Changes in Vegetation and Assessment of Meteorological Condition in Karst Ecologically Fragile Areas

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  • Corresponding author: Weihua MO, mwh-0419@163.com
  • Funds:

    Supported by the Guangxi Science and Technology Project (AB20159022), Guangxi Science and Technology Project (AB17292051), and Guangxi Natural Science Foundation (2018GXNSFAA281338)

  • doi: 10.1007/s13351-020-9170-2
  • Note: This paper has been peer-reviewed and is just accepted by J. Meteor. Res. Professional editing and proof reading are underway. Please use with caution.

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  • Meteorological conditions have an important impact on changes in vegetation in karst ecologically fragile areas. This study aims to explore a method for quantitative evaluation of these meteorological conditions. We analyzed the changing trend characteristics of vegetation during 2000 to 2018 and the correlations between vegetation change and various meteorological factors in the karst rocky areas of Guangxi. The characteristics of meteorological factors in vegetation areas with varying degrees of improvement were also analyzed. Key meteorological factors at seasonal scale were selected for meteorological condition evaluation. A quantitative evaluation model of comprehensive influence of meteorological factors on vegetation was built using partial least-square regression (PLS). About 91.45% of the vegetation tended to be improved, while only 8.55% of the vegetation showed a trend of degradation from 2000 to 2018. Areas with evident vegetation improvement were mainly distributed in the middle and northeast, and those with obvious vegetation degradation were scattered. Meteorological factors affecting vegetation were significantly different among the four seasons. Overall, high air humidity, small temperature difference in spring and autumn, and low daily minimum temperature and air pressure were favorable conditions. Low temperature in winter and high temperature in summer and autumn were unfavorable conditions. Climate Vegetation Index (CVI) model was established by PLS using the maximum temperature, minimum temperature, average temperature, vapor pressure, rainfall, and air pressure as key meteorological factors. (Ehanced Vegetation Index (EVI) was well fitted by the CVI model, with R2 and RMSE average of 0.856 and 0.042, respectively. Finally, the assessment model of comprehensive meteorological conditions was built based on interannual differences in CVI. The meteorological conditions in the study area in 2014 were successfully evaluated by combining the model and selected seasonal key meteorological factors.
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  • Fig. 1.  Distribution of rocky desertification areas and meteorological stations in Guangxi.

    Fig. 2.  Vegetation change trend in the karst rocky areas from 2000 to 2018.

    Fig. 3.  Scatter plots of EVI and CVI during 2000 to 2018.

    Fig. 4.  Evaluation results of meteorological conditions in the karst rocky areas of Guangxi in 2014.

    Table 1.  SLOPE Grading Standard in the rocky desertification areas of Guangxi

    GradeChange of vegetationRange of SLOPEProportion (%)
    1Noticeably worse[−1,−0.2) 0.04
    2Noticeably worse[−0.2,−0.15) 0.08
    3Noticeably worse[−0.15,−0.1) 0.22
    4Worse[−0,1,−0.05) 0.84
    5Slightly worse[−0.05,0) 7.37
    6Slightly better[0,0.05)58.37
    7Better[0.05,0.1)30.35
    8Noticeably better[0.1,0.15) 2.55
    9Noticeably better[0.15,0.2) 0.18
    10Noticeably better[0.2,1) 0.01
    Download: Download as CSV

    Table 2.  Correlation between vegetation EVI and meteorological factors in karst rocky areas

    TimeVapor pressureRainfallRelative humidityAverage temperatureThe maximum temperatureThe minimum temperatureSunshine durationAir pressureDew point
    Spring+/+/////+
    Summer///////
    Autumn/+/////
    Winter+//+++++
    Note: + indicates significant positive correlation; - indicates significant negative correlation; / indicates that correlation is not significant
    Download: Download as CSV

    Table 3.  Climatic factors characteristics in the five categories of SLOPE

    CategoryVapor pressureRainfallRelative humidityAverage temperatureThe maximum temperatureThe minimum temperatureSunshine durationAir
    pressure
    Dew
    point
    Spring<5016.5170.767.421.328.416.4443.8928.614.0
    50-6017.8218.674.920.225.816.3352.6939.715.0
    60-7019.0297.175.521.126.117.6293.5976.316.1
    70-8019.9273.476.521.626.218.4292.9972.716.8
    >8020.2297.077.221.625.818.5259.5980.917.0
    Summer<5025.2625.879.325.430.922.0500.0922.721.2
    50-6026.3770.783.625.130.121.8477.8935.321.8
    60-7028.1861.181.126.831.923.7483.6970.222.9
    70-8028.1797.580.726.831.623.7516.7966.222.9
    >8028.9838.781.127.231.824.2494.9974.623.4
    Autumn<5018.6173.978.420.326.116.7410.0933.115.9
    50-6018.8199.480.519.825.316.2431.0944.015.9
    60-7019.6204.176.621.327.117.9439.7980.116.5
    70-8020.2211.575.921.927.418.3482.3975.316.9
    >8020.3222.273.822.527.619.0490.9983.717.0
    Winter<5010.659.375.612.418.58.5307.2934.97.4
    50-6010.770.477.211.716.88.3275.4946.27.3
    60-7011.1105.475.212.417.29.4243.5984.07.7
    70-8012.099.074.713.718.210.6274.7980.08.7
    >8011.8114.674.113.517.910.6256.2988.88.4
    Download: Download as CSV

