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The utility of fused airborne laser scanning and multispectral data for improved wind damage risk assessment over a managed forest landscape in Finland / Ranjith Gopalakrishnan in Annals of Forest Science [en ligne], vol 77 n° 4 (December 2020)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 77 n° 4 (December 2020) . - 18 p. Titre : The utility of fused airborne laser scanning and multispectral data for improved wind damage risk assessment over a managed forest landscape in Finland Type de document : Article/Communication Auteurs : Ranjith Gopalakrishnan, Auteur ; Petteri Packalen, Auteur ; Veli-Pekka Ikonen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] Finlande [Termes descripteurs IGN] forêt boréale [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] paysage forestier [Termes descripteurs IGN] risque naturel [Termes descripteurs IGN] tempête [Termes descripteurs IGN] vent [Termes descripteurs IGN] zone à risque Résumé : (auteur) Key message: The potential of airborne laser scanning (ALS) and multispectral remote sensing data to aid in generating improved wind damage risk maps over large forested areas is demonstrated. This article outlines a framework to generate such maps, primarily utilizing the horizontal structural information contained in the ALS data. Validation was done over an area in Eastern Finland that had experienced sporadic wind damage. Context: Wind is the most prominent disturbance element for Finnish forests. Hence, tools are needed to generate wind damage risk maps for large forested areas, and their possible changes under planned silvicultural operations. Aims: (1) How effective are ALS-based forest variables (e.g. distance to upwind forest stand edge, gap size) for identifying high wind damage risk areas? (2) Can robust estimates of predicted critical wind speeds for uprooting of trees be derived from these variables? (3) Can these critical wind speed estimates be improved using wind multipliers, which factor in topography and terrain roughness effects? Methods: We first outline a framework to generate several wind damage risk–related parameters from remote sensing data (ALS + multispectral). Then, we assess if such parameters have predictive power. That is, whether they help differentiate between damaged and background points. This verification exercise used 42 wind damaged points spread over a large area. Results: Parameters derived from remote sensing data are shown to have predictive power. Risk models based on critical wind speeds are not that robust, but show potential for improvement. Conclusion: Overall, this work described a framework to get several wind risk–related parameters from remote sensing data. These parameters are shown to have potential in generating wind damage risk maps over large forested areas. Numéro de notice : A2020-629 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-00992-8 date de publication en ligne : 09/10/2020 En ligne : https://doi.org/10.1007/s13595-020-00992-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96045 [article]

Active and incremental learning for semantic ALS point cloud segmentation / Yaping Lin in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 169 (November 2020) . - pp 73 - 92 Titre : Active and incremental learning for semantic ALS point cloud segmentation Type de document : Article/Communication Auteurs : Yaping Lin, Auteur ; M. George Vosselman, Auteur ; Yanpeng Cao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 73 - 92 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] entropie [Termes descripteurs IGN] incertitude des données [Termes descripteurs IGN] itération [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] semis de points Résumé : (auteur) Supervised training of a deep neural network for semantic segmentation of point clouds requires a large amount of labelled data. Nowadays, it is easy to acquire a huge number of points with high density in large-scale areas using current LiDAR and photogrammetric techniques. However it is extremely time-consuming to manually label point clouds for model training. In this paper, we propose an active and incremental learning strategy to iteratively query informative point cloud data for manual annotation and the model is continuously trained to adapt to the newly labelled samples in each iteration. We evaluate the data informativeness step by step and effectively and incrementally enrich the model knowledge. The data informativeness is estimated by two data dependent uncertainty metrics (point entropy and segment entropy) and one model dependent metric (mutual information). The proposed methods are tested on two datasets. The results indicate the proposed uncertainty metrics can enrich current model knowledge by selecting informative samples, such as considering points with difficult class labels and choosing target objects with various geometries in the labelled training pool. Compared to random selection, our metrics provide valuable information to significantly reduce the labelled training samples. In contrast with training from scratch, the incremental fine-tuning strategy significantly save the training time. Numéro de notice : A2020-638 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.09.003 date de publication en ligne : 14/09/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.09.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96061 [article]

