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Auteur Anahita Khosravipour |
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Effect of slope on treetop detection using a LiDAR Canopy Height Model / Anahita Khosravipour in ISPRS Journal of photogrammetry and remote sensing, vol 104 (June 2015)
[article]
Titre : Effect of slope on treetop detection using a LiDAR Canopy Height Model Type de document : Article/Communication Auteurs : Anahita Khosravipour, Auteur ; Tiejun Wang, Auteur ; Martin Isenburg, Auteur ; Kourosh Khoshelham, Auteur Année de publication : 2015 Article en page(s) : pp 44 - 52 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] houppier
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] pente
[Termes IGN] Pinus mugo subsp. uncinata
[Termes IGN] Pinus sylvestris
[Termes IGN] semis de pointsRésumé : (auteur) Canopy Height Models (CHMs) or normalized Digital Surface Models (nDSM) derived from LiDAR data have been applied to extract relevant forest inventory information. However, generating a CHM by height normalizing the raw LiDAR points is challenging if trees are located on complex terrain. On steep slopes, the raw elevation values located on either the downhill or the uphill part of a tree crown are height-normalized with parts of the digital terrain model that may be much lower or higher than the tree stem base, respectively. In treetop detection, a highest crown return located in the downhill part may prove to be a “false” local maximum that is distant from the true treetop. Based on this observation, we theoretically and experimentally quantify the effect of slope on the accuracy of treetop detection. The theoretical model presented a systematic horizontal displacement of treetops that causes tree height to be systematically displaced as a function of terrain slope and tree crown radius. Interestingly, our experimental results showed that the effect of CHM distortion on treetop displacement depends not only on the steepness of the slope but more importantly on the crown shape, which is species-dependent. The influence of the systematic error was significant for Scots pine, which has an irregular crown pattern and weak apical dominance, but not for mountain pine, which has a narrow conical crown with a distinct apex. Based on our findings, we suggest that in order to minimize the negative effect of steep slopes on the CHM, especially in heterogeneous forest with multiple species or species which change their morphological characteristics as they mature, it is best to use raw elevation values (i.e., use the un-normalized DSM) and compute the height after treetop detection. Numéro de notice : A2015-700 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.02.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.02.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78336
in ISPRS Journal of photogrammetry and remote sensing > vol 104 (June 2015) . - pp 44 - 52[article]Generating pit-free canopy height models from airborne lidar / Anahita Khosravipour in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 9 (September 2014)
[article]
Titre : Generating pit-free canopy height models from airborne lidar Type de document : Article/Communication Auteurs : Anahita Khosravipour, Auteur ; Andrew K. Skidmore, Auteur ; Martin Isenburg, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 863 - 872 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] canopée
[Termes IGN] détection de cible
[Termes IGN] données lidar
[Termes IGN] hauteur des arbres
[Termes IGN] modélisation
[Termes IGN] semis de pointsRésumé : (Auteur)Canopy height models (CHMs) derived from lidar data have been applied to extract forest inventory parameters. However, variations in modeled height cause data pits, which form a challenging problem as they disrupt CHM smoothness, negatively affecting tree detection and subsequent biophysical measurements. These pits appear where laser beams penetrate deeply into a tree crown, hitting a lower branch or ground before producing the first return. In this study, we develop a new algorithm that generates a pit-free CHM raster, by using subsets of the lidar points to close pits. The algorithm operate robustly on high-density lidar data as well as on a thinned lidar dataset. The evaluation involves detecting the finding to those achieved by using a Gaussian smoothed CHM. The results show that our pit-free CHMs derived from high-and low-density lidar data significantly improve the accuracy of tree detection. Numéro de notice : A2014-599 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.80.9.863 En ligne : https://doi.org/10.14358/PERS.80.9.863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74889
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 9 (September 2014) . - pp 863 - 872[article]