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).
Grade Change of vegetation Range of SLOPE Proportion (%) 1 Noticeably worse [−1，−0.2) 0.04 2 Noticeably worse [−0.2，−0.15) 0.08 3 Noticeably worse [−0.15,−0.1) 0.22 4 Worse [−0,1，−0.05) 0.84 5 Slightly worse [−0.05,0) 7.37 6 Slightly better [0,0.05) 58.37 7 Better [0.05,0.1) 30.35 8 Noticeably better [0.1,0.15) 2.55 9 Noticeably better [0.15,0.2) 0.18 10 Noticeably 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.
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.
Time Vapor pressure Rainfall Relative humidity Average temperature The maximum temperature The minimum temperature Sunshine duration Air pressure Dew 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).
Category Vapor pressure Rainfall Relative humidity Average temperature The maximum temperature The minimum temperature Sunshine duration Air
Spring <50 16.5 170.7 67.4 21.3 28.4 16.4 443.8 928.6 14.0 50-60 17.8 218.6 74.9 20.2 25.8 16.3 352.6 939.7 15.0 60-70 19.0 297.1 75.5 21.1 26.1 17.6 293.5 976.3 16.1 70-80 19.9 273.4 76.5 21.6 26.2 18.4 292.9 972.7 16.8 >80 20.2 297.0 77.2 21.6 25.8 18.5 259.5 980.9 17.0 Summer <50 25.2 625.8 79.3 25.4 30.9 22.0 500.0 922.7 21.2 50-60 26.3 770.7 83.6 25.1 30.1 21.8 477.8 935.3 21.8 60-70 28.1 861.1 81.1 26.8 31.9 23.7 483.6 970.2 22.9 70-80 28.1 797.5 80.7 26.8 31.6 23.7 516.7 966.2 22.9 >80 28.9 838.7 81.1 27.2 31.8 24.2 494.9 974.6 23.4 Autumn <50 18.6 173.9 78.4 20.3 26.1 16.7 410.0 933.1 15.9 50-60 18.8 199.4 80.5 19.8 25.3 16.2 431.0 944.0 15.9 60-70 19.6 204.1 76.6 21.3 27.1 17.9 439.7 980.1 16.5 70-80 20.2 211.5 75.9 21.9 27.4 18.3 482.3 975.3 16.9 >80 20.3 222.2 73.8 22.5 27.6 19.0 490.9 983.7 17.0 Winter <50 10.6 59.3 75.6 12.4 18.5 8.5 307.2 934.9 7.4 50-60 10.7 70.4 77.2 11.7 16.8 8.3 275.4 946.2 7.3 60-70 11.1 105.4 75.2 12.4 17.2 9.4 243.5 984.0 7.7 70-80 12.0 99.0 74.7 13.7 18.2 10.6 274.7 980.0 8.7 >80 11.8 114.6 74.1 13.5 17.9 10.6 256.2 988.8 8.4
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).
Time Vapor pressure Rainfall Relative humidity Average temperature The maximum temperature The minimum temperature Sunshine duration Air pressure Dew point Spring H H H H / H L H H Summer H H / H / H / H H Autumn H H L H H H H H H Winter H H / L / / H L H / / 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.
Time Vapor pressure Rainfall Average temperature The maximum temperature The minimum temperature Air 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 factors X variance Cumulative X variance Y variance Cumulative Y variance 1 0.830 0.830 0.807 0.807 2 0.084 0.915 0.025 0.832 3 0.057 0.972 0.018 0.850 4 0.023 0.995 0.009 0.859 5 0.003 0.998 0.012 0.871 6 0.001 0.999 0.007 0.878
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).
Index V T Tmax Tmin P R VIP 1.087 1.066 1.065 1.053 0.988 0.842
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).
Model Sum of Squares df R2 Adjusted R2 Std.Error of the estimate F Sig. 1 2.335 6 0.856 0.852 0.042 220.325 0.000 0.394 431 2.728 439
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).
Grade Good General Bad 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).
2013 2014 Multi-year
Vapor pressure (spring)/hPa 20.0 20.7 19.4 The minimum temperature (summer)/°C 23.8 24.0 23.6 Air pressure (summer)/hPa 965.1 964.8 964.8 Rainfall (autumn)/mm 239.9 347.5 210.6 Average temperature (autumn)/°C 20.9 22.1 21.6 The minimum temperature (autumn)/°C 17.8 19.1 18.1 Average temperature (winter)/°C 12.7 11.5 13.0 The maximum temperature (winter)/°C 16.4 16.7 17.7 The minimum temperature (winter)/°C 10.3 8.1 9.9 Vapour pressure (winter)/hPa 11.9 10.0 11.5
Table 10. Meteorological elements in the rocky desertification areas of Guangxi in 2013 and 2014