基于Google Earth Engine的红树林年际变化监测研究

doi:10.12082/dqxxkx.2019.180354

地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (5): 731-739.doi: 10.12082/dqxxkx.2019.180354

基于Google Earth Engine的红树林年际变化监测研究

刘凯¹(), 彭力恒¹, 李想¹, 谭敏¹, 王树功²

1. 中山大学地理科学与规划学院,广东省城市化与地理环境空间模拟重点实验室,广东省公共安全与灾害工程技术研究中心,广州 510275
2. 中山大学地球科学与工程学院,广东省地质过程与矿产资源探查重点实验室,广州 510275

收稿日期:2018-07-31 修回日期:2019-01-22 出版日期:2019-05-25 发布日期:2019-05-25
作者简介:
作者简介：刘凯(1979-),男,黑龙江伊春人,博士,副教授,主要从事环境遥感研究。E-mail: liuk6@mail.sysu.edu.cn
基金资助:
广东省省级科技计划项目(2017A020217003);广东省自然科学基金项目(2016A030313261、2016A030313188)

Monitoring the Inter-annual Change of Mangroves based on the Google Earth Engine

Kai LIU^1,^*(), Liheng PENG¹, Xiang LI¹, Min TAN¹, Shugong WANG²

1. School of Geography and Planning, Sun Yat-sen University, Guangdong Key Laboratory for Urbanization and Geo-Simulation, Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China
2. School of Earth Science and Engineering, Guangdong Provincial Key Laboratory of Mineral Resources and Geological Processes, Sun Yat-sen University, Guangzhou 510275, China

Received:2018-07-31 Revised:2019-01-22 Online:2019-05-25 Published:2019-05-25
Contact: Kai LIU E-mail:liuk6@mail.sysu.edu.cn
Supported by:
Science and Technology Planning Project of Guangdong Province, No.2017A020217003;Natural Science Foundation of Guangdong, No.2016A030313261, 2016A030313188.

摘要/Abstract

摘要：

遥感技术已广泛应用于红树林资源调查与动态监测中,但仍然存在遥感数据获取困难、数据预处理工作量大、监测时间长而周期过大等问题,影响了学者对红树林演变过程的精细刻画与理解。本文基于Google Earth Engine（GEE）云遥感数据处理平台,选取Landsat系列卫星数据,生成长时间序列年际极少云影像集（云量少于5%）,利用3个红外波段反射率（NIR、SWIR1、SWIR2）和3个特征指数（NDVI、NDWI、NDMI）建立阈值规则集,实现对实验区越南玉显县红树林、红树林-虾塘、不透水面-裸地、水体4种目标地物的专家知识决策树分类和土地覆盖的制图,并基于分类结果监测该区域1993-2017年的红树林年际动态变化。结果表明：GEE平台可满足多云多雨地区红树林的长时间序列年际变化监测需求;本文阈值分类方法可以有效提取红树林及红树林-虾塘,实验区有86%年份的影像分类精度达到80%以上;年际变化监测可精细刻画实验区红树林面积先增后减再增的变化过程,也能准确反映红树林与红树林-虾塘养殖系统面积之间的负相关关系。红树林年际动态监测结果可以降低红树林演变分析的不确定性,并能更精细地量化红树林与其他土地覆盖类型的转化过程,从而评估经济发展、政策等因素对红树林演变的影响。

关键词: 红树林, Google Earth Engine, 年际变化监测, 长时间序列, 特征指数, 虾塘养殖

Abstract:

