Centre de documentation
scientifique

Dépouillements

A fully-automated approach to land cover mapping with airborne LiDAR and high resolution multispectral imagery in a forested suburban landscape / Jason R. Parent 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 18 - 29 Titre : A fully-automated approach to land cover mapping with airborne LiDAR and high resolution multispectral imagery in a forested suburban landscape Type de document : Article/Communication Auteurs : Jason R. Parent, Auteur ; John C. Volin, Auteur ; Daniel L. Civco, Auteur Année de publication : 2015 Article en page(s) : pp 18 - 29 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] carte d'occupation du sol [Termes IGN] classification automatique [Termes IGN] classification pixellaire [Termes IGN] Connecticut (Etats-Unis) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] feuillu [Termes IGN] forêt ripicole [Termes IGN] image multibande [Termes IGN] Pinophyta Résumé : (auteur) Information on land cover is essential for guiding land management decisions and supporting landscape-level ecological research. In recent years, airborne light detection and ranging (LiDAR) and high resolution aerial imagery have become more readily available in many areas. These data have great potential to enable the generation of land cover at a fine scale and across large areas by leveraging 3-dimensional structure and multispectral information. LiDAR and other high resolution datasets must be processed in relatively small subsets due to their large volumes; however, conventional classification techniques cannot be fully automated and thus are unlikely to be feasible options when processing large high-resolution datasets. In this paper, we propose a fully automated rule-based algorithm to develop a 1 m resolution land cover classification from LiDAR data and multispectral imagery. The algorithm we propose uses a series of pixel- and object-based rules to identify eight vegetated and non-vegetated land cover features (deciduous and coniferous tall vegetation, medium vegetation, low vegetation, water, riparian wetlands, buildings, low impervious cover). The rules leverage both structural and spectral properties including height, LiDAR return characteristics, brightness in visible and near-infrared wavelengths, and normalized difference vegetation index (NDVI). Pixel-based properties were used initially to classify each land cover class while minimizing omission error; a series of object-based tests were then used to remove errors of commission. These tests used conservative thresholds, based on diverse test areas, to help avoid over-fitting the algorithm to the test areas. The accuracy assessment of the classification results included a stratified random sample of 3198 validation points distributed across 30 1 × 1 km tiles in eastern Connecticut, USA. The sample tiles were selected in a stratified random manner from locations representing the full range of rural to urban landscapes in eastern Connecticut. The overall land cover accuracy was 93% with accuracies exceeding 90% for deciduous trees, low vegetation, water, buildings, and low impervious cover. Slight confusion occurred between coniferous and deciduous trees; major confusion occurred between water and riparian wetlands; and moderate confusion occurred between medium vegetation and other vegetation classes. The algorithm was robust for the forested suburban landscape of eastern Connecticut, which is typical for much of the northeastern U.S., and the algorithm shows promise for applications in similar landscapes with similar datasets. Further research is needed to test the applicability of the algorithm to more diverse landscapes as well as with different LiDAR and multispectral datasets. Numéro de notice : A2015-698 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.02.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.02.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78334 [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]

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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 104 (June 2015) . - pp 44 - 52 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 points Résumé : (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 [article]

