S, Quantifying influences of natural factors on vegetation NDVI changes based on geographical detector in Sichuan, western China, Journal of Cleaner Production (2019), doi: https://doi.org/10.1016/j.jclepro.2019.05.355.

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ACCEPTED ACCEPTED MANUSCRIPT

Quantifying influences of natural factors on vegetation NDVI

changes based on geographical detector in Sichuan, western China

Abstract : Many studies have revealed that Normalized Difference Vegetation Index (NDVI) is of vital importance for research of ground surface processes and climatic changes. However, identification of the causes responsible for vegetation changes remain challenging. Using Geographical Detector, a new spatial statistical approach, individual and interactive influences of natural factors on vegetation NDVI changes were quantified for the Sichuan province. The optimal characteristics of key natural factors that are beneficial to vegetation growth were determined.The vegetation cover in Sichuan for 2000 and 2015 is good overall; areas with upper-intermediate and high vegetation cover account for more than 88% of the total area. Regions with an NDVI higher than 0.4 are noticeably transformed; regions with upper-intermediate vegetation cover (NDVI from 0.4 to 0.8) tend to decrease significantly,whereas regions with high vegetation cover (NDVI>0.8) tend to increase significantly. Spatiotemporal variation in vegetation cover is very significant. Regions with high vegetation cover are distributed in the northeast part of the Sichuan Basin and on the northwestern Sichuan Plateau, whereas regions with low vegetation cover are distributed in urban agglomerative regions in the central part of the Sichuan Basin. Soil types, elevation, and annual mean temperature can satisfactorily account for vegetation changes. Natural factors have an interactive influence on NDVI. The synergistic effect of natural factors is manifested as mutual enhancement and nonlinear enhancement, and the interaction of two natural factors strengthens the influence of each individual natural factor. Natural factors within a favourable value range or favourable landform factors serve to promote and intervene in vegetation changes, adapt to climatic changes, and buffer against the effects of vegetation changes. Key words: vegetation NDVI; Geographical Detector; natural MANUSCRIPT factors; Geographic Information System (GIS); Sichuan province

1. Introduction As an important part of terrestrial ecosystems, ground vegetation serves to promote ecological balance, climatic changes, and water circulation (Gong et al., 2017). Meanwhile, ground vegetation is highly reliant on and sensitive to a variety of natural and anthropogenic factors, and can reflect the impact of climatic changes and anthropogenic activities within a short time (Zhao et al., 2018; Parmesan C et al., 2003). Therefore, investigation of regional and global vegetation changes is of great significance to understanding the interaction between changesACCEPTED in natural factors and terrestrial ecosystems (He et al., 2017). With the development of earth observation technology, there is an important opportunity to study global and regional scale vegetation cover changes based on high temporal resolution and low-cost MODIS data (Liu et al., 2015; Piao et al., 2001). The Normalized Difference

Vegetation Index (NDVI) can effectively reflect the optimal status of vegetation growth and is closely correlated with ground biomass (Lin et al., 2018; Fu et al., 2017). In particular NDVI time-series data are important for many regional and global ecological and environmental ACCEPTED MANUSCRIPT applications (Cao et al., 2017).

The mechanism by which vegetation changes are influenced by historical changes and

predicted future natural factors has become an issue of great concern. In recent years, studies

have revealed a range of factors driving vegetation changes. Based on ground observation

data, scholars in China and around the world have used different methods to study vegetation

changes and their driving factors. A series of pixel-based VEgetation Dynamics

Stepwise-cluster Prediction models have been proposed to establish the relations between

NDVI and climate conditions through using the data series of remotely sensed precipitation

and temperature. Precipitation has been identified as the critical climatic factor resulting in

differences in NDVI values (Zheng et al., 2018). Kong et al.(2017) studied the climatic causes

of vegetation phenological changes in Qinghai-Tibet Plateau using partial least squares

regression, and concluded that temperature has a dominant influence on phenology and

precipitation has a large monthly influence on vegetation phenology. Based on a multiple

linear regression method, the spatial and temporal variation and influencing factors of

vegetation NDVI in Yulin, Shaanxi Province from 2000 to 2016 were analysed, and it is believed that meteorological factors promote the MANUSCRIPT growth and evolution of vegetation, and human activities make the vegetation index level more even (Luan et al., 2018). Based on

multivariate regression analysis with multiple time scale analysis, climate change had a good

and significant influence on vegetation dynamics in 54.1% in southwest China; and

specifically, in Guangxi Province and the north of Guizhou Province, the impact of climate

change on vegetation dynamics was greater than that of human activities (Liu et al.,2015).

Applying trend analysis methods, correlation analysis and other aspects, Wang et al.(2017)

found that there was a negative correlation between vegetation NDVI and air temperature in

the Qinling mountains, and a positive correlation between NDVI and precipitation. Louise Leroux et al.ACCEPTED (2017) found that 16% of the Sahel is re-greening, but found strong evidence that rainfall is not the only important driver of biomass increase. Moreover, a decrease found

in 5% of the Sahel can be chiefly attributed to factors other than rainfall (88%). We observed

negative trends (29% of the Niger site area) mainly in tiger bush areas located on lateritic

plateaus, which are particularly prone to pressures from overgrazing and over logging. The

significant role of accessibility factors in biomass production trends was also highlighted.

Zheng et al. (2016) found that the change of vegetation NDVI in Sichuan Province has a ACCEPTED MANUSCRIPT significant linear correlation with the precipitation and temperature, using the method of trend analysis, linear regression, and variation coefficient. Using other quantitative analysis methods, Benewinde J-B et al.(2018) found that significant decreasing NDVI trends (p<0.05) indicated negative modifications of natural vegetation in Burkina Faso, West Africa,

Spearman's correlation showed that accessibility, climatic and topographic conditions led to degradation of vegetation degradation. Zhang et al.(2016) found that the general climate conditions were favourable to vegetation recovery, whereas human activities had a weaker negative impact on vegetation growth in the Three-Rivers Source Region, China, from 2001 to 2012, climate conditions began to have a negative impact on vegetation growth, whereas human activities made a favourable impact on vegetation recovery. Pang et al. (2017) used the third-generation Global Inventory Modelling and Mapping Studies NDVI and climate data

(temperature and precipitation) to examine recent (1982–2012) spatial and temporal variations in vegetation, and relationships between climate and vegetation for both the growing period and for different seasons, on the Tibetan Plateau. Zhao et al. (2018) suggests that historical anthropogenic forcing (mainly increases in greenhouse gases) explains about two-thirds of the growing-season normalized differe MANUSCRIPTnce vegetation index (NDVI gs ) trend from 1982 to 2013, with the rest explained mainly by the Atlantic Multi-decadal Oscillation in global vegetation changes activity. Lamchin et al. (2018) suggest that an increasing trend for evapotranspiration and air temperature accompanied a decreasing trend for vegetation greenness and rainfall in Asia. The temperature was found to be the main driver of the changing vegetation greenness in Kazakhstan, northern Mongolia, Northeast and Central

China, North Korea, South Korea, and northern Japan, showing an indirect relationship

(R=0.84-0.96). Qu et al. (2018) suggest that temperature is a controlling factor determining the vegetation greenness in the Yangtze River Basin, and the response of vegetation to precipitation ACCEPTEDis relatively lower because of the abundant water. Meanwhile, land use changes caused by the ecological restoration project is the major driving factor for improving vegetation conditions in Yangtze River Basin. Gu et al. (2018) suggest that precipitation plays a more important role than temperature in the interaction between NDVI and climatic factors, and that human activities, especially the implementation of the Grain for Green Project, has affected the spatial and temporal patterns of vegetation coverage in the Red River Basin,

China. ACCEPTED MANUSCRIPT

Although related studies have analysed dynamic correlation between vegetation changes and driving factors, most studies on the factors influencing spatiotemporal variation in NDVI are conducted by regression, trend, and correlation analyses. However, there is a crucial deficiency in these studies they assume that there is a significant linear relation exists between driving forces and vegetation productivity across an entire time series. In fact, a rigorous statistical linear relation may not exist during the complex process of vegetation growth responses to climatic changes (L.Hein et al., 2016). Although K-means and SOM algorithms are used in studies on classification and subzones, statistical methods to study spatial heterogeneity are less well-developed (Wang et al., 2017).

The improvement of vegetation cover in Sichuan contributes to the construction of ecological barriers in the upper reaches of the Yangtze River. Therefore, research the relationship between vegetation change and natural factors in Sichuan, and promote the growth of vegetation and slow down the impact of natural factor change on vegetation change by selecting the appropriate range or type of natural factors. It is nevertheless difficult to quantitatively analyse factors influencing differences in NDVI variation (Louise Leroux et al., 2017; Luan J K et al., 2018; Wang et al., 2016). MANUSCRIPT Geographical Detector is a new spatial statistical method that is used, independent of any linear hypothesis, to identify driving forces by detecting spatial heterogeneity (Wang et al., 2017; Wang et al., 2011). Geographical

Detector uses spatial variance to quantify the relative importance of single factors and their implicit interactions with response variables (Wang et al., 2017).

