山西省植被覆盖度的时空变化及驱动因子

doi:10.13287/j.1001-9332.202403.023

摘要/Abstract

摘要： 探究区域植被覆盖度的时空变化特征及其驱动因子,对于科学制定区域生态环境保护方案、维护区域生态平衡具有重要指导意义。本研究基于Google Earth Engine(GEE)平台,使用Landsat Collection 2数据,结合自然和社会经济数据,借助像元二分模型、趋势分析、分区统计和地理探测器等方法,探究山西省1990—2020年间植被覆盖度时空变化特征及其驱动因子。结果表明: 1990—2020年,山西省植被覆盖度呈波动上升趋势,44.4%区域的植被覆盖得到显著改善,显著退化区域占7.4%。山西省植被覆盖度与高程、坡度和山地地势起伏呈正相关。台地和丘陵地区植被覆盖度增长面积比例最高。因子探测结果表明,土地利用类型、地貌类型、年平均降水量、土壤类型是山西省植被覆盖空间分异的主要影响因素。交互探测发现,驱动因子间的交互作用均表现为增强。研究期间,自然因子间的交互结果呈下降趋势,而社会因子间的交互结果呈增强趋势,反映出人类活动对山西省植被覆盖的影响逐步增大。

关键词: 植被覆盖度, 趋势分析, 时空变化, 谷歌地球引擎, 地理探测器

Abstract: Understanding the spatiotemporal variations and driving factors of regional vegetation coverage is crucial for developing scientific plans for ecological environment protection and maintaining regional ecological balance. Based on the Google Earth Engine (GEE) platform and using Landsat Collection 2 data, we investigated the spatiotemporal variation and driving factors of vegetation coverage in Shanxi Province, China, from 1990 to 2020, by employing methods such as pixel-based binary model, trend analysis, zonal statistics, and geodetector. The results showed that vegetation coverage in Shanxi Province showed a fluctuating upward trend from 1990 to 2020. Vegetation coverage in 44.4% of this region had been significantly improved, and the area with significant degradation accounted for 7.4%. Vegetation coverage in Shanxi Province was positively correlated with elevation, slope, and mountain terrain relief. The area proportion of vegetation coverage growth was the highest in the plateau and hilly regions. Factor detection results showed that land use type, landform type, annual average precipitation, and soil type were the main influencing factors of the spatial differentiation of vegetation coverage in Shanxi Province. Results of the interaction detection showed that the interaction between driving factors all showed enhancement. The interaction between natural factors showed a downward trend, while the interaction results of social factors showed an upward trend, reflecting that the impacts of human activities on vegetation coverage in Shanxi Province were gradually increasing.

Key words: vegetation cover, trend analysis, spatiotemporal variation, Google Earth Engine (GEE), geodetector

贾一越, 齐璇璇, 黄蕊, 周义. 山西省植被覆盖度的时空变化及驱动因子[J]. 应用生态学报, 2024, 35(4): 1073-1082.

JIA Yiyue, QI Xuanxuan, HUANG Rui, ZHOU Yi. Spatiotemporal variation and driving factors of vegetation coverage in Shanxi Province, China[J]. Chinese Journal of Applied Ecology, 2024, 35(4): 1073-1082.

