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Improving precision of field inventory estimation of aboveground biomass through an alternative view on plot biomass / Christoph Kleinn in Forest ecosystems, vol 7 (2020)
[article]
Titre : Improving precision of field inventory estimation of aboveground biomass through an alternative view on plot biomass Type de document : Article/Communication Auteurs : Christoph Kleinn, Auteur ; Magnussen, Steen, Auteur ; Nils Nölke, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 57 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Basse-Saxe (Allemagne)
[Termes IGN] biomasse aérienne
[Termes IGN] biomasse forestière
[Termes IGN] écologie forestière
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] parcelle forestière
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) We contrast a new continuous approach (CA) for estimating plot-level above-ground biomass (AGB) in forest inventories with the current approach of estimating AGB exclusively from the tree-level AGB predicted for each tree in a plot, henceforth called DA (discrete approach). With the CA, the AGB in a forest is modelled as a continuous surface and the AGB estimate for a fixed-area plot is computed as the integral of the AGB surface taken over the plot area. Hence with the CA, the portion of the biomass of in-plot trees that extends across the plot perimeter is ignored while the biomass from trees outside of the plot reaching inside the plot is added. We use a sampling simulation with data from a fully mapped two hectare area to illustrate that important differences in plot-level AGB estimates can emerge. Ideally CA-based estimates of mean AGB should be less variable than those derived from the DA. If realized, this difference translates to a higher precision from field sampling, or a lower required sample size. In our case study with a target precision of 5% (i.e. relative standard error of the estimated mean AGB), the CA required a 27.1% lower sample size for small plots of 100 m2 and a 10.4% lower sample size for larger plots of 1700 m2. We examined sampling induced errors only and did not yet consider model errors. We discuss practical issues in implementing the CA in field inventories and the potential in applications that model biomass with remote sensing data. The CA is a variation on a plot design for above-ground forest biomass; as such it can be applied in combination with any forest inventory sampling design. Numéro de notice : A2020-812 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1186/s40663-020-00268-7 Date de publication en ligne : 23/10/2020 En ligne : https://doi.org/10.1186/s40663-020-00268-7 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96985
in Forest ecosystems > vol 7 (2020) . - n° 57[article]Genetic variation of introduced red oak (Quercus rubra) stands in Germany compared to North American populations / Tim Pettenkofer in European Journal of Forest Research, vol 139 n° 2 (April 2020)
[article]
Titre : Genetic variation of introduced red oak (Quercus rubra) stands in Germany compared to North American populations Type de document : Article/Communication Auteurs : Tim Pettenkofer, Auteur ; Reiner Finkeldey, Auteur ; Markus Müller, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 321 – 331 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Allemagne
[Termes IGN] Amérique du nord
[Termes IGN] analyse comparative
[Termes IGN] génétique forestière
[Termes IGN] Quercus rubra
[Termes IGN] variationRésumé : (auteur) Although Northern red oak (Quercus rubra L.) is the most important introduced deciduous tree species in Germany, only little is known about its genetic variation. For the first time, we describe patterns of neutral and potentially adaptive nuclear genetic variation in Northern red oak stands across Germany. For this purpose, 792 trees were genotyped including 611 trees from 12 stands in Germany of unknown origin and 181 trees from four populations within the natural distribution area in North America. Our marker set included 12 potentially adaptive (expressed sequence tag-derived simple sequence repeat = EST SSR) and 8 putatively selectively neutral nuclear microsatellite (nSSR) markers. Our results showed that German stands retain comparatively high levels of genetic variation at both EST-SSRs and nSSRs, but are more similar to each other than to North American populations. These findings are in agreement with earlier chloroplast DNA analyses which suggested that German populations originated from a limited geographic area in North America. The comparison between potentially adaptive and neutral microsatellite markers did not reveal differences in the analyzed diversity and differentiation measures for most markers. However, locus FIR013 was identified as a potential outlier locus. Due to the absence of signatures of selection in German stands, we suggest that introduced populations were established with material from provenances that were adapted to environmental conditions similar to those in Germany. However, we analyzed only a limited number of loci which are unlikely to be representative of adaptive genetic differences among German stands. Our results suggest that the apparent introduction from a limited geographic range in North America may go along with a reduced adaptive potential. Numéro de notice : A2020-345 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s10342-019-01256-5 Date de publication en ligne : 18/01/2020 En ligne : https://doi.org/10.1007/s10342-019-01256-5 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95225