    Table 4.  Characteristics of meteorological elements in areas where vegetation was obviously improved

    TimeVapor pressureRainfallRelative humidityAverage temperatureThe maximum temperatureThe minimum temperatureSunshine durationAir pressureDew point
    SpringHHHH/HLHH
    SummerHH/H/H/HH
    AutumnHHLHHHHHH
    WinterHH /L//HLH //
    Note: H indicates that the high-value feature is obvious; L indicates that the low-value feature is obvious; / indicates that the feature is not obvious.
    Download: Download as CSV

    Table 5.  Meteorological evaluation factors in the study area

    TimeVapor pressureRainfallAverage temperatureThe maximum temperatureThe minimum temperatureAir pressure
    Spring
    Summer
    Autumn
    Winter
    Note: ↑ indicates that a high value is favorable, ↓ indicates that a low value is favorable.
    Download: Download as CSV

    Table 6.  Variation percentage explained by the partial least-square (PLS) factors

    Latent factorsX varianceCumulative X varianceY varianceCumulative Y variance
    10.8300.8300.8070.807
    20.0840.9150.0250.832
    30.0570.9720.0180.850
    40.0230.9950.0090.859
    50.0030.9980.0120.871
    60.0010.9990.0070.878
    Download: Download as CSV

    Table 7.  Analysis of variable projection importance index

    IndexVTTmaxTminPR
    VIP1.0871.0661.0651.0530.9880.842
    Download: Download as CSV

    Table 8.  Error and variance analysis of the partial-least square regression model

    ModelSum of SquaresdfR2Adjusted R2Std.Error of the estimateFSig.
    12.33560.8560.8520.042220.3250.000
    0.394431
    2.728439
    Download: Download as CSV

    Table 9.  Classification standard of meteorological conditions in karst rocky areas

    GradeGoodGeneralBad
    Classification standard[0.02, 1][0, 0.02)[−1, 0.0)
    Download: Download as CSV

    Table 10.  Meteorological elements in the rocky desertification areas of Guangxi in 2013 and 2014

    Meteorological
    factor/unit
    20132014Multi-year
    average
    Vapor pressure (spring)/hPa20.020.719.4
    The minimum temperature (summer)/°C23.824.023.6
    Air pressure (summer)/hPa965.1964.8964.8
    Rainfall (autumn)/mm239.9347.5210.6
    Average temperature (autumn)/°C20.922.121.6
    The minimum temperature (autumn)/°C17.819.118.1
    Average temperature (winter)/°C12.711.513.0
    The maximum temperature (winter)/°C16.416.717.7
    The minimum temperature (winter)/°C10.38.19.9
    Vapour pressure (winter)/hPa11.910.011.5
    Download: Download as CSV
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Changes in Vegetation and Assessment of Meteorological Condition in Karst Ecologically Fragile Areas

    Corresponding author: Weihua MO, mwh-0419@163.com
  • 1. Guangxi Institute of Meteorological Sciences, Nanning 530022
  • 2. Guangxi Hechi Meteorological Bureau, Hechi 547000
  • 3. College of Geography and Planning of Nanning Normal University, Nanning 530001
Funds: Supported by the Guangxi Science and Technology Project (AB20159022), Guangxi Science and Technology Project (AB17292051), and Guangxi Natural Science Foundation (2018GXNSFAA281338)

Abstract: Meteorological conditions have an important impact on changes in vegetation in karst ecologically fragile areas. This study aims to explore a method for quantitative evaluation of these meteorological conditions. We analyzed the changing trend characteristics of vegetation during 2000 to 2018 and the correlations between vegetation change and various meteorological factors in the karst rocky areas of Guangxi. The characteristics of meteorological factors in vegetation areas with varying degrees of improvement were also analyzed. Key meteorological factors at seasonal scale were selected for meteorological condition evaluation. A quantitative evaluation model of comprehensive influence of meteorological factors on vegetation was built using partial least-square regression (PLS). About 91.45% of the vegetation tended to be improved, while only 8.55% of the vegetation showed a trend of degradation from 2000 to 2018. Areas with evident vegetation improvement were mainly distributed in the middle and northeast, and those with obvious vegetation degradation were scattered. Meteorological factors affecting vegetation were significantly different among the four seasons. Overall, high air humidity, small temperature difference in spring and autumn, and low daily minimum temperature and air pressure were favorable conditions. Low temperature in winter and high temperature in summer and autumn were unfavorable conditions. Climate Vegetation Index (CVI) model was established by PLS using the maximum temperature, minimum temperature, average temperature, vapor pressure, rainfall, and air pressure as key meteorological factors. (Ehanced Vegetation Index (EVI) was well fitted by the CVI model, with R2 and RMSE average of 0.856 and 0.042, respectively. Finally, the assessment model of comprehensive meteorological conditions was built based on interannual differences in CVI. The meteorological conditions in the study area in 2014 were successfully evaluated by combining the model and selected seasonal key meteorological factors.

1.   Introduction
  • Karst, loess, desert, and alpine regions are four ecologically fragile areas in China. In the karst fragile ecological environment, the gradual exposure of rocks similar to desert landscape on the Earth’s surface due to vegetation destruction, soil erosion, and decline or loss of land production capacity is called rocky desertification (Yuan, 2008). The low disaster-tolerance capacity of these areas is a major hidden danger of ecological security and has attracted great attention from all walks of life (Li et al., 2006).

