{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T17:36:16Z","timestamp":1777484176066,"version":"3.51.4"},"reference-count":73,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,25]],"date-time":"2020-06-25T00:00:00Z","timestamp":1593043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["15-CMS15-0055"],"award-info":[{"award-number":["15-CMS15-0055"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006196","name":"Jet Propulsion Laboratory","doi-asserted-by":"publisher","award":["281945.02.03.08.58-TROPICAL PEATLAND"],"award-info":[{"award-number":["281945.02.03.08.58-TROPICAL PEATLAND"]}],"id":[{"id":"10.13039\/100006196","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We introduce a multiscale superpixel approach that leverages repeat-pass interferometric coherence and sparse AGB estimates from a simulated spaceborne lidar in order to extend the NISAR mission\u2019s applicable range of aboveground biomass (AGB) in tropical forests. Airborne and spaceborne L-band radar and full-waveform airborne lidar data are used to simulate the NISAR and GEDI mission, respectively. In addition to UAVSAR data, we use spaceborne ALOS-2\/PALSAR-2 imagery with 14-day temporal baseline, which is comparable to NISAR\u2019s 12-day baseline. Our reference AGB maps are derived from the airborne LVIS data during the AfriSAR campaign for three sites (Mondah, Ogooue, and Lope). Each tropical site has mean AGB of at least 125 Mg\/ha in addition to areas with AGB exceeding 700 Mg\/ha. Spatially sampling from these LVIS-derived AGB reference maps, we approximate GEDI AGB estimates. To evaluate our methodology, we perform several different analyses. First, we partition each study site into low (\u2264100 Mg\/ha) and high (&gt;100 Mg\/ha) AGB areas, in conformity with the NISAR mission requirement to provide AGB estimates for forests between 0 and 100 Mg\/ha with a RMSE below 20 Mg\/ha. In the low AGB areas, this RMSE requirement is satisfied in Lope and Mondah and it fell short of the requirement in Ogooue by less 3 Mg\/ha with UAVSAR and 6 Mg\/ha with PALSAR-2. We note that our maps have finer spatial resolution (50 m) than NISAR requires (1 hectare). In the high AGB areas, the normalized RMSE increases to 51% (i.e., &lt;90 Mg\/ha), but with negligible bias for all three sites. Second, we train a single model to estimate AGB across both high and low AGB regimes simultaneously and obtain a normalized RMSE that is &lt;60% (or &lt;100 Mg\/ha). Lastly, we show the use of both (a) multiscale superpixels and (b) interferometric coherence significantly improves the accuracy of the AGB estimates. The InSAR coherence improved the RMSE by approximately 8% at Mondah with both sensors, lowering the RMSE from 59 Mg\/ha to 47.4 Mg\/h with UAVSAR and from 57.1 Mg\/ha to 46 Mg\/ha. This work illustrates one of the numerous synergistic relationships between the spaceborne lidars, such as GEDI, with L-band SAR, such as PALSAR-2 and NISAR, in order to produce robust regional AGB in high biomass tropical regions.<\/jats:p>","DOI":"10.3390\/rs12122048","type":"journal-article","created":{"date-parts":[[2020,6,25]],"date-time":"2020-06-25T10:36:54Z","timestamp":1593081414000},"page":"2048","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5633-7153","authenticated-orcid":false,"given":"Charlie","family":"Marshak","sequence":"first","affiliation":[{"name":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91101, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9442-4562","authenticated-orcid":false,"given":"Marc","family":"Simard","sequence":"additional","affiliation":[{"name":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91101, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura","family":"Duncanson","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"},{"name":"Biospheric Sciences Lab, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7844-3560","authenticated-orcid":false,"given":"Carlos Alberto","family":"Silva","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"},{"name":"School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Denbina","sequence":"additional","affiliation":[{"name":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91101, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tien-Hao","family":"Liao","sequence":"additional","affiliation":[{"name":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91101, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1130-6748","authenticated-orcid":false,"given":"Lola","family":"Fatoyinbo","sequence":"additional","affiliation":[{"name":"Biospheric