参考文献

[1] Piao SL, Wang XH, Park T, et al. Characteristics, drivers and feedbacks of global greening. Nature Review Earth & Environment, 2020, 1: 14-27
[2] Zhang M, Wang JM, Li SJ. Tempo-spatial changes and main anthropogenic influence factors of vegetation fractional coverage in a large-scale opencast coal mine area from 1992 to 2015. Journal of Cleaner Production, 2019, 232: 940-952
[3] 朴世龙, 岳超, 丁金枝, 等. 试论陆地生态系统碳汇在“碳中和”目标中的作用. 中国科学:地球科学, 2022, 52(7): 1419-1426
[4] Wang YM, Zhang ZX, Chen X. Quantifying influences of natural and anthropogenic factors on vegetation changes based on geodetector: A case study in the Poyang Lake Basin, China. Remote Sensing, 2021, 13: 5081
[5] Zhang ZX, Chang J, Xu CY, et al. The response of lake area and vegetation cover variations to climate change over the Qinghai-Tibetan Plateau during the past 30 years. Science of the Total Environment, 2018, 635: 443-451
[6] 龙爽, 郭正飞, 徐粒, 等. 基于 Google Earth Engine 的中国植被覆盖度时空变化特征分析. 遥感技术与应用, 2020, 35(2): 326-334
[7] 张良侠, 樊江文, 张海燕, 等. 黄土高原地区生态脆弱性时空变化及其驱动因子分析. 环境科学, 2022, 43(9): 4902-4910
[8] 窦永静, 王让虎, 付含培, 等. 山西省植被NDVI时空变化及驱动力研究. 山西大学学报:自然科学版, 2023, 46(1): 244-255
[9] 刘庚, 毕如田, 禇雅红. 基于MODIS 影像的山西省植被指数分析. 测绘与空间地理信息, 2008(1): 43-45
[10] 宋晓静, 周淑琴, 荆耀栋, 等. 吕梁山植被时空分布规律及地形差异影响. 湖北农业科学, 2021, 60(5): 53-58
[11] 陈学兄, 毕如田, 张小军, 等. 太原市城区植被覆盖变化地形分异效应. 水土保持通报, 2020, 40(5): 299-309
[12] 曹会, 刘立文, 李雨珂, 等. 基于景观尺度的黄土丘陵区植被覆盖时空变化:以山西省晋城市为例. 地质与资源, 2022, 31(4): 530-538
[13] 刘立文, 徐立帅, 段永红, 等. 晋城市植被覆盖时空变化与地形效应耦合. 测绘与空间地理信息, 2021, 44(8): 1-6
[14] 王国芳, 毕如田, 张吴平, 等. 典型矿区植被覆盖度时空分布特征及影响因素. 生态学报, 2020, 40(17): 6046-6056
[15] 卫宇婷, 杨怀卿. 基于NDVI的近30 a来山西省植被动态变化特征及与气候因子间的关系分析. 山西农业科学, 2018, 46(8): 1354-1361
[16] 覃巧婷, 陈建军, 杨艳萍, 等. 黄河源植被时空变化及其对地形和气候的响应. 中国环境科学, 2021, 41(8): 3832-3841
[17] 王一, 郝利娜, 许强, 等. 2001—2019年黄土高原植被覆盖度时空演化特征及地理因子解析. 生态学报, 2023, 43(6): 2397-2407
[18] 刘静, 温仲明, 刚成诚. 黄土高原不同植被覆被类型NDVI对气候变化的响应. 生态学报, 2020, 40(2): 678-691
[19] 刘旻霞, 赵瑞东, 邵鹏, 等. 近15 a黄土高原植被覆盖时空变化及驱动力分析. 干旱区地理, 2018, 41(1): 99-108
[20] 李婷, 吕一河, 任艳姣, 等. 黄土高原植被恢复成效及影响因素. 生态学报, 2020, 40(23): 8593-8605
[21] 邓椿, 蒋晓辉, 聂桐, 等. 山西省植被覆盖度多因子探测特征与驱动力分析. 环境科学与技术, 2022, 45(2): 182-191
[22] 李梦华, 韩颖娟, 赵慧, 等. 基于地理探测器的宁夏植被覆盖度时空变化特征及其驱动因子分析. 生态环境学报, 2022, 31(7): 1317-1325
[23] 武永利, 栾青, 赵永强, 等. 近25年山西植被指数时空变化特征分析. 生态环境, 2008, 17(6): 2330-2335
[24] 张茹, 程朋, 张珺, 等. 山西省植被覆盖变化及其影响因素研究. 西北师范大学学报:自然科学版, 2017, 53(6): 114-122
[25] Jiang HL, Xu X, Guan MX, et al. Determining the contributions of climate change and human activities to vegetation dynamics in agro-pastural transitional zone of northern China from 2000 to 2015. Science of the Total Environment, 2020, 718: 134871
[26] Shi SY, Yu JJ, Wang F, et al. Quantitative contributions of climate change and human activities to vegetation changes over multiple time scales on the Loess Plateau. Science of the Total Environment, 2021, 755: 142419
[27] 徐勇, 郑志威, 郭振东, 等. 2000—2020年长江流域植被NDVI动态变化及影响因素探测. 环境科学, 2022, 43(7): 3730-3740
