{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T11:30:34Z","timestamp":1781782234169,"version":"3.54.5"},"reference-count":92,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,3,27]],"date-time":"2019-03-27T00:00:00Z","timestamp":1553644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31770677"],"award-info":[{"award-number":["31770677"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31660202"],"award-info":[{"award-number":["31660202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Project of Forestry Public Welfare Industry in China","award":["201404309"],"award-info":[{"award-number":["201404309"]}]},{"name":"Expert Workstation of Yunnan Province of China","award":["2018IC100"],"award-info":[{"award-number":["2018IC100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Optical remote sensing data have been widely used for estimating forest aboveground biomass (AGB). However, the use of optical images is often restricted by the saturation of spectral reflectance for forests that have multilayered and complex canopy structures and high AGB values and by the effect of spectral reflectance from underlayer shrub, grass, and bare soil for young stands. This usually leads to overestimations and underestimations for smaller and larger values, respectively, and makes it very challenging to improve the estimation accuracy of forest AGB. In this study, a novel methodology was proposed by incorporating stand age as a dummy variable into four models to improve the estimation accuracy of the Pinus densata forest AGB in Yunnan of Southwestern China. A total of eight models, including two parametric models (LM: linear regression model and LMC: LM with combined variables), two nonparametric models (RF: random forest and ANN: artificial neural network) without the age dummy variable, and four corresponding models with the age dummy variable (DLM, DLMC, DRF, and DANN), were compared to estimate AGB. Landsat 8 Operational Land Imager (OLI) images and 147 sample plots were acquired and utilized. The results showed that (1) compared with the two parametric models, the two nonparametric algorithms resulted in significantly greater estimation accuracies of Pinus densata forest AGB, and the increases of accuracy varied from 8% to 32% for 100 modeling plots and from 12% to 35% for 47 test plots based on root mean square error (RMSE); (2) compared with the models without the age dummy variable, the models with the age dummy variable greatly reduced the overestimations for the plots with AGB values smaller than 70 Mg\/ha and the underestimations for the plots with AGB values larger than 180 Mg\/ha and, thus, significantly improved the overall estimation accuracy by 14% to 42% for the modeling plots and by 32% to 44% for the test plots based on RMSE; and (3) the texture measures derived from the Landsat 8 OLI images contributed more to improving the estimation accuracy than the original spectral bands and other transformations. This implied that two nonparametric models, coupled with the use of the age dummy variable and texture measures, offered a great potential for improving the estimation accuracy of Pinus densata forest AGB.<\/jats:p>","DOI":"10.3390\/rs11070738","type":"journal-article","created":{"date-parts":[[2019,3,29]],"date-time":"2019-03-29T03:38:52Z","timestamp":1553830732000},"page":"738","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Improving Aboveground Biomass Estimation of Pinus densata Forests in Yunnan Using Landsat 8 Imagery by Incorporating Age Dummy Variable and Method Comparison"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1925-6690","authenticated-orcid":false,"given":"Guanglong","family":"Ou","sequence":"first","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanyu","family":"Lv","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anchao","family":"Wei","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hexian","family":"Xiong","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5419-4547","authenticated-orcid":false,"given":"Guangxing","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China"},{"name":"Department of Geography, Southern Illinois University, Carbondale, IL 62901, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1111\/j.1744-7429.2009.00543.x","article-title":"Short-term temporal changes in tree live biomass in a central Amazonian forest Brazil","volume":"42","author":"Magnusson","year":"2010","journal-title":"Biotropica"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1177\/0309133310364934","article-title":"Tropical savannas: Biomass, plant ecology, and the role of fire and soil on vegetation","volume":"34","author":"Furley","year":"2010","journal-title":"Prog. Phys. Geog."