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• W4293232017 abstract "Mangroves play an extremely important role in purifying the atmosphere and responding to global temperature changes. The analysis of chemical elements (carbon, nitrogen, phosphorus, etc.) in mangroves is an effective way to investigate physiological activities, such as vegetation growth, development, and material metabolism. Therefore, the monitoring of mangrove stoichiometry is extremely important for mangrove restoration. Here, two mangrove species, Kandelia candel (KC) and Aegiceras corniculatum (AC), were studied in the Quanzhou Bay Estuary Wetland Nature Reserve. Two machine learning models [random forest (RF) and back propagation neural network (BPNN)] and partial least squares (PLS) were established with the original spectral data as independent variables, and the optimal model was selected by comparing the simple cross-validation VEcv, the ratio of performance to deviation, and the root mean square error (RMSE). The results showed that: (1) the contents of total phosphorous and total nitrogen decreased gradually and the content of total carbon of mangroves increased gradually with an increase in age at restoration; (2) hyperspectral modeling can invert the ecological stoichiometries of KC and AC, and it can be used to effectively monitor the growth status of the species studied; and (3) model performance ranking, PLS > RF > BPNN, where PLS (VEcv ≥ 0.60 and RMSE < 40) was significantly better than the other two models, and BPNN was the least effective and not suitable for hyperspectral inversion modeling of KC and AC ecological stoichiometries. This study provides a methodological basis for long-term and large-scale dynamic monitoring of mangrove ecological stoichiometries and mangrove restoration quality based on hyperspectral data." @default.
• W4293232017 created "2022-08-27" @default.
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• W4293232017 date "2022-08-25" @default.
• W4293232017 modified "2023-10-15" @default.
• W4293232017 title "Hyperspectral prediction of mangrove leaf stoichiometries in different restoration areas based on machine learning models" @default.
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• W4293232017 doi "https://doi.org/10.1117/1.jrs.16.034525" @default.
• W4293232017 hasPublicationYear "2022" @default.
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