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Forest canopy stratification based on fused, imbalanced and collinear LiDAR and Sentinel-2 metrics / Jakob Wernicke in Remote sensing of environment, vol 279 (15 September 2022)  

Public [article]  inRemote sensing of environment > vol 279 (15 September 2022) . - n° 113134 Titre : Forest canopy stratification based on fused, imbalanced and collinear LiDAR and Sentinel-2 metrics Type de document : Article/Communication Auteurs : Jakob Wernicke, Auteur ; Christian Torsten Seltmann, Auteur ; Ralf Wenzel, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113134 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Allemagne [Termes IGN] analyse comparative [Termes IGN] canopée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] fusion d'images [Termes IGN] image Sentinel-MSI [Termes IGN] indice de végétation [Termes IGN] inventaire forestier étranger (données) [Termes IGN] semis de points [Termes IGN] stratification Résumé : (auteur) Knowledge about the forest canopy stratification is of essential importance for forest management and planning. Collecting structural information (e.g. natural regeneration) still depends on cost and labour intensive forest inventories with a coarse spatio-temporal resolution. Remote sensing partly overcomes these limitations and particularly active sensors of type light detection and ranging (LiDAR) have proven their great potential of separating forest strata. The applicability of LiDAR metrics for the differentiation of the spruce dominated forest strata in Central Germany has not been tested yet. Additionally, studying the potential of Sentinel-2 metrics for the classification of forest strata is lacking too. In this study, we investigated the capabilities of six different classification approaches for the differentiation of five forest strata that are typical for the study region. Reference data were derived from forest inventory measurements surveyed on a dense 200 × 200 m grid. The six classification approaches were trained with fused and un-fused LiDAR and Sentinel-2 inferred metrics. The classification results were compared using the overall mean accuracy, sensitivity and specificity via receivers operating characteristics of multi-class problems. We were interested in the classification abilities of Sentinel-2 metrics due to the obvious advantages of Sentinel-2 based metrics (free of charge, high spatio-temporal coverage). We assumed that the canopy structure determines the reflection on stand level and thus might facilitate the classification of different canopy strata. Beforehand, it was important to examine the influence of distinctly imbalanced and collinear reference data on the classification results. We found that the Random Forest classifier most accurately separated the five forest strata with a mean overall accuracy of 83.3% (Kappa = 76.2%). These values were achieved from balanced training data and the classification capability was confirmed by classification results from an independent test data set. Fused predictors of active (LiDAR) and passive (Sentinel-2) remote sensing revealed no substantial improvement in the classification accuracy due to the dominant role of LiDAR metrics. Herein, we identified that especially the height variability, top height, portion of LiDAR-returns between 2 m and 10 m and the standard deviation of the return number between the 25th and 50th height percentile, predominately contributed to the classification accuracy. Classification results purely based on Sentinel-2 metrics revealed a rather small overall mean accuracy of 54.7%. The metrics (e.g. median, variance, entropy) were derived from Sentinel-2 indices, covering the visible and near to short infrared spectrum. Variable importance computations unraveled a detectable but minor contribution of MSI, TCG, NDVI to the classification result. Finally, our data driven observations illustrated serious drawbacks associated to data imbalance, collinearity and autocorrelation and presented practical guidance to cope with these issues. Numéro de notice : A2022-510 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113134 Date de publication en ligne : 28/06/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113134 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101047 [article]

Towards a global seasonal and permanent reference water product from Sentinel-1/2 data for improved flood mapping / Sandro Martinis in Remote sensing of environment, vol 278 (September 2022)  

Public [article]  inRemote sensing of environment > vol 278 (September 2022) . - n° 113077 Titre : Towards a global seasonal and permanent reference water product from Sentinel-1/2 data for improved flood mapping Type de document : Article/Communication Auteurs : Sandro Martinis, Auteur ; Sandro Groth, Auteur ; Marc Wieland, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113077 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] Allemagne [Termes IGN] Australie [Termes IGN] carte thématique [Termes IGN] fusion d'images [Termes IGN] image radar moirée [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] Inde [Termes IGN] inondation [Termes IGN] Mozambique [Termes IGN] prévention des risques [Termes IGN] série temporelle [Termes IGN] Soudan [Termes IGN] surveillance hydrologique [Termes IGN] variation saisonnière [Termes IGN] zone à risque Résumé : (auteur) Satellite-based flood mapping has become an important part of disaster response. In order to accurately distinguish flood inundation from normally present conditions, up-to-date, high-resolution information on the seasonal water cover is crucial. This information is usually neglected in disaster management, which may result in a non-reliable representation of the flood extent, mainly in regions with highly dynamic hydrological conditions. In this study, we present a fully automated method to generate a global reference water product specifically designed for the use in global flood mapping applications based on high resolution Earth Observation data. The proposed methodology combines existing processing pipelines for flood detection based on Sentinel-1/2 data and aggregates permanent as well as seasonal water masks over an adjustable reference time period. The water masks are primarily based on the analysis of Sentinel-2 data and are complemented by Sentinel-1-based information in optical data scarce regions. First results are demonstrated in five selected study areas (Australia, Germany, India, Mozambique, and Sudan), distributed across different climate zones and are systematically compared with external products. Further, the proposed product is exemplary applied to three real flood events in order to evaluate the impact of the used reference water mask on the derived flood extent. Results show, that it is possible to generate a consistent reference water product at 10–20 m spatial resolution, that is more suitable for the use in rapid disaster response than previous masks. The proposed multi-sensor approach is capable of producing reasonable results, even if only few or no information from optical data is available. Further it becomes clear, that the consideration of seasonality of water bodies, especially in regions with highly dynamic hydrological and climatic conditions, reduces potential over-estimation of the inundation extent and gives a more reliable picture on flood-affected areas. Numéro de notice : A2022-467 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113077 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113077 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100801 [article]

