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Termes IGN > sciences naturelles > sciences de la vie > biologie > botanique > formation végétale > forêt
forêt
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Bois (forêts), Boisé, Espace boisé, Espace forestier, Essence forestière, Forêt et sylviculture, Groupement forestier (écologie), Massif forestier, Milieu forestier, Peuplement forestier, Région forestière Ressource forestière, Zone forestière. Campagne, Espace naturel. >> Arbre, Archéologie des forêts, Écologie des forêts, Foresterie, Paysage forestier, Politique forestière, Produit forestier, Sylviculture. Voir aussi aux noms des forêts, par ex. : Fontainebleau, Forêt de (Seine-et-Marne) ; Bayerischer Wald (Allemagne). >>Terme(s) spécifique(s) : Biomasse des forêts, Canopée, Forêt domaniale, Forêt privée, Plante des forêts, Réserve forestière, Sol forestier, Station forestière -- Typologie. Source(s) : Grand Larousse universel . - Terminologie forestière / A. Métro, 1975. Equiv. LCSH : Forests and forestry. Domaine(s) : 577, 580. Synonyme(s)paysage forestierVoir aussi |
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Strong gradients in forest sensitivity to climate change revealed by dynamics of forest fire cycles in the post Little Ice Age Era / Igor Drobyshev in Journal of geophysical research : Biogeosciences, vol 122 n° 10 (October 2017)
[article]
Titre : Strong gradients in forest sensitivity to climate change revealed by dynamics of forest fire cycles in the post Little Ice Age Era Type de document : Article/Communication Auteurs : Igor Drobyshev, Auteur ; Yves Bergeron, Auteur ; Martin P. Girardin, Auteur ; Sylvie Gauthier, Auteur ; Clémentine Ols , Auteur ; John Ojal, Auteur Année de publication : 2017 Projets : PREREAL / Ali, Ahmed Adam Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Amérique du nord
[Termes IGN] analyse diachronique
[Termes IGN] changement climatique
[Termes IGN] circulation atmosphérique
[Termes IGN] forêt boréale
[Termes IGN] forêt tempérée
[Termes IGN] incendie de forêt
[Termes IGN] Moyen-Age
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) The length of the fire cycle is a critical factor affecting the vegetation cover in boreal and temperate regions. However, its responses to climate change remain poorly understood. We reanalyzed data from earlier studies of forest age structures at the landscape level, in order to map the evolution of regional fire cycles across Eastern North American boreal and temperate forests, following the termination of the Little Ice Age (LIA). We demonstrated a well‐defined spatial pattern of post‐LIA changes in the length of fire cycles toward lower fire activity during the 1800s and 1900s. The western section of Eastern North America (west of 77°W) experienced a decline in fire activity as early as the first half of the 1800s. By contrast, the eastern section showed these declines as late as the early 1900s. During a regionally fire‐prone period of the 1910s–1920s, forests in the western section of Eastern boreal North America burned more than forests in the eastern section. The climate appeared to dominate over vegetation composition and human impacts in shaping the geographical pattern of the post‐LIA change in fire activity. Changes in the atmospheric circulation patterns following the termination of the LIA, specifically changes in Arctic Oscillation and the strengthening of the Continental Polar Trough, were likely drivers of the regional fire dynamics. Numéro de notice : A2017-912 Affiliation des auteurs : LIF+Ext (2012-2019) Autre URL associée : vers HAL Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1002/2017JG003826 Date de publication en ligne : 20/10/2017 En ligne : https://doi.org/10.1002/2017JG003826 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96696
in Journal of geophysical research : Biogeosciences > vol 122 n° 10 (October 2017)[article]The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas / Emanuele Santi in Remote sensing of environment, vol 200 (October 2017)
[article]
Titre : The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas Type de document : Article/Communication Auteurs : Emanuele Santi, Auteur ; Simonetta Paloscia, Auteur ; Simone Pettinato, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 63 - 73 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande C
[Termes IGN] bande L
[Termes IGN] biomasse forestière
[Termes IGN] capacité de stockage
[Termes IGN] classification par réseau neuronal
[Termes IGN] forêt méditerranéenne
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image Envisat-ASAR
[Termes IGN] image radar moirée
