{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T14:15:22Z","timestamp":1768832122261,"version":"3.49.0"},"reference-count":82,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,12]],"date-time":"2018-04-12T00:00:00Z","timestamp":1523491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate assessment of above-ground biomass (AGB) is important for the sustainable management of forests, especially buffer zone (areas within the protected area, where restrictions are placed upon resource use and special measure are undertaken to intensify the conservation value of protected area) areas with a high dependence on forest products. This study presents a new AGB estimation method and demonstrates the potential of medium-resolution Sentinel-2 Multi-Spectral Instrument (MSI) data application as an alternative to hyperspectral data in inaccessible regions. Sentinel-2 performance was evaluated for a buffer zone community forest in Parsa National Park, Nepal, using field-based AGB as a dependent variable, as well as spectral band values and spectral-derived vegetation indices as independent variables in the Random Forest (RF) algorithm. The 10-fold cross-validation was used to evaluate model effectiveness. The effect of the input variable number on AGB prediction was also investigated. The model using all extracted spectral information plus all derived spectral vegetation indices provided better AGB estimates (R2 = 0.81 and RMSE = 25.57 t ha\u22121). Incorporating the optimal subset of key variables did not improve model variance but reduced the error slightly. This result is explained by the technically-advanced nature of Sentinel-2, which includes fine spatial resolution (10, 20 m) and strategically-positioned bands (red-edge), conducted in flat topography with an advanced machine learning algorithm. However, assessing its transferability to other forest types with varying altitude would enable future performance and interpretability assessments of Sentinel-2.<\/jats:p>","DOI":"10.3390\/rs10040601","type":"journal-article","created":{"date-parts":[[2018,4,12]],"date-time":"2018-04-12T12:19:27Z","timestamp":1523535567000},"page":"601","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":121,"title":["Estimating Above-Ground Biomass in Sub-Tropical Buffer Zone Community Forests, Nepal, Using Sentinel 2 Data"],"prefix":"10.3390","volume":"10","author":[{"given":"Santa","family":"Pandit","sequence":"first","affiliation":[{"name":"Graduate School of Agricultural and Life Sciences, University of Tokyo, 1-1 Yayoi, Bunkyo-Ku, Tokyo 113-8567, Japan"}]},{"given":"Satoshi","family":"Tsuyuki","sequence":"additional","affiliation":[{"name":"Graduate School of Agricultural and Life Sciences, University of Tokyo, 1-1 Yayoi, Bunkyo-Ku, Tokyo 113-8567, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3456-8991","authenticated-orcid":false,"given":"Timothy","family":"Dube","sequence":"additional","affiliation":[{"name":"Institute for Water Studies, Dept. of Earth Sciences, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,12]]},"reference":[{"key":"ref_1","unstructured":"Ebregt, A., and Greve, P.D. (2000). Buffer Zones and Their Management: Policy and Best Practices for Terrestrial Ecosystems in Developing Countries, National Reference Centre for Nature Management."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.jenvman.2005.03.017","article-title":"Beyond buffer zone protection: A comparative study of park and buffer zone products\u2019 importance to villagers living inside Royal Chitwan National Park and to villagers living in its buffer zone","volume":"78","author":"Treue","year":"2006","journal-title":"J. Environ. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s11053-013-9216-6","article-title":"The precision of C stock estimation in the Ludhikola watershed using model-based and design-based approaches","volume":"22","author":"Chinembiri","year":"2013","journal-title":"Nat. Res. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"237","DOI":"10.2989\/20702620.2014.965981","article-title":"Estimating wood volume from canopy area in deciduous woodlands of Zimbabwe","volume":"76","author":"Gara","year":"2014","journal-title":"South. For."},{"key":"ref_5","first-page":"119","article-title":"Estimation of floodplain aboveground biomass using multispectral remote sensing and nonparametric modeling","volume":"33","author":"Filippi","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160500486732","article-title":"The potential and challenges of remote sensing-based biomass estimation","volume":"27","author":"Lu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"477","DOI":"10.14214\/sf.38","article-title":"Estimating tree biomass of Sub-Saharan African forests: A review of available allometric equations","volume":"45","author":"Henry","year":"2011","journal-title":"Silva Fenn."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/S0034-4257(02)00130-X","article-title":"Remote Sensing estimates of boreal and temperate forest woody biomass: Carbon pools, sources, and sinks","volume":"84","author":"Dong","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6497","DOI":"10.1080\/01431160902882496","article-title":"Estimating aboveground biomass of grassland having a high canopy cover; an exploratory analysis of in situ hyperspectral data","volume":"30","author":"Chen","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","first-page":"15348","article-title":"Intra-and-inter species biomass prediction in a plantation forest: Testing the utility of high spatial resolution space borne multispectral RapidEye sensor and advance machine learning algorithms","volume":"14","author":"Dube","year":"2014","journal-title":"Remote Sens."