1990—2020年黄土高原典型县域植被覆盖变化及影响因素

doi:10.13866/j.azr.2024.01.14

摘要/Abstract

摘要：

为探究吉县近30 a植被动态演变规律及其影响因素，本研究基于Landsat影像，结合气象、土地利用、夜间灯光等数据，采用趋势分析、偏相关分析、随机森林、残差分析等方法探究吉县植被覆盖度时空变化特征及气候和人为因素对植被变化的影响。结果显示：（1）1990—2020年研究区植被覆盖度（FVC）整体呈显著上升趋势，FVC年均增长速率约为0.49%，植被质量明显改善。（2）吉县FVC具有明显的“低值-高值”交错分布的空间特征。1990—2020年FVC显著增加区占51%，FVC显著减少区占7%。（3）气候因素对部分FVC高值区和建筑区植被生长起抑制作用，对其余地区植被覆盖起促进作用。将人类活动作为全域影响因素考虑时气候和人类活动对植被动态的贡献率分别为53.43%、46.57%，将其作为局域变量时相对贡献率减少至13.07%。人类活动在特定地区如吉县中部和东部是植被退化的重要影响因素，西侧和南部的植被恢复也与之相关。研究结果可为区域生态修复工作进一步开展提供科学依据。

关键词: 植被覆盖度, Google Earth Engine, 气候变化, 人类活动, 黄土高原

Abstract:

To explore the dynamic evolution of vegetation and its influencing factors in Ji County during the last 30 years, this study used Landsat images, along with meteorological, land use, and night light data. This study adopted trend, partial correlation, random forest, and residual analysis methods to explore the temporal and spatial variation-related characteristics in vegetation coverage and the influence of the climate and human factors on the vegetation changes in the County. (1) FVC in the study area demonstrated a significant upward trend from 1990 to 2020, with an annual growth rate of 0.49%, and the vegetation quality was distinctly higher. (2) The low and high rates of FVC had an obvious staggered spatial distribution. The proportion of areas with a marked enhancement in FVC was 51%, and a remarkable reduction in FVC was 7%. (3) Temperature and precipitation inhibited vegetation growth in the FVC high-value and built-up areas, but promoted vegetation cover in others. The contribution rates of climate change and human activities to vegetation dynamics were 53.43% and 46.57%, respectively, and were considered global influencing factors. When used as local variables, the relative contribution rates were reduced to 13.07%. Human activity was an essential factor affecting vegetation degradation in certain areas, such as the central and eastern parts of Jixian County, and the vegetation restoration in the west and south. This study can provide a scientific basis for the follow-up work of regional ecological restoration.

Key words: fractional vegetation cover, Google Earth Engine, climate change, human activity, Loess Plateau

赵雨琪, 魏天兴. 1990—2020年黄土高原典型县域植被覆盖变化及影响因素[J]. 干旱区研究, 2024, 41(1): 147-156.

ZHAO Yuqi, WEI Tianxing. Changes in vegetation cover and influencing factors in typical counties of the Loess Plateau from 1990 to 2020[J]. Arid Zone Research, 2024, 41(1): 147-156.

图/表 10

图1

表1

表2

气候因子与人类活动对植被覆盖度的相对贡献"

驱动因素划分			驱动因素贡献率/%		分类结果
Slope_FVCobs	Slope_FVCcc	Slope_FVCres	气候变化	人类活动	分类结果
>0	>0	>0	$S l o p e F V C c c S l o p e F V C o b s$	$S l o p e F V C r e s S l o p e F V C o b s$	气候变化和人类活动
	>0	<0	100	0	气候变化
	<0	>0	0	100	人类活动
<0	<0	<0	$S l o p e F V C c c S l o p e F V C o b s$	$S l o p e F V C r e s S l o p e F V C o b s$	气候变化和人类活动
	<0	>0	100	0	气候变化
	>0	<0	0	100	人类活动

