Descripteur
Documents disponibles dans cette catégorie (477)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Shrub biomass estimates in former burnt areas using Sentinel 2 images processing and classification / Jose Aranha in Forests, vol 11 n° 5 (May 2020)
[article]
Titre : Shrub biomass estimates in former burnt areas using Sentinel 2 images processing and classification Type de document : Article/Communication Auteurs : Jose Aranha, Auteur ; Teresa Enes, Auteur ; Ana Calvão, Auteur ; Hélder Viana, Auteur Année de publication : 2020 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arbuste
[Termes IGN] biomasse
[Termes IGN] classification dirigée
[Termes IGN] gestion forestière
[Termes IGN] image proche infrarouge
[Termes IGN] image RVB
[Termes IGN] image Sentinel-MSI
[Termes IGN] incendie de forêt
[Termes IGN] modèle de croissance végétale
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Portugal
[Termes IGN] signature spectrale
[Termes IGN] sous-bois
[Termes IGN] système d'information géographique
[Termes IGN] zone sinistréeRésumé : (auteur) Shrubs growing in former burnt areas play two diametrically opposed roles. On the one hand, they protect the soil against erosion, promote rainwater infiltration, carbon sequestration and support animal life. On the other hand, after the shrubs’ density reaches a particular size for the canopy to touch and the shrubs’ biomass accumulates more than 10 Mg ha−1, they create the necessary conditions for severe wild fires to occur and spread. The creation of a methodology suitable to identify former burnt areas and to track shrubs’ regrowth within these areas in a regular and a multi temporal basis would be beneficial. The combined use of geographical information systems (GIS) and remote sensing (RS) supported by dedicated land survey and field work for data collection has been identified as a suitable method to manage these tasks. The free access to Sentinel images constitutes a valuable tool for updating the GIS project and for the monitoring of regular shrubs’ accumulated biomass. Sentinel 2 VIS-NIR images are suitable to classify rural areas (overall accuracy = 79.6% and Cohen’s K = 0.754) and to create normalized difference vegetation index (NDVI) images to be used in association to allometric equations for the shrubs’ biomass estimation (R2 = 0.8984, p-value Numéro de notice : A2020-654 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f11050555 Date de publication en ligne : 14/05/2020 En ligne : https://doi.org/10.3390/f11050555 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96116
in Forests > vol 11 n° 5 (May 2020) . - 19 p.[article]What Is threatening forests in protected areas? A global assessment of deforestation in protected areas, 2001–2018 / Christopher M. Wade in Forests, vol 11 n° 5 (May 2020)
[article]
Titre : What Is threatening forests in protected areas? A global assessment of deforestation in protected areas, 2001–2018 Type de document : Article/Communication Auteurs : Christopher M. Wade, Auteur ; Kemen G. Austin, Auteur ; James Cajka, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 14 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] aire protégée
[Termes IGN] Amérique du nord
[Termes IGN] analyse diachronique
[Termes IGN] arbuste
[Termes IGN] biodiversité
[Termes IGN] Brésil
[Termes IGN] Congo
[Termes IGN] couvert forestier
[Termes IGN] déboisement
[Termes IGN] gaz à effet de serre
[Termes IGN] habitat forestier
[Termes IGN] indicateur de service écosystémique
[Termes IGN] Indonésie
[Termes IGN] protection des forêts
[Termes IGN] risque environnemental
[Vedettes matières IGN] Ecologie forestièreRésumé : (auteur) The protection of forests is crucial to providing important ecosystem services, such as supplying clean air and water, safeguarding critical habitats for biodiversity, and reducing global greenhouse gas emissions. Despite this importance, global forest loss has steadily increased in recent decades. Protected Areas (PAs) currently account for almost 15% of Earth’s terrestrial surface and protect 5% of global tree cover and were developed as a principal approach to limit the impact of anthropogenic activities on natural, intact ecosystems and habitats. We assess global trends in forest loss inside and outside of PAs, and land cover following this forest loss, using a global map of tree cover loss and global maps of land cover. While forests in PAs experience loss at lower rates than non-protected forests, we find that the temporal trend of forest loss in PAs is markedly similar to that of all forest loss globally. We find that forest loss in PAs is most commonly—and increasingly—followed by shrubland, a broad category that could represent re-growing forest, agricultural fallows, or pasture lands in some regional contexts. Anthropogenic forest loss for agriculture is common in some regions, particularly in the global tropics, while wildfires, pests, and storm blowdown are a significant and consistent cause of forest loss in more northern latitudes, such as the United States, Canada, and Russia. Our study describes a process for screening tree cover loss and agriculture expansion taking place within PAs, and identification of priority targets for further site-specific assessments of threats to PAs. We illustrate an approach for more detailed assessment of forest loss in four case study PAs in Brazil, Indonesia, Democratic Republic of Congo, and the United States. Numéro de notice : A2020-655 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f11050539 Date de publication en ligne : 12/05/2020 En ligne : https://doi.org/10.3390/f11050539 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96119
