大湄公河次区域植被覆盖时空变化特征及其与气象因子的关系

doi:10.12302/j.issn.1000-2006.202010020

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南京林业大学学报（自然科学版） ›› 2022, Vol. 46 ›› Issue (2): 187-195.doi: 10.12302/j.issn.1000-2006.202010020

大湄公河次区域植被覆盖时空变化特征及其与气象因子的关系

邱凤婷¹^,²^,³(), 过志峰¹, 张宗科¹^,^*(), 魏显虎¹, 李俊杰³, 吕争³

1.中国科学院空天信息创新研究院,中国科学院中国-斯里兰卡水技术研究与示范联合中心,北京100094
2.中国科学院大学电子电气与通信工程学院,北京 100049
3.中国四维测绘技术有限公司,北京 100094

收稿日期:2020-10-14 接受日期:2021-01-24 出版日期:2022-03-30 发布日期:2022-04-08
通讯作者: 张宗科

Spatio-temporal change characteristics of vegetation coverage and its relationship with meteorological factors in the Greater Mekong Subregion

QIU Fengting¹^,²^,³(), GUO Zhifeng¹, ZHANG Zongke¹^,^*(), WEI Xianhu¹, LI Junjie³, LYU Zheng³

1. Aerospace Information Research Institute, Chinese Academy of Sciences, China-Sri Lanka Joint Center for Water Technology Research and Demonstration by the Chinese Academy of Sciences (CAS), Beijing 100094, China
2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
3. China Siwei Surveying and Mapping Technology Co. Ltd., Beijing 100094,China

Received:2020-10-14 Accepted:2021-01-24 Online:2022-03-30 Published:2022-04-08
Contact: ZHANG Zongke

摘要/Abstract

摘要：

【目的】分析大湄公河次区域植被覆盖的时空分布变化规律及其与气象因子之间的关系,为全球变暖环境下大湄公河次区域植被保护及生态环境修复提供理论依据。【方法】以大湄公河次区域为研究区,使用MOD13Q1-NDVI数据,借助Google Earth Engine(GEE)平台反演区域2005—2019年植被覆盖,采用线性回归分析、马尔科夫模型等分析区域植被覆盖的时空分布规律,并利用偏相关分析法探究植被覆盖与气象因子之间的关系。【结果】大湄公河次区域高植被覆盖的面积占总面积的61.9%,空间上呈现北低南高、东高西低的特点;2005—2016年,区域植被以改善为主,主要是中高植被向高植被类型转化;2016—2019年,区域植被发生明显退化,以高覆盖植被类型退化为主;15年来,呈改善趋势的面积占总面积12.7%,呈退化趋势的面积占总面积3.0%,基于Hurst指数分析发现,区域植被未来显著改善面积大于显著退化,南部地区未来会发生退化;年际变化趋势上,归一化植被指数(NDVI)与气温呈显著正相关,相关系数为0.61,与降水相关性较弱;空间上,区域植被NDVI变化受到气温和降水影响,北部与降水显著负相关,南部与气温显著负相关。【结论】大湄公河次区域植被覆盖整体较好,改善趋势大于退化趋势。综合来看,大湄公河次区域植被变化与气温和降水有一定关系,尤其是北部和南部。

关键词: 大湄公河次区域(GMS), 植被覆盖, 时空变化, 气温, 降水

Abstract:

【Objective】The aim of this reseach is to analyze the temporal and spatial distribution of vegetation coverage in the Greater Mekong Subregion (GMS), and the relationship between vegetation coverage and meteorological factors, so as to provide a theoretical basis for vegetation protection and ecological environment restoration in GMS under the background of global warming environment.【Method】Taking the GMS as the study area, based on MOD13Q1-NDVI data and Google Earth Engine (GEE) platform, the linear regression analysis and Markov model were employed to analyze the spatio-temporal change of its vegetation coverage from 2005 to 2019, and the partial correlation analysis was used to analyze the relationship between vegetation coverage and meteorological factors. 【Result】The 61.9% of the GMS was a high vegetation type, showing the characteristics of low in the north and high in the south, high in the east and low in the west. From 2005 to 2016, the vegetation was improved and mainly the medium-high vegetation transformed to the high vegetation. From 2016 to 2019, the vegetation degraded significantly, and mainly the high vegetation transformed to the low vegetation. In the past 15 years, 12.7% of the area in GMS showed an improvement trend, and 3.0% showed a degradation trend. Based on the overlay analysis of the trend change and Hurst index, the area of significant improvement on vegetation will be greater than a significant degradation in the future, and the vegetation coverage in the south of GMS will be degraded in the future. In terms of an interannual variation trend, normalized vegetation index (NDVI) was significantly positively correlated with temperature, with a correlation coefficient of 0.61, and weakly correlated with precipitation. Spatially, the change of NDVI was affected obviously by temperature and precipitation, and the north was significantly negatively correlated with precipitation, and the south was significantly negatively correlated with temperature.【Conclusion】The vegetation coverage in GMS is good in the whole, and the improvement trend is greater than the degradation. Generally speaking, the vegetation change in GMS is related to temperature and precipitation, especially in the north and south.

Key words: Greater Mekong Subregion(GMS), vegetation coverage, spatial and temporal change, temperature, precipitation

中图分类号:

TP79

邱凤婷,过志峰,张宗科,等. 大湄公河次区域植被覆盖时空变化特征及其与气象因子的关系[J]. 南京林业大学学报（自然科学版）, 2022, 46(2): 187-195.

QIU Fengting, GUO Zhifeng, ZHANG Zongke, WEI Xianhu, LI Junjie, LYU Zheng. Spatio-temporal change characteristics of vegetation coverage and its relationship with meteorological factors in the Greater Mekong Subregion[J].Journal of Nanjing Forestry University (Natural Science Edition), 2022, 46(2): 187-195.DOI: 10.12302/j.issn.1000-2006.202010020.

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年份 years	初始类型 initial type	转换占比/% conversion proportion					合计/% total
年份 years	初始类型 initial type	裸地	低	中	中高	高	合计/% total
2005— 2016	裸地	0.64	0.21	—	—	—	0.85
	低	0.15	0.68	0.31	0.08	—	1.22
	中	—	0.30	1.51	1.76	0.19	3.75
	中高	—	0.05	1.00	19.34	13.34	33.73
	高	—	—	0.09	5.39	54.97	60.45
	合计	0.79	1.24	2.91	26.7	68.50	100.00
2016— 2019	裸地	0.61	0.17	—	—	—	0.79
	低	0.13	0.77	0.31	—	—	1.21
	中	—	0.23	1.85	0.80	0.05	2.93
	中高	—	—	1.30	20.70	4.53	26.54
	高	—	—	0.11	10.84	57.58	68.53
	合计	0.74	1.17	3.57	32.34	62.17	100.00

大湄公河次区域植被覆盖时空变化特征及其与气象因子的关系

Spatio-temporal change characteristics of vegetation coverage and its relationship with meteorological factors in the Greater Mekong Subregion

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