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Classifying and mapping forest types on a large scale across the country is challenging due to the complex composition of forest types, the similarity of spectral features among forest types, and the need to collect and process large amounts of data. In this study, we generated a medium-resolution (30 m) forest classification map of China using multi-source remote sensing images and local samples. A mapping framework based on Google Earth Engine (GEE) was constructed mainly using the spectral, textural, and structural features of Sentinel-1 and Sentinel-2 remote sensing images, while local acquisition data were utilized as the mapping channel for training. The proposed method includes the following steps. First, local data processing is performed to obtain training and validation samples. Second, Sentinel-1 and Sentinel-2 data are processed to improve the classification accuracy by using the enhanced vegetation index (EVI) and the red-edge position index (REPI) computed based on the S2A data. Third, to improve classification efficiency, useless bands are removed and important bands are retained through feature importance analysis. Finally, random forest (RF) is used as a classifier to train the above features, and the classification results are used for mapping and accuracy evaluation. The validation of the samples showed an accuracy of 82.37% and a Kappa value of 0.72. The results showed that the total forest area in China is 21,662,261.17 km2, of which 1,127,294.42 km2 of coniferous forests account for 52% of the total area, 981,690.98 km2 of broad-leaf forests account for 45.3 % of the total area, and 57,275.77 km2 of mixed coniferous and broad-leaf forests account for 2.6% of the total area. Upon further evaluation, we found that textural and structural features play a greater role in classification compared to spectral features. Our study shows that combining multi-source high-resolution remote sensing imagery with locally collected samples can produce forest maps for large areas. Our maps can accurately reflect the distribution of forests in China, which is conducive to forest conservation and development.<\/jats:p>","DOI":"10.3390\/rs15205026","type":"journal-article","created":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T07:15:36Z","timestamp":1697699736000},"page":"5026","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Classification of Coniferous and Broad-Leaf Forests in China Based on High-Resolution Imagery and Local Samples in Google Earth Engine"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7895-1899","authenticated-orcid":false,"given":"Xiaoguang","family":"Yuan","sequence":"first","affiliation":[{"name":"Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"Xi\u2019an Key Laboratory of Advanced Remote Sensing, Xi\u2019an 710071, China"},{"name":"Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Yiduo","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"Xi\u2019an Key Laboratory of Advanced Remote Sensing, Xi\u2019an 710071, China"},{"name":"Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Wei","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"Xi\u2019an Key Laboratory of Advanced Remote Sensing, Xi\u2019an 710071, China"},{"name":"Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Junhang","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Soil and Water Conservation & Ecological Restoration of Loess Plateau, Shaanxi Academy of Forestry, Xi\u2019an 710082, China"}]},{"given":"Hongtao","family":"Ren","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Soil and Water Conservation & Ecological Restoration of Loess Plateau, Shaanxi Academy of Forestry, Xi\u2019an 710082, China"}]},{"given":"Shuo","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Mengqi","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.foreco.2015.06.003","article-title":"Assessing change in national forest monitoring capacities of 99 tropical countries","volume":"352","author":"Romijn","year":"2015","journal-title":"For. 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