Nonlinear relationship of vegetation greening with nature and human factors and its forecast – A case study of Southwest China

Ecological Indicators 111 (2020) 106009

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Original Articles

Nonlinear relationship of vegetation greening with nature and human factors and its forecast – A case study of Southwest China

T

Huiyu Liua,b,c,d, , Fusheng Jiaoa,b,c,d, Jingqiu Yine, Tingyou Lif, Haibo Gonga,b,c,d, Zhaoyue Wanga,b,c,d, Zhenshan Lina,b,c,d ⁎

a

School of Geography Science, Nanjing Normal University, Nanjing 210023, China Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China c Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China d State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing Normal University, Nanjing 210023, China e College of Geography Science, Nanjing University of Information Science & Technology, Nanjing 210044, China f College of Pharmacy and Chemistry & Chemical Engineering, Taizhou University, Taizhou 225300, China b

ARTICLE INFO

ABSTRACT

Keywords: Nonlinear relationship Variable interactions Bioclimatic variables Human activities of different kinds Boosted Regression Tree method (BRT)

Vegetation showed a greening trend in most Southwest China. However, the nonlinear relationship of vegetation greening with nature and human factors remains unclear. In this study, we studied the nonlinear relationship and predicted the future changes of the greening with Boosted Regression Tree (BRT) method. Results showed that: (1) Precipitation of Driest Month (Bio14, 20.64%), land use changes (10.39%) and population density (8%) were the three most important factors limiting vegetation greening. Climate changes (42.655%) and human activities (33.163%) were the two most important variable types, but topography was also important (18.481%), which cannot be ignored; (2) Bio14 and elevation had the strongest variable interactions. Climate changes had strong interactions with both human activities and elevation, but human activities and elevation had less interactions; (3) vegetation greening was facilitated by the increasing of Bio14, distance to residential area, temperature annual range (Bio7), but inhibited by the increasing of GDP and precipitation of coldest quarter (Bio19) with changing rates. These factors would have no further impacts when approaching threshold values. The increase of population density improved vegetation greening greatly when it was low, while inhibited the greening strongly when it was high. Elevation increase promoted vegetation greening when elevation < 300 m, then inhibited it. For land use changes, ‘Grain for Green’ improved vegetation greening, but urbanization decreased it; (4) Under current environmental condition, area percentage of vegetation browning will greatly increase from 13.88% to 37.69%, which is mostly from insignificant vegetation changes and located in the west. However, future climate changes will facilitate vegetation greening in the most area except the northwest. Our results highlight the importance of nonlinear analysis for determining the drivers and predicting future changes of vegetation greening, and developing adaptation and alleviation strategies for climate changes and human activities in fragile ecosystem.

1. Introduction Vegetation, a most essential component of terrestrial ecosystems, plays an important role in global carbon cycle and energy conversion, and is a sensitive index for monitoring global environmental changes at different spatial and temporal scales (Piao et al., 2011; Peng et al., 2015; Jiang et al., 2015). The Normal Difference Vegetation Index (NDVI) is strongly correlated with vegetation coverage, growth conditions, biomass and photosynthesis intensity. NDVI is widely used for detection and attribution of vegetation greening trend, which has

⁎

attracted considerable attention (Piao et al., 2011, 2015; Qu et al., 2018; Tong et al., 2018). Climate changes and human activities are considered as the main driving forces for vegetation greening (Piao et al., 2011; Qu et al., 2018; Tong et al., 2018). However, their driving effects are also affected by topography (elevation, slope and aspect) and soil properties (Djebou and Singh, 2015; Tao et al., 2018; Qu et al., 2018). Thus, the relationship of nature factors (climate, topography and soil properties), and human factors with vegetation greening is not as simple as a linear relationship when considering the complex and nonlinear interactions and responses (Zheng et al., 2018). Thus, to

Corresponding author at: School of Geography Science, Nanjing Normal University, Nanjing 210023, China. E-mail address: [email protected] (H. Liu).

https://doi.org/10.1016/j.ecolind.2019.106009 Received 4 September 2019; Received in revised form 23 November 2019; Accepted 10 December 2019 1470-160X/ © 2019 Published by Elsevier Ltd.

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deepen our understanding of the driving mechanism of vegetation greening, it is urgently needed to assess the nonlinear relationship of nature and human factors with vegetation greening, and their relative importance. With global warming and availability of long time series of NDVI and climate data, numerous studies have investigated the relationship between climate changes and vegetation greening (Wang et al., 2015; Piao et al., 2015; Tao et al., 2018). Temperature and precipitation were reported to be the controlling factors of vegetation greenness in humid and arid regions, respectively (Piao et al., 2015; Huang et al., 2016; Xie et al., 2015; Tao et al., 2018). However, most of these studies focused on the average temperature and total precipitation during growing season. Bioclimatic variables, representing annual trends, seasonality and extreme or limiting environmental factors, are much more biologically meaningful (Hijmans et al., 2005; Deblauwe et al., 2016). They are widely used in species distribution models (Hijmans et al 2014; Liu et al., 2018). Thus, bioclimatic variables are more suitable for examining the relationship between climate changes and vegetation greening. With population growth and economic development, human impacts on vegetation greening increase rapidly. A growing number of researches have investigated the effects of both climate changes and human activities (Lü et al., 2015; Liu et al., 2015a, b; Jiang et al., 2017a; Zhao et al., 2017; Hua et al., 2017). Most of them explored the relationship between human activities and vegetation greening by correlating social-economic data, human impact index and human footprint with NDVI. Due to the data limitation of human activities, these studies were mostly based on administrative division by treating it as one unity, which ignored the spatial heterogeneity of human activities. However, human activities have different types with different intensities in different areas, and so, human impacts on vegetation greening have high spatial heterogeneity (Liu et al., 2015a; Jiang et al., 2017b; Qu et al., 2018). Moreover, these studies were mostly based on correlation analysis, which cannot quantify the relative importance of climate changes and human activities on vegetation greening, and thus overestimated or underestimated their impacts on vegetation greening. Recently, residual trends method was widely used to quantify the relative importance of climate changes and human activities (Sun et al., 2015; Huang et al., 2016; Wang et al., 2016; Jiang et al., 2017a; Tong et al., 2017; Qu et al., 2018; Qi et al., 2019). It calculated the residuals between the observed and the predicted vegetation greenness to reflect the human signal, i.e. the vegetation trends which cannot be explained by climate. However, vegetation greening is affected not only by climate changes and human activities, but also by topography and soil properties. Thus, using only climate data added model uncertainty. More importantly, it cannot differentiate human impacts from different kinds of human activities such as land use changes, population pressure, and economic development. The relationship between human activities and vegetation greening can be positive or negative due to different kinds of human activities with different intensity (Lü et al., 2015; Liu et al., 2015a; John et al., 2016; Hua et al., 2017). Thus, differentiating the impacts from different types of human activities will be beneficial to accurately extract human impacts. Due to strong interactions between nature and human factors, their relationships with vegetation greening are nonlinear, which remains unclear. Correlation analysis and residual trends method are good for revealing the linear relationship, but not suitable for extracting the nonlinear relationship. Thus, a nonlinear method is urgently needed. Boosted Regression Tree (BRT) is a machine-learning method based on multivariable regression trees and a boosting technique to improve performance of multiple single models (Elith et al., 2008; Yang et al., 2016). BRT method does not require prior knowledge of the system to build a model, and they can effectively choose relevant variables, handle collinear variables of various types, model complex variable interactions, identify nonlinear relationships between response variable and its predictors, and thus avoid overfitting(Elith et al., 2008; Soykan et al.,2014; Mitchell et al.,2018). It provides a higher predictive

