{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:24:07Z","timestamp":1767831847207,"version":"3.49.0"},"reference-count":71,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research University Grant, GERAN UNIVERSITI PENYELIDIKAN","award":["GUP-2021-073"],"award-info":[{"award-number":["GUP-2021-073"]}]},{"name":"Research University Grant, GERAN UNIVERSITI PENYELIDIKAN","award":["FRGS\/1\/2020\/WAB03\/UKM\/02\/1"],"award-info":[{"award-number":["FRGS\/1\/2020\/WAB03\/UKM\/02\/1"]}]},{"name":"FUNDAMENTAL RESEARCH GRANT SCHEME","award":["GUP-2021-073"],"award-info":[{"award-number":["GUP-2021-073"]}]},{"name":"FUNDAMENTAL RESEARCH GRANT SCHEME","award":["FRGS\/1\/2020\/WAB03\/UKM\/02\/1"],"award-info":[{"award-number":["FRGS\/1\/2020\/WAB03\/UKM\/02\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Canopy height is a fundamental parameter for determining forest ecosystem functions such as biodiversity and above-ground biomass. Previous studies examining the underlying patterns of the complex relationship between canopy height and its environmental and climatic determinants suffered from the scarcity of accurate canopy height measurements at large scales. NASA\u2019s mission, the Global Ecosystem Dynamic Investigation (GEDI), has provided sampled observations of the forest vertical structure at near global scale since late 2018. The availability of such unprecedented measurements allows for examining the vertical structure of vegetation spatially and temporally. Herein, we explore the most influential climatic and environmental drivers of the canopy height in tropical forests. We examined different resampling resolutions of GEDI-based canopy height to approximate maximum canopy height over tropical forests across all of Malaysia. Moreover, we attempted to interpret the dynamics underlining the bivariate and multivariate relationships between canopy height and its climatic and topographic predictors including world climate data and topographic data. The approaches to analyzing these interactions included machine learning algorithms, namely, generalized linear regression, random forest and extreme gradient boosting with tree and Dart implementations. Water availability, represented as the difference between precipitation and potential evapotranspiration, annual mean temperature and elevation gradients were found to be the most influential determinants of canopy height in Malaysia\u2019s tropical forest landscape. The patterns observed are in line with the reported global patterns and support the hydraulic limitation hypothesis and the previously reported negative trend for excessive water supply. Nevertheless, different breaking points for excessive water supply and elevation were identified in this study, and the canopy height relationship with water availability observed to be less significant for the mountainous forest on altitudes higher than 1000 m. This study provides insights into the influential factors of tree height and helps with better comprehending the variation in canopy height in tropical forests based on GEDI measurements, thereby supporting the development and interpretation of ecosystem modeling, forest management practices and monitoring forest response to climatic changes in montane forests.<\/jats:p>","DOI":"10.3390\/rs14133172","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T20:59:18Z","timestamp":1656968358000},"page":"3172","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Analyzing Canopy Height Patterns and Environmental Landscape Drivers in Tropical Forests Using NASA\u2019s GEDI Spaceborne LiDAR"],"prefix":"10.3390","volume":"14","author":[{"given":"Esmaeel","family":"Adrah","sequence":"first","affiliation":[{"name":"Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7813-088X","authenticated-orcid":false,"given":"Wan Shafrina","family":"Wan Mohd Jaafar","sequence":"additional","affiliation":[{"name":"Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"},{"name":"Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8565-1122","authenticated-orcid":false,"given":"Hamdan","family":"Omar","sequence":"additional","affiliation":[{"name":"Forest Research Institute Malaysia, Kepong 52019, Malaysia"}]},{"given":"Shaurya","family":"Bajaj","sequence":"additional","affiliation":[{"name":"United Nations Volunteering Program, Morobe Development Foundation, Lae 00411, Papua New Guinea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7840-3905","authenticated-orcid":false,"given":"Rodrigo Vieira","family":"Leite","sequence":"additional","affiliation":[{"name":"Department of Forest