Monitoring the Inter-annual Change of Mangroves based on the Google Earth Engine
LIU Kai1, *, , PENG Liheng1, LI Xiang1, TAN Min1, WANG Shugong2
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, China2. School of Earth Science and Engineering, Guangdong Provincial Key Laboratory of Mineral Resources and Geological Processes, Sun Yat-sen University, Guangzhou 510275, China
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.
Keywords：mangrove
;
Google Earth Engine
;
inter-annual change monitoring
;
long time series
;
normalized difference indices
;
shrimp aquaculture
LIUKai, PENGLiheng, LIXiang, TANMin, WANGShugong. Monitoring the Inter-annual Change of Mangroves based on the Google Earth Engine[J]. Journal of Geo-information Science, 2019, 21(5): 731-739 https://doi.org/10.12082/dqxxkx.2019.180354
因此,GEE适用于地物的长时间序列动态监测,以及大尺度范围数据产品的生成。目前,GEE已在林业[20,21]、农业[22]、城镇提取[23]等领域发挥了一定的作用。在红树林遥感的应用上,Chen等[24]提出一种基于植被指数等指标来构建分类规则集的分类方法,利用GEE平台Landsat系列卫星多光谱影像和哨兵一号卫星合成孔径雷达数据生成2015年1期的数据,从而对中国的红树林进行制图研究。基于GEE平台研究红树林的年际变化、缩短常规遥感数据处理分析的时间,有望能更精细地刻画其演变过程,避免研究中的面积动态变化不符合红树林演变规律[25];而使用同一来源的多光谱遥感数据,力求降低分类结果和年际变化监测的不确定性。因此,基于GEE平台的长时序红树林变化监测潜力是一个值得研究的主题,它包括以下3项内容： ① GEE的红树林监测时间序列遥感影像潜力分析;② GEE中高分辨率数据的红树林识别方法; ③ GEE下的红树林遥感动态监测。
The mangrove forests of Southeast Asia are highly biodiverse and provide multiple ecosystem services upon which millions of people depend. Mangroves enhance fisheries and coastal protection, and store among the highest densities of carbon of any ecosystem globally. Mangrove forests have experienced extensive deforestation owing to global demand for commodities, and previous studies have identif...
[8]
Cárdenas NY, Joyce KE, Maier SW.
Monitoring mangrove forests: Are we taking full advantage of technology?
[J]. International Journal of Applied Earth Observation and Geoinformation, 2017,63:1-14.
Mangrove forests grow in the estuaries of 124 tropical countries around the world. Because in-situ monitoring of mangroves is difficult and time-consuming, remote sensing technologies are commonly used to monitor these ecosystems. Landsat satellites have provided regular and systematic images of mangrove ecosystems for over 30 years, yet researchers often cite budget and infrastructure constraints to justify the underuse this resource. Since 2001, over 50 studies have used Landsat or ASTER imagery for mangrove monitoring, and most focus on the spatial extent of mangroves, rarely using more than five images. Even after the Landsat archive was made free for public use, few studies used more than five images, despite the clear advantages of using more images (e.g. lower signal-to-noise ratios). The main argument of this paper is that, with freely available imagery and high performance computing facilities around the world, it is up to researchers to acquire the necessary programming skills to use these resources. Programming skills allow researchers to automate repetitive and time-consuming tasks, such as image acquisition and processing, consequently reducing up to 60% of the time dedicated to these activities. These skills also help scientists to review and re-use algorithms, hence making mangrove research more agile. This paper contributes to the debate on why scientists need to learn to program, not only to challenge prevailing approaches to mangrove research, but also to expand the temporal and spatial extents that are commonly used for mangrove research.
[9]
SonN, ChenC, ChangN, et al.
Mangrove mapping and change detection in Ca Mau Peninsula, Vietnam, using Landsat data and object-based image analysis
[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015,8(2):503-510.
Uncovering the spatio-temporal dynamics of land cover change and fragmentation of mangroves in the Ca Mau peninsula, Vietnam using multi-temporal SPOT satellite imagery (2004-2013)
Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine
Aquaculture and the environment: The supply of and demand for environmental goods and services by Asian aquaculture and the implications for sustainability
Abstract Eco-certification has been used as a tool to mitigate adverse effects of aquaculture production and might thus be understood as a private approach to sustainable ecosystem management. In production forests in Ca Mau, Vietnam, where mangrove have suffered degradation despite legal protection, different projects have targeted reversing this trend by means of private certification using the ‘Naturland’ organic standard as a reference. So far the outcomes have, however, been proven unsatisfactory. With the aim to better understand the reasons for these poor outcomes, a survey of forty households was conducted in a production forest in Rach Goc commune, Ngoc Hien District. We evaluated farmers’ perceptions on mangrove management, the drivers guiding shrimp farming, and whether there was a difference between participants and non-participants in a former ‘Naturland’ organic project. To complement the survey, a range of stakeholders involved in shrimp value chains were interviewed to better understand the terms and benefits of certification. The results of this survey suggested that, when applied to shrimp–mangrove farming systems in production forests in Ca Mau, ‘eco-certification’ and associated benefits are not very satisfactory. The survey results revealed that certified farms do not show significant differences to non-certified farms in terms of social and environmental benefits. As far as the implementation process was concerned, the survey results showed that a failure to integrate local farmers as participants consequently resulted in households becoming ‘objects’ for certification and not project partners with equal weight and power. It appears that rather than being a tool for improvement, ‘Naturland’ certification for shrimp–mangrove farming systems in Ca Mau’s production forests has become an end in itself.
