{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T22:11:37Z","timestamp":1783030297291,"version":"3.54.6"},"reference-count":81,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,9]],"date-time":"2018-05-09T00:00:00Z","timestamp":1525824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Federal Ministry for Economic Affairs and Energy (BMWi)","award":["50EE1416"],"award-info":[{"award-number":["50EE1416"]}]},{"name":"HGF-Helmholtz Alliance \u201cRemote Sensing and Earth System Dynamics\u201d","award":["HA-310"],"award-info":[{"award-number":["HA-310"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring of changes in forest biomass requires accurate transfer functions between remote sensing-derived changes in canopy height (\u0394H) and the actual changes in aboveground biomass (\u0394AGB). Different approaches can be used to accomplish this task: direct approaches link \u0394H directly to \u0394AGB, while indirect approaches are based on deriving AGB stock estimates for two points in time and calculating the difference. In some studies, direct approaches led to more accurate estimations, while, in others, indirect approaches led to more accurate estimations. It is unknown how each approach performs under different conditions and over the full range of possible changes. Here, we used a forest model (FORMIND) to generate a large dataset (&gt;28,000 ha) of natural and disturbed forest stands over time. Remote sensing of forest height was simulated on these stands to derive canopy height models for each time step. Three approaches for estimating \u0394AGB were compared: (i) the direct approach; (ii) the indirect approach and (iii) an enhanced direct approach (dir+tex), using \u0394H in combination with canopy texture. Total prediction accuracies of the three approaches measured as root mean squared errors (RMSE) were RMSEdirect = 18.7 t ha\u22121, RMSEindirect = 12.6 t ha\u22121 and RMSEdir+tex = 12.4 t ha\u22121. Further analyses revealed height-dependent biases in the \u0394AGB estimates of the direct approach, which did not occur with the other approaches. Finally, the three approaches were applied on radar-derived (TanDEM-X) canopy height changes on Barro Colorado Island (Panama). The study demonstrates the potential of forest modeling for improving the interpretation of changes observed in remote sensing data and for comparing different methodologies.<\/jats:p>","DOI":"10.3390\/rs10050731","type":"journal-article","created":{"date-parts":[[2018,5,10]],"date-time":"2018-05-10T03:48:27Z","timestamp":1525924107000},"page":"731","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Model-Assisted Estimation of Tropical Forest Biomass Change: A Comparison of Approaches"],"prefix":"10.3390","volume":"10","author":[{"given":"Nikolai","family":"Knapp","sequence":"first","affiliation":[{"name":"Department of Ecological Modeling, Helmholtz Centre for Environmental Research (UFZ), 04318 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Huth","sequence":"additional","affiliation":[{"name":"Department of Ecological Modeling, Helmholtz Centre for Environmental Research (UFZ), 04318 Leipzig, Germany"},{"name":"Institute for Environmental Systems Research, Department of Mathematics\/Computer Science, University of Osnabr\u00fcck, 49076 Osnabr\u00fcck, Germany"},{"name":"German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, 04103 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian","family":"Kugler","sequence":"additional","affiliation":[{"name":"Microwaves and Radar Institute, German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5736-0379","authenticated-orcid":false,"given":"Konstantinos","family":"Papathanassiou","sequence":"additional","affiliation":[{"name":"Microwaves and Radar Institute, German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Condit","sequence":"additional","affiliation":[{"name":"Field Museum of Natural History, Chicago, IL 60605, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen P.","family":"Hubbell","sequence":"additional","affiliation":[{"name":"Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0482-0095","authenticated-orcid":false,"given":"Rico","family":"Fischer","sequence":"additional","affiliation":[{"name":"Department of Ecological Modeling, Helmholtz Centre for Environmental Research (UFZ), 04318 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2009JG000935","article-title":"Importance of biomass in the global carbon cycle","volume":"114","author":"Houghton","year":"2009","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1126\/science.1201609","article-title":"A Large and Persistent Carbon Sink in the World\u2019s Forests","volume":"333","author":"Pan","year":"2011","journal-title":"Science"},{"key":"ref_3","unstructured":"Intergovernmental Panel on Climate Change (IPCC) (2007). Climate Change 2007: The Physical Science Basis, IPCC."