The objectives of this study were to: (1) identify the main natural factors and their relative roles in vegetation changes; (2) discern whether the natural factors are independent or dependent influences on vegetation variation; and (3) determine the optimal characteristics of each natural factor beneficial to vegetation growth, as indicated by the maximum NDVI values. This ACCEPTEDpaper consists of five sections: the first section is the introduction; the second section introduces the study area, data source and research method; the third section reports the main results and findings of NDVI changes and influences of natural factors on vegetation

NDVI changes; the fourth section gives a full discussion on natural factors within a favourable value range or favourable landform factors serve to promote vegetation changes; and the fifth section summarizes the core results of quantifying influences of natural factors on vegetation NDVI Changes. ACCEPTED MANUSCRIPT

2. Materials and methods

2.1. Study area

The study was conducted in Sichuan, western China, located between 97°21’–108°33’ E

and 26°03’–34°19’ N, which covers an area of 48.6×10 4 km 2 (Fig.1). It lies in a transitional

zone between the Qinghai-Tibet Plateau (the first rung on the terrain ladder of China) and the

Yangtze Plain (the second rung). Topographically, Sichuan is low-lying in the east and

high-lying in the west, descending from the northwest to the southeast. Its topography is

complex and diverse. The land forms are dominated by mountains and plateaus, as well as

hills, with very few plains. In Sichuan Province, climate varies according to the region. In the

plateau mountain regions in western Sichuan, vertical climatic changes are very significant. A

cold temperate climate dominates. It is cold in winter and cool in summer, and sunlight is

ample. The annual mean temperature is 4°C to 12°C, and the annual mean precipitation is

500 mm to 700 mm. In the basin regions in eastern Sichuan, a subtropical monsoon climate

dominates. It is warm in winter and hot in summer, sunlight is insufficient, the annual mean

temperature is 16°C to 18°C, and the annual mean precipitation is 1000 mm to 1300 mm. Soil types are diverse: there are 25 soil groups and 63 MANUSCRIPT soil sub-types, arable soils include purple, paddy, yellow, and red soils. Vegetation types are dominated by subtropical shrubs and

evergreen broad-leaf forests, and alpine meadows in the mountain plateaus.

2.2. Data sources

This study data in this paper included vegetation NDVI, Digital Elevation Model (DEM),

climate, soil, topography and vegetation types. Vegetation NDVI data are derived from the

natural spatial data cloud (http://www.gscloud.cn/), and the rest of the data are from the

Resources and Environmental Science Data Center of the Chinese Academy of Aciences

(http://www.resdc.cn). Vegetation NDVI data for 2000 and 2015 originated from China 500 m vegetation NDVIACCEPTED synthesis products,which was calculated by MODND1D, and the calculation method is gotten the maximum NDVI per day in a month. Climate was extracted the interpolation of inverse distance weighted average method and DEM correction based on

1915 meteorological stations in China. The extracted climate data was cut access according to the administrative region of Sichuan province vector boundary.Climate zoning data were compiled by the national meteorological administration in 1978, by using climatic data from

1951 to 1970. Soil map was compiled and published by the Chinese Soil Census Office in ACCEPTED MANUSCRIPT

1995. The spatial distribution data of 1:100,000 landform type in China was derived from the

Geomorphological Atlas of the People’s Republic of China. The vegetation map was digitized by 1:10 000 vegetation map. Terrain data, i.e. elevation, aspect and slope were derived from the DEM.

MANUSCRIPT

Fig.1. Location of study area

2.3. Methods

2.3.1. Synthesis method of NDVI and its grading

(1) Compositing method for NDVI: Sichuan’s vegetation types are very diverse. One ecosystem may comprise a variety of vegetation types that span multiple natural belts, and some vegetation types have obvious seasonal characteristics. To describe the characteristics of interannual vegetationACCEPTED change more comprehensively, this study uses the maximum value composite (MVC) method of the Environment for Visualizing Images (ENVI), specifically, extracting maximum pixel values to regenerate from monthly composites, and compositing

NDVI data from 2000 and 2015.

(2) Grading: for better vegetation dynamics analysis, the vegetation NDVI was divided into five grades based on equal-interval method and the actual status of vegetation NDVI, namely the low vegetation coverage (0<0.2), middle and lower vegetation coverage [0.2-0.4), the ACCEPTED MANUSCRIPT vegetation coverage [0.4-0.6), the high vegetation coverage [0.6-0.8), high vegetation coverage [0.8-1) (Peng et al., 2016). Microsoft Excel was used for data analysis.

2.3.2. Image density segmentation and difference image algorithm

Considering the actual vegetation cover of Sichuan, field survey data, and thresholds of vegetation cover (if the threshold is set to 10%, regions with a decrease in vegetation cover of

0–10% may be omitted) (Xu et al., 2013; Peng et al., 2016), dynamic changes in vegetation cover were extracted using image density segmentation and image differencing algorithms.

Vegetation cover is considered to remain unchanged, decrease, or increase if the difference values of vegetation cover are zero, negative, or positive, respectively (Peng et al., 2016).

2.3.3. Index selection and information extraction

(1) Index selection: although vegetation NDVI changes are deeply influenced by natural and cultural factors, complex and diverse natural conditions have an important impact on vegetation NDVI changes in Sichuan. Relative studies have shown that climate factors have a significant impact on vegetation, and the variation trend of NDVI in the western Sichuan

Plateau is mainly closely related to climate fluctuations. Vegetation growth in Sichuan is negatively correlated with precipitation, but MANUSCRIPT positively correlated with temperature. Vegetation cover shows a downward trend with the increase of temperature and precipitation, and the negative correlation between vegetation and average annual temperature is significantly greater than that of precipitation. The change of vegetation cover in the western

Sichuan Plateau is mainly driven by the negative influence of precipitation, while the Sichuan

Basin and Panxi region are greatly affected by the influence of temperature (Du et al., 2016).

Sichuan has a complex and diverse topography, which leads to the diversity of climate, plants and soil, adding to the complexity of the environment. In the western high mountains and plateaus, the terrain is steep and the traffic is inconvenient. Although there has been overgrazing inACCEPTED the past on the western Sichuan Plateau, overgrazing has been effectively controlled after the government increased efforts to protect the grassland and other resources.

According to the index system of selection of systematic, typicality, dynamic, scientific, quantifiable, and can obtain the principle, 12 natural factors were selected to detect the effect of natural factors on NDVI changes of vegetation in Sichuan (Table 1).

Table1 The index of natural factors in study area Natural factors types Code Index Unit ACCEPTED MANUSCRIPT

Climate x1 Average annual precipitation mm

x2 Dryness index class

x3 Humidity index class

x4 Cumulative temperature ( ≥10 °C) °C

x5 Annual average temperature °C 2 x6 Global radiation MJ/m

Vegetation x7 Vegetation types types

Physiognomy x8 Physiognomy types types

Soil x9 Soil types types

Topography x10 Elevation m

x11 Slope degree °

x12 Aspect °

(2) Information extraction: some 4861 randomly sampling points files were generated (as shown in Fig.1) based on 1 km×1 km grids by using a GIS. Then, according to spatial positions, the NDVI of sampling points was correlated with all natural factor data, thus generating an attribute table. Finally, quantitative relations between NDVI and the selected indices were determined.

2.3.4. Grading of natural factors Using the natural breakpoint method in theMANUSCRIPT GIS (Liu et al., 2017), annual mean precipitation, dryness index, humidity index, cumul ative temperature (daily mean temperature

is higher than or equal to 10 °C), annual mean temperature, total radiation, and elevation were each divided into six classes. Slope was classified into nine classes, and aspect into ten classes.

Meanwhile, vegetation was classified into one of five types, and soil into one of 14 types.

2.3.5. Geographical Detector

Geographical Detectors represents a new spatial statistics method that is used to detect spatial heterogeneity and identify driving factors based on risk, factors, ecology, and interaction (Wang et al., 2017). (1) DetectionACCEPTED of spatial heterogeneity and factors. The calculation method comprises the following steps: (a) spatial overlay analysis was performed for the NDVI layer and natural factor layer; (b) natural factors were divided into different spatial types or subzones; and (c) a significance test for the differences of mean values of natural factors was conducted, to detect relative importance of natural factors. The calculation model of the explanatory power of each natural factor is as follows: ACCEPTED MANUSCRIPT

L σ 2 ∑ N hh = − h=1 = − SSW PD 1 2 1 (1) Nσ SST

Where PD is the explanatory power of natural factors on vegetation NDVI, h = 1, …, L

are the stratification of y or factor x, that is, classification or partition; Nh and N are the number of units in h and the whole region,respectively. N and σ2 are the total number of

samples and the variance of y value in the whole region. Nh is the variance of units h.

The range of PD value is [0, 1], and the larger the PD value is, the more obvious the

spatial differentiation of y is. In the extreme case, the PD value of 1 indicates that factor x completely controls the spatial distribution of Y, the PD value of 0 indicates that the factor x

has nothing to do with Y.

The variance calculation formula of the y value in the whole region is as follows:

1 N 2 σ 2 = (Y −Y) (2) − ∑ i N 1 i=1

Where Yj and Y are the of the jth sample and the mean value of region Y in study area, respectively. MANUSCRIPT Nh σ 2 = 1 − 2 h ∑(Y ,ih Y h ) (3) N −1 = h i 1

where Yh,i and Y is the value of i th sample and the mean of Y in zone h, respectively.

