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Mapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery / Donato Morresi in Remote sensing of environment, vol 269 (February 2022)  

Public [article]  inRemote sensing of environment > vol 269 (February 2022) . - n° 112800 Titre : Mapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery Type de document : Article/Communication Auteurs : Donato Morresi, Auteur ; Raffaella Marzano, Auteur ; Emanuele Lingua, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112800 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte de la végétation [Termes IGN] détection de changement [Termes IGN] forêt alpestre [Termes IGN] image multibande [Termes IGN] image Sentinel-MSI [Termes IGN] incendie de forêt [Termes IGN] phénologie [Termes IGN] Piémont (Italie) [Termes IGN] réflectance spectrale [Termes IGN] variation saisonnière Résumé : (auteur) Deriving burn severity from multispectral satellite data is a widely adopted approach to infer the degree of environmental change caused by fire. Burn severity maps obtained by thresholding bi-temporal indices based on pre- and post-fire Normalized Burn Ratio (NBR) can vary substantially depending on temporal constraints such as matched acquisition and optimal seasonal timing. Satisfying temporal requirements is crucial to effectively disentangle fire and non-fire induced spectral changes and can be particularly challenging when only a few cloud-free images are available. Our study focuses on 10 wildfires that occurred in mountainous areas of the Piedmont Region (Italy) during autumn 2017 following a severe and prolonged drought period. Our objectives were to: (i) generate reflectance composites using Sentinel-2 imagery that were optimised for seasonal timing by embedding spatial patterns of long-term land surface phenology (LSP); (ii) produce and validate burn severity maps based on the modelled relationship between bi-temporal indices and field data; (iii) compare burn severity maps obtained using either a pair of cloud-free Sentinel-2 images, i.e. paired images, or reflectance composites. We proposed a pixel-based compositing algorithm coupling the weighted geometric median and thematic spatial information, e.g. long-term LSP metrics derived from the MODIS Collection 6 Land Cover Dynamics Product, to rank all the clear observations available in the growing season. Composite Burn Index data and bi-temporal indices exhibited a strong nonlinear relationship (R2 > 0.85) using paired images or reflectance composites. Burn severity maps attained overall classification accuracy ranging from 76.9% to 83.7% (Kappa between 0.61 and 0.72) and the Relative differenced NBR (RdNBR) achieved the best results compared to other bi-temporal indices (differenced NBR and Relativized Burn Ratio). Improvements in overall classification accuracy offered by the calibration of bi-temporal indices with the dNBR offset were limited to burn severity maps derived from paired images. Reflectance composites provided the highest overall classification accuracy and differences with paired images were significant using uncalibrated bi-temporal indices (4.4% to 5.2%) while they decreased (2.8% to 3.2%) when we calibrated bi-temporal indices derived from paired images. The extent of the high severity category increased by ~19% in burn severity maps derived from reflectance composites (uncalibrated RdNBR) compared to those from paired images (calibrated RdNBR). The reduced contrast between healthy and burnt conditions associated with suboptimal seasonal timing caused an underestimation of burnt areas. By embedding spatial patterns of long-term LSP metrics, our approach provided consistent reflectance composites targeted at a specific phenological stage and minimising non-fire induced inter-annual changes. Being independent from the multispectral dataset employed, the proposed pixel-based compositing approach offers new opportunities for operational change detection applications in geographic areas characterised by persistent cloud cover. Numéro de notice : A2022-095 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112800 Date de publication en ligne : 22/11/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112800 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99534 [article]

Multi-temporal remote sensing data to monitor terrestrial ecosystem responses to climate variations in Ghana / Ram Avtar in Geocarto international, vol 37 n° 2 ([06/01/2022])  

