{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T18:49:19Z","timestamp":1779216559872,"version":"3.51.4"},"reference-count":84,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T00:00:00Z","timestamp":1602547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"'National MRV Capacity Building towards Climate Resilient Development in Ethiopia' Project","award":["ETH 14\/0002"],"award-info":[{"award-number":["ETH 14\/0002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Periodic assessment of forest aboveground biomass (AGB) is essential to regulate the impacts of the changing climate. However, AGB estimation using field-based sample survey (FBSS) has limited precision due to cost and accessibility constraints. Fortunately, remote sensing technologies assist to improve AGB estimation precisions. Thus, this study assessed the role of remotely sensed (RS) data in improving the precision of AGB estimation in an Afromontane forest in south-central Ethiopia. The research objectives were to identify RS variables that are useful for estimating AGB and evaluate the extent of improvement in the precision of the remote sensing-assisted AGB estimates beyond the precision of a pure FBSS. Reference AGB data for model calibration and estimation were collected from 111 systematically distributed circular sample plots (SPs) of 1000 m2 area. Independent variables were derived from Landsat-8, Sentinel-2 and PlanetScope images acquired in January 2019. The area-weighted mean and standard deviation of the spectral reflectance, spectral index and texture (only for PlanetScope) variables were extracted for each SP. A maximum of two independent variables from each image type was fitted to a generalized linear model for AGB estimation using model-assisted estimators. The results of this study revealed that the Landsat-8 model with the predictor variable of shortwave infrared band reflectance and the PlanetScope model with the predictor variable of green band reflectance had estimation efficiency of 1.40 and 1.37, respectively. Similarly, the Sentinel-2 model, which had predictor variables of shortwave infrared reflectance and standard deviation of green leaf index, improved AGB estimation with the relative efficiency of 1.68. Utilizing freely available Sentinel-2 data seems to enhance the AGB estimation efficiency and reduce cost and extensive fieldwork in inaccessible areas.<\/jats:p>","DOI":"10.3390\/rs12203335","type":"journal-article","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T21:24:39Z","timestamp":1602710679000},"page":"3335","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Use of Remotely Sensed Data to Enhance Estimation of Aboveground Biomass for the Dry Afromontane Forest in South-Central Ethiopia"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0381-8874","authenticated-orcid":false,"given":"Habitamu","family":"Taddese","sequence":"first","affiliation":[{"name":"Faculty of Science and Technology, Norwegian University of Life Sciences, P.O. Box 5003, 1432 \u00c5s, Norway"},{"name":"Wondo Genet College of Forestry and Natural Resources, Hawassa University, P.O. Box 128,  Shashemene 3870006, Ethiopia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3535-0786","authenticated-orcid":false,"given":"Zerihun","family":"Asrat","sequence":"additional","affiliation":[{"name":"Wondo Genet College of Forestry and Natural Resources, Hawassa University, P.O. Box 128,  Shashemene 3870006, Ethiopia"},{"name":"Faculty Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 \u00c5s, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ingunn","family":"Burud","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Norwegian University of Life Sciences, P.O. Box 5003, 1432 \u00c5s, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5534-049X","authenticated-orcid":false,"given":"Terje","family":"Gobakken","sequence":"additional","affiliation":[{"name":"Faculty Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 \u00c5s, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7492-8608","authenticated-orcid":false,"given":"Hans","family":"\u00d8rka","sequence":"additional","affiliation":[{"name":"Faculty Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 \u00c5s, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00d8ystein","family":"Dick","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Norwegian University of Life Sciences, P.O. 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Decisions adopted by the Conference of the Parties. Proceedings of the Conference of the Parties on Its Fifteenth Session, Copenhagen, Denmark. Available online: https:\/\/unfccc.int\/resource\/docs\/2009\/cop15\/eng\/11a01.pdf."