Sciences Lab, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ghislain","family":"Moussavou","sequence":"additional","affiliation":[{"name":"Minist\u00e8re des For\u00eats, de la Mer et de l\u2019Environnement, Libreville 3241, Gabon"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1232-3424","authenticated-orcid":false,"given":"John","family":"Armston","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,25]]},"reference":[{"key":"ref_1","unstructured":"Eggleston, S., Buendia, L., Miwa, K., Ngara, T., and Tanabe, K. (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Institute for Global Environmental Strategies."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kumar, L., and Mutanga, O. (2017). Remote Sensing of Above-Ground Biomass. Remote Sens., 9.","DOI":"10.3390\/rs9090935"},{"key":"ref_3","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_4","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_5","doi-asserted-by":"crossref","first-page":"2906","DOI":"10.1016\/j.rse.2011.03.021","article-title":"Forest Biomass Mapping from Lidar and Radar Synergies","volume":"115","author":"Sun","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_6","unstructured":"NISAR Science Team (2020, January 01). NASA-ISRO SAR Mission Science Users Handbook, Available online: https:\/\/nisar.jpl.nasa.gov\/files\/nisar\/NISAR_Science_Users_Handbook.pdf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1007\/s13762-015-0750-0","article-title":"A Review of Radar Remote Sensing for Biomass Estimation","volume":"12","author":"Sinha","year":"2015","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Woodhouse, I.H. (2017). Introduction to Microwave Remote Sensing, CRC Press.","DOI":"10.1201\/9781315272573"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2017.12.030","article-title":"An Above-Ground Biomass Map of African Savannahs and Woodlands at 25 m Resolution Derived from ALOS PALSAR","volume":"206","author":"Bouvet","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Denbina, M., and Simard, M. (2017, January 23\u201328). Kapok: An Open Source Python Library for PolInSAR Forest Height Estimation using UAVSAR Data. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127956"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2880","DOI":"10.1109\/TGRS.2011.2174367","article-title":"A Temporal Decorrelation Model for Polarimetric Radar Interferometers","volume":"50","author":"Lavalle","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4752","DOI":"10.1109\/TGRS.2015.2409066","article-title":"Extraction of Structural and Dynamic Properties of Forests from Polarimetric-Interferometric SAR Data Affected by Temporal Decorrelation","volume":"53","author":"Lavalle","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.crte.2019.01.001","article-title":"Mapping of Aboveground Biomass in Gabon","volume":"351","author":"Baghdadi","year":"2019","journal-title":"Comptes Rendus Geosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"42","DOI":"10.3390\/rs5010042","article-title":"Using InSAR Coherence to Map Stand Age in a Boreal Forest","volume":"5","author":"Pinto","year":"2013","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"975","DOI":"10.3390\/rs4040975","article-title":"An Empirical Assessment of Temporal Decorrelation using the Uninhabited Aerial Vehicle Synthetic Aperture Radar over Forested Landscapes","volume":"4","author":"Simard","year":"2012","journal-title":"Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1109\/TGRS.2018.2860590","article-title":"Generation of Large-Scale Moderate-Resolution Forest Height Mosaic With Spaceborne Repeat-Pass SAR Interferometry and Lidar","volume":"57","author":"Lei","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1109\/JSTARS.2017.2761338","article-title":"An Assessment of Temporal Decorrelation Compensation Methods for Forest Canopy Height Estimation using Airborne L-band Same-day Repeat-Pass Polarimetric SAR Interferometry","volume":"11","author":"Simard","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Simard, M., Pinto, N., Fisher, J.B., and