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Open land cover from OpenStreetMap and remote sensing / Michael Schultz in International journal of applied Earth observation and geoinformation, vol 63 (December 2017)
[article]
Titre : Open land cover from OpenStreetMap and remote sensing Type de document : Article/Communication Auteurs : Michael Schultz, Auteur ; Janek Voss, Auteur ; Michael Auer, Auteur ; Sarah Carter, Auteur ; Alexander Zipf, Auteur Année de publication : 2017 Article en page(s) : pp 206 - 213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] analyse diachronique
[Termes IGN] Bade-Wurtemberg (Allemagne)
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image Landsat-8
[Termes IGN] occupation du sol
[Termes IGN] OpenStreetMap
[Termes IGN] segmentation sémantiqueRésumé : (auteur) OpenStreetMap (OSM) tags were used to produce a global Open Land Cover (OLC) product with fractional data gaps available at osmlanduse.org. Data gaps in the global OLC map were filled for a case study in Heidelberg, Germany using free remote sensing data, which resulted in a land cover (LC) prototype with complete coverage in this area. Sixty tags in the OSM were used to allocate a Corine Land Cover (CLC) level 2 land use classification to 91.8% of the study area, and the remaining gaps were filled with remote sensing data. For this case study, complete are coverage OLC overall accuracy was estimated 87%, which performed better than the CLC product (81% overall accuracy) of 2012. Spatial thematic overlap for the two products was 84%. OLC was in large parts found to be more detailed than CLC, particularly when LC patterns were heterogeneous, and outperformed CLC in the classification of 12 of the 14 classes. Our OLC product represented data created in different periods; 53% of the area was 2011–2016, and 46% of the area was representative of 2016–2017. Numéro de notice : A2017-638 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2017.07.014 En ligne : https://doi.org/10.1016/j.jag.2017.07.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86989
in International journal of applied Earth observation and geoinformation > vol 63 (December 2017) . - pp 206 - 213[article]Multi-model estimation of understorey shrub, herb and moss cover in temperate forest stands by laser scanner data / Hooman Latifi in Forestry, an international journal of forest research, vol 90 n° 4 (October 2017)
[article]
Titre : Multi-model estimation of understorey shrub, herb and moss cover in temperate forest stands by laser scanner data Type de document : Article/Communication Auteurs : Hooman Latifi, Auteur ; Steven Hill, Auteur ; Bastian Schumann, Auteur ; Marco Heurich, Auteur ; Stefan Dech, Auteur Année de publication : 2017 Article en page(s) : pp 496 - 514 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation statistique
[Termes IGN] forêt tempérée
[Termes IGN] habitat forestier
[Termes IGN] sous-boisRésumé : (Auteur) In temperate forests, the highest plant richness is regularly found in the understorey, i.e. shrub, tree regeneration, herbal and moss covers, which provides important food and shelter for other plant and animal species. Here, Light Detection And Ranging (LiDAR) remote sensing was investigated as a surrogate to laborious field surveys to improve understanding of the causal and predictive attributes of understorey. We designed a study in which we used a high-density LiDAR point cloud and applied a thinning algorithm to simulate two lower density point clouds including first and last returns and half of the remaining points (half-thinned data) and only first and last returns (F/L-thinned data). From each dataset, several over- and understorey-related statistical metrics were derived. Each of the three sets of LiDAR metrics was then combined with the forest habitat information to estimate the recorded proportions of shrub, herb and moss coverages. We used three different model procedures including zero-and-one-inflated beta regression (ZOINBR), ordinary least squares with logit-transformed response variables (logistic model) and a machine learning random forest (RF) method. The logistic and ZOINBR model results showed highly significant relationships between LiDAR metrics and habitat types in explaining understorey coverage. The highest coefficients of determination included r2 = 0.80 for shrub cover (estimated by F/L-thinned data and ZOINBR model), r2 = 0.53 for herb cover (estimated by half-thinned data and logistic model) and r2 = 0.48 for moss cover (estimated by half-thinned data and logistic model). RF models returned the best predictive performances (i.e. the lowest root mean square errors). Despite slight differences, no substantial difference was observed amongst the performances achieved by the original, half-thinned and F/L-thinned point clouds. Moreover, the ZOINBR models did not improve predictive performances compared with the logistic model, which suggests that the latter should be preferred due to its greater simplicity and parsimony. Despite the differences between our simulated data and the real-world LiDAR point clouds of different point densities, the results of this study are thought to mostly reflect how LiDAR and forest habitat data can be combined for deriving ecologically relevant information on temperate forest understorey vegetation layers. This, in turn, increases the applicability of prediction results for overarching aims such as forest and wildlife management. Numéro de notice : A2017-906 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1093/forestry/cpw066 Date de publication en ligne : 27/01/2017 En ligne : https://doi.org/10.1093/forestry/cpw066 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93195