accuracy than other generalized models, and a better interpretability of nonlinear relationship and interactions between variables (Elith et al., 2008; Luo et al., 2017; Wang et al., 2018). It is now widely used to determine the drivers of some complex ecological process (Luo et al., 2017; Mitchell et al., 2018; Venter et al., 2018; Wang et al., 2018; Zu et al., 2018), while is not yet used to reveal the nonlinear relationship of nature and human factors with vegetation greening, and their relative importance. Although numerous studies have revealed the trend of vegetation greenness in the past decades (Piao et al., 2015; Tong et al., 2016, 2018), the changing trend in the future remains unclear. The purpose of studying vegetation greening is not only to explain the past, but also to serve for the current and to predict the future (Fu, 2017). Prediction of the future vegetation changes will provide early warning of environmental changes. Several studies revealed the persistent of the trend of vegetation greenness with Hurst exponent (Jiang et al., 2015; Tong et al., 2016; Jiang et al., 2017b), but did not take future climate changes into consideration. With global climate changes, there is great uncertainty in the changes of vegetation greening. Thus, it is necessary to predict the future changes of vegetation greening under future climate changes. Southwest China is one of the most fragile regions threatening by severe rocky desertification with low environmental capacity and poor self-recovery capability in China (Cai et al., 2014; Zhang et al., 2016; Tong et al., 2017). To improve the ecological conditions, the national and local Chinese governments since 2000 have implemented ecological engineering such as Grain for Green, Natural Forest Protection Project, Yangtze River Basin Shelter Forest System Project, and Controlling of Karst Rocky Desertification Project (Tong et al., 2016). In the meantime, it is one of the most undeveloped regions in China with large population pressure and underdeveloped economy (Li et al., 2017). Thus, vegetation greenness is greatly influenced by human activities. Moreover, the area has experienced frequent floods and droughts during the past decades, which poses a severe threat to vegetation (Liu et al., 2018; Tao et al., 2018). Therefore, vegetation greenness is undergoing great challenges induced by both climate changes and human activities. Several studies have reported an increasing vegetation greenness with NDVI time series in Southwest China (Cai et al., 2014; Wang et al., 2015; Tong et al., 2016, 2017, 2018). Yet, the nonlinear relationships of vegetation greening with different nature and human factors and its future changes remain unclear. In this study, based on BRT method, nonlinear relationship of nature and human factors with vegetation greening and their relative importance, and the prediction of the future changes of vegetation greening, were studied to deepen our understanding of the driving mechanism of vegetation greening, and help develop management strategies to ensure ecological and economic sustainability in fragile ecosystem. More specifically, the objectives of this work were to address the following: (1) Relative importance of nature factors (bioclimatic variables, topography, and soil properties), and human activities on vegetation greening; (2) variable interactions between nature and human factors; (3) nonlinear responses of vegetation greening to main nature and human factors; (4) prediction of the changes of vegetation greening under future climate changes. 2. Materials and methods 2.1. Study area Southwest China (97°38′E-112°10′E, 21°6′N − 29°16′N), including Yunnan, Guizhou and Guangxi provinces, is located in the subtropical/ tropical climate zone with annual precipitation > 1100 mm and average temperature > 20 °C (Fig. 1). It is dominated by karst landform occupying 48.32% of the total area. 2

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Fig. 1. Study area. (a) Location of study area in China, spatial distribution of Elevation (downloaded from http://westdc. westgis.ac.cn), Karst Area with red solid lines and province boundary with black solid lines; (b) Spatial distribution of vegetation type (classified into needle leaf forest, broadleaf forest, mixed forest, cultivated vegetation, alpine vegetation, shrubland, grassland and meadow which extracted from the 1:1000000 Vegetation Atlas of China, http://westdc.westgis.ac.cn/).

with the model of HadGEM2-CC were downloaded from Worldclim (http://www.worldclim.com/CMIP5v1) to represent the future climate changes.

