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Unsupervised semantic and instance segmentation of forest point clouds / Di Wang in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 165 (July 2020) . - pp 86 - 97 Titre : Unsupervised semantic and instance segmentation of forest point clouds Type de document : Article/Communication Auteurs : Di Wang, Auteur Année de publication : 2020 Article en page(s) : pp 86 - 97 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] étiquetage sémantique [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] houppier [Termes descripteurs IGN] indice foliaire [Termes descripteurs IGN] interprétation automatique [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] télémètre laser terrestre Résumé : (auteur) Terrestrial Laser Scanning (TLS) has been increasingly used in forestry applications including forest inventory and plant ecology. Tree biophysical properties such as leaf area distributions and wood volumes can be accurately estimated from TLS point clouds. In these applications, a prerequisite is to properly understand the information content of large scale point clouds (i.e., semantic labelling of point clouds), so that tree-scale attributes can be retrieved. Currently, this requirement is undergoing laborious and time consuming manual works. In this work, we jointly address the problems of semantic and instance segmentation of forest point clouds. Specifically, we propose an unsupervised pipeline based on a structure called superpoint graph, to simultaneously perform two tasks: single tree isolation and leaf-wood classification. The proposed method is free from restricted assumptions of forest types. Validation using simulated data resulted in a mean Intersection over Union (mIoU) of 0.81 for single tree isolation, and an overall accuracy of 87.7% for leaf-wood classification. The single tree isolation led to a relative root mean square error (RMSE%) of 2.9% and 19.8% for tree height and crown diameter estimations, respectively. Comparisons with existing methods on other benchmark datasets showed state-of-the-art results of our method on both single tree isolation and leaf-wood classification tasks. We provide the entire framework as an open-source tool with an end-user interface. This study closes the gap for using TLS point clouds to quantify tree-scale properties in large areas, where automatic interpretation of the information content of TLS point clouds remains a crucial challenge. Numéro de notice : A2020-347 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.020 date de publication en ligne : 28/05/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.020 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95228 [article]

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Monitoring clearcutting and subsequent rapid recovery in Mediterranean coppice forests with Landsat time series / Gherardo Chirici in Annals of Forest Science [en ligne], Vol 77 n° 2 (June 2020)  

Public [article]  inAnnals of Forest Science [en ligne] > Vol 77 n° 2 (June 2020) Titre : Monitoring clearcutting and subsequent rapid recovery in Mediterranean coppice forests with Landsat time series Type de document : Article/Communication Auteurs : Gherardo Chirici, Auteur ; Francesca Giannetti, Auteur ; Erica Mazza, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] brûlis [Termes descripteurs IGN] canopée [Termes descripteurs IGN] coupe rase (sylviculture) [Termes descripteurs IGN] dégradation du signal [Termes descripteurs IGN] forêt méditerranéenne [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] reconstruction du signal [Termes descripteurs IGN] régénération (sylviculture) [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] taillis [Termes descripteurs IGN] télémètre laser aéroporté Résumé : (auteur) Key message: This work analyses the rate of recovery of the spectral signal from clearcut areas of coppice Mediterranean forests using Landsat Time Series (LTS). The analysis revealed a more rapid rate of spectral signal recovery than what was found in previous investigations in boreal and temperate forests. Context: The rate of post-disturbance vegetation recovery is an important component of forest dynamics. Aims: In this study, we analyze the recovery of the spectral signal from forest clearcut areas in Mediterranean conditions when the coppice system of forest management is applied. Methods: We used LTS surface reflectance data (1999–2015). We generated an annual reference database of clearcuts using visual interpretation and local forest inventory data, and then derived the Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) spectral trajectories for these clearcuts. From these spectral trajectories, we calculated the Years to Recovery or Y2R, the number of years it takes for a pixel to return to within a specified threshold (i.e., 70%, 80%, 90%, 100%) of its pre-disturbance value. Spectral recovery rates were then corroborated using measures of canopy height derived from airborne laser scanning (ALS) data. Results: The coppice system is associated with rapid recovery rates when compared to rates of recovery from seeds or seedlings in temperate and boreal forest conditions. We found that the Y2R derived from the spectral trajectories of post-clearcut NBR and NDVI provided similar characterizations of rapid recovery for the coppice system of forest management applied in our study area. The ALS measures of canopy height indicated that the Y2R metric accurately captured the rapid regeneration of coppice systems. Conclusion: The rapid rate of spectral recovery associated with the coppice system is 2–4 years, which contrasts with values reported in boreal and temperate forest environments, where spectral recovery was attained in approximately 10 years. NBR is an effective index for assessing rapid recovery in this forest system. Numéro de notice : A2020-293 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-00936-2 date de publication en ligne : 15/04/2020 En ligne : https://doi.org/10.1007/s13595-020-00936-2 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95123 [article]