Engineering, Federal University of Vi\u00e7osa, Vi\u00e7osa 36570-900, Brazil"}]},{"given":"Siti Munirah","family":"Mazlan","sequence":"additional","affiliation":[{"name":"Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7844-3560","authenticated-orcid":false,"given":"Carlos Alberto","family":"Silva","sequence":"additional","affiliation":[{"name":"Forest Biometrics, Remote Sensing and Artificial Intelligence Lab (SilvaLab), School of Forest Resources and Conservation, University of Florida, Gainesville, FL 110410, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3325-0386","authenticated-orcid":false,"given":"Maggie","family":"Chel Gee Ooi","sequence":"additional","affiliation":[{"name":"Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}]},{"given":"Mohd Nizam","family":"Mohd Said","sequence":"additional","affiliation":[{"name":"Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9215-2778","authenticated-orcid":false,"given":"Khairul Nizam","family":"Abdul Maulud","sequence":"additional","affiliation":[{"name":"Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"},{"name":"Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0185-3959","authenticated-orcid":false,"given":"Adri\u00e1n","family":"Cardil","sequence":"additional","affiliation":[{"name":"Technosylva Inc., San Diego, CA 92108, USA"},{"name":"Joint Research Unit CTFC\u2014AGROTECNIO\u2014CERCA, 25280 Solsona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1824-1841","authenticated-orcid":false,"given":"Midhun","family":"Mohan","sequence":"additional","affiliation":[{"name":"United Nations Volunteering Program, Morobe Development Foundation, Lae 00411, Papua New Guinea"},{"name":"Department of Geography, University of California\u2014Berkeley, Berkeley, CA 94709, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"075003","DOI":"10.1088\/1748-9326\/aacadd","article-title":"Forest Drought Resistance Distinguished by Canopy Height","volume":"13","author":"Xu","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11635","DOI":"10.1073\/pnas.0901970106","article-title":"Re-Evaluation of Forest Biomass Carbon Stocks and Lessons from the World\u2019s Most Carbon-Dense Forests","volume":"106","author":"Keith","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_3","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: ICESAT ESTIMATES OF CANOPY HEIGHT. Geophys. Res. Lett., 32.","DOI":"10.1029\/2005GL023971"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"094013","DOI":"10.1088\/1748-9326\/ab2dcd","article-title":"Exploring the Relation between Remotely Sensed Vertical Canopy Structure and Tree Species Diversity in Gabon","volume":"14","author":"Marselis","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xu, P., Zhou, T., Yi, C., Luo, H., Zhao, X., Fang, W., Gao, S., and Liu, X. (2018). Impacts of Water Stress on Forest Recovery and Its Interaction with Canopy Height. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15061257"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Dubayah, R.O., Sheldon, S.L., Clark, D.B., Hofton, M.A., Blair, J.B., Hurtt, G.C., and Chazdon, R.L. (2010). Estimation of Tropical Forest Height and Biomass Dynamics Using Lidar Remote Sensing at La Selva, Costa Rica: FOREST DYNAMICS USING LIDAR. J. Geophys. Res., 115.","DOI":"10.1029\/2009JG000933"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1641\/0006-3568(2001)051[0723:CCAFD]2.0.CO;2","article-title":"Climate Change and Forest Disturbances: Climate Change Can Affect Forests by Altering the Frequency, Intensity, Duration, and Timing of Fire, Drought, Introduced Species, Insect and Pathogen Outbreaks, Hurricanes, Windstorms, Ice Storms, or Landslides","volume":"51","author":"Dale","year":"2001","journal-title":"BioScience"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1038\/nature02417","article-title":"The Limits to Tree Height","volume":"428","author":"Koch","year":"2004","journal-title":"Nature"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1111\/j.1365-2745.2009.01526.x","article-title":"Global Patterns in Plant Height","volume":"97","author":"Moles","year":"2009","journal-title":"J. Ecol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1007\/s00468-021-02138-3","article-title":"Wind and Gravity in Shaping Picea Trunks","volume":"35","author":"Larjavaara","year":"2021","journal-title":"Trees"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, B., Fang, S., Wang, Y., Guo, Q., Hu, T., Mi, X., Lin, L., Jin, G., Coomes, D.A., and Yuan, Z. (2022). The Shift from Energy to Water Limitation in Local Canopy Height from Temperate to Tropical Forests in