},{"key":"ref_4","unstructured":"UNFCCC (2015, January 21). Adoption of the Paris Agreement Proposal by the President. Proceedings of the Paris Climate Change Conference\u2014COP 21, Paris, France. Available online: https:\/\/unfccc.int\/resource\/docs\/2015\/cop21\/eng\/l09.pdf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"361","DOI":"10.2307\/3235761","article-title":"Natural dynamics and regeneration methods in tropical dry forests\u2014An introduction","volume":"3","author":"Gerhardt","year":"1992","journal-title":"J. Veg. Sci."},{"key":"ref_6","unstructured":"Price, M., Gratzer, G., Alemayehu Duguma, L., Kohler, T., and Maselli, D. (2011). Mountain Forests in a Changing World: Realizing Values, Addressing Challenges, Food and Agriculture Organization of the United Nations (FAO) and Centre of Development and Environment (CDE). Available online: http:\/\/www.fao.org\/3\/a-i2481e.pdf."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Solomon, N., Segnon, A.C., and Birhane, E. (2019). Ecosystem Service Values Changes in Response to Land-Use\/Land-Cover Dynamics in Dry Afromontane Forest in Northern Ethiopia. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16234653"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/978-3-642-19986-8_17","article-title":"Dry Forests of Ethiopia and Their Silviculture","volume":"Volume 8","author":"Weber","year":"2011","journal-title":"Silviculture in the Tropics"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.envsci.2013.04.011","article-title":"Natural forest disturbances and the design of REDD+ initiatives","volume":"33","author":"Nguon","year":"2013","journal-title":"Environ. Sci. Policy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/20964129.2018.1433951","article-title":"Allometric equations for aboveground biomass estimation of Olea europaea L. subsp.cuspidatain Mana Angetu Forest","volume":"4","author":"Kebede","year":"2018","journal-title":"Ecosyst. Health Sustain."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep17153","article-title":"Small Sample Sizes Yield Biased Allometric Equations in Temperate Forests","volume":"5","author":"Duncanson","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5751\/ES-05670-180306","article-title":"Uncertain Emission Reductions from Forest Conservation: REDD in the Bale Mountains, Ethiopia","volume":"18","author":"Watson","year":"2013","journal-title":"Ecol. Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1088\/1755-1315\/18\/1\/012011","article-title":"Tropical forest degradation monitoring using ETM+ and MODIS remote sensing data in the Peninsular Malaysia","volume":"18","author":"Hashim","year":"2014","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s40899-015-0012-9","article-title":"Advance application of geospatial technology for fisheries perspective in Tarai region of Himalayan state of Uttarakhand","volume":"1","author":"Ingole","year":"2015","journal-title":"Sustain. Water Resour. Manag."},{"key":"ref_15","unstructured":"Koch, B. (2015). Remote Sensing supporting national forest inventories NFA. FAO Knowledge Reference for National Forest Assessments, FAO. Available online: http:\/\/www.fao.org\/3\/a-i4822e.pdf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.rse.2016.01.006","article-title":"Mapping and estimating forest area and aboveground biomass in miombo woodlands in Tanzania using data from airborne laser scanning, TanDEM-X, RapidEye, and global forest maps: A comparison of estimated precision","volume":"175","author":"Solberg","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2011.09.026","article-title":"Sentinels for science: Potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land","volume":"120","author":"Rott","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_18","first-page":"1011","article-title":"Free access to Landsat imagery","volume":"320","author":"Woodcock","year":"2008","journal-title":"Sci. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","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":"Gizachew","year":"2016","journal-title":"Carbon Balance Manag."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, C., Li, Y., and Li, M. (2019). Improving Forest Aboveground Biomass (AGB) Estimation by Incorporating Crown Density and Using Landsat 8 OLI Images of a Subtropical Forest in Western Hunan in Central China. Forests, 10.","DOI":"10.3390\/f10020104"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Navarro, J.A., Algeet, N., Fern\u00e1ndez-Landa, A., Esteban, J., Rodr\u00edguez-Noriega, P., and Guill\u00e9n-Climent, M.L. (2019). Integration of UAV, Sentinel-1, and Sentinel-2 Data for Mangrove Plantation Aboveground Biomass Monitoring in Senegal. Remote Sens., 11.","DOI":"10.3390\/rs11010077"},{"key":"ref_22","first-page":"1","article-title":"Exploring parameter selection for carbon monitoring based on Landsat-8 imagery of the aboveground forest biomass on Mount Tai","volume":"52","author":"Qiu","year":"2019","journal-title":"Eur. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Risdiyanto, I., and Fakhrul, M. (2017). Examination of Multi-Spectral Radiance of the Landsat 8 Satellite Data for Estimating Biomass Carbon Stock at Wetland Ecosystem. Preprints, 1\u201314.","DOI":"10.20944\/preprints201704.0020.v1"},{"key":"ref_24","first-page":"47","article-title":"Above-Ground Biomass Estimation with High Spatial Resolution Satellite Images","volume":"Volume 2017","author":"Tumuluru","year":"2017","journal-title":"Biomass Volume Estimation and Valorization for Energy"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.isprsjprs.2014.12.004","article-title":"Biomass estimation with high resolution satellite images: A case study of Quercus rotundifolia","volume":"101","author":"Sousa","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"29","DOI":"10.5194\/isprs-annals-IV-3-29-2018","article-title":"Estimation of Mangrove Forest Aboveground Biomass Using Multispectral Bands, Vegetation Indices and Biophysical Variables Derived from Optical Satellite Imageries: Rapideye, Planetscope and Sentinel-2","volume":"IV-3","author":"Baloloy","year":"2018","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_27","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":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1080\/07038992.2016.1217485","article-title":"A Comparison of Machine Learning Techniques Applied to Landsat-5 TM Spectral Data for Biomass Estimation","volume":"42","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_29","first-page":"289","article-title":"Estimating aboveground biomass using Landsat TM imagery: A case study of Anatolian Crimean pine forests in Turkey","volume":"57","author":"Ercanli","year":"2014","journal-title":"Ann. For. Res."},{"key":"ref_30","first-page":"1","article-title":"Correlation analysis between biomass and spectral vegetation indices of forest ecosystem","volume":"1","author":"Das","year":"2012","journal-title":"Int. J. Eng. Res. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"33","DOI":"10.2307\/2845602","article-title":"Vegetation spectral reflectance along a north-south vegetation gradient in northern Australia","volume":"21","author":"Ringrose","year":"1994","journal-title":"J. Biogeogr."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/0034-4257(89)90112-0","article-title":"Tropical forest biomass and successional age class relationships to a vegetation index derived from Landsat TM data","volume":"28","author":"Sader","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3468","DOI":"10.1016\/j.rse.2011.08.010","article-title":"Comparison of different vegetation indices for the remote assessment of green leaf area index of crops","volume":"115","author":"Gitelson","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1080\/22797254.2018.1521250","article-title":"Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images","volume":"51","author":"Macedo","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_35","first-page":"97","article-title":"Narrow band based and broadband derived vegetation indices using Sentinel-2 Imagery to estimate vegetation biomass","volume":"6","author":"Imran","year":"2020","journal-title":"Glob. J. Environ. Sci. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Motohka, T., Nasahara, K.N., Oguma, H., and Tsuchida, S. (2010). Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology. Remote Sens., 2.","DOI":"10.3390\/rs2102369"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Larrinaga, A.R., and Brotons, L. (2019). Greenness Indices from a Low-Cost UAV Imagery as Tools for Monitoring Post-Fire Forest Recovery. Drones, 3.","DOI":"10.3390\/drones3010006"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.agrformet.2011.09.009","article-title":"Digital repeat photography for phenological research in forest ecosystems","volume":"152","author":"Sonnentag","year":"2012","journal-title":"Agric. For. Meteorol."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4314\/sajg.v4i1.1","article-title":"Estimation and mapping of above ground biomass and carbon of Bwindi impenetrable National Park using ALOS PALSAR data","volume":"4","author":"Otukei","year":"2015","journal-title":"S. Afr. J. Geomat."