| Autor: |
Huazhe Li, Lijuan Cui, Zhiguo Dou, Junjie Wang, Xiajie Zhai, Jing Li, Xinsheng Zhao, Yinru Lei, Jinzhi Wang, Wei Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Forests, Vol 14, Iss 8, p 1566 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1999-4907 |
| DOI: |
10.3390/f14081566 |
| Popis: |
Mangroves have important roles in regulating climate change, and in reducing the impact of wind and waves. Analysis of the chlorophyll content of mangroves is important for monitoring their health, and their conservation and management. Thus, this study aimed to apply four regression models, eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Partial Least Squares (PLS) and Adaptive Boosting (AdaBoost), to study the inversion of Soil Plant Analysis Development (SPAD) values obtained from near-ground hyperspectral data of three dominant species, Bruguiera sexangula (Lour.) Poir. (B. sexangula), Ceriops tagal (Perr.) C. B. Rob. (C. tagal) and Rhizophora apiculata Blume (R. apiculata) in Qinglan Port Mangrove Nature Reserve. The accuracy of the model was evaluated using R2, RMSE, and MAE. The mean SPAD values of R. apiculata (SPADavg = 66.57), with a smaller dispersion (coefficient of variation of 6.59%), were higher than those of C. tagal (SPADavg = 61.56) and B. sexangula (SPADavg = 58.60). The first-order differential transformation of the spectral data improved the accuracy of the prediction model; R2 was mostly distributed in the interval of 0.4 to 0.8. The accuracy of the XGBoost model was less affected by species differences with the best stability, with RMSE at approximately 3.5 and MAE at approximately 2.85. This study provides a technical reference for large-scale detection and management of mangroves. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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