Crown allometry and growing space requirements of four rare domestic tree species compared to oak and beech: implications for adaptive forest management / Julia Schmucker in European Journal of Forest Research, vol 141 n° 4 (August 2022)  

Public [article]  inEuropean Journal of Forest Research > vol 141 n° 4 (August 2022) . - pp 587 - 604 Titre : Crown allometry and growing space requirements of four rare domestic tree species compared to oak and beech: implications for adaptive forest management Type de document : Article/Communication Auteurs : Julia Schmucker, Auteur ; Enno Uhl, Auteur ; Mathias Steckel, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 587 - 604 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Acer campestre [Termes IGN] Allemagne [Termes IGN] allométrie [Termes IGN] Carpinus betulus [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] gestion forestière adaptative [Termes IGN] hauteur des arbres [Termes IGN] houppier [Termes IGN] régression par quantile [Termes IGN] Sorbus torminalis [Termes IGN] Ulmus (genre) [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Rare domestic tree species are increasingly being viewed as promising alternatives and additions to current main tree species in forests facing climate change. For a feasible management of these rare species, it is, however, necessary to know their growth patterns and space requirements. This information has been lacking in management and science up to now. Our study investigated the basic crown allometries of four rare domestic tree species (European hornbeam, European white elm, field maple and wild service tree) and compared them to the more established and assessable European beech and oak (sessile oak and pedunculate oak). For our analysis, we used data from eight temporary research plots located on seven sites across south-eastern Germany, augmented by data from long-term plots. Using quantile regression, we investigated the fundamental relationships between crown projection area and diameter, and height and diameter. Subsequently, we used a mixed-effect model to detect the dependence of crown allometry on different stand variables. We derived maximum stem numbers per hectare for each species at different stand heights, thus providing much-needed practical guidelines for forest managers. In the early stages of stand development, we found that European white elm and field maple can be managed with higher stem numbers than European beech, similar to those of oak. European hornbeam and wild service tree require lower stem numbers, similar to European beech. However, during first or second thinnings, we hypothesise that the rare domestic tree species must be released from competitors, as shade tolerance and competitiveness decrease with age. Furthermore, we argue that thinnings must be performed at a higher frequency in stands with admixed European beech because of the species’ high shade tolerance. When properly managed, rare species can reach target diameters similar to oak and beech. Numéro de notice : A2022-639 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10342-022-01460-w Date de publication en ligne : 31/05/2022 En ligne : https://doi.org/10.1007/s10342-022-01460-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101446 [article]

Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series / Maximilian Lange in Remote sensing of environment, vol 277 (August 2022)  

Public [article]  inRemote sensing of environment > vol 277 (August 2022) . - n° 112888 Titre : Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series Type de document : Article/Communication Auteurs : Maximilian Lange, Auteur ; Hannes Feilhauer, Auteur ; Ingolf Kühn, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112888 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Allemagne [Termes IGN] apprentissage automatique [Termes IGN] bande spectrale [Termes IGN] carte d'utilisation du sol [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] échantillonnage de données [Termes IGN] image Sentinel-MSI [Termes IGN] indice de végétation [Termes IGN] prairie [Termes IGN] série temporelle Résumé : (auteur) Information on grassland land-use intensity (LUI) is crucial for understanding trends and dynamics in biodiversity, ecosystem functioning, earth system science and environmental monitoring. LUI is a major driver for numerous environmental processes and indicators, such as primary production, nitrogen deposition and resilience to climate extremes. However, large extent, high resolution data on grassland LUI is rare. New satellite generations, such as Copernicus Sentinel-2, enable a spatially comprehensive detection of the mainly subtle changes induced by land-use intensification by their fine spatial and temporal resolution. We developed a methodology quantifying key parameters of grassland LUI such as grazing intensity, mowing frequency and fertiliser application across Germany using Convolutional Neural Networks (CNN) on Sentinel-2 satellite data with 20 m × 20 m spatial resolution. Subsequently, these land-use components were used to calculate a continuous LUI index. Predictions of LUI and its components were validated using comprehensive in situ grassland management data. A feature contribution analysis using Shapley values substantiates the applicability of the methodology by revealing a high relevance of springtime satellite observations and spectral bands related to vegetation health and structure. We achieved an overall classification accuracy of up to 66% for grazing intensity, 68% for mowing, 85% for fertilisation and an r2 of 0.82 for subsequently depicting LUI. We evaluated the methodology's robustness with a spatial 3-fold cross-validation by training and predicting on geographically distinctly separated regions. Spatial transferability was assessed by delineating the models' area of applicability. The presented methodology enables a high resolution, large extent mapping of land-use intensity of grasslands. Numéro de notice : A2022-468 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112888 Date de publication en ligne : 13/05/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112888 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100805 [article]