2.2. Data sources 2.2.1. Modis NDVI dataset The Modis NDVI dataset (MOD13A2) at a spatial resolution of 1 km and 16-day interval is widely used to monitor vegetation dynamic due to high quality (Wang et al., 2015; Tong et al., 2016). It was downloaded from https://modis.gsfc.nasa.gov/ during 2001–2015.To improve the quality of remote sensing data, especially to eliminate the cloud-contaminated data and abnormal data, the Savitzky–Golay filter was applied to smooth the NDVI data. The maximum-value composite (MVC) method was used to choose the higher value of semi-monthly NDVI to compile the monthly NDVI. To reflect the vegetation more appropriately, the NDVI products were averaged over the entire growing season from April to November to get growing-season NDVI in Southwest China. NDVI values of no vegetation cover are always smaller than 0.1, such as desert, bare earth, water body, icepack, glacier, cloud and so on(Jamali et al., 2014; Zhang et al., 2017; Pan et al., 2015). Regions of growing-season NDVI values below 0.1 are assumed as nonvegetated areas, which is widely used in China to reduce the influence of soil and snow on NDVI (Piao et al., 2003; Zhang et al., 2017; Pan et al., 2018). Therefore, only the pixels with growing season NDVI > 0.1 were used to represent the vegetated areas in this study.

2.2.3. Topography data (elevation, slope, aspect and karst), vegetation type and soil properties DEM data was downloaded from the Cold and Arid Regions Science Data Centre at Lanzhou (WestDC, http://westdc.westgis.ac.cn). Elevation, slope and aspect were derived from DEM in ArcGIS 10.0 (ESRI, Inc., Redlands, CA, USA).The karst data in vector format were obtained from World Map of Carbonate Rock Outcrops v3.0 (http:// web.env.auckland.ac.nz/our_research/ karst/), and converted to raster data with 1 km spatial resolution in ArcGIS 10.0. Vegetation type was extracted from the 1:1000000 Vegetation Atlas of China (http://westdc.westgis.ac.cn/), and classified into eight types such as needle leaf forest, broadleaf forest, mixed forest, cultivated vegetation, alpine vegetation, shrubland, grassland and meadow. Soil properties data includes soil type, percentage sand, percentage clay, percentage silt. Soil type was downloaded from Resource and environment data cloud platform (http://www.resdc.cn/data.aspx? DATAID = 207). While clay, sand and silt percentages were derived from the Harmonized World Soil Database Version 1.2.1(HWSD, http:// webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/ HTML/HWSD_Data.html?sb = 4). The spatial resolutions of all topography data and soil properties were resampled into 1 km.

2.2.2. Bioclimatic data The meteorological data, including monthly max and min temperature and total precipitation data, were collected by approximately 279 weather stations provided by China Meteorological Science Data Sharing Service Network (http://www.nmic.cn/) during1982-2015. We used thin-plate-smoothing spline-fitting techniques in ANUSPLIN 4.4 (Xu and Hutchinson, 2011,http://fennerschool.anu.edu.au/research/ products/anusplin-vrsn-44) to interpolate the meteorological data into same spatial resolution as NDVI data (1 km) with elevation, latitude, and longitude as covariates. Then we extracted 19 bioclimatic variables with “biovars” function in the R package “dismo” (Hijmans et al., 2014). We selected the least correlated variables (correlation coefficient < 0.7) to reduce multicollinearity with the Remove Highly Correlated Variables tools in SDMtools 2.2 (Brown et al., 2017; http:// sdmtoolbox.org/), and then five bioclimatic variables were kept (Table 1). Bioclimatic variables under the scenario of RCP4.5 in 2050

2.2.4. Human factors (land use data , population density, GDP, distance to residential areas, distance to roads) Land use data in 2000 and 2015, population density and GDP data in 2000, 2005, 2010, 2015, residential area and road network with 1 km spatial resolution were downloaded from Resource and environment data cloud platform (http://www.resdc.cn/data.aspx? DATAID = 207) supported by the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. Land use types were simplified into six types: cropland, forest, grassland, water body, urban area and unutilized land. Land use changes data were extracted with land use data in 2000 and 2015 by spatial analysis in ArcGIS 10.0. The population density and GDP data were averaged with the data in 2000, 2005, 2010 and 2015 to reflect the average 3

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Table 1 Climate, topography, soil and human factors and their ranges. Variable types

Variable

Unit

Range

Climate

Precipitation of Driest Month (Bio14) Temperature Annual Range (Bio7) Precipitation of Coldest Quarter (Bio19) Annual Precipitation (Bio12) Annual Mean Temperature (Bio1) Elevation (Elev) Slope (Slp) Aspect (Asp) Karst Land use change (Lchg)

mm

0.02–26.15

℃

20.16–33.94

mm

14.86–237.36

mm ℃

496.65–2028.12 4.36–23.57

meter degree –

Population density (Pop) GDP Distance to residential area (Distra) Distance to roads (Distrd) Soil type (Stp) Percentage Sand (Psa) Percentage Silt (Psi) Percentage Clay (Pcl)

Person/km2 Yuan/km2 degree

−1–5625 0–41.82 1 Flat, 2 North,3Northeast, 4 East, 5 Southeast, 6 South, 7 Southwest, 8 West, 9 Northwest, 10 North 1 karst; 0 nonkarst 11 cropland, 12 cropland to forest, 13 cropland to grassland, 15 cropland to urban area, 21 forest to cropland, 22 forest, 23 forest to grassland, 24 forest to waterbody, 25 forest to urban area, 31 grassland to cropland, 32 grassland to forest, 33 grassland, 44 waterbodies, 51 urban area to forest, 55 Urban area, 66 Unutilized land 5.42–10000 5.38–40000 0–0.70

degree – % % %

0–1.30 1 Ferralsol, 2 Entisols,3 Alfisols, 4 Anthrosol, 5 alpine soil, 6 semi-Alfisols, 7 rock, 8 Semi-Aquatic soil 0–100 0–100 0–100

topography

Human

Soil properties

–

population density and GDP in these areas from 2000 to 2015. The distances to residential area and roads were derived by buffering from the residential area and roads in ArcGIS 10.0.