Public [article]  inGeocarto international > vol 37 n° 2 [06/01/2022] . - pp 396 - 412 Titre : Multi-temporal remote sensing data to monitor terrestrial ecosystem responses to climate variations in Ghana Type de document : Article/Communication Auteurs : Ram Avtar, Auteur ; Ali P. Yunus, Auteur ; Osamu Saito, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 396 - 412 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] changement climatique [Termes IGN] données multitemporelles [Termes IGN] écosystème [Termes IGN] Ghana [Termes IGN] image Landsat [Termes IGN] image SPOT [Termes IGN] image Terra-MODIS [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] phénologie [Termes IGN] variation temporelle Résumé : (auteur) Operational monitoring of vegetation and its response to climate change involves the use of vegetation indices (VIs) in relation to relevant climatic data. This study analyses the temporal variations of vegetation indices in response to climatic data (temperature and precipitation) to better understand the phenological changes in the Wa-West and Tolon districts of Ghana during 1999–2011. This study also examines the inter-annual variation of vegetation indices and lag effects of climate variables (temperature and precipitation) using simple regression and correlation approaches. Results indicate that the mean Normalized Difference Vegetation Index (NDVI) and Normalized Difference Soil Index (NDSI) were significantly correlated with the mean temperature, whereby the value of NDVI increases with a decrease in temperature and value of NDSI increases with an increase in temperature. On examining seasonal variations, our findings indicated that the months of August and September have the highest mean NDVI values. This study confirms that consistently rising temperature and altered precipitation patterns have exerted a strong influence on temporal distributions and productivities of the terrestrial ecosystems of the Tolon and Wa-West districts of Ghana. Furthermore, this research demonstrates how vegetation indices can be used as an indicator to monitor phenological changes in the terrestrial ecosystem. Numéro de notice : A2022-050 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1723716 Date de publication en ligne : 11/02/2020 En ligne : https://doi.org/10.1080/10106049.2020.1723716 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99442 [article]

Growth recovery and phenological responses of juvenile beech (fagus sylvatica L.) exposed to spring warming and late spring frost / Kristine Vander Mijnsbrugge in Forests, vol 12 n° 11 (November 2021)  

Public [article]  inForests > vol 12 n° 11 (November 2021) . - n° 1604 Titre : Growth recovery and phenological responses of juvenile beech (fagus sylvatica L.) exposed to spring warming and late spring frost Type de document : Article/Communication Auteurs : Kristine Vander Mijnsbrugge, Auteur ; Jessa May Malanguis, Auteur ; Stefaan Moreels, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 1604 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Belgique [Termes IGN] chaleur [Termes IGN] croissance des arbres [Termes IGN] dommage forestier causé par facteurs naturels [Termes IGN] Fagus sylvatica [Termes IGN] gelée [Termes IGN] phénologie [Termes IGN] phénomène climatique extrême [Termes IGN] semis (sylviculture) [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Global change increases the risk of extreme climatic events. The impact of extreme temperature may depend on the tree species and also on the provenance. Ten provenances of Fagus sylvatica L. were grown in a common garden environment in Belgium and subjected to different temperature treatments. Half of the one year old seedlings were submitted to a high thermal stress in the spring of the first year, and all plants were exposed to a late spring frost in the second year. The high-temperature treated plants displayed reduced growth in the first year, which was fully compensated (recovery with exact compensation) in the second year for radial growth and in the third year for height growth. Frost in the spring of the second year damaged part of the saplings and reduced their growth. The frost damaged plants regained the pre-stress growth rate one year later (recovery without compensation). The high temperature treatment in the first year and the frost damage in the second year clearly influenced the phenological responses in the year of the event and in the succeeding year. Little population differentiation was observed among the provenances for growth and for phenological responses. Yet, a southern provenance, a non-autochthonous provenance (original German provenance that was planted in Belgium about a century ago) and a more continental provenance flushed earlier than the local Atlantic provenances in the year of the frost event, resulting in more frost damage. Some caution should therefore be taken when translocating provenances as an anticipation of the predicted climate warming. Numéro de notice : A2021-879 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f12111604 Date de publication en ligne : 20/11/2021 En ligne : https://doi.org/10.3390/f12111604 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99169 [article]

Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies / Guangqin Song in ISPRS Journal of photogrammetry and remote sensing, vol 183 (January 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 183 (January 2022) . - pp 19 - 33 Titre : Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies Type de document : Article/Communication Auteurs : Guangqin Song, Auteur ; Shengbiao Wu, Auteur ; Calvin K.F. Lee, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 19 - 33 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] canopée [Termes IGN] classification dirigée [Termes IGN] diagnostic foliaire [Termes IGN] Enhanced vegetation index [Termes IGN] feuille (végétation) [Termes IGN] forêt tropicale [Termes IGN] Panama [Termes IGN] phénologie [Termes IGN] photosynthèse [Termes IGN] segmentation sémantique [Termes IGN] série temporelle [Termes IGN] SLIC algorithm [Termes IGN] superpixel [Termes IGN] variation saisonnière Résumé : (auteur) Tropical leaf phenology—particularly its variability at the tree-crown scale—dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red–greenblue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. We first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams. Numéro de notice : A2022-009 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.10.023 Date de publication en ligne : 10/11/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.10.023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99057 [article]

