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自然资源遥感  2022, Vol. 34 Issue (1): 249-256    DOI: 10.6046/zrzyyg.2021086
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基于GEE的诺木洪洪积扇植被时空变化特征、成因及趋势分析
姚金玺1(), 张志1(), 张焜2
1.中国地质大学(武汉)地球物理与空间信息学院,武汉 430074
2.青海省青藏高原北部地质过程与矿产资源重点实验室,西宁 810300
An analysis of the characteristics, causes, and trends of spatio-temporal changes in vegetation in the Nuomuhong alluvial fan based on Google Earth Engine
YAO Jinxi1(), ZHANG Zhi1(), ZHANG Kun2
1. Institute of Geophysics and Geomatics, China University of Geosciences(Wuhan), Wuhan 430074, China
2. Key Laboratory of the Northern Qinghai-Tibet Plateau Geological Processes and Mineral Resources, Xining 810300,China
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摘要 

Google Earth Engine(GEE)平台使植被遥感监测突破了数据获取难、本地存储量大和处理效率低的限制。基于GEE平台,利用空间分辨率为30 m的Landsat卫星数据和空间分辨率为250 m的MODIS卫星数据,结合温度和降水气象数据研究2000—2017年间青海省诺木洪洪积扇地表植被的时空变化趋势及持续性,并分析不同时代洪积扇上枸杞种植园和盐碱化区的植被关系及未来变化。结果表明: ①2000—2017年间最大化合成归一化差异植被指数年均值从0.029上升到0.054,增幅为0.025,最大增强植被指数(enhanced vegetation index,EVI)年均值从0.633上升到0.771,增幅为0.138,多年EVI最大化的均值结果显示峰值区间在每年的5—10月; ②对最大EVI均值与温度、降水量数据进行相关分析和偏相关分析,最大EVI均值与温度相关系数为0.839,表现为强相关性,与降水量相关系数为0.457,表现为弱相关性,且最大EVI均值与温度、降水均存在显著的正相关关系; ③在18 a内枸杞种植园植被改善较快,而盐碱化区植被有所衰减; ④未来枸杞种植区与盐碱化区植被变化均具有强持续性,枸杞种植区植被增长对盐碱化区植被有一定的制约效应,且在未来一段时间会持续存在。

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姚金玺
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张焜
关键词 诺木洪洪积扇植被变化植被指数Hurst指数Google Earth Engine    
Abstract

Google Earth Engine enables some limitations in the remote sensing monitoring of vegetation to be overcome, including difficult data acquisition, large local storage capacity, and low processing efficiency. Using GEE, the data of satellites Landsat and MODIS (spatial resolution: 30 m and 250 m, respectively), and temperature and precipitation data, this study investigated the spatio-temporal change trends and sustainability of the vegetation in the Nuomuhong alluvial fan in Qinghai Province during 2000—2017. Moreover, this study analyzed the relationships of vegetation between wolfberry plantations and salinized areas on the alluvial fans of different eras and the future changes of the relationships. The results are as follows: ① The average annual maximum synthetic normalized difference vegetation index (NDVI) increased from 0.029 to 0.054 during 2000—2017, with an increased amplitude of 0.025. The average annual enhanced vegetation index (EVI) increased from 0.633 to 0.771 during these years, with an increased amplitude of 0.138. The multiyear average maximum EVI showed that EVI peaks occurred from May to October each year. ② A correlation analysis and a partial correlation analysis were conducted between the average maximum EVI and the temperature and precipitation data. According to the analytical results, the correlation coefficient between the average maximum EVI and temperature was 0.839, indicating a strong positive correlation. Meanwhile, the correlation coefficient between the average maximum EVI and precipitation amount was 0.457, indicating a weak positive correlation. ③ Over 18 years, the vegetation in wolfberry plantations was rapidly improved, while the vegetation in the salinized area degenerated. ④ The future changes in the vegetation in wolfberry planting areas and salinized areas will have strong sustainability. The vegetation growth in wolfberry planting areas will continuously restrict the vegetation in the salinized area to a certain extent in a period in the future.

Key wordsNuomuhong alluvial fan    vegetation change    vegetation index    Hurst index    Google Earth Engine
收稿日期: 2021-03-23      出版日期: 2022-03-14
ZTFLH:  TP79  
基金资助:青海省青藏高原北部地质过程与矿产资源重点实验室开放课题“青海柴南缘林草湿资源长时间系列遥感自动提取方法——以察汗乌苏镇—诺木洪乡段为例”编号(2019-kz-01);青海省科技厅创新平台建设专项项目“青海省自然资源要素与生态状况一体化遥感监测应用平台”共同资助编号(2019-ZJ-T04)
通讯作者: 张志
作者简介: 姚金玺(1997-),男,硕士研究生,研究方向为生态环境遥感。Email: 1812283850@qq.com
引用本文:   
姚金玺, 张志, 张焜. 基于GEE的诺木洪洪积扇植被时空变化特征、成因及趋势分析[J]. 自然资源遥感, 2022, 34(1): 249-256.
YAO Jinxi, ZHANG Zhi, ZHANG Kun. An analysis of the characteristics, causes, and trends of spatio-temporal changes in vegetation in the Nuomuhong alluvial fan based on Google Earth Engine. Remote Sensing for Natural Resources, 2022, 34(1): 249-256.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2021086      或      https://www.gtzyyg.com/CN/Y2022/V34/I1/249
Fig.1  诺木洪洪积扇位置示意图
Fig.2  技术路线
Fig.3  2000年和2017年研究区植被覆盖度
Fig.4  2000—2017年研究区植被年际变化
Fig.5  2000—2017年研究区植被月际变化
Fig.6  枸杞种植区和盐碱化区植被覆盖度年际变化
Fig.7  2000—2017年间诺木洪洪积扇枸杞种植区和盐碱化区NDVI变化k
Fig.8  研究区Hurst指数分布
Fig.9  枸杞种植区和盐碱化区Hurst指数分布
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