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.rse.2012.10.017","article-title":"A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing","volume":"128","author":"Zolkos","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lu, D., Chen, Q., Wang, G., Liu, L., Li, G., and Moran, E. (2014). A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. Int. J. Digit. Earth, 1\u201343.","DOI":"10.1080\/17538947.2014.990526"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1641\/0006-3568(2002)052[0019:LRSFES]2.0.CO;2","article-title":"Lidar remote sensing for ecosystem studies","volume":"52","author":"Lefsky","year":"2002","journal-title":"Bioscience"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2012.02.001","article-title":"Lidar sampling for large-area forest characterization: A review","volume":"121","author":"Wulder","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/LGRS.2014.2334140","article-title":"Tropical-Forest Biomass Estimation at X-Band From the Spaceborne TanDEM-X Interferometer","volume":"12","author":"Treuhaft","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1046\/j.1466-822x.2002.00303.x","article-title":"Lidar remote sensing of above-ground biomass in three biomes","volume":"11","author":"Lefsky","year":"2002","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.rse.2016.07.023","article-title":"Forest aboveground biomass mapping and estimation across multiple spatial scales using model-based inference","volume":"184","author":"Chen","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.rse.2017.11.018","article-title":"Linking lidar and forest modeling to assess biomass estimation across scales and disturbance states","volume":"205","author":"Knapp","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2014.07.028","article-title":"Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass","volume":"154","author":"Fassnacht","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/S0034-4257(01)00281-4","article-title":"Estimation of tropical forest structural characteristics, using large-footprint lidar","volume":"79","author":"Drake","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/j.rse.2013.09.023","article-title":"Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric","volume":"140","author":"Asner","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"E5224","DOI":"10.1073\/pnas.1412999111","article-title":"Amazonian landscapes and the bias in field studies of forest structure and biomass","volume":"111","author":"Marvin","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.1007\/s10980-017-0550-7","article-title":"Using airborne LiDAR to assess spatial heterogeneity in forest structure on Mount Kilimanjaro","volume":"32","author":"Getzin","year":"2017","journal-title":"Landsc. Ecol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.rse.2017.03.017","article-title":"Area-based vs. tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data","volume":"194","author":"Coomes","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_19","unstructured":"Asner, G.P., Knapp, D.E., Martin, R.E., Tupayachi, R., Anderson, C.B., Mascaro, J., Sinca, F., Chadwick, K.D., Sousan, S., and Higgins, M. (2014). The High-Resolution Carbon Geography of Per\u00fa, Minuteman Press."},{"key":"ref_20","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_21","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_22","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1126\/science.1217962","article-title":"Baseline Map of Carbon Emissions from Deforestation in Tropical Regions","volume":"336","author":"Harris","year":"2012","journal-title":"Science"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.rse.2016.03.012","article-title":"Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data","volume":"178","author":"Cao","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s10260-012-0220-5","article-title":"Detection of biomass change in a Norwegian mountain forest area using small footprint airborne laser scanner data","volume":"22","author":"Gregoire","year":"2013","journal-title":"Stat. Methods Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2012.02.023","article-title":"Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys","volume":"123","author":"Hudak","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.rse.2017.09.007","article-title":"Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux","volume":"204","author":"Zhao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2009JG000933","article-title":"Estimation of tropical forest height and biomass dynamics using lidar remote sensing at la Selva, Costa Rica","volume":"115","author":"Dubayah","year":"2010","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5421","DOI":"10.5194\/bg-10-5421-2013","article-title":"Detecting tropical forest biomass dynamics from repeated airborne lidar measurements","volume":"10","author":"Meyer","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13021-014-0005-2","article-title":"Forest biomass change estimated from height change in interferometric SAR height models","volume":"9","author":"Solberg","year":"2014","journal-title":"Carbon Balance Manag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s13021-015-0023-8","article-title":"Monitoring forest carbon in a Tanzanian woodland using interferometric SAR: A novel methodology for REDD+","volume":"10","author":"Solberg","year":"2015","journal-title":"Carbon Balance Manag."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Puliti, S., Solberg, S., N\u00e6sset, E., Gobakken, T., Zahabu, E., Mauya, E., and Malimbwi, R.E. (2017). Modelling above ground biomass in Tanzanian miombo woodlands using TanDEM-X WorldDEM and field data. Remote Sens., 9.","DOI":"10.3390\/rs9100984"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1111\/geb.12365","article-title":"A global, remote sensing-based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling","volume":"24","author":"Tuanmu","year":"2015","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s10980-005-2166-6","article-title":"Textural ordination based on fourier spectral decomposition: A method to analyze and compare landscape patterns","volume":"21","author":"Couteron","year":"2006","journal-title":"Landsc. Ecol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Proisy, C., Barbier, N., Gu\u00e9roult, M., and P\u00e9lissier, R. (2011). Biomass Prediction in Tropical Forests: The Canopy Grain Approach. Remote Sensing of Biomass: Principles and Applications, InTech.","DOI":"10.5772\/17185"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5057","DOI":"10.3390\/rs70505057","article-title":"Mapping Above-Ground Biomass in a Tropical Forest in Cambodia Using Canopy Textures Derived from Google Earth","volume":"7","author":"Singh","year":"2015","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"8917","DOI":"10.1080\/01431161.2013.858846","article-title":"Canopy height model characteristics derived from airbone laser scanning and its effectiveness in discriminating various tropical moist forest types","volume":"34","author":"Kennel","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1080\/02827581.2012.686625","article-title":"Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM","volume":"27","author":"Bohlin","year":"2012","journal-title":"Scand. J. For. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.rse.2015.12.012","article-title":"Prediction of stem volume in complex temperate forest stands using TanDEM-X SAR data","volume":"174","author":"Abdullahi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1890\/140327","article-title":"Computer and remote-sensing infrastructure to enhance large-scale testing of individual-based forest models","volume":"13","author":"Shugart","year":"2015","journal-title":"Front. Ecol. Environ."},{"key":"ref_40","unstructured":"Shugart, H.H. (2003). A Theory of Forest Dynamics: The Ecological Implications of Forest Succession Models, The Blackburn Press."},{"key":"ref_41","unstructured":"Moser, J.W. (1980). Historical chapters in the development of modern forest growth and yield theory. Forecasting Forest and Stand Dynamics, Proceedings of the Workshop held at the School of Forestry, Wageningen, The Netherlands, 10\u201314 November 1980, Lakehead University."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"849","DOI":"10.2307\/2258570","article-title":"Some Ecological Consequences of a Computer Model of Forest Growth","volume":"60","author":"Botkin","year":"1972","journal-title":"J. Ecol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"682","DOI":"10.2307\/1310870","article-title":"New Models Unify Computer be explained by interactions among individual organisms","volume":"38","author":"Huston","year":"1988","journal-title":"Bioscience"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1023\/A:1012525626267","article-title":"A review of forest gap models","volume":"51","author":"Bugmann","year":"2001","journal-title":"Clim. Chang."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"033001","DOI":"10.1088\/1748-9326\/aaaacc","article-title":"Gap models and their individual-based relatives in the assessment of the consequences of global change","volume":"13","author":"Shugart","year":"2018","journal-title":"Environ. Res. Lett"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.ecolmodel.2016.01.001","article-title":"Structural realism, emergence, and predictions in next-generation ecological modelling: Synthesis from a special issue","volume":"326","author":"Grimm","year":"2016","journal-title":"Ecol. Model."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1890\/02-5317","article-title":"Beyond Potential Vegetation: Combining Lidar Data and a Height-Structured Model for Carbon Studies","volume":"14","author":"Hurtt","year":"2004","journal-title":"Ecol. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1111\/geb.12639","article-title":"Spatial heterogeneity of biomass and forest structure of the Amazon rain forest: Linking remote sensing, forest modelling and field inventory","volume":"26","author":"Cuntz","year":"2017","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"R\u00f6dig, E., Cuntz, M., Rammig, A., Fischer, R., Taubert, F., and Huth, A. (2018). The importance of forest structure for carbon flux estimates in the Amazon rainforest. Environ. Res. Lett., in press.","DOI":"10.1088\/1748-9326\/aabc61"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.5194\/bg-7-2531-2010","article-title":"Towards ground-truthing of spaceborne estimates of above-ground life biomass and leaf area index in tropical rain forests","volume":"7","author":"Huth","year":"2010","journal-title":"Biogeosciences"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.01.020","article-title":"Estimating forest structure in a tropical forest using field measurements, a synthetic model and discrete return lidar data","volume":"161","author":"Palace","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1016\/j.rse.2010.10.008","article-title":"Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass","volume":"115","author":"Frazer","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Cazcarra-Bes, V., Tello-Alonso, M., Fischer, R., Heym, M., and Papathanassiou, K. (2017). Monitoring of Forest Structure Dynamics by Means of L-Band SAR Tomography. Remote Sens., 9.","DOI":"10.3390\/rs9121229"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.ecolmodel.2015.11.018","article-title":"Lessons learned from applying a forest gap model to understand ecosystem and carbon dynamics of complex tropical forests","volume":"326","author":"Fischer","year":"2016","journal-title":"Ecol. Model."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1641\/0006-3568(2001)051[0389:TSOTPC]2.0.CO;2","article-title":"The Status of the Panama Canal Watershed and Its Biodiversity at the Beginning of the 21st Century","volume":"51","author":"Condit","year":"2001","journal-title":"Bioscience"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Condit, R. (1998). Tropical Forest Census Plots, R. G. Landes Company.","DOI":"10.1007\/978-3-662-03664-8"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1126\/science.283.5401.554","article-title":"Light-gap disturbances, recruitment limitation, and tree diversity in a neotropical forest","volume":"283","author":"Hubbell","year":"1999","journal-title":"Science"},{"key":"ref_58","unstructured":"Hubbell, S.P., Condit, R., and Foster, R.B. (2017, November 06). Barro Colorado Forest Census Plot Data. Available online: http:\/\/ctfs.si.edu\/webatlas\/datasets\/bci."