سابِق|تال

Estimation of mangrove canopy chlorophyll content using hyperspectral image and stacking ensemble regression algorithm

  • role： First author第一作者
  • Affiliation：

    College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China

  • Email：fbl2012@126.com
  • Introduction：付波霖，研究方向为主被动湿地遥感应用。E-mail： fbl2012@126.com
FU Bolin1,  
  • Affiliation：

    College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China

DENG Liangchao1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：zhangli@radi.ac.cn
  • Introduction：张丽，研究方向为植被生态遥感，土地资源遥感调查。E-mail： zhangli@radi.ac.cn
ZHANG Li2*,  
  • Affiliation：

    College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China

QIN Jiaoling1,  
  • Affiliation：

    College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China

LIU Man1,  
  • Affiliation：

    Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130000, China

JIA Mingming3,  
  • Affiliation：

    College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China

HE Hongchang1,  
  • Affiliation：

    College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China

DENG Tengfang1,  
  • Affiliation：

    College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China

GAO Ertao1,  
  • Affiliation：

    College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China

FAN Donglin1

ملخص

Mangroves are one of the most productive and valuable wetland ecosystems in the world. Canopy Chlorophyll Content (CCC), as an important biophysical parameter, is an important indicator to evaluate mangroves’ productivity and health status.This study calculated traditional vegetation index, combined vegetation index using Zhuhai-1 Hyperspectral Satellite (OHS) and Sentinel-2A multispectral images, and produced high-dimensional datasets. Dimension reduction and variable selection were carried out by combining normal distribution test, correlation analysis, and importance evaluation of feature variables. The optimal inversion model of mangrove CCC in the Beibu Gulf was built using single linear regression, machine learning regression, and stacking ensemble learning regression algorithm. This study demonstrated the inversing accuracy difference between OHS image and Sentinel-2A data, and evaluated the applicability of SNAP-SL2P algorithm in mangrove CCC inversion.Results showed that (1) eight optimal feature variables are selected from the high-dimensional datasets of OHS image by statistical test, maximum correlation coefficient, and variable importance evaluation. Combining vegetation indexes (RSI(12,17), DSI(12,18), and NDSI(6,12)) highly contributes to the inversion of mangrove CCC. (2) The training accuracy of the GBRT-based optimal stacking ensemble learning regression model using OHS data (Score=0.999, RMSE=0.963 μg/cm2) was higher than that of the optimal RF regression model (RMSE reduced by 7.531 μg/cm2), which is better than the optimal Lasso linear regression model (RMSE reduced by 19.383 μg/cm2). (3) The inversion accuracy of the optimal stacking ensemble learning regression model using OHS data (R2=0.761, RMSE=16.738 μg/cm2) is higher than that from Sentinel-2A image (R2=0.615, RMSE=20.701 μg/cm2). (4) The optimal stacking ensemble learning regression model using OHS and Sentinel-2A data in estimating mangrove CCC outperforms the SNAP-SL2P algorithm (R2=0.356, RMSE=49.419 μg/cm2).The conclusion demonstrated that normal distribution test, maximum correlation coefficient method, and XGBoost-based feature selection method can effectively reduce the redundancy of high-dimensional datasets, and obtain the optimal feature variables. The optimal stack GBRT ensemble learning regression model with the OHS data has the highest training accuracy, which is the optimal inversion model for estimating CCC of the mangrove. The R2 of OHS and Sentinel-2A data is over 0.61, which indicated that OHS and Sentinel-2A data can effectively estimate mangrove CCC. SNAP-SL2P algorithm cannot effectively inverse mangrove CCC (R2 is less than 0.4) and systematically underestimates CCC value.

مفهوم

mangrove;canopy chlorophyll content;Zhuhai-1 Hyperspectral Satellite;stacking ensemble learning regression algorithm;feature dimension reduction;remote sensing inversion

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