Generation of the Forest Cover Map of the Qinghai–Tibet Plateau Based on the Multisource Dataset and Random Forest

The Qinghai–Tibet Plateau (QTP) plays a significant role in global climate change and biodiversity conservation. As the third pole of the Earth, it has a wide range and complex terrain. QTP has a vertical distribution of vegetation, and its forest ecosystems play a key role in the region. Forest ext...

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Bibliographic Details
Published in:Frontiers in environmental science 2022-07, Vol.10
Main Authors: Guo, Yantao, Jiao, Weili, Long, Tengfei, Zhang, Xiaomei
Format: Article
Language:English
Subjects:
forest cover map
Google Earth engine
multisource dataset
Qinghai-Tibet plateau
random forest
Sentinel satellite images
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Summary:The Qinghai–Tibet Plateau (QTP) plays a significant role in global climate change and biodiversity conservation. As the third pole of the Earth, it has a wide range and complex terrain. QTP has a vertical distribution of vegetation, and its forest ecosystems play a key role in the region. Forest extraction in this region is still a comprehensive problem because of the phenological periods of different forest types in distinct regions of the QTP and the characteristics of frequent rain and cloudy weather in the south. Taking these factors into consideration, multiple features, including reflectance, spectral indices, statistical backscattering coefficients, topographic slope, and aspect, derived from a multisource dataset incorporating optical remote sensing data, synthetic radar, and digital elevation models, were applied to extract forest in the QTP based on the random forest (RF) classification method. As more than 30 features were involved, the 5-folded cross-validation method was used to determine the optimal parameters and features for the RF model. Using 14,919 forest samples and a multifeature optimized RF classification model, a 10-m resolution forest cover map of QTP in 2021 was generated based on the Sentinel series of satellite datasets and digital elevation model datasets on the Google Earth Engine (GEE) platform. After verification, the overall accuracy of the forest cover map generated in this article is 98.3%, and the Kappa coefficient is 0.95, which is better than the European Space Agency (ESA) WorldCover forest layer.
ISSN:2296-665X
2296-665X
DOI:10.3389/fenvs.2022.939151