[Termes IGN] modèle de transfert radiatif
[Termes IGN] production primaire brute
[Termes IGN] Toscane (Italie)Résumé : (auteur) The extraction of forest information from SAR images is particularly complex in Mediterranean areas, since they are characterized by high spatial fragmentation and heterogeneity. We have investigated the use of multi-frequency SAR data from different sensors (ALOS/PALSAR and ENVISAT/ASAR) for estimating forest biomass in two test areas in Central Italy (San Rossore and Molise), where detailed in-situ measurements and Airborne Laser Scanning (ALS) data were available. The study focused on the estimation of growing stock volume (GS, in m3/ha) by using an inversion algorithm based on artificial neural networks (ANN). The ANN algorithm was first appropriately trained using the available GS estimates obtained from ALS data. The potential of this algorithm was then improved through the innovative use of a simulated dataset, generated by a forward electromagnetic model based on the Radiative Transfer Theory (RTT). The algorithm is able to merge SAR data at L and C bands for predicting GS in diversified Mediterranean environments. The performed analyses indicated that GS was correctly estimated by integrating information from L and C bands on both test areas, with the following statistics: R > 0.97 and RMSE = 28.5 m3/ha for the independent test, and R = 0.86 and RMSE ≈ 77 m3/ha for the final independent validation, the latter performed on the forest stands of both areas not included in the ALS acquisitions and where conventional measurements were available. The research then illustrates the potential of using the obtained GS estimates from SAR data to drive the simulations of forest net primary production (NPP). This experiment produced spatially explicit estimates of GS current annual increments that are slightly less accurate than those obtained from ground observations (R = 0.75 and RMSE ≈ 1.5 m3/ha/year). Numéro de notice : A2017-415 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2017.07.038 En ligne : https://doi.org/10.1016/j.rse.2017.07.038 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86307
in Remote sensing of environment > vol 200 (October 2017) . - pp 63 - 73[article]Crown bulk density and fuel moisture dynamics in Pinus pinaster stands are neither modified by thinning nor captured by the Forest Fire Weather Index / Marc Soler Martin in Annals of Forest Science, vol 74 n° 3 (September 2017)
[article]
Titre : Crown bulk density and fuel moisture dynamics in Pinus pinaster stands are neither modified by thinning nor captured by the Forest Fire Weather Index Type de document : Article/Communication Auteurs : Marc Soler Martin, Auteur ; José Antonio Bonet, Auteur ; Juan Martínez De Aragón, Auteur ; et al., Auteur Année de publication : 2017 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse (combustible)
[Termes IGN] éclaircie (sylviculture)
[Termes IGN] Espagne
[Termes IGN] forêt méditerranéenne
[Termes IGN] houppier
[Termes IGN] incendie de forêt
[Termes IGN] indice d'humidité
[Termes IGN] Pinus pinaster
[Termes IGN] risque naturel
[Termes IGN] surveillance forestière
[Termes IGN] traitement d'image
[Termes IGN] variation saisonnière
[Vedettes matières IGN] Ecologie forestièreRésumé : (Auteur)
Key message : No temporal change was recorded during summer in fuel availability in Pinus pinaster stands, contrary to predictions from the Forest Fire Weather Index. Also, thinning had no mid-term effect on fuel moisture or canopy structure.
Context : Forest fires are a major problem in Mediterranean countries. Management actions, such as fuel reductions, are one of the main tools to diminish fire risk, but the midterm efficacy of such tools remains largely untested with empirical data.
Aims : Here, we test for midterm effects of thinning on fuel moisture and crown bulk density in P. pinaster stands and whether temporal variations in fuel moisture correlated with predictions from the Fire Weather Index, a commonly used index on fire risk, and its components.
Methods : We compared fuel moisture over a fire season and crown bulk density in nine pairs of thinned/unthinned plots 7 years after treatments were applied.
Results : We observed that fuel moisture remained stable during a fire season, as a likely result of drought-induced physiological adjustments, including stomatal regulation and others, which allow leaves to maintain a large humidity even during drought, and that thinning had no midterm effect on fuel moisture or crown bulk density. Moreover, the Fire Weather Index and its components displayed different temporal dynamics than those observed in fuel moisture.