(2) Detection of factor interaction. Interaction detection is used to identify the interaction

between natural factors, that is, to evaluate the accountability of the combined effect

(enhancing or weakening) and respective effect on the NDVI. First, the PD values of two

natural factors with respect to NDVI were calculated ( PD (x1) and PD (x2). Then, PD values regarding theACCEPTED interaction between natural factors was calculated ( PD (x1∩x2)) and compared with PD (x1) and PD (x2).

(3) Detection of risk zones. Risk detection is used to judge whether there is a significant difference in mean attribute values between the subzones of two natural factors, and can be used to find regions with high vegetation coverage. The risk detection is examined by using T statistic value: ACCEPTED MANUSCRIPT

Y h=1 −Y h=2 t = (4) Var (Y ) Var (Y ) [ h=1 + h=2 ] 2/1 n =1 n = 2 h h (4) Ecological detection: Ecological detection is used to determine whether there is a significant difference between two natural factors ( x1 and x2) in terms of influence on the

spatial distribution of NDVI, i.e., whether x1 will influence the spatial distribution of NDVI more significantly than x2.

N ×(N − )1 × SSW F = x1 x2 x1 (5) N ×(N − )1 × SSW x2 x1 x2

L1 L2 = σ 2 = σ 2 SSW x1 ∑ N hh SSW x2 ∑ N hh h=1 h=1 (6) ，

where Nx1, Nx2 represent the sample number of two natural factors,respectively. SSW x1,

SSW x2 represent the sum of intra-layer variance formed by two natural factors,respectively. L1,

L2 represent the number of stratification of variables x1 and x2, respectively. MANUSCRIPT 3. Results

3.1. Dynamic variation in NDVI

Regions with upper intermediate and high vegetation cover areas accounted for 24–69% of the total area of Sichuan in 2000, and 19–75% in 2015. Regions with low, lower intermediate, and intermediate vegetation cover collectively accounted for less than 6% of the total area(Table 2). This shows that vegetation cover in the study area is high; it is at an upper intermediate or high level in most regions, and there was a significant increase in regions with high vegetation cover between 2000 and 2015. From 2000 to 2015, regions with low, lower intermediate,ACCEPTED intermediate, and upper intermediate vegetation cover decreased; as a proportion of the total area of Sichuan, they decreased by 0.076%, 0.146%, 0.390%, and

5.598%, respectively. Among these regions, those with upper intermediate vegetation cover decreased most significantly. In contrast, regions with high vegetation cover increased significantly; the proportion of such regions to Sichuan’s total area increased by 6.210%.

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Table 2. Vegetation NDVI dynamic changes in 2000-2015 Year 2000 2015 2000—2015 NDVI Area (km 2) Proportion (%) Area (km 2) Proportion (%) Area change (km2) Proportion (%) (0.0—0.2) 2072.104 0.429 1703.75 0.353 -368.354 -0.076 [0.2—0.4) 5358.020 1.110 4655.25 0.964 -702.770 -0.146 [0.4—0.6) 19194.216 3.975 17313.5 3.586 -1880.716 -0.390 [0.6—0.8) 119556.267 24.761 92525.75 19.163 -27030.517 -5.598 [0.8—1.0) 336651.193 69.724 366633.5 75.934 29982.307 6.210

From 2000 to 2015, there is an obvious variation in the spatial distribution of vegetation coverage. Overall, vegetation coverage is high in the northwestern Sichuan Plateau and in the northeast part of the Sichuan Basin, but low in urban agglomerate regions in the Sichuan

Basin (Fig. 2).

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Fig. 2. Vegetation NDVI patterns in study area (2000 —2015)

Regions with significant variation in vegetation coverage mainly include three autonomous prefectures (i.e., Ganzi, Aba, and Liang MANUSCRIPTshan) in the western Sichuan Plateau (Fig. 3). Regions with a rise in NDVI are mainly distributed in the north central part of the Sichuan

Basin (i.e., Guangyuan, Nanchong, Mianyang, Deyang, Ya'an, Dazhou, Leshan, and Yibin) and in the western Sichuan Plateau (i.e., Litang, Batang, and Dege). Regions with a decline in

NDVI are mainly distributed in the northwestern Sichuan Plateau, and regions with an obvious decline in vegetation coverage are mainly distributed in Chengdu Plain.

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Fig. 3. Vegetation NDVI Change patterns in study area (2000 —2015)

The transfer matrix for different classes of NDVI was calculated based on the statistics of NDVI spatial distribution from 2000 to 2015 MANUSCRIPT (Table 3). From 2000 to 2015, NDVI variation is mainly manifested in the obvious transformation of regions with NDVI higher than 0.4. As a result, there was a significant decrease in regions with NDVI from 0.4 to 0.8, and a significant increase in regions with NDVI higher than 0.8 (Table 3). Over this time period, 28 904.25 km 2 and 29 999.25 km 2 is removed from regions with NDVI from 0.4 to 0.8, and with NDVI higher than 0.8, respectively. Meanwhile, 35 722.25 km 2 and 48 475.50 km 2 is added to these two types of regions, respectively. As a result, there is a decrease in the area of regions with NDVI from 0.4 to 0.8, and an increase in the area of regions with NDVI higher than 0.8. ACCEPTED Table 3 The transfer matrix of vegetation NDVI changes in 2000—2015 unit: km 2 NDVI (0.0— 0.2) [0.2—0.4) [0.4—0.6) [0.6—0.8) [0.8—1.0) 2015 total Transfer in (0.0— 0.2) 1487.75 182.01 7.50 2.25 0 1703.75 216.00 [0.2—0.4) 536.75 3059.65 1008.05 50.00 1.00 4655.25 1595.75 [0.4—0.6) 41.25 1931.10 11377.55 3739.19 225.26 17313.5 10826.75 [0.6—0.8) 4.00 154.76 6487.06 67633.64 18250.92 92525.75 24895.50 [0.8—1.0) 2.25 30.50 314.02 48131.17 318173.97 366633.5 48475.50 2000 total 2072 5358.02 19194.17 119556.25 336651.15 ACCEPTED MANUSCRIPT

Transfer out 584.25 2298.25 7816.25 51920 18476.25 Change -368.25 -702.5 -1879.75 -27024.5 29999.25

3.2. Influence analysis of detection factors

3.2.1. Detection factor influence

The factor detector was used to uncover the magnitude of influence exerted by each

environmental factor on NDVI. By calculating the PD value of each natural factor (Table 4),

this study identified the influence exerted by each natural factor on NDVI. From 2000 to 2015,

natural factors could be ranked in descending order by the magnitude of their influence on

NDVI: soil type > elevation > annual mean temperature > dryness index > cumulative

temperature (≥10°C) > landform types > global radiation > vegetation types > annual mean

precipitation > slope degree > humidity index > slope aspect (Table 4).

Table 4 PD values of natural factors from 2000 to 2015 in study area

Natural factors x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12

PD 0.1030 0.2337 0.0078 0.2013 0.3171 0.1341 0.0957 0.1741 0.3401 0.3316 0.0042 0.0042 p value 0.000 0.000 1 0.000 0.000 0.000 MANUSCRIPT 0.000 0.000 0.000 0.000 1 1

Table 4 shows that the PD values of soil type and elevation are the largest (0.3401 and

0.3316 respectively), and both accounted for more than 30% of variation in NDVI (Table 4).

Therefore soil type was the primary natural factor affecting vegetation changes. The PD

values of annual mean temperature and dryness index are 0.3171 and 0.2337, respectively,

and both account for more than 23% of variation in NDVI. The PD values of cumulative

temperature, landform type, global radiation, and vegetation type are all higher than 0.1018

and tended to increase, and all accounted for more than 10% of NDVI variation. Annual mean precipitation,ACCEPTED slope, humidity index, and aspect accounted for a very small amount of variation in NDVI. However, these factors could be combined with others to produce a

remarkable influence on vegetation changes.

3.2.2. Temporal variation of detection factors

From 2000 to 2015, the PD value of average annual precipitation, moisture index,

dryness index, cumulative temperature ( ≥10°C), annual average temperature, geomorphology

types, soil type,elevation and slope showed an increasing trend. From 2000 to 2005, the PD ACCEPTED MANUSCRIPT value of the total radiation, vegetation type, slope, etc. is generally reduced. Except the decrease of PD value of the average annual precipitation,moisture index, cumulative temperature (≥10 0C), aspect, etc., the PD value of other factors showed an increasing trend.

The PD values such as annual precipitation, humidity index, geomorphology types, slope and

slope direction showed an increasing trend, while PD values of other factors showed a decreasing trend during the 2005-2010. From 2010 to 2015, except the decrease of PD value of slope and aspect, the PD value of other factors showed an increasing trend (Fig. 4).

Fig. 4. PD value changes of naturalMANUSCRIPT factors from 2000 to 2015

3.2.3. Difference analysis of the detection factors

(1) Differences in geomorphologic types. The PD values of natural factors of different geomorphological types differ greatly (Table 5). For example, the PD values of all natural factors of plains and terraces did not exceed 0.13. The PD values of global radiation, elevation, soil type and other factors in small hilly areas all exceeded 0.23. The PD values of such factors as elevation, total radiation, soil type, annual mean temperature and dryness index of middle and fluctuating mountainous areas all exceeded 0.29. The PD values of

elevation, average annual temperature, soil type and other factors in the hilly region all

exceeded 0.29.ACCEPTED The PD values of soil type, vegetation type, elevation and other factors in the

extremely undulating mountainous areas all exceeded 0.26.

