Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China

A high resolution mangrove map (e.g., 10-m), including mangrove patches with small size, is urgently needed for mangrove protection and ecosystem function estimation, because more small mangrove patches have disappeared with influence of human disturbance and sea-level rise. However, recent national-scale mangrove forest maps are mainly derived from 30-m Landsat imagery, and their spatial resolution is relatively coarse to accurately characterize the extent of mangroves, especially those with small size. Now, Sentinel imagery with 10-m resolution provides an opportunity for generating high-resolution mangrove maps containing these small mangrove patches. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and/or Sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features of random forest to classify mangroves in China. We found that Sentinel-2 (F1-Score of 0.895) is more effective than Sentinel-1 (F1-score of 0.88) in mangrove extraction, and a combination of SAR and MSI imagery can get the best accuracy (F1-score of 0.94). The 10-m mangrove map was derived by combining SAR and MSI data, which identified 20003 ha mangroves in China, and the area of small mangrove patches (<1 ha) is 1741 ha, occupying 8.7% of the whole mangrove area. At the province level, Guangdong has the largest area (819 ha) of small mangrove patches, and in Fujian, the percentage of small mangrove patches is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest map is expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of the mangrove forest.

[1]  W. Salas,et al.  Baseline Map of Carbon Emissions from Deforestation in Tropical Regions , 2012, Science.

[2]  S. Robeson,et al.  Mapping spatial distribution and biomass of coastal wetland vegetation in Indonesian Papua by combining active and passive remotely sensed data , 2016 .

[3]  Stuart E. Hamilton,et al.  Global carbon stocks and potential emissions due to mangrove deforestation from 2000 to 2012 , 2016, Nature Climate Change.

[4]  I. Valiela,et al.  Mangrove Forests: One of the World's Threatened Major Tropical Environments , 2001 .

[5]  D. Alongi.  Carbon sequestration in mangrove forests , 2012 .

[6]  Derek Karssenberg,et al.  Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method , 2020 .

[7]  Edward B. Barbier,et al.  Better restoration policies are needed to conserve mangrove ecosystems , 2019, Nature Ecology & Evolution.

[8]  D. Richards,et al.  Rates and drivers of mangrove deforestation in Southeast Asia, 2000–2012 , 2015, Proceedings of the National Academy of Sciences.

[9]  D. Donato,et al.  Estimating Global “Blue Carbon” Emissions from Conversion and Degradation of Vegetated Coastal Ecosystems , 2012, PloS one.

[10]  M. Hill.  Vegetation index suites as indicators of vegetation state in grassland and savanna: An analysis with simulated SENTINEL 2 data for a North American transect , 2013 .

[11]  Clare Duncan,et al.  The value of small mangrove patches , 2019, Science.

[12]  P. Kareiva,et al.  Coastal habitats shield people and property from sea-level rise and storms , 2013 .

[13]  Mark Huxham,et al.  The future of Blue Carbon science , 2019, Nature Communications.

[14]  George Alan Blackburn,et al.  High resolution wheat yield mapping using Sentinel-2 , 2019, Remote Sensing of Environment.

[15]  Bing Xu,et al.  Monitoring mangrove forest change in China from 1990 to 2015 using Landsat-derived spectral-temporal variability metrics , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[16]  R. O'Neill,et al.  The value of the world's ecosystem services and natural capital , 1997, Nature.

[17]  Mika Siljander,et al.  Auxiliary datasets improve accuracy of object-based land use/land cover classification in heterogeneous savanna landscapes , 2019, Remote Sensing of Environment.

[18]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[19]  Tom G. Farr,et al.  The Shuttle Radar Topography Mission: A Global DEM , 1999 .

[20]  Suhua Shi,et al.  The State of the World's Mangrove Forests: Past, Present, and Future , 2019, Annual Review of Environment and Resources.

[21]  Sudip Manna,et al.  Mapping distribution of Sundarban mangroves using Sentinel-2 data and new spectral metric for detecting their health condition , 2020, Geocarto International.

[22]  Ashbindu Singh,et al.  Status and distribution of mangrove forests of the world using earth observation satellite data , 2011 .

[23]  Inigo J. Losada,et al.  The role of coastal plant communities for climate change mitigation and adaptation , 2013 .

[24]  N. Clinton,et al.  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 , 2017 .

[25]  M. Kanninen,et al.  Mangroves among the most carbon-rich forests in the tropics , 2011 .

[26]  Carlos M. Duarte,et al.  A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2 , 2011 .

[27]  Martin Brandt,et al.  From woody cover to woody canopies: How Sentinel-1 and Sentinel-2 data advance the mapping of woody plants in savannas , 2019 .

[28]  Jun Yang,et al.  The first all-season sample set for mapping global land cover with Landsat-8 data. , 2017, Science bulletin.

[29]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[30]  James N Sanchirico,et al.  Global economic potential for reducing carbon dioxide emissions from mangrove loss , 2012, Proceedings of the National Academy of Sciences.

[31]  Chao Wang,et al.  Monitoring loss and recovery of mangrove forests during 42 years: The achievements of mangrove conservation in China , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[32]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[33]  J. Dubois,et al.  Evaluation Of The Grey-level Co-occurrence Matrix Method For Land-cover Classification Using Spot Imagery , 1990 .

[34]  Mryka Hall-Beyer,et al.  Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales , 2017 .

[35]  Przemyslaw Kupidura,et al.  The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery , 2019, Remote. Sens..

[36]  R. Twilley,et al.  Carbon sinks in mangroves and their implications to carbon budget of tropical coastal ecosystems , 1992 .

[37]  James B Grace,et al.  ScholarWorks @ UTRGV ScholarWorks @ UTRGV Beyond just sea-level rise: considering macroclimatic drivers Beyond just sea-level rise: considering macroclimatic drivers within coastal wetland vulnerability assessments to climate within coastal wetland vulnerability assessments to climate change change , 2022 .

[38]  P. Gong,et al.  Continuous monitoring of coastline dynamics in western Florida with a 30-year time series of Landsat imagery , 2016 .

[39]  Karen E. Joyce,et al.  Monitoring mangrove forests: Are we taking full advantage of technology? , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[40]  K. Moffett,et al.  Remote Sens , 2015 .

[41]  D. Murdiyarso,et al.  The potential of Indonesian mangrove forests for global climate change mitigation , 2015 .

[42]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[43]  Oscar Serrano,et al.  Global patterns in mangrove soil carbon stocks and losses , 2017 .

[44]  Na Chen,et al.  Mapping mangrove in Dongzhaigang, China using Sentinel-2 imagery , 2020 .

[45]  Stuart E. Hamilton,et al.  Assessing the Role of Commercial Aquaculture in Displacing Mangrove Forest , 2013 .

[46]  Marc Simard,et al.  Global-scale dispersal and connectivity in mangroves , 2018, Proceedings of the National Academy of Sciences.

[47]  Masanobu Shimada,et al.  The Global Mangrove Watch - A New 2010 Global Baseline of Mangrove Extent , 2018, Remote. Sens..

[48]  K. Didan,et al.  Detecting large scale conversion of mangroves to aquaculture with change point and mixed-pixel analyses of high-fidelity MODIS data , 2013 .

[49]  P. Travassos,et al.  Impact of aquaculture on mangrove areas in the northern Pernambuco Coast (Brazil) using remote sensing and geographic information system , 2009 .

[50]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[51]  Hui Lin,et al.  Textural-Spectral Feature-Based Species Classification of Mangroves in Mai Po Nature Reserve from Worldview-3 Imagery , 2015, Remote. Sens..