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.agrformet.2011.07.019","article-title":"Dynamics of carbon exchange in a Eucalyptus forest in response to interacting disturbance factors","volume":"153","author":"Keith","year":"2012","journal-title":"Agric. For. Meteorol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1126\/science.282.5388.439","article-title":"Changes in the carbon balance of tropical forests: Evidence from long-term plots","volume":"282","author":"Phillips","year":"1998","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2320","DOI":"10.1126\/science.1058629","article-title":"Changes in forest biomass carbon storage in China between 1949 and 1998","volume":"292","author":"Fang","year":"2001","journal-title":"Science"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2019","DOI":"10.1111\/gcb.12512","article-title":"Forest biomass carbon sinks in East Asia, with special reference to the relative contributions of forest expansion and forest growth","volume":"20","author":"Fang","year":"2014","journal-title":"Glob. Chang. Biol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1016\/j.foreco.2011.07.028","article-title":"Topographic and biotic regulation of aboveground carbon storage in subtropical broad-leaved forests of Taiwan","volume":"262","author":"McEwan","year":"2011","journal-title":"For. Ecol. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1126\/science.1201609","article-title":"A large and persistent car bon sink in the world\u2019s forests","volume":"333","author":"Pan","year":"2011","journal-title":"Science"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"399","DOI":"10.2307\/1930126","article-title":"The trophic-dynamic aspect of ecology","volume":"23","author":"Lindeman","year":"1942","journal-title":"Ecology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"567","DOI":"10.2307\/2258999","article-title":"Estimates of above-ground biomass and primary productivity in a Missouri Forest","volume":"62","author":"Rochow","year":"1974","journal-title":"J. Ecol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.isprsjprs.2014.11.001","article-title":"Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa","volume":"101","author":"Dube","year":"2015","journal-title":"ISPRS J. Photogramm."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1111\/j.1365-2486.2005.00955.x","article-title":"Aboveground forest biomass and the global carbon balance","volume":"11","author":"Houghton","year":"2005","journal-title":"Glob. Chang. Biol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gibbs, H.K., Brown, S., Niles, J.O., and Foley, J.A. (2007). Monitoring and estimating tropical forest carbon stocks: Making REDD a reality. Environ. Res. Lett., 2.","DOI":"10.1088\/1748-9326\/2\/4\/045023"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1762","DOI":"10.1111\/gcb.12822","article-title":"Observing terrestrial ecosystems and the carbon cycle from space","volume":"21","author":"Schimel","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Goetz, S.J., Hansen, M., Houghton, R.A., Walker, W., Laporte, N., and Busch, J. (2015). Measurement and monitoring needs, capabilities and potential for addressing reduced emissions from deforestation and forest degradation under REDD+. Environ. Res. Lett., 10.","DOI":"10.1088\/1748-9326\/10\/12\/123001"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"11635","DOI":"10.1073\/pnas.0901970106","article-title":"Re-evaluation of forest biomass carbon stocks and lessons from the world\u2019s most carbon-dense forests","volume":"106","author":"Keith","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2005.12.001","article-title":"A temporal analysis of urban forest carbon storage using remote sensing","volume":"101","author":"Myeong","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1080\/02827581.2011.564204","article-title":"Uncertainties of mapping forest carbon due to plot locations using national forest inventory plot and remotely sensed data","volume":"26","author":"Wang","year":"2011","journal-title":"Scand. J. For. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.rse.2016.07.023","article-title":"Forest aboveground biomass mapping and estimation across multiple spatial scales using model-based inference","volume":"184","author":"Chen","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_21","first-page":"1","article-title":"Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates","volume":"1","author":"Lu","year":"2012","journal-title":"Int. J. For. Res."},{"key":"ref_22","first-page":"1","article-title":"Forest aboveground biomass estimation in Zhejiang province using the integration of Landsat TM and ALOS PALSAR data","volume":"53","author":"Zhao","year":"2016","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.3390\/rs5031001","article-title":"Impacts of Spatial Variability on Aboveground Biomass Estimation from L-Band Radar in a Temperate Forest","volume":"5","author":"Robinson","year":"2013","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1046\/j.1466-822X.2001.00248.x","article-title":"Mapping the biomass of Bornean tropical rain forest from remotely sensed data","volume":"10","author":"Foody","year":"2001","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1016\/j.foreco.2009.06.056","article-title":"Mapping and spatial uncertainty analysis of forest vegetation carbon by combining national forest inventory data and satellite images","volume":"258","author":"Wang","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"15114","DOI":"10.3390\/rs71115114","article-title":"Increasing the accuracy of mapping urban forest carbon density by combining spatial modeling and spectral unmixing analysis","volume":"7","author":"Sun","year":"2015","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s10342-014-0838-y","article-title":"Comparison of methods toward multi-scale forest carbon mapping and spatial uncertainty analysis: Combining national forest inventory plot data and Landsat TM images","volume":"134","author":"Fleming","year":"2015","journal-title":"Eur. J. For. Res."