Descripteur
Documents disponibles dans cette catégorie (6838)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
Etendre la recherche sur niveau(x) vers le bas
The Impact of ash dieback on veteran trees in Southwestern Sweden / Vikki Bengtsson in Baltic forestry, vol 27 n° 1 ([01/01/2021])
![]()
[article]
Titre : The Impact of ash dieback on veteran trees in Southwestern Sweden Type de document : Article/Communication Auteurs : Vikki Bengtsson, Auteur ; Anna Stenström, Auteur ; C. Philip Wheater, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2 - 9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Chalara fraxinea
[Termes IGN] coupe (sylviculture)
[Termes IGN] dépérissement
[Termes IGN] Fraxinus excelsior
[Termes IGN] maladie phytosanitaire
[Termes IGN] mortalité
[Termes IGN] ombre
[Termes IGN] Suède
[Vedettes matières IGN] SylvicultureRésumé : (auteur) Ash dieback (Hymenoscyphus fraxineus) is a fungal disease which affects ash throughout Sweden. Monitoring to study of the impact of ash dieback on veteran trees was undertaken in southwest Sweden in 2009, 2011, 2013, 2015, and 2020. The study found that 94.5% of the ash trees observed were affected by ash dieback disease in 2020 compared with 62% in 2009. 70 of the studied ash trees have died (21%) since the monitoring began. In 2009 there was no relationship between girth and ash dieback, but in 2020 the correlation between girth and the impact of ash dieback was statistically significant. In 2020, also for the first time during monitoring, the ash trees in the shade were significantly more affected by ash dieback, compared with trees standing in the open. This difference was not detected in 2013 or 2015. The effect of ash dieback on pollarded trees has varied between the years studied, but in 2020 there is no longer any significant difference between the pollarded and the non-pollarded
ash trees. There was however a significant difference in the mortality rates between the groups of trees, with ash trees pollarded in more recent times having the highest mortality. Therefore, the recommendation in relation to veteran trees with ash dieback is that all pruning on veteran ash trees should be avoided. Pollarding should only be done on ash pollards that are in a regular cutting cycle and are not showing any symptoms of ash dieback. If possible, clear around old ash trees if they are in shaded conditions. Given that there are relatively few studies on the impact of ash dieback on veteran ash trees, the results of this study should also be relevant outside of Sweden and for the management of ash trees in non-woodland situations.Numéro de notice : A2021-824 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.46490/BF558 Date de publication en ligne : 02/06/2021 En ligne : https://doi.org/10.46490/BF558 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98942
in Baltic forestry > vol 27 n° 1 [01/01/2021] . - pp 2 - 9[article]The potential of LiDAR and UAV-photogrammetric data analysis to interpret archaeological sites: A case study of Chun Castle in South-West England / Israa Kadhim in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)
![]()
[article]
Titre : The potential of LiDAR and UAV-photogrammetric data analysis to interpret archaeological sites: A case study of Chun Castle in South-West England Type de document : Article/Communication Auteurs : Israa Kadhim, Auteur ; Fanar M. Abed, Auteur Année de publication : 2021 Article en page(s) : n° 41 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] château
[Termes IGN] classification ISODATA
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Cornouailles
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image captée par drone
[Termes IGN] photogrammétrie aérienne
[Termes IGN] semis de points
[Termes IGN] site archéologique
[Termes IGN] structure-from-motionRésumé : (auteur) With the increasing demands to use remote sensing approaches, such as aerial photography, satellite imagery, and LiDAR in archaeological applications, there is still a limited number of studies assessing the differences between remote sensing methods in extracting new archaeological finds. Therefore, this work aims to critically compare two types of fine-scale remotely sensed data: LiDAR and an Unmanned Aerial Vehicle (UAV) derived Structure from Motion (SfM) photogrammetry. To achieve this, aerial imagery and airborne LiDAR datasets of Chun Castle were acquired, processed, analyzed, and interpreted. Chun Castle is one of the most remarkable ancient sites in Cornwall County (Southwest England) that had not been surveyed and explored by non-destructive techniques. The work outlines the