{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T19:04:33Z","timestamp":1781377473496,"version":"3.54.1"},"reference-count":94,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,18]],"date-time":"2019-02-18T00:00:00Z","timestamp":1550448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Project of China","award":["2016YFC0500300"],"award-info":[{"award-number":["2016YFC0500300"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate forest above-ground biomass (AGB) mapping is crucial for sustaining forest management and carbon cycle tracking. The Shuttle Radar Topographic Mission (SRTM) and Sentinel satellite series offer opportunities for forest AGB monitoring. In this study, predictors filtered from 121 variables from Sentinel-1 synthetic aperture radar (SAR), Sentinal-2 multispectral instrument (MSI) and SRTM digital elevation model (DEM) data were composed into four groups and evaluated for their effectiveness in prediction of AGB. Five evaluated algorithms include linear regression such as stepwise regression (SWR) and geographically weighted regression (GWR); machine learning (ML) such as artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that the RF model used predictors from both the Sentinel series and SRTM DEM performed the best, based on the independent validation set. The RF model achieved accuracy with the mean error, mean absolute error, root mean square error, and correlation coefficient in 1.39, 25.48, 61.11 Mg\u00b7ha\u22121 and 0.9769, respectively. Texture characteristics, reflectance, vegetation indices, elevation, stream power index, topographic wetness index and surface roughness were recommended predictors for AGB prediction. Predictor variables were more important than algorithms for improving the accuracy of AGB estimates. The study demonstrated encouraging results in the optimal combination of predictors and algorithms for forest AGB mapping, using openly accessible and fine-resolution data based on RF algorithms.<\/jats:p>","DOI":"10.3390\/rs11040414","type":"journal-article","created":{"date-parts":[[2019,2,19]],"date-time":"2019-02-19T04:08:20Z","timestamp":1550549300000},"page":"414","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":108,"title":["Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9270-1626","authenticated-orcid":false,"given":"Lin","family":"Chen","sequence":"first","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Key Laboratory of Wetland Ecology and Environment, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Department of Natural Resources Science, University of Rhode Island, 1 Greenhouse Rd., Kingston, RI 02881, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeqiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Natural Resources Science, University of Rhode Island, 1 Greenhouse Rd., Kingston, RI 02881, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunying","family":"Ren","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Key Laboratory of Wetland Ecology and Environment, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Key Laboratory of Wetland Ecology and Environment, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongming","family":"Wang","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Key Laboratory of Wetland Ecology and Environment, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1126\/science.1201609","article-title":"A large and persistent carbon sink in the world\u2019s forests","volume":"333","author":"Pan","year":"2011","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1073\/pnas.1515160113","article-title":"The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times","volume":"113","author":"Bloom","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rse.2017.07.038","article-title":"The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas","volume":"200","author":"Santi","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1111\/1365-2745.12847","article-title":"Above-ground biomass is driven by mass-ratio effects and stand structural attributes in a temperate deciduous forest","volume":"106","author":"Fotis","year":"2018","journal-title":"J. Ecol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1038\/nature25138","article-title":"Unexpectedly large impact of forest management and grazing on global vegetation biomass","volume":"553","author":"Erb","year":"2018","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/BF01105003","article-title":"The carbon cycle and global forest ecosystem","volume":"70","author":"Sedjo","year":"1993","journal-title":"Water Air Soil Poll."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1007\/s10661-018-6725-0","article-title":"Estimating and mapping forest biomass using regression models and Spot-6 images (case study: Hyrcanian forests of north of Iran)","volume":"190","author":"Motlagh","year":"2018","journal-title":"Environ. Monit. Assess."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/S0378-1127(97)00044-3","article-title":"Aboveground biomass distribution of US eastern hardwood forests and the use of large trees as an indicator of forest development","volume":"96","author":"Brown","year":"1997","journal-title":"For. Ecol. Manag."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1016\/j.rse.2010.02.011","article-title":"Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California","volume":"114","author":"Gonzalez","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.rse.2018.04.056","article-title":"Potential value of combining ALOS PALSAR and Landsat-derived tree cover data for forest biomass retrieval in Madagascar","volume":"213","author":"Minha","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_12","first-page":"202","article-title":"Mapping boreal forest biomass from a SRTM and TanDEM-X based on canopy height model and Landsat spectral indices","volume":"68","author":"Sadeghi","year":"2018","journal-title":"Int. J. Appl. Earth. Obs. Geoinf."