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Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest / Aline Bernarda Debastiani 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 109 - 122 Titre : Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest Type de document : Article/Communication Auteurs : Aline Bernarda Debastiani, Auteur ; Carlos Roberto Sanquetta, Auteur ; Ana Paula Dalla Corte, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 109 - 122 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] Amazonie [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] arbre aléatoire [Termes descripteurs IGN] bande C [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] réseau neuronal convolutif Résumé : (auteur) The aim of the present study is to evaluate the potential of C-band SAR data from the Sentinel-1/2 instruments and machine learning algorithms for the estimation of forest above ground forest biomass (AGB) in a high-biomass tropical ecosystem. This study was carried out in Jamari National Forest, located in the Brazilian Amazon. The response variable was AGB (Mg/ha) estimated from airborne laser surveys. The following treatments were considered as model predictors: 1) Sentinel-1 Sigma 0 at VV and VH polarizations; 2) (1) plus Sentinel-1 textural metrics; 3) (2) plus Sentinel-2 bands and derived vegetation indices (LAI, RVI, SAVI, NDVI).Our modeling design estimated the relative importance of SAR vs. optical variables in explaining AGB. The modeling was performed with twelve machine-learning algorithms including, neural network and regression tree. The addition of texture and optical data provided a noticeable improvement (3%) over models with SAR backscatter only. The best model performance was achieved with the Random Tree algorithm. Our results demonstrate the potential of freely-available SAR data and machine learning for mapping AGB in tropical ecosystems. Numéro de notice : A2019-335 Thématique : IMAGERIE Nature : Article DOI : 10.15287/afr.2018.1267 date de publication en ligne : 30/07/2019 En ligne : http://dx.doi.org/10.15287%2Fafr.2018.1267 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93349 [article]

On the spatial distribution of buildings for map generalization / Zhiwei Wei in Cartography and Geographic Information Science, Vol 45 n° 6 (November 2018)  

Public [article]  inCartography and Geographic Information Science > Vol 45 n° 6 (November 2018) . - pp 539 - 555 Titre : On the spatial distribution of buildings for map generalization Type de document : Article/Communication Auteurs : Zhiwei Wei, Auteur ; Qingsheng Guo, Auteur ; Lin Wang, Auteur ; Fen Yan, Auteur Année de publication : 2018 Article en page(s) : pp 539 - 555 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] arbre aléatoire minimum [Termes descripteurs IGN] bati [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] généralisation cartographique automatisée [Termes descripteurs IGN] OpenStreetMap [Vedettes matières IGN] Généralisation Résumé : (Auteur) Information on spatial distribution of buildings must be explored as part of the process of map generalization. A new approach is proposed in this article, which combines building classification and clustering to enable the detection of class differences within a pattern, as well as patterns within a class. To do this, an analysis of existing parameters describing building characteristics is performed via principal component analysis (PCA), and four major parameters (i.e. convex hull area, IPQ compactness, number of edges, and smallest minimum bounding rectangle orientation) are selected for further classification based on similarities between building characteristics. A building clustering method based on minimum spanning tree (MST) considering rivers and roads is then applied. Theory and experiments show that use of a relative neighbor graph (RNG) is more effective in detecting linear building patterns than either a nearest neighbor graph (NNG), an MST, or a Gabriel graph (GssG). Building classification and clustering are therefore conducted separately using experimental data extracted from OpenStreetMap (OSM), and linear patterns are then recognized within resultant clusters. Experimental results show that the approach proposed in this article is both reasonable and efficient for mining information on the spatial distribution of buildings for map generalization. Numéro de notice : A2018-480 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2018.1433068 date de publication en ligne : 15/02/2018 En ligne : https://doi.org/10.1080/15230406.2018.1433068 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91258 [article]

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Automated extraction of 3D vector topographic feature line from terrain point cloud / Wei Zhou in Geocarto international, vol 33 n° 10 (October 2018)  

Public [article]  inGeocarto international > vol 33 n° 10 (October 2018) . - pp 1036 - 1047 Titre : Automated extraction of 3D vector topographic feature line from terrain point cloud Type de document : Article/Communication Auteurs : Wei Zhou, Auteur ; Rencan Peng, Auteur ; Jian Dong, Auteur ; Tao Wang, Auteur Année de publication : 2018 Article en page(s) : pp 1036 - 1047 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] arbre aléatoire minimum [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] ligne caractéristique [Termes descripteurs IGN] lissage de données [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] objet géographique linéaire [Termes descripteurs IGN] repère de Laplace [Termes descripteurs IGN] segmentation par croissance de régions [Termes descripteurs IGN] semis de points Résumé : (auteur) This paper presents an automated topographic feature lines detection method that directly extracts 3D vector topographic feature lines from terrain point cloud. First, signed surface variation (SSV) is introduced to extract the potential feature points. Secondly, the potential feature points are segmented to different clusters by combining region growing segmentation and conditional Euclidean clustering. In order to extract feature points, the potential feature points in each cluster are iteratively thinned using a HC-Laplacian smoothing method with SSV weighted taken into account. Besides, SSV-based and elevation-based simple rules are added for accelerating this thinning process. Finally, the feature lines are obtained by constructing the minimum spanning tree of the extracted feature points. By comparing with manually digitized reference lines, the correctness and the completeness of extracted results are about 80% or even higher, which are much higher than those extracted by D8 algorithm. Numéro de notice : A2019-046 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1325521 date de publication en ligne : 18/05/2017 En ligne : https://doi.org/10.1080/10106049.2017.1325521 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92064 [article]