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Effects of radiometric correction on cover type and spatial resolution for modeling plot level forest attributes using multispectral airborne LiDAR data / Wai Yeung Yan in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 169 (November 2020) . - pp 152 - 165 Titre : Effects of radiometric correction on cover type and spatial resolution for modeling plot level forest attributes using multispectral airborne LiDAR data Type de document : Article/Communication Auteurs : Wai Yeung Yan, Auteur ; Karin Y. Van Ewijk, Auteur ; Paul M. Treitz, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 152 - 165 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] artefact [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] correction d'image [Termes descripteurs IGN] correction radiométrique [Termes descripteurs IGN] couvert forestier [Termes descripteurs IGN] délignage [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] forêt tempérée [Termes descripteurs IGN] intensité lumineuse [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] Ontario [Termes descripteurs IGN] peuplement mélangé [Termes descripteurs IGN] restauration d'image [Termes descripteurs IGN] semis de points Résumé : (auteur) In order to use the airborne LiDAR intensity in conjunction with the height-derived information for forest modeling and classification purposes, radiometric correction is deemed to be a critical pre-processing requirement. In this study, we implemented a LiDAR scan line correction (LSLC) and an overlap-driven intensity correction (OIC) to remove the stripe artifacts that appeared within the individual flight lines and overlapping regions of adjacent flight lines of a multispectral LiDAR dataset. We tested the effectiveness of these corrections in various land/forest cover types in a temperate mixed mature forest in Ontario, Canada. Subsequently, we predicted three plot level forest attributes, i.e., basal area (BA), quadratic mean diameter (QMD), and trees per hectare (TPH), using different combinations of height and intensity metrics derived from the multispectral LiDAR data to determine if LiDAR intensity data (corrected and uncorrected) improved predictions over models that utilize LiDAR height-derived information only. The results show that LSLC can reduce the intensity banding effect by 0.19–23.06% in channel 1 (1550 nm) and 4.79–66.87% in channel 2 (1064 nm) at the close-to-nadir region. The combined effect of LSLC and OIC is notable particularly at the swath edges. After implementing both methods, the intensity homogeneity is improved by 5.51–12% in channel 1, 6.37–42.93% in channel 2, and 6.48–33.77% in channel 3 (532 nm). Our results further demonstrate that BA and QMD predictions in our study area gained little from additional LiDAR intensity metrics. Intensity metrics from multiple LiDAR channels and intensity normalized difference vegetation index (NDVI) metrics did improve TPH predictions up to 7.2% in RMSE and 1.8% in Bias. However, our lowest TPH prediction errors (%RMSE) were still approximately 10% larger than for BA and QMD. We observed only minimal differences in plot level BA, QMD, and TPH predictions between models using original and corrected intensity. We attribute this to: (i) the lower effectiveness of radiometric correction in forest versus grassland, bare soil and road land cover types, and (ii) the effect of spatial resolution on intensity noise. Numéro de notice : A2020-640 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.09.001 date de publication en ligne : 22/09/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.09.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96063 [article]

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081-2020112	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Topographic connection method for automated mapping of landslide inventories, study case: semi urban sub-basin from Monterrey, Northeast of México / Nelly L. Ramirez Serrato in Geocarto international, vol 35 n° 15 ([01/11/2020])  

Public [article]  inGeocarto international > vol 35 n° 15 [01/11/2020] . - pp 1706 - 1721 Titre : Topographic connection method for automated mapping of landslide inventories, study case: semi urban sub-basin from Monterrey, Northeast of México Type de document : Article/Communication Auteurs : Nelly L. Ramirez Serrato, Auteur ; Fabiola D. Yepez-Rincon, Auteur ; Adrian L. Ferrino Fierro, Auteur Année de publication : 2020 Article en page(s) : pp 1706 - 1721 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] chaîne de traitement [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] inventaire [Termes descripteurs IGN] Mexique [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] visualisation 3D Résumé : (auteur) By nature, slopes are conformed by forces that are in constant balance. Altering this natural balance causes the sliding of soil towards lower zones. Landslides are a constant danger that compromises the general welfare of society. Landslides mapping is especially important for urban areas or development plans. The innovative aspect of this study is the creation of the Topographic Connection Method (TPCM) to automatically map landslides using two types of landslides 1) falls and 2) flows. TPCM cartography results were compared to a previously proven method (Contour Connection Method), as well as to the manual inventory method. Each method was run four times to locate changes through time by using satellite imagery, digital elevations models and 3D relief visualizations with data covering a period from 2012 to 2017. Results showed both falls and flows with all three methods and demonstrated that TPCM can improve mapping accuracy by up to 14%. Numéro de notice : A2020-659 Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581269 date de publication en ligne : 01/04/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96132 [article]