Table 5 PD values of natural factors in different geomorphic types

GMT x1 x2 x3 x4 x6 x7 x8 x9 x10 x11 x12

PL 0.1133 0.0357 0.0781 0.0333 0.0495 0.0628 0.0125 0.0748 0.0557 0.0121 0.0288

PLF 0.0503 0.0690 0.0203 0.0922 0.0815 0.1222 0.0115 0.0782 0.1080 0.0067 0.0061 ACCEPTED MANUSCRIPT

SUM 0.1998 0.1715 0.0103 0.1861 0.1953 0.3339 0.0829 0.2367 0.2903 0.0162 0.0210

MFM 0.1810 0.2927 0.0200 0.2554 0.3004 0.3351 0.0898 0.3246 0.3715 0.0383 0.0087

HFM 0.1379 0.2905 0.0048 0.2340 0.4532 0.0893 0.2011 0.3617 0.4620 0.0384 0.0096

EUM 0.2429 0.0489 0.1104 0.0080 0.2084 0.0942 0.4068 0.5262 0.2625 0.1055 0.0617 Note: GMT,eomorphic types; PL,Plain; PLF,Platform; SUM,Small undulating mountains; MFM,Middle fluctuating mountains; HFM,Height fluctuating mountains; EUM,Extremely undulating mountains.

（2）Soil types. Soil is the object of change under the comprehensive influence of multiple

factors, and it is generally believed that the parent material, climate, biology, topography,

time and human activities are the main factors of soil formation. PD values of different soil

types are different, reflecting the role of soil formation (Table 6). PD values of eluvial soil

(yellow brown soil and brown spots soil, brown soil, dark brown soil, brown coniferous forest

soil) and iron bauxite (latosolic red soil, red soil and yellow soil) were small, and its

explanatory power was also small. The PD values of natural factors such as elevation,

average annual temperature and geomorphology of semi-leached soils (dry red soil and brown

soil) reached 0.4194, 0.3155 and 0.2899, respectively, and the explanatory power of factors

exceeded 28%. The PD value of primary soil (such as purple soil, etc.) was small, but the interaction with the parent material of the soil hadMANUSCRIPT an important influence on the formation of the purple soil in Sichuan and lime (rock) soil bas ed on the parent material of the soil. The PD

values of annual precipitation, slope direction and geomorphology types of semi-hydrated

soils were 0.4057, 0.4586 and 0.3365, respectively. The explanatory power of annual

precipitation, moisture index and geomorphology types of artificial soil (rice) were all over

10%. PD values of alpine soil (alpine meadow soil, subalpine meadow soil, alpine cold desert

soil) and annual temperature were respectively 0.2360 and 0.2729, respectively, indicating

high explanatory power.

Table 6 PD value of natural factors in different soil types Soil types ACCEPTEDx1 x2 x3 x4 x6 x7 x8 x9 x10 x11 x12 Eluvial soil 0.0059 0.0276 0.0201 0.0618 0.0546 0.0602 0.0454 0.0876 0.0648 0.0645 0.0126

Semi-Luvisols 0.1914 0.1081 0.0435 0.2581 0.3155 0.0607 0.0500 0.2898 0.4194 0.1006 0.0752

Primitive soil 0.0104 0.0185 0.0238 0.0501 0.0270 0.1072 0.0177 0.0357 0.0432 0.0182 0.0082

Semi-aquatic soil 0.4057 0.0143 0.0512 0.0487 0.0252 0.0207 0.0234 0.0298 0.0137 0.0070 0.1748

Aquatic soil 0.1098 0.1619 0.1231 0.0063 0.1338 0.2520 0.0063 0.3365 0.1579 0.0877 0.4586

Anthrosols 0.1351 0.0003 0.1245 0.0151 0.0011 0.0084 0.0133 0.1026 0.0000 0.0362 0.0118

Tierras 0.0392 0.0930 0.0441 0.0321 0.2360 0.0207 0.0454 0.1097 0.2729 0.0257 0.0188

Ferralsol 0.0303 0.0048 0.0292 0.0624 0.0410 0.1712 0.0206 0.0449 0.0171 0.0487 0.0149

ACCEPTED MANUSCRIPT

（3）Climatic differences. The PD values of natural factors in different climate zones are

different (Table 7). From Table 7, the north subtropical Qinba area, climate transitional zone

from subtropical to warm temperate, the PD values of elevation, soil and annual temperature

for the north subtropical Qinba area (climate transitional zone from subtropical to warm

temperate) were all above 0.32, elevation has an obvious barrier to climate. The PD values of

soil, elevation and topography in the central tropical climate region and Sichuan region are

the highest, all above 0.20. The landform is surrounded by mountains and plateaus, and the

Sichuan Basin is mainly mountainous and hilly, the purplish red sandstone is widely

distributed in the basin and the soil is also affected by the parent material.The transition area

from the southeast hills to the southwest high mountain plateau in Guizhou and northern

Yunnan, Guizhou has a humid climate, more cloud, less sunshine, and high PD values of

global radiation, soil and moisture index for the transition area from the southeast hills to the

southwest high mountain plateau in Guizhou and northern Yunnan, Guizhou were all above

0.90. The influence of plateau topography and elevation in northern Yunnan has enriched the

natural landscape and climate of Yunnan, the PD values of the elevation, slope and aspect for northern Yunnan were all above 0.38. Plateau climat MANUSCRIPTe area radiation intensity, more sunshine, low temperature, less accumulated temperature,the temperature decreases with elevation and

latitude, the PD values of elevation, average annual temperature and soil in Changdu were all

above 0.18, the PD values of soil, elevation and average annual temperature in Qingnan

district were the highest, all above 0.32. Therefore, even in the subtropical climate zone,

natural factors influence degree in Sichuan, Guizhou, and north of Yunnan is different, the

elevation and average annual soil temperature in the plateau climate zones has significant

influence on vegetation NDVI.

Table7 PD values of naturalACCEPTED factors in different climatic zones

Primary Subname x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 climate zone

North Qinba 0.0735 0.1949 0.0624 0.2074 0.3250 0.0559 0.0291 0.0676 0.3193 0.4073 0.0563 0.0715 subtropics

Sichuan 0.0678 0.1162 0.0403 0.1084 0.1805 0.0509 0.0145 0.2444 0.2129 0.2034 0.0407 0.0058

Middle Guizhou 0.0172 0.0022 0.9004 0.0509 0.0022 0.9175 0.8952 0.0038 0.9014 0.0009 0.0540 0.0460 subtropics Northern 0.0047 0.0165 0.1901 0.1260 0.0645 0.2739 0.1082 0.1213 0.2928 0.4317 0.4048 0.3876 Yunnan ACCEPTED MANUSCRIPT

Plateau Changdu 0.0739 0.1438 0.0414 0.0887 0.2085 0.0048 0.1408 0.1243 0.1878 0.3188 0.1186 0.1354 climate zone Qingnan 0.1819 0.1419 0.0166 0.1012 0.3245 0.0428 0.2432 0.1330 0.3749 0.3339 0.0312 0.0146

3.3. Statistical analysis of significant differences between factors

Ecological detection was used to determine whether there is a significant difference

between natural factors in terms of the relative importance of their influence on NDVI. Table

8 showed the results of ecological detection and statistically significant differences between

natural factors are presented in Table 8.

Table 8 Statistical significance of detection factors (95% confidence level)

Factors x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12

x1

x2 Y

x3 N N

x4 Y N Y

x5 Y Y Y Y

x6 N N Y N N

x7 N N Y N N N

x8 Y N Y N N Y Y

x9 Y Y Y Y N Y Y Y x10 Y Y Y Y N Y MANUSCRIPT Y Y N x11 N N N N N N N N N N

x12 N N N N N N N N N N N Note: Y indicates that there is a significant difference in the effect of two factors on vegetation NDVI (confidence is 95%), N means no significant difference.

In terms of its influence on spatial distribution of NDVI, soil type is significantly

different from annual mean precipitation, dryness index, humidity index, cumulative

temperature, global radiation, vegetation types, and landform types, whereas there is no

significant difference between annual mean temperature, elevation, slope, and slope aspect.

There is a significant difference between the elevation and average annual rainfall, dryness index, the moistACCEPTED index, or accumulated temperature of 10°C, total radiation, vegetation types and landform, and has no significant influence on slope, slope direction, soil type and annual

temperature. There are significant differences between average annual temperature and

average annual precipitation, dryness index, humidity index, cumulative temperature (≥10°C),

and are not significantly different from soil type, elevation, slope, total radiation, vegetation

type, landform, etc.

3.4. Analysis of indicative effect of factors ACCEPTED MANUSCRIPT

Using the Geographical Detector, this study analysed the optimal types or value ranges

of natural factors beneficial to vegetation growth (Table 9), and conducted a test of statistical

significance at a confidence level of 95%. The higher the NDVI , the more beneficial to

vegetation growth the characteristics of natural factors are. Mean NDVI varies widely among

the natural factors (Table 9).