that allows producing a large number of simple tree models and then combining them to a final optimized model (Elith et al. 2008). They are useful for handling different types of predictor variables without prior data transformation or outliers elimination, and generally regarded as producing superior predictive performance compared with more traditional modelling approaches (Elith et al. 2008).In this study, BRT models were conducted using the Bernoulli family of presence/absence to study the nonlinear relationships of vegetation greening with nature and human variables, and further to predict the future changes of vegetation greening. Firstly, we used Mann–Kendall trend detecting technology to reveal the trend of vegetation changes in the study area (Fig. 2). We assumed significantly increasing trend as vegetation greening, and decreasing trend (either significantly or insignificantly) as vegetation browning. Secondly, we used five bioclimatic variables, four topography variables, five human variables, and four soil variables as predictors (Table 1), vegetation greening/vegetation browning (presence/absence) as response variable in BRT models using the Bernoulli family. Thirdly, in previous study, the number of samples was very small, and so, they used 75% of the samples for training, and the rest for test. However, in our study, there were a great number of samples for vegetation greening and vegetation browning, BRT models were trained using a subset of data containing 10% pixels of vegetation greening and vegetation browning randomly selected as samples for reducing simulation time. Then 10-fold cross-valid method was performed for BRT models. The rest 90% pixels were used for evaluating the predictive performance of the models. Finally, the trained BRT model was used to predict the future changes of vegetation greening by assuming human activities unchanged but climate changed under the scenario of RCP4.5 in 2050. The fitting of a BRT model is controlled by the learning rate (LR), tree complexity (TC) and bag fraction (BF). LR determines the contribution of each tree to the growing model, TC controls the size of trees and whether the interactions between variables should be considered, and BF sets the proportion of observations used in selecting variables (Wang et al., 2018). The number of trees (NT) is set based on the combination of LR and TC. All the models were built, evaluated and projected using “dismo” version 0.8-17 (Hijmans et al., 2005) and “gbm” version 2.1 (Elith et al., 2008) packages in R 3.5 environment. In

2.3. Methods 2.3.1. Mann–Kendall trend test method The Mann–Kendall trend test (Mann, 1945; Kendall, 1975) is a nonparametric statistical test, which does not require the independence and normality of the time series data and can well detect the long-term trend. In the Mann–Kendall trend test, the test statistics Z can be calculated as follows: S

1

S>0

var(S )

Z= 0

S=0 S<0

S+1 var(S )

(1)

where: n-1

n

S=

sign (xk

xi )

i =1 k =i+1

var(S )=

n(n - 1)(2n+5) 18

sign(xk

x i) =

1 0 -1

(2) (3)

xk x i > 0 xk x i = 0 xk x i < 0

(4)

where n is the length of time series, xk and x i are the data values at times k and i (k > i), and sign(xk x i ) is the symbolic function. At a given significance level α, when |Z| > u1 /2 , it represents a significant change in the time series at α level. Moreover, Z > 0 and Z < 0 indicate an increasing and decreasing trend, respectively. In this study, α = 0.05 was used, and then Z = 1.96. Thus, Z > 1.96 / Z < 1.96 indicated a significantly increasing / decreasing trend. 2.3.2. Boosted Regression Tree method (BRT) BRT models combine both regression trees and boosting technique 4

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Table 2 Model evaluation metrics for BRT model. AUC

TSS

Sensitivity

Specificity

Overall correct probability

0.89

0.67

0.86

0.81

0.85

3. Results 3.1. Model performance As shown from Table.2, AUC value was 0.89, > 0.8, and TSS was 0.67, > 0.4, indicating a good model performance. Moreover, sensitivity, specificity and overall correct probability were all > 0.8, indicating a good prediction accuracy. Thus, the BRT model is suitable for determining the nonlinear relationships of vegetation greening to nature and human factors, and further for predicting the future changes of vegetation greening. 3.2. Relative importance of nature and human factors for vegetation greening and their interactions Precipitation of driest month (Bio14, 20.640%), land use change (Lchg, 10.39%) and population density (Pop, 8.004%) were the three most important variables influencing vegetation greening (Table.3). However, Bio14 was by far more important than the other two. Elevation of topography factors ranked the fourth. Moreover, the importance of distance to residential area (Distra), Temperature Annual Range (Bio7), GDP and Precipitation of Coldest Quarter (Bio19) was greater than the average importance (1/18 = 5.56%).The importance of each soil variable was much less than climatic and human variables. For different variable types, the importance of bioclimatic variables (42.655%) was greater than human variables (33.163%). However, except Bio14, the other bioclimatic variables were less important than human variables except distance to road (Distrd). That is, land use change, population density, and distance to residential area had important impacts on vegetation greening. The total importance of human activities was more than double that of topography variables (18.481%). The importance of soil properties was very low (5.701%). So, vegetation greening was mainly controlled by climatic and human variables, but the importance of topography cannot be ignored. Table 4 showed the ten strongest variable interactions. Bio14, the most important variable, had the strongest variable interactions with elevation, Bio19, land use change, Distra, and Bio7, which enhanced its

Fig. 2. Mann–Kendall trend of vegetation in Southwest China.

our study, the final optimal values of TC, LR, BF were set as 7, 0.003, and 0.5 respectively, producing the highest cross-validated the area under the receiver operating characteristic (ROC) curve (AUC) score. The number of trees (NT) was optimized using the step.gbm function (Wang et al., 2018). Maximizing the sensitivity (i.e., proportion of correctly predicted presences) and the specificity (i.e., proportion of correctly predicted absences) was chosen as the optimal threshold for presence/absence of the vegetation greening detection. To measure the model accuracy, we provided sensitivity and specificity, as well as AUC, and the True Skill Statistic (TSS; Allouche et al., 2006). The model accuracy can be judged as excellent if AUC > 0.9, good if 0.9 > AUC > 0.8, fair if 0.8 > AUC > 0.7, poor if 0.7 > AUC > 0.6, and failed if 0.6 > AUC > 0.5 (Swets, 1988). TSS scores interpretation followed: excellent TSS > 0.75, good 0.40 < TSS < 0.75 and poor TSS < 0.40 (Allouche et al. 2006). The relative importance of variables in BRT model was assessed based on the number of times that the variable was selected in splitting the boosted regression tree, weighted by the squared improvement to each split (Friedman and Meulman, 2010). Partial dependence functions estimate the impact of a given predictor on the dependent variable by statistically accounting for the average effect of all other predictors. The partial dependence plots were produced to visualize dependencies between the response and predictors, and further to explain their nonlinear relationships (Elith et al., 2008). The two-way interactive effect of variables, representing as pairwise interaction size, was quantified as the variance caused by the two predictors while controlling for the average effect of all other predictors (Luo et al., 2017). To reduce model uncertainty, the BRT models were run for 100 iterations and all the results were averaged.