},{"key":"ref_41","unstructured":"Deakin, L., Kshatriya, M., and Sunderland, T. (2016). Understanding people and forest interrelations along an intensification gradient in Arsi-Negele, Ethiopia. Agrarian Change in Tropical Landscapes, Center for International Forestry Research (CIFOR)."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.foreco.2020.118335","article-title":"Aboveground tree biomass prediction options for the Dry Afromontane forests in south-central Ethiopia","volume":"473","author":"Asrat","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_43","unstructured":"(2019, September 16). Topcon Positioning Systems Inc. Available online: https:\/\/www.topconpositioning.com\/gb\/gnss-network-solutions."},{"key":"ref_44","unstructured":"Kouba, J. (2020, May 18). A Guide to Using International GNSS Service (IGS) Products. Available online: https:\/\/www.researchgate.net\/profile\/Jan_Kouba\/publication\/228663800_A_guide_to_using_International_GNSS_Service_IGS_products\/links\/54fcc30c0cf270426d102cd3.pdf."},{"key":"ref_45","unstructured":"(2012). MAGNET Tools 1.0, Topcon Positioning Systems Inc.. Available online: https:\/\/www.tigersupplies.com\/files\/bcf31975-d2e6-44c2-ba66-7bad3a95cdb3HLP_MAGNET_Office_Tools_v1_0_EN.pdf."},{"key":"ref_46","unstructured":"(2019, November 12). Hagl\u00f6f Company Group. Available online: http:\/\/www.haglofsweden.com\/index.php\/en\/products\/instruments\/height\/541-the-vertex-laser-geo-all-you-need-in-a-rangefinder-hypsometer."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1111\/2041-210X.12962","article-title":"Field methods for sampling tree height for tropical forest biomass estimation","volume":"9","author":"Sullivan","year":"2018","journal-title":"Methods Ecol. Evol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Asrat, Z., Eid, T., Gobakken, T., and Negash, M. (2020). Modeling and quantifying tree biometric properties of Dry Afromontane forests of South-central Ethiopia. Trees, under review.","DOI":"10.1016\/j.foreco.2020.118335"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"113","DOI":"10.2989\/20702620.2013.805511","article-title":"Models for estimation of carbon sequestered by Cupressus lusitanica plantation stands at Wondo Genet, Ethiopia","volume":"75","author":"Berhe","year":"2013","journal-title":"South For."},{"key":"ref_50","first-page":"48","article-title":"Development of general biomass allometric equations for Tectona grandis Linn. f. and Eucalyptus camaldulensis Dehnh. plantations in Thailand","volume":"50","author":"Ounban","year":"2016","journal-title":"Agric. Nat. Resour."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Owate, O.A., Mware, M.J., and Kinyanjui, M.J. (2018). Allometric Equations for Estimating Silk Oak (Grevillea robusta) Biomass in Agricultural Landscapes of Maragua Subcounty, Kenya. Int. J. For. Res., 1\u201314.","DOI":"10.1155\/2018\/6495271"},{"key":"ref_52","unstructured":"USGS (2019, August 23). USGS Earth Explorer, Available online: https:\/\/earthexplorer.usgs.gov\/."},{"key":"ref_53","unstructured":"Planet (2019, September 03). Planet Explorer. Available online: https:\/\/www.planet.com\/explorer\/."},{"key":"ref_54","unstructured":"QGIS Development Team (2019, November 23). QGIS\u2014A Free and Open Source Geographic Information System. Available online: https:\/\/www.qgis.org\/en\/site\/."},{"key":"ref_55","unstructured":"Huete, A., Justice, C., and Van Leeuwen, W. (2019, October 16). MODIS Vegetation Index (MOD13). Algorithm Theoretical Basis Document, Available online: https:\/\/modis.gsfc.nasa.gov\/data\/atbd\/atbd_mod13.pdf."},{"key":"ref_56","unstructured":"Rouse, J.W., Hass, R.H., Schell, J.A., Deering, D.W., and Harlan, J.C. (1974). Monitoring the Vernal Advancement and Netrogradation (Greenwave Effect) of Natural Vegetation, Texas A&M University."},{"key":"ref_57","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":"5","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"719","DOI":"10.2135\/cropsci1999.0011183X003900030019x","article-title":"Measuring wheat senescence with a digital camera","volume":"39","author":"Adamsen","year":"1999","journal-title":"Crop Sci."},{"key":"ref_59","first-page":"1541","article-title":"Distinguishing vegetation from soil background information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1080\/10106040108542184","article-title":"Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat","volume":"16","author":"Louhaichi","year":"2001","journal-title":"Geocarto Int."},{"key":"ref_61","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_62","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_63","unstructured":"Qi, J., Kerr, Y., and Chehbouni, A. (1994, January 17\u201321). External factor consideration in vegetation index development. Proceedings of the 6th International Symposium on Physical Measurements and Signatures in Remote Sensing, Val d\u2019ls\u00e8re, France."