    The karst rocky areas in southwestern China are located between tropics and the subtropics, and the hot and rainy climate in southwestern subtropics provides a powerful driving force for karst development, water and soil loss, and rocky desertification (Wang et al., 2003; Su et al., 2006). The correlation (Melillo et al., 1993; Keeling et al., 1996; Zhou and Zhang, 1996; Field et al., 1998; Knapp and Smith, 2001; Nemani et al., 2003; Weltzin et al., 2003; Zhang et al., 2013) and hysteresis (Guo et al., 2009; Liu et al., 2009; Hou et al., 2012; Yu et al., 2013) between climate change and vegetation change have been verified at global and regional scales. Scholars have also studied vegetation change and climate change in karst rocky areas. Using GIMMS(无全称) Normalized Difference Vegetation Index (NDVI) and Net Primary Productivity (NPP) datasets, Meng and Wang (2007) studied the response of vegetation change to climate change in southwestern karst rocky areas and found the higher effect of temperature change than precipitation change. Zheng et al.(2009) investigated the change in vegetation cover and its relationship to major climatic factors in Guizhou based on GIMMS NDVI dataset and corresponding climatological data during 1982–2003 by extracting and analyzing pixel-by-pixel information through regression and correlation analysis. The interannual variation trends of NDVI and temperature are synchronous, and a certain lag exist between NDVI and precipitation. Wu et al (2012) analyzed the correlation between rocky desertification and the spatial distribution of climate in the karst mountainous areas of northwestern Guangxi in 2008; the incidence of rocky desertification increased steadily with increasing annual average temperature and increased rapidly with increasing annual average precipitation. In existing studies on the correlation between vegetation and climate in karst rocky areas, NDVI is often used to characterize vegetation, and climatic factors are only represented by temperature and precipitation. However, the influence of climate on vegetation is strongly integrated. In most studies, NDVI is used to characterize vegetation change (Fu et al., 2006; Zhao et al., 2006; Zhang et al., 2009 本条文献指代信息不明确; Chen et al., 2011). Ehanced Vegetation Index (EVI) has the advantages of the widely used NDVI, and its shortcomings have been improved. In recent years, studies on the correlation between vegetation and climate in karst rocky areas based on EVI sequences have made progress (Nan et al., 2010; Chen et al., 2014 本条文献指代信息不明确).

    Located in the south of China, Guangxi is one of the main sources of the Yangtze River basin, the Pearl River basin, and the transnational Red River basin as well as one of the three provinces with the richest biodiversity in China and has an irreplaceable ecological location of important regions (Ban et al., 2018). The karst rocky areas in southwestern Guangxi are one of the most important areas for biodiversity conservation. However, the widespread distribution of karst landform, prominent rocky desertification, and serious natural disasters (Huang et al., 2015] have posed an important threat to regional ecological security and have seriously restricted local economic and social development. Therefore, the ecology of karst rocky areas in Guangxi is always a hot spot for government departments and researchers in Guangxi.

    The authors of this paper previously carried out a lot of research on the karst rocky areas of Guangxi. Basing on multi-source remote sensing data, they established an identification model of rocky desertification, drew spatial distribution maps of rocky desertification in different remote sensing periods, and studied the distribution characteristics of rocky desertification in the karst rocky areas of Guangxi (Wang et al., 2014; Chen et al., 2018). The sensitivity of NDVI and EVI to vegetation monitoring in some rocky desertification counties in the karst rocky areas was compared. EVI considers the influence of soil background in the index structure and is more objective to reflect the vegetation characteristics of the areas (Chen et al., 2014 本条文献指代信息不明确). The correlation and hysteresis of EVI and meteorological factors were analyzed. The effects of various meteorological factors on EVI were studied. The response of EVI to meteorological factors is sensitive, and their correlation is very high (Chen et al., 2014, 2015 本2条文献指代信息不明确). According to the response mode of the two indices, the climate fitting model of EVI was established by using stepwise regression method for fitting and predicting EVI accurately (Chen et al., 2015 本条文献指代信息不明确).

    Many results have been obtained in studies of karst rocky areas in Guangxi. Although the multi-temporal spatial distribution of rocky desertification has been interpreted, its interannual vegetation evolution remains unclear. Meteorological factors have an important influence on vegetation in karst rocky areas, but no models and factors are established to quantitatively evaluate meteorological conditions. In this study, a mathematical model was used to quantitatively analyze the structural characteristics of rocky desertification grade change. According to the characteristics of meteorological conditions in vegetation change areas, a quantitative evaluation model of meteorological conditions affecting vegetation EVI in the karst rocky areas of Guangxi was established using the modeling idea of the climate fitting model of vegetation EVI. Results can provide reference for evaluation of meteorological conditions and rocky desertification pattern change in karst rocky areas.

2.   Data and methods
  • Guangxi Zhuang Autonomous Region (104°26’-112°04’E, 20°54’-26°24’N) is located in the south of China, and the Tropic of Cancer crosses the middle of Guangxi. The boarders of the region are the tropical ocean in the south, the Nanling Mountains in the north, and the Yunnan-Guizhou Plateau in the west. The region is higher than surrounding areas but low in the middle and looks like a basin. The region has more mountains but less plains, and the area of Karst landform accounts for 37.8%. Poor land, harsh ecological environment, prominent rocky desertification, and serious natural disasters seriously restrict local economic and social development, so it is a poverty-stricken area. The distribution of karst rocky areas in Guangxi is shown in Fig. 1.

