سابِق|تال

Mapping high-resolution mangrove forests in China using GF-2 imagery under the tide

  • role： First author第一作者
  • Affiliation：

    School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China

  • Email：xiaqing@csust.edu.cn
  • Introduction：夏清，研究方向为滨海湿地遥感。E-mail：xiaqing@csust.edu.cn
XIA Qing1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Hydraulic Engineering, Yunnan Agricultural University, Kunming 650201, China

  • Email：wenniforever@126.com
  • Introduction：李建华，研究方向为土地利用与环境遥感。E-mail：wenniforever@126.com
LI Jianghua2*,  
  • Affiliation：

    School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China

DAI Shuo1,  
  • Affiliation：

    School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China

ZHANG Han1,  
  • Affiliation：

    School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China

XING Xuemin1

ملخص

Remote sensing technology is widely used in mangrove forest mapping with the advantages of real-time mapping, accuracy, multiscalability, and repeatability. Until now, the mangrove forest dataset with the highest spatial resolution in China is produced by 10 m Sentinel data. In addition, most existing mangrove forest datasets in China ignore the influence of tide, leading to low spatial resolution and inaccurate mapping. On the basis of Chinese GF-2 images, this study aims to map Chinese mangrove forests in 2020 with a spatial resolution of 1 m under the tide. Specifically, 312 scenes of GF-2 image covering China's coastline (24 scenes of GF-1 image covers the areas without GF-2 images) were selected. First, the selected images were segmented using the object-based multiscale method, and the submerged mangrove recognition index was used as a tidal influence indicator. Finally, high-resolution mapping of mangrove forests in China was conducted using the random forest classifier. Results show that the mangrove area in China in 2020 was 29,576.48 ha, 95% of which was mainly distributed in the Guangxi, Guangdong, and Hainan Provinces. The overall classification accuracy was 92%, and the Kappa coefficient was 0.89. The mangrove area without tidal influence was 2,531.24 ha less than that with tidal influence. The high-resolution mangrove dataset generated in this study can provide high-precision data support for the monitoring, management, and evaluation of mangrove ecosystems in China, and it is valuable for practical application.

مفهوم

remote sensing;GF-2;Mangrove forests;object-oriented;tide;random forest classifier