Table 9 The suitable limits of the natural factors (95% confidence level) Natural factor Suitable types or range Mean value of NDVI Average annual precipitation /mm 1394 －1739 0.906 Dryness index 0－0.2 0.908 Humid index 96 －186 0.906 Cumulative temperature ( ≥ 10 0C) / ℃ 3638 －4709 0.907 Annual average temperature /℃ 11.48 －16.40 0.911 Global radiation /MJ/m 2 3779.82 －4215.35 0.912 Elevation /m 1885 －2852 0.902 Slope /° 4.64 －9.63 0.881 Aspect /° 0－22.5 、157.5 －202.5 0.881 Vegetation types /types Coniferous forest, broad-leaved forest 0.907 Landform types /types Hills, small ups and downs and middle 0.902 ups and downs, etc. Soil type /types Red soil, yellow MANUSCRIPTsoil, dark brown soil, 0.903 meadow soil, brown soil, brown soil, etc.

When annual mean precipitation ranges from 1372 mm to 1839 mm, the mean value of

NDVI reaches a maximum (0.906). When the dryness index increases, mean NDVI increases

gradually at first, and then decreases rapidly. When the dryness index ranges from 0 to 0.2,

mean NDVI reaches its maximum. With a rise in the humidity index, mean NDVI increases

gradually. In humidity index subzone 5, mean NDVI reaches a maximum (0.908), implying

that this range of humidity index is beneficial to vegetation growth. As cumulative

temperature increases, mean NDVI increases gradually. When cumulative temperature ranges from 3638 °CACCEPTED to 4709 °C, mean NDVI reaches a maximum (0.907). With a rise in annual mean temperature, mean NDVI gradually increases. When annual mean temperature ranges from

11.29 °C to 15.25 °C or from 11.48 °C to 16.4 °C, mean NDVI reaches a maximum (0.911).

When total radiation increases, mean NDVI fluctuates. When total radiation ranges from

3779.82 to 4215.35 MJ/m 2, mean NDVI reaches a maximum (0.912). Mean NDVI fluctuates

with vegetation type. In coniferous and broad-leaf forests, mean NDVI reaches a maximum

(0.907). Mean NDVI fluctuates with landform type. When the landform type is hills, or ACCEPTED MANUSCRIPT low-or meso-relief mountains, mean NDVI reaches a maximum (0.902). Mean NDVI also fluctuates with soil type and elevation. When soil type is red, yellow, dark brown, meadow, cinnamon, or brown soils, mean NDVI reaches a maximum (0.903). When elevation ranges from 1885 m to 2852 m, mean NDVI reaches a maximum (0.902). Finally, mean NDVI fluctuates with slope and aspect. When slope ranges from 4.64 ° to 14.66 °, vegetation growth is promoted and mean NDVI reaches 0.881. When the aspect is west or southwest, vegetation growth is promoted and mean NDVI values exceed 0.881.

3.5. Analysis of interaction between factors

By identifying the interactive influence of different alternative natural factors ( xi) on

NDVI, interaction detection is used to analyse whether accountability for NDVI (a dependent variable) is enhanced or weakened, or whether the influence of these factors on NDVI is independent. The interaction detector can be used to detect the interactive influence of driving factors on NDVI (Table 10). Studies show that natural factors have an interactive influence on

NDVI . The interaction effect of natural factors is manifested as mutual enhancement and

nonlinear enhancement. None of the natural factors influence NDVI independently from one other (Table 10). MANUSCRIPT Table 10

Interaction detection of natural factors

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x1 0.103 x2 0.294 0.234 x3 0.125 0.266 0.008 x4 0.277 0.246 0.238 0.201 x5 0.383 0.331 0.360 0.325 0.317 x6 0.211 0.263 0.191 0.239 0.355 0.134 x7 0.198 0.314 0.133 0.295 0.377 0.291 0.096 x8 0.300 0.327 0.208 0.307 0.400 0.288 0.300 0.174 x9 0.398 0.381 0.380 0.377 0.432 0.373 0.380 0.423 0.340 x 0.415 0.344ACCEPTED 0.366 0.354 0.374 0.386 0.400 0.426 0.446 0.332 1

0 x11 0.136 0.265 0.034 0.229 0.346 0.167 0.167 0.207 0.378 0.372 0.004

x1 0.123 0.250 0.017 0.218 0.329 0.159 0.122 0.189 0.372 0.342 0.022 0.004

2

According to Table 11, the PD values of interactions between most natural factors are higher than the PD values of any individual factor, and the interaction effect of natural factors ACCEPTED MANUSCRIPT

is manifested as mutual enhancement and nonlinear enhancement.Elevation, annual mean

temperature, landform type, and annual mean precipitation all interact with soil type, thus

producing mutual and nonlinear enhancement effects. In addition, the interaction effect of

slope aspect with soil type, elevation, annual mean precipitation, vegetation type, total

radiation, and annual mean temperature is manifested as mutual and nonlinear enhancement,

having a mutually enhanced influence on NDVI. Overall, the interactive influence of natural

factors on NDVI is not a simple superposition process, but is manifested as mutual or

nonlinear enhancement.

Table 11 Interaction detection between vegetation NDVI influence factors

C A+B Result Interpretation C A+B Result Interpretation x1∩x2 =0.294 ＜0.337 ＝x1＋x2 C＜A＋B ↑ x4∩x8 =0.307 ＜0.375 ＝x4＋x8 C＜A＋B ↑ x1∩x3 =0.125 ＞0.121 ＝x1＋x3 ＞A＋B ↑↑ x4∩x9 =0.377 ＜0.541 ＝x4＋x9 C＜A＋B ↑ x1∩x4 = 0.277 ＜0.304 ＝x1＋x4 C＜A＋B ↑ x4∩x10 =0.354 ＜0.533 ＝x4＋x10 C＜A＋B ↑ x1∩x5 =0.383 ＜0.420 ＝x1＋x5 C＜A＋B ↑ x4∩x11 =0.229 ＞0.205 ＝x4＋x11 C＞A＋B ↑↑ x1∩x6 =0.211 ＜0.237 ＝x1＋x6 C＜A＋B ↑ x4∩x12 =0.218 ＞0.205 ＝x4＋x12 C＞A＋B ↑↑ x1∩x7 =0.198 ＜0.199 ＝x1＋x7 C＜A＋B ↑ x5∩x6 =0.355 ＜0.451 ＝x5＋x6 C＜A＋B ↑ ＞ ＝ ＋ ＜ ＋ ＜ ＝ ＋ ＜ ＋ x1∩x8 =0.300 0.277 x1 x8 C A B ↑↑ x5∩xMANUSCRIPT7 =0.377 0.413 x5 x7 C A B ↑ ＜ ＝ ＋ ＜ ＋ ＜ ＝ ＋ ＜ ＋ x1∩x9 =0.398 0.443 x1 x9 C A B ↑ x5 ∩x8 =0.400 0.491 x5 x8 C A B ↑ x1∩x10 =0.415 ＜0.435 ＝x1＋x10 C＜A＋B ↑ x5∩x9 =0.432 ＜0.675 ＝x5＋x9 C＜A＋B ↑ x1∩x11 =0.136 ＞0.107 ＝x1＋x11 C＞A＋B ↑↑ x5∩x10 =0.374 ＜0.649 ＝x5＋x10 C＜A＋B ↑ x1∩x12 =0.123 ＞0.107 ＝x1＋x12 C＞A＋B ↑↑ x5∩x11 =0.346 ＞0.321 ＝x5＋x11 C＞A＋B ↑↑ x2∩x3 =0.266 ＞0.242 ＝x2＋x3 C＞A＋B ↑↑ x5∩x12 =0.329 ＞0.324 ＝x5＋x12 C＞A＋B ↑↑ x2∩x4 =0.246 ＜0.435 ＝x2＋x4 C＜A＋B ↑ x6∩x7 =0.291 ＞0.230 ＝x6＋x7 C＞A＋B ↑↑ x2∩x5 =0.331 ＜0.551 ＝x2＋x5 C＜A＋B ↑ x6∩x8 =0.288 ＜0.308 ＝x6＋x8 C＞A＋B ↑ x2∩x6 =0.263 ＜0.368 ＝x2＋x6 C＜A＋B ↑ x6∩x9 =0.373 ＜0.474 ＝x6＋x9 C＜A＋B ↑ x2∩x7 =0.314 ＜0.330 ＝x2＋x7 C＜A＋B ↑ x6∩x10 =0.386 ＜0.466 ＝x6＋x10 C＜A＋B ↑ x2∩x8 =0.327 ＜0.408 ＝x2＋x8 C＜A＋B ↑ x6∩x11 =0.167 ＞0.138 ＝x6＋x11 C＞A＋B ↑↑ x2∩x9 =0.381 ＜0.574 ＝x2＋x9 C＜A＋B ↑ x6∩x12 =0.159 ＞0.138 ＝x6＋x12 C＞A＋B ↑↑ x2∩x10 =0.344 ＜0.566 ＝x2＋x10 C＜A＋B ↑ x7∩x8 =0.300 ＞0.270 ＝x7＋x8 C＜A＋B ↑↑ ＞ ＝ ＋ ＞ ＋ ＜ ＝ ＋ ＜ ＋ x2∩x11 =0.265 0.238ACCEPTEDx2 x11 C A B ↑↑ x7∩x9 =0.380 0.436 x7 x9 C A B ↑ x2∩x12 =0.250 ＜0.238 ＝x2＋x12 C＞A＋B ↑ x7∩x10 =0.400 ＜0.428 ＝x7＋x10 C＜A＋B ↑ x3∩x4 =0.238 ＞0.209 ＝x3＋x4 C＞A＋B ↑↑ x7∩x11 =0.167 ＞0.100 ＝x7＋x11 C＞A＋B ↑↑ x3∩x5 =0.360 ＞0.325 ＝x3＋x5 C＞A＋B ↑↑ x7∩x12 =0.122 ＞0.100 ＝x7＋x12 C＞A＋B ↑↑ x3∩x6 =0.191 ＞0.142 ＝x3＋x6 C＞A＋B ↑↑ x8∩x9 =0.423 ＜0.541 ＝x8＋x9 C＜A＋B ↑ x3∩x7 =0.133 ＞0.104 ＝x3＋x7 C＞A＋B ↑↑ x8∩x10 =0.426 ＜0.506 ＝x8＋x10 C＜A＋B ↑ x3∩x8 =0.208 ＞0.182 ＝x3＋x8 C＞A＋B ↑↑ x8∩x11 =0.207 ＞0.178 ＝x8＋x11 C＞A＋B ↑↑ x3∩x9 =0.380 ＞0.348 ＝x3＋x9 C＞A＋B ↑↑ x8∩x12 =0.189 ＞0.178 ＝x8＋x12 C＞A＋B ↑↑ x3∩x10 =0.366 ＞0.340 ＝x3＋x10 C＞A＋B ↑↑ x9∩x10 =0.446 ＞0.672 ＝x9＋x10 C＞A＋B ↑↑ x3∩x11 =0.034 ＞0.012 ＝x3＋x11 C＞A＋B ↑↑ x9∩x11 =0.378 ＞0.344 ＝x9＋x11 ＜A＋B ↑↑ ACCEPTED MANUSCRIPT x3∩x12 =0.017 ＞0.012 ＝x3＋x12 C＞A＋B ↑↑ x9∩x12 =0.342 ＞0.344 ＝x9＋x12 C＞A＋B ↑↑ x4∩x5 =0.325 ＜0.518 ＝x4＋x5 C＜A＋B ↑ x10 ∩x11 =0.372 ＞0.336 ＝x10 ＋x11 C＞A＋B ↑↑ x4∩x6 =0.239 ＜0.335 ＝x4＋x6 C＜A＋B ↑ x10 ∩x12 =0.342 ＞0.336 ＝x10 ＋x12 C＞A＋B ↑↑ x4∩x7 =0.295 ＜0.297 ＝x4＋x7 C＜A＋B ↑ x11 ∩x12 =0.022 ＜0.044 ＝x11 ＋x12 C＞A＋B ↑

