{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:02:16Z","timestamp":1775559736965,"version":"3.50.1"},"reference-count":80,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2016,6,2]],"date-time":"2016-06-02T00:00:00Z","timestamp":1464825600000},"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":["#41571411"],"award-info":[{"award-number":["#41571411"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The data saturation problem in Landsat imagery is well recognized and is regarded as an important factor resulting in inaccurate forest aboveground biomass (AGB) estimation. However, no study has examined the saturation values for different vegetation types such as coniferous and broadleaf forests. The objective of this study is to estimate the saturation values in Landsat imagery for different vegetation types in a subtropical region and to explore approaches to improving forest AGB estimation. Landsat Thematic Mapper imagery, digital elevation model data, and field measurements in Zhejiang province of Eastern China were used. Correlation analysis and scatterplots were first used to examine specific spectral bands and their relationships with AGB. A spherical model was then used to quantitatively estimate the saturation value of AGB for each vegetation type. A stratification of vegetation types and\/or slope aspects was used to determine the potential to improve AGB estimation performance by developing a specific AGB estimation model for each category. Stepwise regression analysis based on Landsat spectral signatures and textures using grey-level co-occurrence matrix (GLCM) was used to develop AGB estimation models for different scenarios: non-stratification, stratification based on either vegetation types, slope aspects, or the combination of vegetation types and slope aspects. The results indicate that pine forest and mixed forest have the highest AGB saturation values (159 and 152 Mg\/ha, respectively), Chinese fir and broadleaf forest have lower saturation values (143 and 123 Mg\/ha, respectively), and bamboo forest and shrub have the lowest saturation values (75 and 55 Mg\/ha, respectively). The stratification based on either vegetation types or slope aspects provided smaller root mean squared errors (RMSEs) than non-stratification. The AGB estimation models based on stratification of both vegetation types and slope aspects provided the most accurate estimation with the smallest RMSE of 24.5 Mg\/ha. Relatively low AGB (e.g., less than 40 Mg\/ha) sites resulted in overestimation and higher AGB (e.g., greater than 140 Mg\/ha) sites resulted in underestimation. The smallest RMSE was obtained when AGB was 80\u2013120 Mg\/ha. This research indicates the importance of stratification in mitigating the data saturation problem, thus improving AGB estimation.<\/jats:p>","DOI":"10.3390\/rs8060469","type":"journal-article","created":{"date-parts":[[2016,6,2]],"date-time":"2016-06-02T10:19:08Z","timestamp":1464862748000},"page":"469","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":230,"title":["Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation"],"prefix":"10.3390","volume":"8","author":[{"given":"Panpan","family":"Zhao","sequence":"first","affiliation":[{"name":"Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, School of Environmental &amp; Resource Sciences, Zhejiang Agriculture and Forestry University, Lin\u2019an 311300, China"}]},{"given":"Dengsheng","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, School of Environmental &amp; Resource Sciences, Zhejiang Agriculture and Forestry University, Lin\u2019an 311300, China"},{"name":"Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48823, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5419-4547","authenticated-orcid":false,"given":"Guangxing","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, School of Environmental &amp; Resource Sciences, Zhejiang Agriculture and Forestry University, Lin\u2019an 311300, China"},{"name":"Department of Geography, Southern Illinois University Carbondale, Carbondale, IL 62901, USA"}]},{"given":"Chuping","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang Forestry Academy, Hangzhou 310023, China"}]},{"given":"Yujie","family":"Huang","sequence":"additional","affiliation":[{"name":"Zhejiang Forestry Academy, Hangzhou 310023, China"}]},{"given":"Shuquan","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Forestry and Biotechnology, Zhejiang Agriculture and Forestry University, Lin\u2019an 311300, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,6,2]]},"reference":[{"key":"ref_1","unstructured":"IPCC (Intergovernmental Panel on Climate Change) (2000). Land Use, Land-Use Change and Forestry, Cambridge Univ. Press."},{"key":"ref_2","unstructured":"Smith, J.E., and Heath, L.S. (2008). Carbon Stocks and Stock Changes in U.S. Forests and appendix C, U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990\u20132005, Available online: http:\/\/www.usda.gov\/oce\/global_change\/ AFGGInventory1990_2005.htm."},{"key":"ref_3","unstructured":"US Climate Change Science Program The North American Carbon Budget and Implications for the Global Carbon Cycle. Available online: http:\/\/www.cfr.org\/climate-change\/north-american-carbon-budget-implications-global-carbon-cycle\/p14868."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160500486732","article-title":"The potential and challenge of remote sensing-based biomass estimation","volume":"27","author":"Lu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lu, D., Chen, Q., Wang, G., Moran, E., Batistella, M., Zhang, M., Laurin, G.V., and Saah, D. (2012). Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates. Int. J. For. Res., 1\u201316.","DOI":"10.1155\/2012\/436537"},{"key":"ref_6","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","volume":"9","author":"Lu","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1016\/j.foreco.2009.06.056","article-title":"Mapping and spatial uncertainty analysis of forest vegetation carbon by combining national forest inventory data and satellite images","volume":"258","author":"Wang","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/S0378-1127(97)00026-1","article-title":"A generalized model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance, and partitioning","volume":"95","author":"Landsberg","year":"1997","journal-title":"For. Ecol. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/0304-3800(88)90112-3","article-title":"A general model of forest ecosystem processes for regional applications I. hydrologic balance, canopy gas exchange and primary production processes","volume":"42","author":"Running","year":"1988","journal-title":"Ecol. Model."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1093\/treephys\/20.11.761","article-title":"Regional assessment of boreal forest productivity using an ecological process model and remote sensing parameter maps","volume":"20","author":"Kimball","year":"2000","journal-title":"Tree Physiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1117\/1.JRS.9.097696","article-title":"Review of the use of remote sensing for biomass estimation to support renewable energy generation","volume":"9","author":"Kumar","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Barbosa, J.M., Broadbent, E.N., and Bitencourt, M.D. (2014). Remote sensing of aboveground biomass in tropical secondary forests: A review. Int. J. For. Res., 14.","DOI":"10.1155\/2014\/715796"},{"key":"ref_13","first-page":"125","article-title":"Remote sensing of aboveground forest biomass: A review","volume":"57","author":"Timothy","year":"2016","journal-title":"Trop. Ecol."},{"key":"ref_14","first-page":"393","article-title":"Quantifying aboveground biomass in African environments: A review of the trade-offs between sensor estimation accuracy and costs","volume":"57","author":"Timothy","year":"2016","journal-title":"Trop. Ecol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"116","DOI":"10.4172\/2157-7625.1000116","article-title":"Methods to estimate aboveground biomass and carbon stock in natural forests\u2014A review","volume":"2","author":"Vashum","year":"2012","journal-title":"J. Ecosyst. Ecogr."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/1750-0680-4-2","article-title":"Mapping and monitoring carbon stocks with satellite observations: A comparison of methods","volume":"4","author":"Goetz","year":"2009","journal-title":"Carbon Balance Manag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1177\/0309133312471367","article-title":"Optical remote sensing of forest leaf area index and biomass","volume":"37","author":"Song","year":"2012","journal-title":"Prog. Phys. Geogr."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.rse.2005.09.011","article-title":"Estimating biomass for boreal forests using ASTER satellite data combined with standwise forest inventory data","volume":"99","author":"Muukkonen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1016\/j.jaridenv.2010.04.007","article-title":"Assessing multitemporal Landsat 7 ETM+ images for estimating aboveground biomass in subtropical dry forests of Argentina","volume":"74","author":"Gasparri","year":"2010","journal-title":"J. Arid Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.isprsjprs.2012.03.011","article-title":"Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions","volume":"70","author":"Cutler","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","first-page":"45","article-title":"Estimation of aboveground biomass in Mediterranean forests by statistical modelling of ASTER fraction images","volume":"31","author":"Quintano","year":"2014","journal-title":"Int. J. Appl. Earth Observ. Geoinf."},{"key":"ref_23","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 Observ. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.isprsjprs.2014.03.008","article-title":"Historical forest biomass dynamics modelled with Landsat spectral trajectories","volume":"93","author":"White","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.rse.2010.11.010","article-title":"Improved forest biomass using ALOS AVNIR-2 texture indices","volume":"115","author":"Sarker","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.isprsjprs.2015.06.002","article-title":"Investigating the robustness of the new Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas","volume":"108","author":"Dube","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6407","DOI":"10.3390\/rs6076407","article-title":"Estimates of aboveground biomass from texture analysis of Landsat imagery","volume":"6","author":"Kelsey","year":"2014","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.isprsjprs.2014.11.001","article-title":"Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa","volume":"101","author":"Dube","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","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_30","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_31","doi-asserted-by":"crossref","first-page":"967","DOI":"10.14358\/PERS.71.8.967","article-title":"Satellite estimation of aboveground biomass and impacts of forest stand structure","volume":"71","author":"Lu","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_32","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_33","first-page":"451","article-title":"Estimating aboveground biomass in interior Alaska with Landsat data and field measurements","volume":"18","author":"Ji","year":"2012","journal-title":"Int. J. Appl. Earth Observ. Geoinf."