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Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations / Shengbiao Wu in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 36 - 48 Titre : Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations Type de document : Article/Communication Auteurs : Shengbiao Wu, Auteur ; Jing Wang, Auteur ; Zhengbing Yan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 36 - 48 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Chine [Termes descripteurs IGN] forêt tempérée [Termes descripteurs IGN] houppier [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] image MODIS [Termes descripteurs IGN] image PlanetScope [Termes descripteurs IGN] phénologie [Termes descripteurs IGN] photosynthèse [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surveillance forestière Résumé : (auteur) In temperate forests, autumn leaf phenology signals the end of leaf growing season and shows large variability across tree-crowns, which importantly mediates photosynthetic seasonality, hydrological regulation, and nutrient cycling of forest ecosystems. However, critical challenges remain with the monitoring of autumn leaf phenology at the tree-crown scale due to the lack of spatially explicit information for individual tree-crowns and high (spatial and temporal) resolution observations with nadir view. Recent availability of the PlanetScope constellation with a 3 m spatial resolution and near-daily nadir view coverage might help address these observational challenges, but remains underexplored. Here we developed an integration of PlanetScope with drone observations for improved monitoring of crown-scale autumn leaf phenology in a temperate forest in Northeast China. This integration includes: 1) visual identification of individual tree-crowns (and species) from drone observations; 2) extraction of time series of PlanetScope vegetation indices (VIs) for each identified tree-crown; 3) derivation of three metrics of autumn leaf phenology from the extracted VI time series, including the start of fall (SOF), middle of fall (MOF), and end of fall (EOF); and 4) accuracy assessments of the PlanetScope-derived phenology metrics with reference from local phenocams. Our results show that (1) the PlanetScope-drone integration captures large inter-crown phenological variations, with a range of 28 days, 25 days, and 30 days for SOF, MOF, and EOF, respectively, (2) the extracted crown-level phenology metrics strongly agree with those derived from local phenocams, with a root-mean-square-error (RMSE) of 4.1 days, 3.0 days and 5.4 days for SOF, MOF, and EOF, respectively, and (3) PlanetScope maps large variations in autumn leaf phenology over the entire forest landscape with spatially explicit information. These results demonstrate the ability of our proposed method in monitoring the large spatial heterogeneity of crown-scale autumn leaf phenology in the temperate forest, suggesting the potential of using high-resolution satellites to advance crown-scale phenology studies over large geographical areas. Numéro de notice : A2021-011 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.017 date de publication en ligne : 13/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.017 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96305 [article]

Recent growth trends of conifers across Western Europe are controlled by thermal and water constraints and favored by forest heterogeneity / Clémentine Ols in Science of the total environment, vol 742 ([10/11/2020])  

Public [article]  inScience of the total environment > vol 742 [10/11/2020] . - n° 140453 Titre : Recent growth trends of conifers across Western Europe are controlled by thermal and water constraints and favored by forest heterogeneity Type de document : Article/Communication Auteurs : Clémentine Ols , Auteur ; Jean-Christophe Hervé , Auteur ; Jean-Daniel Bontemps , Auteur Année de publication : 2020 Projets : GRECOFOR-CC / Bontemps, Jean-Daniel Article en page(s) : n° 140453 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] inventaire forestier national (données France) [Termes descripteurs IGN] modèle de croissance [Termes descripteurs IGN] pinophyta [Termes descripteurs IGN] surveillance forestière [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Tree growing conditions are changing rapidly in the face of climate change. Capturing tree-growth response to such changes across environmental contexts and tree species calls for a continuous forest monitoring over space. Based on >10,000 tree-ring measurements sampled across the systematic grid of the continuous French national forest inventory (NFI) over the 2006–2016 period, we evaluated the radial growth trends of eight conifer tree species prevalent in European forests across their native and introduced ranges and various bioclimatic contexts (n = 16 forest systems). For each forest system, radial increments were filtered out from tree, plot, soil and climatic normal influences to isolate environment-driven growth signals and quantify residual time-series. Associated growth trends across forest systems were then confronted against environmental variables (e.g. short-term averages and trends in seasonal climate). Trends for a given species were systematically more positive in cooler contexts (higher elevations or northern distribution margins) than in warmer contexts (plains). Decreases and increases in precipitation regimes were found to be associated with negative and positive tree growth trends, respectively. Remarkably, positive growth trends were mainly observed for native forest systems (7/9) and negative trends for introduced systems (5/7). Native forests showed a more heterogeneous forest structure as compared to introduced forests that, in line with observed positive dependence of tree growth trends onto both water availability and forest heterogeneity, appears to modulate the competitive pressure on water resource with ongoing summer maximum temperature increase. Over a short annually-resolved study period, we were able to capture tree growth responses coherent with climate change across diverse forest ecosystems. With ongoing accumulation of data, the continuous French NFI hence arises as powerful support to monitoring climate change effects on forests. Numéro de notice : A2020-509 Affiliation des auteurs : LIF (2012-2019) Autre URL associée : vers HAL Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.scitotenv.2020.140453 date de publication en ligne : 23/06/2020 En ligne : https://doi.org/10.1016/j.scitotenv.2020.140453 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95767 [article]

