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Forest canopy stratification based on fused, imbalanced and collinear LiDAR and Sentinel-2 metrics / Jakob Wernicke in Remote sensing of environment, vol 279 (September-15 2022)
[article]
Titre : Forest canopy stratification based on fused, imbalanced and collinear LiDAR and Sentinel-2 metrics Type de document : Article/Communication Auteurs : Jakob Wernicke, Auteur ; Christian Torsten Seltmann, Auteur ; Ralf Wenzel, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113134 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Allemagne
[Termes IGN] analyse comparative
[Termes IGN] canopée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] fusion d'images
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] semis de points
[Termes IGN] stratificationRésumé : (auteur) Knowledge about the forest canopy stratification is of essential importance for forest management and planning. Collecting structural information (e.g. natural regeneration) still depends on cost and labour intensive forest inventories with a coarse spatio-temporal resolution. Remote sensing partly overcomes these limitations and particularly active sensors of type light detection and ranging (LiDAR) have proven their great potential of separating forest strata. The applicability of LiDAR metrics for the differentiation of the spruce dominated forest strata in Central Germany has not been tested yet. Additionally, studying the potential of Sentinel-2 metrics for the classification of forest strata is lacking too. In this study, we investigated the capabilities of six different classification approaches for the differentiation of five forest strata that are typical for the study region. Reference data were derived from forest inventory measurements surveyed on a dense 200 × 200 m grid. The six classification approaches were trained with fused and un-fused LiDAR and Sentinel-2 inferred metrics. The classification results were compared using the overall mean accuracy, sensitivity and specificity via receivers operating characteristics of multi-class problems. We were interested in the classification abilities of Sentinel-2 metrics due to the obvious advantages of Sentinel-2 based metrics (free of charge, high spatio-temporal coverage). We assumed that the canopy structure determines the reflection on stand level and thus might facilitate the classification of different canopy strata. Beforehand, it was important to examine the influence of distinctly imbalanced and collinear reference data on the classification results. We found that the Random Forest classifier most accurately separated the five forest strata with a mean overall accuracy of 83.3% (Kappa = 76.2%). These values were achieved from balanced training data and the classification capability was confirmed by classification results from an independent test data set. Fused predictors of active (LiDAR) and passive (Sentinel-2) remote sensing revealed no substantial improvement in the classification accuracy due to the dominant role of LiDAR metrics. Herein, we identified that especially the height variability, top height, portion of LiDAR-returns between 2 m and 10 m and the standard deviation of the return number between the 25th and 50th height percentile, predominately contributed to the classification accuracy. Classification results purely based on Sentinel-2 metrics revealed a rather small overall mean accuracy of 54.7%. The metrics (e.g. median, variance, entropy) were derived from Sentinel-2 indices, covering the visible and near to short infrared spectrum. Variable importance computations unraveled a detectable but minor contribution of MSI, TCG, NDVI to the classification result. Finally, our data driven observations illustrated serious drawbacks associated to data imbalance, collinearity and autocorrelation and presented practical guidance to cope with these issues. Numéro de notice : A2022-510 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113134 Date de publication en ligne : 28/06/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113134 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101047
in Remote sensing of environment > vol 279 (September-15 2022) . - n° 113134[article]Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts / Shengli Tao in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 119 n° 37 (2022)
[article]
Titre : Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts Type de document : Article/Communication Auteurs : Shengli Tao, Auteur ; Jérôme Chave, Auteur ; Pierre-Louis Frison , Auteur ; et al., Auteur Année de publication : 2022 Projets : 3-projet - voir note / Article en page(s) : n° e2116626119 Note générale : bibliographie
This study was supported by an Investissement d’Avenir grant managed by the Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01; TULIP, ref. ANR-10-LABX-0041; ANAEE-France: ANR-11-INBS-0001), and by the National Natural Science Foundation of China (grant no. 31988102). This research was also supported by a Centre National d' Etudes Spatiales (CNES) postdoctoral fellowship to S.T., the CNES-BIOMASS pluriannual project, and the European Space Agency (ESA) Climate Change Initiative (CCI) Biomass project (contract no. 4000123662/18/I-NB).Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] forêt tropicale