Table 3 Percentage contributions of different environmental variables and variable types (%). Variable type

Variable name

Percentage contribution

Rank

Total percentage contribution

Climatic variables

Bio14 Bio7 Bio19 Bio12 Bio1 Elev Slp Asp Karst Lchg Pop GDP Distra Distrd Stp Psa Psi Pcl

20.640 6.104 5.650 5.021 5.241 7.530 5.502 5.304 0.145 10.39 8.004 5.901 6.919 1.953 2.201 1.396 1.160 0.944

1 6 8 12 11 4 9 10 18 2 3 7 5 14 13 15 16 17

42.655

Topography

Human activities

Soil properties

5

18.481

33.163

5.701

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Table 4 Pairwise interaction size of different variables for vegetation greening. Variable 1

Variable2

Interaction size

Variable 1

Variable 2

Interaction size

Bio14 Bio14 Bio14 Bio14 Elev

Elev Bio19 Lchg Distra Bio12

12.694 9.107 8.812 7.913 5.563

Elev Bio7 Bio1 Elev Lchg

Bio7 Bio14 Bio12 Distra Bio19

4.949 3.804 2.663 2.352 2.189

importance. Elevation had the second strongest variable interaction with Bio14, Bio12, Bio7 and Distra. Moreover, land use changes had strong interaction with Bio14 and Bio19. Thus, climatic factors had strong interactions with both elevation and human activities, while elevation and human activities almost had no interaction except for elevation and Distra. 3.3. Nonlinear responses of vegetation greening to nature and human factors The partial dependence plots, reporting the variations of relative likelihood of vegetation greening upon the variation in the selected predictors, were produced to reveal the nonlinear responses of vegetation greening to Bio14, Lchg, Pop, Elev, Distra, Bio7, GDP, and Bio19 with importance above the average (Fig. 3). As shown from Fig. 3, vegetation greening showed nonlinear responses to all variables. The relative probability of greening increased linearly with the increase of Bio14 when Bio14 < 8 mm. However, when above the threshold, it almost kept unchanged. Therefore, if Bio14 exceeded 8 mm, its increase would not further affect vegetation greening. For land use changes, cropland to forest, and grassland to forest, were more likely to promote vegetation greening. However, urban area, forest to urban area and cropland to urban area had lower probabilities for vegetation greening, which meant that urbanization hindered vegetation greening. For different land use types, unutilized land, grassland, and forest had the highest potential for vegetation greening. Vegetation greening showed a non-monotonic relationship with population density. The relative probability increased very rapidly as population density increased, peaked when it reached 400 person/ km2, and then it decreased very rapidly, and finally kept stable under a low level when it exceeded 800 person/km2. Therefore, when population density was low, population growth facilitated vegetation greening, while when it was high, its growth prevented vegetation from greening. When elevation < 300 m, elevation increase promoted vegetation greening slowly, however, when > 300 m, hindered the greening slowly, and then rapidly when > 1500 m. When elevation > 3500 m, it was impossible for vegetation greening. With the increase of the distance to residential area, the relative probability of vegetation greening increased very rapidly, and then keep unchanged, which means when the distance increased to a threshold, the activities of local residents would have less influence on vegetation greening. As Bio7 increased, vegetation greening probability increased quickly and then slowly, however, when it exceeded 25℃, it had no further influence. When GDP increased, the relative probability of vegetation greening decreased rapidly and then slowly, when it reached 4000 Yuan/km2, there was a very low possibility for vegetation greening. So, economic development is accompanying with vegetation browning. With the decrease of Bio19, the relative probability of vegetation greening decreased with different rates.

Fig. 3. Nonlinear relationship of vegetation greening with natural and human variables explained by partial dependence plots (a) Bio14,(b) land use change type (11 croplands, 12 croplands to forest, 13, cropland to grassland, 15 croplands to urban area, 21 forest to cropland, 22 forest, 23 forest to grassland, 24 forest to waterbody, 25 forest to urban area, 31 grasslands to cropland, 32 grasslands to forest, 33 grasslands, 44 waterbodies, 51 urban area to forest, 55 urban area, 66 Unutilized land), (c) population density, (d)elevation, (e) Distra, (f) Bio7, (g) GDP, (h) Bio19.

3.4. Potential changes of vegetation greening under current conditions and future climate changes

insignificantly increasing (Fig. 2). However, it is possible that the actual trend has not reached the final equilibrium due to time lags for vegetation to adapt to environmental changes. Therefore, we simulated the potential distribution of vegetation trend under current environment (Table 5 and Fig. 4a). Under current condition, the predicted area with

The actual trends of vegetation changes in the east of the study area (Guangxi and Guizhou provinces) were significantly increasing, while in the west (Yunnan province), most were decreasing, and 6

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Fig. 3. (continued)

potentially vegetation browning increased from 13.88% to 37.69%, among which 16.26% were transformed from the area with insignificantly increasing trend (Table 5). The area with potential vegetation greening increased from 56.51% to 62.31%, of which 13.35% were changed from insignificantly increasing, mainly located in the east. There are about 10.92% areas changed from greening to browning, which mainly located in the west (Table 5 and Fig. 4a). Thus, if environmental conditions kept unchanged, vegetation would show a browning trend in the most part of the west, while a greening trend in the east. In 2050, vegetation in almost the whole area showed a greening trend (Fig. 4b and Table 5). Only 9.78% of the area showed a browning trend, mainly located in the northwest. Therefore, current environmental conditions will not favor vegetation greening in the west, while future climate changes would favor vegetation greening except the northwest. To reveal why future climate changes would improve the greening, we have drawn the changes of the main bioclimatic factors (Bio14, Bio7 and Bio19) influencing the greening from current to 2050 (Fig. 5). Bio14 will increase in 2050 in most study area, especially in Guangxi province (Fig. 5). Thus, the changes of Bio14 will promote vegetation greening in most Southwest China in the future. For Bio7, it will decrease in most study area, except the south and west of Yunnan province and the north of Guizhou Province, which means the decrease of Bio7 will be unfavorable for the greening in most study area in the future. For Bio19, it will increase in most of Guangxi province; however, it will decrease in most area of Guizhou and Yunnan provinces. Therefore, in 2050, the increase of Bio19 will hinder the greening in

