Remote Sensing Classification and Mapping of Forest Dominant Tree Species in the Three Gorges Reservoir Area of China Based on Sample Migration and Machine Learning

The distribution of forest-dominant tree species is crucial for ecosystem assessment. Remote sensing monitoring requires annual ground sample data, but consistent field surveys are challenging. This study addresses this by combining sample migration learning and machine learning for multi-year tree...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-07, Vol.16 (14), p.2547
Main Authors: Zhang, Wenbo, Liu, Xiaohuang, Xu, Bin, Liu, Jiufen, Li, Hongyu, Zhao, Xiaofeng, Luo, Xinping, Wang, Ran, Xing, Liyuan, Wang, Chao, Zhao, Honghui
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Canyons
change detection
China
Classification
Coniferous trees
Dominant species
Ecosystem assessment
ecosystems
Eucalyptus
forest dominant tree species
forest ecosystem
forest trees
forests
Geographical distribution
Learning algorithms
Machine learning
Migration
migration learning
Pine
Plant species
Remote monitoring
Remote sensing
Reservoirs
Species classification
Support vector machines
wetlands
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Summary:The distribution of forest-dominant tree species is crucial for ecosystem assessment. Remote sensing monitoring requires annual ground sample data, but consistent field surveys are challenging. This study addresses this by combining sample migration learning and machine learning for multi-year tree species classification in the Three Gorges Reservoir area in China. Using the continuous change detection and classification (CCDC) algorithm, sample data from 2023 were successfully migrated to 2018–2022, achieving high migration accuracy (R2 = 0.8303, RMSE = 4.64). Based on migrated samples, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) algorithms classified forest tree species with overall accuracies above 70% and Kappa coefficients above 0.6. XGB. They outperformed other algorithms, with classification accuracy of over 80% and Kappa above 0.75 in almost all years. The final map indicates stable distribution from 2018 to 2023, with eucalyptus covering over 40% of the forest area, followed by horsetail pine, fir, cypress, and wetland pine.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16142547