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Rapid ecosystem change at the southern limit of the Canadian Arctic, Torngat Mountains National Park / Emma L. Davis in Remote sensing, vol 13 n° 11 (June-1 2021)  

Public [article]  inRemote sensing > vol 13 n° 11 (June-1 2021) . - n° 2085 Titre : Rapid ecosystem change at the southern limit of the Canadian Arctic, Torngat Mountains National Park Type de document : Article/Communication Auteurs : Emma L. Davis, Auteur ; Andrew Trant, Auteur ; Robert G. Way, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2085 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] arbuste [Termes IGN] Arctique [Termes IGN] Canada [Termes IGN] changement climatique [Termes IGN] classification par forêts aléatoires [Termes IGN] détection de changement [Termes IGN] écosystème [Termes IGN] écotone [Termes IGN] géostatistique [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-OLI [Termes IGN] image Terra-MODIS [Termes IGN] modèle de simulation [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] parc naturel national [Termes IGN] régression logistique [Termes IGN] surveillance de la végétation [Termes IGN] toundra Résumé : (auteur) Northern protected areas guard against habitat and species loss but are themselves highly vulnerable to environmental change due to their fixed spatial boundaries. In the low Arctic, Torngat Mountains National Park (TMNP) of Canada, widespread greening has recently occurred alongside warming temperatures and regional declines in caribou. Little is known, however, about how biophysical controls mediate plant responses to climate warming, and available observational data are limited in temporal and spatial scope. In this study, we investigated the drivers of land cover change for the 9700 km2 extent of the park using satellite remote sensing and geostatistical modelling. Random forest classification was used to hindcast and simulate land cover change for four different land cover types from 1985 to 2019 with topographic and surface reflectance imagery (Landsat archive). The resulting land cover maps, in addition to topographic and biotic variables, were then used to predict where future shrub expansion is likely to occur using a binomial regression framework. Land cover hindcasts showed a 235% increase in shrub and a 105% increase in wet vegetation cover from 1985/89 to 2015/19. Shrub cover was highly persistent and displaced wet vegetation in southern, low-elevation areas, whereas wet vegetation expanded to formerly dry, mid-elevations. The predictive model identified both biotic (initial cover class, number of surrounding shrub neighbors), and topographic variables (elevation, latitude, and distance to the coast) as strong predictors of future shrub expansion. A further 51% increase in shrub cover is expected by 2039/43 relative to 2014 reference data. Establishing long-term monitoring plots within TMNP in areas where rapid vegetation change is predicted to occur will help to validate remote sensing observations and will improve our understanding of the consequences of change for biotic and abiotic components of the tundra ecosystem, including important cultural keystone species. Numéro de notice : A2021-442 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13112085 date de publication en ligne : 26/05/2021 En ligne : https://doi.org/10.3390/rs13112085 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97832 [article]

Mask R-CNN and OBIA fusion improves the segmentation of scattered vegetation in very high-resolution optical sensors / Emilio Guirado in Sensors, vol 21 n° 1 (January 2021)  

Public [article]  inSensors > vol 21 n° 1 (January 2021) . - n° 320 Titre : Mask R-CNN and OBIA fusion improves the segmentation of scattered vegetation in very high-resolution optical sensors Type de document : Article/Communication Auteurs : Emilio Guirado, Auteur ; Javier Blanco-Sacristán, Auteur ; Emilio Rodríguez-Caballero, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 320 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] apprentissage profond [Termes IGN] arbuste [Termes IGN] capteur optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de changement [Termes IGN] image à très haute résolution [Termes IGN] segmentation d'image [Termes IGN] service écosystémique [Termes IGN] surveillance de la végétation [Termes IGN] zone aride Résumé : (auteur) Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands. Numéro de notice : A2021-157 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/s21010320 date de publication en ligne : 05/01/2021 En ligne : https://doi.org/10.3390/s21010320 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97072 [article]

Shrub biomass estimates in former burnt areas using Sentinel 2 images processing and classification / Jose Aranha in Forests, vol 11 n° 5 (May 2020)  

Public [article]  inForests > vol 11 n° 5 (May 2020) . - 19 p. 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 sinistrée Résumé : (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 [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)  

Public [article]  inForests > vol 11 n° 5 (May 2020) . - 14 p. 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 forestière Résumé : (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 [article]

Using 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)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 82 (October 2019) . - 15 p. Titre : Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia Type de document : Article/Communication Auteurs : Neil Flood, Auteur ; Fiona Watson, Auteur ; Lisa Collett, Auteur Année de publication : 2019 Article en page(s) : 15 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] arbre (flore) [Termes IGN] arbuste [Termes IGN] bois sur pied [Termes IGN] carte de la végétation [Termes IGN] données d'apprentissage [Termes IGN] image à haute résolution [Termes IGN] image satellite [Termes IGN] méthode de Monte-Carlo [Termes IGN] mosaïque d'images [Termes IGN] Queensland (Australie) [Termes IGN] réseau neuronal convolutif [Termes IGN] texture d'image Résumé : (auteur) Convolutional neural networks offer a new approach to classifying high resolution imagery. We use the U-net neural network architecture to map the presence or absence of trees and large shrubs across the Australian state of Queensland. From a state-wide mosaic of 1 m resolution 3-band Earth-i imagery, a selection of 827 squares (1 km2) are manually labeled for the presence of trees or large shrubs, and these are used to train the neural network. The training is intended to capture the textures which are primary visual cues of such vegetation. The trained neural network has an accuracy on independent data of around 90%. The resulting map over the whole of Queensland (1.73 million km2) is intended to be manually checked, and edited where necessary, to provide a high quality map of woody vegetation extent to serve a range of government policy objectives. Numéro de notice : A2019-474 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.101897 date de publication en ligne : 28/06/2019 En ligne : https://doi.org/10.1016/j.jag.2019.101897 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93635 [article]

Testing the generality of below-ground biomass allometry across plant functional types / Keryn I. Paul in Forest ecology and management, vol 432 (15 January 2019)  

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Response of Swiss forests to management and climate change in the last 60 years / Meinrad Küchler in Annals of Forest Science [en ligne], vol 72 n° 3 (May 2015)  

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Influence of a dense, low-height shrub species on the accuracy of a lidar-derived DEM / Samuel B. Gould in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 5 (May 2013)  

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Small-footprint Lidar estimations of sagebrush canopy characteristics / J. Mitchell in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 5 (May 2011)  

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Flore des arbres, arbustes et arbrisseaux, 1. Plaines et collines / René Rol (1975)

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