},{"key":"ref_11","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 multi-spectral sensor in quantifying aboveground biomass in Umgeni catchment, South Africa","volume":"101","author":"Dube","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3920","DOI":"10.3390\/rs4123920","article-title":"Utilizing a multi-source forest inventory technique, MODIS data and Landsat TM images in the production of forest cover and volume maps for the Terai Physiographic Zone in Nepal","volume":"4","author":"Muinonen","year":"2012","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2014.04.012","article-title":"Effect of field plot location on estimating tropical forest above ground biomass in Nepal using airborne laser scanning data","volume":"94","author":"Rana","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Shen, W., Li, M., Huang, C., and Wei, A. (2016). Quantifying live aboveground biomass and forest disturbance of mountainous natural and plantation forests in Northern Guangdong, China, based on Multi-temporal Landsat, PALSAR and field plot data. Remote Sens., 8.","DOI":"10.3390\/rs8070595"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"793","DOI":"10.14358\/PERS.70.7.793","article-title":"Methodology for hyperspectral band selection","volume":"70","author":"Bajcsy","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_16","first-page":"145","article-title":"A review of hyperspectral remote sensing and its application in vegetation and water resource studies","volume":"33","author":"Govender","year":"2007","journal-title":"Water S. Afr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1139\/x00-142","article-title":"An evaluation of alternative remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forests in western Oregon","volume":"31","author":"Lefsky","year":"2001","journal-title":"Can. J. For. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.rse.2007.07.028","article-title":"Recent advances in techniques for hyperspectral image processing","volume":"113","author":"Plaza","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.rse.2013.07.011","article-title":"Toward structural assessment of semi-arid African savannahs and woodlands: The potential of multitemporal polarimetric RADARSAT-2 fine beam images","volume":"138","author":"Mathieu","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"045023","DOI":"10.1088\/1748-9326\/2\/4\/045023","article-title":"Monitoring and estimating tropical forest carbon stocks: Making REDD a reality","volume":"2","author":"Gibbs","year":"2007","journal-title":"Environ. Res. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.1016\/j.rse.2011.01.024","article-title":"Characterizing 3D vegetation structure from space; mission requirements","volume":"115","author":"Hall","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.isprsjprs.2014.01.001","article-title":"Aboveground 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_24","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_25","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.foreco.2006.01.014","article-title":"Modeling forest stand structure attributes using Landsat ETM+ data: Application of mapping of aboveground biomass and stand volume","volume":"225","author":"Hall","year":"2006","journal-title":"For. Ecol. Manag."},{"key":"ref_26","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_27","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_28","unstructured":"Kasischke, E.S., Goetz, S., Hansen, M.C., Ozdogan, M., Rogan, J., Ustin, S.L., and Woodcock, C.E. (2014). Remote Sensing for Natural Resource Management and Environmental Monitoring, John and Wiley and Sons, Inc.. [3rd ed.]."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1080\/014311600210119","article-title":"Satellite estimation of tropical secondary forest above-ground biomass: Data from Brazil and Bolivia","volume":"21","author":"Steininger","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/S0034-4257(01)00332-7","article-title":"Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data","volume":"81","author":"Broge","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S0034-4257(00)00197-8","article-title":"Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density","volume":"76","author":"Broge","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_32","first-page":"305","article-title":"Imaging spectrometry for ecological applications","volume":"3","author":"Curran","year":"2001","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3999","DOI":"10.1080\/01431160310001654923","article-title":"Narrow band vegetation indices to overcome the saturation problem in biomass estimation","volume":"25","author":"Mutanga","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s00267-005-0228-9","article-title":"A comparison of spatial and spectral image resolution for mapping invasive plants in coastal California","volume":"39","author":"Underwood","year":"2007","journal-title":"Environ. Manag."