Note ：“↑” denotes x1 and x2 enhance each other; “↑↑ ” denotes a non-linear enhancement of x1 and x2.

According to Table 11, the PD values of interactions between most natural factors are

higher than the PD values of any individual factors, and the interactive influence of natural

factors is manifested as mutual enhancement and nonlinear enhancement. The PD values of

the interactive influence of natural factors on NDVI, annual mean temperature, elevation,

cumulative temperature, dryness index, and annual mean precipitation interact with soil type,

thus producing mutual and nonlinear enhancement effects. In addition, annual mean

precipitation and humidity index interact with annual mean temperature, cumulative

temperature and dryness index interact with annual mean temperature, and dryness index and

elevation interact with cumulative temperature, thus producing mutual and nonlinear

enhancement effects. The superposition of x8∩x9 also produces a mutually enhanced influence

on NDVI. In summary, the influence of natural factors on NDVI is not independent, instead factors interact significantly with each other. The MANUSCRIPT interactive influence of multiple natural factors on NDVI is not a simple superposition process, but is manifested as mutual or

nonlinear enhancement.

4. Discussion

With advances in earth observation technologies, there is an important role for the use of

cost-effective MODIS data with a fine temporal resolution to study global and regional

changes in vegetation cover. Although significantly influenced by various natural and

anthropogenic factors, China’s vegetation coverage has increased dramatically from 1997 to

2012. IncreasingACCEPTED the vegetation cover of Sichuan is important for increasing the ecological

resilience of the Yangtze River. Therefore, it is of great significance to study the correlation

between natural factors and changes in vegetation in Sichuan. According to our findings,

appropriate interventions can be made to alleviate the influence of natural factors on

vegetation coverage and to restore vegetation and the ecological environment.

4.1. Soil types

The mean NDVI exceeded 0.869 in the soil type subzones 1, 3, 6, 8 and 11, mean NDVI ACCEPTED MANUSCRIPT of subzone 8 reaches a maximum (0.903, Table 12 ), implying that soil type subzones 1, 3, 6,

8, and 11 are beneficial to vegetation growth. Statistical tests show that the mean NDVI of soil type subzones 1, 3, 6, 8, and 11 is significantly different from that of other soil type subzones. Related studies propose that soil type has a significant influence on vegetation growth and rainwater reuse efficiency in regions where precipitation is the main factor restricting production (Piao et al., 2001; Liu et al., 2015). In contrast, this study shows that soil type is the primary factor influencing NDVI in Sichuan. Mean NDVI fluctuates with soil type. For soil types such as red, yellow, dark brown, meadow, cinnamon, and brown soils, mean NDVI is higher than 0.845 and vegetation cover is at the highest level. For yellow soils, dark brown soils, and meadow soils, mean NDVI reaches a maximum (0.903).

Table 12 Vegetation NDVI mean and its statistical significance of every two sub regions in soil types(confidence level 95%)

Statistical tests 1 2 3 4 5 6 7 8 9 10 11 12 14

1

2 Y

3 N Y

4 Y N Y 5 Y Y Y Y MANUSCRIPT 6 Y N Y Y Y

7 Y Y Y Y N Y

8 Y Y N Y Y Y Y

9 Y Y Y Y Y Y Y Y

10 N N N N N N N N N

11 N Y N Y Y Y Y N Y N

12 Y N Y N Y Y Y Y Y N Y

14 Y Y Y Y Y Y Y Y Y N Y Y

Soil subzones 1 2 3 4 5 6 7 8 9 10 11 12 14

Mean NDVI 0.898 0.824 0.902 0.845 0.829 0.869 0.746 0.903 0.651 0.648 0.897 0.866 0.167 Note: (1) Y and N same as Table 8. (2) The numbers from 1 to 14 indicate respectively: Leached soils, semi-leached soils, primary soils, semi-hydrated soils, water-formed soils, artificial soils, alpine soils, ferroalumina soils, urban areas,ACCEPTED rocks, lakes and reservoirs, rivers and rivers, sandbanks and glacier snow covers within rivers.

Due to the interactive effect of soil type with annual mean temperature, elevation,

cumulative temperature, landform type, humidity index, and total radiation, the influence of

soils on NDVI is significantly enhanced. Compared with soils, slope and aspect only have a

slight influence on NDVI. This may be because hydrothermal conditions vary with slope and

aspect. Soil type can also interact with slope and aspect to significantly enhance the influence ACCEPTED MANUSCRIPT of soils on NDVI .

4.2. Elevation

Although ground vegetation growth involves other direct and indirect factors, elevation

has a great influence on the growth of ground vegetation. Elevation can interact with

landform type, annual mean precipitation, annual mean temperature, humidity index, and

cumulative temperature to significantly enhance its influence on vegetation NDVI (e.g.

x8∩x10 =0.426 > x10 , x5∩x10 =0.374 > x10 , x3∩x10 =0.366 > x10 , x10 ∩x4=0.354 > x10 ).

Mean NDVI fluctuates with elevation. In elevation subzones 2 and 3 (that is elevation

<2852 m) mean NDVI is higher than 0.892 (Table 13). In elevation subzone 2, mean NDVI reaches its maximum (0.903), implying that this elevation subzone (1885 m to 2852 m) is beneficial to vegetation growth. Statistical tests show that the mean NDVI of elevation subzones 1 to 4 is significantly different from that of elevation subzones 5 and 6. Therefore, vegetation cover is at its highest when elevation is less than 2852 m.

Table 13 Vegetation NDVI mean and its statistical significance of every two sub regions in elevation (confidence level 95%) Statistical test 1 2 MANUSCRIPT 3 4 5 6 1 2 Y 3 Y N 4 N Y Y 5 Y Y Y Y 6 Y Y Y Y Y Subzones 1 2 3 4 5 6 Mean NDVI 0.890 0.903 0.892 0.873 0.843 0.648 Note: (1) Y and N same as Table 8. (2) The numbers from 1 to 6 are represent different elevation levels, respectively: level 1< 942m、942 < level 2< 1885m、1885 < level 3 < 2852m、2852 < level 4 < 3638m、3638 < level 5 < 4523m、level 6> 4523m.

4.3. Annual meanACCEPTED temperature Temperature variation influences the growth and development of plants. Environmental

temperatures above or below the bearable temperature range of plants will negatively impact

plant growth and development. Due to the interaction of annual mean temperature with soil

type, landform type, precipitation,vegetation type, elevation, humidity index, and cumulative

temperature, the influence of annual mean temperature on NDVI is significantly enhanced

(e.g. x5∩x9=0.432> x5, x5∩x8=0.400> x5, x5∩x1=0.383> x5, x5∩x7=0.377> x5, x5∩x10 =0.374> x5, ACCEPTED MANUSCRIPT x5∩x4=0.325 >x5, x5∩x3=0.360> x5). Therefore, annual mean temperature variation will lead to changes in other environmental factors (e.g. humidity and precipitation), producing a great superposition effect on plant growth and development.

