{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T20:25:08Z","timestamp":1773779108577,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T00:00:00Z","timestamp":1626998400000},"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>In this paper, we consider a new method for forest canopy height estimation using TanDEM-X single-pass radar interferometry. We exploit available information from sample-based, space-borne LiDAR systems, such as the Global Ecosystem Dynamics Investigation (GEDI) sensor, which offers high-resolution vertical profiling of forest canopies. To respond to this, we have developed a new extended Fourier-Legendre series approach for fusing high-resolution (but sparsely spatially sampled) GEDI LiDAR waveforms with TanDEM-X radar interferometric data to improve wide-area and wall-to-wall estimation of forest canopy height. Our key methodological development is a fusion of the standard uniform assumption for the vertical structure function (the SINC function) with LiDAR vertical profiles using a Fourier-Legendre approach, which produces a convergent series of approximations of the LiDAR profiles matched to the interferometric baseline. Our results showed that in our test site, the Petawawa Research Forest, the SINC function is more accurate in areas with shorter canopy heights (&lt;~27 m). In taller forests, the SINC approach underestimates forest canopy height, whereas the Legendre approach avails upon simulated GEDI forest structural vertical profiles to overcome SINC underestimation issues. Overall, the SINC + Legendre approach improved canopy height estimates (RMSE = 1.29 m) compared to the SINC approach (RMSE = 4.1 m).<\/jats:p>","DOI":"10.3390\/rs13152882","type":"journal-article","created":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T04:29:14Z","timestamp":1627014554000},"page":"2882","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Using GEDI Waveforms for Improved TanDEM-X Forest Height Mapping: A Combined SINC + Legendre Approach"],"prefix":"10.3390","volume":"13","author":[{"given":"Hao","family":"Chen","sequence":"first","affiliation":[{"name":"Canadian Forest Service, Natural Resources Canada, 506 West Burnside Road, Victoria, BC V8Z 1M5, Canada"}]},{"given":"Shane R.","family":"Cloude","sequence":"additional","affiliation":[{"name":"AEL Consultants, Cupar KY15 5AA, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4674-0373","authenticated-orcid":false,"given":"Joanne C.","family":"White","sequence":"additional","affiliation":[{"name":"Canadian Forest Service, Natural Resources Canada, 506 West Burnside Road, Victoria, BC V8Z 1M5, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3084","DOI":"10.1109\/JSTARS.2021.3058837","article-title":"Forest Height Estimation by Means of TanDEM-X InSAR and Waveform Lidar Data","volume":"14","author":"Guliaev","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","unstructured":"Zhao, L., Chen, E., Li, Z., Zhang, W., and Fan, Y. (2021). A New Approach for Forest Height Inversion Using X-Band Single-Pass InSAR Coherence Data. IEEE Trans. Geosci. Remote Sens., 1\u201318.","DOI":"10.1109\/TGRS.2021.3072125"},{"key":"ref_4","unstructured":"Cazcarra-Bes, V., Albrecht, L., Guliaev, R., Choi, C., Pardini, M., and Papathanassiou, K. (2020, November 30). Mapping global forest canopy height through integration of GEDI and Landsat data. In Proceedings of the Polinsar 2021, Online Event. Available online: https:\/\/next.brella.io\/events\/P2021oe\/home."},{"key":"ref_5","unstructured":"Pardini, M., Cazcarra-Bes, V., and Papathanassiou, K. (2020, November 30). Forest Height and Structure Estimation by Means of InSAR Data\u2014Using P-band profiles to estimate forest height. In Proceedings of the Polinsar 2021, Online Event. Available online: https:\/\/next.brella.io\/events\/P2021oe\/home."