Précédent|Prochain

Construction method of a Spartina alterniflora index based on hyperspectral satellite images

  • role： First author第一作者
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

  • Email：935863330@qq.com
  • Introduction：邵春晨，研究方向为遥感影像处理和信息提取。E-mail： 935863330@qq.com
SHAO Chunchen1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

    Donghai Academy, Ningbo University, Ningbo 315211, China

    Ningbo Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research, Ningbo 315211, China

  • Email：yanggang@nbu.edu.cn
  • Introduction：杨刚，研究方向为遥感影像数据质量改善与信息提取、遥感滨海健康监测技术与应用。E-mail： yanggang@nbu.edu.cn
YANG Gang123*,  
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

    Donghai Academy, Ningbo University, Ningbo 315211, China

    Ningbo Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research, Ningbo 315211, China

SUN Weiwei123,  
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

ZUO Yangyan1,  
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

GE Weiting1,  
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

YANG Susu1

résumé

Spartina alterniflora, an introduced coastal wetland plant, has rapidly propagated and expanded in recent years due to its strong adaptability and tolerance to climate and environment. Owing to the rapid reproduction and expansion of S. alterniflora, it has invaded the ecological niche of native vegetation and caused serious damage to the local ecosystem. Remote sensing technology can realize long-term, large-scale, real-time dynamic and accurate surveys and be effectively applied to the precise monitoring of S. alterniflora. Furthermore, it can provide guidance and a basis for the management of S. alterniflora and the restoration of coastal wetland ecosystems. Wetland management provides accurate, real-time, and dynamic information and technical support.To solve the problem of rapid and precise identification of S. alterniflora, this study proposed a method to construct the growth period of a S. alterniflora index based on ZY1-02D hyperspectral data. On the basis of the characteristics of S. alterniflora and other salt marsh wetland vegetation in near-infrared and short-wavelength infrared bands, the differentially sensitive bands were selected to construct an S. alterniflora index, increasing the spectral difference between S. alterniflora and other salt marsh vegetation in the complex coastal wetland environment. This outcome effectively reduces the problem of difficult vegetation discrimination caused by the phenomenon of different spectra of the same objects and the same spectrum of different objects in surface cover.In this study, two national nature reserves, the Yellow River Delta wetland and Yancheng coastal wetland, were selected as the study area, and ZY1-02D hyperspectral images of September were selected as the data source. First, image preprocessing was performed to obtain the reflectance data of the study area. Second, the differentially sensitive bands were determined, the Growing Period Normalized Difference Spartina Alterniflora Index (GNDSAI) was constructed, and the decision tree was constructed to extract the information of S. alterniflora accurately. Finally, the qualitative and quantitative accuracy of the classification results were evaluated, with Normalized Differential Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Support Vector Machine (SVM), Random Forest (RF), Maximum Likelihood Classifier (MLC), and Artificial Neural Network (ANN) selected for comparative evaluation.Experimental results show that the proposed method has strong regional adaptability in the two study areas. S. alterniflora can be well separated from other wetland salt marsh vegetation in the box plot of the GNDSAI value. The optimal lower threshold of GNDSAI for the Yellow River Delta wetland is 0.4 and that of the Yancheng coastal wetland is 0.27. The average producer’s accuracy and user’s accuracy of S. alterniflora were 92.00% and 91.68%, respectively, which were better than those of the other classification methods. Spectral heterogeneity of S. alterniflora, selection of band combinations, variability of thresholds, and influence and uncertainty of tide levels are discussed in the text.In this study, GNDSAI was proposed using ZY1-02D hyperspectral images. Considering the phenological characteristics of S. alterniflora, GNDSAI was constructed by using the four bands of near-infrared (765 nm), near-infrared (842 nm), short-wave infrared (1644 nm), and short-wave infrared (2216 nm) to enhance the difference between S. alterniflora and other wetland salt marsh vegetation through the calculation of spectral bands. Combined with MNDWI and prior knowledge, the decision tree classification model based on GNDSAI was designed, which realized simple, rapid, and accurate extraction of S. alterniflora, providing a new idea or method for information extraction of S. alterniflora.

