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Offering the appetite for the monitoring of European forests a diversified diet / Jean-Daniel Bontemps in Annals of Forest Science [en ligne], vol 79 n° 1 (2022)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 79 n° 1 (2022) . - n° 19 Titre : Offering the appetite for the monitoring of European forests a diversified diet Type de document : Article/Communication Auteurs : Jean-Daniel Bontemps , Auteur ; Olivier Bouriaud , Auteur ; Cédric Vega , Auteur ; Laura Bouriaud , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 19 Note générale : bibliographie NB article d'opinion Langues : Anglais (eng) Descripteur : [Termes IGN] Europe (géographie politique) [Termes IGN] intégration [Termes IGN] inventaire forestier étranger (données) [Termes IGN] inventaire forestier national (données France) [Termes IGN] politique publique [Termes IGN] ressources forestières [Termes IGN] santé des forêts [Termes IGN] surveillance forestière [Termes IGN] Union Européenne [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Forest monitoring in Europe is turning matter of renewed political concern, and a possible role for ICP Forests health monitoring has been suggested to meet this goal (Ann For Sci 78:94, 2021). Multipurpose national forest inventory (NFI) surveys yet offer a sampling effort by two orders of magnitude greater than ICP level 1, have accomplished substantial methodological and harmonization progresses in the recent years, and therefore form a decisive contributor to future European forest monitoring incentives. Possible paths for the future development of a pan-European, comprehensive and more accurate monitoring are designed that stress a crucial need to build on the assets of the existing forest monitoring programs and favor their cooperation, in order to limit the co-existence of distinct forest monitoring processes. Numéro de notice : A2022-320 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1186/s13595-022-01139-7 Date de publication en ligne : 11/04/2022 En ligne : http://dx.doi.org/10.1186/s13595-022-01139-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100432 [article]

Recent advances in forest insect pests and diseases monitoring using UAV-based data: A systematic review / André Duarte in Forests, vol 13 n° 6 (June 2022)  

Public [article]  inForests > vol 13 n° 6 (June 2022) . - n° 911 Titre : Recent advances in forest insect pests and diseases monitoring using UAV-based data: A systematic review Type de document : Article/Communication Auteurs : André Duarte, Auteur ; Nuno Borralho, Auteur ; Pedro Cabral, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 911 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse d'image orientée objet [Termes IGN] apprentissage profond [Termes IGN] données lidar [Termes IGN] extraction de traits caractéristiques [Termes IGN] image captée par drone [Termes IGN] insecte nuisible [Termes IGN] maladie parasitaire [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] santé des forêts [Termes IGN] structure-from-motion [Termes IGN] surveillance forestière Résumé : (auteur) Unmanned aerial vehicles (UAVs) are platforms that have been increasingly used over the last decade to collect data for forest insect pest and disease (FIPD) monitoring. These machines provide flexibility, cost efficiency, and a high temporal and spatial resolution of remotely sensed data. The purpose of this review is to summarize recent contributions and to identify knowledge gaps in UAV remote sensing for FIPD monitoring. A systematic review was performed using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) protocol. We reviewed the full text of 49 studies published between 2015 and 2021. The parameters examined were the taxonomic characteristics, the type of UAV and sensor, data collection and pre-processing, processing and analytical methods, and software used. We found that the number of papers on this topic has increased in recent years, with most being studies located in China and Europe. The main FIPDs studied were pine wilt disease (PWD) and bark beetles (BB) using UAV multirotor architectures. Among the sensor types, multispectral and red–green–blue (RGB) bands were preferred for the monitoring tasks. Regarding the analytical methods, random forest (RF) and deep learning (DL) classifiers were the most frequently applied in UAV imagery processing. This paper discusses the advantages and limitations associated with the use of UAVs and the processing methods for FIPDs, and research gaps and challenges are presented. Numéro de notice : A2022-483 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13060911 Date de publication en ligne : 10/06/2022 En ligne : https://doi.org/10.3390/f13060911 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100897 [article]

