{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T06:09:05Z","timestamp":1772345345144,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T00:00:00Z","timestamp":1614816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program (class A) of the Chinese Academy of Sciences","award":["XDA19040501"],"award-info":[{"award-number":["XDA19040501"]}]},{"name":"The 13th Five-Year Informatization Plan of the Chinese Academy of Sciences","award":["XXH13505-07"],"award-info":[{"award-number":["XXH13505-07"]}]},{"name":"Construction Project of China Knowledge Center for Engineering Sciences and Technology","award":["CKCEST-2020-2-4"],"award-info":[{"award-number":["CKCEST-2020-2-4"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The comprehensive application of spectral, spatial, and temporal (SST) features derived from remote sensing images is a significant technique for classifying and mapping forest types. Facing limitations in the availability of detailed forest type identification processes for large regions, a forest type classification framework based on SST features was developed in this study. The advantages of Sentinel-2 and Landsat series imagery were used to extract SST forest type classification features, using red-edge bands, a gray-level co-occurrence matrix, and harmonic analysis, with the assistance of the Google Earth Engine platform. Considering four representative Chinese geographic regions\u2014middle and high latitudes, complex mountainous areas, cloudy and rainy areas, and the N\u2013S climate transition zone\u2014our method was proven to be effective, with overall classification accuracies &gt; 85%. The scheme to assess the importance of SST features for forest classification in various regions was designed using the Gini criterion in the random forest algorithm and revealed that spectral features were more effective in classifying forest types with complex compositions. Temporal features were found to be favorable for identifying forest types with obvious evergreen and deciduous growth patterns, while spatial features produced better classification results for forest types with different spatial structures, such as needle- or broad-leaved forests. The findings of this study can provide a reference for feature selection in remote sensing forest type classification processes, and identifying forest types in this way could provide support for the accurate and sustainable management of forest resources.<\/jats:p>","DOI":"10.3390\/rs13050973","type":"journal-article","created":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T00:39:07Z","timestamp":1614904747000},"page":"973","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Mapping Forest Types in China with 10 m Resolution Based on Spectral\u2013Spatial\u2013Temporal Features"],"prefix":"10.3390","volume":"13","author":[{"given":"Kai","family":"Cheng","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5641-0813","authenticated-orcid":false,"given":"Juanle","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"China-Pakistan Earth Science Research Center, Islamabad 45320, Pakistan"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9481-2397","authenticated-orcid":false,"given":"Xinrong","family":"Yan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,4]]},"reference":[{"key":"ref_1","unstructured":"Food and Agriculture Organization of the United Nations (2018). The State of the World\u2019s Forests 2018\u2014Forest Pathways to Sustainable Development, Food and Agriculture Organization of the United Nations."},{"key":"ref_2","unstructured":"UNSC (2015). Revised List of Global Sustainable Development Goal Indicators, United Nations Statistical, UNSC."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/S0305-9006(03)00066-7","article-title":"Remote sensing technology for mapping and monitoring land-cover and land-use change","volume":"61","author":"Rogan","year":"2004","journal-title":"Prog. Plan."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, Y., Gong, W., Hu, X., and Gong, J. (2018). Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data. Remote Sens., 10.","DOI":"10.3390\/rs10060946"},{"key":"ref_5","first-page":"104","article-title":"Extraction of Shrub Vegetation by Object-Oriented Classification Method Based on ENVI ZOOM in High-Altitude Area: A Case of Dingri County","volume":"26","author":"Zhang","year":"2010","journal-title":"Geogr. Geo-Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2018.02.064","article-title":"Improved mapping of forest type using spectral-temporal Landsat features","volume":"210","author":"Pasquarella","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Grabska, E., Hostert, P., Pflugmacher, D., and Ostapowicz, K. (2019). Forest Stand Species Mapping Using the Sentinel-2 Time Series. Remote Sens., 11.","DOI":"10.3390\/rs11101197"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cheng, K., and Wang, J. (2019). Forest-Type Classification Using Time-Weighted Dynamic Time Warping Analysis in Mountain Areas: A Case Study in Southern China. Forest, 10.","DOI":"10.3390\/f10111040"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2020.04.001","article-title":"Google Earth Engine for geo-big data applications: A meta-analysis and systematic review","volume":"164","author":"Tamiminia","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-Resolution Global Maps of 21st-Century Forest Cover Change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2014.04.014","article-title":"New global forest\/non-forest maps from ALOS PALSAR data (2007\u20132010)","volume":"155","author":"Shimada","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2016.02.016","article-title":"Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine","volume":"185","author":"Dong","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_15","first-page":"413","article-title":"Statistical analysis about the changes of forest resource and precipitation in China over the past 50 years","volume":"16","author":"Ge","year":"2001","journal-title":"J. Nat. Resour."