Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine

Huang, Huabing; Chen, Yanlei; Clinton, Nicholas; Wang, Jie; Wang, Xiaoyi; Liu, Caixia; Gong, Peng; Yang, Jun; Bai, Yuqi; Zheng, Yunpu; Zhu, Zhiliang

doi:10.1016/j.rse.2017.02.021

Cited by 364 publications

(211 citation statements)

References 32 publications

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“…The 17% global application is a significant percentage, which is attributed to the processing as well as data storage capabilities of GEE [11]. To analyze data at a global scale requires more than 10 petabytes of data and efficient processing power, a strength engrained in GEE [11][12][13].…”

Section: Discussionmentioning

confidence: 99%

Google Earth Engine Applications Since Inception: Usage, Trends, and Potential

2018

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The Google Earth Engine (GEE) portal provides enhanced opportunities for undertaking earth observation studies. Established towards the end of 2010, it provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. However, the uptake and usage of the opportunity remains varied and unclear. This study was undertaken to investigate the usage patterns of the Google Earth Engine platform and whether researchers in developing countries were making use of the opportunity. Analysis of published literature showed that a total of 300 journal papers were published between 2011 and June 2017 that used GEE in their research, spread across 158 journals. The highest number of papers were in the journal Remote Sensing, followed by Remote Sensing of Environment. There were also a number of papers in premium journals such as Nature and Science. The application areas were quite varied, ranging from forest and vegetation studies to medical fields such as malaria. Landsat was the most widely used dataset; it is the biggest component of the GEE data portal, with data from the first to the current Landsat series available for use and download. Examination of data also showed that the usage was dominated by institutions based in developed nations, with study sites mainly in developed nations. There were very few studies originating from institutions based in less developed nations and those that targeted less developed nations, particularly in the African continent.

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Section: Discussionmentioning

confidence: 99%

Google Earth Engine Applications Since Inception: Usage, Trends, and Potential

2018

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“…We used a threshold on the Normalized Difference Vegetation Index (NDVI) computed from a cloud-free median composite of 3 years (2009-2011) of the Landsat 5 Collection 1 archive (30 m resolution). Specifically, we used a threshold of 0.3 to determine vegetation, a relatively conservative estimate based on previous research (Huang et al, 2016). We designate urban vegetation as any 30 m pixels with NDVI >0.3 that are within 500 m pixels classified as urban in the 2010 Moderate Resolution Imaging SpectroRadiometer (MODIS) land cover (MCD12Q1) (Friedl et al, 2002(Friedl et al, , 2010 dataset.…”

Section: Estimation Of Vegetation and Available Space From Uamentioning

confidence: 99%

A Global Geospatial Ecosystem Services Estimate of Urban Agriculture

et al. 2018

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Though urban agriculture (UA), defined here as growing of crops in cities, is increasing in popularity and importance globally, little is known about the aggregate benefits of such natural capital in built‐up areas. Here, we introduce a quantitative framework to assess global aggregate ecosystem services from existing vegetation in cities and an intensive UA adoption scenario based on data‐driven estimates of urban morphology and vacant land. We analyzed global population, urban, meteorological, terrain, and Food and Agriculture Organization (FAO) datasets in Google Earth Engine to derive global scale estimates, aggregated by country, of services provided by UA. We estimate the value of four ecosystem services provided by existing vegetation in urban areas to be on the order of $33 billion annually. We project potential annual food production of 100–180 million tonnes, energy savings ranging from 14 to 15 billion kilowatt hours, nitrogen sequestration between 100,000 and 170,000 tonnes, and avoided storm water runoff between 45 and 57 billion cubic meters annually. In addition, we estimate that food production, nitrogen fixation, energy savings, pollination, climate regulation, soil formation and biological control of pests could be worth as much as $80–160 billion annually in a scenario of intense UA implementation. Our results demonstrate significant country‐to‐country variability in UA‐derived ecosystem services and reduction of food insecurity. These estimates represent the first effort to consistently quantify these incentives globally, and highlight the relative spatial importance of built environments to act as change agents that alleviate mounting concerns associated with global environmental change and unsustainable development.

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“…This algorithm scores Landsat pixels by their relative cloudiness with a value ranging from 0 to 100 [40]. A threshold value of 20 was used to mask the cloudy pixels.…”

Section: Reference Samples Collectionmentioning

confidence: 99%

Green Spaces as an Indicator of Urban Health: Evaluating Its Changes in 28 Mega-Cities

Huang

Yang

et al. 2017

Remote Sensing

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Urban green spaces can yield considerable health benefits to urban residents. Assessing these health benefits is a key step for managing urban green spaces for human health and wellbeing in cities. In this study, we assessed the change of health benefits generated by urban green spaces in 28 megacities worldwide between 2005 and 2015 by using availability and accessibility as proxy indicators. We first mapped land covers of 28 megacities using 10,823 scenes of Landsat images and a random forest classifier running on Google Earth Engine. We then calculated the availability and accessibility of urban green spaces using the land cover maps and gridded population data. The results showed that the mean availability of urban green spaces in these megacities increased from 27.63% in 2005 to 31.74% in 2015. The mean accessibility of urban green spaces increased from 65.76% in 2005 to 72.86% in 2015. The increased availability and accessibility of urban green spaces in megacities have brought more health benefits to their residents.

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Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine

Cited by 364 publications

References 32 publications

Google Earth Engine Applications Since Inception: Usage, Trends, and Potential

Google Earth Engine Applications Since Inception: Usage, Trends, and Potential

A Global Geospatial Ecosystem Services Estimate of Urban Agriculture

Green Spaces as an Indicator of Urban Health: Evaluating Its Changes in 28 Mega-Cities

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