in Forests > vol 11 n° 5 (May 2020) . - 14 p.[article]Geocoding of trees from street addresses and street-level images / Daniel Laumer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)
[article]
Titre : Geocoding of trees from street addresses and street-level images Type de document : Article/Communication Auteurs : Daniel Laumer, Auteur ; Nico Lang, Auteur ; Natalie Van Doorn, Auteur Année de publication : 2020 Article en page(s) : pp 125 - 136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse des correspondances
[Termes IGN] apprentissage profond
[Termes IGN] arbre urbain
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'arbres
[Termes IGN] détection d'objet
[Termes IGN] géocodage par adresse postale
[Termes IGN] image panoramique
[Termes IGN] image Streetview
[Termes IGN] inventaire
[Termes IGN] service écosystémique
[Termes IGN] zone urbaineRésumé : (auteur) We introduce an approach for updating older tree inventories with geographic coordinates using street-level panorama images and a global optimization framework for tree instance matching. Geolocations of trees in inventories until the early 2000s where recorded using street addresses whereas newer inventories use GPS. Our method retrofits older inventories with geographic coordinates to allow connecting them with newer inventories to facilitate long-term studies on tree mortality etc. What makes this problem challenging is the different number of trees per street address, the heterogeneous appearance of different tree instances in the images, ambiguous tree positions if viewed from multiple images and occlusions. To solve this assignment problem, we (i) detect trees in Google street-view panoramas using deep learning, (ii) combine multi-view detections per tree into a single representation, (iii) and match detected trees with given trees per street address with a global optimization approach. Experiments for trees in 5 cities in California, USA, show that we are able to assign geographic coordinates to 38% of the street trees, which is a good starting point for long-term studies on the ecosystem services value of street trees at large scale. Numéro de notice : A2020-124 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.001 Date de publication en ligne : 21/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94749
in ISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 125 - 136[article]How far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study / Enrico Borgogno Mondino in International Journal of Remote Sensing IJRS, vol 41 n° 12 (20 - 30 March 2020)
[article]
Titre : How far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study Type de document : Article/Communication Auteurs : Enrico Borgogno Mondino, Auteur ; Vanina Fissore, Auteur ; Michael J. Falkowski, Auteur ; Brian Palik, Auteur Année de publication : 2020 Article en page(s) : pp 4551 - 4569 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] auscultation topographique
[Termes IGN] diamètre des arbres
[Termes IGN] données dendrométriques
[Termes IGN] données lidar
[Termes IGN] feuillu
[Termes IGN] hauteur des arbres
[Termes IGN] image Landsat-OLI
[Termes IGN] inventaire forestier local
[Termes IGN] Minnesota (Etats-Unis)
[Termes IGN] modèle d'erreur
[Termes IGN] Pinophyta
[Termes IGN] semis de points
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] surface forestière
[Termes IGN] télémètre laser aéroportéRésumé : (auteur) Aerial discrete return LiDAR (Light Detection And Ranging) technology (ALS – Aerial Laser Scanner) is now widely used for forest characterization due to its high accuracy in measuring vertical and horizontal forest structure. Random and systematic errors can still occur and these affect the native point cloud, ultimately degrading ALS data accuracy, especially when adopting datasets that were not natively designed for forest applications. A detailed understanding of how uncertainty of ALS data could affect the accuracy of derivable forest metrics (e.g. tree height, stem diameter, basal area) is required, looking for eventual error biases that can be possibly modelled to improve final accuracy. In this work a low-density ALS dataset, originally acquired by the State of Minnesota (USA) for non-forestry related purposes (i.e. topographic mapping), was processed attempting to characterize forest inventory parameters for the Cutfoot Sioux Experimental Forest (north-central Minnesota, USA). Since accuracy of estimates strictly depends on the applied species-specific dendrometric models a first required step was to map tree species over the forest. A rough classification, aiming at separating conifers from broadleaf, was achieved by processing a Landsat 8 OLI (Operational Land Imager) scene. ALS-derived forest metrics initially greatly overestimated those measured at the ground in 230 plots. Conversely, ALS-derived tree density was greatly underestimated. To reduce ALS uncertainty, trees belonging to the dominated plane were removed from the ground dataset, assuming that they could not properly be detected by low-density ALS measures. Consequently, MAE (Mean Absolute Error) values significantly decreased to 4.0 m for tree height and to 0.19 cm for diameter estimates. Remaining discrepancies were related to a bias affecting the native ALS point cloud, which was modelled and removed. Final MAE values were 1.32 m for tree height, 0.08 m for diameter, 8.5 m2 ha−1 for basal area, and 0.06 m for quadratic mean diameter. Specifically focusing on tree height