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Large-area high spatial resolution albedo retrievals from remote sensing for use in assessing the impact of wildfire soot deposition on high mountain snow and ice melt / André Bertoncini in Remote sensing of environment, vol 278 (September 2022)  

Public [article]  inRemote sensing of environment > vol 278 (September 2022) . - n° 113101 Titre : Large-area high spatial resolution albedo retrievals from remote sensing for use in assessing the impact of wildfire soot deposition on high mountain snow and ice melt Type de document : Article/Communication Auteurs : André Bertoncini, Auteur ; Caroline Aubry-Wake, Auteur ; John W. Pomeroy, Auteur Année de publication : 2022 Article en page(s) : n° 113101 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] albedo [Termes IGN] Colombie-Britannique (Canada) [Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes IGN] fonte des glaces [Termes IGN] glacier [Termes IGN] Google Earth Engine [Termes IGN] image Sentinel-MSI [Termes IGN] image SRTM [Termes IGN] image Terra-MODIS [Termes IGN] incendie de forêt [Termes IGN] montagne [Termes IGN] neige [Termes IGN] pouvoir de résolution radiométrique Résumé : (auteur) Soot deposition from wildfires decreases snow and ice albedo and increases the absorption of shortwave radiation, which advances and accelerates melt. Soot deposition also induces algal growth, which further decreases snow and ice albedo. In recent years, increasingly severe and widespread wildfire activity has occurred in western Canada in association with climate change. In the summers of 2017 and 2018, westerly winds transported smoke from extensive record-breaking wildfires in British Columbia eastward to the Canadian Rockies, where substantial amounts of soot were deposited on high mountain glaciers, snowfields, and icefields. Several studies have addressed the problem of soot deposition on snow and ice, but the spatiotemporal resolution applied has not been compatible with studying mountain icefields that are extensive but contain substantial internal variability and have dynamical albedos. This study evaluates spatial patterns in the albedo decrease and net shortwave radiation (K*) increase caused by soot from intense wildfires in Western Canada deposited on the Columbia Icefield (151 km2), Canadian Rockies, during 2017 and 2018. Twelve Sentinel-2 images were used to generate high spatial resolution albedo retrievals during four summers (2017 to 2020) using a MODIS bidirectional reflectance distribution function (BRDF) model, which was employed to model the snow and ice reflectance anisotropy. Remote sensing estimates were evaluated using site-measured albedo on the icefield's Athabasca Glacier tongue, resulting in a R2, mean bias, and root mean square error (RMSE) of 0.68, 0.019, and 0.026, respectively. The biggest inter-annual spatially averaged soot-induced albedo declines were of 0.148 and 0.050 (2018 to 2020) for southeast-facing glaciers and the snow plateau, respectively. The highest inter-annual spatially-averaged soot-induced shortwave radiative forcing was 203 W/m2 for southeast-facing glaciers (2018 to 2020) and 106 W/m2 for the snow plateau (2017 to 2020). These findings indicate that snow albedo responded rapidly to and recovered rapidly from soot deposition. However, ice albedo remained low the year after fire, and this was likely related to a bio-albedo feedback involving microorganisms. Snow and ice K* were highest during low albedo years, especially for south-facing glaciers. These large-scale effects accelerated melt of the Columbia Icefield. The findings highlight the importance of using large-area high spatial resolution albedo estimates to analyze the effect of wildfire soot deposition on snow and ice albedo and K* on icefields, which is not possible using other approaches. Numéro de notice : A2022-466 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113101 Date de publication en ligne : 30/05/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113101 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100800 [article]

DART-Lux: An unbiased and rapid Monte Carlo radiative transfer method for simulating remote sensing images / Yingjie Wang in Remote sensing of environment, vol 274 (June 2022)  

Public [article]  inRemote sensing of environment > vol 274 (June 2022) . - n° 112973 Titre : DART-Lux: An unbiased and rapid Monte Carlo radiative transfer method for simulating remote sensing images Type de document : Article/Communication Auteurs : Yingjie Wang, Auteur ; Abdelaziz Kallel, Auteur ; Xuebo Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112973 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bande spectrale [Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes IGN] image à haute résolution [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle de transfert radiatif [Termes IGN] radiance [Termes IGN] réflectance directionnelle [Termes IGN] scène forestière [Termes IGN] scène urbaine Résumé : (auteur) Accurate and efficient simulation of remote sensing images is increasingly needed in order to better exploit remote sensing observations and to better design remote sensing missions. DART (Discrete Anisotropic Radiative Transfer), developed since 1992 based on the discrete ordinates method (i.e., standard mode DART-FT), is one of the most accurate and comprehensive 3D radiative transfer models to simulate the radiative budget and remote sensing observations of urban and natural landscapes. Recently, a new method, called DART-Lux, was integrated into DART model to address the requirements of massive remote sensing data simulation for large-scale and complex landscapes. It is developed based on efficient Monte Carlo light transport algorithms (i.e., bidirectional path tracing) and on DART model framework. DART-Lux can accurately and rapidly simulate the bidirectional reflectance factor (BRF) and spectral images of arbitrary landscapes. This paper presents its theory, implementation, and evaluation. Its accuracy, efficiency and advantages are also discussed. The comparison with standard DART-FT in a variety of scenarios shows that DART-Lux is consistent with DART-FT (relative differences Numéro de notice : A2022-398 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112973 Date de publication en ligne : 26/03/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112973 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100698 [article]

