Analysis of Factors Driving Subtropical Forest Phenology Differentiation, Considering Temperature and Precipitation Time-Lag Effects: A Case Study of Fujian Province

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Analysis of Factors Driving Subtropical Forest Phenology Differentiation, Considering Temperature and Precipitation Time-Lag Effects: A Case Study of Fujian Province


Figure 1.
The geographical location of the study area: (a) the geographical location of the study area in China; (b) the distribution of forest type, site area, demarcation line, and main rivers in Fujian Province. I, II, III, and IV, respectively, represent the mountainous region of Wuyi Mountain; the coastal low mountain and hill region of Zhejiang and Fujian; the mountainous and hill region in Jiangxi, Fujian, and Guangdong; and the coastal hill and plain region of Fujian and Guangdong. The demarcation line is that between the middle and southern subtropical zones.

Figure 1.
The geographical location of the study area: (a) the geographical location of the study area in China; (b) the distribution of forest type, site area, demarcation line, and main rivers in Fujian Province. I, II, III, and IV, respectively, represent the mountainous region of Wuyi Mountain; the coastal low mountain and hill region of Zhejiang and Fujian; the mountainous and hill region in Jiangxi, Fujian, and Guangdong; and the coastal hill and plain region of Fujian and Guangdong. The demarcation line is that between the middle and southern subtropical zones.

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Figure 2.
(a) The proportions of individual regions in the area as to distinguished forest types. (b) The elevation distribution of different vegetation types in Fujian Province. In the figure, ENF, EBF, DBF1, DBF2, and SHL, respectively, represent evergreen needleleaf forests, evergreen broadleaf forests, deciduous broadleaf forests, closed (>40%) deciduous broadleaf forests, and shrubland.

Figure 2.
(a) The proportions of individual regions in the area as to distinguished forest types. (b) The elevation distribution of different vegetation types in Fujian Province. In the figure, ENF, EBF, DBF1, DBF2, and SHL, respectively, represent evergreen needleleaf forests, evergreen broadleaf forests, deciduous broadleaf forests, closed (>40%) deciduous broadleaf forests, and shrubland.

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Figure 3.
The division of the (a) elevation data, (b) slope data, and (c) aspect data in Fujian Province.

Figure 3.
The division of the (a) elevation data, (b) slope data, and (c) aspect data in Fujian Province.

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Figure 4.
The research framework of this study. SRTM is the elevation data that can produce slope, as well as the aspect data. ESA CCI represents the land-cover data from the European Space Agency’s Climate Change Initiative product. SOS, EOS, LOS, and POP represent the start of the growth season, the end of the growth season, the length of the growth season, and the date-position of peak value, respectively. Sen + MK represents the methods of Theil–Sen median analysis and the Mann–Kendall test.

Figure 4.
The research framework of this study. SRTM is the elevation data that can produce slope, as well as the aspect data. ESA CCI represents the land-cover data from the European Space Agency’s Climate Change Initiative product. SOS, EOS, LOS, and POP represent the start of the growth season, the end of the growth season, the length of the growth season, and the date-position of peak value, respectively. Sen + MK represents the methods of Theil–Sen median analysis and the Mann–Kendall test.

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Figure 5.
The spatial distributions of the (a) SOS, (b) EOS, (c) LOS, and (d) POP in the Fujian province. The histograms present the proportion of pixels in the phenological interval. DOY indicates the day of the year.

Figure 5.
The spatial distributions of the (a) SOS, (b) EOS, (c) LOS, and (d) POP in the Fujian province. The histograms present the proportion of pixels in the phenological interval. DOY indicates the day of the year.

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Figure 6.
The fluctuations of the (a) SOS, (b) EOS, (c) LOS, and (d) POP among different forest types and site areas.

Figure 6.
The fluctuations of the (a) SOS, (b) EOS, (c) LOS, and (d) POP among different forest types and site areas.

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Figure 7.
The phenological time trends of (a) evergreen needleleaf forests, (b) evergreen broadleaf forests, (c) deciduous broadleaf forests, (d) deciduous broadleaf forests mixed with other vegetation, and (e) shrubland, in the four site areas, as well as that of (f) all forest pixels.

