{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T18:04:41Z","timestamp":1773684281289,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T00:00:00Z","timestamp":1638230400000},"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>Sparse mixed forest with trees, shrubs, and green herbaceous vegetation is a typical landscape in the afforestation areas in northwestern China. It is a great challenge to accurately estimate the woody aboveground biomass (AGB) of a sparse mixed forest with heterogeneous woody vegetation types and background types. In this study, a novel woody AGB estimation methodology (VI-AGB model stratified based on herbaceous vegetation coverage) using a combination of Landsat-8, GaoFen-2, and unmanned aerial vehicle (UAV) images was developed. The results show the following: (1) the woody and herbaceous canopy can be accurately identified using the object-based support vector machine (SVM) classification method based on UAV red-green-blue (RGB) images, with an average overall accuracy and kappa coefficient of 93.44% and 0.91, respectively; (2) compared with the estimation uncertainties of the woody coverage-AGB models without considering the woody vegetation types (RMSE = 14.98 t\u2219ha\u22121 and rRMSE = 96.31%), the woody coverage-AGB models stratified based on five woody species (RMSE = 5.82 t\u2219ha\u22121 and rRMSE = 37.46%) were 61.1% lower; (3) of the six VIs used in this study, the near-infrared reflectance of pure vegetation (NIRv)-AGB model performed best (RMSE = 7.91 t\u2219ha\u22121 and rRMSE = 50.89%), but its performance was still seriously affected by the heterogeneity of the green herbaceous coverage. The normalized difference moisture index (NDMI)-AGB model was the least sensitive to the background. The stratification-based VI-AGB models considering the herbaceous vegetation coverage derived from GaoFen-2 and UAV images can significantly improve the accuracy of the woody AGB estimated using only Landsat VIs, with the RMSE and rRMSE of 6.6 t\u2219ha\u22121 and 42.43% for the stratification-based NIRv-AGB models. High spatial resolution information derived from UAV and satellite images has a great potential for improving the woody AGB estimated using only Landsat images in sparsely vegetated areas. This study presents a practical method of estimating woody AGB in sparse mixed forest in dryland areas.<\/jats:p>","DOI":"10.3390\/rs13234859","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4859","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Improving Estimation of Woody Aboveground Biomass of Sparse Mixed Forest over Dryland Ecosystem by Combining Landsat-8, GaoFen-2, and UAV Imagery"],"prefix":"10.3390","volume":"13","author":[{"given":"Yonglei","family":"Shi","sequence":"first","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China"},{"name":"Key Laboratory of Soil and Water Conservation on the Loess Plateau of Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8836-7111","authenticated-orcid":false,"given":"Zhihui","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Soil and Water Conservation on the Loess Plateau of Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7987-037X","authenticated-orcid":false,"given":"Liangyun","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, China Academy of Sciences, Beijing 100190, China"}]},{"given":"Chunyi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China"}]},{"given":"Dailiang","family":"Peng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, China Academy of Sciences, Beijing 100190, China"}]},{"given":"Peiqing","family":"Xiao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Soil and Water Conservation on the Loess Plateau of Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1016\/j.rse.2019.111383","article-title":"Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years","volume":"233","author":"Xiao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_2","first-page":"100127","article-title":"Carbon neutrality: Toward a sustainable future","volume":"2","author":"Chen","year":"2021","journal-title":"Innovation"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1016\/j.rse.2019.111401","article-title":"Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities","volume":"233","author":"Smith","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.ecolind.2015.09.041","article-title":"Multiple afforestation programs accelerate the greenness in the \u2018Three North\u2019 region of China from 1982 to 2013","volume":"61","author":"Zhang","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_5","first-page":"151","article-title":"Opportunities of Mapping Forest Carbon Stock and its Annual Increment Using Landsat Time-Series Data","volume":"4","author":"Liu","year":"2016","journal-title":"Geoinformatics Geostat. Overv."},{"key":"ref_6","first-page":"2239","article-title":"Biomass and vegetation coverage survey in the Mu Us sandy land-based on unmanned aerial vehicle RGB images","volume":"94","author":"Guo","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2013.08.010","article-title":"Monitoring coniferous forest biomass change using a Landsat trajectory-based approach","volume":"139","author":"Cohen","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7293","DOI":"10.1007\/s10661-014-3927-y","article-title":"Improving artificial forest biomass estimates using afforestation age information from time series Landsat stacks","volume":"186","author":"Liu","year":"2014","journal-title":"Environ. Monit. Assess."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Peng, D.L., Zhang, H.L., and Liu, L.Y. (2019). Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental Variables. Remote Sens., 11.","DOI":"10.3390\/rs11192270"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7307","DOI":"10.3390\/rs6087303","article-title":"The Penetration Depth Derived from the Synthesis of ALOS\/PALSAR InSAR Data and ASTER GDEM for the Mapping of Forest Biomass","volume":"6","author":"Ni","year":"2014","journal-title":"Remote Sens."