Spatial and temporal variation and driving forces for the net primary productivity of vegetation on the Loess Plateau
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摘要:
目的 探究黄土高原地区植被净初级生产力（NPP）多年时空演变规律以及同期人类活动及自然因子对其产生的复合影响，为当地生态修复规划和实施提供参考。 方法 利用CASA模型计算并分析2001—2019年黄土高原地区植被净初级生产力及其时空分布格局，并基于地理探测器对植被NPP进行驱动因子和机制分析。 结果 （1）2001—2019年黄土高原地区植被NPP整体呈显著上升趋势，年均增加速率为5.59 g/（m2·a），显著增加的区域主要分布在黄土高原中部沟壑区以及丘陵沟壑区。基于重心模型对NPP在空间上重心的分析结果表明：黄土高原NPP重心迁移呈现出阶段性变化特征，平均NPP重心点南部的NPP增量与增速在多数年间均高于北部。不同土地利用类型中，林地的NPP均值最高。由于土地利用转移主要在耕地与草地之间相互转化，占总变化面积的75%，因此，耕地与草地NPP均值变化的线性趋势率最高。（2）地理探测器结果显示年降水量与干燥度指数是影响黄土高原地区植被NPP的主导自然因素，随后依次为土地利用类型、年均气温、坡度等。交互探测器结果表明各因子交互作用均呈现增强趋势，且对植被NPP无显著影响的因子通过与其他因子发生交互作用的方式对NPP产生显著影响。风险探测器识别的适宜植被生长的范围在不同土地利用类型中存在差异，多数地类NPP的年降水量适宜区间在500 ~ 1 000 mm之间。除未利用土地外，其他地类的NPP适宜温度区间在10 ~ 14 ℃之间。耕地NPP的适宜海拔高度区间在19.62 ~ 548.43 m之间，而其他地类在1 000 ~ 2 500 m之间。林地NPP的坡度适宜范围相对较大，不同地类最适宜的坡向不相同。 结论 黄土高原2001—2019年间植被恢复工程对生态系统NPP贡献显著，环境因子间的交互作用会增强单因子对植被NPP的影响，不同环境因子的NPP适宜累积区间在不同土地利用类型下存在差异，本研究结果为该地区实际植被恢复与管理工作提供了理论依据。 Abstract:Objective This paper aims to explore the spatial and temporal evolution of net primary productivity (NPP) of vegetation in the Loess Plateau over many years and the combined impact of human activities and natural factors in the same period on it, so as to provide reference for the local ecological restoration planning and implementation. Method CASA model was used to calculate and analyze the NPP and its spatial and temporal distribution pattern of vegetation in the Loess Plateau from 2001 to 2019, and the driving factors and mechanisms of vegetation NPP were analyzed based on Geodetector. Result (1) The vegetation NPP in the Loess Plateau showed an overall significant increasing trend from 2001 to 2019, with an average annual increase rate of 5.59 g/(m2·year). The areas with significant increase in NPP were mainly distributed in the central gully and hilly gully areas of the Loess Plateau. The analysis of the spatial center of gravity of NPP based on the center of gravity model showed that the migration of NPP center of gravity in the Loess Plateau exhibited a periodic change feature, with the average NPP increment and growth rate in the southern part of the center of gravity being higher than in the northern part for most years. The significant NPP increased was detected mainly in the central gully area and hilly gully area of the Loess Plateau. Among different land use types, forest land had the highest mean NPP. Land use transfer was mainly transformed between cultivated land and grassland, accounting for 75% of the total change area, leading to the highest rate of linear changes in NPP. (2) The results of geographic detectors showed that annual precipitation and dryness index were the dominant natural factors affecting vegetation NPP in the Loess Plateau, followed by land use type, annual average temperature and slope. The results of the interaction detector showed that the interaction of factors was mainly bi-factor enhancement or nonlinear enhancement, and the factors that had no significant impact on vegetation NPP had a significant impact on NPP when interacting with other factors. The optimal range of vegetation NPP identified by the risk detector varied among different land use types. The optimum annual precipitation range for NPP of most land use types was 500−1000 mm. Except for unused land, the suitable temperature range for NPP of other land use types was between 10 and 14 ℃. The suitable altitude ranged for NPP of cultivated land and other land use types were 19.62−548.43 m and 1000−2500 m, respectively. The suitable slope range for NPP of the woodland was relatively spanned, and the suitable slope aspects varied among different land types. Conclusion The results of this study show that vegetation restoration on the Loess Plateau from 2001 to 2019 contributes significantly to ecosystem NPP. The interactions between environmental factors enhance the influence of single factors on vegetation NPP. Moreover, the optimal accumulation ranges of NPP of different environmental factors are different for varied land use types. Our study provides a theoretical basis for the vegetation restoration and management practices in this area. -
图 1 不同因子分区空间分布及植被类型空间分布
采用自然断点法将图A ~ F各指标数据分别分为 10 类。The natural breakpoint method is used to divide the data of each index in figures A to F into 10 categories.
