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Fusion of Landsat 8 and Sentinel-2 data for mangrove phenology information extraction and classification

  • role： First author第一作者
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China

  • Email：zhaohui.xue@hhu.edu.cn
  • Introduction：薛朝辉，研究方向为遥感图像处理与模式识别、机器学习在遥感地学中的应用等。E-mail： zhaohui.xue@hhu.edu.cn
XUE Zhaohui,  
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China

QIAN Siyu

Resümee

Scientific and accurate monitoring of mangroves is the basis and premise for the protection of marine coastal and transitional ecosystem. However, mangroves are mainly distributed in the intertidal zone, result in large-scale manual monitoring a tough task. Although remote sensing technology can map mangrove within for long time and large area, the existing studies have some shortcomings. On the one hand, mangroves are often distributed in tropical and subtropical regions, where long-term coverage of effective optical remote sensing data is difficult to obtain due to weather conditions. On the other hand, mangroves are easily confused with other terrestrial vegetation by only using spectral information.In this paper, we choose the Sundarbans located in the Ganges River Delta as the study area. Landsat 8 OLI and Sentinel-2 MSI data in 2016 are obtained based on GEE (Google Earth Engine) to conduct mangrove extraction in this research. Firstly, the relation between the two sensors for the same index is constructed based on a least square regression model, which is used to reconstruct the time series data. In this phase, EVI (Enhanced Vegetation Index) and LSWI (Land Surface Water Index) are selected according to the separability criterion. Secondly, Savitzky-Golay filtering is applied to the time series data of the two indices, and 13 phenology metrics are extracted. Finally, these metrics of the two indices are cascaded, and Random Forest (RF) is used to extract the area of mangrove.Fusing the Landsat 8 OLI and Sentinel-2 MSI can effectively improve the quality of time series data. Compared with the classification results based on single sensor data, the overall accuracy is improved by 1.58%. Phenology information can significantly enhance the separability between mangrove and other vegetation, with a 1.92% improvement of overall accuracy compared with the classification results using time series data directly. Considering both EVI and LSWI indices can greatly improve, the classification effect, with 14.11% and 9.69% improvements compared with using a single index. Therefore, the method in this paper can effectively extract mangroves, with the overall accuracy and Kappa coefficient reaching to 91.02% and 0.892 respectively.This research takes full account of the deficiency of optical remote sensing monitoring, biological characteristics and geographical characteristics of mangroves, which can extract the area of mangroves more objectively and accurately. Compared with other similar studies, the differences and characteristics of this study are: (1) Jointly using EVI and LSWI time series to describe the phenological information of mangroves can effectively differentiate mangroves and evergreen forests; (2) We introduce phenology information into mangrove classification using remote sensing for the first time, and verify the feasibility of using phenology information to monitor the range of mangrove. The method proposed in this paper may be benefit for scientific and accurate monitoring of global or regional mangroves.

Schlüsselwort

remote sensing;mangrove;data fusion;phenology information;time series;GEE;random forests

