{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T18:49:19Z","timestamp":1779216559285,"version":"3.51.4"},"reference-count":67,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,4,25]],"date-time":"2020-04-25T00:00:00Z","timestamp":1587772800000},"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>Inventories of tropical forest aboveground biomass (AGB) are often imprecise and sparse. Increasingly, airborne Light Detection And Ranging (LiDAR) and satellite optical wavelength sensor data are used to map tree height and to estimate AGB. In the tropics, cloud cover is particularly prevalent and so several years of satellite observations must be considered. This may reduce mapping accuracy because of seasonal and inter-annual changes in the forest reflectance. In this paper, the sensitivity of airborne LiDAR and Landsat-8 Operational Land Imager (OLI) based dominant canopy height and AGB 30 m mapping is assessed with respect to the season of Landsat acquisition for a ~10,000 Km2 tropical forest area in the Democratic Republic of the Congo. A random forest regression estimator is used to predict and assess the 30 m dominant canopy height using LiDAR derived test and training data. The AGB is mapped using an allometric model parameterized with the dominant canopy height and is assessed by comparison with field based 30 m AGB estimates. Experiments are undertaken independently using (i) only a wet season Landsat-8 image, (ii) only a dry season Landsat-8 image, and (iii) both Landsat-8 images. At the study area level there is little reported sensitivity to the season of Landsat image used. The mean dominant canopy height and AGB values are similar between seasons, within 0.19 m and 5 Mg ha\u22121, respectively. The mapping results are improved when both Landsat-8 images are used with Root Mean Square Error (RMSE) values that correspond to 18.8% of the mean study area mapped tree height (20.4 m) and to 41% of the mean study area mapped AGB (204 Mg ha\u22121). The mean study area mapped AGB is similar to that reported in other Congo Basin forest studies. The results of this detailed study are illustrated and the implications for tropical forest tree height and AGB mapping are discussed.<\/jats:p>","DOI":"10.3390\/rs12091360","type":"journal-article","created":{"date-parts":[[2020,4,28]],"date-time":"2020-04-28T10:30:58Z","timestamp":1588069858000},"page":"1360","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Democratic Republic of the Congo Tropical Forest Canopy Height and Aboveground Biomass Estimation with Landsat-8 Operational Land Imager (OLI) and Airborne LiDAR Data: The Effect of Seasonal Landsat Image Selection"],"prefix":"10.3390","volume":"12","author":[{"given":"Herve B.","family":"Kashongwe","sequence":"first","affiliation":[{"name":"Department of Geography, Environment &amp; Spatial Sciences Michigan State University, East Lansing, MI 48824, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1347-0250","authenticated-orcid":false,"given":"David P.","family":"Roy","sequence":"additional","affiliation":[{"name":"Department of Geography, Environment &amp; Spatial Sciences Michigan State University, East Lansing, MI 48824, USA"},{"name":"Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48824, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean Robert B.","family":"Bwangoy","sequence":"additional","affiliation":[{"name":"Department of Natural Resource Management, Faculty of Agronomy, University of Kinshasa, Kinshasa, Democratic Republic of Congo"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1111\/j.1365-2486.2005.00955.x","article-title":"Aboveground Forest Biomass and the Global Carbon Balance","volume":"11","author":"Houghton","year":"2005","journal-title":"Glob. Chang. Biol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s13021-019-0117-9","article-title":"Forest degradation and biomass loss along the Choco region of Colombia","volume":"14","author":"Meyer","year":"2019","journal-title":"Carbon Balance Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1038\/s41586-020-2035-0","article-title":"Asynchronous carbon sink saturation in African and Amazonian tropical forests","volume":"579","author":"Hubau","year":"2020","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0378-1127(99)00327-8","article-title":"Landscape-scale variation in forest structure and biomass in a tropical rain forest","volume":"137","author":"Clark","year":"2000","journal-title":"For. Ecol. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2836","DOI":"10.1016\/j.rse.2010.07.015","article-title":"Impact of spatial variability of tropical forest structure on radar estimation of aboveground biomass","volume":"115","author":"Saatchi","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.rse.2016.05.028","article-title":"Lidar detection of individual tree size in tropical forests","volume":"183","author":"Ferraz","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s00442-005-0100-x","article-title":"Tree allometry and improved estimation of carbon stocks and balance in tropical forests","volume":"145","author":"Chave","year":"2005","journal-title":"Oecologia"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"E12","DOI":"10.1073\/pnas.1015854108","article-title":"Implications of allometry","volume":"108","author":"Skole","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1111\/j.1654-1103.2012.01471.x","article-title":"Tropical forest biomass estimation and the fallacy of misplaced concreteness","volume":"23","author":"Clark","year":"2012","journal-title":"J. Veg. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3177","DOI":"10.1111\/gcb.12629","article-title":"Improved allometric models to estimate the aboveground biomass of tropical trees","volume":"20","author":"Chave","year":"2014","journal-title":"Glob. Chang. Biol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/S0034-4257(03)00039-7","article-title":"Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions","volume":"85","author":"Foody","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1080\/01431160500142145","article-title":"Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon","volume":"26","author":"Lu","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"20170048","DOI":"10.1098\/rsfs.2017.0048","article-title":"Weighing trees with lasers: Advances, challenges and opportunities","volume":"8","author":"Disney","year":"2018","journal-title":"Interface Focus"},{"key":"ref_14","first-page":"17831","article-title":"Monitoring tropical forest carbon stocks and emissions using Planet satellite data","volume":"9","author":"Csillik","year":"2019","journal-title":"Nat. Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"034008","DOI":"10.1088\/1748-9326\/11\/3\/034008","article-title":"Humid tropical forest disturbance alerts using Landsat data","volume":"11","author":"Hansen","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_16","first-page":"15030","article-title":"Spatial Distribution of Carbon Stored in Forests of the Democratic Republic of Congo","volume":"7","author":"Xu","year":"2017","journal-title":"Nat. Sci. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.rse.2017.12.020","article-title":"Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and lidar plots","volume":"209","author":"Matasci","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3855","DOI":"10.1080\/01431160010006926","article-title":"Cloud cover in Landsat observations of the Brazilian Amazon","volume":"22","author":"Asner","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7269","DOI":"10.1080\/01431160802275890","article-title":"The suitability of decadal image data sets for mapping tropical forest cover change in the Democratic Republic of Congo: Implications for the global land survey","volume":"29","author":"Lindquist","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.rse.2012.12.003","article-title":"The global availability of Landsat 5 TM and Landsat 7 ETM+ land surface observations and implications for global 30m Landsat data product generation","volume":"130","author":"Kovalskyy","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dommo, A., Philippon, N., Vondou, D.A., S\u00e8ze, G., and Eastman, R. (2018). The June\u2013September Low Cloud Cover in Western Central Africa: Mean Spatial Distribution and Diurnal Evolution, and Associated Atmospheric Dynamics. J. Clim., 31.","DOI":"10.1175\/JCLI-D-17-0082.1"},{"key":"ref_22","first-page":"666","article-title":"Modelling LiDAR derived tree canopy height from Landsat TM, ETM+ and OLI satellite imagery\u2014A machine learning approach","volume":"73","author":"Staben","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4820","DOI":"10.1073\/pnas.0611338104","article-title":"Large seasonal swings in leaf area of Amazon rainforests","volume":"104","author":"Myneni","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"14685","DOI":"10.1073\/pnas.0908741107","article-title":"Seasonal and interannual variability of climate and vegetation indices across the Amazon","volume":"107","author":"Brando","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.1111\/gcb.14056","article-title":"Amazon drought and forest response: Largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015\/2016","volume":"24","author":"Yang","year":"2018","journal-title":"Glob. Chang. Biol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1029\/2017JG004282","article-title":"Dry-Season Greening and Water Stress in Amazonia: The Role of Modeling Leaf Phenology","volume":"123","author":"Manoli","year":"2018","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Egorov, A., Roy, D., Zhang, H., Hansen, M., and Kommareddy, A. (2018). Demonstration of Percent Tree Cover Mapping Using Landsat Analysis Ready Data (ARD) and Sensitivity with Respect to Landsat ARD Processing Level. Remote Sens., 10.","DOI":"10.3390\/rs10020209"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s00704-007-0298-0","article-title":"Rainfall and temperature variations over Congo-Brazzaville between 1950 and 1998","volume":"91","author":"Samba","year":"2007","journal-title":"Theor. Appl. Climatol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Einzmann, K., Haarpaintner, J., and Larsen, Y. (2012, January 22\u201327). Forest monitoring in Congo Basin with combined use of SAR C- & L-band. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6352093"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2009.08.004","article-title":"Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices","volume":"114","author":"Bwangoy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/S0034-4257(99)00073-5","article-title":"Central African Forest Cover Revisited","volume":"71","author":"Mayaux","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s11273-012-9277-z","article-title":"Identifying nascent wetland forest conversion in the Democratic Republic of the Congo","volume":"21","author":"Bwangoy","year":"2012","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"094009","DOI":"10.1088\/1748-9326\/10\/9\/094009","article-title":"Forest cover dynamics of shifting cultivation in the Democratic Republic of Congo: A remote sensing-based assessment for 2000\u20132010","volume":"10","author":"Molinario","year":"2015","journal-title":"Environ. Res. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"024034","DOI":"10.1088\/1748-9326\/8\/2\/024034","article-title":"Satellite-based primary forest degradation assessment in the Democratic Republic of the Congo, 2000\u20132010","volume":"8","author":"Zhuravleva","year":"2013","journal-title":"Environ. Res. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s13021-016-0054-9","article-title":"Using fragmentation to assess degradation of forest edges in Democratic Republic of Congo","volume":"11","author":"Shapiro","year":"2016","journal-title":"Carbon Balance Manag."},{"key":"ref_36","unstructured":"Xu, L., Saatchi, S.S., Shapiro, A., Meyer, V., Ferraz, A., Yang, Y., Bastin, J.