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Drought impacts in forest canopy and deciduous tree saplings in Central European forests / Mirela Beloiu in Forest ecology and management, vol 509 (1 April 2022)  

Public [article]  inForest ecology and management > vol 509 (1 April 2022) . - n° 120075 Titre : Drought impacts in forest canopy and deciduous tree saplings in Central European forests Type de document : Article/Communication Auteurs : Mirela Beloiu, Auteur ; Reinhold Stahlmann, Auteur ; Carl Beierkuhnlein, Auteur Année de publication : 2022 Article en page(s) : n° 120075 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Bavière (Allemagne) [Termes IGN] bois mort [Termes IGN] canopée [Termes IGN] dendrométrie [Termes IGN] données de terrain [Termes IGN] écosystème forestier [Termes IGN] jeune arbre [Termes IGN] mortalité [Termes IGN] peuplement mélangé [Termes IGN] phénomène climatique extrême [Termes IGN] Pinophyta [Termes IGN] régénération (sylviculture) [Termes IGN] résilience écologique [Termes IGN] sécheresse [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Forests worldwide are increasingly exposed to extreme weather events. Drought deteriorates the health, structure, and functioning of forests, which can lead to reduced diversity, decreased productivity, and increased tree mortality. Therefore, it is an urgent need to assess the impact of drought on tree species. Due to differences in tree physiology, saplings and mature trees are likely to respond specifically to drought conditions. In contrast to mature trees, little is known about the response of saplings to drought. Here, we combine in-situ field measurements for saplings of deciduous tree species with remote sensing for forest canopy to assess drought damage, recovery, and sapling mortality patterns during a centennial drought (2018, 2019) and beyond (2020). We measured 2051 saplings out of 214 plots in Central Germany. Forest canopy health was assessed using 10 × 10 m resolution satellite observations for the same locations. We (1) demonstrate that forest canopy exhibits long-lasting drought-induced effects, (2) show that saplings have a remarkable capacity to recover from drought and survive a subsequent drought, (3) demonstrate that reduced sapling recovery leads to their mortality, (4) reveal that drought damage on saplings increases from pioneer to non-pioneer species, and mortality is ranking from Sorbus aucuparia > Sambucus nigra > Fraxinus excelsior, Acer campestre, Frangula alnus > Ulmus glabra > Carpinus betulus > Betula pendula, Fagus sylvatica > Acer pseudoplatanus > Quercus petraea > Corylus avellana, Crataegus spp., > Prunus avium, Quercus robur; and (5) link drought response to site conditions, indicating that species diversity and winter precipitation as relevant indicators of tree health. If periods of drought become more frequent, as expected, this could negatively impact mid-term forest recovery, alter long-term tree species assemblages and reduce biodiversity and functional resilience of forest ecosystems. We suggest that models of forest response to drought should differentiate between the forest canopy and understory and also consider species-specific responses as we found a broad spectrum of responses within the same plant functional type of deciduous tree species in terms of drought damage and recovery. Numéro de notice : A2022-191 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120075 Date de publication en ligne : 12/02/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120075 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99947 [article]

A combination of convolutional and graph neural networks for regularized road surface extraction / Jingjing Yan in IEEE Transactions on geoscience and remote sensing, vol 60 n° 2 (February 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 2 (February 2022) . - n° 4409113 Titre : A combination of convolutional and graph neural networks for regularized road surface extraction Type de document : Article/Communication Auteurs : Jingjing Yan, Auteur ; Shunping Ji, Auteur ; Yao Wei, Auteur Année de publication : 2022 Article en page(s) : n° 4409113 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Bavière (Allemagne) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de contours [Termes IGN] extraction de traits caractéristiques [Termes IGN] extraction du réseau routier [Termes IGN] image aérienne [Termes IGN] jeu de données [Termes IGN] optimisation (mathématiques) [Termes IGN] régression [Termes IGN] réseau neuronal de graphes [Termes IGN] Wuhan (Chine) Résumé : (auteur) Road surface extraction from high-resolution remote sensing images has many engineering applications; however, extracting regularized and smooth road surface maps that reach the human delineation level is a very challenging task, and substantial and time-consuming manual work is usually unavoidable. In this article, to solve this problem, we propose a novel regularized road surface extraction framework by introducing a graph neural network (GNN) for processing the road graph that is preconstructed from the easily accessible road centerlines. The proposed framework formulates the road surface extraction problem as two-sided width inference of the road graph and consists of a convolutional neural network (CNN)-based feature extractor and a GNN model for vertex attribute adjustment. The CNN extracts the high-level abstract features of each vertex in the graph as the input of the GNN and also the road boundary features that allow us to distinguish roads from the background. The GNN propagates and aggregates the features of the vertices in the graph to achieve global optimization of the regression of the regularized widths of the vertices. At the same time, a biased centerline map can also be corrected based on the width prediction result. To the best of the authors’ knowledge, this is the first study to have introduced a GNN to regularized human-level road surface extraction. The proposed method was evaluated on four diverse datasets, and the results show that the proposed method comprehensively outperforms the recent CNN-based segmentation methods and other regularization methods in the intersection over union (IoU) and smoothness score, and a visual check shows that a majority of the prediction results of the proposed method approach the human delineation level. Numéro de notice : A2022-297 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3151688 Date de publication en ligne : 15/02/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3151688 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100355 [article]