Table 14 Vegetation NDVI mean and its statistical significance of every two sub regions in annual mean temperature (confidence level 95%) Statistical test 1 2 3 4 5 6 1 2 Y 3 Y Y 4 Y Y N 5 Y Y Y Y 6 Y Y N Y Y Subzones 1 2 3 4 5 6 Mean NDVI 0.640 0.821 0.879 0.895 0.912 0.887 Note: (1) Y and N same as Table 8. (2) The numbers from 1 to 6 indicate different annual mean temperature levels, respectively: level 1 < 1.07 ℃、1.07 < level 2 < 4.30 ℃、4.30 < level 3 <8.18 ℃、8.18 16.40 ℃.

According to Table 14, Mean NDVI gradually increases with the annual mean temperature goes up and reached its maximumMANUSCRIPT value of 0.912 in the range of the 5th temperature subzone, indicating that this range of temperature promoted the growth of

vegetation. Chen et al. (2013) argue that the vegetation coverage of mainland China is

significantly correlated with precipitation and temperature,the correlation with the latter is

more significant than with the former. However, this study finds that the NDVI of Sichuan is

influenced more significantly by temperature. Statistical tests showed that the mean NDVI of

the 5th temperature subzone is not significantly different from that of the 3th temperature

subzone (Table 14), but is significantly different from that of temperature subzones 1, 2, 4,

and 6. Therefore, vegetation coverage is highest when annual mean temperature is in the range of 12.48ACCEPTED°C to 16.40 °C. 4.4. Dryness index

Studies have shown that the interaction effect between dryness index and soil

types,average annual precipitation, and accumulative temperature ( ≥10°C)significantly

enhanced the effect of dryness index on vegetation NDVI (e.g. x2∩x9 = 0.381 > x2, x2∩x5 =

0.331 > x2, x2∩x1 = 0.294 > x2, x2∩x4 = 0.246 > x2). As the subzone number increases, the degree of dryness grows. In humid regions, mean NDVI increases gradually with rises in ACCEPTED MANUSCRIPT dryness index. In dryness index subzone 3, mean NDVI reaches a maximum (0.908), implying that this dryness index range is beneficial to vegetation growth. Statistical tests showed that the mean NDVI of dryness index subzone 3 is significantly different from that of subzones 1 and 6, but not significantly different from that of subzones 4 and 5 (Table 15).

Therefore, vegetation coverage is highest when the dryness index is between 0 and 0.2. Due to interaction of the dryness index with soil type, annual mean temperature, average annual precipitation, and accumulative temperature (≥10°C), the influence of the dryness index on

NDVI is significantly enhanced.

Table 15 Vegetation NDVI mean and its statistical significance of every two sub regions in dryness index(confidence level 95%) Statistical test 1 2 3 4 5 6 1 2 Y 3 Y N 4 Y N N 5 N N N N 6 N Y Y Y Subzones 1 2 3 4 5 6 Mean NDVI 0.746 0.892 0.908MANUSCRIPT 0.875 0.889 0.475 Note: (1)Y and N same as Table 8. (2) The numbers from 1 to 6 indicate different dryness index levels, respectively: level 1 < -1、-1 < level 2 < 0 、0 < level 3 < 2 、2 < level 4 <7 、7 < level 5 < 13 、level 6 > 13. 4.5. Cumulative temperature (≥ 10 °C)

Analysis shows that due to the interaction of cumulative temperature ( ≥10°C) with average annual precipitation, annual mean temperature, and global radiation, the influence of cumulative temperature ( ≥10°C) on NDVI is significantly enhanced (x4∩x1 = 0.374 > x4, x4∩x5 = 0.436 > x4, x4∩x6 = 0.372 > x4). Thermal conditions vary remarkably from region to

region. Increasing subzone numbers indicate improvement of thermal conditions. As cumulative temperatureACCEPTED ( ≥10°C) rises, mean NDVI gradually increases. In cumulative temperature ( ≥10°C) subzone 4, mean NDVI reaches a maximum (0.686), implying that this

cumulative temperature ( ≥10°C) range is beneficial to vegetation growth. Statistical tests

showed that the mean NDVI of CT subzone 4 is significantly different from that of subzones

1, 2, 5, and 6 (Table 16). Therefore, vegetation cover is highest when cumulative temperature

(≥10°C) is between 3638 °C and 4709 °C. This may be because temperature rise facilitates

vegetation growth, but causes a decline in soil humidity, thus also negatively influencing ACCEPTED MANUSCRIPT vegetation growth.

Table 16 Vegetation NDVI mean and its statistical significance of every two sub regions in 10°C or higher cumulative temperature ( ≥10°C) (confidence level 95%) Statistical test 1 2 3 4 5 6 1 2 Y 3 Y Y 4 Y Y N 5 Y Y Y Y 6 N Y Y Y Y Subzone 1 2 3 4 5 6 Mean NDVI 0.769 0.892 0.906 0.907 0.886 0.811 Note: (1) Y and N same as Table 8. (2) The numbers from 1 to 6 denotes cumulative temperature ( ≥10C) levels, respectively: level 1 < 771 ℃、771 < level 2 < 2396 ℃、2396 < level 3 < 3638 ℃、3638 < level 4 < 4709 ℃、4709 < level 5 < 6083 ℃、level 6 >6083 ℃。

4.6. Landform types

Mean NDVI fluctuates with landform types. In landform type subzones 3, 4, and 5, mean NDVI is higher than 0.888. In subzone 3, mean NDVIMANUSCRIPT reaches its maximum (0.902), implying that landform type subzones 3, 4 and 5 are benefici al to vegetation growth. Statistical tests showed that the mean NDVI of subzones 3, 4 and 5 is significantly different from that of subzones 1, 2, 6 and 7 (Table 17). Therefore, vegetation coverage is highest when landform types are hills, low-relief mountains, large ups and downs mountains regions.

Table 17 Vegetation NDVI mean and its statistical significance of every two sub regions in geomorphic types(confidence level 95%) Statistical test 1 2 3 4 5 6 1 2 Y 3 Y Y ACCEPTED4 Y Y Y 5 Y Y N Y 6 Y Y Y Y Y Subzone 1 2 3 4 5 6 Mean NDVI 0.841 0.882 0.902 0.889 0.888 0.848 Note: (1) Y and N same as Table 8. (2) The numbers from 1 to 6 denotes geomorphic types, respectively: plains, terraces, small undulating mountains, middle undulating mountains, large undulating mountains and extremely undulating mountains.

4.7. Global radiation ACCEPTED MANUSCRIPT

Mean NDVI fluctuates with increases in global radiation. In global radiation subzone 2,

mean NDVI reaches its maximum (0.686), implying that this range of total radiation is

beneficial to vegetation growth. Statistical tests show that the mean NDVI of subzone 5 is

significantly different from that of subzones 1 and 3 (Table 18). Therefore, vegetation

coverage is highest when global radiation is between 3779.82 W/m 2 and 4215.35 MJ/m 2.

Table 18 Vegetation NDVI mean and its statistical significance of every two sub regions in global radiation (confidence level 95%) Statistical test 1 2 3 4 5 6 1 2 Y 3 Y Y 4 Y Y Y 5 Y Y N Y 6 Y Y Y Y Y Subzone 1 2 3 4 5 6 Mean NDVI 0.648 0.686 0.623 0.573 0.610 0.547 Note: (1) Y and N same as Table 8. (2) The numbers from 1 to 6 denotes global radiation levels, respectively: level 1 < 3779.82WJ/m 2、3779.82 < level 2 < 4215.35WJ/m 2、4215.35 < level 3 < 4710.11WJ/m 2、4710.11 < level 4 < 5152.40WJ/m 2、5152.40 < level 5 < 5506.23WJ/m 2、level 6 >5506.23WJ/m 2. MANUSCRIPT 4.8. Annual mean precipitation

Climatic factors are usually believed to be a crucial biophysical element affecting

vegetation growth. For example, precipitation is closely correlated with vegetation diversity

and quantity. Results show that annual mean precipitation has a remarkable influence on

NDVI. As annual mean precipitation rises, vegetation cover tends to increase (Table 19),

further proving that availability of water is a restrictive factor for NDVI. The higher the

number, the more annual mean precipitation. With the annual mean precipitation increase,

mean NDVI gradually increased, and in annual mean precipitation subzone 6,mean NDVI reaches its maximumACCEPTED (0.906), implying that this precipitation range is beneficial to vegetation growth. Statistical tests showed that the mean NDVI of subzone 5 is significantly different

from that of subzones 4 and 6 (Table 16). Therefore, vegetation coverage is highest when

annual mean precipitation is between 1394 mm and 1739 mm. Wang et al. (2018) argue that

from 2000 to 2015, interannual variation in grassland vegetation of Qinghai-Tibet Plateau was

primarily influenced by precipitation. This view is consistent with the findings of this study.