},{"key":"ref_35","unstructured":"G\u00f3mez, M.G.C. (2017). Joint Use of Sentinel-1 and Sentinel-2 for Land Cover Classification: A Machine Learning Approach. [Master\u2019s. Thesis, Lund University]."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.rse.2011.08.028","article-title":"The European Earth monitoring (GMES) programme: Status and perspectives","volume":"120","author":"Aschbacher","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_37","first-page":"516","article-title":"Potential of Sentinel-2 spectral configuration to assess rangeland quality","volume":"124","author":"Ramoelo","year":"2015","journal-title":"J. Appl. Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.isprsjprs.2017.04.016","article-title":"Examining the strength of the newly-launched Sentinel 2 MSI sensor in detecting and discriminating subtle differences between C3 and C4 grass species","volume":"129","author":"Shoko","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Forkuor, G.F., Dimobe, K., Serme, I., and Tondoh, J.E. (2017). Landsat-8 vs. Sentinel-2: Examining the added value of Sentinel-2\u2019s red-edge bands to land-use and land cover mapping in Burkina Faso. GISci. Remote Sens., 1\u201324.","DOI":"10.1080\/15481603.2017.1370169"},{"key":"ref_40","first-page":"344","article-title":"Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3","volume":"23","author":"Clevers","year":"2012","journal-title":"Int. J Appl. Earth Obs. Geoinf."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"271","DOI":"10.5194\/isprs-annals-IV-4-W4-271-2017","article-title":"Mapping and monitoring wetlands using Sentinel-2 satellite imagery","volume":"IV-4\/W4","author":"Kaplan","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Godinho, S., Guiomar, N., and Gil, A. (2017). Estimating tree canopy percentage in Mediterranean slivopastoral systems in suing Sentinel-2A imagery and the stochastic gradient boosting algorithm. Int. J. Remote Sens., 1\u201323.","DOI":"10.1080\/01431161.2017.1399480"},{"key":"ref_43","first-page":"280","article-title":"Integration of WorldView-2 and airborne LiDAR data for tree species level carbon stock mapping in Kayar Khola watershed, Nepal","volume":"38","author":"Karna","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_44","unstructured":"Baral, S. (2011). Mapping Carbon Stock Using High Resolution Satellite Images in Sub-Tropical Forest of Nepal. [Ph.D. Thesis, University of Twente]."},{"key":"ref_45","unstructured":"FRA\/DFRS (2014). Terai Forests of Nepal (2010-2012)."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Murthy, M.S.R., Wesselman, S., and Gilani, H. (2015). Multi-Scale Forest Biomass Assessment and Monitoring in the Hindu Kush Himalayan Region: A Geospatial Perspective, International Centre for Integrated Mountain Development (ICIMOD).","DOI":"10.53055\/ICIMOD.605"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Strobl, C., Boulesteix, A.L., Kneib, T., Augustin, T., and Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinf., 9.","DOI":"10.1186\/1471-2105-9-307"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"10017","DOI":"10.3390\/rs70810017","article-title":"Mapping tree canopy cover and aboveground biomass in Sudano-Sahelian Woodlands using Landsat 8 and Random forest","volume":"7","author":"Karlson","year":"2015","journal-title":"Remote Sens."},{"key":"ref_49","unstructured":"Dunne, K., Cunningham, P., and Azuaje, F. (2002). Solution to instability problems with sequential wrapper-based approaches to feature selection. J. Mach. Learn. Res., 1\u201322. Available online: https:\/\/www.scss.tcd.ie\/publications\/tech-reports\/reports.02\/TCD-CS-2002-28.pdf."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A review of feature selection techniques in bioinformatics","volume":"23","author":"Saeys","year":"2007","journal-title":"Bioinformatics"},{"key":"ref_51","first-page":"87","article-title":"Tree allometry and improved estimation of carbon stocks and balance of tropical forest","volume":"145","author":"Chave","year":"2005","journal-title":"Ecosyst. Ecol."},{"key":"ref_52","unstructured":"Chaturvedi, A.N., and Khanna, L.S. (1982). Forest Mensuration, International Book Distributors."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/0034-4257(88)90019-3","article-title":"An improved-dark object subtraction technique for atmospheric scattering correction of multispectral data","volume":"24","author":"Chavez","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2013.04.007","article-title":"Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation","volume":"82","author":"Frampton","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1016\/j.rse.2009.12.006","article-title":"Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: An application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS\/PROBA observations","volume":"114","author":"Stagakis","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and Photographic infrared linear combination for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and development stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of leaf-area index from quality of light on the forest floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.rse.2013.06.004","article-title":"Vegetation index suites as indicators of vegetation state in grassland and savanna: An analysis with simulated SENTINEL 2 data for a North American transect","volume":"137","author":"Hill","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1034\/j.1399-3054.1999.106119.x","article-title":"Non-destructive optical detection of pigment changes during leaf senescence and fruits ripening","volume":"106","author":"Merzlyak","year":"1999","journal-title":"Physiol. Plant."