Modelling the impact of climate change on the occurrence of frost damage in Sitka spruce (Picea sitchensis) in Great Britain / A.A. Atucha-Zamkova in Forestry, an international journal of forest research, vol 94 n° 5 (December 2021)  

Public [article]  inForestry, an international journal of forest research > vol 94 n° 5 (December 2021) . - p 664 - 676 Titre : Modelling the impact of climate change on the occurrence of frost damage in Sitka spruce (Picea sitchensis) in Great Britain Type de document : Article/Communication Auteurs : A.A. Atucha-Zamkova, Auteur ; K.A. Steele, Auteur ; A.R. Smith, Auteur Année de publication : 2021 Article en page(s) : p 664 - 676 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] changement climatique [Termes IGN] cycle climatique [Termes IGN] gelée [Termes IGN] Grande-Bretagne [Termes IGN] historique des données [Termes IGN] modèle de simulation [Termes IGN] phénologie [Termes IGN] Picea sitchensis [Termes IGN] température [Termes IGN] variation saisonnière [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Climate change is predicted to increase temperature and seasonal temperature variance in Great Britain (GB). Sitka spruce (Picea sitchensis (Bong.) Carr) is the most important tree species used in commercial plantations throughout Europe and GB. Frosts that occur outside the winter dormancy period can negatively affect trees, since they happen after dehardening. Damage can be especially severe at bud burst, before emerging needles mature and form protective barriers. Here, we modelled the impact of climate change on frost sensitivity in Sitka spruce with temperature data from five climate projections. The UKCP09 climate model HadRm3 uses emission scenario SRESA1B for the years 2020–2099. The global and downscaled versions of the UKCP18 HadGem3 model use the emissions scenario RCP 8.5. The global model CMCC-CM uses the RCP 4.5 and RCP 8.5 emissions scenarios. The predictions based on these models were compared with results from gridded historical data for the period 1960–2015. Three indicators that assessed the frost sensitivity of Sitka spruce were explored: the total number of frosts between the onset of dehardening and the end of summer, which use three different temperature thresholds (Index 10°C, 1–3°C, 1–5°C); the total number of frosts after bud burst (Index 2); the number of days with minimum temperatures below the resistance level (backlashes) during the hardening–dehardening period (September–August) (Index 3). The indices were validated with historical data for frost damage across GB, and Index 1–3°C, Index 1–5°C and Index 3 were shown to be significantly correlated. The frequency of all frosts and backlashes is expected to decrease with climate change, especially under higher emissions scenarios. Post-bud burst frosts have been historically very rare in GB and remain so with climate change. Downscaled regional climate models detect geographic variability within GB and improve prediction of overall trends in frost damage in comparison to global climate change models for GB. Numéro de notice : A2021-825 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1093/forestry/cpab020 Date de publication en ligne : 17/05/2021 En ligne : https://doi.org/10.1093/forestry/cpab020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98944 [article]

Uncertainties in measurements of leaf optical properties are small compared to the biological variation within and between individuals of European beech / Fanny Petibon in Remote sensing of environment, vol 264 (October 2021)  

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Improving the accuracy of spring phenology detection by optimally smoothing satellite vegetation index time series based on local cloud frequency / Jiaqi Tian in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)  

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Phenotypic variability and differences in the drought response of Norway spruce pendula and pyramidalis half-sib families / Marius Budeanu in Forests, vol 12 n° 7 (July 2021)  

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Assessing forest phenology: A multi-scale comparison of near-surface (UAV, spectral reflectance sensor, PhenoCam) and satellite (MODIS, Sentinel-2) remote sensing / Shangharsha Thapa in Remote sensing, vol 13 n° 8 (April-2 2021)  

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Artificial neural networks in agriculture / Sebastian Kujawa (2021)  

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Investigation of Sentinel-1 time series for sensitivity to fern vegetation in an European temperate forest / Marlin Mueller (2021)  

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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)  

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Reply to Elmendorf and Ettinger: Photoperiod plays a dominant and irreplaceable role in triggering secondary growth resumption / Jian-Guo Huang in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 117 n° 52 (December 2020)  

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CNN-based tree species classification using high resolution RGB image data from automated UAV observations / Sebastian Egli in Remote sensing, vol 12 n° 23 (December-2 2020)  

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A novel intelligent classification method for urban green space based on high-resolution remote sensing images / Zhiyu Xu in Remote sensing, vol 12 n° 22 (December 2020)  

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