Deep learning for the detection of early signs for forest damage based on satellite imagery / Dennis Wittich in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 307 - 315 Titre : Deep learning for the detection of early signs for forest damage based on satellite imagery Type de document : Article/Communication Auteurs : Dennis Wittich, Auteur ; Franz Rottensteiner, Auteur ; Mirjana Voelsen, Auteur ; Christian Heipke, Auteur ; Sönke Müller, Auteur Année de publication : 2022 Article en page(s) : pp 307 - 315 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 de la flore [Termes IGN] dommage forestier causé par facteurs naturels [Termes IGN] fonction de perte [Termes IGN] image Sentinel-MSI [Termes IGN] régression [Termes IGN] série temporelle [Termes IGN] surveillance forestière Résumé : (auteur) We present an approach for detecting early signs for upcoming forest damages by training a Convolutional Neural Network (CNN) for the pixel-wise prediction of the remaining life-time (RLT) of trees in forests based on Sentinel-2 imagery. We focus on a scenario in which reference data are only available for a related task, namely for a bi-temporal pixel-wise classification of forest degradation. This reference is used to train a CNN for the pixel-wise prediction of forest degradation. In this context, we propose a new sub-sampling-based approach for compensating the effects of a heavy class imbalance in the training data. Using the resulting classification model, we predict semi-labels for images of a Sentinel-2 time series, from which training data for a CNN designed to regress the RLT can be derived after some label cleansing. However, due to data gaps in the time series, e.g. caused by clouds, only intervals can be derived for the target variable to be regressed, and for some training pixels one of the interval limits may even be unknown. Consequently, we propose a new loss function for training a CNN for regressing the RLT that only requires the known interval limits. The method is evaluated on a data set in Germany, covering a time-span of 5 years. We show that the proposed sub-sampling strategy for dealing with strong label imbalance when training the classifier significantly reduces the training time compared to other approaches. We further show that our model predicts the RLT with a maximum error of two months for 80% of the forest pixels that die within one year from the acquisition date of the Sentinel-2 image. Numéro de notice : A2022-432 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-307-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-307-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100738 [article]

Unveiling the complex canopy spatial structure of a Mediterranean old-growth beech (Fagus sylvatica L.) forest from UAV observations / Francesco Solano in Ecological indicators, vol 138 (May 2022)  

Public [article]  inEcological indicators > vol 138 (May 2022) . - n° 108807 Titre : Unveiling the complex canopy spatial structure of a Mediterranean old-growth beech (Fagus sylvatica L.) forest from UAV observations Type de document : Article/Communication Auteurs : Francesco Solano, Auteur ; Giuseppe Modica, Auteur ; Salvatore Praticò, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108807 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] Calabre [Termes IGN] écosystème forestier [Termes IGN] Fagus sylvatica [Termes IGN] forêt ancienne [Termes IGN] forêt méditerranéenne [Termes IGN] forêt primaire [Termes IGN] image à très haute résolution [Termes IGN] image captée par drone [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] orthophotoplan numérique [Termes IGN] photogrammétrie aérienne [Termes IGN] structure spatiale [Termes IGN] structure-from-motion [Termes IGN] surveillance forestière Résumé : (auteur) In front of climate change scenarios and global loss of biodiversity, it is essential to monitor the structure of old-growth forests to study ecosystem status and dynamics to inform future conservation and restoration programmes. We propose an Unmanned Aerial Vehicle (UAV)-based framework to monitor fine-grained forest top canopy structure in a primary old-growth beech (Fagus sylvatica L.) forest in Pollino National Park, Italy, which belongs to the UNESCO World Heritage (UNESCO WH) serial site “Ancient and Primeval beech forests of the Carpathians and other regions of Europe”. Canopy profile, gap properties and their spatial distribution patterns were analysed using the canopy height model (CHM) derived from UAV surveys. Very high-resolution orthomosaic images coupled with direct field measurement data were used to assess gap detection accuracy and CHM validation. Forest canopy properties along with the vertical layering of the canopy were further explored using second-order statistics. The reconstructed canopy profile revealed a bimodal top height frequency distribution. The upper canopy layer (h > 14 m) was the most represented canopy height, with the remaining 50% split between the medium and lowest layer; 551 gaps were identified within 11.5 ha. Gap size varied between 2 m2 and 353 m2, and 19 m2was the mean gap size; the gap size-frequency relationship reflected a power-law probability distribution. About 97 % of the gaps were Numéro de notice : A2022-369 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ecolind.2022.108807 Date de publication en ligne : 01/04/2022 En ligne : https://doi.org/10.1016/j.ecolind.2022.108807 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100598 [article]