Mangroverelated policy and institutional frameworks in Pakistan, Thailand and Viet Nam
[M]. Bangkok: Food and Agriculture Organization of the United Nations Regional Office for Asia and the Pacific, International Union for Conservation of Nature, 2016.
Uncovering the spatio-temporal dynamics of land cover change and fragmentation of mangroves in the Ca Mau peninsula, Vietnam using multi-temporal SPOT satellite imagery (2004-2013)
Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine
High-resolution global maps of 21st-century forest cover change
1
2013
... 因此,GEE适用于地物的长时间序列动态监测,以及大尺度范围数据产品的生成.目前,GEE已在林业[20,21]、农业[22]、城镇提取[23]等领域发挥了一定的作用.在红树林遥感的应用上,Chen等[24]提出一种基于植被指数等指标来构建分类规则集的分类方法,利用GEE平台Landsat系列卫星多光谱影像和哨兵一号卫星合成孔径雷达数据生成2015年1期的数据,从而对中国的红树林进行制图研究.基于GEE平台研究红树林的年际变化、缩短常规遥感数据处理分析的时间,有望能更精细地刻画其演变过程,避免研究中的面积动态变化不符合红树林演变规律[25];而使用同一来源的多光谱遥感数据,力求降低分类结果和年际变化监测的不确定性.因此,基于GEE平台的长时序红树林变化监测潜力是一个值得研究的主题,它包括以下3项内容： ① GEE的红树林监测时间序列遥感影像潜力分析;② GEE中高分辨率数据的红树林识别方法; ③ GEE下的红树林遥感动态监测. ...
Combining satellite data for better tropical forest monitoring
1
2016
... 因此,GEE适用于地物的长时间序列动态监测,以及大尺度范围数据产品的生成.目前,GEE已在林业[20,21]、农业[22]、城镇提取[23]等领域发挥了一定的作用.在红树林遥感的应用上,Chen等[24]提出一种基于植被指数等指标来构建分类规则集的分类方法,利用GEE平台Landsat系列卫星多光谱影像和哨兵一号卫星合成孔径雷达数据生成2015年1期的数据,从而对中国的红树林进行制图研究.基于GEE平台研究红树林的年际变化、缩短常规遥感数据处理分析的时间,有望能更精细地刻画其演变过程,避免研究中的面积动态变化不符合红树林演变规律[25];而使用同一来源的多光谱遥感数据,力求降低分类结果和年际变化监测的不确定性.因此,基于GEE平台的长时序红树林变化监测潜力是一个值得研究的主题,它包括以下3项内容： ① GEE的红树林监测时间序列遥感影像潜力分析;② GEE中高分辨率数据的红树林识别方法; ③ GEE下的红树林遥感动态监测. ...
Automated cropland mapping of continental Africa using Google Earth Engine cloud computing
1
2017
... 因此,GEE适用于地物的长时间序列动态监测,以及大尺度范围数据产品的生成.目前,GEE已在林业[20,21]、农业[22]、城镇提取[23]等领域发挥了一定的作用.在红树林遥感的应用上,Chen等[24]提出一种基于植被指数等指标来构建分类规则集的分类方法,利用GEE平台Landsat系列卫星多光谱影像和哨兵一号卫星合成孔径雷达数据生成2015年1期的数据,从而对中国的红树林进行制图研究.基于GEE平台研究红树林的年际变化、缩短常规遥感数据处理分析的时间,有望能更精细地刻画其演变过程,避免研究中的面积动态变化不符合红树林演变规律[25];而使用同一来源的多光谱遥感数据,力求降低分类结果和年际变化监测的不确定性.因此,基于GEE平台的长时序红树林变化监测潜力是一个值得研究的主题,它包括以下3项内容： ① GEE的红树林监测时间序列遥感影像潜力分析;② GEE中高分辨率数据的红树林识别方法; ③ GEE下的红树林遥感动态监测. ...