in European Journal of Forest Research > vol 139 n° 2 (April 2020) . - pp 321 – 331[article]Jahresbericht 2019 des Bundesamtes für Kartographie und Geodäsie / Bundesamt für Kartographie und Geodäsie (2020)
Titre : Jahresbericht 2019 des Bundesamtes für Kartographie und Geodäsie Titre original : Annual report 2019 Federal Agency for Cartography and Geodesy Type de document : Rapport Auteurs : Bundesamt für Kartographie und Geodäsie, Auteur Editeur : Francfort sur le Main : Bundesamt für Kartographie und Geodäsie Année de publication : 2020 Importance : 40 p. Format : 21 x 30 cm Langues : Allemand (ger) Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie
[Termes IGN] activité cartographique
[Termes IGN] activité géodésique
[Termes IGN] Allemagne
[Termes IGN] base de données localisées
[Termes IGN] géomatique
[Termes IGN] système de référence localNote de contenu : 1 Looking back: This was BKG´s year 2019
2 Facts and figures 2019
3 Determine changes on the Earth: The global infrastructure GGRF and the BKG
4 The new Satellite-Based Crisis and Spatial Information Service – The SKD
5 Easier and faster: Products and services of the BKG
6 Where you can find us: Locations and contact detailsNuméro de notice : 17742 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Rapport d'activité DOI : sans En ligne : https://sg.geodatenzentrum.de/web_public/gdz/schriften/BKG-Jahresbericht-2019-DE [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103111 Documents numériques
en open access
rapport 2019 - pdf éditeurAdobe Acrobat PDF
Titre : Remotely sensing the species of individual trees Type de document : Thèse/HDR Auteurs : Yifang Shi, Auteur ; Andrew K. Skidmore, Directeur de thèse ; Tiejun Wang, Directeur de thèse Editeur : Enschede [Pays Bas] : University of Twente Année de publication : 2020 Collection : ITC Dissertation num. 376 Importance : 163 p. Format : 21 x 30 cm Note générale : bibliographie
Doctor of Philosophy, Faculty of Geo-Information Science and Earth Observation, University of TwenteLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Abies alba
[Termes IGN] analyse comparative
[Termes IGN] analyse diachronique
[Termes IGN] Bavière (Allemagne)
[Termes IGN] chlorophylle
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt tempérée
[Termes IGN] image hyperspectrale
[Termes IGN] image infrarouge couleur
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Leaf Mass per Area
[Termes IGN] orthoimageRésumé : (auteur) The accurate identification of tree species is critical for the management of forest ecosystems. Mapping of tree species is an important task as it can assist a wide range of environmental applications, such as biodiversity monitoring, ecosystem services assessment, invasive species detection, and sustainable forest management. Compared to the conventional approaches based on labor-intensive field measurements, remote sensing has supplied a large variety of cutting-edge techniques to accomplish forest inventory. However, individual tree species classification in natural mixed forests, as it is typical in central Europe, is still a challenging task. High spectral and structural intra-species variability and inter-species similarity, due to phenological effects, differences in tree age and openness of canopies, shadowing effects, and environment variability, restrict tree species separability. An in-depth understanding of the relationship between species-specific features and remote sensing observations for tree species classification needs further investigation. This thesis aimed to accurately map the species of individual trees using multi-source remotely sensed data, including aerial photographs, airborne LiDAR and hyperspectral data. The research in the thesis firstly evaluated the performance of geometric and radiometric metrics from airborne LiDAR data under leaf-on and leaf-off conditions for individual tree species discrimination. The results empathized the importance of intensity-related LiDAR metrics for tree species identification under both leaf-on and leaf-off conditions. Then, the thesis examined whether multi-temporal digital CIR orthophotos could be used to further increase the accuracy of airborne LiDAR-based individual tree species mapping. The results showed that the texture features generated from multi-temporal digital CIR orthophotos under different view-illumination conditions are species-specific. Combining these texture features with LiDAR metrics significantly improved the accuracy of individual tree species mapping. To explore more valuable species-specific features, the thesis consequently integrated three plant functional traits (i.e. equivalent water thickness, leaf mass per area and leaf chlorophyll) retrieved from hyperspectral data with hyperspectral derived spectral features and airborne LiDAR derived metrics for mapping five tree species. Three selected plant functional traits were accurately retrieved using radiative transfer model and further improved the accuracy of tree species classification. Eventually, the thesis focused on an important tree species silver fir, and accurately mapped individuals of this species based on one-class classifiers using integrated airborne hyperspectral and LiDAR data. The mapping results provided the references locating the areas with a high occurrence probability of silver fir trees and hence increase the efficiency in subsequent field campaigns for forest management and biodiversity monitoring. This thesis explored the potential of various remotely sensed datasets for individual tree species mapping. The methodologies and findings in this thesis can be applied in the mapping of other tree species, which enriches the knowledge of species-specific characteristics and related remotely sensed signatures. The emerging of UAVs and the upcoming hyperspectral missions such as EnMAP and HySPIRI deliver valuable datasets with multi-scale coverage and revisit observations, which can be used for mapping the diversity of tree species at stand or regional level. Note de contenu : - General introduction