Figure 7.
The phenological time trends of (a) evergreen needleleaf forests, (b) evergreen broadleaf forests, (c) deciduous broadleaf forests, (d) deciduous broadleaf forests mixed with other vegetation, and (e) shrubland, in the four site areas, as well as that of (f) all forest pixels.

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Figure 8.
The trend figures of the (a) SOS, (b) EOS, (c) LOS, and (d) POP. In this figure, ESA (ESD), SA (SD), SSA (SSD), NA (ND), and NF, respectively, represent the trends of extremely significantly advanced (or delayed), significantly advanced (or delayed), slightly significantly advanced (or delayed), not significantly advanced (or delayed), and no fluctuation.

Figure 8.
The trend figures of the (a) SOS, (b) EOS, (c) LOS, and (d) POP. In this figure, ESA (ESD), SA (SD), SSA (SSD), NA (ND), and NF, respectively, represent the trends of extremely significantly advanced (or delayed), significantly advanced (or delayed), slightly significantly advanced (or delayed), not significantly advanced (or delayed), and no fluctuation.

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Figure 9.
The factor detection q-values of the SOS and EOS in regions (a) I, (b) II, (c) III, and (d) IV. In the figure, “*” means that the factor has passed the significance test. The lagged impacts of precipitation and temperature are, respectively, presented in the formats “#month_pre” and “#month_tmp,” where “#” represents the number of preseason months. Moreover, the cumulative impacts are, respectively, presented in the formats “acc#_pre” and “acc#_tmp,” where “#” denotes the cumulative length. Finally, DEM is the elevation, Slope and Aspect are terrain factors, and FC_type is the forest type.

Figure 9.
The factor detection q-values of the SOS and EOS in regions (a) I, (b) II, (c) III, and (d) IV. In the figure, “*” means that the factor has passed the significance test. The lagged impacts of precipitation and temperature are, respectively, presented in the formats “#month_pre” and “#month_tmp,” where “#” represents the number of preseason months. Moreover, the cumulative impacts are, respectively, presented in the formats “acc#_pre” and “acc#_tmp,” where “#” denotes the cumulative length. Finally, DEM is the elevation, Slope and Aspect are terrain factors, and FC_type is the forest type.

Forests 15 00334 g009aForests 15 00334 g009b

Figure 10.
The mean SOS and EOS for (a) forest type, (b) elevation, (c) slope, and (d) aspect factors.

Figure 10.
The mean SOS and EOS for (a) forest type, (b) elevation, (c) slope, and (d) aspect factors.

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Figure 11.
The ecological detection results of the (a) SOS and (b) EOS in site areas. Variables X0 to X15 represent the driving factors, Y indicates that the factor had a significant difference in the 95% confidence interval for the SOS or EOS, and N represents “no significant difference.” The factors with no significant difference were labeled according to different regions, and all the other factors except the labeled ones had significant differences.

Figure 11.
The ecological detection results of the (a) SOS and (b) EOS in site areas. Variables X0 to X15 represent the driving factors, Y indicates that the factor had a significant difference in the 95% confidence interval for the SOS or EOS, and N represents “no significant difference.” The factors with no significant difference were labeled according to different regions, and all the other factors except the labeled ones had significant differences.

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Figure 12.
The results of the relationships of lagged and accumulated (a) temperature and (b) precipitation with phenology. TMP indicates temperature and PRE represents precipitation.

Figure 12.
The results of the relationships of lagged and accumulated (a) temperature and (b) precipitation with phenology. TMP indicates temperature and PRE represents precipitation.

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Table 1.
The significance classification of the phenological trends.

Table 1.
The significance classification of the phenological trends.

β Z Trend Significance Classification
β > 0 2.58 < Z Extremely significantly delayed
1.96 < Z 2.58 Significantly delayed
1.65 < Z 1.96 Slightly significantly delayed
Z 1.65 Not significantly delayed
β = 0 Z No fluctuations
β < 0 Z 1.65 Not significantly advanced
1.65 < Z 1.96 Slightly significantly advanced
1.96 < Z 2.58 Significantly advanced
2.58 < Z Extremely significantly advanced

Table 2.
The descriptions of the GeoDetector factors.

Table 2.
The descriptions of the GeoDetector factors.

