Remote Sensing in Ecology and Conservation (Jun 2023)

Mapping mangrove leaf area index (LAI) by combining remote sensing images with PROSAIL‐D and XGBoost methods

  • Demei Zhao,
  • Jianing Zhen,
  • Yinghui Zhang,
  • Jing Miao,
  • Zhen Shen,
  • Xiapeng Jiang,
  • Junjie Wang,
  • Jincheng Jiang,
  • Yuzhi Tang,
  • Guofeng Wu

DOI
https://doi.org/10.1002/rse2.315
Journal volume & issue
Vol. 9, no. 3
pp. 370 – 389

Abstract

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Abstract Leaf area index (LAI) is a vital parameter reflecting vegetation structure, physio‐ecological process and growth development. Accurate estimation of mangrove LAI is fundamental for assessing the ecological restoration and sustainable development of mangrove ecosystems. To date, very few studies have explored the hybrid method of radiative transfer model (RTM) and machine‐learning model in retrieving mangrove LAI with different satellite sensors. This study investigated the capabilities of combining the PROSAIL‐D model, XGBoost (extreme gradient boosting) and remote sensing images in estimating mangrove LAI, considering the spatial resolutions and spectral vegetation indices (VIs) of Landsat‐8, Sentinel‐2, Worldview‐2 and Zhuhai‐1 images, and further explored the role of eco‐environmental factors in the spatial distribution of LAI in Gaoqiao Mangrove Reserve, China. The results showed that the Zhuhai‐1 acquires the best estimation accuracy (RVal2 (the determination coefficient of validation) = 0.86, RPD (residual prediction deviation) = 3.36 and RMSE (root mean square error) = 0.31), followed by Worldview‐2 (RVal2 = 0.84, RPD = 2.64 and RMSE = 0.33), Sentinel‐2 (RVal2 = 0.34, RPD = 1.33 and RMSE = 0.62) and Landsat‐8 (RVal2 = 0.29, RPD = 1.03 and RMSE = 0.71). The newly developed three‐band VIs (B443−B864/B443+B864−2×B561 with Landsat‐8, B490−B842/B490+B842−2×B705 with Sentinel‐2, B427−B832/B908−B832 with Worldview‐2 and B896−B700/B776−B700 with Zhuhai‐1) were efficient estimators of mangrove LAI. Moreover, elevation and species composition could greatly affect the spatial distribution of mangrove LAI. We concluded that the hybrid method of PROSAIL‐D and XGBoost model using VIs derived from Zhuhai‐1 hyperspectral image could be deemed as basic method and input variables of mapping mangrove LAI, and could be effectively and widely applied in generating mangrove LAI products at the regional and national scales.

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