{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T13:56:18Z","timestamp":1768485378413,"version":"3.49.0"},"reference-count":94,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,15]],"date-time":"2020-01-15T00:00:00Z","timestamp":1579046400000},"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":["41871216"],"award-info":[{"award-number":["41871216"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The accurate quantification of biomass helps to understand forest productivity and carbon cycling dynamics. Research on uncertainty during pretreatment is still lacking despite it being one of the major sources of uncertainty and an essential step in biomass estimation. In this study, we investigated pretreatment uncertainty and conducted a comparative study on the uncertainty of three optical imagery preprocessing stages (radiometric calibration, atmospheric and terrain correction) in biomass estimation. A combination of statistical models (random forest) and multisource data (Landsat enhanced thematic mapper plus (ETM+), Landsat operational land imager (OLI), national forest inventory (NFI)) was used to estimate forest biomass. Particularly, mean absolute error (MAE) and relative error (RE) were used to assess and quantify the uncertainty of each pretreatment, while the coefficient of determination (R2) was employed to evaluate the accuracy of the model. The results obtained show that random forest (RF) and 10-fold cross validation algorithms provided reliable accuracy for biomass estimation to better understand the uncertainty in pretreatments. In this study, there was a considerable uncertainty in biomass estimation using original OLI and ETM+ images from. Uncertainty was lower after data processing, emphasizing the importance of pretreatments for improving accuracy in biomass estimation. Further, the effects of three pretreatments on uncertainty of biomass estimation were objectively quantified. In this study (results of test sample), a 33.70% uncertainty was found in biomass estimation using original images from the OLI, and a 34.28% uncertainty in ETM+. Radiometric calibration slightly increased the uncertainty of biomass estimation (OLI increased by 1.38%, ETM+ increased by 2.08%). Moreover, atmospheric correction (5.56% for OLI, 4.41% for ETM+) and terrain correction (1.00% for OLI, 1.67% for ETM+) significantly reduced uncertainty for OLI and ETM+, respectively. This is an important development in the field of improving the accuracy of biomass estimation by remote sensing. Notably, the three pretreatments presented the same trend in uncertainty during biomass estimation using OLI and ETM+. This may exhibit the same effects in other optical images. This article aims to quantify uncertainty in pretreatment and to analyze the resultant effects to provide a theoretical basis for improving the accuracy of biomass estimation.<\/jats:p>","DOI":"10.3390\/ijgi9010048","type":"journal-article","created":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T04:14:41Z","timestamp":1579234481000},"page":"48","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Uncertainty Analysis of Remote Sensing Pretreatment for Biomass Estimation on Landsat OLI and Landsat ETM+"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6467-1987","authenticated-orcid":false,"given":"Qi","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Environment and Resources Science, Zhejiang A &amp; F University, Hangzhou 311300, China"}]},{"given":"Lihua","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Environment and Resources Science, Zhejiang A &amp; F University, Hangzhou 311300, China"},{"name":"School of Landscape Architecture, Zhejiang A &amp; F University, Hangzhou 311300, China"}]},{"given":"Maozhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Environment and Resources Science, Zhejiang A &amp; F University, Hangzhou 311300, China"}]},{"given":"Zhi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Environment and Resources Science, Zhejiang A &amp; F University, Hangzhou 311300, China"}]},{"given":"Zhangfeng","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Landscape Architecture, Zhejiang A &amp; F University, Hangzhou 311300, China"}]},{"given":"Yaqi","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Landscape Architecture, Zhejiang A &amp; F University, Hangzhou 311300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3099-4379","authenticated-orcid":false,"given":"Yijun","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Landscape Architecture, Zhejiang A &amp; F University, Hangzhou 311300, China"}]},{"given":"Zhangwei","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Landscape Architecture, Zhejiang A &amp; F University, Hangzhou 311300, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Achard, F., Eva, H.D., Mayaux, P., Stibig, H.J., and Belward, A. (2004). Improved estimates of net carbon emissions from land cover change in the tropics for the 1990s. Glob. Biogeochem. Cycles, 18.","DOI":"10.1029\/2003GB002142"},{"key":"ref_2","first-page":"544","article-title":"Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure","volume":"114","author":"Frolking","year":"2015","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-century forest cover change","volume":"342","author":"Hansen","year":"2014","journal-title":"Science"},{"key":"ref_4","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":"2010","journal-title":"Glob. Chang. Biol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.rse.2004.09.006","article-title":"Global biomass mapping for an improved understanding of the co 2 balance\u2014The earth observation mission carbon-3d","volume":"94","author":"Hese","year":"2005","journal-title":"Remote Sens. Environ."