    Figure 1.  Distribution of rocky desertification areas and meteorological stations in Guangxi.

  • EVI data, with format of V005, are the MODIS(无全称) vegetation index product MOD13Q1(无全称) developed by the NASA MODIS terrestrial product group according to statistical algorithm, namely global synthetic vegetation index product that was synthesized in 16 d and has a resolution ratio of 250 m. MODIS vegetation index product is improved and designed based on existing vegetation indices, including two vegetation index products NDVI and EVI. MODIS NDVI is a continuation of NOAA NDVI series accumulating for 20 a and can provide long-term data for business monitoring and research. EVI takes advantage of MODIS radiometer to correct surface reflectance to increase sensitivity to high biomass areas and to improve vegetation monitoring accuracy by coupling canopy background signal and reducing atmospheric effects.

    The EVI data used were obtained using internationally accepted maximum value composite (MVC) method, which can further eliminate the interference of clouds, atmosphere, and solar elevation angle.

    where EVImi is the maximized composite value of EVI in the ith 16 d; and EVIij is the value of EVI on the jth day in the ith 16 d.

    The obtained MOD13Q1 remote sensing data set was preprocessed by subset extraction, image mounting, data format conversion, projection conversion, and quality inspection to obtain a reliable EVI data set. Many cloud pollution pixels still existed in the Guangxi EVI data set because of the influence of cloud and rain weather. In this study, spline interpolation method was used to deal with cloud pollution pixels and reconstruct high-quality EVI data series (Zhang et al., 2009 本条文献指代信息不明确).

  • The data of daily vapor pressure, precipitation, maximum temperature, minimum temperature, average temperature, dew point temperature, sunshine duration, and air pressure at 25 meteorological stations from 2000 to 2018 were provided by Guangxi Meteorological Information Center. The 16-day statistical values of various climatic factors and EVI in corresponding periods (including the same period and the previous period) were calculated. Precipitation and sunshine duration are cumulative values, and the remaining climatic factors are averages in the periods. The meteorological data and the remote sensing EVI were matched by near matching method, that is, the vegetation EVI of karst area in the county is matched with the data of meteorological observation station in that county.

  • Geographic information data include the administrative boundary of counties, the vector boundary of Karst rocky areas, and the latitude and longitude information of meteorological stations in Guangxi.

  • Trend line method means that regression analysis is performed on a set of variables that change over time to predict their changing trend. This method can be used to simulate the interannual changing trend of EVI. The calculation formula is as follows:

    where k is the number of years from 1 to n; EVIyk is the average of EVI in the kth year; and SLOPE is the changing trend. If SLOPE > 0, then EVI increases in n years, that is, the vegetation in the area is improved; otherwise it degrades.

  • The EVI value of a certain area was calculated using averaging method, namely, the EVI average of all pixels in the statistical area. The formula is as follows:

    where EVIap is the EVI average of a certain area; p is the code of an area; x is the line number of pixels in the statistical area; y is the column number of pixels in the statistical area; and n is the total number of pixels in the statistical area.

  • PLS is a multivariate statistical analysis method that combines multiple linear regression analysis, principal component analysis of variables, and canonical correlation analysis between variables to make full use of sample information. Regression modeling can be performed under conditions of small sample size, multiple independent variables, and severe multiple correlations. The principle (taking the PLS of a single dependent variable as an example) is as follows: let the known dependent variable be y and k independent variables be x1, x2, ···, xk, the number of samples is n, constituting the data table X = [x1, x2,···,xk]n × k and y = [y]n × 1. Component t1 is extracted from X, and t1 is a linear combination of x1, x2, ···, xk. t1 should carry the information of variation in X as much as possible, and the degree of correlation with y is the largest. After the first principal component t1 is extracted, the regression of y and X is performed. If the regression equation has reached satisfactory accuracy at this time, then the algorithm is stopped; otherwise, the second principal component t2 is extracted by using the participation information after X is explained by t1 and y is explained by t2, and the regression of y and X to t1 and t2 is continued until satisfactory accuracy can be achieved. If a total of m components t1, t2, ···, tm (mn) are finally extracted from X, then the PLS will be used for the regression of y to t1, t2, ···, tm. Since t1, t2, ···, tm are linear combinations of x1, x2, ···, xk, they can be expressed as a regression equation of y and dependent variable X.

    In the PLS analysis, the Variable Projection Importance Index (VIP) is used to measure the explanatory power of the independent variable to the dependent variable. This index is defined as follows:

    where Whj is the jth component of the axis Wh, which is used to measure the contribution of xj to the structure component th; r(y,th) is the correlation coefficient between the dependent variable y and the component th; k is the number of independent variables; and m is the number of components. For xj with a large VIPj, xj has a more important role in explaining y.

  • Coefficient of determination (R2) and root mean square error (RMSE) were used to test the model’s accuracy. The larger the R2 is, the better the model is. The smaller the RMSE is, the better the fitting effect is.

3.   Results
  • Univariate regression trend line method was used to study the changing trend of vegetation in the karst rocky areas of Guangxi from 2000 to 2018. Given that the SLOPE values were mostly concentrated within −0.2 to 0.2, the classification standard of SLOPE was formulated to classify it (Table 1).