approaches that were applied to the remotely sensed data to reveal potential remains: Visualization methods (e.g., hillshade and slope raster images), ISODATA clustering, and Support Vector Machine (SVM) algorithms. The results display various archaeological remains within the study site that have been successfully identified. Applying multiple methods and algorithms have successfully improved our understanding of spatial attributes within the landscape. The outcomes demonstrate how raster derivable from inexpensive approaches can be used to identify archaeological remains and hidden monuments, which have the possibility to revolutionize archaeological understanding. Numéro de notice : A2021-146 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10010041 Date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.3390/ijgi10010041 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97053
in ISPRS International journal of geo-information > vol 10 n° 1 (January 2021) . - n° 41[article]The strong and the stronger: The effects of increasing ozone and nitrogen dioxide concentrations in pollen of different forest species / Sónia Pereira in Forests, vol 12 n° 1 (January 2021)
![]()
[article]
Titre : The strong and the stronger: The effects of increasing ozone and nitrogen dioxide concentrations in pollen of different forest species Type de document : Article/Communication Auteurs : Sónia Pereira, Auteur ; Maria Fernández-González, Auteur ; Alexandra Guedes, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 88 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Acer negundo
[Termes IGN] analyse comparative
[Termes IGN] Betula pendula
[Termes IGN] Corylus avellana
[Termes IGN] dioxyde d'azote
[Termes IGN] Europe (géographie politique)
[Termes IGN] indice de stress
[Termes IGN] ozone
[Termes IGN] pollen
[Termes IGN] pollution atmosphérique
[Termes IGN] protection de l'environnement
[Termes IGN] Quercus pedunculata
[Vedettes matières IGN] Ecologie forestièreRésumé : (auteur) The knowledge of pollen sensitivity and tolerance to stress factors such as air pollution is important for forest sustainability, ensuring the most efficient production with the highest benefits and lowest resource losses. This study intended to evaluate the influence of common air pollutants in four forest trees species, Betula pendula Roth, Corylus avellana L., Acer negundo L. and Quercus robur L., through a comparative analysis at the same experimental conditions. We aimed to investigate the effect that may occur in pollen fertility, protein content, oxidative stress and wall composition after exposure in vitro to ozone and nitrogen dioxide at concentration levels for vegetation protection in Europe. Our results suggest changes in pollen viability, protein content and differential sensitivity related to ROS synthesis, NADPH oxidase activity, as well as in wall composition. The results indicate that NO2 exposure affected more the pollen species studied mostly at the highest concentration exposure. As for ozone, there were less significant differences between samples; however, a different behavior occurs in O3 expositions, where the most influence happens at the legal limit for vegetation protection in Europe. Our study showed that significant pollen functions could be compromised even at common air pollutant’s concentrations. Numéro de notice : A2021-143 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f12010088 Date de publication en ligne : 15/01/2021 En ligne : https://doi.org/10.3390/f12010088 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97046
in Forests > vol 12 n° 1 (January 2021) . - n° 88[article]The use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution / Dimitri I. Rukhovitch in Remote sensing, vol 13 n° 1 (January-1 2021)
![]()
[article]
Titre : The use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution Type de document : Article/Communication Auteurs : Dimitri I. Rukhovitch, Auteur ; Polina V. Koroleva, Auteur ; Danila D. Rukhovitch, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 155 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] dégradation des sols
[Termes IGN] distribution spatiale
[Termes IGN] érosion
[Termes IGN] image Landsat
[Termes IGN] image Sentinel-MSI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Russie
[Termes IGN] surface cultivée
[Termes IGN] système d'information géographiqueRésumé : (auteur) Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps. Numéro de notice : A2021-074 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13010155 Date de publication en ligne : 05/01/2021 En ligne : https://doi.org/10.3390/rs13010155 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96810
