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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)
[article]
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 forestièreRésumé : (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
in International journal of applied Earth observation and geoinformation > vol 106 (February 2022) . - n° 102663[article]Dynamic modelling of rice leaf area index with quad-source optical imagery and machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 3 ([01/02/2022])
[article]
Titre : Dynamic modelling of rice leaf area index with quad-source optical imagery and machine learning regression models Type de document : Article/Communication Auteurs : Lamin R. Mansaray, Auteur ; Adam Sheka Kanu, Auteur ; Lingbo Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 828 - 840 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] Chine
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Extreme Gradient Machine
[Termes IGN] Green Leaf Area Index
[Termes IGN] image Gaofen
[Termes IGN] image HJ-1A
[Termes IGN] image HJ-1B
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice foliaire
[Termes IGN] modèle de régression
[Termes IGN] rizièreRésumé : (auteur) Optical satellite imagery has been widely used to monitor leaf area index (LAI). However, most studies have focussed on single- or dual-source data, thus making little use of a growing repository of freely available optical imagery. Hence this study has evaluated the feasibility of quad-source optical satellite imagery involving Landsat-8, Sentinel-2A, China’s environment satellite constellation (HJ-1 A and B) and Gaofen-1 (GF-1) in modelling rice green LAI over a test site located in southeast China at two growing seasons. With the application of machine learning regression models including Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbour (k-NN) and Gradient Boosting Decision Tree (GBDT), results indicated that regression models based on an ensemble of decision trees (RF and GBDT) were more suitable for modelling rice green LAI. The current study has demonstrated the feasibility of quad-source optical imagery in modelling rice green LAI and this is relevant for cloudy areas. Numéro de notice : A2022-346 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1745299 Date de publication en ligne : 03/04/2020 En ligne : https://doi.org/10.1080/10106049.2020.1745299 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100530
in Geocarto international > vol 37 n° 3 [01/02/2022] . - pp 828 - 840[article]Fast local adaptive multiscale image matching algorithm for remote sensing image correlation / Niccolò Dematteis in Computers & geosciences, vol 159 (February 2022)
[article]
Titre : Fast local adaptive multiscale image matching algorithm for remote sensing image correlation Type de document : Article/Communication Auteurs : Niccolò Dematteis, Auteur ; Daniele Giordan, Auteur ; Bruno Crippa, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104988 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement automatique
[Termes IGN] appariement d'images
[Termes IGN] données multiéchelles
[Termes IGN] fonte des glaces
[Termes IGN] glacier
[Termes IGN] image Sentinel-MSI
[Termes IGN] implémentation (informatique)
[Termes IGN] Matlab
[Termes IGN] PatagonieRésumé : (auteur) Various studies have shown that image correlation calculated in the space domain outperforms frequency-based methods. However, such an approach usually requires great computational efforts, making it challenging to adopt for surveying fast moving processes like glaciers, particularly over wide areas. We present a local adaptive multiscale image matching algorithm (LAMMA), which repeatedly applies image correlation on grids of increasing spatial resolution and adapts the size of the interrogation area according to the local range of displacements. LAMMA allows reducing the number of calculi of several orders of magnitude and limits the occurrence of displacement outliers. We show an example of LAMMA application on Sentinel-2 images to measure glaciers flow of the Southern Patagonian Icefield, where LAMMA's runtime was comparable to that of frequency-based correlation. LAMMA's Matlab code is freely available on GitHub. Numéro de notice : A2022-094 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2021.104988 Date de publication en ligne : 19/11/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104988 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99528
in Computers & geosciences > vol 159 (February 2022) . - n° 104988[article]Generating 2m fine-scale urban tree cover product over 34 metropolises in China based on deep context-aware sub-pixel mapping network / Da He in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)
[article]
