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National scale mapping of larch plantations for Wales using the Sentinel-2 data archive / Suvarna M. Punalekar in Forest ecology and management, vol 501 (December-1 2021)
[article]
Titre : National scale mapping of larch plantations for Wales using the Sentinel-2 data archive Type de document : Article/Communication Auteurs : Suvarna M. Punalekar, Auteur ; Carole Planque, Auteur ; Richard M. Lucas, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119679 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] arbre de décision
[Termes IGN] carte forestière
[Termes IGN] coupe rase (sylviculture)
[Termes IGN] gestion forestière
[Termes IGN] image infrarouge
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] Larix decidua
[Termes IGN] maladie phytosanitaire
[Termes IGN] modélisation de la forêt
[Termes IGN] Pays de Galles
[Termes IGN] surveillance forestièreRésumé : (auteur) Accurate spatial information regarding forest types and tree species is immensely important for efficient forest management strategies. In the UK and particularly in Wales, creating a spatial inventory of larch (Larix sps.) plantations that encompasses both the public and private forests has become one of the highest priorities of woodland management policies, particularly given the need to respond to the rapid spread of Phytophthora ramorum fungal disease. For directing disease control measures, national scale, regularly updated mapping of larch distributions is essential. In this study, we applied a ExtraTree classifier machine learning algorithm to multi-year (June 2015 and December 2019) multi-path composites of vegetation indices derived from 10 m Sentinel-2 satellite data (spectral range used in this study: 490–2190 nm) to map the extent of larch plantations across Wales. For areas identified as woody vegetation, areas under larch plantations were associated with a needle-leaved leaf type and deciduous phenology, allowing differentiation from broad-leaved deciduous and needle-leaved evergreen types. The model accuracies for validation, which included overall accuracy, producer’s and user’s accuracies, exceeded 95% and the F1-score was greater than 0.97 for all forest types. Comparison against an independent reference dataset indicated all map accuracies above 90% (F1-score higher than 0.92) with the lowest value being 90.3% for the producer’s accuracy for larch. Short wave infrared and red-edge based indices were particularly useful for discriminating larch from other forest types. Capacity for updating information on clear-felling of larch stands through annual updates of a woody mask was also introduced. The resulting maps of larch plantations for Wales are the most current for Wales covering public as well as private woodlands and can be routinely updated. The classification approach has potential to be transferred to a wider geographical area given the availability of open-source multi-year Sentienl-2 datasets and robust calibration datasets. Numéro de notice : A2021-741 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2021.119679 Date de publication en ligne : 20/09/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119679 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98657
in Forest ecology and management > vol 501 (December-1 2021) . - n° 119679[article]Land degradation assessment in an African dryland context based on the Composite Land Degradation Index and mapping method / Felicia Akinyemi in Geocarto international, vol 36 n° 16 ([01/09/2021])
[article]
Titre : Land degradation assessment in an African dryland context based on the Composite Land Degradation Index and mapping method Type de document : Article/Communication Auteurs : Felicia Akinyemi, Auteur ; Laura T. Tlhalerwa, Auteur ; Peter N. Eze, Auteur Année de publication : 2021 Article en page(s) : pp 1838 - 1854 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] Botswana
[Termes IGN] carte de la végétation
[Termes IGN] dégradation de l'environnement
[Termes IGN] dégradation des sols
[Termes IGN] données de terrain
[Termes IGN] système d'information géographique
[Termes IGN] utilisation du sol
[Termes IGN] zone arideRésumé : (auteur) Increasing environmental and socioeconomic transformations in African drylands are driving land degradation. Using the Composite Land Degradation Index, this study assessed physical, chemical and biological degradation by determining their extent and severity. Palapye, an agro-pastoral region in eastern Botswana was used as a case study. Land degradation maps (status and indicators) were created with data from the field, soil chemical properties and image interpretation. Areas in the vicinity of settlements with Luvisols at elevations between 773 and 893 m were most degraded, implying impacts from human activities. This study developed a comprehensive list of of land degradation indicators for Botswana and created additional symbols for mapping indicators. Creation of these reference data for 2015 will facilitate the monitoring of land degradation in Palapye. The integrative and spatially explicit procedure utilized in this study can be adapted for assessing and validating local-level land degradation baseline and estimates towards operationalizing Land Degradation Neutrality in all countries. Numéro de notice : A2021-582 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1678673 Date de publication en ligne : 25/10/2019 En ligne : https://doi.org/10.1080/10106049.2019.1678673 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98194
in Geocarto international > vol 36 n° 16 [01/09/2021] . - pp 1838 - 1854[article]Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data / Laura Elena Cué La Rosa in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)
[article]
Titre : Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data Type de document : Article/Communication Auteurs : Laura Elena Cué La Rosa, Auteur ; Camile Sothe, Auteur ; Raul Queiroz Feitosa, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 35 - 49 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Brésil
[Termes IGN] carte de la végétation
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] densité de la végétation
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] espèce végétale
[Termes IGN] forêt tropicale
[Termes IGN] image captée par drone