},{"key":"ref_34","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_35","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 to mapping of aboveground biomass and stand volume","volume":"225","author":"Hall","year":"2006","journal-title":"For. Ecol. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.foreco.2006.01.030","article-title":"A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland","volume":"226","author":"Labrecque","year":"2006","journal-title":"For. Ecol. Manag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"129","DOI":"10.5589\/m10-037","article-title":"Integration of GLAS and Landsat TM data for aboveground biomass estimation","volume":"36","author":"Duncanson","year":"2010","journal-title":"Can. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"045011","DOI":"10.1088\/1748-9326\/3\/4\/045011","article-title":"A first map of tropical Africa\u2019s above-ground biomass derived from satellite imagery","volume":"3","author":"Baccini","year":"2008","journal-title":"Environ. Res. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1016\/j.rse.2007.08.021","article-title":"Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information","volume":"112","author":"Blackard","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1080\/014311699213055","article-title":"The relationship between the biomass of Cameroonian tropical forests and radiation reflected in middle infrared wavelengths (3.0-5.0 mu m)","volume":"20","author":"Boyd","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","first-page":"160","article-title":"Estimation of forest aboveground biomass using multi-parameter remote sensing data over a cold and arid area","volume":"14","author":"Tian","year":"2012","journal-title":"Int. J. Appl. Earth Observ. Geoinf."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1016\/j.ecolmodel.2009.04.025","article-title":"A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China","volume":"220","author":"Xie","year":"2009","journal-title":"Ecol. Model."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1046\/j.1466-822X.2001.00248.x","article-title":"Mapping the biomass of Bornean tropical rain forest from remotely sensed data","volume":"10","author":"Foody","year":"2001","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.rse.2009.01.007","article-title":"Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors","volume":"113","author":"Chander","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1016\/j.rse.2011.01.019","article-title":"C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter","volume":"115","author":"Reese","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.isprsjprs.2014.08.014","article-title":"Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series","volume":"102","author":"Zhu","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1109\/TGRS.2010.2068574","article-title":"Improved biomass estimation using the texture parameters of two high-resolution optical sensors","volume":"49","author":"Nichol","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.isprsjprs.2012.03.002","article-title":"Potential of texture measurements of two-date dual polarization PALSAR data for the improvement of forest biomass estimation","volume":"69","author":"Sarker","year":"2012","journal-title":"ISPRS J. Photogram. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.foreco.2004.03.048","article-title":"Relationships between forest stand parameters and Landsat Thematic Mapper spectral responses in the Brazilian Amazon basin","volume":"198","author":"Lu","year":"2004","journal-title":"For. Ecol. Manag."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1590\/S0044-59672005000200015","article-title":"Exploring TM image texture and its relationships with biomass estimation in Rond\u00f4nia, Brazilian Amazon","volume":"35","author":"Lu","year":"2005","journal-title":"Acta Amaz\u00f4n."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4829","DOI":"10.1080\/01431160500239107","article-title":"Relating SAR image texture to the biomass of regenerating tropical forests","volume":"26","author":"Kuplich","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3544","DOI":"10.3390\/rs4113544","article-title":"Estimating CO2 sequestration by forests in Oita prefecture, Japan, by combining Landsat ETM plus and ALOS Satellite remote sensing data","volume":"4","author":"Iizuka","year":"2012","journal-title":"Remote Sens."},{"key":"ref_55","unstructured":"Zhejiang. Available online: https:\/\/en.wikipedia.org\/wiki\/Zhejiang\/."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3925","DOI":"10.5846\/stxb201301230134","article-title":"Biomass conversion coefficients of Chinese fir forests of Zhejiang Province based on LULUCF greenhouse gas emission","volume":"33","author":"Zhu","year":"2013","journal-title":"Acta Ecol. Sin."},{"key":"ref_57","unstructured":"Forestry Department of Zhejiang Province: Introduction of Forestry. Available online: http:\/\/baike.baidu.com\/view\/4532835.htm#5."