},{"key":"ref_62","first-page":"77","article-title":"The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies","volume":"49","author":"Hardisky","year":"1983","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_63","first-page":"71","article-title":"Leaf chlorophyll content and surface spectral reflectance of tree species along a terrain gradient in Taiwan\u2019s Kenting National Park","volume":"48","author":"Chen","year":"2007","journal-title":"Bot. Stud."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_65","first-page":"235","article-title":"Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops","volume":"34","author":"Kross","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0176-1617(11)81633-0","article-title":"Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer plantanoides L. leaves. Spectral features and relation to chlorophyll estimation","volume":"143","author":"Gitelson","year":"1994","journal-title":"J. Plant Physiol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"27832","DOI":"10.3390\/s151127832","article-title":"Active-optical sensors using red NDVI compared to red edge NDVI for prediction of corn grain yield in North Dakota, USA","volume":"15","author":"Sharma","year":"2015","journal-title":"Sensors"},{"key":"ref_68","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_69","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s10021-005-0054-1","article-title":"Newer classification and regression tree techniques: Bagging and random forests for ecological prediction","volume":"9","author":"Prasad","year":"2006","journal-title":"Ecosystems"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.compbiomed.2011.03.001","article-title":"Random forest ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis","volume":"41","author":"Ozcift","year":"2011","journal-title":"Comput. Biol. Med."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1021\/ci060164k","article-title":"Random forest model to predict aqueous solubility","volume":"47","author":"Palmer","year":"2007","journal-title":"J. Chem. Inf. Model."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Reif, D.M., Motsinger, A.A., McKinney, B.A., Crowe, J.E., and Moore, J.H. (2006). Feature selection using a random forests classifier for the integrated analysis of multiple data types. IEEE Symp. Comput. Intell. Bioinf. Comput. Biol., 1\u20138.","DOI":"10.1109\/CIBCB.2006.330987"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s11273-009-9169-z","article-title":"Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review","volume":"18","author":"Adam","year":"2010","journal-title":"Wetlands Ecol. Manag."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.rse.2005.09.011","article-title":"Estimating biomass for boreal forest using ASTER satellite data combined with standwise forest inventory data","volume":"99","author":"Muukkonen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_75","unstructured":"Adan, M.S. (2017). Integrating Sentinel-2 Derived Vegetation Indices and Terrestrial Laser Scanner to Estimate Above-Ground Biomass\/Carbon in Ayer Hitam Tropical Forest Malaysia. [Master\u2019s Thesis, The University of Twente]."},{"key":"ref_76","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 stimulated multispectral Sentinel-2 data","volume":"176","author":"Laurin","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1080\/01431161.2013.870676","article-title":"Estimating standing biomass in papyrus (Cyperus papyrus L.) swamp: Exploratory of in situ hyperspectral indices and random forest regression","volume":"35","author":"Adam","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Liu, K., Wang, J., Zeng, W., and Song, J. (2017). Comparison and evaluation of three models for estimating forest above ground biomass using TM and GLAS data. Remote Sens., 9.","DOI":"10.3390\/rs9040341"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.isprsjprs.2015.06.002","article-title":"Investigating the robustness of the newly Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass","volume":"108","author":"Dube","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Wu, C., Shen, H., Wang, K., Shen, A., Deng, J., and Gan, M. (2016). Landsat Imagery-based aboveground biomass estimation and change investigation related to human activities. Sustainability, 8.","DOI":"10.3390\/su8020159"},{"key":"ref_82","first-page":"399","article-title":"High density biomass estimation for wetland vegetation using Worldview-2 imagery and random forest regression algorithm","volume":"18","author":"Mutanga","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/601\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:00:31Z","timestamp":1760194831000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/601"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,12]]},"references-count":82,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["rs10040601"],"URL":"https:\/\/doi.org\/10.3390\/rs10040601","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,12]]}}}