表2

图2

图3

图4

图5

表3

图6

图7

参考文献 33

[1]	Piao S L, Wang X H, Park T, et al. Characteristics, drivers and feedbacks of global greening[J]. Nature Reviews Earth & Environment, 2020, 1(1): 14-27.
[2]	张宝庆, 吴普特, 赵西宁. 近30a黄土高原植被覆盖时空演变监测与分析[J]. 农业工程学报, 2011, 27(4): 287-293.
	[Zhang Baoqing, Wu Pute, Zhao Xining. Detecting and analysis of spatial and temporal variation of vegetation cover in the Loess Plateau during 1982-2009[J]. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(4): 287-293.]
[3]	Chen C, Park T, Wang X H, et al. China and India lead in greening of the world through land-use management[J]. Nature Sustainability, 2019, 2(2): 122-129. doi: 10.1038/s41893-019-0220-7 pmid: 30778399
[4]	邵全琴, 樊江文, 刘纪远, 等. 重大生态工程生态效益监测与评估研究[J]. 地球科学进展, 2017, 32(11): 1174-1182. doi: 10.11867/j.issn.1001-8166.2017.11.1174
	[Shao Quanqin, Fan Jiangwen, Liu Jiyuan, et al. Approaches for monitoring and assessment of ecological benefits of national key ecological projects[J]. Advances in Earth Science, 2017, 32(11): 1174-1182.] doi: 10.11867/j.issn.1001-8166.2017.11.1174
[5]	Li J J, Peng S Z, Li Z. Detecting and attributing vegetation changes on China’s Loess Plateau[J]. Agricultural and Forest Meteorology, 2017, 247: 260-270. doi: 10.1016/j.agrformet.2017.08.005
[6]	Shi Y, Jin N, Ma X L, et al. Attribution of climate and human activities to vegetation change in China using machine learning techniques[J]. Agricultural and Forest Meteorology, 2020, 294: 108146. doi: 10.1016/j.agrformet.2020.108146
[7]	Zheng K, Wei J Z, Pei J Y, et al. Impacts of climate change and human activities on grassland vegetation variation in the Chinese Loess Plateau[J]. Science of The Total Environment, 2019, 660: 236-244. doi: 10.1016/j.scitotenv.2019.01.022
[8]	谢宝妮. 黄土高原近30年植被覆盖变化及其对气候变化的响应[D]. 杨凌: 西北农林科技大学, 2016.
	[Xie Baoni. Vegetation Dynamics and Climate Change on the Loess Plateau, China: 1982-2014[D]. Yangling: Northwest A & F University, 2016.]
[9]	Yu Y, Zhao W, Martinez-Murillo J F, et al. Loess Plateau: From degradation to restoration[J]. Science of the Total Environment, 2020, 738: 140206. doi: 10.1016/j.scitotenv.2020.140206
[10]	马炳鑫, 和彩霞, 靖娟利, 等. 1982—2019年中国西南地区植被变化归因研究[J]. 地理学报, 2023, 78(3): 714-728. doi: 10.11821/dlxb202303013
	[Ma Bingxin, He Caixia, Jing Juanli, et al. Attribution of vegetation dynamics in Southwest China from 1982 to 2019[J]. Acta Geographica Sinica, 2023, 78(3): 714-728.] doi: 10.11821/dlxb202303013
[11]	尹振良, 冯起, 王凌阁, 等. 2000—2019年中国西北地区植被覆盖变化及其影响因子[J]. 中国沙漠, 2022, 42(4): 11-21. doi: 10.7522/j.issn.1000-694X.2021.00200
	[Yin Zhenliang, Feng Qi, Wang Lingge, et al. Vegetation coverage change and its influencing factors across the Northwest region of China during 2000-2019[J]. Journal of Desert Research, 2022, 42(4): 11-21.] doi: 10.7522/j.issn.1000-694X.2021.00200
[12]	燕丹妮, 武心悦, 王博恒, 等. 1982—2015年黄土高原植被变化特征及归因分析[J]. 生态学报, 2023: 1-11. doi: 10.20103/j.stxb.202210233017.
	[Yan Danni, Wu Xinwu, Wang Fuheng, et al. Characteristics and driving forces of changes in vegetation coverage on the Loess Plateau, 1982-2015[J]. Acta Ecological Sinica, 2023: 1-11. doi: 10.20103/j.stxb.202210233017.]
[13]	赵胜楠, 王宇, 乔旭宁. 1987—2021年淮河流域植被覆盖度时空变化与驱动因素分析[J]. 农业机械学报, 2023, 54(4): 180-190.