},{"key":"ref_58","first-page":"17","article-title":"Bioass and carbon stocks of commonwealth forests for Central Zhejiang","volume":"49","author":"Qia","year":"2013","journal-title":"For. Sci."},{"key":"ref_59","first-page":"1","article-title":"Study on biomass models of important commonwealth forests for Zhejiang Province","volume":"29","author":"Yuan","year":"2009","journal-title":"J. Zhejiang For. Technol."},{"key":"ref_60","first-page":"134","article-title":"Analysis and comparison test on C-correction strategies and their scale effects with TM images in rugged mountainous terrain","volume":"16","author":"Li","year":"2014","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"061706","DOI":"10.1117\/1.JRS.6.061706","article-title":"A comparative analysis of classification algorithms and multiple sensor data for land use\/land cover classification in the Brazilian Amazon","volume":"6","author":"Li","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1080\/17538947.2013.866173","article-title":"Methods to extract impervious surface areas from satellite images","volume":"7","author":"Lu","year":"2014","journal-title":"Int. J. Digit. Earth"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"265","DOI":"10.3390\/rs8030265","article-title":"Examining urban impervious surface distribution and its dynamic change in Hangzhou metropolis","volume":"8","author":"Li","year":"2016","journal-title":"Remote Sens."},{"key":"ref_64","first-page":"589","article-title":"A study on information extraction of water body with the modified normalized difference water index (MNDWI)","volume":"5","author":"Xu","year":"2005","journal-title":"J. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press. [2nd ed.].","DOI":"10.1201\/9781420055139"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.rse.2012.10.007","article-title":"Inference for lidar-assisted estimation of forest growing stock volume","volume":"128","author":"McRoberts","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/19479830903561035","article-title":"Multi-source remote sensing data fusion: Status and trends","volume":"1","author":"Zhang","year":"2010","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.inffus.2011.08.001","article-title":"Multisensor data fusion: A review of the state-of-the-art","volume":"14","author":"Khaleghi","year":"2013","journal-title":"Inf. Fusion"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Andersen, J.R., Hardy, E.E., Roach, J.T., and Witmer, R.E. (1976). A Land Use and Land Cover Classification System for Use with Remote Sensor Data, Available online: http:\/\/www.pbcgis.com\/data_basics\/anderson.pdf.","DOI":"10.3133\/pp964"},{"key":"ref_70","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_71","doi-asserted-by":"crossref","first-page":"3649","DOI":"10.1080\/01431160110114538","article-title":"Improvement in mapping vegetation cover factor for universal soil loss equation by geo-statistical methods with Landsat TM images","volume":"23","author":"Wang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_72","unstructured":"Fotheringham, A.S., Brunsdon, C., and Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, John Wiley & Sons Ltd."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1016\/j.cageo.2006.02.010","article-title":"An empirical evaluation of spatial regression models","volume":"32","author":"Gao","year":"2006","journal-title":"Comput. Geosci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.ecolmodel.2005.01.007","article-title":"Spatial residual analysis of six modeling techniques","volume":"186","author":"Zhang","year":"2005","journal-title":"Ecol. Model."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1984","DOI":"10.1890\/13-1574.1","article-title":"Aboveground biomass mapping of African forest mosaics using canopy texture analysis: Toward a regional approach","volume":"24","author":"Bastin","year":"2014","journal-title":"Ecol. Appl."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0378-1127(99)00327-8","article-title":"Landscape-scale variation in forest structure and biomass in a tropical rain forest","volume":"137","author":"Clark","year":"2000","journal-title":"For. Ecol. Manag."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"6827","DOI":"10.5194\/bg-11-6827-2014","article-title":"Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks","volume":"11","author":"Detto","year":"2014","journal-title":"Biogeosciences"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1080\/02827581.2011.564204","article-title":"Uncertainties of mapping forest carbon due to plot locations using national forest inventory plot and remotely sensed data","volume":"26","author":"Wang","year":"2011","journal-title":"Scand. J. For. Res."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1109\/LGRS.2013.2260719","article-title":"Impacts of plot location errors on accuracy of mapping and up-scaling aboveground forest carbon using sample plot and Landsat TM data","volume":"10","author":"Zhang","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_80","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/6\/469\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:24:54Z","timestamp":1760210694000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/6\/469"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,6,2]]},"references-count":80,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2016,6]]}},"alternative-id":["rs8060469"],"URL":"https:\/\/doi.org\/10.3390\/rs8060469","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,6,2]]}}}