    GradeChange of vegetationRange of SLOPEProportion (%)
    1Noticeably worse[−1,−0.2) 0.04
    2Noticeably worse[−0.2,−0.15) 0.08
    3Noticeably worse[−0.15,−0.1) 0.22
    4Worse[−0,1,−0.05) 0.84
    5Slightly worse[−0.05,0) 7.37
    6Slightly better[0,0.05)58.37
    7Better[0.05,0.1)30.35
    8Noticeably better[0.1,0.15) 2.55
    9Noticeably better[0.15,0.2) 0.18
    10Noticeably better[0.2,1) 0.01

    Table 1.  SLOPE Grading Standard in the rocky desertification areas of Guangxi

    According to Table 1 and Fig. 2, 91.45% of vegetation in the karst rocky areas of Guangxi tended to improve from 2000 to 2018, among which 58.37% of the vegetation was improved slightly; 30.35% of the vegetation was improved; and 2.73% of the vegetation was improved significantly. Only 8.55% of the vegetation trended to deteriorate, among which 7.37% of the vegetation deteriorated slightly; 0.84% of the vegetation deteriorated; and 0.34% of the vegetation deteriorated significantly. Areas with obvious vegetation improvement were mainly distributed in Laibin City, Liuzhou City, and Guilin City in the middle of the karst rocky areas. Areas with obvious vegetation degradation were scattered, of which the vegetation degradation in Chongzuo City, Guilin City and Nanning City was more obvious than that in the other areas.

    Figure 2.  Vegetation change trend in the karst rocky areas from 2000 to 2018.

  • Meteorological conditions are very important for vegetation growth in karst rocky areas, and the influences of meteorological factors on vegetation vary in different periods. When a business service product report is established, the impact of meteorological factors needs to be specified, such as the specific impact of various meteorological factors in different seasons. Therefore, the correlation between EVI and meteorological factors in the four periods including spring (from March to May), summer (from June to August), autumn (from September to November), and winter (December to next February) was discussed. The correlation between the same meteorological factors and the vegetation in different areas are different because of the difference in topography and vegetation, that is, not only the size difference but also the positive correlation and negative correlation may exist at the same time. In our study, the correlation between meteorological elements and EVI was defined by the relevant attributes of 80% stations passing through the significance test. If 80% of the sites are positively (or negatively) correlated with a weather factor, then we assume that the correlation is positive (or negative).

    The influence of meteorological factors on vegetation is quite different in different seasons (Table 2). In spring, the three meteorological factors (vapor pressure, relative humidity, and dew point) that characterize humidity were positively correlated with EVI, that is, the more humid the air was, the better the vegetation grew. In summer, the minimum temperature was negatively correlated with EVI. The minimum temperature generally occurred in the early morning, that is, the low temperature at night was not conducive to vegetation growth. In autumn, precipitation was positively correlated with EVI, while the average temperature and the minimum temperature were negatively correlated with EVI. The high temperature and less rainfall in autumn in Guangxi could easily lead to drought. Sufficient precipitation and mild temperature in autumn were conducive to vegetation growth. In winter, vapor pressure and dew point, which characterize humidity, were positively correlated with EVI, that is, humid air was conducive to vegetation growth. The average temperature, the maximum temperature, the minimum temperature, and sunshine duration were positively correlated with EVI, that is, warm winter was conducive to vegetation growth. If temperature was too low, then soil in the karst rocky areas was not conducive to heat storage, and vegetation roots were likely to be frozen, which was not conducive to vegetation growth.

    TimeVapor pressureRainfallRelative humidityAverage temperatureThe maximum temperatureThe minimum temperatureSunshine durationAir pressureDew point
    Spring+/+/////+
    Summer///////
    Autumn/+/////
    Winter+//+++++
    Note: + indicates significant positive correlation; - indicates significant negative correlation; / indicates that correlation is not significant

    Table 2.  Correlation between vegetation EVI and meteorological factors in karst rocky areas

    In further analysis, we aimed to determine the characteristics of meteorological factors in different vegetation improvement/degradation areas. The vegetation improvement was obvious when SLOPE ≥ 0.05 according to the analysis of vegetation change trend. The vegetation change in these areas has less man-made disturbance mainly due to the influence of meteorological factors. Thus, the sites included in the study area were divided into five categories, and the characteristics of the meteorological factors of each category were analyzed. The five categories were classified according to the proportion of pixels with SLOPE ≥ 0.05 in each county, and the proportion intervals were >80%, 70%–80%, 60%–70%, 50%–60%, and < 50%. Four periods were studied including spring, summer, autumn, and winter (Table 3).

