{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T07:09:38Z","timestamp":1773385778943,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T00:00:00Z","timestamp":1606176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004761","name":"Natural Science Foundation of Hainan Province","doi-asserted-by":"publisher","award":["2019RC329"],"award-info":[{"award-number":["2019RC329"]}],"id":[{"id":"10.13039\/501100004761","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41701510, 31770591, 31760181"],"award-info":[{"award-number":["41701510, 31770591, 31760181"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010038","name":"Earmarked Fund for China Agriculture Research System","doi-asserted-by":"publisher","award":["CARS-33-ZP3, CARS-33-ZP1"],"award-info":[{"award-number":["CARS-33-ZP3, CARS-33-ZP1"]}],"id":[{"id":"10.13039\/501100010038","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Joint Special Project of Agricultural Basic Research in Yunnan Province","award":["2017FG001-034"],"award-info":[{"award-number":["2017FG001-034"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rubber (Hevea brasiliensis Muell.) plantations constitute one of the most important agro-ecosystems in the tropical region of China and Southeast Asia, playing an important role in the carbon budget there. Accurately obtaining their biomass over a large area is challenging because of difficulties in acquiring the Diameter at Breast Height (DBH) through remote sensing and the problem of biomass saturation. The stand age, which is closely related to the forest biomass, was proposed for biomass estimation in this study. A stand age map at an annual scale for Hainan Island, which is the second largest natural rubber production base in China, was generated using all Landsat and Sentinel-2 (LS2) data (1987\u20132017). Scatter plots and the correlation coefficient method were used to explore the relationship (e.g., biomass saturation) between rubber biomass and different LS2-based variables. Subsequently, a regression model fitted with the stand age (R2 = 0.96) and a Random Forest (RF) model parameterizing with LS2-based variables and\/or the stand age were respectively employed to estimate rubber biomass for Hainan Island. The results show that rubber biomass was saturated around 65 Mg\/ha with all LS2-based variables. The regression model estimated biomass accurately (R2 = 0.79 and Root Mean Square Error (RMSE) = 14.00 Mg\/ha) and eliminated the saturation problem significantly. In addition to LS2-based variables, adding a stand age parameter to the RF models was found to significantly improve the prediction accuracy (R2 = 0.82\u20130.96 and RMSE = 4.08\u201310.59 Mg\/ha, modeling using samples of different biomass sizes). However, all RF models overestimated the biomass of young plantations and underestimated the biomass of old plantations. A hybrid model integrating the optimal results of RF and regression models reduced estimation bias and generated the best performance (R2 = 0.83 and RMSE = 12.48 Mg\/ha). The total rubber biomass of Hainan Island in 2017 was about 5.40 \u00d7 107 Mg. The northward and westward expansions after 2000 had great impact on the biomass distribution, leading to a higher biomass density for the inland coastal strip from south to northeast and a lower biomass density in the northern and western regions.<\/jats:p>","DOI":"10.3390\/rs12233853","type":"journal-article","created":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T09:06:28Z","timestamp":1606208788000},"page":"3853","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["High-Precision Stand Age Data Facilitate the Estimation of Rubber Plantation Biomass: A Case Study of Hainan Island, China"],"prefix":"10.3390","volume":"12","author":[{"given":"Bangqian","family":"Chen","sequence":"first","affiliation":[{"name":"Danzhou Investigation &amp; Experiment Station of Tropical Cops, State Key Laboratory Incubation Base for Cultivation &amp; Physiology of Tropical Crops, Rubber Research Institute (RRI), Chinese Academy of Tropical Agricultural Sciences (CATAS), Haikou 571101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4294-8337","authenticated-orcid":false,"given":"Ting","family":"Yun","sequence":"additional","affiliation":[{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3412-7766","authenticated-orcid":false,"given":"Jun","family":"Ma","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, Fudan University, No. 2005, Songhu Road, Shanghai 200438, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weili","family":"Kou","sequence":"additional","affiliation":[{"name":"College of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming 650224, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hailiang","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Scientific and Technical Information, Chinese Academy of Tropical Agricultural Sciences (CATAS), Haikou 571101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuan","family":"Yang","sequence":"additional","affiliation":[{"name":"Danzhou Investigation &amp; Experiment Station of Tropical Cops, State Key Laboratory Incubation Base for Cultivation &amp; Physiology of Tropical Crops, Rubber Research Institute (RRI), Chinese Academy of Tropical Agricultural Sciences (CATAS), Haikou 571101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0956-7428","authenticated-orcid":false,"given":"Xiangming","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Danzhou Investigation &amp; Experiment Station of Tropical Cops, State Key Laboratory Incubation Base for Cultivation &amp; Physiology of Tropical Crops, Rubber Research Institute (RRI), Chinese Academy of Tropical Agricultural Sciences (CATAS), Haikou 