Baccini, A. (2011). Mapping Forest Canopy Height Globally with Spaceborne Lidar. J. Geophys. Res. Biogeosci., 116.","DOI":"10.1029\/2011JG001708"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lefsky, M.A., Harding, D.J., Keller, M., Cohen, W.B., Carabajal, C.C., Del Bom Espirito-Santo, F., Hunter, M.O., and De Oliveira, R. (2005). Estimates of forest canopy height and aboveground biomass using ICESat. Geophys. Res. Lett., 32.","DOI":"10.1029\/2005GL023971"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1111\/j.1365-2486.2007.01323.x","article-title":"Distribution of Aboveground Live Biomass in the Amazon Basin","volume":"13","author":"Saatchi","year":"2007","journal-title":"Glob. Chang. Biol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"045011","DOI":"10.1088\/1748-9326\/3\/4\/045011","article-title":"A First Map of Tropical Africa\u2019s Above-Ground Biomass Derived from Satellite Imagery","volume":"3","author":"Baccini","year":"2008","journal-title":"Environ. Res. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100002","DOI":"10.1016\/j.srs.2020.100002","article-title":"The Global Ecosystem Dynamics Investigation: High-Resolution Laser Ranging of the Earth\u2019s Forests and Topography","volume":"1","author":"Dubayah","year":"2020","journal-title":"Sci. Remote Sens."},{"key":"ref_24","first-page":"U14A-07","article-title":"The Global Ecosystem Dynamics Investigation","volume":"2014","author":"Dubayah","year":"2014","journal-title":"AGUFM"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Saarela, S., Holm, S., Healey, S., Andersen, H.E., Petersson, H., Prentius, W., Patterson, P., N\u00e6sset, E., Gregoire, T., and St\u00e5hl, G. (2018). Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data. Remote Sens., 10.","DOI":"10.3390\/rs10111832"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, M., Sun, R., and Xiao, Z. (2018). Estimation of Forest Canopy Height and Aboveground Biomass from Spaceborne LiDAR and Landsat Imageries in Maryland. Remote Sens., 10.","DOI":"10.3390\/rs10020344"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.rse.2016.10.018","article-title":"Combining Tandem-X InSAR and Simulated GEDI Lidar Observations for Forest Structure Mapping","volume":"187","author":"Qi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1016\/j.rse.2018.11.035","article-title":"Improved Forest Height Estimation by Fusion of Simulated GEDI Lidar Data and TanDEM-X InSAR Data","volume":"221","author":"Qi","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5574","DOI":"10.3390\/rs5115574","article-title":"Model-based Biomass Estimation of a Hemi-boreal Forest from Multitemporal TanDEM-X Acquisitions","volume":"5","author":"Askne","year":"2013","journal-title":"Remote Sens."},{"key":"ref_30","first-page":"B44E-05","article-title":"Fusing GEDI, ICESat-2 and NISAR data for Aboveground Biomass Mapping in Sonoma County, California, USA","volume":"2018","author":"Silva","year":"2018","journal-title":"AGUFM"},{"key":"ref_31","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_32","unstructured":"MacDicken, K. (2015). Forest Resources Assessment Working Paper, FAO."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1111\/j.1755-263X.2009.00067.x","article-title":"Critical Need for New Definitions of \u201cForest\u201d and \u201cForest Degradation\u201d in Global Climate Change Agreements","volume":"2","author":"Sasaki","year":"2009","journal-title":"Conserv. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1038\/nclimate2277","article-title":"Primary Forest Cover Loss in Indonesia over 2000\u20132012","volume":"4","author":"Margono","year":"2014","journal-title":"Nat. Clim. Chang."},{"key":"ref_35","unstructured":"Armston, J., Tang, H., Hancock, S., Marselis, S., Duncanson, L., Hofton, M., Blair, J.B., Fatoyinbo, L., and Dubayah, R. (2020, January 01). AfriSAR: Gridded Aboveground Biomass, Canopy Height and Vertical Profile Metrics from LVIS, Gabon. Available online: https:\/\/doi.org\/10.3334\/ORNLDAAC\/1775."