{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T07:32:29Z","timestamp":1767771149757,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,14]],"date-time":"2019-01-14T00:00:00Z","timestamp":1547424000000},"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>Australia has historically used structural descriptors of height and cover to characterize, differentiate, and map the distribution of woody vegetation across the continent but no national satellite-based structural classification has been available. In this study, we present a new 30-m spatial resolution reference map of Australian forest and woodland structure (height and cover), with this generated by integrating Landsat Thematic Mapper (TM) and Enhanced TM, Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) and Ice, Cloud, and land Elevation (ICESat),and Geoscience Laser Altimeter System (GLAS) data. ALOS PALSAR and Landsat-derived Foliage Projective Cover (FPC) were used to segment and classify the Australian landscape. Then, from intersecting ICESat waveform data, vertical foliage profiles and height metrics (e.g., 95% percentile height, mean height and the height to maximum vegetation density) were extracted for each of the classes generated. Within each class, and for selected areas, the variability in ICESat profiles was found to be similar with differences between segments of the same class attributed largely to clearance or disturbance events. ICESat metrics and profiles were then assigned to all remaining segments across Australia with the same class allocation. Validation against airborne LiDAR for a range of forest structural types indicated a high degree of correspondence in estimated height measures. On this basis, a map of vegetation height was generated at a national level and was combined with estimates of cover to produce a revised structural classification based on the scheme of the Australian National Vegetation Information System (NVIS). The benefits of integrating the three datasets for segmenting and classifying the landscape and retrieving biophysical attributes was highlighted with this leading the way for future mapping using ALOS-2 PALSAR-2, Landsat\/Sentinel-2, Global Ecosystem Dynamics Investigation (GEDI), and ICESat-2 LiDAR data. The ability to map across large areas provides considerable benefits for quantifying carbon dynamics and informing on biodiversity metrics.<\/jats:p>","DOI":"10.3390\/rs11020147","type":"journal-article","created":{"date-parts":[[2019,1,14]],"date-time":"2019-01-14T12:20:07Z","timestamp":1547468407000},"page":"147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["A Structural Classification of Australian Vegetation Using ICESat\/GLAS, ALOS PALSAR, and Landsat Sensor Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5091-7915","authenticated-orcid":false,"given":"Peter","family":"Scarth","sequence":"first","affiliation":[{"name":"Joint Remote Sensing Research Program, School of Earth and Environmental Sciences, University of Queensland, Brisbane, QLD 4072, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1232-3424","authenticated-orcid":false,"given":"John","family":"Armston","sequence":"additional","affiliation":[{"name":"Joint Remote Sensing Research Program, School of Earth and Environmental Sciences, University of Queensland, Brisbane, QLD 4072, Australia"},{"name":"Department of Geographical Sciences, University of Maryland, 2181 Samuel J. LeFrak Hall, 7251 Preinkert Drive, College Park, MD 20742, USA"}]},{"given":"Richard","family":"Lucas","sequence":"additional","affiliation":[{"name":"Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, Ceredigion, SY23 3DB, UK"},{"name":"School of Biological, Earth and Environmental Sciences, University of New South Wales, High Street, Kensington, NSW 2052, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7435-0148","authenticated-orcid":false,"given":"Peter","family":"Bunting","sequence":"additional","affiliation":[{"name":"Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, Ceredigion, SY23 3DB, UK"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1080\/01431161.2012.712224","article-title":"Height and biomass of mangroves in Africa from ICESat\/GLAS and SRTM","volume":"34","author":"Fatoyinbo","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.rse.2006.09.007","article-title":"Quality assessment of SRTM C- and X-band interferometric data: Implications for the retrieval of vegetation canopy height","volume":"106","author":"Walker","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3876","DOI":"10.1016\/j.rse.2008.06.003","article-title":"Regional aboveground forest biomass using airborne and spaceborne LiDAR in Qu\u00e9bec","volume":"112","author":"Boudreau","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1139\/X09-002","article-title":"Estimating Quebec provincial forest resources using ICESat\/GLAS","volume":"39","author":"Nelson","year":"2009","journal-title":"Can. J. For. Res."