References

  1. 1.
    Behera M D, Barnwal S, Paramanik S, Das P, Bhattyacharya B K, Jagadish B, Roy P S, Ghosh S M and Behera S K. 2021. Species-level classification and mapping of a mangrove forest using random forest—utilisation of AVIRIS-NG and sentinel data. Remote Sensing, 13(11): 2027
  2. 2.
    Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1): 2-16
  3. 3.
    Brown M I, Pearce T, Leon J, Sidle R and Wilson R. 2018. Using remote sensing and traditional ecological knowledge (TEK) to understand mangrove change on the Maroochy River, Queensland, Australia. Applied Geography, 94: 71-83
  4. 4.
    Cao J J, Leng W C, Liu K, Liu L, He Z and Zhu Y H. 2018. Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sensing, 10(1): 89
  5. 5.
    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
  6. 6.
    Chen G, Weng Q H, Hay G J and He Y N. 2018. Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities. GIScience and Remote Sensing, 55(2): 159-182
  7. 7.
    Conchedda G, Durieux L and Mayaux P. 2008. An object-based method for mapping and change analysis in mangrove ecosystems. ISPRS Journal of Photogrammetry and Remote Sensing, 63(5): 578-589
  8. 8.
    Diniz C, Cortinhas L, Nerino G, Rodrigues J, Sadeck L, Adami M and Souza-Filho P W M. 2019. Brazilian mangrove status: three decades of satellite data analysis. Remote Sensing, 11(7): 808
  9. 9.
    Elmahdy S I, Ali T A, Mohamed M M, Howari F M, Abouleish M and Simonet D. 2020. Spatiotemporal mapping and monitoring of mangrove forests changes from 1990 to 2019 in the Northern Emirates, UAE using random forest, kernel logistic regression and naive Bayes tree models. Frontiers in Environmental Science, 8: 102
  10. 10.
    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
  11. 11.
    Ghorbanian A, Zaghian S, Asiyabi R M, Amani M, Mohammadzadeh A and Jamali S. 2021. Mangrove ecosystem mapping using Sentinel-1 and Sentinel-2 satellite images and random forest algorithm in Google Earth Engine. Remote Sensing, 13(13): 2565
  12. 12.
    Gilani H, Naz H I, Arshad M, Nazim K, Akram U, Abrar A and Asif M. 2021. Evaluating mangrove conservation and sustainability through spatiotemporal (1990-2020) mangrove cover change analysis in Pakistan. Estuarine, Coastal and Shelf Science, 249: 107128
  13. 13.
    Giri C, Long J and Tieszen L. 2011. Mapping and monitoring Louisiana’s mangroves in the aftermath of the 2010 Gulf of Mexico Oil Spill. Journal of Coastal Research, 27(6): 1059-1064
  14. 14.
    Green E P, Clark C D, Mumby P J, Edwards A J and Ellis A C. 1998. Remote sensing techniques for mangrove mapping. International Journal of Remote Sensing, 19(5): 935-956
  15. 15.
    Hauser L T, Binh N A, Hoa P V, Quan N H and Timmermans J. 2020. Gap-free monitoring of annual mangrove forest dynamics in Ca Mau province, Vietnamese Mekong delta, using the Landsat-7-8 archives and post-classification temporal optimization. Remote Sensing, 12(22): 3729
  16. 16.
    Heumann B W. 2011. Satellite remote sensing of mangrove forests: recent advances and future opportunities. Progress in Physical Geography, 35(1): 87-108
  17. 17.
    Hossain M D and Chen D M. 2019. Segmentation for object-based image analysis (OBIA): a review of algorithms and challenges from remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing, 150: 115-134
  18. 18.
    Huang K, Meng X Z, Yang G, Sun W W. Spatio-temporal probability threshold method of remote sensing for mangroves mapping in China. National Remote Sensing Bulletin, 2022, 26(6): 1083-1095.
  19. 19.
    Jia K, Chen S M, Jiang W G. Long time-series remote sensing monitoring of mangrove forests in the Guangdong-Hong Kong-Macao Greater Bay Area, National Remote Sensing Bulletin, 2022, 26(6): 1096-1111
  20. 20.
    Jia M M, Wang Z M, Mao D H, Huang C L and Lu C Y. 2021. Spatial-temporal changes of China’s mangrove forests over the past 50 years: an analysis towards the Sustainable Development Goals (SDGs). Chinese Science Bulletin, 66(30): 3886-3901
  21. 21.
    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
  22. 22.
    Jiang Y F, Zhang L, Yan M, Qi J G, Fu T M, Fan S X and Chen B W. 2021. High-resolution mangrove forests classification with machine learning using Worldview and UAV hyperspectral data. Remote Sensing, 13(8): 1529
  23. 23.
    Kamal M, Phinn S and Johansen K. 2015. Object-based approach for multi-scale mangrove composition mapping using multi-resolution image datasets. Remote Sensing, 7(4): 4753-4783
  24. 24.
    Kirui K B, Kairo J G, Bosire J, Viergever K M, Rudra S, Huxham M and Briers R A. 2013. Mapping of mangrove forest land cover change along the Kenya coastline using Landsat imagery. Ocean and Coastal Management, 83: 19-24
  25. 25.
    Koedsin W and Vaiphasa C. 2013. Discrimination of tropical mangroves at the species level with EO-1 Hyperion data. Remote Sensing, 5(7): 3562-3582
  26. 26.
    Kong H and Liu J B. 2021. High spatial resolution remote sensing image classification based on pixel shape index method. Journal of Physics: Conference Series, 1952: 022054
  27. 27.
    Kuenzer C, Bluemel A, Gebhardt S, Quoc T V and Dech S. 2011. Remote sensing of mangrove ecosystems: a review. Remote Sensing, 3(5): 878-928.
  28. 28.
    Li B. 2016. A multiple bands pixel shape index for classification of LiDAR and hyperspectral data. Geomatics Science and Technology, 4(4): 117-127
