{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:59:49Z","timestamp":1778860789192,"version":"3.51.4"},"reference-count":83,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Science and Technology Plan Project of Inner Mongolia, China (Forest Ecosystem National Observation and Research Station of Greater Khingan Mountains in Inner Mongolia)","award":["32260389"],"award-info":[{"award-number":["32260389"]}]},{"name":"the Science and Technology Plan Project of Inner Mongolia, China (Forest Ecosystem National Observation and Research Station of Greater Khingan Mountains in Inner Mongolia)","award":["21-Y20B01-9001-19\/22"],"award-info":[{"award-number":["21-Y20B01-9001-19\/22"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32260389"],"award-info":[{"award-number":["32260389"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["21-Y20B01-9001-19\/22"],"award-info":[{"award-number":["21-Y20B01-9001-19\/22"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the National Science and Technology Major Project of China\u2019s High-Resolution Earth Observation System","award":["32260389"],"award-info":[{"award-number":["32260389"]}]},{"name":"the National Science and Technology Major Project of China\u2019s High-Resolution Earth Observation System","award":["21-Y20B01-9001-19\/22"],"award-info":[{"award-number":["21-Y20B01-9001-19\/22"]}]},{"name":"Postgraduate Scientific Research Innovation Project of Inner Mongolia Autonomous Region","award":["32260389"],"award-info":[{"award-number":["32260389"]}]},{"name":"Postgraduate Scientific Research Innovation Project of Inner Mongolia Autonomous Region","award":["21-Y20B01-9001-19\/22"],"award-info":[{"award-number":["21-Y20B01-9001-19\/22"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mapping the distribution of coniferous forests is of great importance to the sustainable management of forests and government decision-making. The development of remote sensing, cloud computing and deep learning has provided the support of data, computing power and algorithms for obtaining large-scale forest parameters. However, few studies have used deep learning algorithms combined with Google Earth Engine (GEE) to extract coniferous forests in large areas and the performance remains unknown. In this study, we thus propose a cloud-enabled deep-learning approach using long-time series Landsat remote sensing images to map the distribution and obtain information on the dynamics of coniferous forests over 35 years (1985\u20132020) in the northwest of Liaoning, China, through the combination of GEE and U2-Net. Firstly, to assess the reliability of the proposed method, the U2-Net model was compared with three Unet variants (i.e., Resnet50-Unet, Mobile-Unet and U-Net) in coniferous forest extraction. Secondly, we evaluated U2-Net\u2019s temporal transferability of remote sensing images from Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI. Finally, we compared the results obtained by the proposed approach with three publicly available datasets, namely GlobeLand30-2010, GLC_FCS30-2010 and FROM_GLC30-2010. The results show that (1) the cloud-enabled deep-learning approach proposed in this paper that combines GEE and U2-Net achieves a high performance in coniferous forest extraction with an F1 score, overall accuracy (OA), precision, recall and kappa of 95.4%, 94.2%, 96.6%, 95.5% and 94.0%, respectively, outperforming the other three Unet variants; (2) the proposed model trained by the sample blocks collected from a specific time can be applied to predict the coniferous forests in different years with satisfactory precision; (3) Compared with three global land-cover products, the distribution of coniferous forests extracted by U2-Net was most similar to that of actual coniferous forests; (4) The area of coniferous forests in Northwestern Liaoning showed an upward trend in the past 35 years. The area of coniferous forests has grown from 945.64 km2 in 1985 to 6084.55 km2 in 2020 with a growth rate of 543.43%. This study indicates that the proposed approach combining GEE and U2-Net can extract coniferous forests quickly and accurately, which helps obtain dynamic information and assists scientists in developing sustainable strategies for forest management.<\/jats:p>","DOI":"10.3390\/rs15051235","type":"journal-article","created":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T01:37:52Z","timestamp":1677202672000},"page":"1235","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Mapping the Distribution and Dynamics of Coniferous Forests in Large Areas from 1985 to 2020 Combining Deep Learning and Google Earth Engine"],"prefix":"10.3390","volume":"15","author":[{"given":"Lizhi","family":"Liu","sequence":"first","affiliation":[{"name":"College of Forestry, Inner Mongolia Agricultural University, Hohhot 010019, China"},{"name":"College of Art and Architectural Engineering, Heilongjiang University of Technology, Jixi 158100, China"},{"name":"National Field Scientific Observation and Research Station of Greater Khingan Forest Ecosystem, Genhe 022350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5776-5440","authenticated-orcid":false,"given":"Qiuliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Forestry, Inner Mongolia Agricultural University, Hohhot 010019, China"},{"name":"National Field Scientific Observation and Research Station of Greater Khingan Forest Ecosystem, Genhe 022350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4229-0002","authenticated-orcid":false,"given":"Ying","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Chinese Academy of Forestry, Beijing 100091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erxue","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Chinese Academy of Forestry, Beijing 100091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zengyuan","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Chinese Academy of Forestry, Beijing 100091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0015-5611","authenticated-orcid":false,"given":"Bing","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Forestry, Inner Mongolia Agricultural University, Hohhot 010019, China"},{"name":"National Field Scientific Observation and Research Station of Greater Khingan Forest Ecosystem, Genhe 022350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ana","family":"Ri","sequence":"additional","affiliation":[{"name":"College of Forestry, Inner Mongolia Agricultural University, Hohhot 010019, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"ref_1","unstructured":"(2022, November 10). 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