High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform
1
2018
... 因此,GEE适用于地物的长时间序列动态监测,以及大尺度范围数据产品的生成.目前,GEE已在林业[20,21]、农业[22]、城镇提取[23]等领域发挥了一定的作用.在红树林遥感的应用上,Chen等[24]提出一种基于植被指数等指标来构建分类规则集的分类方法,利用GEE平台Landsat系列卫星多光谱影像和哨兵一号卫星合成孔径雷达数据生成2015年1期的数据,从而对中国的红树林进行制图研究.基于GEE平台研究红树林的年际变化、缩短常规遥感数据处理分析的时间,有望能更精细地刻画其演变过程,避免研究中的面积动态变化不符合红树林演变规律[25];而使用同一来源的多光谱遥感数据,力求降低分类结果和年际变化监测的不确定性.因此,基于GEE平台的长时序红树林变化监测潜力是一个值得研究的主题,它包括以下3项内容： ① GEE的红树林监测时间序列遥感影像潜力分析;② GEE中高分辨率数据的红树林识别方法; ③ GEE下的红树林遥感动态监测. ...
A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform
1
2017
... 因此,GEE适用于地物的长时间序列动态监测,以及大尺度范围数据产品的生成.目前,GEE已在林业[20,21]、农业[22]、城镇提取[23]等领域发挥了一定的作用.在红树林遥感的应用上,Chen等[24]提出一种基于植被指数等指标来构建分类规则集的分类方法,利用GEE平台Landsat系列卫星多光谱影像和哨兵一号卫星合成孔径雷达数据生成2015年1期的数据,从而对中国的红树林进行制图研究.基于GEE平台研究红树林的年际变化、缩短常规遥感数据处理分析的时间,有望能更精细地刻画其演变过程,避免研究中的面积动态变化不符合红树林演变规律[25];而使用同一来源的多光谱遥感数据,力求降低分类结果和年际变化监测的不确定性.因此,基于GEE平台的长时序红树林变化监测潜力是一个值得研究的主题,它包括以下3项内容： ① GEE的红树林监测时间序列遥感影像潜力分析;② GEE中高分辨率数据的红树林识别方法; ③ GEE下的红树林遥感动态监测. ...
红树林空间分布遥感监测精度的影响因素及应对措施
1
2018
... 因此,GEE适用于地物的长时间序列动态监测,以及大尺度范围数据产品的生成.目前,GEE已在林业[20,21]、农业[22]、城镇提取[23]等领域发挥了一定的作用.在红树林遥感的应用上,Chen等[24]提出一种基于植被指数等指标来构建分类规则集的分类方法,利用GEE平台Landsat系列卫星多光谱影像和哨兵一号卫星合成孔径雷达数据生成2015年1期的数据,从而对中国的红树林进行制图研究.基于GEE平台研究红树林的年际变化、缩短常规遥感数据处理分析的时间,有望能更精细地刻画其演变过程,避免研究中的面积动态变化不符合红树林演变规律[25];而使用同一来源的多光谱遥感数据,力求降低分类结果和年际变化监测的不确定性.因此,基于GEE平台的长时序红树林变化监测潜力是一个值得研究的主题,它包括以下3项内容： ① GEE的红树林监测时间序列遥感影像潜力分析;② GEE中高分辨率数据的红树林识别方法; ③ GEE下的红树林遥感动态监测. ...
红树林空间分布遥感监测精度的影响因素及应对措施
1
2018
... 因此,GEE适用于地物的长时间序列动态监测,以及大尺度范围数据产品的生成.目前,GEE已在林业[20,21]、农业[22]、城镇提取[23]等领域发挥了一定的作用.在红树林遥感的应用上,Chen等[24]提出一种基于植被指数等指标来构建分类规则集的分类方法,利用GEE平台Landsat系列卫星多光谱影像和哨兵一号卫星合成孔径雷达数据生成2015年1期的数据,从而对中国的红树林进行制图研究.基于GEE平台研究红树林的年际变化、缩短常规遥感数据处理分析的时间,有望能更精细地刻画其演变过程,避免研究中的面积动态变化不符合红树林演变规律[25];而使用同一来源的多光谱遥感数据,力求降低分类结果和年际变化监测的不确定性.因此,基于GEE平台的长时序红树林变化监测潜力是一个值得研究的主题,它包括以下3项内容： ① GEE的红树林监测时间序列遥感影像潜力分析;② GEE中高分辨率数据的红树林识别方法; ③ GEE下的红树林遥感动态监测. ...
Integrated shrimp-mangrove farming systems in the Mekong Delta of Vietnam
Aquaculture and the environment: The supply of and demand for environmental goods and services by Asian aquaculture and the implications for sustainability
, 彭力恒