Unmixing-based spatiotemporal image fusion accounting for complex land cover changes / Xiaolu Jiang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 5 (May 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 5 (May 2022) . - n° 5623010 Titre : Unmixing-based spatiotemporal image fusion accounting for complex land cover changes Type de document : Article/Communication Auteurs : Xiaolu Jiang, Auteur ; Bo Huang, Auteur Année de publication : 2022 Article en page(s) : n° 5623010 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] changement d'occupation du sol [Termes IGN] données spatiotemporelles [Termes IGN] fusion d'images [Termes IGN] image Landsat [Termes IGN] image Terra-MODIS [Termes IGN] réflectance spectrale [Termes IGN] régression géographiquement pondérée Résumé : (auteur) Spatiotemporal reflectance fusion has received considerable attention in recent decades. However, various challenges remain despite varying levels of success, especially regarding the recovery of spatial details with complex land cover changes. Taking the blending of Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) images as an example, this article presents a locally weighted unmixing-based spatiotemporal image fusion model (LWU-STFM) that focuses on recovering complex land cover changes. The core idea is to redefine the land use class of each pixel featuring land cover change at the prediction date. The spatial unmixing process is enhanced using a proposed geographically spectrum-weighted regression (GSWR), and then, we optimize similar neighboring pixels for the final weighted-based prediction. Experiments are conducted using semisimulated and actual time-series Landsat–MODIS datasets to demonstrate the performance of the proposed LWU-STFM compared with the classic spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), two enhanced FSDAF models (SFSDAF and FSDAF 2.0), and a virtual image pair-based spatiotemporal fusion model for spatial weighting (VIPSTF-SW). The results reveal that the proposed LWU-STFM outperforms the other five models with the best quantitative accuracy. In terms of the relative dimensionless global error (ERGAS) index, the errors of Landsat-like images generated using LWU-STFM are 2.8%–63.4% lower than those of other models. From visual comparisons, LWU-STFM predictions illustrate encouraging improvements in recovering spatial details of pixels with complex land cover changes in heterogeneous landscapes and, thus, advancing applications of spatiotemporal image fusion for continuous and fine-scale land surface monitoring. Numéro de notice : A2022-409 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3173172 Date de publication en ligne : 05/05/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3173172 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100744 [article]

A convolution neural network for forest leaf chlorophyll and carotenoid estimation using hyperspectral reflectance / Shuo Shi in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 108 (April 2022) . - n° 102719 Titre : A convolution neural network for forest leaf chlorophyll and carotenoid estimation using hyperspectral reflectance Type de document : Article/Communication Auteurs : Shuo Shi, Auteur ; Lu Xu, Auteur ; Wei Gong, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102719 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] chlorophylle [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] écosystème forestier [Termes IGN] feuille (végétation) [Termes IGN] modèle de transfert radiatif [Termes IGN] processus gaussien [Termes IGN] réflectance spectrale [Termes IGN] régression Résumé : (auteur) Forest leaf chlorophyll (Cab) and carotenoid (Cxc) are key functional indicators for the state of the forest ecosystem. Current machine learning models based on hyperspectral reflectance are widely applied to estimate leaf Cab and Cxc contents at leaf scale. However, these models have certain accuracy for non-independent datasets but have poor generalization for independent datasets when they are used to estimate leaf Cab and Cxc contents. This fact limits that hyperspectral remote sensing completely replaces destructive measurements for leaf Cab and Cxc contents. Thus, the development of an estimation model with high accuracy and satisfactory generalization is necessary. Convolutional neural networks (CNNs) have certain accuracy and generalization in many domains, and have the potential to solve above-mentioned problem. Therefore, this study developed a CNN using one-dimensional hyperspectral reflectance, which aimed to improve the model's accuracy and generalization in leaf Cab and Cxc content estimation at leaf scale. The proposed CNN was developed by three steps. First, in consideration of the correlation between leaf Cab and Cxc contents in natural leaves, 2500 physical data with leaf reflectance and corresponding Cab and Cxc contents were generated by leaf radiative transfer model and multivariable gaussian distribution function. Then, the proposed CNN was built by five strategies based on the architecture of the AlexNet. Finally, five-fold cross validation was performed with 70% of the physical data to determine the best strategy to develop the proposed CNN. These were executed to ensure the proposed CNN with the maximum accuracy and generalization. In addition, the accuracy and generalization of the proposed CNN were tested using a non-independent dataset and an independent dataset, respectively. The proposed CNN was also compared with back propagation neural network (BPNN), support vector regression (SVR) and gaussian process regression (GPR). Results showed that the best CNN could be developed with one input, five convolutional, three max-pooling and three fully-connected layers. Comprehensively considering the model's accuracy and generalization, the proposed CNN was the best model for leaf Cab and Cxc content estimation compared with BPNN, SVR and GPR. This study provides a development strategy of CNN estimation model using one-dimensional hyperspectral reflectance at leaf scale. The proposed CNN could further promote the practical application of hyperspectral remote sensing in leaf Cab and Cxc content estimation. Numéro de notice : A2022-231 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102719 Date de publication en ligne : 16/02/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102719 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100119 [article]