},{"key":"ref_11","first-page":"229","article-title":"Stratified aboveground forest biomass estimation by remote sensing data","volume":"38","author":"Latifi","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","first-page":"62","article-title":"Review of forest above ground biomass inversion methods based on remote sensing technology","volume":"19","author":"Liu","year":"2015","journal-title":"J. Remote Sens."},{"key":"ref_13","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":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.rse.2016.12.029","article-title":"The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): Science requirements, concept, and implementation","volume":"190","author":"Markus","year":"2017","journal-title":"Remote Sens. Env."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2850","DOI":"10.1016\/j.rse.2011.03.020","article-title":"The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle","volume":"115","author":"Quegan","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Rosen, P., Hensley, S., Shaffer, S., Edelstein, W., Kim, Y., Kumar, R., and Misra, T. (2016). An update on the NASA-ISRO dual-frequency dbf SAR(NISAR) mission. 2016 IEEE International Geoscience and Remote Sensing Symposium, IEEE.","DOI":"10.1109\/IGARSS.2016.7729543"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1080\/014311698216071","article-title":"Biomass estimation on grazed and ungrazed rangelands using spectral indices","volume":"19","author":"Todd","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/S0143-6228(02)00048-6","article-title":"Evaluation of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing","volume":"22","author":"Boyd","year":"2002","journal-title":"Appl. Geogr."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2006.08.012","article-title":"Evaluation and comparison of gross primary production estimates for the Northern Great Plains grasslands","volume":"106","author":"Zhang","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.foreco.2006.12.018","article-title":"Satellite-based estimation of biomass carbon stocks for northeast China\u2019s forests between 1982 and 1999","volume":"240","author":"Tan","year":"2006","journal-title":"Forest Ecol. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.agrformet.2014.09.010","article-title":"Estimating spatiotemporal patterns of aboveground biomass using Landsat TM and MODIS images in the Mu Us Sandy Land, China","volume":"200","author":"Yan","year":"2015","journal-title":"Agric. Forest Meteorol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.ecolind.2015.11.005","article-title":"Modeling grassland aboveground biomass using a pure vegetation index","volume":"62","author":"LI","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhao, P.P., Lu, D.S., Wang, G.X., Wu, C.P., Huang, Y.J., Yu, S.Q., Tomppo, E., and McRorberts, B.E. (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_24","first-page":"968","article-title":"Improved forest biomass estimates using ALOS AVNIR-2 texture indices","volume":"115","author":"Rahman","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9497","DOI":"10.1080\/01431161.2011.562255","article-title":"Mapping shrubland biomass along Mediterranean climatic gradients: The synergy of rainfall-based and NDVI-based models","volume":"32","author":"Shoshany","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.rse.2012.01.021","article-title":"Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass","volume":"121","author":"Chen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Feng, Y.Y., Lu, D.S., Chen, Q., Keller, M., Moran, E., Nara, D.S., Luis, B.E., and Batistella, M. (2017). Examining Effective Use of Data Sources and Modeling Algorithms for Improving Biomass Estimation in a Moist Tropical Forest of the Brazilian Amazon, Taylor & Francis.","DOI":"10.1080\/17538947.2017.1301581"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jiang, X.D., Li, G.Y., Lu, D.S., Chen, E.X., and Wei, X.L. (2020). Stratification-Based Forest Aboveground Biomass Estimation in a Subtropical Region Using Airborne Lidar Data. Remote Sens., 12.","DOI":"10.3390\/rs12071101"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.rse.2014.11.007","article-title":"Quantifying dwarf shrub biomass in an arid environment: Comparing empirical methods in a high dimensional setting","volume":"158","author":"Zandler","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"096036","DOI":"10.1117\/1.JRS.9.096036","article-title":"Estimating woody above-ground biomass in an arid zone of central Australia using Landsat imagery","volume":"9","author":"Wang","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.11834\/jrs.20110150","article-title":"Prediction of subtropical forest parameters using airborne laser scanner","volume":"15","author":"Fu","year":"2011","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"036007","DOI":"10.1117\/1.JRS.10.036007","article-title":"Estimating woody aboveground biomass in an area of agroforestry using airborne light detection and ranging and compact airborne spectrographic imager hyperspectral data: Individual tree analysis incorporating tree species information","volume":"10","author":"Wang","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_33","first-page":"1874","article-title":"LiDAR Remote Sensing and Applications","volume":"51","author":"Lato","year":"2019","journal-title":"Math. Geosci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1016\/j.rse.2006.05.025","article-title":"A shadow fraction method for mapping biomass of northern boreal black spruce forests using QuickBird imagery","volume":"110","author":"Leboeuf","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1080\/10106049.2016.1240717","article-title":"Estimating forest standing biomass in savanna woodlands as an indicator of forest productivity using the new generation WorldView-2 sensor","volume":"33","author":"Dube","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"245","DOI":"10.5721\/EuJRS20154814","article-title":"Modeling forest biomass using Very-High-Resolution data\u2014Combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images","volume":"48","author":"Maack","year":"2015","journal-title":"Eur. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.foreco.2005.10.014","article-title":"Taniguchi, M.; Utsugi, H.; Kojima, T.; Yamada, K. Stand biomass estimation method by canopy coverage for application to remote sensing in an arid area of Western Australia","volume":"222","author":"Suganuma","year":"2005","journal-title":"Forest Ecol. Manag."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5643","DOI":"10.1080\/01431160802082155","article-title":"Estimating stem volume by tree crown area and tree shadow area extracted from pan-sharpened Quickbird imagery in open Crimean juniper forests","volume":"29","author":"Ozdemir","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","first-page":"18932","article-title":"A detailed portrait of the forest aboveground biomass pool for the year 2010 obtained from multiple remote sensing observations","volume":"20","author":"Santoro","year":"2018","journal-title":"Geophys. Res. Abstr."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.ecoleng.2017.03.013","article-title":"Spatiotemporal variation of vegetation coverage before and after implementation of Grain for Green Program in Loess Plateau, China","volume":"104","author":"Zhao","year":"2017","journal-title":"Ecol. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.ecolind.2018.07.063","article-title":"Anthropogenic contributions dominate trends of vegetation cover change over the farming-pastoral ecotone of northern China","volume":"95","author":"Liu","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_42","first-page":"19","article-title":"Change trend of vegetation coverage in the mu us sandy region from 2000 to 2015","volume":"21","author":"Guo","year":"2018","journal-title":"Desert Res."},{"key":"ref_43","unstructured":"Li, H.K. (2010). Estimation and Evaluation of Forest Biomass Carbon Storage in China, China Forestry Press. (In Chinese)."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2924","DOI":"10.1109\/TGRS.2017.2656152","article-title":"An internal crown geometric model for conifer species classification with high-density lidar data","volume":"55","author":"Harikumar","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.ecss.2018.10.021","article-title":"Coastline information extraction based on the tasseled cap transformation of Landsat-8 OLI images","volume":"217","author":"Chen","year":"2019","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4407","DOI":"10.1080\/01431161.2011.552923","article-title":"Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment","volume":"32","author":"Gilmore","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2937","DOI":"10.1080\/01431161.2011.620034","article-title":"Derivation of biomass information for semi-arid areas using remote-sensing data","volume":"33","author":"Eisfelder","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2016.01.002","article-title":"The spatiotemporal patterns of vegetation coverage and biomass of the temperate deserts in Central Asia and their relationships with climate controls","volume":"175","author":"Zhang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"He, L., Li, A.N., Yin, G.F., Nan, X., and Bian, J.H. (2019). Retrieval of Grassland Aboveground Biomass through Inversion of the PROSAIL Model with MODIS Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11131597"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"L09402","DOI":"10.1029\/2006GL025879","article-title":"Estimating forest biomass in the USA using generalized allometric models and MODIS land products","volume":"33","author":"Zhang","year":"2006","journal-title":"Geophys. Res. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Luo, Y.P., El-Madany, T.S., Filippa, G., Ma, X.L., Ahrens, B., and Carrara, A. (2018). Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree\u2013Grass Ecosystems. Remote Sens., 10.","DOI":"10.3390\/rs10081293"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez-Mart\u00ednez, M., John, G., Hmimina, G., Filella, L., Balzarolo, M., and Benjamin, S. (2019). Monitoring Spatial and Temporal Variabilities of Gross Primary Production Using Maiac Modis Data. Remote Sens., 11.","DOI":"10.3390\/rs11070874"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Huang, X.J., Xiao, J.F., and Ma, M.G. (2019). Evaluating the Performance of Satellite-Derived Vegetation Indices for Estimating Gross Primary Productivity Using FLUXNET Observations across the Globe. Remote Sens., 11.","DOI":"10.3390\/rs11151823"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Hinojo-Hinojo, C., and Michael, L.G. (2020). Plant Traits Help Explain the Tight Relationship between Vegetation Indices and Gross Primary Production. Remote Sens., 12.","DOI":"10.3390\/rs12091405"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e1602244","DOI":"10.1126\/sciadv.1602244","article-title":"Canopy near-infrared reflectance and terrestrial photosynthesis","volume":"3","author":"Grayson","year":"2017","journal-title":"Sci. Adv."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3731","DOI":"10.1111\/gcb.14729","article-title":"Terrestrial gross primary production: Using NIRV to scale from site to globe","volume":"25","author":"Badgley","year":"2019","journal-title":"Glob. Chang. Biol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"107665","DOI":"10.1016\/j.agrformet.2019.107665","article-title":"Landscape-level vegetation classification and fractional woody and herbaceous vegetation cover estimation over the dryland ecosystems by unmanned aerial vehicle platform","volume":"278","author":"Wang","year":"2019","journal-title":"Agric. For. Meteorol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4859\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:37:50Z","timestamp":1760168270000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4859"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,30]]},"references-count":57,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234859"],"URL":"https:\/\/doi.org\/10.3390\/rs13234859","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,30]]}}}