References

  1. 1.
    Baloloy A B, Blanco A C, Sta. Ana R R C and Nadaoka K. 2020. Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 166: 95-117
  2. 2.
    Berlanga-Robles C A and Ruiz-Luna A. 2020. Assessing seasonal and long-term mangrove canopy variations in Sinaloa, northwest Mexico, based on time series of enhanced vegetation index (EVI) data. Wetlands Ecology and Management, 28(2): 229-249
  3. 3.
    Bunting P, Rosenqvist A, Lucas R M, Rebelo L M, Hilarides L, Thomas N, Hardy A, Itoh T, Shimada M and Finlayson C M. 2018. The global mangrove watch—a new 2010 global baseline of mangrove extent. Remote Sensing, 10(10): 1669
  4. 4.
    Chen B Q, Xiao X M, Li X P, Pan L H, Doughty R, Ma J, Dong J W, Qin Y W, Zhao B, Wu Z X, Sun R, Lan G Y, Xie G S, Clinton N and Giri C. 2017. 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. ISPRS Journal of Photogrammetry and Remote Sensing, 131: 104-120
  5. 5.
    Dan T T, Chen C F, Chiang S H and Ogawa S. 2016. Mapping and change analysis in mangrove forest by using Landsat imagery. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-8: 109-116
  6. 6.
    Descals A, Verger A, Yin G F and Peñuelas J. 2021. A threshold method for robust and fast estimation of land-surface phenology using Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 601-606
  7. 7.
    Dong D, Zeng J S, Wei Z and Yan J H. 2020. Integrating spaceborne optical and SAR imagery for monitoring mangroves and Spartina alterniflora in Zhangjiang Estuary. Journal of Tropical Oceanography, 39(2): 107-117
  8. 8.
    Dutta D, Das P K, Paul S, Sharma J R and Dadhwal V K. 2015. Assessment of ecological disturbance in the mangrove forest of Sundarbans caused by cyclones using MODIS time-series data (2001-2011). Natural Hazards, 79(2): 775-790
  9. 9.
    Fan H Q and Wang W Q. 2017. Some thematic issues for mangrove conservation in China. Journal of Xiamen University (Natural Science), 56(3): 323-330
  10. 10.
    Fu D J, Xiao H, Su F Z, Zhou C H, Dong J W, Zeng Y L, Yan K, Li S W, Wu J, Wu W Z and Yan F Q. 2021. Remote sensing cloud computing platform development and Earth science application. National Remote Sensing Bulletin, 25(1): 220-230
  11. 11.
    Gao F and Zhang X Y. 2021. Mapping crop phenology in near real-time using satellite remote sensing: challenges and opportunities. Journal of Remote Sensing, 2021: 8379391
  12. 12.
    Ghosh A, Schmidt S, Fickert T and Nüsser M. 2015. The Indian sundarban mangrove forests: history, utilization, conservation strategies and local perception. Diversity, 7(2): 149-169
  13. 13.
    Ghosh M K, Kumar L and Roy C. 2016. Mapping long-term changes in mangrove species composition and distribution in the Sundarbans. Forests, 7(12): 305
  14. 14.
    Giri C, Ochieng E, Tieszen L L, Zhu Z, Singh A, Loveland T, Masek J and Duke N. 2011. Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20(1): 154-159
  15. 15.
    Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D and Moore R. 2017. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202: 18-27
  16. 16.
    Gupta K, Mukhopadhyay A, Giri S, Chanda A, Datta Majumdar S, Samanta S, Mitra D, Samal R N, Pattnaik A K and Hazra S. 2018. An index for discrimination of mangroves from non-mangroves using LANDSAT 8 OLI imagery. MethodsX, 5: 1129-1139
  17. 17.
    Islam M, Borgqvist H and Kumar L. 2019. Monitoring Mangrove forest landcover changes in the coastline of Bangladesh from 1976 to 2015. Geocarto International, 34(13): 1458-1476
  18. 18.
    Jia M M. 2014. Remote Sensing analysis of China’s mangrove forests dynamics during 1973 to 2013. Changchun: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences
  19. 19.
    Jia M M, Wang Z M, Wang C, Mao D H and Zhang Y Z. 2019. A new vegetation index to detect periodically submerged mangrove forest using single-tide sentinel-2 imagery. Remote Sensing, 11(17): 2043
  20. 20.
    Jönsson P and Eklundh L. 2004. TIMESAT—a program for analyzing time-series of satellite sensor data. Computers and Geosciences, 30(8): 833-845
  21. 21.
    Kumar T, Mandal A, Dutta D, Nagaraja R and Dadhwal V K. 2019. Discrimination and classification of mangrove forests using EO-1 Hyperion data: a case study of Indian Sundarbans. Geocarto International, 34(4): 415-442
  22. 22.
    Li H Y, Jia M M, Zhang R, Ren Y X and Wen X. 2019. Incorporating the plant phenological trajectory into mangrove species mapping with dense time series sentinel-2 imagery and the Google earth engine platform. Remote Sensing, 11(21): 2479
  23. 23.
    Liang H, Ma Y and Ren G B. 2016. Remote sensing monitoring of the mangrove forests resources of Kra Isthmus in Thailand. Marine Environmental Science, 35(5): 725-731
  24. 24.
    Lin H and Zhang H S. 2021. Tropical and subtropical remote sensing: needs, challenges, and opportunities. National Remote Sensing Bulletin, 25(1): 276-291
  25. 25.
    Liu D Z, Han Z W and Shen C Y. 2019. Remote sensing analysis of the distribution and change of mangrove forest in Lianjiang from 1991 to 2001. Marine Sciences, 43(4): 22-28
  26. 26.
    Liu K, Peng L H, Li X, Tan M and Wang S G. 2019. Monitoring the inter-annual Change of mangroves based on the Google Earth Engine. Journal of Geo-information Science, 21(5): 731-739
  27. 27.