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3177","DOI":"10.1109\/JSTARS.2016.2582722","article-title":"Forest Canopy Height Estimation Using Tandem-X Coherence Data","volume":"9","author":"Chen","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3443","DOI":"10.1109\/JSTARS.2018.2866059","article-title":"Radar forest height estimation in mountainous terrain using tandem-X coherence data","volume":"11","author":"Chen","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cloude, S.R. (2009). Polarisation: Applications in Remote Sensing, Oxford University Press. [2nd ed.].","DOI":"10.1093\/acprof:oso\/9780199569731.001.0001"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"RS4017","DOI":"10.1029\/2005RS003436","article-title":"Polarization Coherence Tomography","volume":"41","author":"Cloude","year":"2006","journal-title":"Radio Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1109\/LGRS.2006.885893","article-title":"Dual Baseline Coherence Tomography","volume":"4","author":"Cloude","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cloude, S.R., Brolly, M., and Woodhouse, I. (2009, January 12\u201317). A study of forest vertical structure estimation using coherence tomography coupled to a macro-ecological scattering model. Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing 2009, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5417477"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"149","DOI":"10.5558\/tfc2019-024","article-title":"The Petawawa Research Forest: Establishment of a Remote Sensing Supersite","volume":"95","author":"White","year":"2019","journal-title":"For. Chron."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1139\/x90-063","article-title":"Advances in remote sensing technologies for forest surveys and management","volume":"20","author":"Leckie","year":"1990","journal-title":"Can. J. For. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"78","DOI":"10.5558\/tfc2021-009","article-title":"Assessing single photon LiDAR for operational implementation of an enhanced forest inventory in diverse mixedwood forests","volume":"97","author":"White","year":"2021","journal-title":"For. Chron."},{"key":"ref_15","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":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","unstructured":"Dubayah, R., Luthcke, S., Blair, J., Hofton, M., Armston, J., and Tang, H. (2020). GEDI L1B Geolocated Waveform Data Global Footprint Level V001 [Data Set], NASA EOSDIS Land Processes DAAC."},{"key":"ref_17","unstructured":"Krehbiel, C. (2020, December 21). Getting Started with GEDI L1B Version 2 Data in Python, Available online: https:\/\/lpdaac.usgs.gov\/resources\/e-learning\/getting-started-gedi-l1b-version-2-data-python\/."},{"key":"ref_18","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_19","unstructured":"Ontario Ministry of Natural Resources and Forestry (2020, November 30). Forest Resources Inventory Technical Specifications. Available online: https:\/\/docs.ontario.ca\/documents\/2837\/fim-tech-spec-forest-resources-inventory.pdf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1029\/96RS01763","article-title":"Vegetation characteristics and underlying topography from interferometric data","volume":"31","author":"Treuhaft","year":"1996","journal-title":"Radio Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1109\/36.964971","article-title":"Single baseline polarimetric SAR interferometry","volume":"39","author":"Papathanassiou","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1049\/ip-rsn:20030449","article-title":"Three-stage inversion process for polarimetric SAR interferometry","volume":"150","author":"Cloude","year":"2003","journal-title":"IEE Proc. Radar Sonar Navig."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1080\/07038992.2019.1604119","article-title":"Mapping Forest Height from TanDEM-X Interferometric Coherence Data in Northwest Territories","volume":"45","author":"Chen","year":"2019","journal-title":"Canada. Can. J. Remote Sens."},{"key":"ref_24","first-page":"386","article-title":"Evaluating the Impacts of Using Different Digital Surface Models to Estimate Forest Height with TanDEM-X Interferometric Coherence Data","volume":"9","author":"Chen","year":"2020","journal-title":"J. Radars"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1080\/17538947.2016.1187673","article-title":"Mass data processing of time series Landsat imagery: Pixels to data products for forest monitoring","volume":"9","author":"Hermosilla","year":"2016","journal-title":"Int. J. Digit. Earth"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2882\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:33:42Z","timestamp":1760164422000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2882"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,23]]},"references-count":25,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13152882"],"URL":"https:\/\/doi.org\/10.3390\/rs13152882","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,23]]}}}