Under-canopy UAV laser scanning for accurate forest field measurements / Eric Hyyppä in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 164 (June 2020) . - pp 41 - 60 Titre : Under-canopy UAV laser scanning for accurate forest field measurements Type de document : Article/Communication Auteurs : Eric Hyyppä, Auteur ; Juha Hyyppä, Auteur ; Teemu Hakala, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 41 - 60 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] balayage laser [Termes descripteurs IGN] canopée [Termes descripteurs IGN] densité du bois [Termes descripteurs IGN] diamètre à hauteur de poitrine [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] erreur moyenne quadratique [Termes descripteurs IGN] Finlande [Termes descripteurs IGN] forêt boréale [Termes descripteurs IGN] hauteur à la base du houppier [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] inventaire forestier local [Termes descripteurs IGN] modèle de croissance [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] Simultaneous Localisation And Mapping [Termes descripteurs IGN] télédétection aérienne [Termes descripteurs IGN] télémètre laser terrestre [Termes descripteurs IGN] télémétrie laser aéroporté [Termes descripteurs IGN] tronc Résumé : (auteur) Surveying and robotic technologies are converging, offering great potential for robotic-assisted data collection and support for labour intensive surveying activities. From a forest monitoring perspective, there are several technological and operational aspects to address concerning under-canopy flying unmanned airborne vehicles (UAV). To demonstrate this emerging technology, we investigated tree detection and stem curve estimation using laser scanning data obtained with an under-canopy flying UAV. To this end, we mounted a Kaarta Stencil-1 laser scanner with an integrated simultaneous localization and mapping (SLAM) system on board an UAV that was manually piloted with the help of video goggles receiving a live video feed from the onboard camera of the UAV. Using the under-canopy flying UAV, we collected SLAM-corrected point cloud data in a boreal forest on two 32 m 32 m test sites that were characterized as sparse ( = 42 trees) and obstructed ( = 43 trees), respectively. Novel data processing algorithms were applied for the point clouds in order to detect the stems of individual trees and to extract their stem curves and diameters at breast height (DBH). The estimated tree attributes were compared against highly accurate field reference data that was acquired semi-manually with a multi-scan terrestrial laser scanner (TLS). The proposed method succeeded in detecting 93% of the stems in the sparse plot and 84% of the stems in the obstructed plot. In the sparse plot, the DBH and stem curve estimates had a root-mean-squared error (RMSE) of 0.60 cm (2.2%) and 1.2 cm (5.0%), respectively, whereas the corresponding values for the obstructed plot were 0.92 cm (3.1%) and 1.4 cm (5.2%). By combining the stem curves extracted from the under-canopy UAV laser scanning data with tree heights derived from above-canopy UAV laser scanning data, we computed stem volumes for the detected trees with a relative RMSE of 10.1% in both plots. Thus, the combination of under-canopy and above-canopy UAV laser scanning allowed us to extract the stem volumes with an accuracy comparable to the past best studies based on TLS in boreal forest conditions. Since the stems of several spruces located on the test sites suffered from severe occlusion and could not be detected with the stem-based method, we developed a separate work flow capable of detecting trees with occluded stems. The proposed work flow enabled us to detect 98% of trees in the sparse plot and 93% of the trees in the obstructed plot with a 100% correction level in both plots. A key benefit provided by the under-canopy UAV laser scanner is the short period of time required for data collection, currently demonstrated to be much faster than the time required for field measurements and TLS. The quality of the measurements acquired with the under-canopy flying UAV combined with the demonstrated efficiency indicates operational potential for supporting fast and accurate forest resource inventories. Numéro de notice : A2020-240 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.03.021 date de publication en ligne : 11/04/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.03.021 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94994 [article]