- Important LiDAR metrics for discriminating tree species
- Improving LiDAR-based tree species mapping using multi-temporal CIR orthophotos
- Tree species classification using remotely sensed plant functional traits
- Mapping individual silver fir trees in a Norway spruce dominated forest
- Synthesis: Mapping individual tree species using multi-source remotely sensed dataNuméro de notice : 17671 Affiliation des auteurs : non IGN Thématique : FORET Nature : Thèse étrangère Note de thèse : PhD thesis : : University of Twente : 2020 DOI : 10.3990/1.978903654953-0 Date de publication en ligne : 31/01/2020 En ligne : https://doi.org/10.3990/1.978903654953-0 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97985 Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods / Wolfgang B. Hamer in ISPRS International journal of geo-information, Vol 9 n° 1 (January 2020)
[article]
Titre : Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods Type de document : Article/Communication Auteurs : Wolfgang B. Hamer, Auteur ; Tim Birr, Auteur ; Joseph-Alexander Verreet, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Allemagne
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] diffusion spatiale
[Termes IGN] données localisées
[Termes IGN] données météorologiques
[Termes IGN] géostatistique
[Termes IGN] maladie phytosanitaire
[Termes IGN] modèle de simulation
[Termes IGN] modèle dynamique
[Termes IGN] rendement agricole
[Termes IGN] risque environnemental
[Termes IGN] temps réelRésumé : (auteur) Real-time identification of the occurrence of dangerous pathogens is of crucial importance for the rapid execution of countermeasures. For this purpose, spatial and temporal predictions of the spread of such pathogens are indispensable. The R package papros developed by the authors offers an environment in which both spatial and temporal predictions can be made, based on local data using various deterministic, geostatistical regionalisation, and machine learning methods. The approach is presented using the example of a crops infection by fungal pathogens, which can substantially reduce the yield if not treated in good time. The situation is made more difficult by the fact that it is particularly difficult to predict the behaviour of wind-dispersed pathogens, such as powdery mildew (Blumeria graminis f. sp. tritici). To forecast pathogen development and spatial dispersal, a modelling process scheme was developed using the aforementioned R package, which combines regionalisation and machine learning techniques. It enables the prediction of the probability of yield- relevant infestation events for an entire federal state in northern Germany at a daily time scale. To run the models, weather and climate information are required, as is knowledge of the pathogen biology. Once fitted to the pathogen, only weather and climate information are necessary to predict such events, with an overall accuracy of 68% in the case of powdery mildew at a regional scale. Thereby, 91% of the observed powdery mildew events are predicted Numéro de notice : A2020-116 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9010044 Date de publication en ligne : 15/01/2020 En ligne : https://doi.org/10.3390/ijgi9010044 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94723
in ISPRS International journal of geo-information > Vol 9 n° 1 (January 2020)[article]Knowing is not enough: exploring the missing link between climate change knowledge and action of German forest owners and managers / Yvonne Hengst-Ehrhart in Annals of Forest Science, Vol 76 n° 4 (December 2019)PermalinkAccurate 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)PermalinkMapping 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)PermalinkMultiscale cartographic visualization of harmonized datasets / Peter Kunz in International journal of cartography, vol 5 n° 2-3 (July - November 2019)PermalinkCNN-based dense image matching for aerial remote sensing images / Shunping Ji in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)PermalinkVirtual Support Vector Machines with self-learning strategy for classification of multispectral remote sensing imagery / Christian Geiss in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkClimate change and mixed forests: how do altered survival probabilities impact economically desirable species proportions of Norway spruce and European beech? / Carola Paul in Annals of Forest Science, vol 76 n° 1 (March 2019)PermalinkTemporal and spatial high-resolution climate data from 1961 to 2100 for the German National Forest Inventory (NFI) / Helge Dietrich in Annals of Forest Science, vol 76 n° 1 (March 2019)PermalinkVariation 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)PermalinkForest conversion from Norway spruce to European beech increases species richness and functional structure of aboveground macrofungal communities / Peggy Heine in Forest ecology and management, vol 432 (15 January 2019)Permalink