文献类型：期刊文献

英文题名：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

作者：Liu, Lizhi[1,2,3] Zhang, Qiuliang[2] Guo, Ying[1,3] Li, Yu[4] Wang, Bing[2] Chen, Erxue[1,3] Li, Zengyuan[1,3] Hao, Shuai[2]

第一作者：Liu, Lizhi

通信作者：Guo, Y[1];Guo, Y[2]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Inner Mongolia Agr Univ, Coll Forestry, Hohhot 010019, Peoples R China;[3]Chinese Acad Forestry, Natl Forestry & Grassland Adm, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China;[4]Liaoning Tech Univ, Sch Geomat, Fuxin 123000, Peoples R China

年份：2024

卷号：15

期号：2

外文期刊名：FORESTS

收录：;EI(收录号：20240915644961);Scopus(收录号：2-s2.0-85185832651);WOS:【SCI-EXPANDED(收录号:WOS:001172086400001)】；

基金：We thank Yaqiu Zhang, Shuhan Jia, and Xiaojun He from Liaoning Technical University for their help in data processing. The authors are also grateful to the editors and referees for their constructive criticism of this paper.

语种：英文

外文关键词：coniferous forests; semi-arid; multi-classifier fusion; Gaofen-1; Sentinel-1; Google Earth Engine

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.