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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1492560

Unlocking Vegetation Health: Optimizing GEDI Data for Accurate Chlorophyll content Estimation

Provisionally accepted
Cuifen Xia Cuifen Xia 1Wenwu Zhou Wenwu Zhou 2*Qingtai Shu Qingtai Shu 1*Zaikun Wu Zaikun Wu 1*Mingxing Wang Mingxing Wang 1*Li Xu Li Xu 1Zhengdao Yang Zhengdao Yang 1*Jinge Yu Jinge Yu 3*Hanyue Song Hanyue Song 4*Dandan Duan Dandan Duan 5*
  • 1 College of Forestry, Southwest Forestry University, Kunming, China
  • 2 Guangyuan Forestry Bureau, Guangyuan, China
  • 3 School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, Jiangsu Province, China
  • 4 College of Forestry, Fujian Agriculture and Forestry University, Fuzhou, Fujian Province, China
  • 5 Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China

The final, formatted version of the article will be published soon.

    Chlorophyll content serves as a vital indicator for evaluating vegetation health and estimating productivity. This study aims to effectively tackle the issue of GEDI data discreteness and explore its potential in estimating chlorophyll content. This study used the EBKRP method to obtain the continuous distribution of GEDI spot parameters in an unknown space. Initially, 52 measured sample data were employed to screen the modeling parameters with the Pearson and RF methods. Next, BO algorithm was applied to optimize the KNN regression model, RFR model, and GBRT model. These steps were taken to establish the most effective RS estimation model for chlorophyll content in Dendrocalamus giganteus (D. giganteus). The results showed that: (1) The R2 of EBKRP method was 0.34~0.99, RMSE was 0.012~3134.005, rRMSE was 0.011~0.854, and CRPS was 965.492~1626.887. (2) The Pearson method selects five parameters (cover, pai, fhd_normal, rv, and rx_energy_a3) with a correlation greater than 0.37. The RF method opts for five parameters (cover, fhd_normal, sensitivity, rh100, and modis_nonvegetated) with a contribution threshold greater than 5.5%. (3) The BO-GBRT model in the RF method was used as the best estimation model (R2 = 0.86, RMSE = 0.219 g/m2, rRMSE = 0.167 g/m2, P = 84.13%) to estimate and map the chlorophyll content of D. giganteus in the study area. The distribution range is 0.20 g/m2~2.50 g/m2. The findings aligned with the distribution of D. giganteus in the experimental area, indicating the reliability of estimating forest biochemical parameters GEDI data.

    Keywords: remote sensing1, EBKRP method2, Modeling factor selection3, Bayesian Optimization algorithm4, Chlorophyll content5, Estimation6

    Received: 09 Sep 2024; Accepted: 08 Nov 2024.

    Copyright: © 2024 Xia, Zhou, Shu, Wu, Wang, Xu, Yang, Yu, Song and Duan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Wenwu Zhou, Guangyuan Forestry Bureau, Guangyuan, China
    Qingtai Shu, College of Forestry, Southwest Forestry University, Kunming, China
    Zaikun Wu, College of Forestry, Southwest Forestry University, Kunming, China
    Mingxing Wang, College of Forestry, Southwest Forestry University, Kunming, China
    Zhengdao Yang, College of Forestry, Southwest Forestry University, Kunming, China
    Jinge Yu, School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, 210044, Jiangsu Province, China
    Hanyue Song, College of Forestry, Fujian Agriculture and Forestry University, Fuzhou, Fujian Province, China
    Dandan Duan, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China

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