Fig. 3. (continued)

most Guangxi province, while the decrease will promote the greening in most area of Guizhou and Yunnan provinces. Although the decrease of Bio7 is unfavorable for vegetation greening, Bio14 is by far more 7

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vegetation greening in Southwest China (Wang et al., 2015; Tong et al., 2016, 2018), while some thought human activities or climate changes played a more important role (Cai et al., 2014; Tao et al., 2018). However, they have not quantified the relative importance of climate changes, topography and human activities. In our studies, human activities (33.163%) were slightly less important than climate changes (42.655%), while much more important than topography (16.481%). However, human factors such as land use changes, population density and distance to residential area were more important than climatic factors except Bio14. The biggest Chinese conservation program, the Grain to Green Project, was launched in 2000/2001 in Southwest China (Tong et al., 2018), which changed land use. Ecological migration project has changed population size and structure, which may, in turn, greatly affected vegetation dynamics (Cai et al., 2014). Thus, human activities played a very important role in vegetation greening. Most previous studies ignored the influence of topography, when quantifying the importance of climate changes and human activities (Sun et al., 2015; Huang et al., 2016; Wang et al., 2016; Jiang et al., 2017a; Tong et al., 2017; Qu et al., 2018; Qi et al., 2019). However, in our study, the importance of topography reached 16.481%, which should not be ignored. Some studies reported vegetation greening in Southwest China was mainly controlled by temperature, and weakly related with precipitation by correlation analysis of vegetation greenness with average temperature and total precipitation (Hou et al., 2015; Tao et al., 2018). However, based on bioclimatic variables, our studies demonstrated that Bio14 was the most important driving factor, which was much more important than the others. Although Southwest China is located in the subtropical monsoon climate zone, with abundant precipitation and moderate temperature, precipitation is concentrated in wet season (Tong et al., 2016). Moreover, drought risks and severity increased significantly in the past decade due to extremely inter-annual/seasonal variation of precipitation (Liu et al., 2017), and heavily affected

Table 5 Area percentages of the conversion from the actual distributions of decreasing (browning), insignificantly and significantly increasing trends (greening) to their potential distributions under current condition and future climate changes in 2050. Actual distribution (%)

Browning Insignificantly increasing trend Greening Sum

Potential distribution (%) Current

2050

Browning

Greening

Browning

Greening

13.88 29.61

10.51 16.26

3.37 13.35

2.68 4.32

11.20 25.29

56.51 100

10.92 37.69

45.59 62.31

2.78 9.78

53.73 90.22

important, and thus most study area will become greening. In northwest Yunnan province, the decrease of Bio7 will inhibit the greening. Although the slight increase of Bio14 and decrease of Bio19 will favor the greening, it is not sufficient for vegetation changing from greening to browning. As a result, vegetation in northwest Yunnan province will remain browning in future. 4. Discussion 4.1. Relative importance of climatic, topography, human factors, and their interactions Similar with the results of Tong et al. (2017), our studies showed that the greening trend of vegetation was mostly concentrated in the Guangxi and Guizhou provinces, while insignificant and decreasing trends were mainly located in Yunnan province. Several studies reported that human activities and climate changes both contributed to

Fig. 4. Changes of vegetation trends from actual distribution to predicted distribution under (a) current conditions and (b) the future climate changes scenario of RCP4.5 in 2050. 8

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Fig. 5. Spatial changes of (a) Bio14, (b) Bio7 and (c) Bio19 from current to future in 2050.

topography (elevation), which made it the most important. Although elevation was the fourth most important, it had the second strongest interaction. It strongly interacted with climatic factors (Bio14, Bio12 and Bio7). Thus, vegetation greening is very vulnerable in the areas with high elevation and low Bio14 where need more conservation efforts. For human activities, land use changes and distance to residential area had strong interactions with climate changes (Bio14 and Bio19). The effectiveness of ‘Grain to Green’ on vegetation greenness was thought to vary with topography (Tong et al., 2017, 2018). However, land use changes had strong interactions with Bio14 and less with topography, which meant the effectiveness may be mainly affected by Bio14. Therefore, to improve the effectiveness, efforts will be made on not just the area with high elevation and tough slope (Tong et al., 2017, 2018), but more on the area with low Bio14.

vegetation growth, which may hinder vegetation greening (Jiang et al., 2014; Tao et al., 2018). Thus, Bio14 was the most important factor limiting vegetation greening. In the meantime, Bio19 was also more important than the temperature related factors. However, annual total precipitation (Bio12) was less important than annual average temperature (Bio1), which was consistent with previous studies (Hou et al., 2015; Wang et al., 2015; Tao et al., 2018). For temperature related factors, Bio1 was less important than Bio7. In brief, different from previous studies (Cai et al., 2014; Wang et al., 2015; Hou et al.,2015), precipitation related factors were more important for vegetation greening than temperature related factors with bioclimatic variables. Therefore, bioclimatic variables were more accurate and suitable than average temperature and total precipitation for examining the effects of climate changes on vegetation greening. Although previous studies demonstrated that ecological engineering had great influences on vegetation greening (Wang et al., 2015; Tong et al., 2017, 2018), they cannot extract it from the effects of the other types of human activities. In our study, land use changes and population density were found to be the second and third most important factors for vegetation greening. Because there was not only the Grain for Green Project (Wang et al., 2015; Tong et al., 2017, 2018), but also Ecological migration project (Cai et al., 2014; Wang et al., 2015), which greatly changed land use and population density, and in turn, greatly changed vegetation greenness. Previous studies found that elevation had great influences on vegetation greening (Tong et al., 2016; Tao et al., 2018), but did not quantify its relative importance. We found that elevation was the fourth most important factor liming vegetation greening with contribution of 7.530%. Moreover, slope and aspect also contributed 5.502% and 5.304% respectively. Because Southwest China has very complex topography, vegetation changes are sensitive to it (Tao et al., 2018). Tong et al. (2017) pointed out that the effectiveness of ecological engineer was closely related to the combined influences from climatic conditions and human management. Moreover, Tao et al. (2018) thought the driving effects of climate changes on vegetation greening was elevation-dependent. Our study also showed that climate changes had strong interactions with both human activities and elevation, and so, the driving effects of climate changes were affected by both elevation and human management. However, human activities and topography had poor interactions with each other. Bio14 had strong interactions with not only climatic factors (Bio19, Bio7), but also human activities (land use changes and distance to residential area), and