	[Zhao Shennan, Wang Yu, Qiao Xuning. Spatiotemporal variation and driving factors for FVC in Huaihe River Basin from 1987 to 2021[J]. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54(4): 180-190.]
[14]	Yi L, Yu Z, Qian J, et al. Evaluation of the heterogeneity in the intensity of human interference on urbanized coastal ecosystems: Shenzhen (China) as a case study[J]. Ecological Indicators, 2021, 122: 107243. doi: 10.1016/j.ecolind.2020.107243
[15]	李雪银, 张志强, 孙爱芝. 1982—2021年黄河流域植被覆盖时空演变及影响因素研究[J]. 地球环境学报, 2022, 13(4): 428-436.
	[Li Xueyin, Zhang Zhiqiang, Sun Aizhi. Study on the spatial-temporal evolution and influence factors of vegetation coverage in the Yellow River Basin during 1982-2021[J]. Journal of Earth Environment, 2022, 13(4): 428-436.]
[16]	李依璇. 黄土高原植被覆盖度变化特征及其影响因素[D]. 北京: 北京林业大学, 2021.
	[Li Yixuan. Change Characteristics and Influencing Factors of Vegetation Coverage in the Loess Plateau[D]. Beijing: Beijing Forestry University, 2021.]
[17]	薛智超, 甄霖, 闫慧敏. 基于土地多功能的黄土丘陵沟壑区生态保护与发展情景评估及多主体模拟[J]. 生态学报, 2023, 43(15): 6081-6098.
	[Xue Zhichao, Zhen Lin, Yan Huimin. The scenario assessment of ecological protection and development in the Loess Hilly and Gully area based on land use functions and agent-based modelling[J]. Acta Ecologica Sinica, 2023, 43(15): 6081-6098.]
[18]	杨灿, 魏天兴, 李亦然, 等. 黄土高原典型县域植被覆盖度时空变化及地形分异特征[J]. 生态学杂志, 2021, 40(6): 1830-1838.
	[Yang Can, Wei Tianxing, Li Yiran, et al. Spatiotemporal variations and topographic differentiation of fractional vegetation cover in typical counties of Loess Plateau[J]. Chinese Journal of Ecology, 2021, 40(6): 1830-1838.]
[19]	刘海龙, 唐飞, 丁娅楠, 等. 山西省县域高质量发展与生态系统服务耦合的时空演变特征[J]. 干旱区研究, 2022, 39(4): 1234-1245.
	[Liu Hailong, Tang Fei, Ding Yanan, et al. Temporal and spatial evolution characteristics of the coupling between county high-quality development and ecosystem services in Shanxi Province[J]. Arid Zone Research, 2022, 39(4): 1234-1245.]
[20]	王琳, 卫伟. 黄土高原典型县域生态系统服务变化特征及驱动因素[J]. 生态环境学报, 2023, 32(6): 1140-1148. doi: 10.16258/j.cnki.1674-5906.2023.06.016
	[Wang Lin, Wei Wei. Characteristics and driving factors of ecosystem services changes in a typical county of the Loess Plateau[J]. Ecology and Environmental Sciences, 2023, 32(6): 1140-1148.] doi: 10.16258/j.cnki.1674-5906.2023.06.016
[21]	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[J]. 资源科学, 2004, 26(4): 153-159.
	[Li Miaomiao, Wu Bingfang, Yan Changzhen, et al. Estimation of vegetation fraction in the upper Basin of Miyun Reservoir by remote sensing[J]. Resources Science, 2004, 26(4): 153-159.]
[22]	张雷, 王琳琳, 张旭东, 等. 随机森林算法基本思想及其在生态学中的应用——以云南松分布模拟为例[J]. 生态学报, 2014, 34(3): 650-659.
	[Zhang Lei, Wang Linlin, Zhang Xudong, et al. The basic principle of random forest and its applications in ecology: A case study of Pinus yunnanensis[J]. Acta Ecologica Sinica, 2014, 34(3): 650-659.]
[23]	张衡. 基于随机森林的汾河流域草地覆盖变化及其环境因子研究[D]. 山西: 太原师范学院, 2023.
	[Zhang Heng. Study on Grassland Coverage Change and its Environmental Factors in Fenhe River Basin based on Random Forest[D]. Shanxi: Taiyuan Normal University, 2023.]
[24]	宋梦来, 陈海涛, 丁晗, 等. 1990—2020年天津市植被覆盖度时空演变特征及影响因素分析[J]. 水土保持研究, 2023, 30(1): 154-163.