in Forestry, an international journal of forest research > vol 90 n° 4 (October 2017) . - pp 496 - 514[article]3D local feature BKD to extract road information from mobile laser scanning point clouds / Yang Bisheng in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
[article]
Titre : 3D local feature BKD to extract road information from mobile laser scanning point clouds Type de document : Article/Communication Auteurs : Yang Bisheng, Auteur ; Yuan Liu, Auteur ; Zhen Dong, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 329 - 343 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classificateur
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] densité des points
[Termes IGN] données localisées 3D
[Termes IGN] estimation par noyau
[Termes IGN] extraction du réseau routier
[Termes IGN] semis de points
[Termes IGN] télémétrie laser mobile
[Termes IGN] variable binaireRésumé : (Auteur) Extracting road information from point clouds obtained through mobile laser scanning (MLS) is essential for autonomous vehicle navigation, and has hence garnered a growing amount of research interest in recent years. However, the performance of such systems is seriously affected due to varying point density and noise. This paper proposes a novel three-dimensional (3D) local feature called the binary kernel descriptor (BKD) to extract road information from MLS point clouds. The BKD consists of Gaussian kernel density estimation and binarization components to encode the shape and intensity information of the 3D point clouds that are fed to a random forest classifier to extract curbs and markings on the road. These are then used to derive road information, such as the number of lanes, the lane width, and intersections. In experiments, the precision and recall of the proposed feature for the detection of curbs and road markings on an urban dataset and a highway dataset were as high as 90%, thus showing that the BKD is accurate and robust against varying point density and noise. Numéro de notice : A2017-517 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.06.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.06.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86479
in ISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 329 - 343[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Image matching as a data source for forest inventory – Comparison of semi-global matching and next-generation automatic terrain extraction algorithms in a typical managed boreal forest environment / Mari Kukkonen in International journal of applied Earth observation and geoinformation, vol 60 (August 2017)
[article]
Titre : Image matching as a data source for forest inventory – Comparison of semi-global matching and next-generation automatic terrain extraction algorithms in a typical managed boreal forest environment Type de document : Article/Communication Auteurs : Mari Kukkonen, Auteur ; Matti Maltamo, Auteur ; Petteri Packalen, Auteur Année de publication : 2017 Article en page(s) : pp 11 - 21 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse comparative
[Termes IGN] appariement d'images
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Finlande
[Termes IGN] forêt boréale
[Termes IGN] image aérienne
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle numérique de terrain
[Termes IGN] semis de points
[Termes IGN] sous-boisRésumé : (auteur) Image matching is emerging as a compelling alternative to airborne laser scanning (ALS) as a data source for forest inventory and management. There is currently an open discussion in the forest inventory community about whether, and to what extent, the new method can be applied to practical inventory campaigns. This paper aims to contribute to this discussion by comparing two different image matching algorithms (Semi-Global Matching [SGM] and Next-Generation Automatic Terrain Extraction [NGATE]) and ALS in a typical managed boreal forest environment in southern Finland. Spectral features from unrectified aerial images were included in the modeling and the potential of image matching in areas without a high resolution digital terrain model (DTM) was also explored. Plot level predictions for total volume, stem number, basal area, height of basal area median tree and diameter of basal area median tree were modeled using an area-based approach. Plot level dominant tree species were predicted using a random forest algorithm, also using an area-based approach. The statistical difference between the error rates from different datasets was evaluated using a bootstrap method. Results showed that ALS outperformed image matching with every forest attribute, even when a high resolution DTM was used for height normalization and spectral information from images was included. Dominant tree species classification with image matching achieved accuracy levels similar to ALS regardless of the resolution of the DTM when spectral metrics were used. Neither of the image matching algorithms consistently outperformed the other, but there were noticeably different error rates depending on the parameter configuration, spectral band, resolution of DTM, or response variable. This study showed that image matching provides reasonable point cloud data for forest inventory purposes, especially when a high resolution DTM is available and information from the understory is redundant. Numéro de notice : A2017-364 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2017.03.012 En ligne : https://doi.org/10.1016/j.jag.2017.03.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85791