571101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0865-5066","authenticated-orcid":false,"given":"Rui","family":"Sun","sequence":"additional","affiliation":[{"name":"Danzhou Investigation &amp; Experiment Station of Tropical Cops, State Key Laboratory Incubation Base for Cultivation &amp; Physiology of Tropical Crops, Rubber Research Institute (RRI), Chinese Academy of Tropical Agricultural Sciences (CATAS), Haikou 571101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guishui","family":"Xie","sequence":"additional","affiliation":[{"name":"Danzhou Investigation &amp; Experiment Station of Tropical Cops, State Key Laboratory Incubation Base for Cultivation &amp; Physiology of Tropical Crops, Rubber Research Institute (RRI), Chinese Academy of Tropical Agricultural Sciences (CATAS), Haikou 571101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7344-8717","authenticated-orcid":false,"given":"Zhixiang","family":"Wu","sequence":"additional","affiliation":[{"name":"Danzhou Investigation &amp; Experiment Station of Tropical Cops, State Key Laboratory Incubation Base for Cultivation &amp; Physiology of Tropical Crops, Rubber Research Institute (RRI), Chinese Academy of Tropical Agricultural Sciences (CATAS), Haikou 571101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,24]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Prediction models for estimating the area, volume, and age of rubber (Hevea brasiliensis) plantations in Malaysia using Landsat TM data","volume":"6","author":"Suratman","year":"2004","journal-title":"Int. For. Rev."},{"key":"ref_2","first-page":"451","article-title":"Estimating Aboveground Biomass of Rubber Tree Using Remote Sensing in Phuket Province, Thailand","volume":"4","author":"Yasen","year":"2015","journal-title":"J. Med. Bioeng."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, B., Xiao, X., Wu, Z., Yun, T., Gan, S., Ye, H., Lin, Q., Doughty, R., Dong, J., and Xiao, X. (2018). Identifying Establishment Year and Pre-Conversion Land Cover of Rubber Plantations on Hainan Island, China Using Landsat Data during 1987. Remote Sens., 10.","DOI":"10.3390\/rs10081240"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7559","DOI":"10.1073\/pnas.1200452109","article-title":"Committed carbon emissions, deforestation, and community land conversion from oil palm plantation expansion in West Kalimantan, Indonesia","volume":"109","author":"Carlson","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.isprsjprs.2015.02.007","article-title":"Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass","volume":"102","author":"Li","year":"2015","journal-title":"ISPRS J. Photogramm."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rse.2015.01.007","article-title":"Potential of high-resolution ALOS\u2013PALSAR mosaic texture for aboveground forest carbon tracking in tropical region","volume":"160","author":"Thapa","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_7","first-page":"932","article-title":"Combining remote sensing imagery and forest age inventory for biomass mapping","volume":"10","author":"Zheng","year":"2006","journal-title":"J. Remote Sens."},{"key":"ref_8","first-page":"1942","article-title":"Biomass and its estimation model of rubber plantations in Xishuangbanna, Southwest China","volume":"28","author":"Tang","year":"2009","journal-title":"Chin. J. Ecol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.jclepro.2013.02.003","article-title":"Greenhouse gas emissions and carbon stock changes in rubber tree plantations in Thailand from 1990 to 2004","volume":"52","author":"Petsri","year":"2013","journal-title":"J. Clean. Prod."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.foreco.2017.08.013","article-title":"Rubber tree allometry, biomass partitioning and carbon stocks in mountainous landscapes of sub-tropical China","volume":"404","author":"Yang","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.biombioe.2018.04.019","article-title":"Estimating biomass stocks and potential loss of biomass carbon through clear-felling of rubber plantations","volume":"115","author":"Brahma","year":"2018","journal-title":"Biomass Bioenergy"},{"key":"ref_12","first-page":"1028","article-title":"Changes of rubber plantation aboveground biomass along elevation gradient in Xishuangbanna","volume":"25","author":"Jia","year":"2006","journal-title":"Chin. J. Ecol."},{"key":"ref_13","first-page":"624","article-title":"Biomass equations for rubber tree in Southern China","volume":"8","author":"Zhou","year":"1995","journal-title":"For. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.foreco.2016.10.051","article-title":"Biomass conversion and expansion factors for a chronosequence of young naturally regenerated silver birch (Betula pendula Roth) stands growing on post-agricultural sites","volume":"384","author":"Zasada","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.foreco.2012.01.004","article-title":"Individual tree biomass equations or biomass expansion factors for assessment of carbon stock changes in living biomass\u2014A comparative study","volume":"270","author":"Petersson","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1016\/j.foreco.2008.11.002","article-title":"Generalized functions of biomass expansion factors for conifers and broadleaved by stand age, growing stock and site index","volume":"257","author":"Teobaldelli","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhang, X., Wang, X., and Ren, Y. (2014). Dissecting variation in biomass conversion factors across China\u2019s forests: Implications for biomass and carbon accounting. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0094777"},{"key":"ref_18","first-page":"1887","article-title":"Biomass, carbon sequestration and its potential of rubber plantation in Xishuangbanna, Southwest China","volume":"29","author":"Song","year":"2010","journal-title":"Chin. J. Ecol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.chnaes.2010.08.005","article-title":"The responses of Moso bamboo (Phyllostachys heterocycla var. pubescens) forest aboveground biomass to Landsat TM spectral reflectance and NDVI","volume":"30","author":"Du","year":"2010","journal-title":"Acta Ecol. Sin."},{"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":"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_22","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.foreco.2016.12.020","article-title":"Estimating aboveground biomass of broadleaf, needleleaf, and mixed forests in Northeastern China through analysis of 25-m ALOS\/PALSAR mosaic data","volume":"389","author":"Ma","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111283","DOI":"10.1016\/j.rse.2019.111283","article-title":"Forest biomass estimation over three distinct forest types using TanDEM-X InSAR data and simulated GEDI lidar data","volume":"232","author":"Qi","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.ecolind.2017.02.045","article-title":"Above-ground biomass estimation using airborne discrete-return and full-waveform LiDAR data in a coniferous forest","volume":"78","author":"Nie","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_25","first-page":"101922","article-title":"Estimating forest aboveground biomass using small-footprint full-waveform airborne LiDAR data","volume":"83","author":"Luo","year":"2019","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4871","DOI":"10.1080\/01431161.2013.777486","article-title":"Estimating tropical forest biomass more accurately by integrating ALOS PALSAR and Landsat-7 ETM+ data","volume":"34","author":"Basuki","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.rse.2016.01.006","article-title":"Mapping and estimating forest area and aboveground biomass in miombo woodlands in Tanzania using data from airborne laser scanning, TanDEM-X, RapidEye, and global forest maps: A comparison of estimated precision","volume":"175","author":"Solberg","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.rse.2015.12.002","article-title":"Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data","volume":"173","author":"Su","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.foreco.2017.10.007","article-title":"Synergistic use of Landsat 8 OLI image and airborne LiDAR data for above-ground biomass estimation in tropical lowland rainforests","volume":"406","author":"Phua","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.rse.2019.03.032","article-title":"The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space","volume":"227","author":"Quegan","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17538947.2014.990526","article-title":"A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems","volume":"9","author":"Lu","year":"2014","journal-title":"Int. J. Digit. Earth"},{"key":"ref_32","first-page":"317","article-title":"Estimating biomass for rubber plantations in Xishuangbanna using remote sensing data","volume":"33","author":"Xu","year":"2011","journal-title":"J. Yunnan Univ."},{"key":"ref_33","first-page":"87","article-title":"Estimation of forest biomass in Jinghong by TM satellite","volume":"22","author":"Zhao","year":"2013","journal-title":"J. Yunnan Minzu Univ."},{"key":"ref_34","first-page":"38","article-title":"Counter-Estimation on Aboveground Biomass of Hevea brasiliensis Plantation by Remote Sensing with Random Forest Algorithm\u2014A Case Study of Jinghong","volume":"33","author":"Wang","year":"2013","journal-title":"J. Southwest For. Univ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"96072","DOI":"10.1117\/1.JRS.9.096072","article-title":"Estimation of biomass and carbon stock in Para rubber plantations using object-based classification from Thaichote satellite data in Eastern Thailand","volume":"9","author":"Charoenjit","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Avtar, R., Suzuki, R., and Sawada, H. (2014). Natural forest biomass estimation based on plantation information using PALSAR data. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0086121"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1080\/10106049.2019.1573855","article-title":"L-band SAR for estimating aboveground biomass of rubber plantation in Java Island, Indonesia","volume":"35","author":"Trisasongko","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_38","first-page":"1","article-title":"Biomass of Hevea Clone PR","volume":"29","author":"Cao","year":"2009","journal-title":"Chin. J. Trop. Agric."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1016\/j.foreco.2007.12.038","article-title":"Carbon stock in rubber tree plantations in Western Ghana and Mato Grosso (Brazil)","volume":"255","author":"Wauters","year":"2008","journal-title":"For. Ecol. Manag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.foreco.2016.04.009","article-title":"Land-use change impact on time-averaged carbon balances: Rubber expansion and reforestation in a biosphere reserve, South-West China","volume":"372","author":"Yang","year":"2016","journal-title":"For. Ecol. Manag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1016\/S1002-0160(17)60327-8","article-title":"Temporal Changes of Ecosystem Carbon Stocks in Rubber Plantations in Xishuangbanna, Southwest China","volume":"27","author":"Sun","year":"2017","journal-title":"Pedosphere"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1126\/science.320.5879.1011a","article-title":"Free Access to Landsat Imagery","volume":"320","author":"Woodcock","year":"2008","journal-title":"Science"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2017.04.003","article-title":"Obtaining rubber plantation age information from very dense Landsat TM&ETM+ time series data and pixel-based image compositing","volume":"196","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_44","unstructured":"SBHP & SONBSH (2018). Hainan Statistical Yearbook 2018."