[28] Zheng ZH, Wu ZF, Chen YB, et al. Exploration of eco-environment and urbanization changes in coastal zones: A case study in China over the past 20 years. Ecological Indicators, 2020, 119: 106847
[29] Huo H, Sun CP. Spatiotemporal variation and influencing factors of vegetation dynamics based on Geodetector: A case study of the northwestern Yunnan Plateau, China. Ecological Indicators, 2021, 130: 108005
[30] Mu XH, Song WJ, Gao Z, et al. Fractional vegetation cover estimation by using multi-angle vegetation index. Remote Sensing of Environment, 2018, 216: 44-56
[31] Li J, Wang JL, Zhang J, et al. Growing-season vegetation coverage patterns and driving factors in the China-Myanmar Economic Corridor based on Google Earth Engine and geographic detector. Ecological Indicators, 2022, 136: 108620
[32] 王晓蕾, 石守海. 基于GEE的黄河流域植被时空变化及其地形效应研究. 地球信息科学学报, 2022, 24(6): 1087-1098
[33] Fu BL, Yang WL, Yao H, et al. Evaluation of spatio-temporal variations of FVC and its relationship with climate change using GEE and Landsat images in Ganjiang River Basin. Geocarto International, 2022, 37: 13658-13688
[34] Masek JG, Wulder MA, Markham B, et al. Landsat 9: Empowering open science and applications through continuity. Remote Sensing of Environment, 2020, 248: 111968
[35] Bai Y, Li SG, Liu MH, et al. Assessment of vegetation change on the Mongolian Plateau over three decades using different remote sensing products. Journal of Environmental Management, 2022, 317: 115509
[36] Peng SZ, Ding YX, Liu WZ, et al. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth System Science Data, 2019, 11: 1931-1946
[37] Yang J, Huang X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019. Earth System Science Data, 2021, 13: 3907-3925
[38] 李苗苗, 吴炳方, 颜长珍, 等. 密云水库上游植被覆盖度的遥感估算. 资源科学, 2004, 26(4): 153-159
[39] 郭永强, 王乃江, 褚晓升, 等. 基于Google Earth Engine分析黄土高原植被覆盖变化及原因. 中国环境科学, 2019, 39(11): 4804-4811
[40] Wei YY, Sun SG, Liang D, et al. Spatial-temporal variations of NDVI and its response to climate in China from 2001 to 2020. International Journal of Digital Earth, 2022, 15: 1463-1484
[41] 方德泉, 胡宝清. 2000—2016年广西西江流域植被覆盖时空变化. 大众科技, 2019, 21(10): 28-31
[42] Tang ZX, Zhou ZX, Wang D, et al. Impact of vegetation restoration on ecosystem services in the Loess plateau: A case study in the Jinghe Watershed, China. Ecological Indicators, 2022, 142: 109183
[43] 王劲峰, 徐成东. 地理探测器:原理与展望. 地理学报, 2017, 72(1): 116-134
[44] 彭文甫, 张冬梅, 罗艳玫, 等. 自然因子对四川植被NDVI变化的地理探测. 地理学报, 2019, 74(9): 1758-1776
[45] Chen Y, Cao RY, Chen J, et al. A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky-Golay filter. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 180: 174-190
[46] Claverie M, Ju JC, Masek JG, et al. The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, 2018, 219: 145-161
[47] 汪瑞. 阿克苏河灌区植被及湖泊生态需水量估算与特征分析研究. 硕士论文. 武汉: 华中师范大学, 2021