},{"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 Pollut."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Waring, R.H., and Running, S.W. (2007). Forest Ecosystems, Analysis at Multiple Scales, Academic Press. [3rd ed.].","DOI":"10.1016\/B978-012370605-8.50005-0"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/0961-9534(95)00113-1","article-title":"The role of forest and bioenergy strategies in the global carbon cycle","volume":"11","author":"Schlamadinger","year":"1996","journal-title":"Biomass Bioenergy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"14855","DOI":"10.1038\/ncomms14855","article-title":"High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle","volume":"8","author":"Brinck","year":"2017","journal-title":"Nat. Commun."},{"key":"ref_10","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_11","first-page":"6362","article-title":"Realising redd+ national strategy and policy options","volume":"18","author":"Mandels","year":"2009","journal-title":"Cent. Int. For. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"014013","DOI":"10.1088\/1748-9326\/5\/1\/014013","article-title":"Predicting pan-tropical climate change induced forest stock gains and losses\u2014Implications for REDD","volume":"5","author":"Gumpenberger","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1080\/01431161.2016.1266113","article-title":"Vegetation biomass estimation with remote sensing: Focus on forest and other wooded land over the mediterranean ecosystem","volume":"38","author":"Galidaki","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"3177","DOI":"10.1111\/gcb.12629","article-title":"Improved allometric models to estimate the aboveground biomass of tropical trees","volume":"20","author":"Chave","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.foreco.2010.05.023","article-title":"Forest structure and live aboveground biomass variation along an elevational gradient of tropical atlantic moist forest (Brazil)","volume":"260","author":"Alves","year":"2010","journal-title":"For. Ecol. Manag."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kumar, L., and Mutanga, O. (2017). Remote sensing of above-ground biomass. Remote Sens., 9.","DOI":"10.3390\/rs9090935"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1002\/2013GB004664","article-title":"Application of remote sensing to understanding fire regimes and biomass burning emissions of the tropical andes","volume":"28","author":"Oliveras","year":"2014","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/S0580-9517(08)70239-3","article-title":"2 Direct methods and biomass estimation","volume":"22","author":"Fry","year":"1990","journal-title":"Methods Microbiol."},{"key":"ref_20","first-page":"881","article-title":"Biomass estimation methods for tropical forests with applications to forest inventory data","volume":"35","author":"Brown","year":"1989","journal-title":"For. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s13595-011-0040-z","article-title":"Review of ground-based methods to measure the distribution of biomass in forest canopies","volume":"68","author":"Seidel","year":"2011","journal-title":"Ann. For. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Durante, P., Mart\u00edn-Alc\u00f3n, S., Gil-Tena, A., Algeet, N., Tom\u00e9, J.L., Recuero, L., Palacios-Orueta, A., and Oyonarte, C. (2019). Improving aboveground forest biomass maps: From high-resolution to national scale. Remote Sens., 11.","DOI":"10.3390\/rs11070795"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"O\u2019Donnell, J.P.R., and Schalles, J.F. (2016). Examination of abiotic drivers and their influence on Spartina alterniflora biomass over a twenty-eight year period using Landsat 5 TM satellite imagery of the Central Georgia Coast. Remote Sens., 8.","DOI":"10.3390\/rs8060477"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.rse.2012.11.010","article-title":"Comparison of precision of biomass estimates in regional field sample surveys and airborne lidar-assisted surveys in Hedmark county, Norway","volume":"130","author":"Gobakken","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_26","first-page":"399","article-title":"High density biomass estimation for wetland vegetation using worldview-2 imagery and random forest regression algorithm","volume":"18","author":"Onisimo","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shi, W. (2009). Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses, CRC Press.","DOI":"10.1201\/9781420059281"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hill, T.C., Williams, M., Bloom, A.A., Mitchard, E.T., and Ryan, C.M. (2013). Are inventory based and remotely sensed above-ground biomass estimates consistent?. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0074170"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1006\/jmsc.2002.1258","article-title":"A model for the uncertainty around the yearly trawl-acoustic estimate of biomass of barents sea capelin, mallotus villosus (m\u00fcller)","volume":"59","author":"Tjelmeland","year":"2002","journal-title":"ICES J. Mar. Sci."},{"key":"ref_30","first-page":"1","article-title":"Aboveground biomass estimation and uncertainties assessing on regional scale with an improved model analysis method","volume":"47","author":"Yu","year":"2018","journal-title":"Hubei For. Sci. Technol."},{"key":"ref_31","first-page":"79","article-title":"Uncertainty assessment in regional-scale above ground biomass estimation of Chinese fir","volume":"50","author":"Yu","year":"2014","journal-title":"Sci. Silvae Sin."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1080\/01431168708948693","article-title":"The effect of subpixel clouds on remote sensing","volume":"8","author":"Kaufman","year":"1987","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.14358\/PERS.72.10.1137","article-title":"Landsat-7 long-term acquisition plan radiometry-evolution over time","volume":"72","author":"Arvidson","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.isprsjprs.2014.09.006","article-title":"Bidirectional effects in landsat reflectance estimates: Is there a problem to solve?","volume":"103","author":"Nagol","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3112","DOI":"10.1016\/j.rse.2008.03.009","article-title":"Multi-temporal modis-landsat data fusion for relative radiometric normalization, gap filling, and prediction of landsat data","volume":"112","author":"Roy","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.rse.2003.08.010","article-title":"Intercalibration of vegetation indices from different sensor systems","volume":"88","author":"Steven","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4485","DOI":"10.1080\/01431160500168686","article-title":"An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data","volume":"26","author":"Tucker","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/S0034-4257(02)00087-1","article-title":"The modis land product quality assessment approach","volume":"83","author":"Roy","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2015.12.024","article-title":"Characterization of landsat-7 to landsat-8 reflective wavelength and normalized difference vegetation index continuity","volume":"185","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chave, J., Davies, S.J., Phillips, O.L., Lewis, S.L., Sist, P., Schepaschenko, D., Armston, J., Baker, T.R., Coomes, D., and Disney, M. (2019). Ground data are essential for biomass remote sensing missions. Surv. Geophys.","DOI":"10.1007\/s10712-019-09528-w"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1648","DOI":"10.1016\/j.foreco.2011.07.018","article-title":"Estimating carbon stock in secondary forests: Decisions and uncertainties associated with allometric biomass models","volume":"262","author":"Van","year":"2011","journal-title":"For. Ecol. Manag."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.eja.2016.06.006","article-title":"Multi-model simulation of soil temperature, soil water content and biomass in euro-mediterranean grasslands: Uncertainties and ensemble performance","volume":"88","author":"Barcza","year":"2017","journal-title":"Eur. J. Agron."},{"key":"ref_43","first-page":"25","article-title":"Quantifying the model-related variability of biomass stock and change estimates in the norwegian national forest inventory","volume":"60","author":"Breidenbach","year":"2014","journal-title":"For. Sci."},{"key":"ref_44","first-page":"436537","article-title":"Aboveground forest biomass estimation with landsat and lidar data and uncertainty analysis of the estimates","volume":"2012","author":"Lu","year":"2012","journal-title":"Int. J. For. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1111\/j.2041-210x.2012.00266.x","article-title":"Error propagation in biomass estimation in tropical forests","volume":"4","author":"Molto","year":"2013","journal-title":"Methods Ecol. Evol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1016\/j.rse.2010.10.008","article-title":"Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of lidar-derived estimates of forest stand biomass","volume":"115","author":"Frazer","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/S0378-1127(01)00509-6","article-title":"Biomass estimation in the Tapajos National Forest, Brazil: Examination of sampling and allometric uncertainties","volume":"154","author":"Keller","year":"2001","journal-title":"For. Ecol. Manag."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1023\/B:GEJO.0000026688.74589.58","article-title":"Biomass estimation using landsat-tm and -etm+. Towards a regional model for southern Africa?","volume":"59","author":"Samimi","year":"2004","journal-title":"GeoJournal"},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2989\/20702620.2018.1463150","article-title":"Mapping tree aboveground biomass and carbon in omo forest reserve Nigeria using landsat 8 oli data","volume":"80","author":"Chenge","year":"2018","journal-title":"South. For."