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1111\/geb.12125","article-title":"Carbon stock and density of northern boreal and temperate forests","volume":"23","author":"Thurner","year":"2014","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Berninger, A., Lohberger, S., St\u00e4ngel, M., and Siegert, F. (2018). SAR-based estimation of above-ground biomass and its changes in tropical forests of Kalimantan using L- and C-band. Remote Sens., 10.","DOI":"10.3390\/rs10060831"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2013.02.002","article-title":"A simulation approach for accuracy assessment of two-phase post-stratified estimation in large-area LiDAR biomass surveys","volume":"133","author":"Ene","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1016\/j.foreco.2017.06.042","article-title":"Tree size thresholds produce biased estimates of forest biomass dynamics","volume":"400","author":"Searle","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1038\/nclimate1354","article-title":"Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps","volume":"2","author":"Baccini","year":"2012","journal-title":"Nat. Clim. Chang."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4442","DOI":"10.3390\/rs70404442","article-title":"L-band SAR backscatter related to forest cover, height and aboveground biomass at multiple spatial scales across Denmark","volume":"7","author":"Joshi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/j.rse.2006.10.011","article-title":"Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: A possibility to verify carbon inventories","volume":"107","author":"Muukkonen","year":"2007","journal-title":"Remote Sens. Environ."},{"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","first-page":"5569","article-title":"Stacked sparse autoencoder modeling using the synergy of airborne LiDAR and satellite optical and SAR data to map forest above-ground biomass","volume":"10","author":"Shao","year":"2017","journal-title":"IEEE J.-STARS"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.rse.2018.11.017","article-title":"Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China","volume":"221","author":"Huang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2014.07.028","article-title":"Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass","volume":"154","author":"Fassnacht","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4927","DOI":"10.3390\/rs6064927","article-title":"On the semi-automatic retrieval of biophysical parameters based on spectral index optimization","volume":"6","author":"Rivera","year":"2014","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/S0034-4257(98)00090-X","article-title":"Spectral mixture analysis and geometric-optical reflectance modeling of boreal forest biophysical structure","volume":"67","author":"Peddle","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.rse.2004.06.016","article-title":"Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models","volume":"93","author":"Atzberger","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yue, J.B., Feng, H.K., Yang, G.J., and Li, Z.H. (2018). A comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy. Remote Sens., 10.","DOI":"10.3390\/rs10010066"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.rse.2004.08.008","article-title":"Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA","volume":"93","author":"Zheng","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_31","first-page":"82","article-title":"Modifying geographically weighted regression for estimating aboveground biomass in tropical rainforests by multispectral remote sensing data","volume":"18","author":"Propastin","year":"2012","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.isprsjprs.2014.01.001","article-title":"Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data","volume":"89","author":"Laurin","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gao, Y.K., Lu, D.S., Li, G.Y., Wang, G.X., Chen, Q., Liu, L.J., and Li, D.Q. (2018). Comparative analysis of modeling algorithms for forest aboveground biomass estimation in a subtropical region. Remote Sens., 10.","DOI":"10.3390\/rs10040627"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2011.09.026","article-title":"Sentinels for science: Potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land","volume":"120","author":"Malenovsky","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.isprsjprs.2017.10.016","article-title":"Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery","volume":"134","author":"Castillo","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","unstructured":"Sentinel-1_Team (2013). Sentinel-1 User Handbook, European Space Agency."},{"key":"ref_38","unstructured":"Sentinel-2_Team (2015). Sentinel-2 User Handbook, European Space Agency."