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Forest inventory sensitivity to UAS-based image processing algorithms / Bonifasius Maturbongs in Annals of forest research, vol 62 n° 1 (January - June 2019)  

Public [article]  inAnnals of forest research > vol 62 n° 1 (January - June 2019) . - pp 87 - 108 Titre : Forest inventory sensitivity to UAS-based image processing algorithms Type de document : Article/Communication Auteurs : Bonifasius Maturbongs, Auteur ; Michael G. Wing, Auteur ; Bogdan M. Strimbu, Auteur ; Jon Burnett, Auteur Année de publication : 2019 Article en page(s) : pp 87 - 108 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] données lidar [Termes IGN] gestion forestière [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] photographie aérienne [Termes IGN] Pseudotsuga menziesii [Termes IGN] segmentation dynamique [Termes IGN] semis de points [Termes IGN] structure-from-motion Résumé : (auteur) Frequent and accurate estimation of forest structure parameters, such as number of trees per hectare or total height, are mandatory for sustainable forest management. Unmanned aircraft system (UAS) equipped with inexpensive sensors can be used to monitor and measure forest structure. The detailed information provided by the UAS allows tree level forest inventory. However, tree identification depends on a variety of parameters defining the image processing and tree segmentation algorithms. The objective of our study was to identify parameter combinations that accurately delineated trees and their heights. We evaluated the impact of different tree segmentation and point cloud generation algorithms on forest inventory from imagery collected with a UAS over a mature Douglas-fir plantation forest. We processed the images with two commonly used commercial software packages, Agisoft PhotoScan and Pix4Dmapper, both implementing image processing algorithms called Structure from Motion. For each software we generated photogrammetric point clouds by varying the parameters defining the implementation. We segmented individual trees and heights using three tree algorithms: Variable Window Filter, Graph-Theoretical, and Watershed Segmentation. We assessed the impact of image processing algorithms on forest inventory by comparing the estimated trees with trees manually identified from the point clouds. We found that the type of tree segmentation and image processing algorithms have a significant effect in accurately identifying trees. For tree height estimation, we found strong evidence that image processing algorithms had significant effects, whereas tree segmentation algorithms did not significantly affect tree height estimation.These findings may be of interest to others that are using high-resolution spatial imagery to estimate forest inventory parameters. Numéro de notice : A2019-580 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.15287/afr.2018.1282 Date de publication en ligne : 30/07/2019 En ligne : http://dx.doi.org/10.15287/afr.2018.1282 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94487 [article]

A posteriori bias correction of three models used for environmental reporting / Bogdan M. Strimbu in Forestry, an international journal of forest research, vol 91 n° 1 (January 2018)  

Public [article]  inForestry, an international journal of forest research > vol 91 n° 1 (January 2018) . - pp 49 - 62 Titre : A posteriori bias correction of three models used for environmental reporting Type de document : Article/Communication Auteurs : Bogdan M. Strimbu, Auteur ; Alexandru Amarioarei, Auteur ; John Paul McTague, Auteur ; Mihaela M. Paun, Auteur Année de publication : 2018 Article en page(s) : pp 49 - 62 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] correction [Termes IGN] erreur systématique [Termes IGN] Louisiane (Etats-Unis) [Termes IGN] modèle mathématique [Termes IGN] Oregon (Etats-Unis) [Termes IGN] Pinus ponderosa [Termes IGN] Pseudotsuga menziesii [Termes IGN] résidu [Termes IGN] Roumanie [Termes IGN] Texas (Etats-Unis) Résumé : (Auteur) A plethora of forest models were developed by transforming the dependent variable, which introduces bias if appropriate corrections are not applied when back-transformed. Many recognized models are still biased and the original data sets are no longer available, which suggests ad hoc bias corrections. The present research presents a procedure for bias correction in the absence of needed information from summary statistics. Additionally, we developed a realistic correction of the square root transformation based on a truncated normal distribution. The transformations considered in this study are the logarithm, the square root and arcsine square root. Using simulated data we found that uncorrected back-transformation created biases by as much as 100 percent. The generated data revealed that depending on available information, that bias can still be present after correction. In addition to generated data we corrected the site index of Douglas-fir and ponderosa pine in Oregon USA, tree volume of 27 species from Romania, stand merchantable volume for longleaf pine in Louisiana and East Texas USA, and canopy fuel weight in Washington USA. Using only the available information, the unbiased back-transformed estimates can change from ≤1 percent (i.e. the site index and canopy fuel weight) to ≥⅓ (tree and stand volume). Numéro de notice : A2018-631 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpx032 Date de publication en ligne : 10/08/2017 En ligne : https://doi.org/10.1093/forestry/cpx032 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93217 [article]

A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data / Victor F. Strimbu in ISPRS Journal of photogrammetry and remote sensing, vol 104 (June 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 104 (June 2015) . - pp 30 - 43 Titre : A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data Type de document : Article/Communication Auteurs : Victor F. Strimbu, Auteur ; Bogdan M. Strimbu, Auteur Année de publication : 2015 Article en page(s) : pp 30 - 43 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données localisées 3D [Termes IGN] extraction de la végétation [Termes IGN] graphe [Termes IGN] houppier [Termes IGN] Louisiane (Etats-Unis) [Termes IGN] segmentation [Termes IGN] structure hiérarchique de données Résumé : (auteur) This work proposes a segmentation method that isolates individual tree crowns using airborne LiDAR data. The proposed approach captures the topological structure of the forest in hierarchical data structures, quantifies topological relationships of tree crown components in a weighted graph, and finally partitions the graph to separate individual tree crowns. This novel bottom-up segmentation strategy is based on several quantifiable cohesion criteria that act as a measure of belief on weather two crown components belong to the same tree. An added flexibility is provided by a set of weights that balance the contribution of each criterion, thus effectively allowing the algorithm to adjust to different forest structures. The LiDAR data used for testing was acquired in Louisiana, inside the Clear Creek Wildlife management area with a RIEGL LMS-Q680i airborne laser scanner. Three 1 ha forest areas of different conditions and increasing complexity were segmented and assessed in terms of an accuracy index (AI) accounting for both omission and commission. The three areas were segmented under optimum parameterization with an AI of 98.98%, 92.25% and 74.75% respectively, revealing the excellent potential of the algorithm. When segmentation parameters are optimized locally using plot references the AI drops to 98.23%, 89.24%, and 68.04% on average with plot sizes of 1000 m2 and 97.68%, 87.78% and 61.1% on average with plot sizes of 500 m2. More than introducing a segmentation algorithm, this paper proposes a powerful framework featuring flexibility to support a series of segmentation methods including some of those recurring in the tree segmentation literature. The segmentation method may extend its applications to any data of topological nature or data that has a topological equivalent. Numéro de notice : A2015-699 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.01.018 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.01.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78335 [article]