[Termes IGN] image radar
[Termes IGN] sécheresse
[Termes IGN] série temporelle
[Termes IGN] vulnérabilitéRésumé : (auteur) Intact tropical rainforests have been exposed to severe droughts in recent decades, which may threaten their integrity, their ability to sequester carbon, and their capacity to provide shelter for biodiversity. However, their response to droughts remains uncertain due to limited high-quality, long-term observations covering extensive areas. Here, we examined how the upper canopy of intact tropical rainforests has responded to drought events globally and during the past 3 decades. By developing a long pantropical time series (1992 to 2018) of monthly radar satellite observations, we show that repeated droughts caused a sustained decline in radar signal in 93%, 84%, and 88% of intact tropical rainforests in the Americas, Africa, and Asia, respectively. Sudden decreases in radar signal were detected around the 1997–1998, 2005, 2010, and 2015 droughts in tropical Americas; 1999–2000, 2004–2005, 2010–2011, and 2015 droughts in tropical Africa; and 1997–1998, 2006, and 2015 droughts in tropical Asia. Rainforests showed similar low resistance (the ability to maintain predrought condition when drought occurs) to severe droughts across continents, but American rainforests consistently showed the lowest resilience (the ability to return to predrought condition after the drought event). Moreover, while the resistance of intact tropical rainforests to drought is decreasing, albeit weakly in tropical Africa and Asia, forest resilience has not increased significantly. Our results therefore suggest the capacity of intact rainforests to withstand future droughts is limited. This has negative implications for climate change mitigation through forest-based climate solutions and the associated pledges made by countries under the Paris Agreement. Numéro de notice : A2022-681 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1073/pnas.2116626119 En ligne : https://doi.org/10.1073/pnas.2116626119 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101538
in Proceedings of the National Academy of Sciences of the United States of America PNAS > vol 119 n° 37 (2022) . - n° e2116626119[article]The FIRST model: Spatiotemporal fusion incorrporting spectral autocorrelation / Shuaijun Liu in Remote sensing of environment, vol 279 (September-15 2022)
[article]
Titre : The FIRST model: Spatiotemporal fusion incorrporting spectral autocorrelation Type de document : Article/Communication Auteurs : Shuaijun Liu, Auteur ; Junxiong Zhou, Auteur ; Yuean Qiu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113111 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] autocorrélation
[Termes IGN] bande spectrale
[Termes IGN] détection de changement
[Termes IGN] données spatiotemporelles
[Termes IGN] fusion de données
[Termes IGN] image Landsat-OLI
[Termes IGN] image Terra-MODIS
[Termes IGN] réflectance de surface
[Termes IGN] réflectance spectrale
[Termes IGN] régression des moindres carrés partiels
[Termes IGN] régression multipleRésumé : (auteur) Over the past decade, spatiotemporal fusion has become an indispensable tool for monitoring land surface dynamics due to its promising ability to produce surface reflectance products with both high spatial and temporal resolutions. However, existing fusion methods usually generate multispectral band products by predicting each spectral band separately, so the useful information of spectral autocorrelation within the spectrum has been ignored and waits to be exploited. To address this issue, we propose a novel spatiotemporal fusion method, the spatiotemporal Fusion Incorrporting Spectral autocorrelaTion (FIRST) model, to fully utilize the multiple spectral bands of surface reflectance products. Compared with other fusion methods, the model has three distinct advantages: (1) it utilizes spectral autocorrelation in a many-to-many regression framework that simultaneously inputs and predicts multispectral bands without the collinearity effect; (2) it maintains high fusion accuracy when the spatiotemporal variation is large with acceptable computational efficiency; and (3) it can produce robust results even with input images contaminated by haze and thin clouds. We tested the FIRST model at several experimental sites and compared it with four typical methods, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF) model, the regression model Fitting, spatial Filtering and residual Compensation (Fit-FC) model and the enhanced STARFM (ESTARFM). The results demonstrate that FIRST yields better overall performance for its simple and effective technical principles. FIRST is thus expected to provide high-quality remotely sensed data with high spatial resolution and frequent observations for various applications. Numéro de notice : A2022-554 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113111 Date de publication en ligne : 16/06/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113111 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101166
in Remote sensing of environment > vol 279 (September-15 2022) . - n° 113111[article]Analytical method for high-precision seabed surface modelling combining B-spline functions and Fourier series / Tyler Susa in Marine geodesy, vol 45 n° 5 (September 2022)
