{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T19:32:08Z","timestamp":1760470328012,"version":"build-2065373602"},"reference-count":84,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,6,11]],"date-time":"2020-06-11T00:00:00Z","timestamp":1591833600000},"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>For tropical countries that do not have extensive ground sampling programs such as national forest inventories, the gain-loss approach for greenhouse gas inventories is often used. With the gain-loss approach, emissions and removals are estimated as the product of activity data defined as the areas of human-caused emissions and removals and emissions factors defined as the per unit area responses of carbon stocks for those activities. Remotely sensed imagery and remote sensing-based land use and land use change maps have emerged as crucial information sources for facilitating the statistically rigorous estimation of activity data. Similarly, remote sensing-based biomass maps have been used as sources of auxiliary data for enhancing estimates of emissions and removals factors and as sources of biomass data for remote and inaccessible regions. The current status of statistically rigorous methods for combining ground and remotely sensed data that comply with the good practice guidelines for greenhouse gas inventories of the Intergovernmental Panel on Climate Change is reviewed.<\/jats:p>","DOI":"10.3390\/rs12111891","type":"journal-article","created":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T05:56:27Z","timestamp":1592200587000},"page":"1891","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Remote Sensing Support for the Gain-Loss Approach for Greenhouse Gas Inventories"],"prefix":"10.3390","volume":"12","author":[{"given":"Ronald","family":"McRoberts","sequence":"first","affiliation":[{"name":"Department of Forest Resources, University of Minnesota, Saint Paul, MN 55108, USA"},{"name":"Raspberry Ridge Analytics, 15111 Elmcrest Avenue North, Hugo, MN 55038, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erik","family":"N\u00e6sset","sequence":"additional","affiliation":[{"name":"Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 \u00c5s, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christophe","family":"Sannier","sequence":"additional","affiliation":[{"name":"Syst\u00e8mes d\u2019Information \u00e0 R\u00e9f\u00e9rence Spatiale, Parc de la Cimaise, 59650 Villeneuve d\u2019Ascq, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen","family":"Stehman","sequence":"additional","affiliation":[{"name":"Department of Sustainable Resources Management, State University of New York, College of Environmental Science and Forestry, Syracuse, NY 13210, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5715-8912","authenticated-orcid":false,"given":"Erkki","family":"Tomppo","sequence":"additional","affiliation":[{"name":"Department of Electronics and Nanoengineering, School of Electrical Engineering, Aalto University, P.O. Box 11000, 00076 Aalto, Finland"},{"name":"Department of Forest Sciences, University of Helsinki, Latokartanonkaari 7, P.O. Box 27, FI-00014 Helsinki, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,11]]},"reference":[{"key":"ref_1","unstructured":"GFOI (2016). Integration of Remote-Sensing and Ground-Based Observations for Estimation of Emissions and Removals of Greenhouse Gases in Forests: Methods and Guidance from the Global Forest Observations Initiative, Ed. 2.0, Food and Agriculture Organization. Available online: https:\/\/www.reddcompass.org\/frontpage."},{"key":"ref_2","unstructured":"Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T., and Tanabe, K. (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 4: Agriculture, Forestry and Other Land Use, Institute for Global Environmental Strategies. Available online: http:\/\/www.ipcc-nggip.iges.or.jp\/public\/2006gl\/index.html."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"014031","DOI":"10.1088\/1748-9326\/7\/1\/014031","article-title":"Assessing REDDC performance of countries with low monitoring capacities: The matrix approach","volume":"7","author":"Bucki","year":"2012","journal-title":"Environ. Res. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.rse.2015.11.012","article-title":"Statistical rigor in lidar-assisted estimation of aboveground forest biomass","volume":"173","author":"Gregoire","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.envsci.2019.02.009","article-title":"Data quality reporting: Good practice for transparent estimates from forestand land cover surveys","volume":"96","author":"Birigazzi","year":"2019","journal-title":"Environ. Sci. Policy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1139\/cjfr-2018-0295","article-title":"Comparing the stock-change and gain\u2013loss approaches for estimating forest carbon emissions for the aboveground biomass pool","volume":"48","author":"McRoberts","year":"2018","journal-title":"Can. J. For. Res."},{"key":"ref_7","unstructured":"(2020, June 08). REDD+ Web Platform. Available online: https:\/\/redd.unfccc.int\/fact-sheets.html."