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Wide-area near-real-time monitoring of tropical forest degradation and deforestation using Sentinel-1 / Dirk Hoekman in Remote sensing, vol 12 n° 19 (October - 1 2020)  

Public [article]  inRemote sensing > vol 12 n° 19 (October - 1 2020) . - 32 p. Titre : Wide-area near-real-time monitoring of tropical forest degradation and deforestation using Sentinel-1 Type de document : Article/Communication Auteurs : Dirk Hoekman, Auteur ; Boris Kooij, Auteur ; Marcela J. Quiñones, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 32 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Amazonie [Termes descripteurs IGN] Bornéo, île de [Termes descripteurs IGN] déboisement [Termes descripteurs IGN] dégradation de l'environnement [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] image radar [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] image TerraSAR-X [Termes descripteurs IGN] modèle physique [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surveillance forestière [Termes descripteurs IGN] tourbière Résumé : (auteur) The use of Sentinel-1 (S1) radar for wide-area, near-real-time (NRT) tropical-forest-change monitoring is discussed, with particular attention to forest degradation and deforestation. Since forest change can relate to processes ranging from high-impact, large-scale conversion to low-impact, selective logging, and can occur in sites having variable topographic and environmental properties such as mountain slopes and wetlands, a single approach is insufficient. The system introduced here combines time-series analysis of small objects identified in S1 data, i.e., segments containing linear features and apparent small-scale disturbances. A physical model is introduced for quantifying the size of small (upper-) canopy gaps. Deforestation detection was evaluated for several forest landscapes in the Amazon and Borneo. Using the default system settings, the false alarm rate (FAR) is very low (less than 1%), and the missed detection rate (MDR) varies between 1.9% ± 1.1% and 18.6% ± 1.0% (90% confidence level). For peatland landscapes, short radar detection delays up to several weeks due to high levels of soil moisture may occur, while, in comparison, for optical systems, detection delays up to 10 months were found due to cloud cover. In peat swamp forests, narrow linear canopy gaps (road and canal systems) could be detected with an overall accuracy of 85.5%, including many gaps barely visible on hi-res SPOT-6/7 images, which were used for validation. Compared to optical data, subtle degradation signals are easier to detect and are not quickly lost over time due to fast re-vegetation. Although it is possible to estimate an effective forest-cover loss, for example, due to selective logging, and results are spatiotemporally consistent with Sentinel-2 and TerraSAR-X reference data, quantitative validation without extensive field data and/or large hi-res radar datasets, such as TerraSAR-X, remains a challenge. Numéro de notice : A2020-633 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs12193263 date de publication en ligne : 08/10/2020 En ligne : https://doi.org/10.3390/rs12193263 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96056 [article]

Using machine learning to synthesize spatiotemporal data for modelling DBH-height and DBH-height-age relationships in boreal forests / Jiaxin Chen in Forest ecology and management, Vol 466 (15 June 2020)  

Public [article]  inForest ecology and management > Vol 466 (15 June 2020) Titre : Using machine learning to synthesize spatiotemporal data for modelling DBH-height and DBH-height-age relationships in boreal forests Type de document : Article/Communication Auteurs : Jiaxin Chen, Auteur ; Hongqiang Yang, Auteur ; Rongzhou Man, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] Canada [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] diamètre à hauteur de poitrine [Termes descripteurs IGN] données environnementales [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] forêt boréale [Termes descripteurs IGN] gestion forestière durable [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] surveillance forestière Résumé : (auteur) Sustainable forest management requires the ability to accurately model forest dynamics under a changing environment, which is difficult using conventional statistical methods as many factors that interactively affect forest growth must be considered. As well, statistical model development is often limited by the lack of broad-scale repeated forest measurements needed to capture changes in 1 or more variables and the corresponding changes in forest dynamics (e.g., growth in diameter and height), while assuming other variables do not change, or their changes do not significantly affect the forest dynamics of interest. In many forested countries, comprehensive monitoring programs have amassed large amounts of diverse forest measurement data. Here we propose a new approach for using artificial neural network-based machine learning to synthesize spatiotemporal tree measurement data collected over a vast area of boreal forest in central Canada to model diameter at breast height (DBH)-height and DBH-height-age relationships for 6 dominant tree species. More than 30 potentially important stand structure, site, and climate variables were considered. We used an individual-based modelling approach by considering each individual tree measurement as an instance of the complex relationships modelled; together, broad-scale long-term monitoring data contain many such instances, representing considerable spatial and temporal scale variation in forest growth and growing conditions. Using this approach, we significantly improved DBH-height and DBH-height-age models. And the models developed allowed us to analyze the effects of environmental conditions or changes in these conditions on forest growth. This may be the first attempt at applying this type of approach, which can be used to more accurately model, for example, forest growth, mortality, and how they are affected by changing climate in a variety of forest types. Numéro de notice : A2020-406 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2020.118104 date de publication en ligne : 04/04/2020 En ligne : https://doi.org/10.1016/j.foreco.2020.118104 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95463 [article]

