{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T22:57:38Z","timestamp":1781564258969,"version":"3.54.5"},"reference-count":50,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2015,2,5]],"date-time":"2015-02-05T00:00:00Z","timestamp":1423094400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The knowledge about spatial distribution of plantation forests is critical for forest management, monitoring programs and functional assessment. This study demonstrates the potential of multi-seasonal (spring, summer, autumn and winter) Landsat-8 Operational Land Imager imageries with random forests (RF) modeling to map larch plantations (LP) in a typical plantation forest landscape in North China. The spectral bands and two types of textures were applied for creating 675 input variables of RF. An accuracy of 92.7% for LP, with a Kappa coefficient of 0.834, was attained using the RF model. A RF-based importance assessment reveals that the spectral bands and bivariate textural features calculated by pseudo-cross variogram (PC) strongly promoted forest class-separability, whereas the univariate textural features influenced weakly. A feature selection strategy eliminated 93% of variables, and then a subset of the 47 most essential variables was generated. In this subset, PC texture derived from summer and winter appeared the most frequently, suggesting that this variability in growing peak season and non-growing season can effectively enhance forest class-separability. A RF classifier applied to the subset led to 91.9% accuracy for LP, with a Kappa coefficient of 0.829. This study provides an insight into approaches for discriminating plantation forests with phenological behaviors.<\/jats:p>","DOI":"10.3390\/rs70201702","type":"journal-article","created":{"date-parts":[[2015,2,5]],"date-time":"2015-02-05T10:54:42Z","timestamp":1423133682000},"page":"1702-1720","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Mapping Spatial Distribution of Larch Plantations from  Multi-Seasonal Landsat-8 OLI Imagery and Multi-Scale Textures Using Random Forests"],"prefix":"10.3390","volume":"7","author":[{"given":"Tian","family":"Gao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Qingyuan Forest CERN, Chinese Academy of Sciences, Shenyang 110016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaojun","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Qingyuan Forest CERN, Chinese Academy of Sciences, Shenyang 110016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Zheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Qingyuan Forest CERN, Chinese Academy of Sciences, Shenyang 110016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guiduo","family":"Shang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Qingyuan Forest CERN, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"University of Chinese Academy of Sciences, Beijing 110164, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyan","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Qingyuan Forest CERN, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"University of Chinese Academy of Sciences, Beijing 110164, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shangrong","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture \/ Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2015,2,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fenning, T. (2014). Challenges and Opportunities for the World\u2019s Forests in the 21st Century, Springer.","DOI":"10.1007\/978-94-007-7076-8"},{"key":"ref_2","unstructured":"Chinese Ministry of Forestry (2014). Forest Resource Statistics of China, Department of Forest Resource and Management, Chinese Ministry of Forestry. (In Chinese)."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1111\/j.1744-7909.2006.00264.x","article-title":"Soil carbon changes following afforestation with Olga Bay Larch (Larix olgensis Henry) in Northeastern China","volume":"48","author":"Wang","year":"2006","journal-title":"J. Integr. Plant Biol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s10310-009-0152-6","article-title":"Feasibility of implementing thinning in even-aged Larix olgensis plantations to develop uneven-aged larch-broadleaved mixed forests","volume":"15","author":"Zhu","year":"2010","journal-title":"J. For. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1007\/s00468-013-0920-y","article-title":"Comparison of spatial patterns of soil seed banks between larch plantations and adjacent secondary forests in Northeast China: Implication for spatial distribution of larch plantations","volume":"27","author":"Yan","year":"2013","journal-title":"Trees"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1093\/jpe\/rtq022","article-title":"Soil microbial biomass carbon and nitrogen in forest ecosystems of Northeast China: A comparison between natural secondary forest and larch plantation","volume":"3","author":"Yang","year":"2010","journal-title":"J. Plant Ecol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s11104-012-1535-6","article-title":"The impact of secondary forests conversion into larch plantations on soil chemical and microbiological properties","volume":"368","author":"Yang","year":"2013","journal-title":"Plant Soil"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.foreco.2013.07.059","article-title":"Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution","volume":"310","author":"Engler","year":"2013","journal-title":"For. Ecol. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.ecolind.2013.11.014","article-title":"Quantifying ecosystem services and indicators for science, policy and practice","volume":"37","author":"Alkemade","year":"2014","journal-title":"Ecol. Indic. A"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s11676-010-0026-y","article-title":"Land-use, biomass and carbon estimation in dry tropical forest of Chhattisgarh region in India using satellite remote sensing and GIS","volume":"21","author":"Bijalwan","year":"2010","journal-title":"J. For. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7796","DOI":"10.1080\/01431161.2013.823000","article-title":"Using MODIS time series data to estimate aboveground biomass and its spatio-temporal variation in Inner Mongolia\u2019s grassland between 2001 and 2011","volume":"34","author":"Gao","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.rse.2013.07.010","article-title":"Monitoring and analysis of grassland desertification dynamics using Landsat images in Ningxia, China","volume":"138","author":"Li","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1007\/s11676-014-0466-x","article-title":"Satellite monitoring of land-use and land-cover changes in northern Togo protected areas","volume":"25","author":"Folega","year":"2014","journal-title":"J. For. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4261","DOI":"10.1016\/j.rse.2008.07.007","article-title":"Scaling-based forest structural change detection using an inverted geometric-optical model in the Three Gorges region of China","volume":"112","author":"Zeng","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.rse.2013.07.008","article-title":"Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Landsat satellite images and the random forests classifier","volume":"139","author":"Grinand","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.3390\/rs5073212","article-title":"Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in Northern Minnesota","volume":"5","author":"Corcoran","year":"2013","journal-title":"Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2011.12.003","article-title":"Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture","volume":"121","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1080\/01431161.2012.748992","article-title":"Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data","volume":"34","author":"Gong","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.apgeog.2012.06.014","article-title":"Land cover change analysis of a Mediterranean area in Spain using different sources of data: Multi-seasonal Landsat images, land surface temperature, digital terrain models and texture","volume":"35","year":"2012","journal-title":"Appl. Geogr."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1016\/j.jaridenv.2010.04.007","article-title":"Assessing multi-temporal Landsat 7 ETM+ images for estimating above-ground biomass in subtropical dry forests of Argentina","volume":"74","author":"Gasparri","year":"2010","journal-title":"J. Arid Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7727","DOI":"10.1080\/01431161.2012.701349","article-title":"Using multiscale texture information from ALOS PALSAR to map tropical forest","volume":"33","author":"Rakwatin","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2793","DOI":"10.1080\/01431160903111077","article-title":"Utilizing image texture to detect land-cover change in Mediterranean coastal wetlands","volume":"31","author":"Akin","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.rse.2010.11.010","article-title":"Improved forest biomass estimates using ALOS AVNIR-2 texture indices","volume":"115","author":"Sarker","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_25","unstructured":"Bellman, R. (2003). Dynamic Programming, Dover Publications. [2nd ed.]."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2838","DOI":"10.3390\/rs5062838","article-title":"The performance of random forests in an operational setting for large area sclerophyll forest classification","volume":"5","author":"Mellor","year":"2013","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1016\/j.rse.2009.04.015","article-title":"Monitoring of cropland practices for carbon sequestration purposes in north central Montana by Landsat remote sensing","volume":"113","author":"Watts","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.rse.2012.07.006","article-title":"Forest biomass estimation from airborne LiDAR data using machine learning approaches","volume":"125","author":"Gleason","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.rse.2005.10.014","article-title":"Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (random Forest)","volume":"100","author":"Lawrence","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2564","DOI":"10.1016\/j.rse.2011.05.013","article-title":"Object-oriented mapping of landslides using Random Forests","volume":"115","author":"Stumpf","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7545","DOI":"10.1080\/01431161.2013.820366","article-title":"Land-cover mapping in the Nujiang Grand Canyon: Integrating spectral, textural, and topographic data in a random forest classifier","volume":"34","author":"Fan","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2009). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press. [2nd ed.].","DOI":"10.1201\/9781420055139"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"263","DOI":"10.4001\/1021-3589-16.2.263","article-title":"Detecting the severity of woodwasp, Sirex noctilio, infestation in a pine plantation in KwaZulu-Natal, South Africa, using texture measures calculated from high spatial resolution imagery","volume":"16","author":"Dye","year":"2008","journal-title":"Afr. Entomol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/S0098-3004(99)00118-1","article-title":"Computing geostatistical image texture for remotely sensed data classification","volume":"26","year":"2000","journal-title":"Comput. Geosci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"147","DOI":"10.14358\/PERS.75.2.147","article-title":"Multivariate image texture by multivariate variogram for multispectral image classification","volume":"75","author":"Li","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/S0098-3004(99)00117-X","article-title":"Geostatistical classification for remote sensing: An introduction","volume":"26","author":"Atkinson","year":"2000","journal-title":"Comput. Geosci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4287","DOI":"10.1080\/0143116042000192367","article-title":"A multiscale texture analysis procedure for improved forest stand classification","volume":"25","author":"Coburn","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1016\/j.envsoft.2010.01.004","article-title":"Predicting the potential habitat of oaks with data mining models and the R system","volume":"25","year":"2010","journal-title":"Environ. Model. Softw."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1016\/j.rse.2008.02.011","article-title":"Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery","volume":"112","author":"Chan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.3390\/rs4092661","article-title":"Tree Species Classification with random forest using very high spatial resolution 8-band worldview-2 satellite data","volume":"4","author":"Immitzer","year":"2012","journal-title":"Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1890\/07-0539.1","article-title":"Random forests for classification in ecology","volume":"88","author":"Cutler","year":"2007","journal-title":"Ecology"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2376","DOI":"10.1016\/j.rse.2011.04.037","article-title":"Comparison of vegetation water contents derived from shortwave-infrared and passive-microwave sensors over central Iowa","volume":"115","author":"Hunt","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1080\/01431168808954845","article-title":"Exploring the relationships between leaf nitrogen content, biomass and the near-infrared\/red reflectance ratio","volume":"9","author":"Plummer","year":"1988","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1672\/08-34.1","article-title":"Assessing the use of multiseason QuickBird imagery for mapping invasive species in a Lake Erie coastal Marsh","volume":"28","author":"Johnston","year":"2008","journal-title":"Wetlands"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.foreco.2012.05.016","article-title":"Detailed maps of tropical forest types are within reach: Forest tree communities for Trinidad and Tobago mapped with multiseason Landsat and multiseason fine-resolution imagery","volume":"279","author":"Helmer","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/2\/1702\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:42:23Z","timestamp":1760215343000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/2\/1702"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,2,5]]},"references-count":50,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2015,2]]}},"alternative-id":["rs70201702"],"URL":"https:\/\/doi.org\/10.3390\/rs70201702","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,2,5]]}}}