    Liu X, Fatoyinbo T E, Thomas N M, Guan W W, Zhan Y N, Mondal P, Lagomasino D, Simard M, Trettin C C, Deo R and Barenblitt A. 2021. Large-scale high-resolution coastal mangrove forests mapping across West Africa with machine learning ensemble and satellite big data. Frontiers in Earth Science, 8: 560933
  28. 28.
    Long J B and Giri C. 2011. Mapping the Philippines' mangrove forests using Landsat imagery. Sensors, 11(3): 2972-2981
  29. 29.
    Lu C Y, Gao Y B, Chen Y L, Jia M M, Fu W W and Xiong Y L. 2019. Dynamic change analysis of mangrove swamps based on RS/GIS in Quanzhou Bay. Journal of Forest and Environment, 39(2): 143-152
  30. 30.
    Mandal M S H, Kamruzzaman M and Hosaka T. 2020. Elucidating the phenology of the Sundarbans mangrove forest using 18-year time series of MODIS vegetation indices. Tropics, 29(2): 41-55
  31. 31.
    Pastor-Guzman J, Dash J and Atkinson P M. 2018. Remote sensing of mangrove forest phenology and its environmental drivers. Remote Sensing of Environment, 205: 71-84
  32. 32.
    Peng L H, Liu K, Cao J J, Zhu Y H, Li F S and Liu L. 2020. Combining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods. International Journal of Remote Sensing, 41(3): 813-838
  33. 33.
    Savitzky A and Golay M J E. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8): 1627-1639
  34. 34.
    Sheng N, Xin K and Liao B W. 2021. Literature analysis concerning studies of ecological functions and values of mangrove wetland. Wetland Science and Management, 17(1): 47-50
  35. 35.
    Talukdar S, Singha P, Mahato S, Shahfahad, Pal S, Liou Y A and Rahman A. 2020. Land-use land-cover classification by machine learning classifiers for satellite observations—a review. Remote Sensing, 12(7): 1135
  36. 36.
    Tamura M and Kikushima K. 2008. Extraction of mangrove forests using a satellite image and a digital elevation model//Proceedings of SPIE 7104, Remote Sensing for Agriculture, Ecosystems, and Hydrology X. Cardiff: SPIE: 710403
  37. 37.
    Thomas N, Bunting P, Lucas R, Hardy A, Rosenqvist A and Fatoyinbo T. 2018. Mapping mangrove extent and change: a globally applicable approach. Remote Sensing, 10(9): 1466
  38. 38.
    Thomas N, Lucas R, Bunting P, Hardy A, Rosenqvist A and Simard M. 2017. Distribution and drivers of global mangrove forest change, 1996-2010. PLoS One, 12(6): e0179302
  39. 39.
    Tieng T, Sharma S, MacKenzie R A, Venkattappa M, Sasaki N K and Collin A. 2019. Mapping mangrove forest cover using Landsat-8 imagery, Sentinel-2, Very High Resolution Images and Google Earth Engine algorithm for entire Cambodia. IOP Conference Series: Earth and Environmental Science, 266: 012010
  40. 40.
    Vo Q T, Oppelt N, Leinenkugel P and Kuenzer C. 2013. Remote sensing in mapping mangrove ecosystems—an object-based approach. Remote Sensing, 5(1): 183-201
  41. 41.
    Wang L, Jia M M, Yin D M and Tian J Y. 2019. A review of remote sensing for mangrove forests: 1956-2018. Remote Sensing of Environment, 231: 111223
  42. 42.
    Wang Z Y, Liu K, Peng L H, Cao J J, Sun Y X, Qian Y X and Shi S Y. 2020. Analysis of mangrove annual changes in Guangdong province during 1986—2018 based on google earth engine. Tropical Geography, 40(5): 881-892
  43. 43.
    Xia Q, Qin C Z, Li H, Huang C and Su F Z. 2018. Mapping mangrove forests based on multi-tidal high-resolution satellite imagery. Remote Sensing, 10(9): 1343
  44. 44.
    Xu F, Zhang Y, Zhai L, Liu J and Gu X H. 2020. Extraction method of intertidal mangrove by using Sentinel－2 images. Bulletin of Surveying and Mapping, (2): 49-54
  45. 45.
    Yang S C, Lu W X, Zou Z and Li S. 2017. Mangrove wetlands: distribution, species composition and protection in China. Subtropical Plant Science, 46(4): 301-310
  46. 46.
    Zhang H K, Roy D P, Yan L, Li Z B, Huang H Y, Vermote E, Skakun S and Roger J C. 2018. Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences. Remote Sensing of Environment, 215: 482-494
  47. 47.
    Zhang L Y, Lei G P, Guo Y Y and Lu Z. 2021. Object-oriented land use classification based on Landsat images: a case study of the lower Liaohe Plain. Journal of Basic Science and Engineering, 29(2): 261-271
  48. 48.
    Zhang W, Chen Z H and Wang J K. 2015. Monitoring the areal variation of mangrove in Beibu Gulf coast of Guangxi China with remote sensing data. Journal of Guangxi University (Natural Science Edition), 40(6): 1570-1576
  49. 49.
    Zhang X H. 2016. Decision tree algorithm of automatically extracting mangrove forests information from Landsat 8 OLI imagery. Remote Sensing for Land and Resources, 28(2): 182-187
  50. 50.
    Zhao C P and Qin C Z. 2021. A detailed mangrove map of China for 2019 derived from Sentinel-1 and -2 images and Google Earth images. Geoscience Data Journal
  51. 51.
    Zhen J N, Liao J J and Shen G Z. 2018. Mapping mangrove forests of Dongzhaigang nature reserve in China using Landsat 8 and radarsat-2 Polarimetric SAR data. Sensors, 18(11): 4012
  52. 52.
    Zhou Z C. 2019. Research on the identification of mangrove forest based on multi-source remote sensing data —A case study of Zhanjiang Mangrove reserve. Changchun: Jilin University
  53. 53.
    Zhou Z C, Li H, Huang C, Liu Q S, Liu G H, He Y and Yu H. 2018. Review on dynamic monitoring of mangrove forestry using remote sensing. Journal of Geo-information Science, 20(11): 1631-1643

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