},{"key":"ref_45","first-page":"117","article-title":"Mapping tropical forests and deciduous rubber plantations in Hainan Island, China by integrating PALSAR 25-m and multi-temporal Landsat images","volume":"50","author":"Chen","year":"2016","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.foreco.2012.01.033","article-title":"Estimation of rubber stand age in typhoon and chilling injury afflicted area with Landsat TM data: A case study in Hainan Island, China","volume":"274","author":"Chen","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_47","unstructured":"HSF (2003). Compilation of Statistics on Hainan State Farm. (1952\u20132001), Hainan State Farm (HSF)."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Housman, I., Chastain, R., and Finco, M. (2018). An Evaluation of Forest Health Insect and Disease Survey Data and Satellite-Based Remote Sensing Forest Change Detection Methods: Case Studies in the United States. Remote Sens., 10.","DOI":"10.20944\/preprints201805.0360.v1"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.rse.2018.11.012","article-title":"Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States","volume":"221","author":"Chastain","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1080\/01431168608948945","article-title":"Characteristics of maximum-value composite images from temporal AVHRR data","volume":"7","author":"Holben","year":"1986","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"G2004","DOI":"10.1029\/2005RG000183","article-title":"The Shuttle Radar Topography Mission","volume":"45","author":"Farr","year":"2007","journal-title":"Rev. Geophys."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forest","volume":"1","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_56","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_57","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.proenv.2015.03.028","article-title":"Random Forest Classification for Mangrove Land Cover Mapping Using Landsat 5 TM and ALOS PALSAR Imageries","volume":"24","author":"Jhonnerie","year":"2015","journal-title":"Procedia Environ. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.isprsjprs.2018.07.017","article-title":"A 30-m Landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform","volume":"144","author":"Teluguntla","year":"2018","journal-title":"ISPRS J. Photogramm."},{"key":"ref_59","first-page":"110","article-title":"Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud","volume":"81","author":"Oliphant","year":"2019","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.foreco.2019.05.057","article-title":"Assessment of multi-wavelength SAR and multispectral instrument data for forest aboveground biomass mapping using random forest kriging","volume":"447","author":"Chen","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2017.07.007","article-title":"Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak","volume":"131","author":"Dash","year":"2017","journal-title":"ISPRS J. Photogramm."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.3390\/rs5031220","article-title":"Area-Based Mapping of Defoliation of Scots Pine Stands Using Airborne Scanning LiDAR","volume":"5","author":"Vastaranta","year":"2013","journal-title":"Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2300","DOI":"10.3390\/rs11192300","article-title":"A Synergetic Analysis of Sentinel-1 and -2 for Mapping Historical Landslides Using Object-Oriented Random Forest in the Hyrcanian Forests","volume":"11","author":"Zeinab","year":"2019","journal-title":"Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm."},{"key":"ref_65","first-page":"296","article-title":"Carbon sequestration in rubber tree plantations established on former arable lands in Xishuangbanna, SW China","volume":"29","author":"Yang","year":"2005","journal-title":"Acta Phytoecol. Sin."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"45","DOI":"10.4186\/ej.2015.19.4.45","article-title":"Mapping Rubber Tree Stand Age Using Pl\u00e9iades Satellite Imagery: A Case Study in Thalang District, Phuket, Thailand","volume":"19","author":"Koedsin","year":"2015","journal-title":"Eng. J."},{"key":"ref_67","first-page":"627","article-title":"Mapping rubber trees based on phenological analysis of Landsat time series data-sets","volume":"33","author":"Razak","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_68","first-page":"182","article-title":"Estimation of rubber stand age using statistical and artificial neutral network approaches with Landsat TM data","volume":"33","author":"Chen","year":"2012","journal-title":"Chin. J. Trop. Crops."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_70","first-page":"132","article-title":"Spatial-temporal characteristics of rubber typhoon disaster in Hainan Island","volume":"42","author":"Liu","year":"2015","journal-title":"Guangdong Agric. Sci."}],"updated-by":[{"DOI":"10.3390\/rs14195044","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T00:00:00Z","timestamp":1606176000000}}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3853\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T13:54:37Z","timestamp":1754229277000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3853"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,24]]},"references-count":70,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["rs12233853"],"URL":"https:\/\/doi.org\/10.3390\/rs12233853","relation":{"correction":[{"id-type":"doi","id":"10.3390\/rs14195044","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,24]]}}}