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Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data / Xiaofang Sun in Geocarto international, vol 36 n° 14 ([01/08/2021])  

Public [article]  inGeocarto international > vol 36 n° 14 [01/08/2021] . - pp 1549 - 1564 Titre : Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data Type de document : Article/Communication Auteurs : Xiaofang Sun, Auteur ; Bai Li, Auteur ; Zhengping Du, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1549 - 1564 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] biomasse aérienne [Termes IGN] biomasse forestière [Termes IGN] carbone [Termes IGN] carte de la végétation [Termes IGN] classification barycentrique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données ICEsat [Termes IGN] données lidar [Termes IGN] données multisources [Termes IGN] Geoscience Laser Altimeter System [Termes IGN] image Terra-MODIS [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Kiangsi (Chine) [Termes IGN] krigeage [Termes IGN] modèle numérique de surface [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] régression des moindres carrés partiels Résumé : (auteur) An accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. In this study, six methods, including partial least squares regression, regression kriging, k-nearest neighbour, support vector machines, random forest and high accuracy surface modelling (HASM), were used to simulate forest AGB. Forest AGB was mapped by combining Geoscience Laser Altimeter System data, optical imagery and field inventory data. The Normalized Difference Vegetation Index (NDVI) and Wide Dynamic Range Vegetation Index (WDRVI0.2) of September and October, which had a stronger correlation with forest AGB than that of the peak growing season, were selected as predictor variables, along with tree cover percentage and three GLAS-derived parameters. The results of the different methods were evaluated. The HASM model had the best modelling accuracy (small MAE, RMSE, NRMSE, RMSV and NMSE and large R2). A forest AGB map of the study area was generated using the optimal model. Numéro de notice : A2021-555 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1655799 Date de publication en ligne : 28/08/2019 En ligne : https://doi.org/10.1080/10106049.2019.1655799 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98108 [article]

Choosing an appropriate training set size when using existing data to train neural networks for land cover segmentation / Huan Ning in Annals of GIS, vol 26 n° 4 (October 2020)  

Public [article]  inAnnals of GIS > vol 26 n° 4 (October 2020) . - pp 329 - 342 Titre : Choosing an appropriate training set size when using existing data to train neural networks for land cover segmentation Type de document : Article/Communication Auteurs : Huan Ning, Auteur ; Zhenlong Li, Auteur ; Cuizhen Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 329 - 342 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] contour [Termes IGN] extraction de traits caractéristiques [Termes IGN] jeu de données [Termes IGN] Kiangsi (Chine) [Termes IGN] occupation du sol [Termes IGN] segmentation d'image [Termes IGN] segmentation sémantique [Termes IGN] taille du jeu de données Résumé : (auteur) Land cover data is an inventory of objects on the Earth’s surface, which is often derived from remotely sensed imagery. Deep Convolutional Neural Network (DCNN) is a competitive method in image semantic segmentation. Some scholars argue that the inadequacy of training set is an obstacle when applying DCNNs in remote sensing image segmentation. While existing land cover data can be converted to large training sets, the size of training data set needs to be carefully considered. In this paper, we used different portions of a high-resolution land cover map to produce different sizes of training sets to train DCNNs (SegNet and U-Net) and then quantitatively evaluated the impact of training set size on the performance of the trained DCNN. We also introduced a new metric, Edge-ratio, to assess the performance of DCNN in maintaining the boundary of land cover objects. Based on the experiments, we document the relationship between the segmentation accuracy and the size of the training set, as well as the nonstationary accuracies among different land cover types. The findings of this paper can be used to effectively tailor the existing land cover data to training sets, and thus accelerate the assessment and employment of deep learning techniques for high-resolution land cover map extraction. Numéro de notice : A2020-800 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475683.2020.1803402 Date de publication en ligne : 10/08/2020 En ligne : https://doi.org/10.1080/19475683.2020.1803402 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96723 [article]

Research on the construction of urban spatial data infrastructure / Xiaosheng Liu (2005)    

Public Titre : Research on the construction of urban spatial data infrastructure Type de document : Article/Communication Auteurs : Xiaosheng Liu, Auteur ; Youliang Chen, Auteur ; Hexia Weng, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2005 Collection : International Archives of Photogrammetry and Remote Sensing, ISSN 0252-8231 num. 36-4/W6 Conférence : ISPRS 2005, Workshop on Service and Application of Spatial Data Infrastructure 14/10/2005 16/10/2005 Hangzhou Chine OA Proceedings Importance : 4 p. ; pp 183 - 186 Format : 21 x 30 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes IGN] données localisées [Termes IGN] infrastructure urbaine de données localisées [Termes IGN] Kiangsi (Chine) [Termes IGN] partage de données localisées [Termes IGN] ville Résumé : (Auteur) This paper discusses the construction of urban spatial data infrastructure, taking Ganzhou for example. First, we set up an organization that manages and harmonizes spatial data, and design the total configuration of the USDI (urban spatial data infrastructure)of Ganzhou ; Second, we constitute the regulations of the producing and updating of spatial data, and probe in the technology of sharing data, and we also design the framework of data sharing in the USDI of Ganzhou, then we build up the framework dataset. Finally we discuss the integrating of GIS and OA. Numéro de notice : 13835 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Communication DOI : sans En ligne : https://www.isprs.org/proceedings/XXXVI/4-W6/papers/183-186XiaoshengLIU-A067.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=64277

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