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Monitoring forest-savanna dynamics in the Guineo-Congolian transition area of the centre region of Cameroon / Le Bienfaiteur Sagang Takougoum (2022)
Titre : Monitoring forest-savanna dynamics in the Guineo-Congolian transition area of the centre region of Cameroon Type de document : Thèse/HDR Auteurs : Le Bienfaiteur Sagang Takougoum, Auteur ; Bonaventure Sonké, Directeur de thèse ; Nicolas Barbier, Directeur de thèse Editeur : Yaoundé : Université de Yaoundé Année de publication : 2022 Importance : 166 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse pour obtenir le grade de Docteur de l'Université de Yaoundé 1, Spécialité Botanique-EcologieLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] Cameroun
[Termes IGN] carte d'utilisation du sol
[Termes IGN] carte de la végétation
[Termes IGN] classification dirigée
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] données de terrain
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] dynamique de la végétation
[Termes IGN] écotone
[Termes IGN] flore locale
[Termes IGN] forêt
[Termes IGN] Google Earth Engine
[Termes IGN] image Landsat
[Termes IGN] image SPOT 6
[Termes IGN] image SPOT 7
[Termes IGN] incendie de forêt
[Termes IGN] modèle statistique
[Termes IGN] savane
[Termes IGN] surveillance forestièreIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Understanding the effects of global change (combining anthropic and climatic pressures) on biome distribution needs innovative approaches allowing to address the large spatial scales involved and the scarcity of available ground data. Characterizing vegetation dynamics at landscape to regional scale requires both a high level of spatial detail (resolution), generally obtained through precise field measurements, and a sufficient coverage of the land surface (extent) provided by satellite images. The difficulty usually lies between these two scales as both signal saturation from satellite data and ground sampling limitations contribute to inaccurate extrapolations. Airborne laser scanning (ALS) data has revolutionized the trade-off between spatial detail and landscape coverage as it gives accurate information of the vegetation’s structure over large areas which can be used to calibrate satellite data. Also recent satellite data of improved spectral and spatial resolutions (Sentinel 2) allow for detailed characterizations of compositional gradients in the vegetation, notably in terms of the abundance of broad functional/optical plant types. Another major obstacle comes from the lack of a temporal perspective on dynamics and disturbances. Growing satellite imagery archives over several decades (45 years; Landsat) and available computing facilities such as Google Earth Engine (GEE) provide new possibilities to track long term successional trajectories and detect significant disturbances (i.e. fire) at a fine spatial detail (30m) and relate them to the current structure and composition of the vegetation. With these game changing tools our objective was to track long-term dynamics of forest-savanna ecotone in the Guineo-Congolian transition area of the Central Region of Cameroon with induced changes in the vegetatio structure and composition within two contrasted scenarios of anthropogenic pressures: 1) the Nachtigal area which is targeted for the dam construction and subject to intense agricultural activities and 2) the Mpem et Djim National Park (MDNP) which has no management plan. The maximum likelihood classification of the Spot 6/7 image aided with the information from the canopy height derived from ALS data discriminated the vegetation types within the Nachtigal area with good accuracy (96.5%). Using field plots data in upscaling aboveground biomass (AGB) form field plots estimates to the satellite estimates with model-based approaches lead to a systematic overestimation in AGB density estimates and a root mean squared prediction error (RMSPE) of up to 65 Mg.ha−1 (90%), whereas calibration with ALS data (AGBALS) lead to low bias and a drop of ~30% in RMSPE (down to 43 Mg.ha−1, 58%) with little effect of the satellite sensor used. However, these results also confirm that, whatever the spectral indices used and attention paid to sensor quality and pre-processing, the signal is not sufficient to warrant accurate pixel wise predictions, because of large relative RMSPE, especially above (200–250 Mg.ha−1). The design-based approach, for which average AGB density values were attributed to mapped land cover classes, proved to be a simple and reliable alternative (for landscape to region level estimations), when trained with dense ALS samples. AGB and species diversity measured within 74 field inventory plots (distributed along a savanna to forest successional gradient) were higher for the vegetation located in the MDNP compared to their pairs in the Nachtigal area. The automated unsupervised long-term (45 years) land cover change monitoring from Landsat image archives based on GEE captured a consistent and regular pattern of forest progression into savanna at an average rate of 1% (ca. 6 km².year-1). No fire occurrence was captured for savanna that transited to forest within five years of monitoring. Distinct assemblages of spectral species are apparent in forest vegetation which is consistent with the age of transition. As forest gets older AGBALS recovers at a rate of 4.3 Mg.ha-1.year-1 in young forest stands ( Note de contenu : Chapter 1. Generalities
