Centre de documentation
scientifique

Analysis of spatio-temporal changes in forest biomass in China / Weiyi Xu in Journal of Forestry Research, vol 33 n° 1 (February 2022)  

Public [article]  inJournal of Forestry Research > vol 33 n° 1 (February 2022) . - pp 261 - 278 Titre : Analysis of spatio-temporal changes in forest biomass in China Type de document : Article/Communication Auteurs : Weiyi Xu, Auteur ; Xiaobin Jin, Auteur ; Jing Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 261 - 278 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatio-temporelle [Termes IGN] biomasse [Termes IGN] Chine [Termes IGN] distribution spatiale [Termes IGN] inventaire forestier étranger (données) [Termes IGN] puits de carbone [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Forests play a central role in the global carbon cycle. China's forests have a high carbon sequestration potential owing to their wide distribution, young age and relatively low carbon density. Forest biomass is an essential variable for assessing carbon sequestration capacity, thus determining the spatio-temporal changes of forest biomass is critical to the national carbon budget and to contribute to sustainable forest management. Based on Chinese forest inventory data (1999–2013), this study explored spatial patterns of forest biomass at a grid resolution of 1 km by applying a downscaling method and further analyzed spatio-temporal changes of biomass at different spatial scales. The main findings are: (1) the regression relationship between forest biomass and the associated influencing factors at a provincial scale can be applied to estimate biomass at a pixel scale by employing a downscaling method; (2) forest biomass had a distinct spatial pattern with the greatest biomass occurring in the major mountain ranges; (3) forest biomass changes had a notable spatial distribution pattern; increase (i.e., carbon sinks) occurred in east and southeast China, decreases (i.e., carbon sources) were observed in the northeast to southwest, with the largest biomass losses in the Hengduan Mountains, Southern Hainan and Northern Da Hinggan Mountains; and, (4) forest vegetation functioned as a carbon sink during 1999–2013 with a net increase in biomass of 3.71 Pg. Numéro de notice : A2022-336 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s11676-021-01299-8 Date de publication en ligne : 09/04/2021 En ligne : https://doi.org/10.1007/s11676-021-01299-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100678 [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 IGN] algorithme de filtrage [Termes IGN] apprentissage profond [Termes IGN] arbre hors forêt [Termes IGN] arbre urbain [Termes IGN] détection d'arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] gestion urbaine [Termes IGN] image captée par drone [Termes IGN] optimisation (mathématiques) [Termes IGN] reconnaissance d'objets [Termes IGN] segmentation [Termes IGN] semis de points [Termes IGN] voxel [Termes 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]

Predictive mapping with small field sample data using semi‐supervised machine learning / Fei Du in Transactions in GIS, Vol 24 n° 2 (April 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 2 (April 2020) . - pp 315 - 331 Titre : Predictive mapping with small field sample data using semi‐supervised machine learning Type de document : Article/Communication Auteurs : Fei Du, Auteur ; A - Xing Zhu, Auteur ; Jing Liu, Auteur ; Lin Yang, Auteur Année de publication : 2020 Article en page(s) : pp 315 - 331 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage semi-dirigé [Termes IGN] covariance [Termes IGN] échantillon [Termes IGN] modèle de simulation [Termes IGN] représentation cartographique Résumé : (Auteur) Existing predictive mapping methods usually require a large number of field samples with good representativeness as input to build reliable predictive models. In mapping practice, however, we often face situations when only small sample data are available. In this article, we present a semi‐supervised machine learning approach for predictive mapping in which the natural aggregation (clustering) patterns of environmental covariate data are used to supplement limited samples in prediction. This approach was applied to two soil mapping case studies. Compared with field sample only approaches (decision trees, logistic regression, and support vector machines), maps using the proposed approach can better capture the spatial variation of soil types and achieve higher accuracy with limited samples. A cross validation shows further that the proposed approach is less sensitive to the specific field sample set used and thus more robust when field sample data are small. Numéro de notice : A2020-174 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12598 Date de publication en ligne : 04/12/2019 En ligne : https://doi.org/10.1111/tgis.12598 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94900 [article]