Figure 1. Spatial distribution of different factor zones and vegetation types
图 2 本研究与MOD17A3模型获取的多年平均NPP 比较
Figure 2. Comparison of multi-year average NPP obtained from this study and the MOD17A3 model
图 3 2001—2019年黄土高原地区NPP时空变化及分布
Figure 3. Temporal and spatial changes and distribution of NPP in the Loess Plateau region from 2001 to 2019
图 4 2001—2019年黄土高原植被NPP重心分布
Figure 4. NPP gravity center distribution of vegetation on the Loess Plateau from 2001 to 2019
图 5 不同土地利用类型净初级生产力
不同小写字母表示不同地类NPP均值存在显著差异（P < 0.05），**表示通过0.05显著性检验。Different lowercase letters indicate significant differences in the mean NPP of different land use types (P < 0.05), ** means passing the 0.05 significance test.
Figure 5. Net primary productivity for different land use types
图 6 2001—2018年影响因子q值变化时间序列
Figure 6. Time series of the changes in the q values of the impact factor from 2001 to 2018
表 1 影响因子交互作用类型
Table 1. Types of interaction between influencing factors
交互作用类型 Type of interaction 判断依据 Judgment basis 非线性减弱 Non-linear reduction q（X1∩X2） < Min[q（X1）, q（X2）] 单因子非线性减弱
Single-factor non-linear reductionMin[q（X1）, q（X2）] < q（X1∩X2） < Max[q（X1）, q（X2）] 双因子增强 Two-factor enhancement q（X1∩X2） > Max[q（X1）, q（X2）] 非线性增强 Non-linear enhancement q（X1∩X2） > q（X1） + q（X2） 独立 Independent q（X1∩X2） = q（X1） + q（X2） 注：X1、X2分别表示两个不同的自变量因子，q为自变量因子X对因变量因子Y空间分异的解释力大小。Notes: X1 and X2 represent two different independent variable factors, and q represents the explanatory power of the independent variable factor X on the spatial differentiation of the dependent variable factor Y. 