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"3599","DOI":"10.1016\/j.rse.2011.08.021","article-title":"Model-assisted regional forest biomass estimation using lidar and insar as auxiliary data: A case study from a boreal forest area","volume":"115","author":"Gobakken","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4513","DOI":"10.1109\/TGRS.2012.2194502","article-title":"Edge-guided multiscale segmentation of satellite multispectral imagery","volume":"50","author":"Chen","year":"2012","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_31","first-page":"27","article-title":"Accuracy and precision for remote sensing applications of nonlinear model-based inference","volume":"6","author":"Mcroberts","year":"2013","journal-title":"IEEE J.-Stars"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160500486732","article-title":"The potential and challenge of remote sensing-based biomass estimation","volume":"27","author":"Lu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, P., Lu, D., Wang, G., Wu, C., Huang, Y., and Yu, S. (2016). Examining spectral reflectance saturation in Landsat imagery and corresponding solutions to improve forest aboveground biomass estimation. Remote Sens., 8.","DOI":"10.3390\/rs8060469"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1080\/01431160701352154","article-title":"The application of artificial neural networks to the analysis of remotely sensed data","volume":"29","author":"Mas","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1016\/j.ecolmodel.2009.04.025","article-title":"A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China","volume":"220","author":"Xie","year":"2009","journal-title":"Ecol. Model."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2014.01.024","article-title":"Airborne multi-temporal L-band polarimetric SAR data for biomass estimation in semi-arid forests","volume":"145","author":"Tanase","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.rse.2013.05.033","article-title":"Using landsat-derived disturbance and recovery history and Lidar to map forest biomass dynamics","volume":"151","author":"Pflugmacher","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.foreco.2011.06.039","article-title":"Estimating forest attribute parameters for small areas using nearest neighbors techniques","volume":"272","author":"McRoberts","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.1080\/01431161.2012.725958","article-title":"Hyperspectral Analysis of Mangrove Foliar Chemistry Using PLSR and Support Vector Regression","volume":"34","author":"Axelsson","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1126\/science.1217962","article-title":"Baseline map of carbon emissions from deforestation in tropical regions","volume":"336","author":"Harris","year":"2012","journal-title":"Science"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"9899","DOI":"10.1073\/pnas.1019576108","article-title":"Benchmark map of forest carbon stocks in tropical regions across three continents","volume":"108","author":"Saatchi","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","unstructured":"Zhang, W., Li, Z., Chen, E., Zhang, Y., Yang, H., Zhao, L., and Ji, Y. (2017). Compact polarimetric response of rape (Brassica napus L.) at C-band: Analysis and growth parameters inversion. Remote Sens., 9.","DOI":"10.3390\/rs9060591"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Noi, P.T., Degener, J., and Kappas, M. (2017). Comparison of multiple linear regression, cubist regression, and random forest algorithms to estimate daily air surface temperature from dynamic combinations of MODIS LST data. Remote Sens., 9.","DOI":"10.3390\/rs9050398"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"8108","DOI":"10.1080\/01431161.2014.978957","article-title":"Estimating daily maximum air temperature from MODIS in British Columbia, Canada","volume":"35","author":"Xu","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.rse.2016.01.015","article-title":"Integrating Landsat pixel composites and change metrics with LiDAR plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada","volume":"176","author":"Zald","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Deo, R.K., Russell, M.B., Domke, G.M., Andersen, H.E., Cohen, W.B., and Woodall, C.W. (2017). Evaluating site-specific and generic spatial models of aboveground forest biomass based on Landsat time-series and LiDAR strip samples in the eastern USA. Remote Sens., 9.","DOI":"10.3390\/rs9060598"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF02478259","article-title":"A logical calculus of the ideas immanent in neural nets","volume":"5","author":"McCulloch","year":"1943","journal-title":"Bull. Math. Biophys."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"12192","DOI":"10.3390\/rs70912192","article-title":"Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images","volume":"7","author":"Zhu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"226802","DOI":"10.1103\/PhysRevLett.102.226802","article-title":"Noise-controlled signal transmission in a multithread semiconductor Neuron","volume":"102","author":"Samardak","year":"2009","journal-title":"Phys. Rev. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/S1364-8152(99)00007-9","article-title":"Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications","volume":"15","author":"Maier","year":"2000","journal-title":"Environ. Model. Softw."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.rse.2014.01.027","article-title":"The uncertainty of biomass estimates from LiDAR and SAR across a boreal forest structure gradient","volume":"154","author":"Montesano","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.rse.2014.01.025","article-title":"Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data","volume":"151","author":"Zhang","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/S0034-4257(03)00039-7","article-title":"Predictive relations of tropical forest biomass from landsat tm data and their transferability between regions","volume":"85","author":"Foody","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_56","first-page":"1","article-title":"Estimation and uncertainty analysis of aboveground forest biomass with Landsat and LiDAR data: Brief overview and case studies","volume":"1","author":"Lu","year":"2012","journal-title":"Int. J. For. Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1029\/1999GB001206","article-title":"Approaches for Reducing Uncertainties in Regional Forest Carbon Balance","volume":"14","author":"Chen","year":"2000","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.foreco.2004.03.048","article-title":"Relationships between forest stand parameters and Landsat Thematic Mapper spectral responses in the Brazilian Amazon basin","volume":"198","author":"Lu","year":"2004","journal-title":"For. Ecol. Manag."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3770","DOI":"10.1016\/j.rse.2011.07.019","article-title":"Evaluating uncertainty in mapping forest carbon with airborne LiDAR","volume":"115","author":"Mascaro","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s10021-008-9221-5","article-title":"Environmental and biotic controls over aboveground biomass throughout a tropical rain forest","volume":"12","author":"Asner","year":"2009","journal-title":"Ecosystems"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.foreco.2008.04.010","article-title":"Comparison of Uncertainties in Carbon Sequestration Estimates for a Tropical and a Temperate Forest","volume":"256","author":"Nabuurs","year":"2008","journal-title":"For. Ecol. Manag."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.isprsjprs.2014.08.014","article-title":"Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series","volume":"102","author":"Zhu","year":"2015","journal-title":"ISPRS J. Photogramm."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1080\/01431160500142145","article-title":"Aboveground Biomass Estimation Using Landsat TM Data in the Brazilian Amazon","volume":"26","author":"Lu","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"15082","DOI":"10.3390\/rs71115082","article-title":"Estimation of CO2 sequestration by the forests in Japan by discriminating precise tree age category using remote sensing techniques","volume":"7","author":"Iizuka","year":"2015","journal-title":"Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/j.jenvman.2006.07.015","article-title":"Combining remote sensing imagery and forest age inventory for biomass mapping","volume":"85","author":"Zheng","year":"2007","journal-title":"J. Environ. Manag."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.rse.2004.12.022","article-title":"Combining lidar estimates of aboveground biomass and Landsat estimates of stand age for spatially extensive validation of modeled forest productivity","volume":"95","author":"Lefsky","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"7293","DOI":"10.1007\/s10661-014-3927-y","article-title":"Improving artificial forest biomass estimates using afforestation age information from time series Landsat stacks","volume":"186","author":"Liu","year":"2014","journal-title":"Environ. Monit. Assess."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3544","DOI":"10.3390\/rs4113544","article-title":"Estimating CO2 sequestration by forests in Oita prefecture, Japan, by combining Landsat ETM plus and ALOS Satellite remote sensing data","volume":"4","author":"Iizuka","year":"2012","journal-title":"Remote Sens."},{"key":"ref_69","first-page":"1411","article-title":"Effects of spatial autocorrelation on individual tree growth model of Picea likiangensis forest in northwest of Yunnan, China","volume":"25","author":"Cheng","year":"2015","journal-title":"J. Anim. Plant Sci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1093\/genetics\/159.1.337","article-title":"Genetic composition and diploid hybrid speciation of a high mountain pine, Pinus densata, native to the Tibetan plateau","volume":"159","author":"Wang","year":"2001","journal-title":"Genetics"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.foreco.2005.10.074","article-title":"Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests","volume":"222","author":"Wang","year":"2006","journal-title":"For. Ecol. Manag."