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How far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study / Enrico Borgogno Mondino in International Journal of Remote Sensing IJRS, vol 41 n°12 (20 - 30 March 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n°12 (20 - 30 March 2020) . - pp 4551 - 4569 Titre : How far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study Type de document : Article/Communication Auteurs : Enrico Borgogno Mondino, Auteur ; Vanina Fissore, Auteur ; Michael J. Falkowski, Auteur ; Brian Palik, Auteur Année de publication : 2020 Article en page(s) : pp 4551 - 4569 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] auscultation topographique [Termes descripteurs IGN] diamètre des arbres [Termes descripteurs IGN] données dendrométriques [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] feuillu [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] inventaire forestier local [Termes descripteurs IGN] Minnesota (Etats-Unis) [Termes descripteurs IGN] modèle d'erreur [Termes descripteurs IGN] pinophyta [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] structure d'un peuplement forestier [Termes descripteurs IGN] surface forestière [Termes descripteurs IGN] télémètre laser aéroporté Résumé : (auteur) Aerial discrete return LiDAR (Light Detection And Ranging) technology (ALS – Aerial Laser Scanner) is now widely used for forest characterization due to its high accuracy in measuring vertical and horizontal forest structure. Random and systematic errors can still occur and these affect the native point cloud, ultimately degrading ALS data accuracy, especially when adopting datasets that were not natively designed for forest applications. A detailed understanding of how uncertainty of ALS data could affect the accuracy of derivable forest metrics (e.g. tree height, stem diameter, basal area) is required, looking for eventual error biases that can be possibly modelled to improve final accuracy. In this work a low-density ALS dataset, originally acquired by the State of Minnesota (USA) for non-forestry related purposes (i.e. topographic mapping), was processed attempting to characterize forest inventory parameters for the Cutfoot Sioux Experimental Forest (north-central Minnesota, USA). Since accuracy of estimates strictly depends on the applied species-specific dendrometric models a first required step was to map tree species over the forest. A rough classification, aiming at separating conifers from broadleaf, was achieved by processing a Landsat 8 OLI (Operational Land Imager) scene. ALS-derived forest metrics initially greatly overestimated those measured at the ground in 230 plots. Conversely, ALS-derived tree density was greatly underestimated. To reduce ALS uncertainty, trees belonging to the dominated plane were removed from the ground dataset, assuming that they could not properly be detected by low-density ALS measures. Consequently, MAE (Mean Absolute Error) values significantly decreased to 4.0 m for tree height and to 0.19 cm for diameter estimates. Remaining discrepancies were related to a bias affecting the native ALS point cloud, which was modelled and removed. Final MAE values were 1.32 m for tree height, 0.08 m for diameter, 8.5 m2 ha−1 for basal area, and 0.06 m for quadratic mean diameter. Specifically focusing on tree height and diameter estimates, the significance of differences between ground and ALS estimates was tested relative to the expected ‘best accuracy’. Results showed that after correction: 94.35% of tree height differences were lower than the corresponding reference value (2.86 m); 70% of tree diameter differences were lower than the corresponding reference value (4.5 cm for conifers and 6.8 cm for broadleaf). Finally, forest parameters were computed for the whole Cutfoot Sioux Experimental Forest. Main findings include: 1) all forest estimates based on a low-density ALS point cloud can be derived at plot level and not at a tree level; 2) tree height estimates obtained by low-density ALS point clouds at the plot level are highly reasonably accurate only after testing and modelling eventual error bias; 3) diameter, basal area, and quadratic mean diameter estimates have large uncertainties, suggesting the need for a higher point density and, probably, a better mapping of tree species (if possible) than achieved with a remote sensing-based approach. Numéro de notice : A2020-450 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2020.1723173 date de publication en ligne : 20/02/2020 En ligne : https://doi.org/10.1080/01431161.2020.1723173 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95535 [article]