and diameter estimates, the significance of differences between ground and ALS estimates was tested relative to the expected ‘best accuracy’. Results showed that after correction: 94.35% of tree height differences were lower than the corresponding reference value (2.86 m); 70% of tree diameter differences were lower than the corresponding reference value (4.5 cm for conifers and 6.8 cm for broadleaf). Finally, forest parameters were computed for the whole Cutfoot Sioux Experimental Forest. Main findings include: 1) all forest estimates based on a low-density ALS point cloud can be derived at plot level and not at a tree level; 2) tree height estimates obtained by low-density ALS point clouds at the plot level are highly reasonably accurate only after testing and modelling eventual error bias; 3) diameter, basal area, and quadratic mean diameter estimates have large uncertainties, suggesting the need for a higher point density and, probably, a better mapping of tree species (if possible) than achieved with a remote sensing-based approach. Numéro de notice : A2020-450 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2020.1723173 Date de publication en ligne : 20/02/2020 En ligne : https://doi.org/10.1080/01431161.2020.1723173 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95535
in International Journal of Remote Sensing IJRS > vol 41 n° 12 (20 - 30 March 2020) . - pp 4551 - 4569[article]City-descriptive input data for urban climate models: Model requirements, data sources and challenges / Valéry Masson in Urban climate, vol 31 (March 2020)
[article]
Titre : City-descriptive input data for urban climate models: Model requirements, data sources and challenges Type de document : Article/Communication Auteurs : Valéry Masson, Auteur ; Wieke Heldens, Auteur ; Erwan Bocher, Auteur ; Marion Bonhomme, Auteur ; Bénédicte Bucher , Auteur ; et al., Auteur Année de publication : 2020 Projets : URCLIM / Masson, Valéry Article en page(s) : n° 100536 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] arbre urbain
[Termes IGN] données localisées numériques
[Termes IGN] données socio-économiques
[Termes IGN] flore urbaine
[Termes IGN] morphologie urbaine
[Termes IGN] occupation du sol
[Termes IGN] ville
[Termes IGN] zone urbaineRésumé : (auteur) Cities are particularly vulnerable to meteorological hazards because of the concentration of population, goods, capital stock and infrastructure. Urban climate services require multi-disciplinary and multi-sectorial approaches and new paradigms in urban climate modelling. This paper classifies the required urban input data for both mesoscale state-of-the-art Urban Canopy Models (UCMs) and microscale Obstacle Resolving Models (ORM) into five categories and reviews the ways in which they can be obtained. The first two categories are (1) land cover, and (2) building morphology. These govern the main interactions between the city and the urban climate and the Urban Heat Island. Interdependence between morphological parameters and UCM geometric hypotheses are discussed. Building height, plan and wall area densities are recommended as the main input variables for UCMs, whereas ORMs require 3D building data. Recently, three other categories of urban data became relevant for finer urban studies and adaptation to climate change: (3) building design and architecture, (4) building use, anthropogenic heat and socio-economic data, and (5) urban vegetation data. Several methods for acquiring spatial information are reviewed, including remote sensing, geographic information system (GIS) processing from administrative cadasters, expert knowledge and crowdsourcing. Data availability, data harmonization, costs/efficiency trade-offs and future challenges are then discussed. Numéro de notice : A2020-003 Affiliation des auteurs : LASTIG+Ext (2016-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.uclim.2019.100536 Date de publication en ligne : 19/11/2019 En ligne : https://doi.org/10.1016/j.uclim.2019.100536 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94290
in Urban climate > vol 31 (March 2020) . - n° 100536[article]Contribution à la segmentation et à la modélisation 3D du milieu urbain à partir de nuages de points / Tania Landes (2020)PermalinkPermalinkFlowering acceleration in native Brazilian tree species for genetic conservation and breeding / Gleidson Guilherme Caldas Mende in Annals of forest research, Vol 63 n° 1 (January - June 2020)PermalinkDeep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees / Hamid Hamraz in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)PermalinkThis is my spot: What are the characteristics of the trees excavated by the Black Woodpecker? A case study in two managed French forests / Camille Puverel in Forest ecology and management, vol 453 (1 December 2019)PermalinkCaractériser et suivre qualitativement et quantitativement les haies et le bocage en France / Sophie Morin in Sciences, eaux & territoires, n° 30 (octobre 2019)PermalinkMapping dead forest cover using a deep convolutional neural network and digital aerial photography / Jean-Daniel Sylvain in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)PermalinkTransferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines / Lauri Korhonen in Silva fennica, vol 53 n° 3 (2019)PermalinkUsing a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia / Neil Flood in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)PermalinkVulnerability of forest ecosystems to fire in the French Alps / Sylvain Dupire in European Journal of Forest Research, Vol 138 n° 5 (octobre 2019)Permalink