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2015.10.005","article-title":"Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments","volume":"110","author":"Sibanda","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.rse.2016.01.017","article-title":"Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data","volume":"176","author":"Laurin","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_41","first-page":"126","article-title":"Exploiting the capabilities of the Sentinel-2 multi spectral instrument for predicting growing stock volume in forest ecosystems","volume":"66","author":"Mura","year":"2018","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"249","DOI":"10.14358\/PERS.72.3.249","article-title":"A global assessment of the SRTM performance","volume":"72","author":"Morris","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"299","DOI":"10.14358\/PERS.72.3.299","article-title":"Mapping height and biomass of mangrove forests in Everglades National Park with SRTM elevation data","volume":"72","author":"Simard","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.foreco.2005.10.074","article-title":"Biomass allometric equations for 10 co\u2013occurring tree species in Chinese temperate forests","volume":"222","author":"Wang","year":"2006","journal-title":"Forest Ecol. Manag."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1007\/s10265-009-0301-1","article-title":"Altitudinal changes in carbon storage of temperate forests on Mt Changbai, Northeast China","volume":"123","author":"Zhu","year":"2010","journal-title":"J. Plant Res."},{"key":"ref_47","unstructured":"Dong, L.H. (2015). Developing Individual and Stand-level Biomass Equations in Northeast China Forest Area. [Ph.D. Thesis, Northeast Forestry University]."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chen, L., Ren, C.Y., Zhang, B., Wang, Z.M., and Xi, Y.B. (2018). Estimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery. Forests, 9.","DOI":"10.3390\/f9100582"},{"key":"ref_49","unstructured":"Veci, L. (2015). Sentinel-1 Toolbox: SAR Basics Tutorial, European Space Agency."},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2014.10.029","article-title":"The uncertainty of biomass estimates from modeled ICESat-2 returns across a boreal forest gradient","volume":"158","author":"Montesano","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/j.rse.2016.07.030","article-title":"Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data","volume":"184","author":"Battude","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1080\/10106049.2017.1316781","article-title":"Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data","volume":"33","author":"Kumar","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3693","DOI":"10.3390\/rs6053693","article-title":"Improving the estimation of above ground biomass using dual polarimetric PALSAR and ETM+ data in the Hyrcanian mountain forest (Iran)","volume":"6","author":"Attarchi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Laurin, G.V., Pirotti, F., Callegari, M., Chen, Q., Cuozzo, G., Lingua, E., Notarnicola, C., and Papale, D. (2016). Potential of ALOS2 and NDVI to estimate forest above-ground biomass, and comparison with Lidar-derived estimates. Remote Sens., 9.","DOI":"10.3390\/rs9010018"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/0098-3004(96)00009-X","article-title":"Automated derivation of geographic window sizes for remote sensing digital image texture analysis","volume":"22","author":"Franklin","year":"1996","journal-title":"Comput. Geosci."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2014.04.003","article-title":"Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation","volume":"149","author":"Byrd","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_59","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_60","doi-asserted-by":"crossref","first-page":"3220","DOI":"10.1109\/TGRS.2013.2271813","article-title":"Empirical estimation of leaf Chlorophyll density in winter wheat canopies using Sentinel-2 spectral resolution","volume":"52","author":"Vincini","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1080\/2150704X.2016.1149251","article-title":"The potential of Sentinel-2 data for estimating biophysical variables in a boreal forest: A simulation study","volume":"7","author":"Majasalmi","year":"2016","journal-title":"Remote Sens. Lett."},{"key":"ref_62","unstructured":"Tang, G.A., and Yang, X. (2013). ArcGIS Experimental Course for Spatial Analysis, Science Press. [2nd ed.]."},{"key":"ref_63","unstructured":"SNAP (2016). Sentinels Application Platform Software ver. 4.0.0, European Space Agency."},{"key":"ref_64","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_65","doi-asserted-by":"crossref","first-page":"S56","DOI":"10.1016\/j.rse.2008.01.026","article-title":"PROSPECT + SAIL models: A review of use for vegetation characterization","volume":"113","author":"Jacquemoud","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_66","unstructured":"Weiss, M., and Baret, F. (2016). Sentinel 2 Toolbox Level 2 Products: LAI, FAPAR, FCOVER, INRA."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Bourgoin, C., Blanc, L., Bailly, J.S., Cornu, G., Berenguer, E., Oszwald, J., Tritsch, I., Laurent, F., Hasan, A.F., Sist, P., and Gond, V. (2018). The potential of multisource remote sensing for mapping the biomass of a degraded Amazonian forest. Forests, 9.","DOI":"10.3390\/f9060303"},{"key":"ref_68","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_69","doi-asserted-by":"crossref","first-page":"243","DOI":"10.2307\/3237145","article-title":"Stream power influence on southern Californian riparian vegetation","volume":"10","author":"Jacob","year":"1999","journal-title":"J. Veg. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1111\/j.1529-8817.2007.00357.x","article-title":"Linking benthic algal biomass to stream substratum topography","volume":"43","author":"Murdock","year":"2007","journal-title":"J. Phycol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"281","DOI":"10.3724\/SP.J.1258.2014.00025","article-title":"Trade-off between height and branch numbers in Stellera chamaejasme on slopes of different aspects in a degraded alpine grassland","volume":"38","author":"Hou","year":"2014","journal-title":"Chin. J. Plant Ecol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.foreco.2015.08.010","article-title":"Topographic and biotic factors determine forest biomass spatial distribution in a subtropical mountain moist forest","volume":"357","author":"Xu","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1007\/s11135-006-9018-6","article-title":"A caution regarding rules of thumb for variance inflation factors","volume":"41","year":"2007","journal-title":"Qual. Quant."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2906","DOI":"10.1016\/j.rse.2011.03.021","article-title":"Forest biomass mapping from lidar and radar synergies","volume":"115","author":"Sun","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1080\/07038992.2016.1252908","article-title":"Comparing modeling methods for predicting forest attributes using LiDAR metrics and ground measurements","volume":"42","author":"Shin","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software: An update","volume":"11","author":"Hall","year":"2009","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_77","unstructured":"IBM Corp (2012). IBM SPSS Statistics 21 Core System User\u2019s Guide, IBM Corp. Somers."},{"key":"ref_78","unstructured":"Nakaya, T., Charlton, M., Lewis, P., Brunsdon, C., Yao, J., and Fotheringham, S. (2014). GWR4 User Manual, Windows Application for Geographically Weighted Regression Modelling, Ritsumeikan University."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"673","DOI":"10.5721\/EuJRS20154837","article-title":"Application of Neural Networks for the retrieval of forest woody volume from SAR multifrequency data at L and C bands","volume":"48","author":"Santi","year":"2015","journal-title":"Eur. J. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"41","DOI":"10.14358\/PERS.83.1.41","article-title":"Estimation of forest biomass using multivariate relevance vector regression","volume":"82","author":"Sharifi","year":"2016","journal-title":"Photogramm. Eng. Rem. S."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1177\/0309133317693443","article-title":"Optimizing spaceborne LiDAR and very high resolution optical sensor parameters for biomass estimation at ICESat\/GLAS footprint level using regression algorithms","volume":"41","author":"Dhanda","year":"2017","journal-title":"Prog. Phys. Geog."},{"key":"ref_82","unstructured":"Isaaks, E.H., and Srivastava, R.M. (1989). An Introduction to Applied Geostatistics, Oxford University Press."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1109\/72.870050","article-title":"Improvements to the SMO algorithm for SVM regression","volume":"11","author":"Shevade","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.apgeog.2018.05.011","article-title":"Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest","volume":"96","author":"Ghosh","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.isprsjprs.2018.03.019","article-title":"A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States","volume":"139","author":"Byrd","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"016008","DOI":"10.1117\/1.JRS.12.016008","article-title":"Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data","volume":"12","author":"Laurin","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1080\/2150704X.2017.1295479","article-title":"Assessing the relationships between growing stock volume and Sentinel-2 imagery in a Mediterranean forest ecosystem","volume":"8","author":"Chrysafis","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Pandit, S., Tsuyuki, S., and Dube, T. (2018). Estimating above-ground biomass in sub-tropical buffer zone community forests, Nepal, using Sentinel 2 data. Remote Sens., 10.","DOI":"10.3390\/rs10040601"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.rse.2017.10.007","article-title":"Combining UAV and Sentinel-2 auxiliary data for forest growing stock volume estimation through hierarchical model-based inference","volume":"204","author":"Puliti","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1080\/02626667909491834","article-title":"A physically based, variable contributing area model of basin hydrology","volume":"24","author":"Beven","year":"1979","journal-title":"Hydrol. Sci. Bull."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1080\/07038992.2016.1217485","article-title":"A comparison of machine learning techniques applied to Landsat-5 TM spectral data for biomass estimation","volume":"42","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s11676-017-0404-9","article-title":"Using nonparametric modeling approaches and remote sensing imagery to estimate ecological welfare forest biomass","volume":"29","author":"Wu","year":"2018","journal-title":"J. For. Res."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.rse.2017.09.007","article-title":"Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux","volume":"204","author":"Zhao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Liu, K., Wang, J.D., Zeng, W.S., and Song, J.L. (2017). Comparison and evaluation of three methods for estimating forest above ground biomass using TM and GLAS data. Remote Sens., 9.","DOI":"10.3390\/rs9040341"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/4\/414\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:32:51Z","timestamp":1760185971000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/4\/414"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,18]]},"references-count":94,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["rs11040414"],"URL":"https:\/\/doi.org\/10.3390\/rs11040414","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,18]]}}}