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically resistant vegetation index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"41","DOI":"10.3897\/natureconservation.35.29588","article-title":"The utility of Sentinel-2 Vegetation Indices (VIs) and Sentinel-1 Synthetic Aperture Radar (SAR) for invasive alien species detection and mapping","volume":"35","author":"Rajah","year":"2019","journal-title":"Nat. Conserv."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.2134\/agronj13.0578","article-title":"Evaluation of Vegetation Indices for Early Assessment of Corn Status and Yield Potential in the Southeastern United States","volume":"106","author":"Torino","year":"2014","journal-title":"Agron. J."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/1011-1344(93)06963-4","article-title":"Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves","volume":"22","author":"Gitelson","year":"1994","journal-title":"J. Photochem. Photobiol. B Biol."},{"key":"ref_69","unstructured":"ESA (2019, August 28). SNAP Version 7.0.0. Available online: http:\/\/step.esa.int\/main\/download\/snap-download\/."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1139\/cjfr-2019-0170","article-title":"An application niche for finite mixture models in forest resource surveys","volume":"49","author":"Magnussen","year":"2019","journal-title":"Can. J. For. Res."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"S\u00e4rndal, C.E., Swensson, B., and Wretman, J. (1992). Model Assisted Survey Sampling, Springer.","DOI":"10.1007\/978-1-4612-4378-6"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1016\/j.rse.2016.07.035","article-title":"A functional regression model for inventories supported by aerial laser scanner data or photogrammetric point clouds","volume":"184","author":"Magnussen","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_73","first-page":"1","article-title":"Predicting aboveground forest biomass with topographic variables in human-impacted tropical dry forest landscapes","volume":"9","author":"Skutsch","year":"2018","journal-title":"Ecosphere"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1213\/ANE.0000000000002864","article-title":"Correlation Coefficients: Appropriate Use and Interpretation","volume":"126","author":"Schober","year":"2018","journal-title":"Anesth. Analg."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Bao, N., Li, W., Gu, X., and Liu, Y. (2019). Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11232855"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1111\/j.1469-8137.1988.tb04173.x","article-title":"Reflectance of blue, green, red and near infrared radiation from wetland vegetation used in a model discriminating live and dead above ground biomass","volume":"108","author":"Lorenzen","year":"1988","journal-title":"New Phytol."},{"key":"ref_77","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_78","doi-asserted-by":"crossref","unstructured":"Wang, Q., Pang, Y., Li, Z., Sun, G., Chen, E., and Ni-Meister, W. (2016). The Potential of Forest Biomass Inversion Based on Vegetation Indices Using Multi-Angle CHRIS\/PROBA Data. Remote Sens., 8.","DOI":"10.3390\/rs8110891"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"485","DOI":"10.4141\/CJPS08137","article-title":"Association of biomass production and canopy spectral reflectance indices in winter wheat","volume":"89","author":"Prasad","year":"2009","journal-title":"Can. J. Plant. Sci."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-019-0402-3","article-title":"Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system","volume":"15","author":"Lu","year":"2019","journal-title":"Plant Methods"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1080\/01431161.2016.1271477","article-title":"Combining ground-based measurements and MODIS-based spectral vegetation indices to track biomass accumulation in post-fire chaparral","volume":"38","author":"Uyeda","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1080\/01431168608954695","article-title":"Forestry information content of Thematic Mapper data","volume":"7","author":"Horler","year":"1986","journal-title":"Int. J. Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/BF02703218","article-title":"Biomass estimation using satellite remote sensing data\u2014an investigation on possible approaches for natural forest","volume":"21","author":"Roy","year":"1996","journal-title":"J. Biosci."},{"key":"ref_84","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. 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