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s13021-016-0055-8","article-title":"Mapping and estimating the total living biomass and carbon in low-biomass woodlands using landsat 8 cdr data","volume":"11","author":"Belachew","year":"2016","journal-title":"Carbon Balance Manag."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.rse.2015.12.054","article-title":"Uncertainty assessment of surface net radiation derived from landsat images","volume":"175","author":"Mira","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1016\/S0034-4257(02)00066-4","article-title":"Mapping and uncertainty of predictions based on multiple primary variables from joint co-simulation with landsat tm image and polynomial regression","volume":"83","author":"Gertner","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_54","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_55","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1109\/TGRS.2009.2024934","article-title":"Validation of landsat-7\/etm+ thermal-band calibration and atmospheric correction with ground-based measurements","volume":"48","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/S0034-4257(01)00247-4","article-title":"Absolute radiometric calibration of landsat 7 etm+ using the reflectance-based method","volume":"78","author":"Thome","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Campbell, J.B. (1987). Introduction to Remote Sensing, The Guilgord Press.","DOI":"10.1080\/10106048709354126"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"12275","DOI":"10.3390\/rs61212275","article-title":"Landsat-8 operational land imager radiometric calibration and stability","volume":"6","author":"Markham","year":"2014","journal-title":"Remote Sens."},{"key":"ref_59","first-page":"185","article-title":"Uncertainty in prediction of soil erod ibility k-factor in subtropical china","volume":"46","author":"Zhang","year":"2009","journal-title":"Acta Pedol. Sin."},{"key":"ref_60","unstructured":"He, L. (2016). A Study on the Uncertainty of Regional Winter Wheat Growth Simulation from a Crop model Using Remote Sensing Data Assimilation. [Ph.D. Thesis, Chinese Academy of Agricultural Sciences]."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/s40663-015-0047-2","article-title":"The national forest inventory in china: History-results-international context","volume":"2","author":"Zeng","year":"2015","journal-title":"For. Ecosyst."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Fang, J., Oikawa, T., Kato, T., Mo, W., and Wang, Z. (2005). Biomass carbon accumulation by Japan\u2019s forests from 1947 to 1995. Glob. Biogeochem. Cycles, 19.","DOI":"10.1029\/2004GB002253"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1982","DOI":"10.1016\/j.rse.2007.03.032","article-title":"Combining national forest inventory field plots and remote sensing data for forest databases","volume":"112","author":"Tomppo","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.rse.2016.10.022","article-title":"A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the national forest inventory","volume":"194","author":"Nilsson","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1016\/j.foreco.2009.09.047","article-title":"Inventory-based estimates of forest biomass carbon stocks in china: A comparison of three methods","volume":"259","author":"Guo","year":"2010","journal-title":"For. Ecol. Manag."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1290","DOI":"10.1126\/science.223.4642.1290","article-title":"Biomass of tropical forests: A new estimate based on forest volumes","volume":"223","author":"Brown","year":"1984","journal-title":"Science"},{"key":"ref_67","first-page":"125","article-title":"Forest inventory data, models, and assumptions for monitoring carbon flux","volume":"57","author":"Birdsey","year":"2001","journal-title":"SSSA Spec. Publ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"18866","DOI":"10.1073\/pnas.0702737104","article-title":"Contributions to accelerating atmospheric co2 growth from economic activity, carbon intensity, and efficiency of natural sinks","volume":"104","author":"Canadell","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Piao, S., Fang, J., Zhu, B., and Tan, K. (2005). Forest biomass carbon stocks in china over the past 2 decades: Estimation based on integrated inventory and satellite data. J. Geophys. Res. Biogeosci., 110.","DOI":"10.1029\/2005JG000014"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.3390\/f5061267","article-title":"Mapping forest biomass using remote sensing and national forest inventory in China","volume":"5","author":"Du","year":"2014","journal-title":"Forests"},{"key":"ref_71","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_72","first-page":"715796","article-title":"Remote sensing of aboveground biomass in tropical secondary forests: A review","volume":"2014","author":"Barbosa","year":"2014","journal-title":"Int. J. For. Res."