AUTHOR=Fu Bolin , Sun Jun , Wang Yeqiao , Yang Wenlan , He Hongchang , Liu Lilong , Huang Liangke , Fan Donglin , Gao Ertao 

TITLE=Evaluation of LAI Estimation of Mangrove Communities Using DLR and ELR Algorithms With UAV, Hyperspectral, and SAR Images

JOURNAL=Frontiers in Marine Science

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2022.944454

DOI=10.3389/fmars.2022.944454

ISSN=2296-7745

ABSTRACT=Abstract:
The high-precision estimation of mangrove leaf area index (LAI) using deep learning regression algorithm (DLR) always requires a large number of training samples data. However, it’s difficult for LAI field measurements to collect sufficient amount of sample data in mangrove wetlands. To tackle this challenge, this paper proposed an approach for expanding training samples, and quantitatively evaluated the performance of estimating LAI for mangrove communities between Deep Neural Networks (DNN) and Transformer algorithms. This study also explored the effects of unmanned aerial vehicle (UAV) and Sentinel-2A multispectral, Orbital Hyper Spectral (OHS) and GF-3 SAR images on LAI estimation of different mangrove communities. Finally, this paper evaluated the LAI estimation ability of mangrove communities between ensemble learning regression (ELR) and DLR algorithms. The results showed that: (1) the UAV images achieved the better LAI estimation of different mangrove communities (R2=0.5974~0.6186), and GF-3 SAR images were better for LAI estimation of Avicennia marina with high coverage (R2=0.567). The optimal spectral range for estimating LAI for mangroves in the optical images was between 650nm~680nm. (2) The ELR model outperformed single base model, and produced the high-accuracy LAI estimation (R2=0.5266~0.713) for different mangrove communities. (3) the average accuracy (R2) of the ELR model was higher 0.0019~0.149 than the DLR models, which demonstrated that the ELR model had a better capability (R2=0.5865~0.6416) in LAI estimation. The Transformer-based LAI estimation of Avicennia marina (R2=0.6355) was better than DNN model, while DNN model produced higher accuracy for Kandelia candel (KC) (R2=0.5577). (4) With the increase of expansion ratio of the training sample (10%~50%), the LAI estimation accuracy (R2) of DNN and Transformer models for different mangrove communities increased by 0.1166~0.2037 and 0.1037~0.1644, respectively. Under the condition of the same estimation accuracy, the sample enhancement method in this paper could reduce by 20%~40% of filed measurements.