    CategoryVapor pressureRainfallRelative humidityAverage temperatureThe maximum temperatureThe minimum temperatureSunshine durationAir
    pressure
    Dew
    point
    Spring<5016.5170.767.421.328.416.4443.8928.614.0
    50-6017.8218.674.920.225.816.3352.6939.715.0
    60-7019.0297.175.521.126.117.6293.5976.316.1
    70-8019.9273.476.521.626.218.4292.9972.716.8
    >8020.2297.077.221.625.818.5259.5980.917.0
    Summer<5025.2625.879.325.430.922.0500.0922.721.2
    50-6026.3770.783.625.130.121.8477.8935.321.8
    60-7028.1861.181.126.831.923.7483.6970.222.9
    70-8028.1797.580.726.831.623.7516.7966.222.9
    >8028.9838.781.127.231.824.2494.9974.623.4
    Autumn<5018.6173.978.420.326.116.7410.0933.115.9
    50-6018.8199.480.519.825.316.2431.0944.015.9
    60-7019.6204.176.621.327.117.9439.7980.116.5
    70-8020.2211.575.921.927.418.3482.3975.316.9
    >8020.3222.273.822.527.619.0490.9983.717.0
    Winter<5010.659.375.612.418.58.5307.2934.97.4
    50-6010.770.477.211.716.88.3275.4946.27.3
    60-7011.1105.475.212.417.29.4243.5984.07.7
    70-8012.099.074.713.718.210.6274.7980.08.7
    >8011.8114.674.113.517.910.6256.2988.88.4

    Table 3.  Climatic factors characteristics in the five categories of SLOPE

    In the karst rocky areas, as the proportion of pixels with SLOPE ≥ 0.05 increased, the vapor pressure, rainfall, average temperature, minimum temperature air pressure, and dew point mostly showed an increasing change trend. That is, higher average temperature, minimum temperature, air pressure, and dew point and sufficient water vapor and rainfall are better for vegetation improvement (Table 4).

    TimeVapor pressureRainfallRelative humidityAverage temperatureThe maximum temperatureThe minimum temperatureSunshine durationAir pressureDew point
    SpringHHHH/HLHH
    SummerHH/H/H/HH
    AutumnHHLHHHHHH
    WinterHH /L//HLH //
    Note: H indicates that the high-value feature is obvious; L indicates that the low-value feature is obvious; / indicates that the feature is not obvious.

    Table 4.  Characteristics of meteorological elements in areas where vegetation was obviously improved

    According to the correlation between EVI and climatic factors (Table 2) and the climate characteristics of different rocky desertification improvement areas in various periods (Table 4), number of rain days, vapor pressure, precipitation, maximum temperature, minimum temperature, average temperature, and air pressure were selected as the key factors for evaluating the meteorological condition in the karst rocky desertification areas of Guangxi (Table 5). Relative humidity, dew point, and sunshine hours were not included. Relative humidity, dew point, and water vapor pressure are all meteorological factors that can reflect air humidity. The correlation between water vapor pressure and vegetation EVI was the highest, and its characteristics was more obvious in different vegetation improvement areas. Hence, relative humidity and dew point were not included as key factors considering the simplicity and representativeness of the evaluation factors. Sunshine hour was also not included because it varied irregularly in different vegetation improvement grades. In specific case evaluation, the multi-year average of meteorological factors is usually used as a basis for measurement.

    TimeVapor pressureRainfallAverage temperatureThe maximum temperatureThe minimum temperatureAir pressure
    Spring
    Summer
    Autumn
    Winter
    Note: ↑ indicates that a high value is favorable, ↓ indicates that a low value is favorable.

    Table 5.  Meteorological evaluation factors in the study area

  • According to the above analysis, meteorological factors had significant effects on EVI in the karst rocky areas of Guangxi. In a previous study, an interaction exists among meteorological factors (Chen et al., 2014 本条文献指代信息不明确). Therefore, the EVI characterizing vegetation growth is the result of the comprehensive effect of meteorological conditions. Stepwise regression method was used to establish the fitting model of EVI and meteorological factors in the previous research; the model was used to analyze the comprehensive impact of meteorological conditions on vegetation in karst rocky areas (Chen et al., 2015 本条文献指代信息不明确). The establishment of the model requires to understand the effect and interaction of meteorological factors and involves complex data operations. In this work, PLS method was used to establish a model according to the multi-collinearity of statistical samples. The independent variables include vapor pressure, precipitation, average temperature, maximum temperature, minimum temperature, and air pressure, and the dependent variable is vegetation EVI.

    In the principal component analysis of PLS modeling, the variation percentages explained by the components Tk (k = 1, 2,···,6) extracted from the independent variable group are 83.0%, 8.4%, 5.7%, 2.3%, 0.3%, and 0.1% respectively. The variation percentages of the dependent variable group explained by Tk (k = 1, 2,,···,6) are 80.7%, 2.5%, 1.8%, 0.9%, 1.2%, and 0.7% respectively. The cumulative contribution rate of x variance of the first three eigenvectors has reached 97.2%, that is, the basic characteristics of the independent variable X can be expressed. In the meantime, the cumulative variance contribution of Y is 85.0%. That is, the regression equation constructed by the first three principal components can achieve a satisfactory accuracy (Table 6). The final regression equation is as follows:

    Latent factorsX varianceCumulative X varianceY varianceCumulative Y variance
    10.8300.8300.8070.807
    20.0840.9150.0250.832
    30.0570.9720.0180.850
    40.0230.9950.0090.859
    50.0030.9980.0120.871
    60.0010.9990.0070.878

    Table 6.  Variation percentage explained by the partial least-square (PLS) factors

    where Climate Vegetation Index (CVI) is the meteorological fitting value of vegetation EVI; V is the vapor pressure; R is the rainfall; T is the average temperature; Tmax is the maximum temperature; Tmin is the minimum temperature; and P is the air pressure.

