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Shallow water bathymetry based on a back propagation neural network and ensemble learning using multispectral satellite imagery

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Acta Oceanologica Sinica 2023年第5期42卷 154-165页

作者： Sensen Chu Liang Cheng Jian Cheng Xuedong Zhang Jie Zhang Jiabing Chen Jinming Liu Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Nanjing UniversityNanjing 210023China Collaborative Innovation Center of South China Sea Studies Nanjing UniversityNanjing 210093China School of Geography and Ocean Science Nanjing UniversityNanjing 210023China Jiangsu Center for Collaborative Innovation in Novel Software Technology and Industrialization Nanjing 210023China Zhejiang Institute of Hydraulics and Estuary Hangzhou 310020China Institute of Defense Engineering Academy of Military SciencesBeijing 100036China

The back propagation(BP)neural network method is widely used in bathymetry based on multispectral satellite ***,the classical BP neural network method faces a potential problem because it easily falls into a local minimum,leading to model training *** study confirmed that the local minimum problem of the BP neural network method exists in the bathymetry field and cannot be ***,to solve the local minimum problem of the BP neural network method,a bathymetry method based on a BP neural network and ensemble learning(BPEL)is ***,the remote sensing imagery and training sample were used as input datasets,and the BP method was used as the base learner to produce multiple water depth inversion ***,a new ensemble strategy,namely the minimum outlying degree method,was proposed and used to integrate the water depth inversion ***,an ensemble bathymetric map was *** Reef,northeastern Jiuzhang Atoll,and Pingtan coastal zone were selected as test cases to validate the proposed *** with the BP neural network method,the root-mean-square error and the average relative error of the BPEL method can reduce by 0.65–2.84 m and 16%–46%in the three test cases at *** results showed that the proposed BPEL method could solve the local minimum problem of the BP neural network method and obtain highly robust and accurate bathymetric maps.

关键词： bathymetry back propagation neural network ensemble learning local minimum problem multispectral satellite imagery

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Comparing leaf area index estimates in a Mediterranean forest using field measurements, Landsat 8, and Sentinel-2 data

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Ecological Processes 2023年第2期12卷 187-199页

作者： Alessandro Sebastiani Riccardo Salvati Fausto Manes Research Centre for Forestry and Wood(FL) Council for Agricultural Research and Economics(CREA) Research Institute On Terrestrial Ecosystems(IRET) National Research Council of Italy(CNR) Presidential Estate of Castelporziano Department of Environmental Biology Sapienza University of Rome

Background Leaf area index（LAI） is a key indicator for the assessment of the canopy's processes such as net primary production and evapotranspiration. For this reason, the LAI is often used as a key input parameter in ecosystem services' modeling, which is emerging as a critical tool for steering upcoming urban reforestation strategies. However, LAI field measures are extremely time-consuming and require remarkable economic and human resources. In this context, spectral indices computed using high-resolution multispectral satellite imagery like Sentinel-2 and Landsat 8, may represent a feasible and economic solution for estimating the LAI at the city scale. Nonetheless, as far as we know, only a few studies have assessed the potential of Sentinel-2 and Landsat 8 data doing so in Mediterranean forest ecosystems. To fill such a gap, we assessed the performance of 10 spectral indices derived from Sentinel-2 and Landsat 8 data in estimating the LAI, using field measurements collected with the LI-COR LAI 2200c as a reference. We hypothesized that Sentinel-2 data, owing to their finer spatial and spectral resolution, perform better in estimating vegetation's structural parameters compared to Landsat *** We found that Landsat 8-derived models have, on average, a slightly better performance, with the best model（the one based on NDVI） showing an R2of 0.55 and NRMSE of 14.74%, compared to R2of 0.52 and NRMSE of 15.15% showed by the best Sentinel-2 model, which is based on the NBR. All models were affected by spectrum saturation for high LAI values（e.g., above 5）.Conclusion In Mediterranean ecosystems, Sentinel-2 and Landsat 8 data produce moderately accurate LAI estimates during the peak of the growing season. Therefore, the uncertainty introduced using satellite-derived LAI in ecosystem services' assessments should be systematically accounted for.

关键词： Mediterranean forest Leaf area index Field measurement multispectral satellite imagery Sentinel-2 Landsat 8 Spectral vegetation index Global change

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