Chen et al. (2013) argue that an increase in temperature and decrease in precipitation in ACCEPTED MANUSCRIPT mainland China from 1982 to 2000 created a divergent increasing trend in NDVI. This view is not consistent with our findings. Because of the interaction between annual mean precipitation and cumulative temperature ( ≥10°C), annual mean temperature, the influence of annual mean precipitation on NDVI is significantly enhanced. As a result of increases in evapotranspiration arising from climatic warming, accurate coupling of precipitation and temperature plays an important role in regulating vegetation growth. This further suggests that dry land vegetation in temperate regions is constrained by hydrothermal conditions, whereas growth of dry land vegetation in tropical regions is primarily determined by precipitation.

Table 19 Vegetation NDVI mean and its statistical significance of every two sub regions in average annual precipitation (confidence level 95%)

Statistical test 1 2 3 4 5 6

1

2 Y

3 Y Y

4 Y Y Y

5 Y Y Y Y

6 Y Y Y N Y

Subzone 1 2 3 4 5 6 MANUSCRIPT Mean NDVI 0.792 0.784 0.859 0.850 0.888 0.906

Note: (1)Y and N same as Table 8. (2) The numbers from 1 to 6 denotes annual mean precipitation levels, respectively: level 1<664mm 、664 < level 2 < 820mm 、820 < level 3 < 986mm 、986 < level 4 < 1141mm 、1141 < level 5 < 1372mm 、level 6 > 1372mm.

4.9. Synergistic effects between other natural factors

Compared with soil types, elevation, and annual mean temperature, factors such as

vegetation type ( PD =0.096), humidity index ( PD =0.080), slope ( PD =0.004), and slope aspect

(PD =0.004) fail to satisfactorily account for NDVI variation. Although such factors have no significant influenceACCEPTED on vegetation growth, they can interact with soil types, elevation, and

AMT to enhance influences on NDVI, e.g. x1∩x10 = 0.415 、x1∩x9 = 0.398 、x2∩x7 = 0.314 、

x4∩x7 = 0.295 、x5∩x7 = 0.377 、x6∩x7 =0.291 、x2∩x11 = 0.265 x2∩x12 = 0.250 、x4∩x1 = 0.229 、

x4∩x12 = 0.218 、x5∩x11 =0.346 、x5∩x12 =0.329 、x9∩x11 =0.378 、x9 ∩x12 =0.342 、x10 ∩x11 =0.372 、

x10 ∩x12 =0.342, and so on.

Mean NDVI fluctuates with vegetation type. In vegetation type subzones 4 mean NDVI

reaches a maximum of 0.886, and in 5subzone 5 it reaches a maximum of 0.907. Statistical ACCEPTED MANUSCRIPT tests showed that mean NDVI of vegetation type subzones 4 and 5 is significantly different from that of subzones 2 and 3. Therefore, NDVI is highest in coniferous and broad-leaf forests. Mean NDVI fluctuates with slope. In slope subzones 2, 3, and 7, mean NDVI is higher than 0.870. In slope subzone 3, mean NDVI reaches a maximum (0.882), implying that slopes from 4.64 ° to 14.66 ° are beneficial to vegetation growth. Statistical tests showed that

the mean NDVI of slope subzones 2 and 3 is significantly different from that of subzone 1,

but not significantly different from that of subzones 4 to 9. Mean NDVI fluctuates with slope

aspect. In slope aspect subzones 2, 3, and 6–10, mean NDVI is higher than 0.870. In slope

aspect subzone 2, mean NDVI reaches a maximum (0.882), implying that western, north

western, southwestern, and southern slopes are beneficial to vegetation growth. Statistical

tests showed that mean NDVI of slope aspect subzone 2 is not significantly different from

that of other slope aspect subzones. The humidity index was divided into 6 subzones. The

degree of humidity increases with increasing subzone number. As the humidity index rises,

mean NDVI gradually increases. In humidity index subzone 5, mean NDVI reaches a

maximum (0.906), implying that this humidity index range is beneficial to vegetation growth. Statistical tests showed that the mean NDVI of MANUSCRIPT humidity index subzone 5 is significantly different from that of humidity index subzones 2 and 3. Therefore, vegetation coverage is

highest when the humidity index is between 96 and 186.

5. Conclusions

In this study, we quantified individual and interactive influences of natural factors on

vegetation NDVI changes, and identified the most suitable characteristics of each principal

factor for stimulating vegetation growth in Sichuan, western China using the Geo Detector.

This is a new spatial statistical approach based on remotely sensed data. It is both timely and

necessary to understand the NDVI changes, its driving forces, and the implications for ecological conservationACCEPTED and restoration that aimed to mitigate environmental degradation in other regions of China experiencing rapid vegetation changes. We have extracted the

vegetation changes in Sichuan, western China from 2000 to 2015 from RS and GIS.

We found a clear variation in the spatial distribution of vegetation cover. Liu et al. (2015)

reported that the vegetation cover in China is significantly affected by natural and human

factors, with a significant increase occurring from 1997 to 2012; however, there is significant

spatiotemporal variation in vegetation cover in Sichuan. Overall, vegetation coverage is high ACCEPTED MANUSCRIPT in the northwestern Sichuan Plateau and in the northeast part of the Sichuan Basin, but low in urban agglomerate regions in the Sichuan Basin. Regions with upper intermediate and high vegetation coverage account for 19% and 69% (or higher) of Sichuan’s total area, respectively, reflecting a good status for vegetation cover. From 2000 to 2015, NDVI variation is mainly manifested in the obvious transformation of regions with NDVI higher than 0.4. There was a significant decrease in regions with NDVI between 0.4 and 0.8, and a significant increase in regions with NDVI higher than 0.8. Accordingly, there was a significant increase in regions with high vegetation cover, and a significant decrease in regions with upper intermediate vegetation cover. Regions with an obvious variation in vegetation cover include Ganzi, Aba, and Liangshan prefectures in the western Sichuan Plateau.

We illustrated the individual influences of natural factors on vegetation NDVI changes.

Natural factors can be ranked in descending order by the magnitude of their influence on

NDVI: soil types > elevation > annual mean temperature > dryness index > cumulative temperature ( ≥10°C) > landform types > global radiation > vegetation types > annual mean precipitation > slope > humidity index > slope aspect. Soil type and elevation both account for more than 30% of variation in NDVI, meaning MANUSCRIPT that they are the primary natural factors affecting vegetation changes. Annual mean temperature and dryness index both account for more than 23% of NDVI variation.

We found that interactive influences of natural factors on vegetation NDVI changes, and the synergistic effect of natural factors is manifested as mutual and nonlinear enhancement.This study reveals the optimal characteristics of key natural factors beneficial to vegetation growth, thus attaining a more in-depth understanding of the influence exerted by natural factors on NDVI. This is a critical step towards discovering the driving mechanism of

NDVI change. To some extent, the findings of this study may be beneficial to intervening in and promotingACCEPTED vegetation changes by determining a favourable value range for natural factors or favourable landform factors, thus facilitating ecological protection and vegetation restoration, and alleviating environmental degradation.

Finally, this study highlights the advantages of the Geographical Detector in detecting spatial heterogeneity and identifying driving factors. To analyse the relationship between

NDVI and driving factors, conventional principal component analysis or classical regression models are usually based on certain hypotheses or restrictions, for example, normal ACCEPTED MANUSCRIPT distribution and a linear hypothesis. Compared with other methods, the Geographical Detector is independent of any linear hypothesis. In terms of spatial heterogeneity, this model is used to detect the consistency of spatial distribution patterns between dependent and independent variables (Wang et al.,2017; Wang et al.,2016) and to measure the contribution of independent variables to dependent variables. Compared with conventional statistical variables, this method is more capable of detecting explanatory factors and analysing the interactive relationship between different variables (Wang et al.,2017; Wang et al.,2016).

The complex and diverse natural conditions have an important influence on the vegetation NDVI change in Sichuan. In the selection of index system, the influence of natural factors and their interactions on vegetation change in Sichuan was fully considered, while the influence of human factors on vegetation change was not. In the future, based on the NDVI variation of vegetation in Sichuan, natural and human factors will be selected as far as possible to scientifically analyse and reveal the influence of driving factors and their interactions on vegetation change, so as to provide more targeted guidance for vegetation protection and establishment in Sichuan. Meanwhile, the classification of natural factors needs to be further improved and improved to helpMANUSCRIPT select the appropriate range or types of natural factors for promoting the growth of vegetation.

Acknowledgments

Funding for this study was provided by the Humanities and Social Science Research

Foundation of Ministry of Education,China (No.17YJA850007) and National Natural Science

Foundation of China (No.41371125). The authors thank the editors and anonymous referees for their valuable comments and suggestions, which helped improve the manuscript. Landsat data was acquired from the USGS EROS Data Center and the Institute of Remote Sensing and

Digital Earth, Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC)ACCEPTED (http://www.resdc.cn), Chinese Academy of Science. The funding sources had no involvement in the collection, analysis and interpretation of data; the writing of the report; and the decision to submit the article for publication.

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ACCEPTED ACCEPTED MANUSCRIPT Highlights

ò Examine spatiotemporal variation of vegetation coverage from 2000 to 2015

ò Quantify individual and interactive influences of natural factors on vegetation Normalized

Difference Vegetation Index (NDVI) changes

ò Soil types, elevation, and annual mean temperature can satisfactorily account for vegetation

changes

ò Determines the optimal characteristics of key natural factors that are beneficial to vegetation

growth.

MANUSCRIPT

ACCEPTED