An open science and open data approach for the statistically robust estimation of forest disturbance areas / Saverio Francini in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 106 (February 2022) . - n° 102663 Titre : An open science and open data approach for the statistically robust estimation of forest disturbance areas Type de document : Article/Communication Auteurs : Saverio Francini, Auteur ; Ronald E. McRoberts, Auteur ; Giovanni d' Amico, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102663 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] changement climatique [Termes IGN] coupe rase (sylviculture) [Termes IGN] détection de changement [Termes IGN] estimation statistique [Termes IGN] Fagus sylvatica [Termes IGN] Google Earth Engine [Termes IGN] image Sentinel-MSI [Termes IGN] Italie [Termes IGN] méthode robuste [Termes IGN] perturbation écologique [Termes IGN] Quercus cerris [Termes IGN] Quercus pedunculata [Termes IGN] Quercus pubescens [Termes IGN] Quercus sessiliflora [Termes IGN] surveillance forestière Résumé : (auteur) Forest disturbance monitoring is critical for understanding forest-related greenhouse gas emissions and for determining the role of forest management in mitigating climate change. Multiple algorithms for the automated mapping of forest disturbance using remotely sensed imagery have been developed and applied; however, variability in natural and anthropogenic disturbance phenomena, as well as image acquisition conditions, can result in maps that may be incomplete or that contain inaccuracies that prevent their use for directly estimating areas of disturbance. To reduce errors in reporting disturbance areas, stratified estimators can be applied to obtain statistically robust area estimates, while simultaneously circumventing the need to conduct a complete census or in situations where such a census may not be possible. We present a semi-automated procedure for implementation in Google Earth Engine, 3I3D-GEE, for regional to global mapping of forest disturbance (including clear-cut harvesting, fire, and wind damage) and sample-based estimation of related areas using data from the processing capacity of Google Earth Engine. Documentation for the application is also provided in Appendix A. Using Sentinel-2 (S2) imagery, our procedure was applied and tested for 2018 in Italy for which the approximately 11 million ha of forests (mostly Q. pubescens, Q. robur, Q. cerris, Q. petraea, and Fagus sylvatica) serve as an appropriate case study because national statistics on forest disturbance areas are not available. To decrease the overall standard errors of the area estimates, the sampling intensities in areas where greater variability in the form of greater commission and omission errors are expected can be increased. To this end, we augmented the predicted forest disturbance map with a buffer class consisting of a two-pixel buffer (20 m) on each side of the disturbance class boundary. We selected a reference sample of 19,300 points: a simple random sample of 9,300 points from the buffer and simple random samples of 5000 from each of the undisturbed and disturbed classes. The reference sample was photointerpreted using fine resolution orthophotos (30 cm) and S2 imagery. While the estimate of the disturbed area obtained by adding the areas of pixels classified as disturbed was 41,732 ha, the estimate obtained using the unbiased stratified estimator was 27% greater at 57,717716 ha. Regarding map accuracy, we found several omission errors in the buffer (53.4%) but none (0%) in the undisturbed map class. Similarly, among the 1035 commission errors, the majority (7 4 4) were in the buffer class. The methods presented herein provide a useful tool that can be used to estimate areas of forest disturbance, which many nations must report as part of their commitment to international conventions and treaties. In addition, the information generated can support forest management, enabling the forest sector to monitor stand-replacing forest harvesting over space and time. Numéro de notice : A2022-072 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2021.102663 En ligne : https://doi.org/10.1016/j.jag.2021.102663 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99437 [article]

European-wide forest monitoring substantiate the neccessity for a joint conservation strategy to rescue European ash species (Fraxinus spp.) / Jan-Peter George in Scientific reports, vol 12 (2022)  

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Landsat-based monitoring of southern pine beetle infestation severity and severity change in a temperate mixed forest / Ran Meng in Remote sensing of environment, vol 269 (February 2022)  

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Monthly mapping of forest harvesting using dense time series Sentinel-1 SAR imagery and deep learning / Feng Zhao in Remote sensing of environment, vol 269 (February 2022)  

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National implementation of the forest Europe indicators for sustainable forest management / Stefanie Linser in Forests, vol 13 n° 2 (February 2022)  

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Monitoring forest-savanna dynamics in the Guineo-Congolian transition area of the centre region of Cameroon / Le Bienfaiteur Sagang Takougoum (2022)  

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National scale mapping of larch plantations for Wales using the Sentinel-2 data archive / Suvarna M. Punalekar in Forest ecology and management, vol 501 (1 December 2021)  

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Progress on incorporating biodiversity monitoring in REDD+ through national forest inventories / Loïc Gillerot in Global ecology and conservation, vol 32 (December 2021)  

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Above-ground biomass change estimation using national forest inventory data with Sentinel-2 and Landsat / Stefano Puliti in Remote sensing of environment, vol 265 (November 2021)  

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Mapping canopy heights in dense tropical forests using low-cost UAV-derived photogrammetric point clouds and machine learning approaches / He Zhang in Remote sensing, vol 13 n° 18 (September-2 2021)  

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Monitoring forest disturbance using time-series MODIS NDVI in Michoacán, Mexico / Yao Gao in Geocarto international, vol 36 n° 15 ([15/08/2021])  

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