in Remote sensing > vol 13 n° 1 (January-1 2021) . - n° 155[article]Threat degree classification according to habitat quality: A case study from the Czech Republic / Pavel Lustyk in Forests, vol 12 n° 1 (January 2021)
![]()
[article]
Titre : Threat degree classification according to habitat quality: A case study from the Czech Republic Type de document : Article/Communication Auteurs : Pavel Lustyk, Auteur ; Petr Vahalik, Auteur Année de publication : 2021 Article en page(s) : n° 85 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carte de la végétation
[Termes IGN] conservation des ressources naturelles
[Termes IGN] habitat forestier
[Termes IGN] plante menacée
[Termes IGN] protection de la biodiversité
[Termes IGN] République Tchèque
[Termes IGN] site Natura 2000
[Termes IGN] Tracheophyta
[Vedettes matières IGN] Ecologie forestièreRésumé : (auteur) Important sources of information in the field of nature protection are red lists, which define the degree of threat to individual species. In practice, an assessment of the quality of the habitats in which a species occurs is used to a very limited extent in the preparation of red lists of vascular plants. At the same time, this parameter is usually essential to determine their degree of threat. At present, habitat quality data are available for the territory of the Czech Republic; these were obtained during Natura 2000 habitat mapping in the years 2000–2019. In this paper we propose the use of habitat quality data to determine the degree of threat to selected species of vascular plants and to compile a national red list. Nine plant species from three habitat types were selected for this study: meadows and wetland habitats in the alluvium of large rivers (Cardamine matthioli Moretti, Gratiola officinalis L., Teucrium scordium L.), fen habitats (Carex appropinquata Schumach., C. cespitosa L., C. lepidocarpa Tausch) and ecotone shrub habitats (Rosa agrestis Savi, R. micrantha Borrer ex Sm., R. spinosissima L.). For these species, the quality of the habitats in which they occur was analysed and grid maps were created, which present (1) the level of knowledge of habitat quality and (2) the average habitat quality. The results were compared with the degree of threat in the current national red list. Habitat quality analysis should also be used in the future to detect threatened species, which today are outside the red list and this assessment may be useful in compiling another updated red list of vascular plants of the Czech Republic. Numéro de notice : A2021-144 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.3390/f12010085 Date de publication en ligne : 14/01/2021 En ligne : https://doi.org/10.3390/f12010085 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97047
in Forests > vol 12 n° 1 (January 2021) . - n° 85[article]Towards a systematic and continuous monitoring of climate change impacts on forest productivity in Europe [diaporama] / Clémentine Ols (2021)
PermalinkUnmixing-based Sentinel-2 downscaling for urban land cover mapping / Fei Xu in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)
PermalinkVolumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements / Mikko Kukkonen in Silva fennica, vol 55 n° 1 (January 2021)
PermalinkClimate sensitive single tree growth modeling using a hierarchical Bayes approach and integrated nested Laplace approximations (INLA) for a distributed lag model / Arne Nothdurft in Forest ecology and management, vol 478 ([15/12/2020])
PermalinkApplication of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy / Vasil Yordanov in Applied geomatics, vol 12 n° 4 (December 2020)
PermalinkDeep learning for detecting and classifying ocean objects: application of YoloV3 for iceberg–ship discrimination / Frederik Hass in ISPRS International journal of geo-information, vol 9 n° 12 (December 2020)
PermalinkDoes recent fire activity impact fire-related traits of Pinus halepensis Mill. and Pinus sylvestris L. in the French Mediterranean area? / Bastien Romero in Annals of Forest Science, vol 77 n° 4 (December 2020)
PermalinkDu drone LiDAR à un nuage de points précis et exact : une chaîne de traitement LiDAR adaptée et quasi automatique / Maxime Lafleur in XYZ, n° 165 (décembre 2020)
PermalinkExploring the heterogeneity of human urban movements using geo-tagged tweets / Ding Ma in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)
PermalinkFlore et végétation d’une portion de côte en accrétion : sud du port de Taverna (côte orientale de la Corse) / Guilhan Paradis in Bulletin de la Société botanique du Centre-Ouest, n° 51 (2020)
Permalink