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.rse.2009.01.007","article-title":"Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors","volume":"113","author":"Chander","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1016\/j.rse.2011.01.019","article-title":"C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter","volume":"115","author":"Reese","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_74","first-page":"134","article-title":"Analysis and comparison test on C-correction strategies and their scale effects with TM images in rugged mountainous terrain","volume":"16","author":"Li","year":"2014","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_75","unstructured":"Editorial Board of China Forest (1999). Volume 2 of China Forest: Coniferous Forest, China Forestry Publishing House."},{"key":"ref_76","unstructured":"Editorial Board of Yunnan Forest (1984). Yunnan Forest, China Forestry Publishing House."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.rse.2011.10.012","article-title":"Capabilities and limitations of Landsat and land cover data for aboveground woody biomass estimation of Uganda","volume":"117","author":"Avitabile","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.rse.2009.12.018","article-title":"Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches","volume":"114","author":"Powell","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.agrformet.2014.06.013","article-title":"Data-driven diagnostics of terrestrial carbon dynamics over north America","volume":"197","author":"Xiao","year":"2014","journal-title":"Agr. For. Meteorol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1590\/S0044-59672005000200015","article-title":"Exploring TM image texture and its relationships with biomass estimation in Rond\u00f4nia, Brazilian Amazon","volume":"35","author":"Lu","year":"2005","journal-title":"Acta Amazon"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1016\/j.jaridenv.2010.04.007","article-title":"Assessing multitemporal Landsat 7 ETM+ images for estimating aboveground biomass in subtropical dry forests of Argentina","volume":"74","author":"Gasparri","year":"2010","journal-title":"J. Arid Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.rse.2010.11.010","article-title":"Improved forest biomass using ALOS AVNIR-2 texture indices","volume":"115","author":"Sarker","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.isprsjprs.2015.06.002","article-title":"Investigating the robustness of the new Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas","volume":"108","author":"Dube","year":"2015","journal-title":"ISPRS J. Photogramm."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"6407","DOI":"10.3390\/rs6076407","article-title":"Estimates of aboveground biomass from texture analysis of Landsat imagery","volume":"6","author":"Kelsey","year":"2014","journal-title":"Remote Sens."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"45011","DOI":"10.1088\/1748-9326\/3\/4\/045011","article-title":"A first map of tropical Africa\u2019s above-ground biomass derived from satellite imagery","volume":"3","author":"Baccini","year":"2008","journal-title":"Environ. Res. Lett."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1139\/X09-086","article-title":"Estimating cavity tree and snag abundance using negative binomial regression models and nearest neighbor imputation methods","volume":"39","author":"Eskelson","year":"2009","journal-title":"Can. J. For. Res."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Zhang, J., Lu, C., Xu, H., and Wang, G. (2018). Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data. J. For. Res.","DOI":"10.1007\/s11676-018-0713-7"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1016\/j.rse.2010.01.016","article-title":"Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics","volume":"114","author":"Vauhkonen","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2012.02.023","article-title":"Quantifying aboveground forest carbon pools and fluxes from repeat lidar surveys","volume":"123","author":"Hudak","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.rse.2015.01.009","article-title":"Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels","volume":"160","author":"Chen","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1978","DOI":"10.1016\/j.rse.2011.04.001","article-title":"Characterizing forest canopy structure with lidar composite metrics and machine learning","volume":"115","author":"Zhao","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Gao, Y., Lu, D., Li, G., Wang, G., Chen, Q., Liu, L., and Li, D. (2018). Comparative analysis of modeling algorithms for forest aboveground biomass estimation in a subtropical region. Remote Sens., 10.","DOI":"10.3390\/rs10040627"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/7\/738\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:40:51Z","timestamp":1760186451000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/7\/738"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,27]]},"references-count":92,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["rs11070738"],"URL":"https:\/\/doi.org\/10.3390\/rs11070738","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,27]]}}}