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1016\/j.rse.2008.11.010","article-title":"Estimating Siberian timber volume using MODIS and ICESat\/GLAS","volume":"113","author":"Nelson","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kellndorfer, J.M., Walker, W.S., LaPoint, E., Kirsch, K., Bishop, J., and Fiske, G. (2010). Statistical fusion of lidar, InSAR, and optical remote sensing data for forest stand height characterization: A regional-scale method based on LVIS, SRTM, Landsat ETM+, and ancillary data sets. J. Geophys. Res. Biogeosci.","DOI":"10.1029\/2009JG000997"},{"key":"ref_8","unstructured":"\u00d8rka, H.O., Wulder, M.A., Gobakken, T., and N\u00e6sset, E. (2010, January 14\u201317). Integrating airborne laser scanner data and ancillary information for delineating the boreal\u2013alpine transition zone in Hedmark County, Norway. Proceedings of the Proceedings of SilviLaser, Freiburg, Germany."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"413","DOI":"10.5194\/gmd-5-413-2012","article-title":"Vegetation height and cover fraction between 60\u00b0 S and 60\u00b0 N from ICESat GLAS data","volume":"5","author":"Los","year":"2012","journal-title":"Geosci. Model Dev."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lefsky, M.A. (2010). A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System. Geophys. Res. Lett.","DOI":"10.1029\/2010GL043622"},{"key":"ref_11","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.","DOI":"10.1029\/2011JG001708"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5534","DOI":"10.3390\/rs70505534","article-title":"National Forest Aboveground Biomass Mapping from ICESat\/GLAS Data and MODIS Imagery in China","volume":"7","author":"Chi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1080\/01431161.2016.1266112","article-title":"A method for mapping Australian woody vegetation cover by linking continental-scale field data and long-term Landsat time series","volume":"38","author":"Gill","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","unstructured":"Thackway, R., Cresswell, I., and Australian Nature Conservation Agency (1995). An Interim Biogeographic Regionalisation for Australia: A Framework for Setting Priorities in the National Reserves System Cooperative Program, Australian Nature Conservation Agency, Reserve Systems Unit. Version 4.0."},{"key":"ref_15","unstructured":"Specht, R.L. (1970). Vegetation. Leeper, G.W. (ed.), \u201cAustralian Environment\u201d, Melbourne University Press."},{"key":"ref_16","unstructured":"Carnahan, J.A. (1976). Natural Vegetation. Atlas of Australian Resources, Department of Natural Resources."},{"key":"ref_17","unstructured":"Department of the Environment and Water Resources (2007). Australia\u2019s Native Vegetation: A Summary of Australia\u2019s Major Vegetation Groups, Australian Government."},{"key":"ref_18","unstructured":"Specht, R.L., and Specht, A. (2000). Australian plant communities: dynamics of structure, growth and biodiversity, Oxford University Press."},{"key":"ref_19","unstructured":"Walker, J., and Hopkins, M.S. (1990). Vegetation. Australian Soil and Land Survey: Field Handbook, Inkata Press."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JSTARS.2010.2077619","article-title":"Generating Large-Scale High-Quality SAR Mosaic Datasets: Application to PALSAR Data for Global Monitoring","volume":"3","author":"Shimada","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1109\/JSTARS.2010.2086436","article-title":"An evaluation of the ALOS PALSAR L-band backscatter\u2014Above ground biomass relationship Queensland, Australia: Impacts of surface moisture condition and vegetation structure","volume":"3","author":"Lucas","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","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 Sensss."},{"key":"ref_23","unstructured":"Shephard, J., Bunting, P., and Dymond, J. Operational large-scale segmentation of imagery based on iterative elimination. Remote Sens., Submitted."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1109\/TGRS.2004.834633","article-title":"Microwave scattering from mixed-species forests, Queensland, Australia","volume":"42","author":"Lucas","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1002\/(SICI)1097-0266(199606)17:6<441::AID-SMJ819>3.0.CO;2-G","article-title":"The application of cluster analysis in strategic management research: An analysis and critique","volume":"17","author":"Ketchen","year":"1996","journal-title":"Strateg. Manag. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2005GL023971","article-title":"de Estimates of forest canopy height and aboveground biomass using ICESat","volume":"32","author":"Lefsky","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2776","DOI":"10.1016\/j.rse.2010.08.026","article-title":"Physically based vertical vegetation structure retrieval from ICESat data: Validation using LVIS in White Mountain National Forest, New Hampshire, USA","volume":"115","author":"Lee","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1080\/01431160903380557","article-title":"Model effects on GLAS-based regional estimates of forest biomass and carbon","volume":"31","author":"Nelson","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1080\/01431160903380631","article-title":"Uncertainty within satellite LiDAR estimations of vegetation and topography","volume":"31","author":"Rosette","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","unstructured":"Held, A., Phinn, S., Soto-Berrelov, M., and Jones, S. (2018). Australian examples of field and airborne TERN Landscape Assessment campaigns. Effective Field Calibration and Nalidation Practices: A Practical Handbook for Calibration and Validation of Satellite and Model-Derived Terrestrial Environmental Variables for Research and Management, TERN Auscover."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.rse.2013.02.021","article-title":"Direct retrieval of canopy gap probability using airborne waveform lidar","volume":"134","author":"Armston","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1016\/j.csda.2009.09.020","article-title":"Robust smoothing of gridded data in one and higher dimensions with missing values","volume":"54","author":"Garcia","year":"2010","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.agrformet.2018.04.016","article-title":"Relating foliage and crown projective cover in Australian tree stands","volume":"259","author":"Fisher","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1126\/science.aam6527","article-title":"The extent of forest in dryland biomes","volume":"356","author":"Bastin","year":"2017","journal-title":"Science"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Dolan, K., Masek, J.G., Huang, C., and Sun, G. (2009). Regional forest growth rates measured by combining ICESat GLAS and Landsat data. J. Geophys. Res. Biogeosci.","DOI":"10.1029\/2008JG000893"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.rse.2014.02.020","article-title":"C- and L-band SAR interoperability: Filling the gaps in continuous forest cover mapping in Tasmania","volume":"155","author":"Mitchell","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2013.11.025","article-title":"Mapping forest growth and degradation stage in the Brigalow Belt Bioregion of Australia through integration of ALOS PALSAR and Landsat-derived foliage projective cover data","volume":"155","author":"Lucas","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_39","unstructured":"(2018, August 05). Regional Ecosystems Descriptions\u2014Methodology for Survey and Mapping of Regional Ecosystems and Vegetation Communities\u2014Publications | Queensland Government Available online:, Available online: https:\/\/publications.qld.gov.au\/dataset\/redd\/resource\/6dee78ab-c12c-4692-9842-b7257c2511e4."},{"key":"ref_40","unstructured":"Scarth, P., Armston, J., and Goodwin, N. (2018, August 05). If You Climb Up A Tree, You Must Climb Down The Same Tree. But How High Was It?. Available online: https:\/\/search.datacite.org\/works\/10.6084\/m9.figshare.94251.v1."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.biocon.2008.10.005","article-title":"Agricultural landscape modification increases the abundance of an important food resource: Mistletoes, birds and brigalow","volume":"142","author":"Bowen","year":"2009","journal-title":"Biol. Conserv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1126\/science.1229931","article-title":"Essential Biodiversity Variables","volume":"339","author":"Pereira","year":"2013","journal-title":"Science"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/147\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:25:50Z","timestamp":1760185550000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/147"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,14]]},"references-count":42,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["rs11020147"],"URL":"https:\/\/doi.org\/10.3390\/rs11020147","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,1,14]]}}}