Journal ArticleDOI
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.
Bangqian Chen,Bangqian Chen,Xiangming Xiao,Xiangming Xiao,Xiangping Li,Lianghao Pan,Lianghao Pan,Russell Doughty,Jun Ma,Jinwei Dong,Yuanwei Qin,Bin Zhao,Zhixiang Wu,Rui Sun,Guoyu Lan,Guishui Xie,Nicholas Clinton,Chandra Giri +17 more
TLDR
In this paper, a new classification algorithm was developed using the biophysical characteristics of mangrove forests in China by identifying: greenness, canopy coverage, and tidal inundation from time series Landsat data, and elevation, slope, and intersection-with-sea criterion.Abstract:
Due to rapid losses of mangrove forests caused by anthropogenic disturbances and climate change, accurate and contemporary maps of mangrove forests are needed to understand how mangrove ecosystems are changing and establish plans for sustainable management. In this study, a new classification algorithm was developed using the biophysical characteristics of mangrove forests in China. More specifically, these forests were mapped by identifying: (1) greenness, canopy coverage, and tidal inundation from time series Landsat data, and (2) elevation, slope, and intersection-with-sea criterion. The annual mean Normalized Difference Vegetation Index (NDVI) was found to be a key variable in determining the classification thresholds of greenness, canopy coverage, and tidal inundation of mangrove forests, which are greatly affected by tide dynamics. In addition, the integration of Sentinel-1A VH band and modified Normalized Difference Water Index (mNDWI) shows great potential in identifying yearlong tidal and fresh water bodies, which is related to mangrove forests. This algorithm was developed using 6 typical Regions of Interest (ROIs) as algorithm training and was run on the Google Earth Engine (GEE) cloud computing platform to process 1941 Landsat images (25 Path/Row) and 586 Sentinel-1A images circa 2015. The resultant mangrove forest map of China at 30 m spatial resolution has an overall/users/producer’s accuracy greater than 95% when validated with ground reference data. In 2015, China’s mangrove forests had a total area of 20,303 ha, about 92% of which was in the Guangxi Zhuang Autonomous Region, Guangdong, and Hainan Provinces. This study has demonstrated the potential of using the GEE platform, time series Landsat and Sentine-1A SAR images to identify and map mangrove forests along the coastal zones. The resultant mangrove forest maps are likely to be useful for the sustainable management and ecological assessments of mangrove forests in China.read more
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Journal ArticleDOI
Google Earth Engine for geo-big data applications: A meta-analysis and systematic review
Haifa Tamiminia,Bahram Salehi,Masoud Mahdianpari,Lindi J. Quackenbush,Sarina Adeli,Brian Brisco +5 more
TL;DR: A meta-analysis investigation of recent peer-reviewed GEE articles focusing on several features, including data, sensor type, study area, spatial resolution, application, strategy, and analytical methods confirmed that GEE has and continues to make substantive progress on global challenges involving process of geo-big data.
Journal ArticleDOI
A review of remote sensing for mangrove forests: 1956–2018
TL;DR: In this paper, the authors identify key milestones in mangrove remote sensing (RS) by associating the emergence of major research topics with the occurrence of new sensors in four historical phases, i.e. before 1989, 1990, 1999, 2000-2009, and 2010-2018.
Journal ArticleDOI
The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform
TL;DR: This study introduces the first detailed, provincial-scale wetland inventory map of one of the richest Canadian provinces in terms of wetland extent and suggests a paradigm-shift from standard static products and approaches toward generating more dynamic, on-demand, large- scale wetland coverage maps through advanced cloud computing resources that simplify access to and processing of the “Geo Big Data.”
Journal ArticleDOI
Tracking annual changes of coastal tidal flats in China during 1986–2016 through analyses of Landsat images with Google Earth Engine
Xinxin Wang,Xiangming Xiao,Zhenhua Zou,Bangqian Chen,Jun Ma,Jinwei Dong,Russell Doughty,Qiaoyan Zhong,Yuanwei Qin,Shengqi Dai,Xiangping Li,Bin Zhao,Bo Li +12 more
TL;DR: The interannual dynamics of coastal tidal flats area in China over the last three decades can be divided into three periods: a stable period during 1986-1992, an increasing period during 1993-2001 and a decreasing period during 2002-2016.
Journal ArticleDOI
Remote sensing monitoring of multi-scale watersheds impermeability for urban hydrological evaluation
TL;DR: In this paper, the authors defined urban multi-scale watersheds and calculated the impermeability ratio at watersheds level for urban hydrological modeling, considering the interconnection of multiple urban water systems.
References
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Terrestrial Ecoregions of the World: A New Map of Life on Earth
Journal ArticleDOI
The Shuttle Radar Topography Mission
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