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表 2 不同植被类型NPP值与其他模型及研究模拟值比较 g/(m2·a)
Table 2. Comparison of NPP values of different vegetation types in this study and other models and studies
g/(m2·year) 数据来源
Data source针叶林
Coniferous forest阔叶林
Broadleaf forest灌丛
Shrub荒漠
Desert草地
Grassland栽培植被
Cultivated vegetation本研究 This research 504 580 356 111 269 363 MOD17A3 438 461 367 126 234 323 GLO_PEM 492 567 287 14 107 173 文献[40] Reference [40] 476 687 274 293 399 文献[41] Reference [41] 346 513 371 235 261 文献[42] Reference [42] 382 679 382 132 405 390
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表 3 2000—2018年黄土高原地区土地利用转移矩阵
Table 3. Land-use transition matrix in the Loess Plateau from 2000 to 2018
km2 项目 Item 2018年 Year of 2018 耕地
Arable land林地
Woodland草地
Grassland水域
Waters建设用地
Construction land未利用土地
Unused land累计
Sum2000年
Year of 2000耕地 Arable land 4 279.76 20 153.91 1 062.80 8 908.56 602.62 35 007.65 林地 Woodland 2 196.75 4 103.47 154.80 789.18 303.06 7 547.26 草地 Grassland 17 146.47 5 946.68 702.28 4 001.39 2 892.32 30 689.14 水域 Waters 891.06 105.34 581.62 319.51 326.34 2 223.87 建设用地 Construction land 2 119.27 82.96 487.18 62.65 35.12 2 787.18 未利用土地 Unused land 1 538.72 402.87 5 302.89 397.08 776.59 8 418.15 累计 Grand total 23 892.27 10 817.61 30 629.07 2 379.60 14 795.23 4 159.45 86 673.25
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表 4 影响因子交互作用q值
Table 4. q values of the interaction between the influencing factors
指标 Index 年降水量
Annual precipitation年均气温
Annual mean temperature海拔
Altitude坡度
Slope坡向
Aspect干燥度
Dryness index土壤水分
Soil moisture土地利用
Land use type年降水量 Annual precipitation 0.47 年均气温 Annual mean temperature 0.58# 0.26 海拔 Altitude 0.56* 0.35* 0.07 坡度 Slope 0.54# 0.41# 0.34* 0.20 坡向 Aspect 0.48* 0.27* 0.07* 0.21# 0.01 干燥度 Dryness index 0.59# 0.58# 0.57* 0.57# 0.54* 0.53 土壤水分 Soil moisture 0.51# 0.37* 0.28* 0.28# 0.10* 0.55# 0.10 土地利用 Land use type 0.60# 0.44# 0.41* 0.41# 0.28# 0.64# 0.35# 0.27 注：#为双因子增强，*为非线性增强。Notes: # is two-factor enhancement; * is non-linear enhancement.
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表 5 不同土地利用类型各驱动因子的适宜区间
Table 5. Ranges of each driving factor suitable for NPP accumulation in different land use types
指标
Index年降水量
Annual precipitation/mm年均气温
Annual mean temperature/℃海拔
Altitude/m坡度
Slope/（°）坡向
Aspect干燥度
Dryness index/
（mm·mm−1）土壤水分
Soil moisture/
（m3·m−3）耕地
Arable land730.09 ~ 907.10
(458.47)12.14 ~ 13.49
(387.61)19.62 ~ 548.43
(375.92)15.47 ~ 20.83
(367.67)南向 South
(448.87)0.68 ~ 0.85
(442.01)0.31 ~ 0.42
(400.90)林地
Woodland710.25 ~ 984.59
(475.89)10.84 ~ 12.14
(458.19)1 393.31 ~ 1 532.39
(430.43)23.43 ~ 48.42
(445.05)东北向 Northeast
(421.96)0.71 ~ 0.86
(464.59)0.07 ~ 0.12
(436.96)草地
Grassland689.64 ~ 837.96
(468.15)12.14 ~ 13.63
(420.54)1 543.73 ~ 2 361.51
(381.88)18.42 ~ 25.83
(379.10)东向 East
(267.09)0.69 ~ 0.82
(429.76)0.11 ~ 0.15
(369.44)建设用地
Construction land590.32 ~ 694.06
(364.22)13.09 ~ 14.57
(323.60)1 061.42 ~ 1 437.18
(323.60)6.51 ~ 9.14
(334.28)北向 North
(403.75)0.68 ~ 0.77
(336.22)0.08 ~ 0.10
(286.08)未利用土地
Unused land334.57 ~ 409.58
(248.68)5.99 ~ 8.41
(262.41)1 026.37 ~ 1 274.53
(262.75)2.51 ~ 6.71
(150.19)无显著朝向
No significant direction
(127.75)0.41 ~ 0.62
(299.76)0.10 ~ 0.15
(150.69)注：括号中为指标相应的NPP均值，g/(m2·a)。Note: the average NPP of the corresponding ranges is presented in the parenthesis, g/(m2·year).
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