},{"key":"ref_59","unstructured":"Condit, R., Lao, S., P\u00e9rez, R., Dolins, S.B., Foster, R.B., and Hubbell, S.P. (2012). Barro Colorado Forest Census Plot Data. Cent. Trop. For. Sci. Databases."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.ecolmodel.2014.05.002","article-title":"A neutral vs. non-neutral parametrizations of a physiological forest gap model","volume":"288","author":"Kazmierczak","year":"2014","journal-title":"Ecol. Model."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.5194\/bg-8-1615-2011","article-title":"Controls over aboveground forest carbon density on Barro Colorado Island, Panama","volume":"8","author":"Mascaro","year":"2011","journal-title":"Biogeosciences"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"20133218","DOI":"10.1098\/rspb.2013.3218","article-title":"Spatial scale and sampling resolution affect measures of gap disturbance in a lowland tropical forest: Implications for understanding forest regeneration and carbon storage","volume":"281","author":"Lobo","year":"2014","journal-title":"Proc. R. Soc. B Biol. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1017\/S0266467405003019","article-title":"Allometry, adult stature and regeneration requirement of 65 tree species on Barro Colorado Island, Panama","volume":"22","author":"Bohlman","year":"2006","journal-title":"J. Trop. Ecol."},{"key":"ref_64","unstructured":"R Development Core Team R (2014). A Language and Environment for Statistical Computing, R Development Core Team R."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1890\/08-0879.1","article-title":"Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics","volume":"91","author":"Murphy","year":"2010","journal-title":"Ecology"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1109\/PROC.1974.9516","article-title":"Synthetic interferometer radar for topographic mapping","volume":"62","author":"Graham","year":"1974","journal-title":"Proc. IEEE"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0266-5611\/14\/4\/001","article-title":"Synthetic aperture radar interferometry","volume":"14","author":"Bamler","year":"1998","journal-title":"Inverse Probl."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1109\/36.718859","article-title":"Polarimetric SAR interferometry","volume":"36","author":"Cloude","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.actaastro.2013.03.008","article-title":"TanDEM-X: A radar interferometer with two formation-flying satellites","volume":"89","author":"Krieger","year":"2013","journal-title":"Acta Astronaut."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"6404","DOI":"10.1109\/TGRS.2013.2296533","article-title":"TanDEM-X Pol-InSAR performance for forest height estimation","volume":"52","author":"Kugler","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1029\/RS013i002p00357","article-title":"Vegetation modeled as a water cloud","volume":"13","author":"Attema","year":"1978","journal-title":"Radio Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1029\/96RS01763","article-title":"Vegetation characteristics and underlying topography from interferometric radar","volume":"31","author":"Treuhaft","year":"1996","journal-title":"Radio Sci."},{"key":"ref_75","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 11year 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_76","doi-asserted-by":"crossref","first-page":"2368","DOI":"10.3390\/rs5052368","article-title":"Quantifying Dynamics in Tropical Peat Swamp Forest Biomass with Multi-Temporal LiDAR Datasets","volume":"5","author":"Englhart","year":"2013","journal-title":"Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2014.10.004","article-title":"Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data","volume":"156","author":"Bouvier","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2017.02.010","article-title":"Biomass and InSAR height relationship in a dense tropical forest","volume":"192","author":"Solberg","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"382","DOI":"10.5589\/m13-046","article-title":"Airborne laser scanning and digital stereo imagery measures of forest structure: Comparative results and implications to forest mapping and inventory update","volume":"39","author":"Vastaranta","year":"2013","journal-title":"Can. J. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Treuhaft, R., Lei, Y., Gon\u00e7alves, F., Keller, M., dos Santos, J.R., Neumann, M., and Almeida, A. (2017). Tropical-forest structure and biomass dynamics from TanDEM-X radar interferometry. Forests, 8.","DOI":"10.3390\/f8080277"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2010GB003942","article-title":"Relationships between net primary productivity and forest stand age in U.S. forests","volume":"26","author":"He","year":"2012","journal-title":"Glob. Biogeochem. Cycles"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/731\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:03:56Z","timestamp":1760195036000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/5\/731"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,9]]},"references-count":81,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["rs10050731"],"URL":"https:\/\/doi.org\/10.3390\/rs10050731","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,9]]}}}