in International journal of applied Earth observation and geoinformation > vol 60 (August 2017) . - pp 11 - 21[article]Reducing classification error of grassland overgrowth by combing low-density lidar acquisitions and optical remote sensing data / Timo P Pitkänen in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
[article]
Titre : Reducing classification error of grassland overgrowth by combing low-density lidar acquisitions and optical remote sensing data Type de document : Article/Communication Auteurs : Timo P Pitkänen, Auteur ; Niina Käyhkö, Auteur Année de publication : 2017 Article en page(s) : pp 150 - 161 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse diachronique
[Termes IGN] arbre (flore)
[Termes IGN] boisement naturel
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] densité des points
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] erreur de classification
[Termes IGN] image Landsat
[Termes IGN] orthoimage
[Termes IGN] prairie
[Termes IGN] structure de données localiséesRésumé : (Auteur) Mapping structural changes in vegetation dynamics has, for a long time, been carried out using satellite images, orthophotos and, more recently, airborne lidar acquisitions. Lidar has established its position as providing accurate material for structure-based analyses but its limited availability, relatively short history, and lack of spectral information, however, are generally impeding the use of lidar data for change detection purposes. A potential solution in respect of detecting both contemporary vegetation structures and their previous trajectories is to combine lidar acquisitions with optical remote sensing data, which can substantially extend the coverage, span and spectral range needed for vegetation mapping. In this study, we tested the simultaneous use of a single low-density lidar data set, a series of Landsat satellite frames and two high-resolution orthophotos to detect vegetation succession related to grassland overgrowth, i.e. encroachment of woody plants into semi-natural grasslands. We built several alternative Random Forest models with different sets of variables and tested the applicability of respective data sources for change detection purposes, aiming at distinguishing unchanged grassland and woodland areas from overgrown grasslands. Our results show that while lidar alone provides a solid basis for indicating structural differences between grassland and woodland vegetation, and orthophoto-generated variables alone are better in detecting successional changes, their combination works considerably better than its respective parts. More specifically, a model combining all the used data sets reduces the total error from 17.0% to 11.0% and omission error of detecting overgrown grasslands from 56.9% to 31.2%, when compared to model constructed solely based on lidar data. This pinpoints the efficiency of the approach where lidar-generated structural metrics are combined with optical and multitemporal observations, providing a workable framework to identify structurally oriented and dynamically organized landscape phenomena, such as grassland overgrowth. Numéro de notice : A2017-513 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.05.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.05.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86459
in ISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 150 - 161[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt A relative evaluation of random forests for land cover mapping in an urban area / Di Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 8 (August 2017)PermalinkDeveloping detailed age-specific thematic maps for coffee (Coffea arabica L.) in heterogeneous agricultural landscapes using random forests applied on Landsat 8 multispectral sensor / Abel Chemura in Geocarto international, vol 32 n° 7 (July 2017)PermalinkPredicting stem total and assortment volumes in an industrial pinus taeda L. forest plantation using airborne laser scanning data and random forest / Carlos Alberto Silva in Forests, vol 8 n° 7 (July 2017)PermalinkDevelopment and Comparison of Species Distribution Models for Forest Inventories / Óscar Rodríguez de Rivera in ISPRS International journal of geo-information, vol 6 n° 6 (June 2017)PermalinkMonitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms / Lien T.H. Pham in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)PermalinkMapping fine-scale population distributions at the building level by integrating multisource geospatial big data / Yao Yao in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)PermalinkA simplified linear feature matching method using decision tree analysis, weighted linear directional mean, and topological relationships / Ick-Hoi Kim in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)PermalinkImproving large area population mapping using geotweet densities / Nirav N. Patel in Transactions in GIS, vol 21 n° 2 (April 2017)PermalinkSpatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration / Yinghai Ke in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)PermalinkClassifying natural-language spatial relation terms with random forest algorithm / Shihong Du in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)Permalink