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rse.2012.10.031","article-title":"Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation","volume":"129","author":"Olofsson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3275","DOI":"10.1080\/01431160902755346","article-title":"The impact of imperfect ground reference data on the accuracy of land cover change estimation","volume":"30","author":"Foody","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1016\/j.rse.2010.05.003","article-title":"Assessing the accuracy of land cover change with imperfect ground reference data","volume":"14","author":"Foody","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1080\/2150704X.2013.798708","article-title":"Ground reference data error and the mis-estimation of the area of land cover change as a function of its abundance","volume":"4","author":"Foody","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.isprsjprs.2018.06.002","article-title":"The effects of imperfect reference data on remote sensing-assisted estimators of land cover class proportions","volume":"142","author":"McRoberts","year":"2018","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhou, C., Su, F., Harvey, F., and Xu, J. (2017). Analyzing the uncertainties of ground validation for remote sensing land cover mapping in the era of big geographic data. Spatial Data Handling in Big Data Era. Advances in Geographic Information Science, Springer.","DOI":"10.1007\/978-981-10-4424-3"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1080\/01431160500106975","article-title":"A method to obtain large quantities of reference data","volume":"27","author":"Mannel","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1016\/j.rse.2009.07.006","article-title":"Model-assisted estimation as a unifying framework for estimating the area of land cover and land-cover change from remote sensing","volume":"113","author":"Stehman","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mowrer, H.T., and Congalton, R.G. (2000). Quantifying Spatial Uncertainty in Natural Resources: Theory and Applications for GIS and Remote Sensing, Sleeping Bear Press.","DOI":"10.1201\/b12417"},{"key":"ref_18","first-page":"1","article-title":"The effect of season upon registrations of stand mean height, crown closure and tree species on aerial photos","volume":"44","year":"1991","journal-title":"Commun. Skogforsk"},{"key":"ref_19","first-page":"1","article-title":"The effect of scale, type of film and focal length upon interpretation of tree species mixture on aerial photos","volume":"45","year":"1992","journal-title":"Commun. Skogforsk"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.rse.2003.12.007","article-title":"Sources of error in accuracy assessment of thematic land-cover maps in the Brazilian Amazon","volume":"90","author":"Powell","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111261","DOI":"10.1016\/j.rse.2019.111261","article-title":"Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program","volume":"238","author":"Pengra","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_22","unstructured":"Guyana Forestry Commission (2020, June 08). Guyana REDD+ Monitoring Reporting & Verification System (MRVS) Interim Measures Report 01 October 2010\u201331 December 2011 Version 1, 15 June 2012. Available online: http:\/\/occguyana.org\/lcds\/index.php\/documents\/reports\/national\/guyana-mrvs-interim-measures-reports-1\/61-guyana-forestry-commission-guyana-redd-monitoring-reporting-verification-system-mrvs-interim-measures-report-01-october-2010-31-december-2011-version-3-26-july-2012\/file."},{"key":"ref_23","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":"2013","journal-title":"Science"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.rse.2015.10.032","article-title":"Suitability of Global Forest Change data to report forest cover estimates at national level in Gabon","volume":"173","author":"Sannier","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1139\/cjfr-2016-0064","article-title":"Methods for evaluating the utilities of local and global maps for increasing the precision of estimates of subtropical forest area","volume":"46","author":"McRoberts","year":"2016","journal-title":"Can. J. For. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"104014","DOI":"10.1088\/1748-9326\/aae3b1","article-title":"Impacts of the forest definitions adopted by African countries on carbon conservation","volume":"13","author":"Mermoz","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_28","unstructured":"De Wasseige, C., Flynn, J., Louppe, D., Hiol Hiol, F., and Mayaux, P. (2013). The Forests of the Congo Basin\u2013State of the Forest, Weyrich."},{"key":"ref_29","unstructured":"Cochran, W.G. (1977). Sampling Techniques, Wiley. [3rd ed.]."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1139\/x11-031","article-title":"Post-stratified estimation: Within-strata and total sample size recommendations","volume":"41","author":"Westfall","year":"2011","journal-title":"Can. J. For. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/S0034-4257(01)00330-3","article-title":"Using a land cover classification based on satellite imagery to improve the precision of forest inventory area estimates","volume":"81","author":"McRoberts","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"111492","DOI":"10.1016\/j.rse.2019.111492","article-title":"Mitigating the effects of omission errors on area and area change estimates","volume":"236","author":"Olofsson","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gregoire, T., and Valentine, H. (2008). Sampling Strategies for Natural Resources and the Environment, Chapman & Hall\/CRC.","DOI":"10.1201\/9780203498880"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s40663-016-0064-9","article-title":"Use of models in large-area forest surveys: Comparing model-assisted, model-based and hybrid estimation","volume":"3","author":"Saarela","year":"2016","journal-title":"For. Ecosyst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.rse.2013.01.016","article-title":"Estimating area from an accuracy assessment error matrix","volume":"132","author":"Stehman","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_37","first-page":"101931","article-title":"Local validation of global biomass maps","volume":"83","author":"McRoberts","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.rse.2013.03.036","article-title":"Post-classification