	[Song Menglai, Chen Haitao, Ding Han, et al. Temporal and spatial variation characteristic and influencing factors of vegetation coverage in Tianjin during 1990-2020[J]. Research of Soil and Water Conservation, 2023, 30(1): 154-163.]
[25]	Sun W, Jin Y, Yu J, et al. Integrating satellite observations and human water use data to estimate changes in key components of terrestrial water storage in a semi-arid region of North China[J]. Science of the Total Environment, 2020, 698: 134171. doi: 10.1016/j.scitotenv.2019.134171
[26]	Ge W, Deng L, Wang F, et al. Quantifying the contributions of human activities and climate change to vegetation net primary productivity dynamics in China from 2001 to 2016[J]. Science of The Total Environment, 2021, 773: 145648. doi: 10.1016/j.scitotenv.2021.145648
[27]	赵安周, 田新乐. 基于GEE平台的1986—2021年黄土高原植被覆盖度时空演变及影响因素[J]. 生态环境学报, 2022, 31(11): 2124-2133. doi: 10.16258/j.cnki.1674-5906.2022.11.003
	[Zhao Anzhou, Tian Xinle. Spatiotemporal evolution and influencing factors of vegetation coverage in the Loess Plateau from 1986 to 2021 based on GEE platform[J]. Ecology and Environmental Sciences, 2022, 31(11): 2124-2133.] doi: 10.16258/j.cnki.1674-5906.2022.11.003
[28]	Liu J, Li S, Ouyang Z, et al. Ecological and socioeconomic effects of China’s policies for ecosystem services[J]. Proceedings of the National Academy of Sciences, 2008, 105(28): 9477-9482. doi: 10.1073/pnas.0706436105
[29]	信忠保, 许炯心, 郑伟. 气候变化和人类活动对黄土高原植被覆盖变化的影响[J]. 中国科学: 地球科学, 2007, 37(11): 1504-1514.
	[Xin Zhongbao, Xu Jiongxin, Zheng Wei. Effects of climate change and human activities on vegetation cover change on the Loess Plateau[J]. Scientia Sinica (Terrae), 2007, 37(11): 1504-1514.]
[30]	Gitelson A A, Kaufman Y J, Stark R, et al. Novel algorithms for remote estimation of vegetation fraction[J]. Remote Sensing of Environment, 2002, 80(1): 76-87. doi: 10.1016/S0034-4257(01)00289-9
[31]	李祎君, 王春乙. 气候变化对我国农作物种植结构的影响[J]. 气候变化研究进展, 2010, 6(2): 123-129.
	[Li Yijun, Wang Chunyi. Impacts of climate change on crop planting structure in China[J]. Climate Change Research, 2010, 6(2): 123-129.]
[32]	赵楠, 赵颖慧, 邹海凤, 等. 1990—2020年黑龙江省植被覆盖度的时空变化趋势及驱动力[J]. 应用生态学报, 2023, 34(5): 1320-1330. doi: 10.13287/j.1001-9332.202305.021
	[Zhao Nan, Zhao Yinghui, Zou Haifeng, et al. Spatial and temporal trends and drivers of fractional vegetation cover in Heilongjiang Province, China during 1990-2020[J]. Chinese Journal of Applied Ecology, 2023, 34(5): 1320-1330.] doi: 10.13287/j.1001-9332.202305.021
[33]	Kou P, Xu Q, Jin Z, et al. Complex anthropogenic interaction on vegetation greening in the Chinese Loess Plateau[J]. Science of the Total Environment, 2021, 778: 146065. doi: 10.1016/j.scitotenv.2021.146065

影响因素	名称	年份	数据来源	数据类别	分辨率
自然因素	气温	1990—2020年	国家青藏高原科学数据中心（https://data.tpdc.ac.cn/）	逐月平均气温数据集	1 km
自然因素	降水	1990—2020年	国家青藏高原科学数据中心（https://data.tpdc.ac.cn/）	逐月降水量数据集	1 km
人为因素	夜间灯光指数	1990—2020年	国家青藏高原科学数据中心（https://data.tpdc.ac.cn/）	中国人工夜间灯光数据集	1 km
人为因素	土地利用	2000年，2018年	中国科学院资源环境科学与数据中心（https://www.resdc.cn/）	土地利用数据集	30 m

		气温	降水	土地利用	夜间灯光指数
单一要素重要性		0.76	0.13	0.04	0.07
组合因素贡献力	气温	0.62
	降水	0.71	0.38
	土地利用	0.63	0.55	0.51
	夜间灯光指数	0.66	0.44	0.5	0.07