基于Google Earth Engine的阿勒泰地区植被时空格局及驱动力分析

doi:10.5814/j.issn.1674-764x.2021.06.002

资源与生态学报 ›› 2021, Vol. 12 ›› Issue (6): 729-742.DOI: 10.5814/j.issn.1674-764x.2021.06.002

基于Google Earth Engine的阿勒泰地区植被时空格局及驱动力分析

何豫川¹(), 熊俊楠¹, 阿布都马南·阿合买提哈力²^,³^,^*(), 程维明⁴^,⁵^,⁶^,⁷, 叶冲冲¹, 贺文¹, 雍志玮¹, 田洁¹

1. 西南石油大学土木工程与测绘学院,成都 610500
2. 中国科学院大学经济与管理学院,北京 100190
3. 共青团阿勒泰地区委员会,新疆阿勒泰 836500
4. 中国科学院地理科学与资源研究所,资源与环境信息系统国家重点实验室,北京 100101
5. 中国科学院大学,北京 100049
6. 江苏省地理信息资源开发与利用协同创新中心,南京 210023
7. 中国南海研究协同创新中心,南京 210093

收稿日期:2021-04-01 接受日期:2021-05-30 出版日期:2021-11-30 发布日期:2022-01-30
通讯作者: 阿布都马南·阿合买提哈力

Spatiotemporal Pattern and Driving Force Analysis of Vegetation Variation in Altay Prefecture based on Google Earth Engine

HE Yuchuan¹(), XIONG Junnan¹, CHENG Weiming⁴^,⁵^,⁶^,⁷, YE Chongchong¹, HE Wen¹, YONG Zhiwei¹, TIAN Jie¹

1. School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China
2. School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
3. Altay Regional Committee of the Communist Youth League, Altay, Xinjiang 836500, China
4. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
5. University of Chinese Academy of Sciences, Beijing 100049, China
6. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
7. Collaborative Innovation Center of South China Sea Studies, Nanjing 210093, China

Received:2021-04-01 Accepted:2021-05-30 Online:2021-11-30 Published:2022-01-30
About author:HE Yuchuan, E-mail: hyccyh897@163.com
Supported by:
The Science and Technology Project of Xizang Autonomous Region(XZ201901-GA-07);The Key Research and Development Project of Sichuan Science and Technology Department(2021YFQ0042);The Science and Technology Bureau of Altay Region in Yili Kazak Autonomous Prefecture(Y99M4600AL)

摘要/Abstract

摘要：

植被动态的定量评价和驱动机制分析对于促进区域可持续发展至关重要。近20年来,全球变暖导致阿勒泰地区的生态环境显著改变。同时,随着人类活动的增强,该地区的植被时空格局和驱动机制尚不明确,难以准确评估。因此,我们基于谷歌地球引擎(GEE)合成归一化植被指数(NDVI),对植被生长进行量化,在像素尺度上分析了植被的时空格局。最后基于NDVI与气象数据的相关性分析结果,利用显著性阈值分割方法,量化气候变化和人类活动对植被变化的贡献。结果表明：（1）阿勒泰地区植被覆盖主要集中在北部,2000-2019年植被显著恢复和显著退化的面积分别占阿勒泰地区的24.08%和1.24%;（2）阿尔泰地区NDVI与气温、降水和日照时数显著相关的区域分别占3.3%、6.9%和20.3%;（3）在植被显著恢复的区域,由多重因素控制主导的显著恢复占18.94%,由人类活动主导的恢复占3.4%,而由气候变化主导的显著恢复占1.74%,在植被显著退化区域,异常退化和气候变化主导的显著退化分别占1.07%和0.17%。本研究揭示了阿勒泰地区植被动态变化及其驱动机制,可为阿勒泰山水林田湖草生命共同体机制机理与关键修复技术研究提供科学依据。

关键词: 植被变化, 气候变化, 人类活动, 驱动机制, 谷歌地球引擎, 阿勒泰地区

Abstract:

Quantitative evaluation and driving mechanism analysis of vegetation dynamics are essential for promoting regional sustainable development. In the past 20 years, the ecological environment in Altay Prefecture has changed significantly due to global warming. Meanwhile, with increasing human activities, the spatiotemporal pattern and driving forces of vegetation variation in the area are uncertain and difficult to accurately assess. Hence, we quantified the vegetation growth by using the Normalized Difference Vegetation Index (NDVI) on the Google Earth Engine (GEE). Then, the spatiotemporal patterns of vegetation from 2000 to 2019 were analyzed at the pixel scale. Finally, significance threshold segmentation was performed using meteorological data based on the correlation analysis results, and the contributions of climate change and human activities to vegetation variation were quantified. The results demonstrated that the vegetation coverage in Altay Prefecture is mainly concentrated in the north. The vegetation areas representing significant restoration and degradation from 2000 to 2019 accounted for 24.08% and 1.24% of Altay Prefecture, respectively. Moreover, spatial correlation analysis showed that the areas with significant correlations between NDVI and temperature, precipitation and sunlight hours accounted for 3.3%, 6.9% and 20.3% of Altay Prefecture, respectively. In the significant restoration area, 18.94% was dominated by multiple factors, while 3.4% was dominated by human activities, and 1.74% was dominated by climate change. Within the significant degradation area, abnormal degradation and climate change controlled 1.07% and 0.17%, respectively. This study revealed the dynamic changes of vegetation and their driving mechanisms in Altay Prefecture, and can provide scientific support for further research on life community mechanism theory and key remediation technology of mountain-water-forest-farmland-lake-grass in Altay Prefecture.

Key words: vegetation variation, climate change, human activities, driving mechanism, Google Earth Engine, Altay Prefecture

何豫川, 熊俊楠, 阿布都马南·阿合买提哈力, 程维明, 叶冲冲, 贺文, 雍志玮, 田洁. 基于Google Earth Engine的阿勒泰地区植被时空格局及驱动力分析[J]. 资源与生态学报, 2021, 12(6): 729-742.

HE Yuchuan, XIONG Junnan, CHENG Weiming, YE Chongchong, HE Wen, YONG Zhiwei, TIAN Jie. Spatiotemporal Pattern and Driving Force Analysis of Vegetation Variation in Altay Prefecture based on Google Earth Engine[J]. Journal of Resources and Ecology, 2021, 12(6): 729-742.

图/表 12

Fig. 1 Overview of the study area

Fig. 2 Flowchart of significance threshold segmentation

Fig. 3 Spatial distribution of NDVI in Altay Prefecture Note：The bottom left in Fig. 3b is the F test result (P<0.05); The statistical graphs in the upper right corner of Fig. 3 are the percentage of each data level in the legend on the right in the entire area.

Table 1 Vegetation cover classification of Altay Prefecture

NDVI	Vegetation cover classification	Area (km²)	Proportion (%)
NDVI<0.2	Low coverage	64716	56.1
0.2≤NDVI<0.4	Medium and low coverage	14253	12.4
0.4≤NDVI<0.6	Medium coverage	8363	7.2
0.6≤NDVI<0.8	Medium and high coverage	7833	6.8
0.8≤NDVI	High coverage	20146	17.5

Table 2 NDVI trend grading table

Slope	NDVI trend grading	Area (km²)	Proportion (%)
< -0.0100	Serious degradation	183	0.2
-0.0100 - -0.0050	Moderate degradation	451	0.4
-0.0050 - -0.0010	Slight degradation	5084	4.3
-0.0010 - 0.0010	Essentially unchanged	52531	44.8
0.0010 - 0.0050	Slight restoration	51090	43.6
0.0050 - 0.0100	Moderate restoration	4279	3.6
> 0.0100	Obvious restoration	3586	3.1

Fig. 4 The NDVI trends in different counties of Altay Prefecture from 2000 to 2019

Fig. 5 The variation trends of meteorological factors. A, B and C respectively represent air temperature, precipitation and sunshine hours; 1, 2, 3 and 4 respectively represent the multi-year mean, temporal trend, broken line chart of average trend and histogram at the pixel scale of the change rates.

Fig. 6 Spatial distribution diagrams of correlation coefficients between NDVI and meteorological factors of temperature (a), precipitation (b), and sunlight hours (c). Note: The inset maps in the bottom left corner of each show the t test results for each factor (P<0.05) and the statistical graphs in the upper right corner of Fig. 4 are the percentage of each data level in the legend on the right in the entire area.

Fig. 7 Spatial distribution diagrams of significantly correlated r values between NDVI and temperature (T, red), precipitation (P, green) and sunlight hours (S, blue).

Fig. 8 Spatial distribution and proportions of the driving forces

Fig. 9 Temporal trend of livestock production

Fig. 10 The evaluation chart of significant changes led by human factors. A and C indicate areas where cultivated land has increased, B indicates artificial reservoir areas, 1, 2 and 3 respectively represent NDVI distribution in 2000, NDVI distribution in 2019, and the numbers of pixels with different NDVI values.

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基于Google Earth Engine的阿勒泰地区植被时空格局及驱动力分析

Spatiotemporal Pattern and Driving Force Analysis of Vegetation Variation in Altay Prefecture based on Google Earth Engine

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