干旱区生态环境知识资源中心(Arid): Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China

Arid

DOI	10.3390/f15020288
	Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China
	Liu, Lizhi; Zhang, Qiuliang; Guo, Ying; Li, Yu; Wang, Bing; Chen, Erxue; Li, Zengyuan; Hao, Shuai
通讯作者	Guo, Y
来源期刊	FORESTS
EISSN	1999-4907
出版年	2024
卷号	15 期号:2
英文摘要	Information about the distribution of coniferous forests holds significance for enhancing forestry efficiency and making informed policy decisions. Accurately identifying and mapping coniferous forests can expedite the achievement of Sustainable Development Goal (SDG) 15, aimed at managing forests sustainably, combating desertification, halting and reversing land degradation, and halting biodiversity loss. However, traditional methods employed to identify and map coniferous forests are costly and labor-intensive, particularly in dealing with large-scale regions. Consequently, a methodological framework is proposed to identify coniferous forests in northwestern Liaoning, China, in which there are semi-arid and barren environment areas. This framework leverages a multi-classifier fusion algorithm that combines deep learning (U2-Net and Resnet-50) and shallow learning (support vector machines and random forests) methods deployed in the Google Earth Engine. Freely available remote sensing images are integrated from multiple sources, including Gaofen-1 and Sentinel-1, to enhance the accuracy and reliability of the results. The overall accuracy of the coniferous forest identification results reached 97.6%, highlighting the effectiveness of the proposed methodology. Further calculations were conducted to determine the area of coniferous forests in each administrative region of northwestern Liaoning. It was found that the total area of coniferous forests in the study area is about 6013.67 km2, accounting for 9.59% of northwestern Liaoning. The proposed framework has the potential to offer timely and accurate information on coniferous forests and holds promise for informed decision making and the sustainable development of ecological environment.
英文关键词	coniferous forests semi-arid multi-classifier fusion Gaofen-1 Sentinel-1 Google Earth Engine
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001172086400001
WOS关键词	MACHINE ; SYSTEM ; SIGNAL ; SAR
WOS类目	Forestry
WOS研究方向	Forestry
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403771
推荐引用方式 GB/T 7714	Liu, Lizhi,Zhang, Qiuliang,Guo, Ying,et al. Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China[J],2024,15(2).
APA	Liu, Lizhi.,Zhang, Qiuliang.,Guo, Ying.,Li, Yu.,Wang, Bing.,...&Hao, Shuai.(2024).Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China.FORESTS,15(2).
MLA	Liu, Lizhi,et al."Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China".FORESTS 15.2(2024).