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fatoyinbo, L., Armston, J., Simard, M., Saatchi, S., Lou, Y., Dubayah, R., Hensley, S., Armston, J., Duncanson, L., and Lavalle, M. The 2016 NASA AfriSAR Campaign: Airborne SAR and Lidar Measurements of Tropical Forest Structure and Biomass in Support of Future Space Missions. Remote. Sens. Environ., 2017.","DOI":"10.1109\/IGARSS.2017.8127949"},{"key":"ref_37","unstructured":"Duncanson, L., Neuenschwander, A., Hancock, S., Thomas, N., Fatoyinbo, T., Simard, M., Luthcke, S., Silva, C., Armston, J., and Hofton, M. Understanding Biomass Errors from Simulated GEDI, ICESat-2 and NISAR Data Across Environmental Gradients in Sonoma County, California. Remote. Sens. Environ., submitted."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4488","DOI":"10.1109\/TGRS.2016.2543142","article-title":"Radiometric Correction of Airborne Radar Images over Forested Terrain with Topography","volume":"54","author":"Simard","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Rosen, P., Hensley, S., Shaffer, S., Edelstein, W., Kim, Y., Kumar, R., Misra, T., Bhan, R., Satish, R., and Sagi, R. (2016, January 10\u201315). An Update on the NASA-ISRO Dual-frequency DBF SAR (NISAR) Mission. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729543"},{"key":"ref_40","unstructured":"UAVSAR Team (2020, February 01). UAVSAR Data Search, Available online: https:\/\/uavsar.jpl.nasa.gov\/cgi-bin\/data.pl."},{"key":"ref_41","unstructured":"Werner, C., Wegm\u00fcller, U., Strozzi, T., and Wiesmann, A. (2000, January 16\u201320). Gamma SAR and Interferometric Processing Software. Proceedings of the ERS-ENVISAT Symposium, Gothenburg, Sweden."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","article-title":"Nonlinear Total Variation Based Noise Removal Algorithms","volume":"60","author":"Rudin","year":"1992","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4389","DOI":"10.1109\/TIP.2017.2713946","article-title":"MuLoG, or How to Apply Gaussian Denoisers to Multi-Channel SAR Speckle Reduction?","volume":"26","author":"Deledalle","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"74","DOI":"10.5201\/ipol.2012.g-tvd","article-title":"Rudin-Osher-Fatemi Total Variation Denoising using Split-Bregman","volume":"2","author":"Getreuer","year":"2012","journal-title":"Image Process. OnLine"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1093\/biomet\/81.3.425","article-title":"Ideal spatial Adaptation by Wavelet Shrinkage","volume":"81","author":"Donoho","year":"1994","journal-title":"Biometrika"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1023\/B:JMIV.0000011321.19549.88","article-title":"An Algorithm for Total Variation Minimization and Applications","volume":"20","author":"Chambolle","year":"2004","journal-title":"J. Math. Imaging Vis."},{"key":"ref_47","unstructured":"CEOS (2020, June 01). A Layman\u2019s Interpretation Guide to L-band and C-band Synthetic Aperture Radar Data. Available online: http:\/\/ceos.org\/document_management\/SEO\/DataCube\/Laymans_SAR_Interpretation_Guide_2.0.pdf."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/36.673687","article-title":"A Three-Component Scattering Model for Polarimetric SAR Data","volume":"36","author":"Freeman","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1109\/5.838084","article-title":"Synthetic Aperture Radar Interferometry","volume":"88","author":"Rosen","year":"2000","journal-title":"Proc. IEEE"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Tsuchiya, M., Fujisawa, T., and Miura, S. (2008, January 12\u201316). ALOS Mission Operation 2008 in JAXA. Proceedings of the SpaceOps 2008 Conference, Heidelberg, Germany.","DOI":"10.2514\/6.2008-3317"},{"key":"ref_51","unstructured":"Rosen, P., Gurrola, E., Agram, P.S., Sacco, G.F., and Lavalle, M. (2015). The InSAR Scientific Computing Environment (ISCE): A Python Framework for Earth Science. AGU Fall Meeting Abstracts, AGU."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., and Roth, L. (2007). The Shuttle Radar Topography Mission. Rev. Geophys., 45.","DOI":"10.1029\/2005RG000183"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3415","DOI":"10.1109\/JSTARS.2018.2841388","article-title":"Forest Height Estimation Using Multibaseline PolInSAR and Sparse Lidar Data Fusion","volume":"11","author":"Denbina","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_54","first-page":"1097","article-title":"A Comparison of Segmentation Programs for High Resolution Remote Sensing Data","volume":"35","author":"Meinel","year":"2004","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"6111","DOI":"10.3390\/rs6076111","article-title":"A