Conclusion : These results are important as they indicate that thinning may only have a limited, short-term impact towards diminishing the potential for crown fire spread in these stands and that current indices to evaluate fire risk may require a re-evaluation.Numéro de notice : A2017-354 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-017-0650-1 Date de publication en ligne : 28/06/2017 En ligne : http://doi.org/10.1007/s13595-017-0650-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85720
in Annals of Forest Science > vol 74 n° 3 (September 2017)[article]Forest change detection in incomplete satellite images with deep neural networks / Salman H. Khan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
[article]
Titre : Forest change detection in incomplete satellite images with deep neural networks Type de document : Article/Communication Auteurs : Salman H. Khan, Auteur ; Xuming He, Auteur ; Fatih Porikli, Auteur ; Mohammed Bennamoun, Auteur Année de publication : 2017 Article en page(s) : pp 5407 - 5423 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] analyse multirésolution
[Termes IGN] apprentissage profond
[Termes IGN] détection de changement
[Termes IGN] forêt
[Termes IGN] réflectance de surface
[Termes IGN] réseau neuronal artificiel
[Termes IGN] retouche
[Termes IGN] surveillance de la végétationRésumé : (Auteur) Land cover change monitoring is an important task from the perspective of regional resource monitoring, disaster management, land development, and environmental planning. In this paper, we analyze imagery data from remote sensing satellites to detect forest cover changes over a period of 29 years (1987-2015). Since the original data are severely incomplete and contaminated with artifacts, we first devise a spatiotemporal inpainting mechanism to recover the missing surface reflectance information. The spatial filling process makes use of the available data of the nearby temporal instances followed by a sparse encoding-based reconstruction. We formulate the change detection task as a region classification problem. We build a multiresolution profile (MRP) of the target area and generate a candidate set of bounding-box proposals that enclose potential change regions. In contrast to existing methods that use handcrafted features, we automatically learn region representations using a deep neural network in a data-driven fashion. Based on these highly discriminative representations, we determine forest changes and predict their onset and offset timings by labeling the candidate set of proposals. Our approach achieves the state-of-the-art average patch classification rate of 91.6% (an improvement of ~16%) and the mean onset/offset prediction error of 4.9 months (an error reduction of five months) compared with a strong baseline. We also qualitatively analyze the detected changes in the unlabeled image regions, which demonstrate that the proposed forest change detection approach is scalable to new regions. Numéro de notice : A2017-663 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2707528 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2707528 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87105
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 5407 - 5423[article]A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform / Bangqian Chen in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)
[article]
Titre : A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform Type de document : Article/Communication Auteurs : Bangqian Chen, Auteur ; Xiangming Xiao, Auteur ; Lianghao Pan, Auteur ; Russell Doughty, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 104 - 120 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] carte forestière
[Termes IGN] Chine
[Termes IGN] Google Earth Engine
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-OLI
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] mangrove
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] série temporelleRésumé : (auteur) Due to rapid losses of mangrove forests caused by anthropogenic disturbances and climate change, accurate and contemporary maps of mangrove forests are needed to understand how mangrove ecosystems are changing and establish plans for sustainable management. In this study, a new classification algorithm was developed using the biophysical characteristics of mangrove forests in China. More specifically, these forests were mapped by identifying: (1) greenness, canopy coverage, and tidal inundation from time series Landsat data, and (2) elevation, slope, and intersection-with-sea criterion. The annual mean Normalized Difference Vegetation Index (NDVI) was found to be a key variable in determining the classification thresholds of greenness, canopy coverage, and tidal inundation of mangrove forests, which are greatly affected by tide dynamics. In addition, the integration of Sentinel-1A VH band and modified Normalized Difference Water Index (mNDWI) shows great potential in identifying yearlong tidal and fresh water bodies, which is related to mangrove forests. This algorithm was developed using 6 typical Regions of Interest (ROIs) as algorithm training and was run on the Google Earth Engine (GEE) cloud computing platform to process 1941 Landsat images (25 Path/Row) and 586 Sentinel-1A images circa 2015. The resultant mangrove forest map of China at 30 m spatial resolution has an overall/users/producer’s accuracy greater than 95% when validated with ground reference data. In 2015, China’s mangrove forests had a total area of 20,303 ha, about 92% of which was in the Guangxi Zhuang Autonomous Region, Guangdong, and Hainan Provinces. This study has demonstrated the potential of using the GEE platform, time series Landsat and Sentine-1A SAR images to identify and map mangrove forests along the coastal zones. The resultant mangrove forest maps are likely to be useful for the sustainable management and ecological assessments of mangrove forests in China. Numéro de notice : A2017-419 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.07.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.07.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86313
in ISPRS Journal of photogrammetry and remote sensing > vol 131 (September 2017) . - pp 104 - 120[article]Réservation
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