Are northern German Scots pine plantations climate smart? The impact of large-scale conifer planting on climate, soil and the water cycle / Christoph Leuschner in Forest ecology and management, vol 507 (1 March 2022)  

Public [article]  inForest ecology and management > vol 507 (1 March 2022) . - n° 120013 Titre : Are northern German Scots pine plantations climate smart? The impact of large-scale conifer planting on climate, soil and the water cycle Type de document : Article/Communication Auteurs : Christoph Leuschner, Auteur ; Agnes Förster, Auteur ; Marco Diers, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120013 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] acidification des sols [Termes IGN] albedo [Termes IGN] Allemagne [Termes IGN] biomasse aérienne [Termes IGN] canopée [Termes IGN] changement climatique [Termes IGN] évapotranspiration [Termes IGN] Fagus sylvatica [Termes IGN] foresterie [Termes IGN] Pinus sylvestris [Termes IGN] puits de carbone [Termes IGN] résilience écologique [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Increasing temperatures and rising atmospheric vapor pressure deficits are exposing forests around the globe to increasing drought and heat stress, demanding a shift to climate-smart forestry for increasing the stress resistance and resilience of production forests and to enhance their climate change mitigation potential. Based on measurements in paired pine and beech forests and the review of literature data, we analyse the biophysical consequences and the carbon cycle impact of large-scale Scots pine (Pinus sylvestris L.) plantations in northern Germany in the face of a warming and aridifying climate. We quantified canopy surface albedo and surface temperature, evapotranspiration and deep seepage, carbon (C) storage in biomass and soil and annual C sequestration, and soil acidification of pine plantations in comparison to beech forests (Fagus sylvatica L.), the natural forest vegetation. We find that near-infrared (NIR, 700–3000 mn) canopy surface albedo is higher by 5.2 percentage points during summer over beech as compared to pine forest, resulting in a 9 % higher net radiation and a 0.6 K higher surface temperature of the pine canopy. Deep seepage is on average by 68 mm yr−1 smaller under pine than beech forest (66 mm yr−1 vs. 134 mm yr−1) due to the higher evapotranspiration of pine. C storage in biomass and soil is by ∼ 48 Mg C ha−1 higher in beech than pine forests, reflecting the higher productivity of beech, demonstrating an unfavorably low C sequestration potential of Scots pine plantations. We conclude that the large-scale Scots pine plantations in northern Germany (>1.7 million ha) are neither environmental-friendly nor climate smart, given their enhancement of climate-warming, low climate change mitigation potential, and negative effect on groundwater recharge. Replacing pine plantations by beech (or other hardwood) forests in northern Germany and adjacent regions is urgently needed for achieving the goals of climate-smart forestry. Numéro de notice : A2022-136 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120013 Date de publication en ligne : 24/01/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99742 [article]

Land surface phenology retrieval through spectral and angular harmonization of Landsat-8, Sentinel-2 and Gaofen-1 data / Jun Lu in Remote sensing, vol 14 n° 5 (March-1 2022)  

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A novel regression method for harmonic analysis of time series / Qiang Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 185 (March 2022)  

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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)  

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Spatiotemporal 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)  

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Detecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation / Guiming Zhang in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)  

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Detection and biomass estimation of phaeocystis globosa blooms off Southern China from UAV-based hyperspectral measurements / Xue Li in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)  

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Early detection of spruce vitality loss with hyperspectral data: Results of an experimental study in Bavaria, Germany / Kathrin Einzmann in Remote sensing of environment, vol 266 (December 2021)  

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Spatial variability of suspended sediments in San Francisco Bay, California / Niky C. Taylor in Remote sensing, vol 13 n° 22 (November-2 2021)  

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A novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery / Lan Xun in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)  

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Deep-learning-based burned area mapping using the synergy of Sentinel-1&2 data / Qi Zhang in Remote sensing of environment, vol 264 (October 2021)  

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