[article]
Titre : Analytical method for high-precision seabed surface modelling combining B-spline functions and Fourier series Type de document : Article/Communication Auteurs : Tyler Susa, Auteur Année de publication : 2022 Article en page(s) : pp 435 - 461 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bathymétrie
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] Extreme Gradient Machine
[Termes IGN] fond marin
[Termes IGN] image Sentinel-MSI
[Termes IGN] littoral
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation
[Termes IGN] Porto Rico
[Termes IGN] profondeur
[Termes IGN] réflectanceRésumé : (auteur) Accurate charting of nearshore bathymetry is critical to the safe and dependable use of coastal waterways frequented by the trading, fishing, tourism, and ocean energy industries. The accessibility of satellite imagery and the availability of various satellite-derived bathymetry (SDB) techniques have provided a cost-effective alternative to traditional in-situ bathymetric surveys. Furthermore, improved algorithms and the advancement of machine learning models have provided opportunity for higher quality bathymetric derivations. However, to date the relative accuracy and performance between traditional physics-based techniques, improved physics-based methods, and machine learning ensemble models have not been adequately quantified. In this study, nearshore bathymetry is derived from Sentinel-2 satellite imagery near La Parguera, Puerto Rico utilizing a traditional band-ratio algorithm, a band-ratio switching method, a random forest machine learning model, and the XGBoost machine learning model. The machine learning models returned comparable results and were markedly more accurate relative to other techniques; however, both machine learning models required an extensive training dataset. All models were constrained by environmental influences and image spatial resolution, which were assessed to be the limiting factors for routine use of satellite-derived bathymetry as a reliable method for hydrographic surveying. Numéro de notice : A2022-609 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2022.2064572 Date de publication en ligne : 04/05/2022 En ligne : https://doi.org/10.1080/01490419.2022.2064572 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101392
in Marine geodesy > vol 45 n° 5 (September 2022) . - pp 435 - 461[article]Attention mechanisms in computer vision: A survey / Meng-Hao Guo in Computational Visual Media, vol 8 n° 3 (September 2022)
[article]
Titre : Attention mechanisms in computer vision: A survey Type de document : Article/Communication Auteurs : Meng-Hao Guo, Auteur ; Tian-Xing Xu, Auteur ; Jiang-Jiang Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 331 - 368 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] saillance
[Termes IGN] scèneRésumé : (auteur) Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multimodal tasks, and self-supervised learning. In this survey, we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention, and branch attention; a related repository https://github.com/MenghaoGuo/Awesome-Vision-Attentions is dedicated to collecting related work. We also suggest future directions for attention mechanism research. Numéro de notice : A2022-329 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s41095-022-0271-y Date de publication en ligne : 15/03/2022 En ligne : https://doi.org/10.1007/s41095-022-0271-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100601
in Computational Visual Media > vol 8 n° 3 (September 2022) . - pp 331 - 368[article]Classification of pine wilt disease at different infection stages by diagnostic hyperspectral bands / Niwen Li in Ecological indicators, vol 142 (September 2022)PermalinkCrowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images / Ekrem Saralioglu in Geocarto international, vol 37 n° 18 ([01/09/2022])PermalinkDiscontinuity interpretation and identification of potential rockfalls for high-steep slopes based on UAV nap-of-the-object photogrammetry / Wei Wang in Computers & geosciences, vol 166 (September 2022)PermalinkFeux de forêt : un drone traque les risques de reprise / Nathalie Da Cruz in Géomètre, n° 2205 (septembre 2022)PermalinkForest tree species classification based on Sentinel-2 images and auxiliary data / Haotian You in Forests, vol 13 n° 9 (september 2022)PermalinkHistorical mapping of rice fields in Japan using phenology and temporally aggregated Landsat images in Google Earth Engine / Luis Carrasco in ISPRS Journal of photogrammetry and remote sensing, vol 191 (September 2022)PermalinkLarge-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)PermalinkLearning indoor point cloud semantic segmentation from image-level labels / Youcheng Song in The Visual Computer, vol 38 n° 9 (September 2022)PermalinkMapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression / Haoyu Wang in Remote sensing of environment, vol 278 (September 2022)PermalinkMapping individual abandoned houses across cities by integrating VHR remote sensing and street view imagery / Shengyuan Zou in International journal of applied Earth observation and geoinformation, vol 113 (September 2022)Permalink