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A deep neural network with spatial pooling (DNNSP) for 3-D point cloud classification / Zhen Wang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 8 (August 2018)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 8 (August 2018) . - pp 4594 - 4604 Titre : A deep neural network with spatial pooling (DNNSP) for 3-D point cloud classification Type de document : Article/Communication Auteurs : Zhen Wang, Auteur ; Liqiang Zhang, Auteur ; Liang Zhang, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 4594 - 4604 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] arbre aléatoire [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] données hétérogènes [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] Perceptron multicouche [Termes descripteurs IGN] pondération [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] semis de points Mots-clés libres : deep neural network with spatial pooling (DNNSP) Résumé : (Auteur) The large number of object categories and many overlapping or closely neighboring objects in large-scale urban scenes pose great challenges in point cloud classification. Most works in deep learning have achieved a great success on regular input representations, but they are hard to be directly applied to classify point clouds due to the irregularity and inhomogeneity of the data. In this paper, a deep neural network with spatial pooling (DNNSP) is proposed to classify large-scale point clouds without rasterization. The DNNSP first obtains the point-based feature descriptors of all points in each point cluster. The distance minimum spanning tree-based pooling is then applied in the point feature representation to describe the spatial information among the points in the point clusters. The max pooling is next employed to aggregate the point-based features into the cluster-based features. To assure the DNNSP is invariant to the point permutation and sizes of the point clusters, the point-based feature representation is determined by the multilayer perception (MLP) and the weight sharing for each point is retained, which means that the weight of each point in the same layer is the same. In this way, the DNNSP can learn the features of points scaled from the entire regions to the centers of the point clusters, which makes the point cluster-based feature representations robust and discriminative. Finally, the cluster-based features are input to another MLP for point cloud classification. We have evaluated qualitatively and quantitatively the proposed method using several airborne laser scanning and terrestrial laser scanning point cloud data sets. The experimental results have demonstrated the effectiveness of our method in improving classification accuracy. Numéro de notice : A2018-471 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2829625 date de publication en ligne : 22/05/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2829625 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91253 [article]

Evaluation of 10-year temporal and spatial variability in structure and growth across contrasting commercial thinning treatments in spruce-fir forests of northern Maine, USA / Christian Kuehne in Annals of Forest Science [en ligne], vol 75 n° 1 (March 2018)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 75 n° 1 (March 2018) Titre : Evaluation of 10-year temporal and spatial variability in structure and growth across contrasting commercial thinning treatments in spruce-fir forests of northern Maine, USA Type de document : Article/Communication Auteurs : Christian Kuehne, Auteur ; Aaron R. Weiskittel, Auteur ; Arne Pommerening, Auteur ; Robert G. Wagner, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] Abies balsamea [Termes descripteurs IGN] arbre aléatoire [Termes descripteurs IGN] éclaircie (sylviculture) [Termes descripteurs IGN] Maine (Etats-Unis) [Termes descripteurs IGN] Picea rubens [Termes descripteurs IGN] Pinus strobus [Termes descripteurs IGN] station forestière [Termes descripteurs IGN] thuja (genre) [Vedettes matières IGN] Sylviculture Résumé : (Auteur) Key message: Detailed measures of growth pattern and structural heterogeneity applied in this study helped to quantify the immediate effects of various thinning regimes on forest structure and the resulting alterations in tree size as well as observed longer term stand dynamics. Context: Forest management, stand structure, and tree growth are highly inter-correlated. Prior analyses, however, have resulted in mixed outcomes with limited success in revealing ecological mechanisms. Aims: The study aimed at evaluating the relationship between forest structure and stand dynamics by applying several sophisticated measures of growth pattern and structural heterogeneity. Methods : Data from a controlled and fully stem-mapped commercial thinning experiment with seven contrasting treatments including a non-thinned control at six locations across the Acadian Forest of Maine, USA, was used. Stand-level attributes examined included tree size and growth heterogeneity, spatial tree distribution, and growth dominance. Results: Thinning generally reduced stand structural heterogeneity compared to the non-thinned control. In addition, the spatial arrangement of trees changed from fully random (non-thinned control) to a more clustered (removal of dominant and co-dominant individuals) or regular distribution (removal of intermediate and suppressed individuals). Overall, stand growth exhibited increasing (non-thinned control, removal of intermediate and suppressed individuals) or decreasing growth dominance of large trees (removal of co-dominant competitors). Forwarder trails increased basal area growth of individual trees up to a distance from the trail of approximately 5 m. Conclusion: Findings of this study validate an earlier insight according to which interactions between management practices, forest structure, and tree growth form a permanent feedback loop. Keywords Numéro de notice : A2018-315 Thématique : BIODIVERSITE/FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-018-0697-7 date de publication en ligne : 26/02/2018 En ligne : https://doi.org/10.1007/s13595-018-0697-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90439 [article]

Efficient structure from motion for oblique UAV images based on maximal spanning tree expansion / San Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)  

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3D tree modeling from incomplete point clouds via optimization and L1-MST / Jie Mei in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

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Reconstruction of itineraries from annotated text with an informed spanning tree algorithm / Ludovic Moncla in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)  

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Automatic itinerary reconstruction from texts / Ludovic Moncla (2014)  

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Verification of 2D building outlines using oblique airborne images / A. Nyaruhuma in ISPRS Journal of photogrammetry and remote sensing, vol 71 (July 2012)

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Efficient regionalization techniques for socio-economic geographical units using minimum spanning trees / R.M. Assuncao in International journal of geographical information science IJGIS, vol 20 n° 7 (august 2006)

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Building displacement over a ductile truss / M. Bader in International journal of geographical information science IJGIS, vol 19 n° 8 - 9 (september 2005)

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Built-up area analysis / Nicolas Regnauld (1995)

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