Factor Description Value Range Unit
X0 Forest type ENF; EBF; DBF1; DBF2; SHL \
X1 Elevation data [0, 2085] m
X2 Slope data [0, 50.4] °
X3 Aspect data [−1.0, 360.0) °
X4 Monthly average precipitation (175.9, 2711.7] 0.1 mm
X5 Monthly average air temperature (3.2, 22.6] °C
X6 Monthly average precipitation in the previous month (174.1, 1972.5] 0.1 mm
X7 Monthly average temperature in the previous month (1.6, 24.4] °C
X8 Monthly average precipitation two months ago (248.8, 2488.7] 0.1 mm
X9 Monthly average temperature two months ago (0.4, 27.0] °C
X10 Monthly average precipitation three months ago (182.8, 3241.7] 0.1 mm
X11 Monthly average temperature three months ago (−0.1, 28.4] °C
X12 Accumulated average precipitation in the past two months (266.7, 1789.4] 0.1 mm
X13 Accumulated average temperature in the past two months (1.5, 25.7] °C
X14 Accumulated average precipitation in the past three months (308.8, 2083.5] 0.1 mm
X15 Accumulated average temperature in the past three months (1.2, 26.4] °C

Table 3.
The types of interaction between two independent variables.

Table 3.
The types of interaction between two independent variables.

Interaction Types Judgement Criteria
Nonlinear weakened q X 1 X 2 < M i n ( q X 1 , q X 2 )
Univariate weakened M i n q X 1 , q X 2 < q X 1 X 2 < M a x q X 1 , q X 2
Bivariate enhanced q X 1 X 2 > M a x ( q X 1 , q X 2 )
Independent q X 1 X 2 = q X 1 + q X 2
Nonlinear enhanced q X 1 X 2 > q X 1 + q X 2

Table 4.
A pixel-proportions table describing site areas as to the same trend and the advanced or delayed trend proportions in different forest types.

Table 4.
A pixel-proportions table describing site areas as to the same trend and the advanced or delayed trend proportions in different forest types.

Period Site Area ESA SA SSA NA NF ND SSD SD ESD Trend ENF EBF DBF1 DBF2 SHL
SOS I 28% 31% 36% 51% 59% 58% 54% 47% 32% Advance 66% 60% 72% 65% 51%
II 40% 42% 36% 19% 13% 8% 4% 3% 2%
III 9% 12% 16% 21% 19% 23% 16% 14% 11% Delay 34% 40% 28% 35% 49%
IV 23% 15% 12% 9% 9% 13% 26% 36% 55%
EOS I 54% 54% 55% 54% 55% 50% 45% 44% 45% Advance 80% 78% 66% 76% 61%
II 26% 23% 20% 16% 12% 10% 8% 7% 5%
III 13% 15% 16% 20% 23% 25% 28% 29% 23% Delay 20% 22% 34% 24% 39%
IV 7% 8% 9% 11% 10% 15% 19% 20% 29%
LOS I 65% 61% 59% 54% 53% 48% 47% 46% 51% Advance 68% 69% 53% 66% 64%
II 16% 16% 16% 15% 17% 16% 18% 19% 18%
III 11% 14% 15% 20% 21% 24% 23% 22% 20% Delay 32% 31% 47% 34% 36%
IV 8% 9% 10% 11% 10% 12% 12% 13% 10%
POP I 54% 56% 57% 55% 54% 48% 41% 37% 40% Advance 77% 72% 74% 78% 67%
II 28% 23% 20% 16% 13% 11% 9% 8% 8%
III 11% 13% 14% 19% 23% 27% 33% 35% 34% Delay 23% 28% 26% 22% 22%
IV 7% 8% 9% 10% 11% 14% 16% 19% 17%

Table 5.
Ranking of q-values for interaction exploration.

Table 5.
Ranking of q-values for interaction exploration.