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s11676-008-0004-9","article-title":"Assessment of forest geospatial patterns over the three giant forest areas of China","volume":"19","author":"Li","year":"2008","journal-title":"J. For. Res."},{"key":"ref_17","first-page":"75","article-title":"Spatio-temporal changes in forest fragmentation, disturbance patterns over the three giant forested regions of China","volume":"37","author":"Shen","year":"2013","journal-title":"J. Nanjing For. Univ. Nat. Sci. Ed."},{"key":"ref_18","first-page":"305","article-title":"Ten major scientific issues concerning the study of China\u2019s north-south transitional zone","volume":"38","author":"Zhang","year":"2019","journal-title":"Prog. Geogr."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cheng, K., and Wang, J. (2019). Forest Type Classification Based on Integrated Spectral-Spatial-Temporal Features and Random Forest Algorithm-A Case Study in the Qinling Mountains. Forests, 10.","DOI":"10.3390\/f10070559"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Traganos, D., Aggarwal, B., Poursanidis, D., Topouzelis, K., Chrysoulakis, N., and Reinartz, P. (2018). Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas. Remote Sens., 10.","DOI":"10.3390\/rs10081227"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5325","DOI":"10.3390\/rs6065325","article-title":"A Circa 2010 Thirty Meter Resolution Forest Map for China","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.rse.2010.10.001","article-title":"Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia","volume":"115","author":"Potapov","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"20069","DOI":"10.1029\/2000JD000115","article-title":"Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999","volume":"106","author":"Zhou","year":"2001","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2013.04.022","article-title":"Forest disturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites","volume":"151","author":"Griffiths","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1109\/JSTARS.2012.2228167","article-title":"Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping","volume":"6","author":"Griffiths","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111225","DOI":"10.1016\/j.rse.2019.111225","article-title":"Assessing the spatial, spectral, and temporal consistency of topographically corrected Landsat time series composites across the mountainous forests of Nepal","volume":"231","author":"Hurni","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., Wang, R., Sun, F., and Wu, X. (2018). Evaluating the Performance of Sentinel-2, Landsat 8 and Pleiades-1 in Mapping Mangrove Extent and Species. Remote Sens., 10.","DOI":"10.3390\/rs10091468"},{"key":"ref_28","unstructured":"Fu, M. (2009). Nitrogen Transform and Release in Typical Temperate Forest Ecosystems in Northeastern China, Northeast Forestry University."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1080\/07038992.2014.945827","article-title":"Pixel-Based Image Compositing for Large-Area Dense Time Series Applications and Science","volume":"40","author":"White","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_30","unstructured":"Sayn-Wittgenstein, L. (1978). Recognition of Tree Species on Aerial Photographs. Information Report FMR-X-118, Forest Management Institute."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, T., Zhang, H., Lin, H., and Fang, C. (2016). Textural-Spectral Feature-Based Species Classification of Mangroves in Mai Po Nature Reserve from Worldview-3 Imagery. Remote Sens., 8.","DOI":"10.3390\/rs8010024"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8424","DOI":"10.3390\/rs6098424","article-title":"A Multichannel Gray Level Co-Occurrence Matrix for Multi\/Hyperspectral Image Texture Representation","volume":"6","author":"Huang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3417","DOI":"10.1080\/01431160701601782","article-title":"Analysis of co-occurrence and discrete wavelet transform textures for differentiation of forest and non-forest vegetation in very-high-resolution optical-sensor imagery","volume":"29","author":"Ouma","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2015.08.010","article-title":"Forest cover maps of China in 2010 from multiple approaches and data sources: PALSAR, Landsat, MODIS, FRA, and NFI","volume":"109","author":"Qin","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1080\/01431161.2014.999167","article-title":"Phenology-based classification of vegetation cover types in Northeast China using MODIS NDVI and EVI time series","volume":"36","author":"Yan","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0034-4257(02)00135-9","article-title":"Monitoring vegetation phenology using MODIS","volume":"84","author":"Zhang","year":"2003","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/973\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:32:51Z","timestamp":1760160771000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/973"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,4]]},"references-count":36,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["rs13050973"],"URL":"https:\/\/doi.org\/10.3390\/rs13050973","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,4]]}}}