Titre : Generating 2m fine-scale urban tree cover product over 34 metropolises in China based on deep context-aware sub-pixel mapping network Type de document : Article/Communication Auteurs : Da He, Auteur ; Qian Shi, Auteur ; Xiaoping Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102667 Note générale : bibliography Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse infrapixellaire
[Termes IGN] apprentissage profond
[Termes IGN] arbre hors forêt
[Termes IGN] arbre urbain
[Termes IGN] base de données localisées
[Termes IGN] Chine
[Termes IGN] image Sentinel-MSI
[Termes IGN] métropole
[Termes IGN] Pékin (Chine)
[Termes IGN] prise en compte du contexte
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Contrast to the global forest, few trees live in cities but contribute significantly to urban environment and human health. However, the classical satellite-derived land cover/forest cover products with limited resolution are not fine enough for the identification of urban tree, which is usually appeared in small size and intersected with infrastructure. To relieve the dilemma, this study developed an urban tree specific sub-pixel mapping (SPM) architecture with deep learning approach, which aimed to generate 2m fine-scale urban tree cover product from 10 m Sentinel-2 images for large-scale area of 34 metropolises in China. The proposed approach has remarkable reconstruction ability for delineating the contextual characteristic of the urban tree patterns, and reliable generalization ability to large-scale area. In addition, this study creates a large-volume urban tree cover dataset (UTCD) with 0.13 billion urban tree samples at 2 m resolution, which fills the deficiency of standard dataset in urban tree cover research field. Quantitative analysis of our products was conducted on two typical study sites of Beijing and Wuhan. The results show that our products recover averagely more than 58.72% of urban tree covers that have been underestimated in the existing land cover/forest cover products, and outperforms the state-of-the-art approach both visually and quantitatively, by averagely 11.31% improvement in overall accuracy. From our annual products during 2016–2020, we found an evolution characteristic of urban tree cover: it is more stable in developed cities like Beijing, while more fluctuated in developing cities like Wuhan, and the alteration are usually concentrated at the outer-ring of downtown, which may be caused by the municipal planning and the land development of real estate industry. Numéro de notice : A2022-073 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2021.102667 En ligne : https://doi.org/10.1016/j.jag.2021.102667 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99438
in International journal of applied Earth observation and geoinformation > vol 106 (February 2022) . - n° 102667[article]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)
[article]
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] cartographie des risques
[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] risque naturel
[Termes IGN] variation saisonnière
[Termes IGN] zone sinistréeRésumé : (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
in Remote sensing of environment > vol 269 (February 2022) . - n° 112800[article]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)PermalinkSpatiotemporal 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)PermalinkCombined use of Sentinel-1 and Sentinel-2 data for improving above-ground biomass estimation / Narissara Nuthammachot in Geocarto international, vol 37 n° 2 ([15/01/2022])PermalinkSoil erosion estimation of Bhandara region of Maharashtra, India, by integrated use of RUSLE, remote sensing, and GIS / Sumedh R. Kashiwar in Natural Hazards, vol 110 n° 2 (January 2022)PermalinkVariations of urban NO2 pollution during the COVID-19 outbreak and post-epidemic era in China: A synthesis of remote sensing and In situ measurements / Chunhui Zhao in Remote sensing, vol 14 n° 2 (January-2 2022)PermalinkAbove-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data / Fardin Moradi in Annals of forest research, vol 65 n° 1 (January - June 2022)PermalinkApplication of deep learning with stratified K-fold for vegetation species discrimation in a protected mountainous region using Sentinel-2 image / Efosa Gbenga Adagbasa in Geocarto international, vol 37 n° 1 ([01/01/2022])PermalinkDétection des prairies de fauche et estimation des périodes de fauche par télédétection / Emma Seneschal (2022)PermalinkEstimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data / Fardin Moradi in Forests, vol 13 n° 1 (January 2022)PermalinkExamining the integration of Landsat operational land imager with Sentinel-1 and vegetation indices in mapping southern yellow pines (Loblolly, Shortleaf, and Virginia pines) / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 1 (January 2022)Permalink