Variation of leaf angle distribution quantified by terrestrial LiDAR in natural European beech forest / Jing Liu in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 148 (February 2019) . - pp 208 - 220 Titre : Variation of leaf angle distribution quantified by terrestrial LiDAR in natural European beech forest Type de document : Article/Communication Auteurs : Jing Liu, Auteur ; Andrew K. Skidmore, Auteur ; Tiejun Wang, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 208 - 220 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] angle (géométrie) [Termes IGN] Bavière (Allemagne) [Termes IGN] croissance végétale [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Fagus sylvatica [Termes IGN] feuille (végétation) [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] semis de points Mots-clés libres : inclinaison longitudinale  Leaf inclination angle  leaf angle distribution Résumé : (Auteur) Leaf inclination angle and leaf angle distribution (LAD) are important plant structural traits, influencing the flux of radiation, carbon and water. Although leaf angle distribution may vary spatially and temporally, its variation is often neglected in ecological models, due to difficulty in quantification. In this study, terrestrial LiDAR (TLS) was used to quantify the LAD variation in natural European beech (Fagus Sylvatica) forests. After extracting leaf points and reconstructing leaf surface, leaf inclination angle was calculated automatically. The mapping accuracy when discriminating between leaves and woody material was very high across all beech stands (overall accuracy = 87.59%). The calculation accuracy of leaf angles was evaluated using simulated point cloud and proved accurate generally (R2 = 0.88, p  Numéro de notice : A2019-075 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.005 Date de publication en ligne : 15/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.005 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92162 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019023	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019022	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Large off-nadir scan angle of airborne LiDAR can severely affect the estimates of forest structure metrics / Jing Liu in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 136 (February 2018) . - pp 13 - 25 Titre : Large off-nadir scan angle of airborne LiDAR can severely affect the estimates of forest structure metrics Type de document : Article/Communication Auteurs : Jing Liu, Auteur ; Andrew K. Skidmore, Auteur ; Simon D. Jones, Auteur ; Tiejun Wang, Auteur ; Marco Heurich, Auteur ; Xi Zhu, Auteur ; Yifang Shi, Auteur Année de publication : 2018 Article en page(s) : pp 13 - 25 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] angle de visée [Termes IGN] Bavière (Allemagne) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] instrument aéroporté [Termes IGN] parc naturel régional [Termes IGN] placette d'échantillonnage [Termes IGN] structure d'un peuplement forestier Résumé : (Auteur) Gap fraction (Pgap) and vertical gap fraction profile (vertical Pgap profile) are important forest structural metrics. Accurate estimation of Pgap and vertical Pgap profile is therefore critical for many ecological applications, including leaf area index (LAI) mapping, LAI profile estimation and wildlife habitat modelling. Although many studies estimated Pgap and vertical Pgap profile from airborne LiDAR data, the scan angle was often overlooked and a nadir view assumed. However, the scan angle can be off-nadir and highly variable in the same flight strip or across different flight strips. In this research, the impact of off-nadir scan angle on Pgap and vertical Pgap profile was evaluated, for several forest types. Airborne LiDAR data from nadir (0°∼7°), small off-nadir (7°∼23°), and large off-nadir (23°∼38°) directions were used to calculate both Pgap and vertical Pgap profile. Digital hemispherical photographs (DHP) acquired during fieldwork were used as references for validation. Our results show that angular Pgap from airborne LiDAR correlates well with angular Pgap from DHP (R2 = 0.74, 0.87, and 0.67 for nadir, small off-nadir and large off-nadir direction). But underestimation of Pgap from LiDAR amplifies at large off-nadir scan angle. By comparing Pgap and vertical Pgap profiles retrieved from different directions, it is shown that scan angle impact on Pgap and vertical Pgap profile differs amongst different forest types. The difference is likely to be caused by different leaf angle distribution and canopy architecture in these forest types. Statistical results demonstrate that the scan angle impact is more severe for plots with discontinuous or sparse canopies. These include coniferous plots, and deciduous or mixed plots with between-crown gaps. In these discontinuous plots, Pgap and vertical Pgap profiles are maximum when observed from nadir direction, and then rapidly decrease with increasing scan angle. The results of this research have many important practical implications. First, it is suggested that large off-nadir scan angle of airborne LiDAR should be avoided to ensure a more accurate Pgap and LAI estimation. Second, the angular dependence of vertical Pgap profiles observed from airborne LiDAR should be accounted for, in order to improve the retrieval of LAI profiles, and other quantitative canopy structural metrics. This is especially necessary when using multi-temporal datasets in discontinuous forest types. Third, the anisotropy of Pgap and vertical Pgap profile observed by airborne LiDAR, can potentially help to resolve the anisotropic behavior of canopy reflectance, and refine the inversion of biophysical and biochemical properties from passive multispectral or hyperspectral data Numéro de notice : A2018-072 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.12.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.12.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89432 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018023	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018022	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Significant effect of topographic normalization of airborne LiDAR data on the retrieval of plant area index profile in mountainous forests / Jing Liu in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)  

Permalink