  29. 29.
    Li H Y, Jia M M, Zhang R, Ren Y X and Wen X. 2019a. 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
  30. 30.
    Li T H, Zhao Z J and Han P. 2002. Detection and analysis of mangrove changes with multi-temporal remotely sensed imagery in the Shenzhen river estuary. Journal of Remote Sensing, 6(5): 364-369
  31. 31.
    Li W Z, El-Askary H, Qurban M A, Li J J, ManiKandan K P and Piechota T. 2019b. Using multi-indices approach to quantify mangrove changes over the Western Arabian Gulf along Saudi Arabia coast. Ecological Indicators, 102: 734-745
  32. 32.
    Lin P and Fu Q. 1995. Environmental Ecology and Economic Utilization of Mangroves in China. Beijing: Higher Education Press
  33. 33.
    Long J B and Giri C. 2011. Mapping the Philippines’ mangrove forests using Landsat imagery. Sensors, 11(3): 2972-2981
  34. 34.
    Maurya K, Mahajan S and Chaube N. 2021. Remote sensing techniques: mapping and monitoring of mangrove ecosystem—a review. Complex and Intelligent Systems, 7(6): 2797-2818
  35. 35.
    Monsef H A E and Smith S E. 2017. A new approach for estimating mangrove canopy cover using Landsat 8 imagery. Computers and Electronics in Agriculture, 135: 183-194
  36. 36.
    Neukermans G, Dahdouh‐Guebas F, Kairo J G and Koedam N. 2008. Mangrove species and stand mapping in Gazi Bay (Kenya) using Quickbird satellite imagery. Journal of Spatial Science, 53(1): 75-86
  37. 37.
    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
  38. 38.
    Pimple U, Simonetti D, Sitthi A, Pungkul S, Leadprathom K, Skupek H, Som-ard J, Gond V and Towprayoon S. 2018. Google earth engine based three decadal Landsat imagery analysis for mapping of mangrove forests and its surroundings in the trat province of Thailand. Journal of Computer and Communications, 6(1): 247-264 RogersK, LymburnerL, SalumR, BrookeB P and WoodroffeCD. 2017. Mapping of mangrove extent and zonation using high and low tide composites of Landsat data. Hydrobiologia, 803(1): 49-68
  39. 39.
    Sun W H, Chen B and Messinger D. 2014. Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images. Optical Engineering, 53(1): 013107
  40. 40.
    Tang H L, Liu K, Zhu Y H, Wang S G, Liu L and Song S. 2015. Mangrove community classification based on WorldView-2 image and SVM method. Acta Scientiarum Naturalium Universitatis Sunyatseni, 54(4): 102-111
  41. 41.
    Tuominen S and Pekkarinen A. 2005. Performance of different spectral and textural aerial photograph features in multi-source forest inventory. Remote Sensing of Environment, 94(2): 256-268
  42. 42.
    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
  43. 43.
    Wang D Z, Wan B, Qiu P H, Su Y J, Guo Q H and Wu X C. 2018. Artificial mangrove species mapping using pléiades-1: an evaluation of pixel-based and object-based classifications with selected machine learning algorithms. Remote Sensing, 10(2): 294
  44. 44.
    Wang H, Ren G B, Wu P Q, Liu A C, Pan L H, Ma Y M, Ma Y and Wang J J. 2020. Analysis on the remote sensing monitoring and landscape pattern change of mangrove in China from 1990 to 2019. Journal of Ocean Technology, 39(5): 1-12
  45. 45.
    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
  46. 46.
    Wang L, Shi C, Tian J Y, Song X N, Jia M M, Li X J, Liu X M, Zhong R F, Yin D M, Yang S S and Guo X X. 2018. Researches on mangrove forest monitoring methods based on multi-source remote sensing. Biodiversity Science, 26(8): 838-849
  47. 47.
    Wang L, Sousa W P, Gong P and Biging G S. 2004. Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama. Remote Sensing of Environment, 91(3/4): 432-440
  48. 48.
    Watkins B and Van Niekerk A. 2019. A comparison of object-based image analysis approaches for field boundary delineation using multi-temporal Sentinel-2 imagery. Computers and Electronics in Agriculture, 158: 294-302
  49. 49.
    Xia Q, Jia M M, HE T T, Xing X M and Zhu L J. 2021. Effect of tide level on submerged mangrove recognition index using multi-temporal remotely-sensed data. Ecological Indicators, 131: 108169 [DOI: 10.1016/j.ecolind.2021.108169].
  50. 50.
    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
  51. 51.
    Xue C H, Qian S Y. Fusion of Landsat 8 and Sentinel-2 data for mangrove phenology information extraction and classification. National Remote Sensing Bulletin, 2022, 26(6): 1121-1142.
  52. 52.
    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
  53. 53.
    Zhang D S, Cong L X, Wang Z Q, Chen H and Wang F. 2012. Object-oriented Zhangjiangkou mangrove communities classification using QuickBird imagery. Advanced Materials Research, 605-607: 2274-2278
  54. 54.
    Zhang X H and Tian Q J. 2013. A mangrove recognition index for remote sensing of mangrove forest from space. Research Communications, 105(8): 1149-1155
  55. 55.
    Zhang X H, Treitz P M, Chen D M, Quan C, Shi L X and Li X H. 2017. Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure. International Journal of Applied Earth Observation and Geoinformation, 62: 201-214
  56. 56.
    Zhao C P and Qin C Z. 2020. 10-m-resolution mangrove maps of China derived from multi-source and multi-temporal satellite observations. ISPRS Journal of Photogrammetry and Remote Sensing, 169: 389-405

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website