UAV photogrammetry for topographic monitoring of coastal areas / J.A. Gonçalves 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 101 - 111 Titre : UAV photogrammetry for topographic monitoring of coastal areas Type de document : Article/Communication Auteurs : J.A. Gonçalves, Auteur ; R. Henriques, Auteur Année de publication : 2015 Article en page(s) : pp 101 - 111 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] Agisoft Photoscan [Termes IGN] chambre non métrique [Termes IGN] drone [Termes IGN] dune [Termes IGN] modèle numérique de surface [Termes IGN] photogrammétrie numérique [Termes IGN] plage [Termes IGN] point d'appui [Termes IGN] Portugal [Termes IGN] semis de points [Termes IGN] surveillance du littoral Résumé : (auteur) Coastal areas suffer degradation due to the action of the sea and other natural and human-induced causes. Topographical changes in beaches and sand dunes need to be assessed, both after severe events and on a regular basis, to build models that can predict the evolution of these natural environments. This is an important application for airborne LIDAR, and conventional photogrammetry is also being used for regular monitoring programs of sensitive coastal areas. This paper analyses the use of unmanned aerial vehicles (UAV) to map and monitor sand dunes and beaches. A very light plane (SwingletCam) equipped with a very cheap, non-metric camera was used to acquire images with ground resolutions better than 5 cm. The Agisoft Photoscan software was used to orientate the images, extract point clouds, build a digital surface model and produce orthoimage mosaics. The processing, which includes automatic aerial triangulation with camera calibration and subsequent model generation, was mostly automated. To achieve the best positional accuracy for the whole process, signalised ground control points were surveyed with a differential GPS receiver. Two very sensitive test areas on the Portuguese northwest coast were analysed. Detailed DSMs were obtained with 10 cm grid spacing and vertical accuracy (RMS) ranging from 3.5 to 5.0 cm, which is very similar to the image ground resolution (3.2–4.5 cm). Where possible to assess, the planimetric accuracy of the orthoimage mosaics was found to be subpixel. Within the regular coastal monitoring programme being carried out in the region, UAVs can replace many of the conventional flights, with considerable gains in the cost of the data acquisition and without any loss in the quality of topographic and aerial imagery data. Numéro de notice : A2015--002 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.02.009 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.02.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78337 [article]

Validation of canopy height profile methodology for small-footprint full-waveform airborne LiDAR data in a discontinuous canopy environment / Karolina D. Fieber 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 144 - 157 Titre : Validation of canopy height profile methodology for small-footprint full-waveform airborne LiDAR data in a discontinuous canopy environment Type de document : Article/Communication Auteurs : Karolina D. Fieber, Auteur ; Ian J. Davenport, Auteur ; Mihai A. Tanase, Auteur ; James M. Ferryman, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 144 - 157 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Australie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forme d'onde pleine [Termes IGN] hauteur des arbres [Termes IGN] Leaf Area Index [Termes IGN] modèle numérique de surface de la canopée Résumé : (auteur) A Canopy Height Profile (CHP) procedure presented in Harding et al. (2001) for large footprint LiDAR data was tested in a closed canopy environment as a way of extracting vertical foliage profiles from LiDAR raw-waveform. In this study, an adaptation of this method to small-footprint data has been shown, tested and validated in an Australian sparse canopy forest at plot- and site-level. Further, the methodology itself has been enhanced by implementing a dataset-adjusted reflectance ratio calculation according to Armston et al. (2013) in the processing chain, and tested against a fixed ratio of 0.5 estimated for the laser wavelength of 1550 nm. As a by-product of the methodology, effective leaf area index (LAIe) estimates were derived and compared to hemispherical photography values. To assess the influence of LiDAR aggregation area size on the estimates in a sparse canopy environment, LiDAR CHPs and LAIes were generated by aggregating waveforms to plot- and site-level footprints (plot/site-aggregated) as well as in 5 m grids (grid-processed). LiDAR profiles were then compared to field biomass profiles generated based on field tree measurements. The correlation between field and LiDAR profiles was very high, with a mean R2 of 0.75 at plot-level and 0.86 at site-level for 55 plots and the corresponding 11 sites. Gridding had almost no impact on the correlation between LiDAR and field profiles (only marginally improvement), nor did the dataset-adjusted reflectance ratio. However, gridding and the dataset-adjusted reflectance ratio were found to improve the correlation between raw-waveform LiDAR and hemispherical photography LAIe estimates, yielding the highest correlations of 0.61 at plot-level and of 0.83 at site-level. This proved the validity of the approach and superiority of dataset-adjusted reflectance ratio of Armston et al. (2013) over a fixed ratio of 0.5 for LAIe estimation, as well as showed the adequacy of small-footprint LiDAR data for LAIe estimation in discontinuous canopy forests. Numéro de notice : A2015-702 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.03.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.03.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78338 [article]