1.1 Introduction
1.2 Literature Review
Chapter 2. Material And Methods
2.1 Material
2.2 Methods
Chapter 3. Results And Discussion
3.1 Results
3.2 Discussion
Chapter 4. Conclusion And Perspectives
4.1 Conclusion
4.2 PerspectivesNuméro de notice : 26820 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Thèse étrangère Note de thèse : Thèse de doctorat : Botanique-Ecologie : Yaoundé : 2022 Organisme de stage : Institut de Recherche pour le Développement IRD nature-HAL : Thèse DOI : sans Date de publication en ligne : 13/04/2022 En ligne : https://hal.inrae.fr/tel-03528875/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100465
Titre : Multi-layer modeling of dense vegetation from aerial LiDAR scans Type de document : Article/Communication Auteurs : Ekaterina Kalinicheva , Auteur ; Loïc Landrieu , Auteur ; Clément Mallet , Auteur ; Nesrine Chehata , Auteur Editeur : Computer vision foundation CVF Année de publication : 2022 Projets : 1-Pas de projet / Conférence : EarthVision 2022, Large Scale Computer Vision for Remote Sensing Imagery, workshop joint to CVPR 2022 19/06/2022 24/06/2022 New Orleans Louisiane - Etats-Unis OA Proceedings Importance : pp 1341 - 1350 Format : 21 x 30 cm Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] canopée
[Termes IGN] carte d'occupation du sol
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] étage de végétation
[Termes IGN] foresterie
[Termes IGN] maillage
[Termes IGN] parcelle forestière
[Termes IGN] reconstruction d'objet
[Termes IGN] segmentation d'image
[Termes IGN] semis de pointsRésumé : (auteur) The analysis of the multi-layer structure of wild forests is an important challenge of automated large-scale forestry. While modern aerial LiDARs offer geometric information across all vegetation layers, most datasets and methods focus only on the segmentation and reconstruction of the top of canopy. We release WildForest3D, which consists of 29 study plots and over 2000 individual trees across 47 000m2 with dense 3D annotation, along with occupancy and height maps for 3 vegetation layers: ground vegetation, understory, and overstory. We propose a 3D deep net- work architecture predicting for the first time both 3D point- wise labels and high-resolution layer occupancy rasters simultaneously. This allows us to produce a precise estimation of the thickness of each vegetation layer as well as the corresponding watertight meshes, therefore meeting most forestry purposes. Both the dataset and the model are released in open access: https://github.com/ ekalinicheva/multi_layer_vegetation. Numéro de notice : C2022-007 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers CVF Thématique : FORET/IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/CVPRW56347.2022.00140 Date de publication en ligne : 25/04/2022 En ligne : https://arxiv.org/abs/2204.11620 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100509 Planning coastal Mediterranean stone pine (Pinus pinea L.) reforestations as a green infrastructure: combining GIS techniques and statistical analysis to identify management options / Luigi Portoghesi in Annals of forest research, vol 65 n° 1 (January - June 2022)
[article]
Titre : Planning coastal Mediterranean stone pine (Pinus pinea L.) reforestations as a green infrastructure: combining GIS techniques and statistical analysis to identify management options Type de document : Article/Communication Auteurs : Luigi Portoghesi, Auteur ; Antonio Tomao, Auteur ; Simone Bollati, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 31 - 46 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement
[Termes IGN] approche hiérarchique
[Termes IGN] carte forestière
[Termes IGN] Italie
[Termes IGN] littoral méditerranéen
[Termes IGN] peuplement pur
[Termes IGN] Pinus pinea
[Termes IGN] reboisement
[Termes IGN] résilience écologique
[Termes IGN] structure de la végétation
[Termes IGN] système d'information géographique
[Termes IGN] utilisation du sol