Tracing drought effects from the tree to the stand growth in temperate and Mediterranean forests: insights and consequences for forest ecology and management / Hans Pretzsch in European Journal of Forest Research, vol 141 n° 4 (August 2022)  

Public [article]  inEuropean Journal of Forest Research > vol 141 n° 4 (August 2022) . - pp 727 - 751 Titre : Tracing drought effects from the tree to the stand growth in temperate and Mediterranean forests: insights and consequences for forest ecology and management Type de document : Article/Communication Auteurs : Hans Pretzsch, Auteur ; Miren del Rio, Auteur ; Rüdiger Grote, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 727 - 751 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Bavière (Allemagne) [Termes IGN] coefficient de Gini [Termes IGN] croissance des arbres [Termes IGN] écologie forestière [Termes IGN] Espagne [Termes IGN] Fagus sylvatica [Termes IGN] forêt méditerranéenne [Termes IGN] forêt tempérée [Termes IGN] gestion forestière [Termes IGN] mortalité [Termes IGN] Picea abies [Termes IGN] sécheresse [Termes IGN] stress hydrique [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) How drought affects tree and stand growth is an old question, but is getting unprecedented relevance in view of climate change. Stress effects related to drought have been mostly studied at the individual tree level, mostly investigating dominant trees and using their responses as indicator for the impact at the stand level. However, findings at tree and stand level may differ, as the stand responses include interactions and feedbacks that may buffer or aggravate what is observed at the individual tree level. Here, we trace drought effects on growth and development from tree to the stand scale. Therefore, we analyse annually measured data from long-term experiments in temperate and Mediterranean forests. With this analysis, we aim to disclose how well results of dominant tree growth reflect stand-level behaviour, hypothesizing that drought resistance of dominant trees’ can strongly deviate from the overall sensitivity of the stand. First, we theoretically derive how drought responses at the stand level emerge from the tree-level behaviour, thereby considering that potential drought resistance of individual trees is modulated by acclimation and tree–tree interactions at the stand level and that the overall stress response at the stand level results from species-specific and size-dependent individual tree growth and mortality. Second, reviewing respective peer-reviewed literature (24 papers) and complementing findings by own measurements (22 experiments) from temperate and Mediterranean monospecific and mixed-species forests, we are able to reveal main causes for deviations of tree-level and stand-level findings regarding drought stress responses. Using a long-term experiment in Norway spruce (Picea abies (L.) KARST.) and European beech (Fagus sylvatica L.), we provide evidence that the species-dependent and size-dependent reactions matter and how the size–frequency distribution affects the scaling. We show by examples that tree-level derived results may overestimate growth losses by 25%. Third, we investigate the development of the growth dominance coefficient based on measurements gathered at the Bavarian forest climate stations. We show that drought changes stand biomass partitioning in favour of small trees, reduce social differentiation, and homogenize the vertical structure of forests. Finally, we discuss the drought-related consequences of the social class-specific growth reaction patterns for inventory and monitoring and highlight the importance of these findings for understanding site-specific stand dynamics, for forest modelling, and for silvicultural management. Numéro de notice : A2022-640 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10342-022-01451-x Date de publication en ligne : 07/05/2022 En ligne : https://doi.org/10.1007/s10342-022-01451-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101447 [article]

How can Sentinel-2 contribute to seagrass mapping in shallow, turbid Baltic Sea waters? / Katja Kuhwald in Remote sensing in ecology and conservation, vol 8 n° 3 (June 2022)  

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Application oriented quality evaluation of Gaofen-7 optical stereo satellite imagery / Jiaojiao Tian in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2022 (2022 edition)  

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Effects of climate and drought on stem diameter growth of urban tree species / Vjosa Dervishi in Forests, vol 13 n° 5 (May 2022)  

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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)  

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Effect of climate change on the growth of tree species: Dendroclimatological analysis / Archana Gauli in Forests, vol 13 n° 4 (April 2022)  

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Fertilization modifies forest stand growth but not stand density: consequences for modelling stand dynamics in a changing climate / Hans Pretzsch in Forestry, an international journal of forest research, vol 95 n° 2 (April 2022)  

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Projections of climate change impacts on flowering-veraison water deficits for Riesling and Müller-Thurgau in Germany / Chenyao Yang in Remote sensing, vol 14 n° 6 (March-2 2022)  

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Are northern German Scots pine plantations climate smart? The impact of large-scale conifer planting on climate, soil and the water cycle / Christoph Leuschner in Forest ecology and management, vol 507 (1 March 2022)  

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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)  

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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)  

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