4.2. Nonlinear responses of vegetation greening to nature and human factors In humid region, the increase of total precipitation was reported to prohibit vegetation growth (Piao et al., 2015). Our studies found that the increase of winter precipitation (Bio19) prohibited vegetation greening, but precipitation in the driest month (Bio14) promoted vegetation greening in some degree. The increase of Bio19will increase the possibility of the cold damage, further prevent plant growth, while the increase of Bio14 will reduce the drought stress, and further favor plant growth. However, when Bio14 reached a threshold value, it would have no further impacts on vegetation greening. Annual temperature can promote vegetation growth (Piao et al., 2015). We found that the increase of Temperature Annual Range (Bio7) favored vegetation greening greatly, but had no further impacts when above a threshold value. Although human activities had great influences on vegetation greenness, we found that different kinds of human activities had different influences. For land use changes, croplands to forests and grasslands to forests due to ‘Grain for Green’ project increased vegetation greenness, while forests and grasslands to urban areas due to urbanization reduced the greenness. For different land use types, unutilized land, waterbodies, and grasslands have higher potential for vegetation greening. Thus, afforestation in these three types of land use will be more effective. Population pressure was reported to bring about vegetation degradation (Cai et al., 2014; Li et al., 2017). However, we found that a certain increase of population would favor vegetation greening when 9

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population density was low. When population density increased to a high level, the increase began to prohibit vegetation greening, and when it exceeded 800 person/km2, vegetation greening was impossible. Therefore, low population pressure favored vegetation greening, while high pressure prohibited it, and finally brought unrecoverable destruction. With the increase of GDP, vegetation greening probability decreased rapidly firstly, and then slowly. It kept at a minimum when GDP > 4000 Yuan/km2. Thus, economy increase prohibits vegetation greening, and even bring unrecoverable degradation of vegetation. The increase of the distance to residential area favored vegetation greening, but when it reached a certain distance, it had no further impacts. Previous studies demonstrated that high elevation was unfavorable for vegetation greening (Qu et al., 2018; Tao et al., 2018). Our studies showed that elevation increase facilitated vegetation greening when < 300 m, and then inhibit it slowly. When elevation > 1500 m, it began to inhibit the greening rapidly, and when elevation > 3500 m, it was impossible for vegetation greening. In brief, all nature and human factors showed nonlinear relationship with vegetation greening, and thus nonlinear analysis is necessary for studying the relationship of climate changes and human activities with vegetation greening. Vegetation greening of different vegetation types may have different responses to bioclimatic variables, human activities, topography and so on, which we did not consider in this paper. To reveal their different responses to different influential factors, it is needed to build different BRT models for each vegetation type, which needs further more works and will be studied in our future work.

identifying variable interactions and revealing nonlinear relationships between response and predict variables (Elith et al., 2008). BRTs are generally regarded to perform much better in prediction than traditional modelling approaches (Elith et al. 2008). With BRTs, our study predicted the future changes of vegetation greening under future climate changes, which can provide scientific references for the sustainability of fragile ecosystem. In practical terms, with global climate warming and rapid increasing human activities, BRTs will be very useful to predict the future changes of vegetation greening under future climate change phenomena and human activities in the developing economic megacities, and further to provide sustainability efforts on the sustainable developments of environment and economy. This will be one of the future directions of the applications of BRT methods. Although BRTs have so many advantages mentioned above, there are also some disadvantages needed further improvements. Like the other machine-learning methods, interpretation of the BRTs is difficult without an explicit regression equation for the final model. However, they can provide good interpretability of resulting input–output relationships from the black box models (Wang et al., 2015). It should be noted that spatial transferability is also an important limit: to what extent a trained model from one location can be used at another (Dickson and Perry, 2016). Thus, landscape heterogeneity limit the wide application of the trained model from one site to another, and so a new model should be built in another site. 5. Conclusions Our studies revealed the nonlinear relationship of vegetation greening to climatic, topography, soil and human factors, and predicted the future changes of the greening based on the nonlinear analysis with BRT method. The following conclusions can be drawn:

4.3. Potential changes of vegetation greening under current conditions and future climate changes Tong et al (2016) demonstrated the vegetation trends in Southwest China were rather persistent with analysis of Hurst exponent. Our studies also found that vegetation trend would keep stable and steady in most area of Guizhou and Guangxi provinces. However, in Yunnan province, most of the area with insignificant increasing trends and part of the area with increasing trend will become browning. Thus, under current climatic condition and human activities, vegetation will degrade in Yunnan province due to time lag effects, which will need more ecological conservation efforts. However, in the future, vegetation in most of the study area will become greening due to the increase of Bio14. It remains browning in the northwest of Yunnan province due to the decrease of Bio7, and slight increase of Bio14. In all, future climate changes will favor vegetation greening in Southwest China, which will reduce the pressures on ecosystem conservation.