Period Site Area Factor Interaction Ranking
SOS I X11X5 (0.782) ↑ > X15X5 (0.730) ↑ > X9X5 (0.718) ↑ > X6X5 (0.711) ↑↑ > X12X5 (0.700) ↑↑
II X11X5 (0.854) ↑↑ > X12X5 (0.849) ↑↑ > X6X5 (0.831) ↑↑ > X12X7 (0.828) ↑ > X14X5 (0.819) ↑↑
III X11X5 (0.766) ↑ > X11X4 (0.739) ↑↑ > X6X5 (0.736) ↑↑ > X5X4 (0.729) ↑↑ > X15X5 (0.728) ↑
IV X5X4 (0.856) ↑↑ > X7X4 (0.827) ↑↑ = X12X5 (0.827) ↑↑ > X6X5 (0.816) ↑↑ > X13X4 (0.811) ↑↑
EOS I X14X13 (0.839) ↑ > X14X9 (0.836) ↑ > X15X14 (0.834) ↑ > X14X7 (0.827) ↑ > X8X7 (0.804) ↑↑
II X14X7 (0.754) ↑ > X14X13 (0.747) ↑ > X14X9 (0.735) ↑ > X14X5 (0.731) ↑ > X8X7 (0.728) ↑↑
III X14X9 (0.774) ↑ > X15X14 (0.770) ↑ > X14X13 (0.767) ↑↑ > X14X7 (0.763) ↑↑ > X9X8 (0.759) ↑↑
IV X14X13 (0.853) ↑↑ > X14X7 (0.852) ↑↑ > X14X9 (0.850) ↑↑ > X15X14 (0.845) ↑↑ > X14X11 (0.833) ↑

Table 6.
The earliest or latest intervals and the mean values of the intervals of the SOS and EOS corresponding to the temperature and precipitation factors.

Table 6.
The earliest or latest intervals and the mean values of the intervals of the SOS and EOS corresponding to the temperature and precipitation factors.

Factor Range of Max SOS Mean Max SOS Mean (d) Range of Min SOS Mean Min SOS Mean (d) Range of Max EOS Mean Max EOS mean (d) Range of Min EOS Mean Min EOS Mean (d)
X4 (2030, 2710] 95 [456, 896] 67 [176, 288] 343 (991, 1460] 321
X5 (17.9, 21.1] 94 [3.2, 8.0] 60 [5.5, 8.9] 344 (17.2, 22.7] 323
X6 (1460, 1970] 98 (251, 580] 72 [174, 399] 340 (961, 1430] 329
X7 (14.2, 18.3] 92 [1.7, 5.6] 64 [9.1, 12.9] 349 (21.4, 24.5] 323
X8 (910, 1480] 89 [266, 557] 80 [249, 635] 344 (1210, 2490] 322
X9 [0.4, 5.2] 89 (13.8, 18.7] 71 [13.8, 17.1] 343 (24.8, 27.0] 324
X10 (681, 749] 86 (965, 1340] 71 [285, 824] 356 (2180, 3240] 324
X11 [0.1, 6.0] 102 (20.1, 23.6] 75 (24.6, 25.3] 333 (27.7, 28.5] 325
X12 (1260, 1680] 99 [310, 532] 73 [267, 624] 342 (954, 1790] 322
X13 (13.4, 18.5] 90 [1.6, 5.6] 72 [11.5, 15.3] 345 (22.9, 25.7] 324
X14 (939, 1440] 92 [309, 536] 77 [343, 803] 345 (1290, 2080] 324
X15 (7.1, 8.2] 87 (12.1, 13.3] 80 [13.3, 17.0] 340 (24.2, 26.5] 325

Table 7.
The operational measures in various regions.

Table 7.
The operational measures in various regions.

Phenological Parameter Enhanced Productivity
(Prolonged LOS)
Site Area Reference
Value
(DOY)
Management Measures
SOS Advanced
(SOS will be advanced by lower temperatures in early spring, higher temperatures in late winter, and less precipitation)
I, III (inland) 73–95 (I)
75–91 (III)
Pay attention to frost disasters, and plant mixed forests near rivers; Selective cutting and interplanting of artificial pure forest.
II, IV (coastal) 81–100 (II)
79–92 (IV)
Thinning and pruning in late winter and early spring; Choosing evergreen trees.
EOS Delayed
(EOS will be delayed by lower temperatures and less precipitation in summer and autumn)
I, III (inland) 317–344 (I)
319–337 (III)
In the process of tree growth, the forest tending should be strengthened, and the areas with a low density of broadleaf forest should be replanted.
II, IV (coastal) 324–342 (II)
324–339 (IV)
Reasonable thinning and pruning in summer and autumn; Prevention of extreme weather and planting more local dominant tree species; The planting scale of pure forest and pure shrub areas should be limited, and the mixed planting of arbors and irrigation should be carried out.

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