基于地理探测器的汀江流域福建段植被覆盖时空变化及驱动力分析

刘陵桦; 孟维彩; 蔡翠婷; 李毅杰; 袁宇奇; 王晓宇; 张翔; 侯晓龙

doi:10.13961/j.cnki.stbctb.2024.04.025

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基于地理探测器的汀江流域福建段植被覆盖时空变化及驱动力分析

试验研究

- 基于地理探测器的汀江流域福建段植被覆盖时空变化及驱动力分析
- Analysis of Spatio-temporal Dynamics and Driving Force of Vegetation Cover in Fujian Province Section of Tingjiang River Basin Based on Geographical Detector
- 水土保持通报 2024年44卷第4期页码：236-246
- 作者机构：
  
  1. 福建农林大学林学院,福建,福州,350002
  2. 南方红壤区水土保持国家林业和草原局重点实验室,福建,福州,350002
  3. 福建长汀红壤丘陵生态系统国家定位观测研究站, 福建长汀,366300
  4. 海峡两岸红壤区水土保持协同创新中心,福建,福州,350002
- 作者简介：
- 基金信息：
  
  国家重点研发计划项目“马尾松人工林林下覆被诱导恢复与土壤侵蚀控制技术”(2023YFF130440304);福建省科技厅自然科学基金项目(2022J01121);福建省科技厅公益类科研院所专项(2021R1015004)
- DOI：10.13961/j.cnki.stbctb.2024.04.025
  中图分类号： Q948
- 纸质出版：2024
- 稿件说明：
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刘陵桦, 孟维彩, 蔡翠婷, 等. 基于地理探测器的汀江流域福建段植被覆盖时空变化及驱动力分析[J]. 水土保持通报, 2024,44(4):236-246.

Liu Linghua, Meng Weicai, Cai Cuiting, et al. Analysis of Spatio-temporal Dynamics and Driving Force of Vegetation Cover in Fujian Province Section of Tingjiang River Basin Based on Geographical Detector[J]. Bulletin of Soiland Water Conservation, 2024, 44(4): 236-246.
刘陵桦, 孟维彩, 蔡翠婷, 等. 基于地理探测器的汀江流域福建段植被覆盖时空变化及驱动力分析[J]. 水土保持通报, 2024,44(4):236-246. DOI： 10.13961/j.cnki.stbctb.2024.04.025.

Liu Linghua, Meng Weicai, Cai Cuiting, et al. Analysis of Spatio-temporal Dynamics and Driving Force of Vegetation Cover in Fujian Province Section of Tingjiang River Basin Based on Geographical Detector[J]. Bulletin of Soiland Water Conservation, 2024, 44(4): 236-246. DOI： 10.13961/j.cnki.stbctb.2024.04.025.

摘要

[目的] 科学评估南方红壤侵蚀区汀江流域植被覆盖的时空变化特征及其驱动力，揭示影响植被覆盖空间分异驱动因子的作用强度和交互作用机制，为汀江流域的生态系统的修复和水土流失的综合治理提供科学依据。[方法] 基于2000—2020年20 a间月尺度MOD13Q1(250 m)数据集，采用一元线性回归方法和皮尔逊相关分析方法探究植被NDVI与时间之间的关系，综合温度、降水量和海拔等自然因子及人为因子，利用地理探测器模型分析流域内植被NDVI时空变化特征及其驱动力强度。[结果] ①时间上，2000—2020年汀江流域植被覆盖度呈现出波动上升的趋势，增长率为7.11%，上升平均速度为0.002 2/a，表明该区域的生态环境正稳定持续改善中。②空间上，整体上以中高和高覆盖度为主，呈现出明显各区县中部低于四周区域的空间分布格局，具有显著的地域性差异，NDVI改善区域面积为86.33%，远大于退化面积区域。③驱动因子探测结果：降水量＞海拔＞温度＞GDP＞人口密度＞土地利用类型＞植被类型＞坡度＞土壤类型。[结论] 汀江流域植被覆盖度时空变化受自然因子和人为因子双重影响，降水量因子解释力为0.705，是影响研究区植被覆盖度变化的主要驱动因子，海拔、温度和GDP等为次要驱动因子，解释力均为0.58以上。各因子之间的交互作用(q)较于单因子表现出了更高的解释力，为双因子之间主要表现为增强和非线性增强效应的复杂关系。

Abstract

[Objective] The spatial and temporal patterns of vegetation cover and its driving forces in the Tingjiang River basin of the southern red soil erosion area， were scientifically evaluated to reveal the strength of the role of each driving factor in the spatial differentiation of vegetation cover and the mechanism of interaction in order to provide a scientific basis for the restoration of ecosystems and comprehensive control of soil erosion. [Methods] Based on the monthly scale MOD13Q1 (250 m) dataset for 20 years from 2000 to 2020

we used one-way linear regression and Pearson’s correlation analysis to explore the relationship between normalised difference vegetation index (NDVI) and time and incorporated natural and anthropogenic factors such as temperature

precipitation

and elevation

as well as the spatial and temporal dynamics of vegetation NDVI in the watershed using the geoprobe model. A geoprobe model was used to analyse the temporal and spatial changes in vegetation NDVI in the watershed. [Results] ① Temporally

the vegetation cover in the Tingjiang River basin showed a fluctuating upward trend from 2000 to 2020

with a growth rate of 7.11% and an average rate of increase of 0.002 2/year

indicating that the ecological environment of the region was stable and continuously improving. ② Spatially

the overall medium-high and high coverage was dominant

showing a spatial distribution pattern that was lower than the surrounding area in the middle of each district and county

with significant geographical differences; the NDVI improved area was 86.33%

which was much larger than the degraded area region. ③ The driving factor detection results were: precipitation ＞ elevation ＞ temperature ＞ GDP＞ population density ＞ land use type ＞ vegetation type ＞ slope ＞ soil type. [Conclusion] Temporal and spatial variations in vegetation cover within the Tingjiang River Basin were affected by both natural and anthropogenic factors. The explanatory power of the precipitation factor was 0.705

which was the main driving factor affecting the changes in vegetation cover in the study area. Elevation

temperature

and GDP were the secondary driving factors

with explanatory powers of 0.58 or more. The interactions (q) between the factors showed higher explanatory power than the single factors

mainly in the form of a complex relationship between the enhancement and nonlinear enhancement effects of the two factors.

关键词

Keywords

references

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