mots-clés

remote sensing;spartina alterniflora;vegetation index;spartina alterniflora index;hyperspectral data;ZY1-02D

References

  1. 1.
    Bannari A, Morin D, Bonn F and Huete A R. 1995. A review of vegetation indices. Remote Sensing Reviews, 13(1/2): 95-120
  2. 2.
    Belgiu M and Drăguţ L. 2016. Random forest in remote sensing: a review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24-31
  3. 3.
    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
  4. 4.
    Feyisa G L, Meilby H, Fensholt R and Proud S R. 2014. Automated Water Extraction Index: a new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140: 23-35
  5. 5.
    Han Y, Ke Y H, Wang Z P, Liang D Y and Zhou D M. 2023. Classification of the Yellow River Delta wetland landscape based on ZY1-02D hyperspectral imagery. National Remote Sensing Bulletin, 27(6): 1387-1399
  6. 6.
    Hou T T, Sun W W, Chen C, Yang G, Meng X C and Peng J T. 2022. Marine floating raft aquaculture extraction of hyperspectral remote sensing images based decision tree algorithm. International Journal of Applied Earth Observation and Geoinformation, 111: 102846
  7. 7.
    Huete A R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3): 295-309
  8. 8.
    Kaufman Y J and Tanré D. 1992. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, 30(2): 261-270
  9. 9.
    Li J, Gao S and Wang Y P. 2010. Invading cord grass vegetation changes analyzed from Landsat-TM imageries: a case study from the Wanggang area, Jiangsu coast, eastern China. Acta Oceanologica Sinica, 29(3): 26-37
  10. 10.
    Li N, Li L W, Zhang Y L and Wu M. 2020. Monitoring of the invasion of Spartina alterniflora from 1985 to 2015 in Zhejiang Province, China. BMC Ecology, 20(1): 7
  11. 11.
    Li Q Q, Lu Y, Hu S B, Hu Z W, Li H Z, Liu P, Shi T Z, Wang C S, Wang J J and Wu G F. 2016. Review of remotely sensed geo-environmental monitoring of coastal zones. Journal of Remote Sensing, 20(5): 1216-1229
  12. 12.
    Li Y, Chen Y N and Li Y. 2017. Remote sensing analysis of the changes in the ecotone of mangrove forests and Spartina alterniflora saltmarshes. Marine Science Bulletin, 36(3): 348-360
  13. 13.
    Liu M Y, Li H Y, Li L, Man W D, Jia M M, Wang Z M and Lu C Y. 2017. Monitoring the invasion of Spartina alterniflora using multi-source high-resolution imagery in the Zhangjiang Estuary, China. Remote Sensing, 9(6): 539
  14. 14.
    Liu R Q, Li J L, Sun C, Sun W W, Cao L D and Tian P. 2021. Classification of Yancheng coastal wetland vegetation based on vegetation phenological characteristics derived from Sentinel-2 time-series, Acta Geographica Sinica, 76(7): 1680-1692
  15. 15.
    Long Y J, Li W L, Huang R Q, Xu Q, Yu B and Liu G. 2020. Automatic extraction and evolution trend analysis of landslides in Mianyuan River Basin in the 10 years after Wenchuan Earthquake. Geomatics and Information Science of Wuhan University, 45(11): 1792-1800
  16. 16.
    Maxwell A E, Warner T A and Fang F. 2018. Implementation of machine-learning classification in remote sensing: an applied review. International Journal of Remote Sensing, 39(9): 2784-2817
  17. 17.
    Meng X Z, Liu D, Huang K, Yang G, Sun W W and Lu W H. 2021. Extraction method of Spartina alterniflora based on vegetation phenology characteristics: a case study of wetlands in the Changjiang River Delta. Marine Science Bulletin, 40(5): 591-600
  18. 18.
    Parikh J A, DaPonte J S, Damodaran M and Sherman P. 1990. Application of neural networks to pattern recognition problems in remote sensing and medical imagery//Proceedings Volume 1294, Applications of Artificial Neural Networks. Orlando: SPIE: 146-160