Streambank topography: an accuracy assessment of UAV-based and traditional 3D reconstructions / Benjamin U. Meinen in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n° 1 (01 - 08 janvier 2020) . - pp 1 - 18 Titre : Streambank topography: an accuracy assessment of UAV-based and traditional 3D reconstructions Type de document : Article/Communication Auteurs : Benjamin U. Meinen, Auteur ; Derek T. Robinson, Auteur Année de publication : 2020 Article en page(s) : pp 1 - 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] carte topographique [Termes descripteurs IGN] cours d'eau [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] érosion [Termes descripteurs IGN] hétérogénéité spatiale [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] photogrammétrie aérienne [Termes descripteurs IGN] reconstruction 3D [Termes descripteurs IGN] rive [Termes descripteurs IGN] structure-from-motion [Termes descripteurs IGN] télémètre laser terrestre Résumé : (auteur) Highly accurate digital surface models are an essential part of change-over-time analyses for monitoring erosion processes. Streambank topography presents a unique challenge for surface mapping due to dense riparian vegetation, canopy cover, and rapidly changing elevation values. The spatial heterogeneity of stream corridors has made the calculation of streambank erosion across larger spatial extents difficult. Contemporary technologies such as terrestrial laser scanners (TLS) and unmanned aerial vehicles (UAVs) offer new approaches for streambank topography mapping at very high spatial resolutions across varying spatial extents. To evaluate the accuracy of different technologies for streambank topography mapping, we compared streambank surface models derived via a UAV using structure-from-motion and from traditional aerial photogrammetry (i.e. Southwestern Ontario Orthoimagery Project; SWOOP) to that of a TLS benchmark across seven streambank segments. Additional comparisons were made for 22 manually measured stream transects to that of a TLS benchmark. Compared to our benchmark, the UAV-derived streambank surface model was the most accurate with an average root-mean-square-error of 0.104 m. Errors in the UAV surface model were correlated with georeferencing error. The UAV had an average 52% success rate for reconstructing the streambank topography across all field campaigns and was able to map up to 2037 m of streambank in one hour. The streambank surface model derived from traditional aerial photogrammetry and manual transect measurements had average root-mean-square-errors of 0.238 m and 0.274 m respectively. Both aerially-derived surface models tended to over measure elevation values compared to the TLS, whereas manual transect measurements consistently under measured elevation. Numéro de notice : A2020-208 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2019.1597294 date de publication en ligne : 26/03/2019 En ligne : https://doi.org/10.1080/01431161.2019.1597294 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94890 [article]

Automatic extraction of accurate 3D tie points for trajectory adjustment of mobile laser scanners using aerial imagery / Zille Hussnain in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

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Empirical stochastic model of detected target centroids: Influence on registration and calibration of terrestrial laser scanners / Tomislav Medic in Journal of applied geodesy, vol 13 n° 3 (July 2019)  

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Mise en place d’un système sondeur multifaisceaux dans une embarcation légère semi-rigide pour campagne de mesure bathymétrique et couplage avec un scanner terrestre, GNSS et INS / Alexandre Girard (2019)

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Methods for quantification of systematic distance deviations under incidence angle with scanning total stations / Miriam Zámečníková in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)  

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Comparing nearest neighbor configurations in the prediction of species-specific diameter distributions / Janne Raty in Annals of Forest Science [en ligne], vol 75 n° 1 (March 2018)  

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Evaluation of the range accuracy and the radiometric calibration of multiple terrestrial laser scanning instruments for data interoperability / Kim Calders in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)  

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Mise en place de l’utilisation d’instruments de mesure 3D dans le cadre d’auscultations de barrages / Cyril Cadiou (2017)  

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Documentation et gestion d'un site archéologique : cas de Masseria Cesareo / D. Constantino in Géomatique expert, n° 109 (mars - avril 2016)

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Qualification de la précision de données topographiques issues d’acquisitions par méthode scanner laser dynamique ferroporté / Audrey Jacquin in XYZ, n° 146 (mars - mai 2016)

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Laser scanner in a backpack / Antero Kukko in GIM international [en ligne], vol 30 n° 1 (January 2016)

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