    Each variable of the regression equation has an important effect on vegetation in the karst rocky areas in the analysis of VIP (Table 7). The factors that dominate vegetation in the karst rocky areas are ranked as follows: vapor pressure (V) > average temperature (T) > the maximum temperature (Tmax) > the minimum temperature (Tmin) > air pressure (P) > rainfall (R).

    IndexVTTmaxTminPR
    VIP1.0871.0661.0651.0530.9880.842

    Table 7.  Analysis of variable projection importance index

    The regression equation was tested, with R2 of 0.856 and adjusted R2 of 0.852 and passed the significance level test. EVI observation values and their fitting values (CVI) are basically distributed near the 1:1 line in the scatter plots of the two (Fig. 3). The CVI model has high fitting precision and good fitting effect (Table 8).

    Figure 3.  Scatter plots of EVI and CVI during 2000 to 2018.

    ModelSum of SquaresdfR2Adjusted R2Std.Error of the estimateFSig.
    12.33560.8560.8520.042220.3250.000
    0.394431
    2.728439

    Table 8.  Error and variance analysis of the partial-least square regression model

    The CVI actually characterizes the meteorological potential value of EVI, namely, the vegetation EVI under the combined influence of meteorological conditions and without the influence of other factors. If the weather conditions are good, then EVI is high; if weather conditions are poor, then EVI is low. Based on this assumption, the difference in meteorological conditions can be determined by the difference in CVI, and the evaluation model of vegetation meteorological conditions in the karst rocky areas can be obtained as follows:

    where CVIi and CVIj are the CVI values of vegetation in the karst rocky areas in years i and j. If DCVI is greater than zero, then the overall meteorological conditions in the karst rocky areas in the evaluated year is superior to that of the compared year.

    In service materials provided to the public, a simple and intuitive comprehensive evaluation of meteorological conditions is required. For example, images with visually good, medium, and poor ratings are more easily understood by readers. From 2000 to 2018, 91.45% of vegetation in the karst rocky areas of Guangxi tended to be improved, of which 58.37% of the vegetation was slightly improved; and 33.08% of the vegetation was improved or noticeably improved (Table 1). The meteorological condition was judged to be moderate in areas with slight vegetation improvement, and that in areas with improvement or noticeable improvement was judged to be good. The DCVI values from 2000 to 2018 was calculated in these areas, and the classification standard of meteorological conditions was formulated (Table 9).

    GradeGoodGeneralBad
    Classification standard[0.02, 1][0, 0.02)[−1, 0.0)

    Table 9.  Classification standard of meteorological conditions in karst rocky areas

  • The model of vegetation meteorological conditions was used to evaluate the meteorological conditions in the karst rocky areas of Guangxi in 2014. The results of the 2014 assessment of the meteorological conditions were compared with those of 2013. In 2014, the meteorological conditions were good mainly in the west and middle of the study area, including Tianlin, Lingyun, Bama, Donglan, Napo, and Jingxi county, northern Longlin county, western and eastern Xilin county, and the area outside the central part of Fengshan county. The meteorological conditions were medium mainly in the middle and south, including Leye, Tianyang, Debao, Tiandeng, Daxin, Long’an, Longzhou, Dahua and Xincheng county, western and southern Du’an county, and central Xilin county, central and southern Longlin county, eastern Luocheng county, and southern Rong’an county. The overall meteorological conditions in the above areas were conducive to vegetation growth. However, the meteorological conditions in Huanjiang, Rongshui, Shanglin, and Ningming county, western and northern Luocheng county, central and northern Rong’an county, northern Du’an county, and southeastern Long’an county were slightly worse, which was not conducive to vegetation growth (Fig. 4).

    Figure 4.  Evaluation results of meteorological conditions in the karst rocky areas of Guangxi in 2014.

    According to the meteorological evaluation factors in the rocky desertification areas, the advantages and disadvantages of the meteorological conditions were analyzed, and the ecological meteorological evaluation model of rocky desertification areas was used to analyze the overall impact of meteorological conditions on vegetation improvement in the rocky desertification areas. Favorable conditions were high air humidity, small temperature difference in spring, autumn, and dry season, and low daily minimum temperature and air pressure. Unfavorable conditions included too frequent rain days, low temperature in winter, and high temperature in summer and autumn.

    The meteorological conditions in spring and summer were quite similar between 2013 and 2014. In 2014, the meteorological conditions were slightly worse in the early stage but slightly better in the later period. Specifically, from December 2013 to February 2014, the weather was cold and persistently cloudy, which was not conducive to the safe wintering of vegetation in the study area; however, the precipitation in the autumn of 2014 was 107.6 mm larger than that in 2013, which was beneficial to vegetation growth (Table 10).