approaches to estimating change in forest area using remotely sensed auxiliary data","volume":"151","author":"McRoberts","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Khan, A., Hansen, M.C., Potapov, P.V., Adusei, B., Pickens, A., Krylov, A., and Stehman, S.V. (2018). Evaluating Landsat and RapidEye data for winter wheat mapping and area estimation in Punjab, Pakistan. Remote Sens., 10.","DOI":"10.3390\/rs10040489"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3019","DOI":"10.1080\/01431160310001619607","article-title":"Remote sensing and land cover area estimation","volume":"25","author":"Gallego","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.rse.2012.10.023","article-title":"Using satellite image-based maps and ground inventory data to estimate the area of the remaining Atlantic forest in the Brazilian state of Santa Catarina","volume":"130","author":"Vibrans","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4923","DOI":"10.1080\/01431161.2014.930207","article-title":"Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes","volume":"35","author":"Stehman","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1016\/j.rse.2009.12.013","article-title":"Probability- and model-based approaches to inference for proportion forest using satellite imagery as ancillary data","volume":"114","author":"McRoberts","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.rse.2013.09.015","article-title":"Using the regression estimator with Landsat data to estimate proportion forest cover and net proportion deforestation in Gabon","volume":"151","author":"Sannier","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.rse.2017.03.022","article-title":"Global bare ground gain from 2000 to 2012 using Landsat imagery","volume":"194","author":"Ying","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.rse.2012.10.008","article-title":"Model-assisted estimation of change in forest biomass over an 11 year period in a sample survey supported by airborne LiDAR: A case study with post-stratification to provide \u201cactivity data\u201d","volume":"128","author":"Gobakken","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.rse.2012.05.011","article-title":"Statistical inference for remote sensing-based estimates of net deforestation","volume":"124","author":"McRoberts","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.rse.2015.02.018","article-title":"Indirect and direct estimation of forest biomass change using forest inventory and airborne laser scanning data","volume":"164","author":"McRoberts","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1111\/j.1752-1688.2010.00424.x","article-title":"Monitoring regional riparian forest cover change using stratified sampling and multiresolution imagery","volume":"46","author":"Claggett","year":"2010","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"eaat2993","DOI":"10.1126\/sciadv.aat2993","article-title":"Congo Basin forest loss dominated by increasing smallholder clearing","volume":"4","author":"Tyukavina","year":"2018","journal-title":"Sci. Adv."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rse.2018.11.018","article-title":"Quantifying the trade-off between cost and precision in estimating area of forest loss and degradation using probability sampling in Guyana","volume":"221","author":"Pickering","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.rse.2012.09.017","article-title":"An accuracy assessment of forest disturbance mapping in the western Great Lakes","volume":"128","author":"Zimmerman","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.rse.2007.03.010","article-title":"Trajectory-based change detection for automated characterization of forest disturbance dynamics","volume":"110","author":"Kennedy","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1016\/j.rse.2010.07.010","article-title":"Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync\u2014Tools for calibration and validation","volume":"114","author":"Cohen","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.rse.2009.08.014","article-title":"Detecting trend and seasonal changes in satellite image time series","volume":"114","author":"Verbesselt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2014.01.011","article-title":"Continuous change detection and classification of land cover using all available Landsat data","volume":"144","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.rse.2011.10.030","article-title":"Continuous monitoring of forest disturbance using all available Landsat imagery","volume":"122","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"4973","DOI":"10.3390\/rs70504973","article-title":"A Bayesian approach to combine Landsat and ALOS PALSAR time series for near real-time deforestation detection","volume":"7","author":"Reiche","year":"2015","journal-title":"Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.rse.2014.10.001","article-title":"Fusing Landsat and SAR time series to detect deforestation in the tropics","volume":"156","author":"Reiche","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"111051","DOI":"10.1016\/j.rse.2019.01.013","article-title":"Continuous monitoring of land change activities and post-disturbance dynamics from Landsat time series: A test methodology for REDD+ reporting","volume":"238","author":"Arevalo","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1139\/cjfr-2014-0203","article-title":"Estimation of standing wood volume in forest compartments by exploiting airborne laser scanning information: Model-based, design-based, and hybrid perspectives","volume":"4","author":"Corona","year":"2014","journal-title":"Can. J. For. Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.foreco.2016.07.007","article-title":"Hybrid estimators for mean aboveground carbon per unit area","volume":"378","author":"McRoberts","year":"2016","journal-title":"For. Ecol. Manag."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.foreco.2017.04.046","article-title":"Updating national forest inventory estimates of growing stock volume using hybrid inference","volume":"400","author":"McRoberts","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_64","unstructured":"IPCC (2020, June 08). User Manual, Database on Greenhouse as Emission Factors. Version 3.0. Available online: https:\/\/www.ipcc-nggip.iges.or.jp\/EFDB\/documents\/EFDB_User_Manual.pdf."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1016\/j.rse.2007.08.021","article-title":"Mapping, U.S. forest biomass using national forest inventory data and moderate resolution information","volume":"112","author":"Blackard","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"9899","DOI":"10.1073\/pnas.1019576108","article-title":"Benchmark map of forest carbon stocks in tropical regions across three continents","volume":"108","author":"Saatchi","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_67","first-page":"EGU2018-18932","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_68","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1038\/nclimate1354","article-title":"Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps","volume":"2","author":"Baccini","year":"2012","journal-title":"Nat. Clim. Chang."},{"key":"ref_69","unstructured":"Santoro, M., and Cartus, O. (2019). ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the year 2017, v1. Cent. Environ. Data Anal."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.rse.2012.07.002","article-title":"Post-stratified estimation of forest area and growing stock volume using lidar-based stratifications","volume":"125","author":"McRoberts","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_71","first-page":"99","article-title":"The calibration approach in survey theory and practice","volume":"33","year":"2007","journal-title":"Surv. Methodol."},{"key":"ref_72","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_73","doi-asserted-by":"crossref","unstructured":"Poorazimy, M., Shataee, S., McRoberts, R.E., and Mohammadi, J. (2020). Integrating airborne laser scanning data, space-borne radar data and digital aerial imagery to estimate aboveground carbon stock in Hyrcanian forests, Iran. Remote Sens. Environ., in press.","DOI":"10.1016\/j.rse.2020.111669"},{"key":"ref_74","unstructured":"Tomppo, E., Haakana, M., Katia, M., and Per\u00e4saari, J. (2008). Multi-Source National Forest Inventory-Methods and Applications, Springer. Managing Forest Ecosystems."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Esteban, J., McRoberts, R.E., Fern\u00e1ndez-Landa, A., Tom\u00e9, J.L., and N\u00e6ssset, E. (2019). Estimating forest volume and biomass and their changes Using random forests and remotely sensed data. Remote Sens., 11.","DOI":"10.3390\/rs11161944"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"3599","DOI":"10.1016\/j.rse.2011.08.021","article-title":"Model-assisted regional forest biomass estimation using LiDAR and InSAR as auxiliary data: A case study from a boreal forest area","volume":"115","author":"Gobakken","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"N\u00e6sset, E., McRoberts, R.E., Pekkarinen, A., Saatchi, S., Santoro, M., Trier, O.D., Zahabu, E., and Gobakken, T. (2020). Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania. Int. J. Appl. Earth Obs. Geoinf., in press.","DOI":"10.1016\/j.jag.2020.102109"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.rse.2015.07.002","article-title":"The effects of field plot size on model-assisted estimation of aboveground biomass change using multitemporal interferometric SAR and airborne laser scanning data","volume":"168","author":"Gobakken","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1139\/X10-195","article-title":"Model-assisted estimation of biomass in a LiDAR sample survey in Hedmark county, Norway","volume":"41","author":"Gregoire","year":"2011","journal-title":"Can. J. For. Res."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s13021-015-0021-x","article-title":"Effects of field plot size on the prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania","volume":"10","author":"Mauya","year":"2015","journal-title":"Carbon Balance Manag."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1080\/02827581.2016.1259425","article-title":"Effects of field plot configurations on the uncertainties of ALS-assisted forest resource estimates","volume":"32","author":"Tomppo","year":"2017","journal-title":"Scand. J. For. Res."},{"key":"ref_82","first-page":"179","article-title":"Harmonizing national forest inventories","volume":"107","author":"McRoberts","year":"2009","journal-title":"J. For."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Tomppo, E., Gschwantner, T., Lawrence, M., and McRoberts, R.E. (2010). National Forest Inventories: Pathways to Common Reporting, Springer.","DOI":"10.1007\/978-90-481-3233-1"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.rse.2015.11.002","article-title":"Effects of positional errors in model-assisted and model-based estimation of growing stock volume","volume":"172","author":"Saarela","year":"2016","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/11\/1891\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:37:52Z","timestamp":1760175472000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/11\/1891"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,11]]},"references-count":84,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["rs12111891"],"URL":"https:\/\/doi.org\/10.3390\/rs12111891","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,6,11]]}}}