China. Forests, 13.","DOI":"10.3390\/f13050639"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3265","DOI":"10.1002\/ecy.1580","article-title":"Global Patterns and Determinants of Forest Canopy Height","volume":"97","author":"Tao","year":"2016","journal-title":"Ecology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1111\/1365-2745.12510","article-title":"Regional and Historical Factors Supplement Current Climate in Shaping Global Forest Canopy Height","volume":"104","author":"Zhang","year":"2016","journal-title":"J. Ecol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.foreco.2018.12.006","article-title":"More than Climate? Predictors of Tree Canopy Height Vary with Scale in Complex Terrain, Sierra Nevada, CA (USA)","volume":"434","author":"Fricker","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"119792","DOI":"10.1016\/j.foreco.2021.119792","article-title":"Forest Canopy Height Variation in Relation to Topography and Forest Types in Central Japan with LiDAR","volume":"503","author":"Onoda","year":"2022","journal-title":"For. Ecol. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1080\/02693799508902046","article-title":"Topographic Solar Radiation Models for GIS","volume":"9","author":"Dubayah","year":"1995","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3836","DOI":"10.1002\/hyp.8281","article-title":"Aspect Influences on Soil Water Retention and Storage: ASPECT AND SOIL WATER RETENTION","volume":"25","author":"Geroy","year":"2011","journal-title":"Hydrol. Processes"},{"key":"ref_18","first-page":"20122532","article-title":"Soil Resources and Topography Shape Local Tree Community Structure in Tropical Forests","volume":"280","author":"Baldeck","year":"2013","journal-title":"Proc. Biol. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1111\/ele.12525","article-title":"Water Availability Predicts Forest Canopy Height at the Global Scale","volume":"18","author":"Klein","year":"2015","journal-title":"Ecol. Lett."},{"key":"ref_20","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., 116.","DOI":"10.1029\/2011JG001708"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112571","DOI":"10.1016\/j.rse.2021.112571","article-title":"Performance Evaluation of GEDI and ICESat-2 Laser Altimeter Data for Terrain and Canopy Height Retrievals","volume":"264","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"012003","DOI":"10.1088\/1755-1315\/275\/1\/012003","article-title":"Tree Species Richness, Diversity and Distribution at Sungai Menyala Forest Reserve, Negeri Sembilan","volume":"269","author":"Mohamed","year":"2019","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_24","unstructured":"Wyatt-Smith, J. (1966). Ecological Studies on Malayan Forests. Composition and Dynamic Studies in Lowland Evergreen Rain Forest in Two 5-Acre Plots in Bukit Lagong and Sungei Menyala Forest Reserves and in Two Half-Acre Plots in Sungei Menyala Forest, Forest Research Institute, Forest Department. Research Pamphlet No. 101."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nunes, M., Ewers, R., Turner, E., and Coomes, D. (2017). Mapping Aboveground Carbon in Oil Palm Plantations Using LiDAR: A Comparison of Tree-Centric versus Area-Based Approaches. Remote Sens., 9.","DOI":"10.3390\/rs9080816"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1111\/gcb.14903","article-title":"Imaging Spectroscopy Reveals the Effects of Topography and Logging on the Leaf Chemistry of Tropical Forest Canopy Trees","volume":"26","author":"Swinfield","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_27","unstructured":"Swinfield, T., Milodowski, D., Jucker, T., Michele, D., and Coomes, D. (2022, March 01). LiDAR Canopy Structure 2014, 2020 [Data set], Zenodo. Available online: https:\/\/doi.org\/10.5281\/zenodo.4020697."},{"key":"ref_28","unstructured":"Orme, D. (2022, March 01). Safe Web Safeproject.net. Available online: https:\/\/www.safeproject.net."},{"key":"ref_29","unstructured":"(2022, March 01). CEDA Archive Web Browser. Available online: https:\/\/data.ceda.ac.uk\/."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"112061","DOI":"10.1016\/j.rse.2020.112061","article-title":"LidR: An R Package for Analysis of Airborne Laser Scanning (ALS) Data","volume":"251","author":"Roussel","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"112845","DOI":"10.1016\/j.rse.2021.112845","article-title":"Aboveground biomass density models for NASA\u2019s Global Ecosystem Dynamics Investigation (GEDI) lidar mission","volume":"270","author":"Duncanson","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_32","unstructured":"Dubayah, R., Hofton, M., Blair, J., Armston, J., Tang, H., and Luthcke, S. (2021). GEDI L2A Elevation and Height Metrics Data Global Footprint Level V002, NASA EOSDIS Land Processes DAAC."