    Meteorological
    factor/unit
    20132014Multi-year
    average
    Vapor pressure (spring)/hPa20.020.719.4
    The minimum temperature (summer)/°C23.824.023.6
    Air pressure (summer)/hPa965.1964.8964.8
    Rainfall (autumn)/mm239.9347.5210.6
    Average temperature (autumn)/°C20.922.121.6
    The minimum temperature (autumn)/°C17.819.118.1
    Average temperature (winter)/°C12.711.513.0
    The maximum temperature (winter)/°C16.416.717.7
    The minimum temperature (winter)/°C10.38.19.9
    Vapour pressure (winter)/hPa11.910.011.5

    Table 10.  Meteorological elements in the rocky desertification areas of Guangxi in 2013 and 2014

4.   Discussion
  • From 2000 to 2018, most of the karst rocky areas of Guangxi had improved vegetation, while areas where vegetation degraded obviously were scattered. In the future research, the reasons for the vegetation degradation should be elucidated to provide additional scientific reference for government decision-making. The obvious improvement trend of vegetation fully demonstrates that a series of measures, such as closing hillsides to facilitate afforestation and returning farmland to forests implemented by the Guangxi government, have achieved remarkable results in recent years; nevertheless, significant vegetation degradation has still occurred in some areas. Xu et al.(2018 本条文献在文后文献中未体现) studied the changes in the rocky desertification pattern in Qiannan Prefecture, Guizhou and found that potential rocky desertification could also be transformed into severe rocky desertification due to vegetation degradation; this finding indicated that the vegetation ecology in the karst rocky areas was highly vulnerable.

    The response of vegetation to climate in karst rocky areas has special characteristics. Rock desertification in these areas occurs at different degrees, and the rate of exposed bedrock is relatively large. As a result, soil layer is shallow and mostly stone gravel soil, and its vegetation community has poor ability to improve microclimate. In the absence of buffering of the upper layer of vegetation, temperature increase in the daytime is large, and heat dissipation is fast at nighttime due to the small specific heat capacity of rock, resulting in a rapid change in temperature, humidity, and surface temperature and a great change in temperature difference in the areas. Therefore, meteorological conditions had a complicated impact on vegetation, and the main meteorological factors affecting vegetation in the areas in the four seasons were quite different. In spring, the three meteorological factors (vapor pressure, relative humidity, and dew point) that characterize humidity were positively correlated with EVI, that is, the more humid the air was, the better the vegetation grew. In summer, the minimum temperature was negatively correlated with EVI, and the minimum temperature generally appeared in the early morning, that is, the high temperature at night was not conducive to vegetation growth. In autumn, precipitation was positively correlated with EVI, while the average temperature and the minimum temperature were negatively correlated with EVI. In Guangxi, the high temperature and less rainfall in autumn could easily lead to drought. Sufficient precipitation and mild temperature in autumn were conducive to vegetation growth. In winter, vapor pressure and dew point that characterize humidity were positively correlated with EVI, that is, humid air was conducive to vegetation growth; the average temperature, the maximum temperature, the minimum temperature, and sunshine duration were positively correlated with EVI, that is, warm winter was conducive to vegetation growth. If the temperature was too low, then soil in the karst rocky areas was not conducive to heat storage, and vegetation roots were likely to be frozen, which was not conducive to vegetation growth.

    Precipitation has a significant effect on vegetation only in autumn probably because of the large spatial and temporal variability of rainfall in Guangxi and the big difference in vegetation canopy density due to different proportions of shrubs, shrubs and trees growing in different regions. For areas with sparse vegetation, the water retention capacity is poor, and excessive concentration of precipitation is not conducive to vegetation growth and may even aggravate water and soil loss. However, for the karst rocky areas with high canopy density, the soil layer is thicker and has strong water retention capacity, so precipitation will promote vegetation growth.

    In this paper, the fitting model of EVI and meteorological factors was established using PLS method. In a previous research, the climate fitting model of vegetation EVI was established using stepwise regression method (Chen et al., 2015 本条文献指代信息不明确). In comparison, the fitting model of EVI established by using the PLS method has higher simulation accuracy possibly due to the different factors of the models. The response of vegetation growth to meteorological factors has lag effect (Zheng et al., 2009; Chen et al., 2014 本条文献指代信息不明确). The method of using early meteorological factors for modeling may be an effective way to improve the accuracy of model simulation. When the meteorological fitting model of the vegetation EVI was established by stepwise regression method, the mixed model based on early meteorological factors has higher simulation accuracy (Chen et al., 2015 本条文献指代信息不明确). In addition, vegetation growth is affected by many factors, such as soil type and lithology, which need to be further studied in model optimization.

5.   Conclusions
  • During 2000 to 2018, 91.45% of vegetation in the karst rocky areas of Guangxi was improved, while only 8.55% of the vegetation tended to degrade. Areas where vegetation was obviously improved were mainly distributed in the middle and northeast of the karst rocky areas. Areas with obvious vegetation degradation were scattered, and the vegetation degradation in the southwest was more obvious than that in other areas.

    The meteorological factors affecting vegetation in the Karst rocky areas were significantly different in the four seasons. Favorable conditions included high air humidity, small temperature difference in spring and autumn, and low daily minimum temperature and air pressure. Unfavorable conditions included low temperature in winter and high temperature in summer and autumn.

    The Climate Vegetation Index (CVI) model in the Karst rocky areas was established by PLS method using the maximum temperature, minimum temperature, average temperature, vapor pressure, rainfall and air pressure as key meteorological factors. EVI was well fitted by the CVI model, with R2 and RMSE average of 0.856 and 0.042, respectively. Finally, the assessment model of the comprehensive meteorological conditions in the Karst rocky areas was built based on interannual differences in CVI. The meteorological conditions in the study area in 2014 were successfully evaluated by combining the model and the selected seasonal key meteorological factors.

    Acknowledgments. The authors thank the China Center for resource satellite data and application and for providing HJ-1 CCD remote sensing data and technical support.

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