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Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape / M.L. Clarke in Remote sensing of environment, vol 91 n° 1 (15/05/2004)  

Public [article]  inRemote sensing of environment > vol 91 n° 1 (15/05/2004) . - pp 68 - 89 Titre : Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape Type de document : Article/Communication Auteurs : M.L. Clarke, Auteur ; D. Clark, Auteur ; D.A. Roberts, Auteur Année de publication : 2004 Article en page(s) : pp 68 - 89 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] corrélation linéaire [Termes IGN] données lidar [Termes IGN] erreur moyenne quadratique [Termes IGN] forêt tropicale [Termes IGN] interpolation inversement proportionnelle à la distance [Termes IGN] modèle numérique de surface [Termes IGN] modèle numérique de terrain [Termes IGN] sous-bois [Termes IGN] télémétrie laser aéroporté Résumé : (Auteur) Meso-scale digital terrain models (DTMs) and canopy-height estimates, or digital canopy models (DCMs), are two lidar products that have immense potential for research in tropical rain forest (TRF) ecology and management. In this study, we used a small-footprint lidar sensor (airborne laser scanner, ALS) to estimate sub-canopy elevation and canopy height in an evergreen tropical rain forest. A fully automated, local-minima algorithm was developed to separate lidar ground returns from overlying vegetation returns. We then assessed inverse distance weighted (IDW) and ordinary kriging (OK) geostatistical techniques for the interpolation of a sub-canopy DTM. OK was determined to be a superior interpolation scheme because it smoothed fine-scale variance created by spurious understory heights in the ground-point dataset. The final DTM had a linear correlation of 1.00 and a root-mean-square error (RMSE) of 2.29 m when compared against 3859 well-distributed ground-survey points. In old-growth forests, RMS error on steep slopes was 0.67 m greater than on flat slopes. On flatter slopes, variation in vegetation complexity associated with land use caused highly significant differences in DTM error distribution across the landscape. The highest DTM accuracy observed in this study was 0.58-m RMSE, under flat, open-canopy areas with relatively smooth surfaces. Lidar ground retrieval was complicated by dense, multi-layered evergreen canopy in old-growth forests, causing DTM overestimation that increased RMS error to 1.95 m. A DCM was calculated from the original lidar surface and the interpolated DTM. Individual and plot-scale heights were estimated from DCM metrics and compared to field data measured using similar spatial supports and metrics. For old-growth forest emergent trees and isolated pasture trees greater than 20 in tall, individual tree heights were underestimated and had 3.67- and 2.33-m mean absolute error (MAE), respectively. Linear-regression models explained 51% (4.15-m RMSE) and 95% (2.41-m RMSE) of the variance, respectively. It was determined that improved elevation and field-height estimation in pastures explained why individual pasture trees could be estimated more accurately than old-growth trees. Mean height of tree stems in 32 young agroforestry plantation plots (0.38 to 18.53 m tall) was estimated with a mean absolute error of 0.90 m (r 2 = 0,97; 1.08-m model RMSE) using the mean of lidar returns in the plot. As in other small-footprint lidar studies, plot mean height was underestimated; however, our plot-scale results have stronger linear models for tropical, leaf-on hardwood trees than has been previously reported for temperate-zone conifer and deciduous hardwoods. Numéro de notice : A2004-237 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.02.008 En ligne : https://doi.org/10.1016/j.rse.2004.02.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26764 [article]