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"524","DOI":"10.3390\/land3020524","article-title":"Mapping woodland cover in the miombo ecosystem: A comparison of machine learning classifiers","volume":"3","author":"Kamusoko","year":"2014","journal-title":"Land"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"189","DOI":"10.14358\/PERS.82.3.189","article-title":"Approximating prediction uncertainty for random forest regression models","volume":"82","author":"Coulston","year":"2016","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_75","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_76","first-page":"S45","article-title":"A comparison of regression tree ensembles: Predicting sirex noctilio induced water stress in pinus patula forests of kwazulu-natal, South Africa","volume":"12","author":"Ismail","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_77","first-page":"18","article-title":"Classification and regression by randomforest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_78","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1974, January 29). Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings of the Third ERTS-1 Symposium NASA, NASA SP-351, Washington, DC, USA."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from eos-modis","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_80","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_81","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/0034-4257(93)90105-7","article-title":"Interpretation of vegetation indices derived from multi-temporal spot images","volume":"44","author":"Qi","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TGRS.1995.8746027","article-title":"A feedback based modification of the ndvi to minimize canopy background and atmospheric noise","volume":"33","author":"Liu","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_83","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_84","doi-asserted-by":"crossref","unstructured":"Yang, Y., Cao, C., Pan, X., Li, X., and Zhu, X. (2017). Downscaling land surface temperature in an arid area by using multiple remote sensing indices with random forest regression. Remote Sens., 9.","DOI":"10.3390\/rs9080789"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Sun, X., Lin, X., Shen, S., and Hu, Z. (2017). High-resolution remote sensing data classification over urban areas using random forest ensemble and fully connected conditional random field. ISPRS Int. J. Geo Inf., 6.","DOI":"10.3390\/ijgi6080245"},{"key":"ref_86","first-page":"125","article-title":"Construction of a drought monitoring model using the random forest based remote sensing","volume":"19","author":"Shen","year":"2017","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.rse.2016.07.032","article-title":"Continuous calibration improvement in solar reflective bands: Landsat 5 through landsat 8","volume":"185","author":"Mishra","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"5509","DOI":"10.1080\/01431160410001719821","article-title":"Satellite remote sensing of groundwater: Quantitative modelling and uncertainty reduction using 6s atmospheric simulations","volume":"25","author":"Ghulam","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_89","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_90","doi-asserted-by":"crossref","first-page":"18865","DOI":"10.3390\/s150818865","article-title":"Optimal atmospheric correction for above-ground forest biomass estimation with the etm+ remote sensor","volume":"15","author":"Jaehoon","year":"2015","journal-title":"Sensors"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Zhu, W., Huang, L., Sun, N., Chen, J., and Pang, S. (2019). Landsat 8-observed water quality and its coupled environmental factors for urban scenery lakes: A case study of west lake. Water Environ. Res.","DOI":"10.1002\/wer.1240"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"055004","DOI":"10.1088\/1748-9326\/aabcd5","article-title":"Evaluating the influence of spatial resolution of landsat predictors on the accuracy of biomass models for large-area estimation across the eastern USA","volume":"13","author":"Deo","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1098\/rstb.2003.1425","article-title":"Error propagation and scaling for tropical forest biomass estimates","volume":"359","author":"Chave","year":"2004","journal-title":"Philos. Trans. R Soc. Lond. B Biol. Sci."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.envsci.2010.03.004","article-title":"Achieving forest carbon information with higher certainty: A five-part plan","volume":"13","author":"Baker","year":"2010","journal-title":"Environ. Sci. Policy"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/1\/48\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:19:48Z","timestamp":1760361588000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/1\/48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,15]]},"references-count":94,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["ijgi9010048"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9010048","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,15]]}}}