1. Precipitation-related variables were much more important than temperature-related variables, especially for Bio14. It indicated bioclimatic variables were more suitable than raw climatic factors for examining the relationship between climate changes and vegetation greening. Land use changes and population density were the two most important human factors, which meant it is necessary to differentiate the different impacts from different types of human activities. Climate changes and Human activities were the two most important variable types (42.655% and 33.163%); however, topography also was very important (16.481%), which should be considered. 2. Climate changes had strong interactions with both human activities and topography, however, human activities and topography had poor interactions with each other. Therefore, human management should pay more attention to the area susceptible to climate changes. 3. The increase of Bio14, distance to residential area, Bio17 promoted vegetation greening, but the increase of GDP and Bio19 prohibited the greening. When they approaching threshold values, they will have no further impacts. The increase of population density will favor vegetation greening when it is low, while prohibit the greening when it is high. Elevation increase promoted vegetation greening when < 300 m, and then prohibited greening. For land use changes, “Grain for Green” improved vegetation greening, but urbanization prohibited it. In all, vegetation greening showed obviously nonlinear relationship with climatic, topography and human factors, indicating the necessity of nonlinear analysis for exploring the relationship of vegetation greening with nature and human factors. 4. Under current environmental condition, vegetation in the most area with insignificantly increasing trend in Yunnan province will show a browning trend due to time lag effects. Thus, more ecological conservation efforts should be made to prevent vegetation degradation in the west. However, in future, almost all the study area will show a

4.4. Performance of BRT method For the BRT model built in our study, AUC value was 0.89, TSS was 0.67, and sensitivity, specificity and overall correct probability were all > 0.8, indicating good performance. Due to shortage of long time series data, it is hard to consider the spatial heterogeneity of human activities of different types based on traditionally correlation analysis. However, with BRT model, we can consider not only spatial heterogeneity of human activities, but also topography and soil properties. For BRT methods, these variables can be either categorical or continuous, with no need for priori information on variables importance and the relationships between predictor and response variables (Elith et al., 2008; Soykan et al.,2014; Mitchell et al., 2018), which is important for complex ecosystem such as vegetation greening. Linear methods are hard to consider variable interactions, and thus hard to reveal the nonlinear relationships between predict and response variables. With BRT method, our results revealed not only the nonlinear relationships of vegetation greening with nature and human factors, but also the strong interactions of climate changes with human activities and topography. Thus, BRT is very useful for selecting relevant variables, 10

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greening trend due to climate changes except the northwest, which will reduce the pressures on ecological conservation.

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CRediT authorship contribution statement Huiyu Liu: Conceptualization, Methodology, Validation, Project administration. Fusheng Jiao: Methodology. Jingqiu Yin: Conceptualization. Tingyou Li: Conceptualization. Haibo Gong: Methodology. Zhaoyue Wang: Validation. Zhenshan Lin: Conceptualization. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This research has been supported by National Natural Science Foundation of China (No. 41971382, 31470519) and funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (164320H116). The authors thank the anonymous reviewers for their helpful suggestions. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecolind.2019.106009. References Allouche, O., Tsoar, A., Kadmon, R., 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43 (6), 1223–1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x. Brown, J.L., Bennett, J.R., French, C.M., 2017. SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. e4095. Peer. J 5. https://doi.org/10.7717/peerj.4095. Cai, H., Yang, X., Wang, K., Xiao, L., 2014. Is forest restoration in the Southwest China Karst promoted mainly by climate change or human-induced factors? Remote Sens. 6, 9895–9910. https://doi.org/10.3390/rs6109895. Deblauwe, V., Droissart, V., Bose, R., Sonké, B., Blach-Overgaard, A., Svenning, J.-C., Wieringa, J.J., Ramesh, B.R., Stévart, T., Couvreur, T.L.P., 2016. Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics. Global. Ecol. Biogeogr. 25, 443–454. https://doi.org/10.1111/geb.12426. Dickson, M., Perry, G., 2016. Identifying the controls on coastal cliff landslides using machine-learning approaches. Environ. Modell. Softw. 76, 117–127. https://doi.org/ 10.1016/j.envsoft.2015.10.029. Djebou, D.C.S., Singh, V.P., 2015. Retrieving vegetation growth patterns from soil moisture, precipitation and temperature using maximum entropy. Ecol. Model. 309–310, 10–21. https://doi.org/10.1016/j.ecolmodel.2015.03.022. Elith, J., Leathwick, J.R., Hastie, T., 2008. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x. Fu, B., 2017. Geography: from knowledge, science to decision making support. Acta Geograph. Sin. 72 (11), 1923–1932. https://doi.org/10.11821/dlxb201711001. Friedman, J.H., Meulman, J.J., 2010. Multiple additive regression trees with application in epidemiology. Statist. Med. 22, 1365–1381. https://doi.org/10.1002/sim.1501. Hijmans, R.J., Phillips, S., Leathwick, J., Elith, J., 2014. dismo: Species distribution modeling. https://CRAN.R-project.org/package=dismo. Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978. https://doi.org/10.1002/joc.1276. Hou, W., Gao, J., Wu, S., Dai, E., 2015. Interannual variations in growing-season NDVI and its correlation with climate variables in the Southwestern Karst Region of China. Remote. Sens. 7, 11105–11124. https://doi.org/10.3390/rs70911105. Hua, W., Chen, H., Zhou, L., Xie, Z., Qin, M., Li, X., Ma, H., Huang, Q., Sun, S., 2017. Observational quantification of climatic and human influences on vegetation greening in China. Remote. Sens. 9, 425. https://doi.org/10.3390/rs9050425. Huang, K., Zhang, Y., Zhu, J., Liu, Y., Zu, J., Zhang, J., 2016. The influences of climate change and human activities on vegetation dynamics in the Qinghai-Tibet Plateau. Remote. Sens. 8, 876. https://doi.org/10.3390/rs8100876. Jamali, S., Seaquist, J., Eklundh, L., Ardö, J., 2014. Automated mapping of vegetation trends with polynomials using NDVI imagery over the Sahel. Remote. Sens. Environ. 141, 79–89. https://doi.org/10.1016/j.rse.2013.10.019. Jiang, L., Jiapaer, G., Bao, A., Guo, H., Ndayisaba, F., 2017a. Vegetation dynamics and responses to climate change and human activities in Central Asia. Sci. Total. Environ.

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