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) This work proposes a multi-task fully convolutional architecture for tree species mapping in dense forests from sparse and scarce polygon-level annotations using hyperspectral UAV-borne data. Our model implements a partial loss function that enables dense tree semantic labeling outcomes from non-dense training samples, and a distance regression complementary task that enforces tree crown boundary constraints and substantially improves the model performance. Our multi-task architecture uses a shared backbone network that learns common representations for both tasks and two task-specific decoders, one for the semantic segmentation output and one for the distance map regression. We report that introducing the complementary task boosts the semantic segmentation performance compared to the single-task counterpart in up to 11% reaching an average user’s accuracy of 88.63% and an average producer’s accuracy of 88.59%, achieving state-of-art performance for tree species classification in tropical forests. Numéro de notice : A2021-575 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.07.001 Date de publication en ligne : 28/07/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.07.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98175
in ISPRS Journal of photogrammetry and remote sensing > vol 179 (September 2021) . - pp 35 - 49[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021091 SL Revue Centre de documentation Revues en salle Disponible 081-2021093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt 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])
[article]
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 partielsRésumé : (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
in Geocarto international > vol 36 n° 14 [01/08/2021] . - pp 1549 - 1564[article]Comparison of classification methods for urban green space extraction using very high resolution worldview-3 imagery / S. Vigneshwaran in Geocarto international, vol 36 n° 13 ([15/07/2021])
[article]
Titre : Comparison of classification methods for urban green space extraction using very high resolution worldview-3 imagery Type de document : Article/Communication Auteurs : S. Vigneshwaran, Auteur ; S. Vasantha Kumar, Auteur Année de publication : 2021 Article en page(s) : pp 1429 - 1442 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte de la végétation
[Termes IGN] classification dirigée
[Termes IGN] classification non dirigée
[Termes IGN] classification orientée objet
[Termes IGN] espace vert
[Termes IGN] flore urbaine
[Termes IGN] image à très haute résolution
[Termes IGN] image Worldview
[Termes IGN] Inde
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] urbanismeRésumé : (auteur) Urban green space (UGS) plays a vital role in maintaining the ecological balance of a city and in ensuring healthy living of the city inhabitants. It is generally suggested that one-third of the city should be covered by green and to ensure this, the city administrators must have an accurate map of the existing UGS. Such a map would be useful to visualize the distribution of the existing green cover and also to find out the areas that can possibly be converted to UGS. Reported studies on UGS mapping have mostly used medium and high resolution images such as Landsat-TM, ETM+, Sentinel-2A, IKONOS, etc. However, studies on the use of very high resolution images for UGS extraction are very limited. The present study is a first attempt in utilizing the very high resolution Worldview-3 image for UGS extraction. Performance of different classification methods such as unsupervised, supervised, object based and normalized difference vegetation index (NDVI) were compared using the pan sharpened Worldview-3 image covering part of New Delhi in India. It was found that the unsupervised classification followed by manual recoding method showed superior performance with overall accuracy (OA) of 99% and κ coefficient of 0.98. Also, the OA achieved in the present study is the highest when compared to other reported studies on UGS extraction. The map of UGS revealed that almost 40% of the study area is covered by green which is more than the recommended value of 33% (one-third). In order to check the universality of the unsupervised classification approach in extracting UGS, Worldview-3 image covering Rio in Brazil was tested. It was found that an OA of 98% and κ coefficient of 0.95 were obtained which clearly indicate that the proposed approach would work very well in extracting UGS from any Worldview-3 imagery. Numéro de notice : A2021-553 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1665714 Date de publication en ligne : 18/09/2019 En ligne : https://doi.org/10.1080/10106049.2019.1665714 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98104
in Geocarto international > vol 36 n° 13 [15/07/2021] . - pp 1429 - 1442[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2021131 RAB Revue Centre de documentation En réserve L003 Disponible Forest cover mapping and Pinus species classification using very high-resolution satellite images and random forest / Laura Alonso-Martinez in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)PermalinkProvisioning forest and conservation science with high-resolution maps of potential distribution of major European tree species under climate change / Debojyoti Chakraborty in Annals of Forest Science, vol 78 n° 2 (June 2021)PermalinkWalking through the forests of the future: using data-driven virtual reality to visualize forests under climate change / Jiawei Huang in International journal of geographical information science IJGIS, vol 35 n° 6 (June 2021)PermalinkPotentialité des données satellitaires Sentinel-2 pour la cartographie de l’impact des feux de végétation en Afrique tropicale : application au Togo / Yawo Konko in Bois et forêts des tropiques, n° 347 ([02/04/2021])PermalinkComplémentarité des images optiques Sentinel-2 avec les images radar Sentinel-1 et ALOS-PALSAR-2 pour la cartographie de la couverture végétale : application à une aire protégée et ses environs au Nord-Ouest du Maroc via trois algorithmes d’apprentissage automatique / Siham Acharki in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)PermalinkApplication of fuzzy analytical hierarchy process for assessment of desertification sensitive areas in North West of Morocco / Hicham Ait Kacem in Geocarto international, vol 36 n° 5 ([15/03/2021])PermalinkImproving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR / Kabir Peerbhay in Geocarto international, vol 36 n° 4 ([01/03/2021])PermalinkSpruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery / Rajeev Bhattarai in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)PermalinkUsing automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain / R. Niederheiser in GIScience and remote sensing, vol 58 n° 1 (February 2021)PermalinkAnalyse spatio-temporaire des dégradations et évolution des forêts par télédétection : cas du Parc National de Theniet El Had (Algérie) / Faouzi Berrichi in Bulletin des sciences géographiques, n° 32 (2019 - 2021)Permalink