Remote Sensing technologies have been widely used in the investigation and dynamics monitoring of mangrove forests. However, problems remained that severely hinder the precise description and deep understanding of mangrove forests' dynamics. The problems include difficulties in remotely sensed data acquisition, the heavy workload of data preprocessing, and the lengthy time period in long time series monitoring. Based on Google Earth Engine (GEE), a cloud platform of remotely sensed data processing, this study used raw images of Landsat series satellites to produce an inter-annual mostly-cloudless (cloud coverage less than 5%) image collection of top-of-atmosphere reflectance (TOA). Then, classification rules were established based on three infrared-band TOAs (NIR, band near infrared; SWIR1, band shortwave infrared 1; SWIR2, band shortwave infrared 2) and three indices (NDVI, normalized difference vegetation index; NDWI, normalized difference water index; NDMI, normalized difference moisture index). Next, four land cover types, i.e., mangrove, mangrove-shrimp pond, impervious surface-bare land, and water body, were classified for mapping our case study area of Ngoc Hien, Vietnam from 1993 to 2017. Finally, the inter-annual land cover maps were used to analyze the characteristics of mangrove dynamics. The results showed that the long time-series inter-annual change monitoring of mangroves in cloudy and rainy regions can be implemented satisfactorily on the GEE platform. The image classification had an overall accuracy of over 80% for 86% of the study years, indicating that our proposed thresholds-based approach can effectively extract mangroves and mangrove-shrimp ponds. Through the analysis of inter-annual changes, the change process of mangroves in this region was depicted in details: it first increased, then decreased, and later, increased again. The correlation between the area changes of mangroves and mangrove-shrimp ponds was accurately detected to be negative. The inter-annual change monitoring of mangroves reduces the uncertainty of researching mangrove evolution processes, and quantifies in more details the conversions between mangroves and other land cover types. In so doing, the impacts of economic development, policies, and other factors on mangrove dynamics can then be assessed.

Key words: mangrove, Google Earth Engine, inter-annual change monitoring, long time series, normalized difference indices, shrimp aquaculture

刘凯, 彭力恒, 李想, 谭敏, 王树功. 基于Google Earth Engine的红树林年际变化监测研究[J]. 地球信息科学学报, 2019, 21(5): 731-739.DOI:10.12082/dqxxkx.2019.180354

Kai LIU, Liheng PENG, Xiang LI, Min TAN, Shugong WANG. Monitoring the Inter-annual Change of Mangroves based on the Google Earth Engine[J]. Journal of Geo-information Science, 2019, 21(5): 731-739.DOI:10.12082/dqxxkx.2019.180354

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年份	传感器	数量/景	年份	传感器	数量/景
1993	Landsat-5 TM	7	2008	Landsat-5 TM	10
1994	Landsat-5 TM	5	2009	Landsat-5 TM	11
1995	Landsat-5 TM	6	2010	Landsat-7 ETM+	8
1997	Landsat-5 TM	8	2014	Landsat-8 OLI	40
1999	Landsat-5 TM	16	2015	Landsat-8 OLI	36
2002	Landsat-7 ETM+	30	2016	Landsat-8 OLI	32
2004	Landsat-5 TM	15	2017	Landsat-8 OLI	32
2005	Landsat-5 TM	15	合计		271

地物类型	解译标志	描述
红树林		主要由生长密集的红树植物群落组成。在影像中主要表现为亮绿色,由于红树植物集中分布,斑块表面没有明显纹理,相对平滑
红树林-虾塘		塘面有红树林分布的虾塘区域(红树林潮沟系统^[26])。在影像中表现为暗绿色,有明显养殖塘轮廓的植被覆盖区,呈条带状分布
不透水面-裸地		未利用地、裸土、人工地物(如村庄、道路等建设用地)或仅局部为建设用地的大片荒地。在影像中呈现为红色或白色
水体		包括海水和塘基、塘面均无红树林覆盖的养殖塘。在影像中呈蓝紫色、深蓝色,纹理相对平滑

光谱特征	统计值	红树林	红树林-虾塘	不透水面-裸地	水体
NDVI	最小值	0.696	-0.057	0.036	-0.389
	最大值	0.818	0.770	0.411	0.064
	均值	0.766	0.475	0.197	-0.247
NDWI	最小值	0.482	0.126	-0.171	0.076
	最大值	0.712	0.679	0.288	0.623
	均值	0.626	0.498	-0.034	0.360
NDMI	最小值	-0.708	-0.665	-0.392	-0.013
	最大值	-0.538	0.172	-0.037	0.460
	均值	-0.642	-0.363	-0.204	0.274

基于Google Earth Engine的红树林年际变化监测研究

Monitoring the Inter-annual Change of Mangroves based on the Google Earth Engine

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