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_34","unstructured":"R Core Team (2020). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4302","DOI":"10.1002\/joc.5086","article-title":"WorldClim 2: New 1-km Spatial Resolution Climate Surfaces for Global Land Areas: NEW CLIMATE SURFACES FOR GLOBAL LAND AREAS","volume":"37","author":"Fick","year":"2017","journal-title":"Int. J. Climatol."},{"key":"ref_36","unstructured":"Trabucco, A., and Zomer, R. (2022). Global Aridity Index and Potential Evapotranspiration (ET0), Figshare. Climate Database."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Safanelli, J., Poppiel, R., Ruiz, L., Bonfatti, B., Mello, F., Rizzo, R., and Dematt\u00ea, J. (2020). Terrain Analysis in Google Earth Engine: A Method Adapted for High-Performance Global-Scale Analysis. ISPRS Int. J. Geoinf., 9.","DOI":"10.3390\/ijgi9060400"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1007\/s11427-015-4847-y","article-title":"Patterns and Determinants of Wood Physical and Mechanical Properties across Major Tree Species in China","volume":"58","author":"Zhu","year":"2015","journal-title":"Sci. China Life Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., and Zeileis, A. (2008). Conditional Variable Importance for Random Forests. BMC Bioinformatics, 9.","DOI":"10.1186\/1471-2105-9-307"},{"key":"ref_40","unstructured":"Strobl, C., and Zeileis, A. (2008). Danger: High Power! Exploring the Statistical Properties of a Test for Random Forest Variable Importance, Universit\u00e4tsbibliothek der Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen."},{"key":"ref_41","unstructured":"Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., and Kenkel., B. (2022, March 01). The Caret Package. Vienna, Austria, 2012. Available online: https:\/\/cran.r-project.org\/package=caret."},{"key":"ref_42","first-page":"18","article-title":"Classification and Regression by random Forest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Arjasakusuma, S., Swahyu Kusuma, S., and Phinn, S. (2020). Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data. ISPRS Int. J. Geoinf., 9.","DOI":"10.3390\/ijgi9090507"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kacic, P., Hirner, A., and Da Ponte, E. (2021). Fusing Sentinel-1 and -2 to Model GEDI-Derived Vegetation Structure Characteristics in GEE for the Paraguayan Chaco. Remote Sens., 13.","DOI":"10.3390\/rs13245105"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4","DOI":"10.3389\/fenvs.2020.00004","article-title":"Toward Operational Mapping of Woody Canopy Cover in Tropical Savannas Using Google Earth Engine","volume":"8","author":"Anchang","year":"2020","journal-title":"Front. Environ. Sci."},{"key":"ref_47","unstructured":"Molnar, C. (2022, May 31). Interpretable Machine Learning. Github.io. Available online: https:\/\/christophm.github.io\/interpretable-ml-book\/."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1038\/s41558-021-01034-5","article-title":"Woody-Biomass Projections and Drivers of Change in Sub-Saharan Africa","volume":"11","author":"Ross","year":"2021","journal-title":"Nat. Clim. Chang."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Francini, S., D\u2019Amico, G., Vangi, E., Borghi, C., and Chirici, G. (2022). Integrating GEDI and Landsat: Spaceborne Lidar and Four Decades of Optical Imagery for the Analysis of Forest Disturbances and Biomass Changes in Italy. Sensors, 22.","DOI":"10.3390\/s22052015"},{"key":"ref_50","first-page":"012031","article-title":"Analyzing Canopy Height Variations in Secondary Tropical Forests of Malaysia Using NASA GEDI. IOP Conf. Ser","volume":"880","author":"Adrah","year":"2021","journal-title":"Earth Environ. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"112165","DOI":"10.1016\/j.rse.2020.112165","article-title":"Mapping Global Forest Canopy Height through Integration of GEDI and Landsat Data","volume":"253","author":"Potapov","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Dorado-Roda, I., Pascual, A., Godinho, S., Silva, C., Botequim, B., Rodr\u00edguez-Gonz\u00e1lvez, P., Gonz\u00e1lez-Ferreiro, E., and Guerra-Hern\u00e1ndez, J. (2021). Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests. Remote Sens., 13.","DOI":"10.3390\/rs13122279"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Adam, M., Urbazaev, M., Dubois, C., and Schmullius, C. (2020). Accuracy Assessment of GEDI Terrain Elevation and Canopy Height Estimates in European Temperate Forests: Influence of Environmental and Acquisition Parameters. Remote Sens., 12.","DOI":"10.3390\/rs12233948"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2210","DOI":"10.3390\/rs4082210","article-title":"Influence of Surface Topography on ICESat\/GLAS Forest Height Estimation and Waveform Shape","volume":"4","author":"Hilbert","year":"2012","journal-title":"Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"112760","DOI":"10.1016\/j.rse.2021.112760","article-title":"Global Canopy Height Regression and Uncertainty Estimation from GEDI LIDAR Waveforms with Deep Ensembles","volume":"268","author":"Lang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1111\/2041-210X.13686","article-title":"Study Becomes Insight: Ecological Learning from Machine Learning","volume":"12","author":"Yu","year":"2021","journal-title":"Methods Ecol. Evol."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Clark, D.B., Hurtado, J., and Saatchi, S.S. (2015). Tropical Rain Forest Structure, Tree Growth and Dynamics along a 2700-m Elevational Transect in Costa Rica. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0122905"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3171","DOI":"10.1111\/j.1365-2486.2010.02323.x","article-title":"Introduction: Elevation Gradients in the Tropics: Laboratories for Ecosystem Ecology and Global Change Research","volume":"16","author":"Malhi","year":"2010","journal-title":"Glob. Change Biol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1126\/science.1206432","article-title":"Rapid Range Shifts of Species Associated with High Levels of Climate Warming","volume":"333","author":"Chen","year":"2011","journal-title":"Science"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1111\/ele.12693","article-title":"Tree Diversity in Relation to Tree Height: Alternative Perspectives","volume":"20","author":"Givnish","year":"2017","journal-title":"Ecol. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/0169-5347(96)10034-3","article-title":"Flooding: The Survival Strategies of Plants","volume":"11","author":"Blom","year":"1996","journal-title":"Trends Ecol. Evol."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Lambers, H., Chapin, F.S., and Pons, T.L. (2008). Plant Physiological Ecology, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-78341-3"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1890\/0012-9658(2003)084[1165:PAGCRT]2.0.CO;2","article-title":"Productivity and Global Climate Revisited: The Sensitivity of Tropical Forest Growth to Precipitation","volume":"84","author":"Schuur","year":"2003","journal-title":"Ecology"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1073\/pnas.0133045100","article-title":"Cloud Cover Limits Net CO2 Uptake and Growth of a Rainforest Tree during Tropical Rainy Seasons","volume":"100","author":"Graham","year":"2003","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1080\/15481603.2021.1894832","article-title":"Maximum Height of Mountain Forests Abruptly Decreases above an Elevation Breakpoint","volume":"58","author":"Ameztegui","year":"2021","journal-title":"GIsci Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.tree.2007.09.006","article-title":"The Use of \u201caltitude\u201d in Ecological Research","volume":"22","year":"2007","journal-title":"Trends Ecol. Evol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1848","DOI":"10.1073\/pnas.1713936115","article-title":"Range Dynamics of Mountain Plants Decrease with Elevation","volume":"115","author":"Rumpf","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1126\/science.aaz4161","article-title":"A Humboldtian View of Mountains","volume":"365","author":"Spehn","year":"2019","journal-title":"Science"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s10533-010-9517-3","article-title":"Topography Strongly Affects Atmospheric Deposition and Canopy Exchange Processes in Different Types of Wet Lowland Rainforest, Southwest Costa Rica","volume":"106","author":"Hofhansl","year":"2011","journal-title":"Biogeochemistry"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2741","DOI":"10.5194\/bg-11-2741-2014","article-title":"Tropical Montane Forests Are a Larger than Expected Global Carbon Store","volume":"11","author":"Spracklen","year":"2014","journal-title":"Biogeosciences"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1111\/1365-2745.13261","article-title":"Effects of topography on tropical forest structure depend on climate context","volume":"108","author":"Muscarella","year":"2020","journal-title":"J. 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