Year-to-year crown condition poorly contributes to ring width variations of beech trees in French ICP level I network / Clara Tallieu in Forest ecology and management, Vol 465 (1st June 2020)  

Public [article]  inForest ecology and management > Vol 465 (1st June 2020) . - 15 p. Titre : Year-to-year crown condition poorly contributes to ring width variations of beech trees in French ICP level I network Type de document : Article/Communication Auteurs : Clara Tallieu, Auteur ; Vincent Badeau, Auteur ; Denis Allard, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 15 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] dendrochronologie [Termes descripteurs IGN] Fagus (genre) [Termes descripteurs IGN] Fagus sylvatica [Termes descripteurs IGN] feuille (végétation) [Termes descripteurs IGN] houppier [Termes descripteurs IGN] indice foliaire [Termes descripteurs IGN] pollution atmosphérique [Termes descripteurs IGN] sécheresse [Termes descripteurs IGN] stress hydrique [Termes descripteurs IGN] surveillance forestière [Termes descripteurs IGN] variation saisonnière [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Since the 1980-90′s episodes of decline in Central European Forests, forest condition has been surveyed thanks to the trans-national network the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests). It has been traditionally accepted that leaf loss is directly related to impairment of physiological condition of the tree. A few studies tried to correlate crown condition and growth trends while others concentrated on linking annual growth with crown observation at one date clustered into fertility classes. However, none focussed on the high frequency synchronism between leaf loss from annual network observations and annual radial growth issued from dendrochronology. Therefore, we jointly studied annual leaf loss observations and tree-ring width measurements on 715 common beech (Fagus sylvatica L.) trees distributed in the French part of the ICP monitoring network. Detrended inter-annual variations of leaf loss and tree-ring width index were used as response variables in the machine-learning algorithm Random Forest to investigate a common response to abiotic (current and lagged) and biotic hazards, to test the extent to which leaf loss helped to predict inter-annual variations in radial growth. Using Random Forest was effective to identify a common sensitivity to soil water deficit at different time lags. Previous-year climatic variables tended to control leaf loss while radial growth was more sensitive to current-year soil water deficit. Late frost damages were observed on crown condition in mountainous regions but no impact was detected on radial growth. Few significant biotic damages were observed on growth or leaf loss. Leaf loss series did not show a clear common signal among trees from a plot as did radial growth and captured fewer pointer years. Radial growth index did not fall below normal until a 20% leaf loss was reached. However, this threshold is driven by a few extreme leaf loss events. As shown by our joint analysis of leaf loss and radial growth pointer years, no relationship occurred in cases of slight or moderate defoliation. Crown condition is a poorer descriptor of tree vitality than radial growth. Numéro de notice : A2020-287 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2020.118071 date de publication en ligne : 01/04/2020 En ligne : https://doi.org/10.1016/j.foreco.2020.118071 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95111 [article]

La croissance des forêts et les changements environnementaux / François Lebourgeois in Sciences, eaux & territoires, n° 33 (avril 2020)  

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Assessing the shape accuracy of coarse resolution burned area identifications / Michael L. Humber in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

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Regional-scale forest mapping over fragmented landscapes using global forest products and Landsat time series classification / Viktor Myroniuk in Remote sensing, vol 12 n° 1 (January 2020)  

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Automated fusion of forest airborne and terrestrial point clouds through canopy density analysis / Wenxia Dai in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

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Le point de vue de l'Inventaire Forestier National (IFN) [sic] / François Morneau in Rendez-vous techniques, n° 58-59-60 ([01/09/2019]) 

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Monitoring the structure of forest restoration plantations with a drone-lidar system / D.R.A. Almeida in International journal of applied Earth observation and geoinformation, vol 79 (July 2019)  

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Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors / Pauline Perbet in International Journal of Remote Sensing IJRS, vol 40 n°19 (February 2019)  

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Évaluation de la dégradation des forêts primaires par télédétection dans un espace de front pionnier consolidé d’Amazonie orientale (Paragominas) / Ali Fadhil Hasan (2019)  

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Can forest structural diversity be a response to anthropogenic stress? A case study in old-growth fir Abies alba Mill. stands / Rafał Podlaski in Annals of Forest Science [en ligne], vol 75 n° 4 (December 2018)  

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Potential of Sentinel-1 data for monitoring temperate mixed forest phenology / Pierre-Louis Frison in Remote sensing, vol 10 n° 12 (December 2018)  

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