Spatiotemporal fusion modelling using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria / Maninder Singh Dhillon in Remote sensing, vol 14 n° 3 (February-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 3 (February-1 2022) . - n° 677 Titre : Spatiotemporal fusion modelling using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria Type de document : Article/Communication Auteurs : Maninder Singh Dhillon, Auteur ; Thorsten Dahms, Auteur ; Carina Kübert-Flock, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 677 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Bavière (Allemagne) [Termes IGN] carte d'occupation du sol [Termes IGN] fusion de données [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] image Terra-MODIS [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] pouvoir de résolution géométrique [Termes IGN] réflectance [Termes IGN] surveillance de la végétation [Termes IGN] utilisation du sol Résumé : (auteur) The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region’s cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16 days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16 days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions’ cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R2 = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R2 = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R2 = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R2 = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R2 = 0.60, RMSE = 0.05) and S-MOD13Q1 (R2 = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution. Numéro de notice : A2022-124 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14030677 Date de publication en ligne : 31/01/2022 En ligne : https://doi.org/10.3390/rs14030677 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99687 [article]

Early detection of spruce vitality loss with hyperspectral data: Results of an experimental study in Bavaria, Germany / Kathrin Einzmann in Remote sensing of environment, vol 266 (December 2021)  

Public [article]  inRemote sensing of environment > vol 266 (December 2021) . - n° 112676 Titre : Early detection of spruce vitality loss with hyperspectral data: Results of an experimental study in Bavaria, Germany Type de document : Article/Communication Auteurs : Kathrin Einzmann, Auteur ; Clement Atzberger, Auteur ; Nicole Pinnel, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112676 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Bavière (Allemagne) [Termes IGN] changement climatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] dépérissement [Termes IGN] détection de changement [Termes IGN] houppier [Termes IGN] image hyperspectrale [Termes IGN] indice de végétation [Termes IGN] insecte nuisible [Termes IGN] phénomène climatique extrême [Termes IGN] Picea abies [Termes IGN] réflectance spectrale [Termes IGN] série temporelle [Termes IGN] stress hydrique Résumé : (auteur) Vitality loss of trees caused by extreme weather conditions, drought stress or insect infestations, are expected to increase with ongoing climate change. The detection of vitality loss at an early stage is thus of vital importance for forestry and forest management to minimize ecological and economical damage. Remote sensing instruments are able to detect changes over large areas down to the level of individual trees. The scope of our study is to investigate whether it is possible to detect stress-related spectral changes at an early stage using hyperspectral sensors. For this purpose, two Norway spruce (Picea abies) forest stands, both different in age and maintenance, were monitored in the field over two vegetation periods. In parallel, time series of airborne hyperspectral remote sensing data were acquired. For each stand 70 trees were artificially stressed (ring-barked) and 70 trees were used as control trees. The data collected in south-eastern Germany consists of measurements at multiple times and at different scales: (1) crown conditions were visually assessed in the field (2) needle reflectance spectra were acquired in the laboratory using a FieldSpec spectrometer, and (3) hyperspectral airborne data (HySpex) were flown at 0.5 m spatial resolution. We aimed for a simultaneous data acquisition at the three levels. This unique data set was investigated whether any feature can be discriminated to detect vitality loss in trees at an early stage. Several spectral transformations were applied to the needle and tree crown spectra, such as spectral derivatives, vegetation indices and angle indices. All features were examined for their separability (ring-barked vs. control trees) with the Random Forest (RF) classification algorithm. As result, the younger, well maintained forest stand only showed minor changes over the 2-year period, whereas changes in the older forest stand were observable both in the needle and in the hyperspectral tree crown spectra, respectively. These changes could even be detected before changes were visible by field observations. The tree spectral reactions to ring-barking were first noticeable 11 months after ring-barking and 6 weeks before they were visible by field inspection. The most discriminative features for separating the two groups were the reflectance spectra and the spectral derivatives, over the VIs or angle indices. The tree crown spectra of the two groups could be separated by the RF classifier with a 79% overall accuracy at the beginning of the second vegetation period and 1 month later with 92% overall accuracy with high kappa index. The results clearly demonstrate the great potential of hyperspectral remote sensing in detecting early vitality changes of stressed trees. Numéro de notice : A2021-921 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112676 Date de publication en ligne : 21/09/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112676 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99274 [article]

Shining light on danger / Anonyme in GEO: Geoconnexion international, Vol 20 n° 5 (Autumn 2021)

Public [article]  inGEO: Geoconnexion international > Vol 20 n° 5 (Autumn 2021) . - pp 41 - 42 Titre : Shining light on danger Type de document : Article/Communication Auteurs : Anonyme, Auteur Année de publication : 2021 Article en page(s) : pp 41 - 42 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Alpes [Termes IGN] Bavière (Allemagne) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] éboulement [Termes IGN] glacier Résumé : (éditeur) A project at the University of Bayreuth is using laser scanners to support rockfall detection in the Alps, as glaciers retreat. Numéro de notice : A2021-905 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99255 [article]

Remotely sensing the species of individual trees / Yifang Shi (2020)  

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Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits / Tawanda W. Gara in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

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Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model / Roshanak Darvishzadeh in International journal of applied Earth observation and geoinformation, vol 79 (July 2019)  

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CNN-based dense image matching for aerial remote sensing images / Shunping Ji in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)  

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Variation of leaf angle distribution quantified by terrestrial LiDAR in natural European beech forest / Jing Liu in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)  

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Simultaneous chain-forming and generalization of road networks / Susanne Wenzel in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

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Estimation and uncertainty of the mixing effects on Scots pine—European beech productivity from national forest inventories data / Sonia Condés in Forests, vol 9 n° 9 (September 2018)  

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Adaptive stopping criterion for top-down segmentation of ALS point clouds in temperate coniferous forests / Nina Amiri in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)  

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Large off-nadir scan angle of airborne LiDAR can severely affect the estimates of forest structure metrics / Jing Liu in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)  

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Experiences with the QDaedalus system for astrogeodetic determination of deflections of the vertical / Markus Hauk in Survey review, vol 49 n° 355 (October 2017)  

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