[Vedettes matières IGN] SylvicultureRésumé : (auteur) Mediterranean stone pine reforestations are common characteristics of the Italian Tyrrhenian coast, which mostly maintain uniform and monolayered stand structures. However, improving structural diversity is an effective climate change adaptation strategy in forest management. The aim of this study was to implement a methodology which allows distinct reforested areas such as a single green infrastructure to be managed according to the surrounding land use and the characteristics of the forest stands. 240 hectares of Mediterranean stone pine forests located along a 16 km strip of the Lazio coast (Central Italy) were mapped. Twelve attributes describing the pine stands and showing possible constraints for future management decisions were associated to each forest patch. A hierarchical cluster analysis was performed to group the pinewood patches according to their similarity level and five different groups were identified. For each group, different silvicultural methods were proposed to guide the compositional and structural evolution of the stands, in order to make them suitable for providing services required locally and increasing overall diversity at landscape scale. The results of the study highlight how coastal land uses can offer effective inputs to differentiate the management of forest systems and therefore achieve greater variety and resilience in the landscape over time. This approach is particularly useful in the case of very homogeneous stands such as the stone pine reforestations under study. Numéro de notice : A2022-798 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.15287/afr.2022.2176 Date de publication en ligne : 27/06/2022 En ligne : https://doi.org/10.15287/afr.2022.2176 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101958
in Annals of forest research > vol 65 n° 1 (January - June 2022) . - pp 31 - 46[article]Predicting AIS reception using tropospheric propagation forecast and machine learning / Zackary Vanche (2022)
Titre : Predicting AIS reception using tropospheric propagation forecast and machine learning Type de document : Article/Communication Auteurs : Zackary Vanche, Auteur ; Ambroise Renaud, Auteur ; Aldo Napoli, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : ISAP 2022, IEEE AP-S/USNC-URSI International Symposium on Antennas & Propagation 10/07/2022 24/06/2022 Denver Colorado - Etats-Unis Proceedings IEEE Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage automatique
[Termes IGN] carte thématique
[Termes IGN] identification automatique
[Termes IGN] navigation maritime
[Termes IGN] navire
[Termes IGN] récepteur
[Termes IGN] troposphèreRésumé : (auteur) The aim of this paper is to present a methodology for modelling and predicting the coverage of an Automatic Identification System (AIS) station based on tropospheric index forecast maps and modelling methods from machine learning. The aim of this work is to cartographically represent the areas in which the AIS signals emitted by ships will be received by a coastal station. This work contributes to the improvement of maritime situational awareness and to the detection of anomalies at sea [1], and in particular to the identification of AIS message falsifications [2] (ubiquity of a vessel by identity theft, falsification of GPS positions and deactivation of AIS). Numéro de notice : C2022-036 Affiliation des auteurs : ENSG+Ext (2020- ) Autre URL associée : vers HAL Thématique : POSITIONNEMENT Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.23919/USNC-URSI52669.2022.9887465 En ligne : https://doi.org/10.23919/USNC-URSI52669.2022.9887465 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101606 Representing vector geographic information as a tensor for deep learning based map generalisation / Azelle Courtial (2022)
Titre : Representing vector geographic information as a tensor for deep learning based map generalisation Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur Editeur : AGILE Alliance Année de publication : 2022 Projets : 1-Pas de projet / Conférence : AGILE 2022, 25th international AGILE Conference on Geographic Information Science, Artificial intelligence in the service of geospatial technologies 14/06/2022 17/06/2022 Vilnius Lithuanie OA Proceedings Importance : 8 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] alignement des données
[Termes IGN] apprentissage profond
[Termes IGN] architecture de réseau
[Termes IGN] bati
[Termes IGN] carte topographique
[Termes IGN] couche
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] données vectorielles
[Termes IGN] information sémantique
[Termes IGN] milieu urbain
[Termes IGN] route
[Termes IGN] tenseur
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Recently, many researchers tried to generate (generalised) maps using deep learning, and most of the proposed methods deal with deep neural network architecture choices. Deep learning learns to reproduce examples, so we think that improving the training examples, and especially the representation of the initial geographic information, is the key issue for this problem. Our article extracts some representation issues from a literature review and proposes different ways to represent vector geographic information as a tensor. We propose two kinds of contributions: 1) the representation of information by layers; 2) the representation of additional information. Then, we demonstrate the interest of some of our propositions with experiments that show a visual improvement for the generation of generalised topographic maps in urban areas. 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