-F., Banks, N., Boeckx, P., and Verbeeck, H. (2020, April 24). Spatial Distribution of Carbon Stored in Forests of the Democratic Republic of Congo. Supplemental information. Available online: https:\/\/static-content.springer.com\/esm\/art%3A10.1038%2Fs41598-017-15050-z\/MediaObjects\/41598_2017_15050_MOESM1_ESM.pdf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3390\/rs10091363","article-title":"Analysis Ready Data: Enabling Analysis of the Landsat Archive","volume":"10","author":"Dwyer","year":"2018","journal-title":"Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.rse.2011.12.025","article-title":"Continental-scale validation of MODIS-based and LEDAPS Landsat ETM+ atmospheric correction methods","volume":"122","author":"Ju","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8\/OLI land surface reflectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_41","unstructured":"World-Bank (2016). Emission Reduction Program, World Bank\/Forest Carbon Partnership Facility."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1175\/EI132.1","article-title":"Multitemporal Analysis of Degraded Forest in the Southern Brazilian Amazon","volume":"9","author":"Souza","year":"2005","journal-title":"Earth Interact."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Qiu, S., Lin, Y., Shang, R., Zhang, J., Ma, L., and Zhu, Z. (2019). Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data. Remote Sens., 11.","DOI":"10.3390\/rs11010051"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Egorov, A., Roy, D., Zhang, H., Li, Z., Yan, L., and Huang, H. (2019). Landsat 4, 5 and 7 (1982 to 2017) Analysis Ready Data (ARD) Observation Coverage over the Conterminous United States and Implications for Terrestrial Monitoring. Remote Sens., 11.","DOI":"10.3390\/rs11040447"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4254","DOI":"10.1080\/01431161.2018.1452075","article-title":"Land cover 2.0","volume":"39","author":"Wulder","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.rse.2017.05.024","article-title":"Using the 500 m MODIS land cover product to derive a consistent continental scale 30 m Landsat land cover classification","volume":"197","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1080\/01431161.2010.519002","article-title":"Continuous fields of land cover for the conterminous United States using Landsat data: First results from the Web-Enabled Landsat Data (WELD) project","volume":"2","author":"Hansen","year":"2011","journal-title":"Remote Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.rse.2014.11.024","article-title":"Improved time series land cover classification by missing-observation-adaptive nonlinear dimensionality reduction","volume":"158","author":"Yan","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_49","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_50","unstructured":"McGaughey, R.J. (2016). FUSION LDV: Software for LiDAR Data Analysis and Visualization, U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, University of Washington."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.cageo.2008.09.001","article-title":"Evaluating error associated with lidar-derived DEM interpolation","volume":"35","author":"Bater","year":"2009","journal-title":"Comput. Geosci."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s13021-015-0013-x","article-title":"Airborne lidar-based estimates of tropical forest structure in complex terrain: Opportunities and trade-offs for REDD+","volume":"10","author":"Leitold","year":"2015","journal-title":"Carbon Balance Manag."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2015.08.001","article-title":"Using repeated small-footprint LiDAR acquisitions to infer spatial and temporal variations of a high-biomass Neotropical forest","volume":"169","author":"Tymen","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s13021-016-0062-9","article-title":"Performance of non-parametric algorithms for spatial mapping of tropical forest structure","volume":"11","author":"Xu","year":"2016","journal-title":"Carbon Balance Manag"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3770","DOI":"10.1016\/j.rse.2011.07.019","article-title":"Evaluating uncertainty in mapping forest carbon with airborne LiDAR","volume":"115","author":"Mascaro","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1890\/100179","article-title":"High-resolution carbon mapping on the million-hectare Island of Hawaii","volume":"9","author":"Asner","year":"2011","journal-title":"Front. Ecol. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.ecolmodel.2006.12.012","article-title":"Incorporating spatial dependence in predictive vegetation models","volume":"202","author":"Miller","year":"2007","journal-title":"Ecol. Model."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"45011","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_60","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2016.01.023","article-title":"A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance","volume":"176","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"111701","DOI":"10.1016\/j.rse.2020.111701","article-title":"A conterminous United States analysis of the impact of Landsat 5 orbit drift on the temporal consistency of Landsat 5 Thematic Mapper data","volume":"240","author":"Roy","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performanceof the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0168-1923(03)00138-2","article-title":"Seasonal variation of tropical forest LAI based on field measurements in Central African Republic","volume":"119","author":"Bastin","year":"2003","journal-title":"Agric. For. Meteorol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1111\/j.1365-2699.2004.01094.x","article-title":"Forest progression modes in littoral Congo, Central Atlantic Africa","volume":"31","author":"Favier","year":"2004","journal-title":"J. Biogeogr."},{"key":"ref_65","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."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_67","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/9\/1360\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:34:02Z","timestamp":1760366042000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/9\/1360"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,25]]},"references-count":67,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["rs12091360"],"URL":"https:\/\/doi.org\/10.3390\/rs12091360","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,25]]}}}