  19. 19.
    Ren G B, Wang J J, Wang A D, Wang J B, Zhu Y L, Wu P Q, Ma Y and Zhang J. 2019. Monitoring the invasion of smooth cordgrass Spartina alterniflora within the modern Yellow River Delta using remote sensing. Journal of Coastal Research, 90(SI): 135-145
  20. 20.
    Ren G B, Zhou L, Liang J, Lu F, Wang A D, Wang J B, Li X M and Ma Y. 2021. Monitoring the invasion of Spartina alterniflora using hyperspectral remote sensing image of GF-5. Advances in Marine Science, 39(2): 312-326
  21. 21.
    Sun W W, Liu K, Ren G B, Liu W W, Yang G, Meng X C and Peng J T. 2021. A simple and effective spectral-spatial method for mapping large-scale coastal wetlands using China ZY1-02D satellite hyperspectral images. International Journal of Applied Earth Observation and Geoinformation, 104: 102572
  22. 22.
    Sun W W, Liu W W, Wang Y M, Zhao R, Huang M Z, Wang Y, Yang G and Meng X C. 2023. Research progress and prospects of hyperspectral remote sensing for global wetland from 2010 to 2022. National Remote Sensing Bulletin, 27(6): 1281-1299
  23. 23.
    Sun W W, Yang G, Chen C, Chang M H, Huang K, Meng X Z and Liu L Y. 2020. Development status and literature analysis of China’s earth observation remote sensing satellites. Journal of Remote Sensing (Chinese), 24(5): 479-510
  24. 24.
    Tian Y C, Jia K, Wu B F and Li Q Z. 2010. Study on spectral reflectance characteristics of hemp canopies. Spectroscopy and Spectral Analysis, 30(12): 3334-3337
  25. 25.
    Tian Y L, Jia M M, Wang Z M, Mao D H, Du B J and Wang C. 2020. Monitoring invasion process of Spartina alterniflora by seasonal Sentinel-2 imagery and an object-based random forest classification. Remote Sensing, 12(9): 1383
  26. 26.
    Tong Q X, Zheng L F, Wang J N, Wang X J, Dong W D, Hu Y M and Dang S X. 1997. Study on imaging spectrometer remote sensing information for wetland vegetation. Journal of Remote Sensing, 1(1): 50-57
  27. 27.
    Wang A Q, Chen J D, Jing C W, Ye G Q, Wu J P, Huang Z X and Zhou C S. 2015. Monitoring the invasion of Spartina alterniflora from 1993 to 2014 with Landsat TM and SPOT 6 satellite data in Yueqing Bay, China. PLoS One, 10(8): e0135538
  28. 28.
    Wang L, Dronova I, Gong P, Yang W B, Li Y R and Liu Q. 2012. A new time series vegetation-water index of phenological-hydrological trait across species and functional types for Poyang Lake wetland ecosystem. Remote Sensing of Environment, 125: 49-63
  29. 29.
    Wu N, Shi R H, Zhuo W, Zhang C, Zhou B C, Xia Z L, Tao Z, Gao W and Tian B. 2021. A classification of tidal flat wetland vegetation combining phenological features with Google Earth Engine. Remote Sensing, 13(3): 443
  30. 30.
    Xu H Q. 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14): 3025-3033 [10. 1080/01431160600589179]
  31. 31.
    Yuan H W, Li S Z, Zheng H Z and Fang Z Y. 2009. Evaluation of the influences of foreign Spartina alterniflora on ecosystem of Chinese coastal wetland and its countermeasures. Marine Science Bulletin, 28(6): 122-128
  32. 32.
    Zhang L, Gong Z N, Wang Q W, Jin D D and Wang X. 2019. Wetland mapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images. Journal of Remote Sensing, 23(2): 313-326
  33. 33.
    Zheng J H, Sun C, Lin J, Li L and Liu Y C. 2023. Classification of salt marsh vegetation based on pixel-level time series from Landsat images. National Remote Sensing Bulletin, 27(6): 1400-1413

Lire l'article complet

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