Python-based Open Source System for Geographic Object-based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables","volume":"6","author":"Clewley","year":"2014","journal-title":"Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ren, X., and Malik, J. (2003, January 13\u201316). Learning a Classification Model for Segmentation. Proceedings of the Ninth IEEE International Conference on Computer Vision, Nice, France.","DOI":"10.1109\/ICCV.2003.1238308"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Szeliski, R. (2010). Computer Vision: Algorithms and Applications, Springer Science & Business Media.","DOI":"10.1007\/978-1-84882-935-0"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3478","DOI":"10.1109\/JSTARS.2016.2514610","article-title":"Change Detection Based on Conditional Random Field With Region Connection Constraints in High-Resolution Remote Sensing Images","volume":"9","author":"Zhou","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_59","first-page":"4023","article-title":"Superpixel Endmember Detection","volume":"48","author":"Thompson","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Marshak, C., Simard, M., and Denbina, M. (2019). Monitoring Forest Loss in ALOS\/PALSAR Time-Series with Superpixels. Remote Sens., 11.","DOI":"10.3390\/rs11050556"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Huang, X., Yang, W., Xia, G., and Liao, M. (2015, January 22\u201324). Superpixel-based Change Detection in High Resolution SAR Images using region Covariance Features. Proceedings of the 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images, Annecy, France.","DOI":"10.1109\/Multi-Temp.2015.7245781"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1023\/B:VISI.0000022288.19776.77","article-title":"Efficient Graph-based Image Segmentation","volume":"59","author":"Felzenszwalb","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Bonev, B., and Yuille, A.L. (2015). Bottom-Up Processing in Complex Scenes: A Unifying Perspective on Segmentation, Fixation Saliency, Candidate Regions, Base-Detail Decomposition, and Image Enhancement. Recent Progress in Brain and Cognitive Engineering, Springer.","DOI":"10.1007\/978-94-017-7239-6_8"},{"key":"ref_65","first-page":"33","article-title":"Pyramid Methods in Image Processing","volume":"29","author":"Adelson","year":"1984","journal-title":"RCA Eng."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 4\u20138). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1109\/36.602545","article-title":"Coherent Effects in Microwave Backscattering Models for Forest Canopies","volume":"35","author":"Saatchi","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1080\/17583004.2017.1396840","article-title":"County-Scale Biomass Map Comparison: A Case Study for Sonoma, California","volume":"8","author":"Huang","year":"2017","journal-title":"Carbon Manag."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1109\/TGRS.2011.2176133","article-title":"Assessing Performance of L-and P-band Polarimetric Interferometric SAR Data in Estimating Boreal Forest Above-Ground Biomass","volume":"50","author":"Neumann","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_70","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_71","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1029\/2018EA000506","article-title":"The GEDI Simulator: A Large-Footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions","volume":"6","author":"Hancock","year":"2019","journal-title":"Earth Space Sci."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"3512","DOI":"10.1109\/JSTARS.2018.2816962","article-title":"Comparison of Small-and Large-footprint Lidar Characterization of Tropical Forest Aboveground Structure and Biomass: A Case Study from Central Gabon","volume":"11","author":"Silva","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/12\/2048\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:42:52Z","timestamp":1760175772000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/12\/2048"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,25]]},"references-count":73,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["rs12122048"],"URL":"https:\/\/doi.org\/10.3390\/rs12122048","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,25]]}}}