Hierarchical instance recognition of individual roadside trees in environmentally complex urban areas from UAV laser scanning point clouds / Yongjun Wang in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 10 (October 2020) . - 26 p. Titre : Hierarchical instance recognition of individual roadside trees in environmentally complex urban areas from UAV laser scanning point clouds Type de document : Article/Communication Auteurs : Yongjun Wang, Auteur ; Tengping Jiang, Auteur ; Jing Liu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 26 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] algorithme de filtrage [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] arbre hors forêt [Termes descripteurs IGN] arbre urbain [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] gestion urbaine [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] optimisation (mathématiques) [Termes descripteurs IGN] reconnaissance d'objets [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] voxel [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Individual tree segmentation is essential for many applications in city management and urban ecology. Light Detection and Ranging (LiDAR) system acquires accurate point clouds in a fast and environmentally-friendly manner, which enables single tree detection. However, the large number of object categories and occlusion from nearby objects in complex environment pose great challenges in urban tree inventory, resulting in omission or commission errors. Therefore, this paper addresses these challenges and increases the accuracy of individual tree segmentation by proposing an automated method for instance recognition urban roadside trees. The proposed algorithm was implemented of unmanned aerial vehicles laser scanning (UAV-LS) data. First, an improved filtering algorithm was developed to identify ground and non-ground points. Second, we extracted tree-like objects via labeling on non-ground points using a deep learning model with a few smaller modifications. Unlike only concentrating on the global features in previous method, the proposed method revises a pointwise semantic learning network to capture both the global and local information at multiple scales, significantly avoiding the information loss in local neighborhoods and reducing useless convolutional computations. Afterwards, the semantic representation is fed into a graph-structured optimization model, which obtains globally optimal classification results by constructing a weighted indirect graph and solving the optimization problem with graph-cuts. The segmented tree points were extracted and consolidated through a series of operations, and they were finally recognized by combining graph embedding learning with a structure-aware loss function and a supervoxel-based normalized cut segmentation method. Experimental results on two public datasets demonstrated that our framework achieved better performance in terms of classification accuracy and recognition ratio of tree. Numéro de notice : A2020-665 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9100595 date de publication en ligne : 10/10/2020 En ligne : https://doi.org/10.3390/ijgi9100595 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96142 [article]

A LiDAR aiding ambiguity resolution method using fuzzy one-to-many feature matching / Chuang Qian in Journal of geodesy, vol 94 n° 10 (October 2020)  

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See the forest and the trees: Effective machine and deep learning algorithms for wood filtering and tree species classification from terrestrial laser scanning / Zhouxin Xi in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)  

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Tree species classification using structural features derived from terrestrial laser scanning / Louise Terryn in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)  

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Planar polygons detection in lidar scans based on sensor topology enhanced Ransac / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

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Provably consistent distributed Delaunay triangulation / Mathieu Brédif in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

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Assessment of the accuracy of DTM river bed model using classical surveying measurement and LiDAR: a case study in Poland / Pawel Kotlarz in Survey review, vol 52 n° 372 (May 2020)  

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Fusing adjacent-track InSAR datasets to densify the temporal resolution of time-series 3-D displacement estimation over mining areas with a prior deformation model and a generalized weighting least-squares method / Yuedong Wang in Journal of geodesy, vol 94 n° 5 (May 2020)  

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La télédétection aéroportée pour la gestion des territoires forestiers de montagne / Jean-Matthieu Monnet in Sciences, eaux & territoires, n° 33 (avril 2020)  

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3D laser scanning of the natural caves: Example of Škocjanske jame / Richard Walters in Geodetski vestnik, Vol 64 n° 1 (March - May 2020)  

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An improved RANSAC algorithm for extracting roof planes from airborne lidar data / Sibel Canaz Sevgen in Photogrammetric record, vol 35 n° 169 (March 2020)  

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