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An improved vertical correction method for the inter-comparison and inter-validation of Integrated Water Vapour measurements [under review] / Olivier Bock in Atmospheric measurement techniques, vol 15 n° 19 ([01/04/2022])
[article]
Titre : An improved vertical correction method for the inter-comparison and inter-validation of Integrated Water Vapour measurements [under review] Type de document : Article/Communication Auteurs : Olivier Bock , Auteur ; Pierre Bosser , Auteur ; Carl Mears, Auteur Année de publication : 2022 Projets : VEGAN / Bock, Olivier Article en page(s) : pp 5643 - 5665 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] analyse comparative
[Termes IGN] correction des altitudes
[Termes IGN] données GPS
[Termes IGN] données météorologiques
[Termes IGN] erreur systématique
[Termes IGN] montagne
[Termes IGN] régression multiple
[Termes IGN] teneur intégrée en vapeur d'eau
[Termes IGN] zone intertropicaleRésumé : (auteur) Integrated Water Vapour (IWV) measurements from similar or different techniques are often inter-compared for calibration and validation purposes. Results are usually assessed in terms of bias (difference of the means), standard deviation of the differences, and linear fit slope and offset (intercept) estimates. When the instruments are located at different elevations, a correction must be applied to account for the vertical displacement between the sites. Empirical formulations are traditionally used for this correction. In this paper, we show that the widely-used correction model based on a standard, exponential, profile for water vapour cannot properly correct the bias, slope, and offset parameters simultaneously. Correcting the bias with this model degrades the slope and offset estimates, and vice-versa. This paper proposes an improved correction model which overcomes these limitations. The model uses a multi-linear regression of slope and offset parameters from a radiosonde climatology. It is able to predict monthly parameters with a root-mean-square error smaller than 0.5 kg m-2 for height differences up to 500 m. The method is applied to the inter-comparison of GPS IWV data in a tropical mountainous area and to the inter-validation of GPS and satellite microwave radiometer data. This paper also emphasizes the need for using a slope and offset regression method that accounts for errors in both variables and for correctly specifying these errors. Numéro de notice : A2022-327 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/amt-15-5643-2022 Date de publication en ligne : 21/04/2022 En ligne : https://doi.org/10.5194/amt-15-5643-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100492
in Atmospheric measurement techniques > vol 15 n° 19 [01/04/2022] . - pp 5643 - 5665[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)
[article]
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] régressionRésumé : (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
in International journal of applied Earth observation and geoinformation > vol 108 (April 2022) . - n° 102719[article]Estimation and testing of linkages between forest structure and rainfall interception characteristics of a Robinia pseudoacacia plantation on China’s Loess Plateau / Changkun Ma in Journal of Forestry Research, vol 33 n° 2 (April 2022)
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Titre : Estimation and testing of linkages between forest structure and rainfall interception characteristics of a Robinia pseudoacacia plantation on China’s Loess Plateau Type de document : Article/Communication Auteurs : Changkun Ma, Auteur ; Yi Luo, Auteur ; Mingan Shao, Auteur ; Xiaoxu Jia, Auteur Année de publication : 2022 Article en page(s) : pp 529 - 542 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] canopée
[Termes IGN] capacité de stockage
[Termes IGN] Chine
[Termes IGN] pluie
[Termes IGN] régression multiple
[Termes IGN] Robinia pseudoacacia
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] zone semi-aride
[Vedettes matières IGN] ForesterieMots-clés libres : Rainfall interception loss Résumé : (auteur) Understanding the interaction between canopy structure and the parameters of interception loss is essential in predicting the variations in partitioning rainfall and water resources as affected by changes in canopy structure and in implementing water-based management in semiarid forest plantations. In this study, seasonal variations in rainfall interception loss and canopy storage capacity as driven by canopy structure were predicted and the linkages were tested using seasonal filed measurements. The study was conducted in nine 50 m × 50 m Robinia pseudoacacia plots in the semiarid region of China’s Loess Plateau. Gross rainfall, throughfall and stemflow were measured in seasons with and without leaves in 2015 and 2016. Results show that measured average interception loss for the nine plots were 17.9% and 9.4% of gross rainfall during periods with leaves (the growing season) and without leaves, respectively. Average canopy storage capacity estimated using an indirect method was 1.3 mm in the growing season and 0.2 mm in the leafless season. Correlations of relative interception loss and canopy storage capacity to canopy variables were highest for leaf/wood area index (LAI/WAI) and canopy cover, followed by bark area, basal area, tree height and stand density. Combined canopy cover, leaf/wood area index and bark area multiple regression models of interception loss and canopy storage capacity were established for the growing season and in the leafless season in 2015. It explained 97% and 96% of the variations in relative interception loss during seasons with and without leaves, respectively. It also explained 98% and 99% of the variations in canopy storage capacity during seasons with and without leaves, respectively. The empirical regression models were validated using field data collected in 2016. The models satisfactorily predicted relative interception loss and canopy storage capacity during seasons with and without leaves. This study provides greater understanding about the effects of changes in tree canopy structure (e.g., dieback or mortality) on hydrological processes. Numéro de notice : A2022-334 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s11676-021-01324-w Date de publication en ligne : 06/06/2021 En ligne : https://doi.org/10.1007/s11676-021-01324-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100668
in Journal of Forestry Research > vol 33 n° 2 (April 2022) . - pp 529 - 542[article]Exploring the association between street built environment and street vitality using deep learning methods / Yunqin Li in Sustainable Cities and Society, vol 79 (April 2022)
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Titre : Exploring the association between street built environment and street vitality using deep learning methods Type de document : Article/Communication Auteurs : Yunqin Li, Auteur ; Nobuyoshi Yabuki, Auteur ; Tomohiro Fukuda, Auteur Année de publication : 2022 Article en page(s) : n° 103656 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] apprentissage profond
[Termes IGN] attractivité (aménagement)
[Termes IGN] bati
[Termes IGN] image Streetview
[Termes IGN] Japon
[Termes IGN] morphologie urbaine
[Termes IGN] OpenStreetMap
[Termes IGN] piéton
[Termes IGN] planification urbaine
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] régression linéaire
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantique
[Termes IGN] système d'information géographique
[Termes IGN] urbanisme
[Termes IGN] ville intelligenteRésumé : (auteur) Street vitality has become an essential indicator for evaluating the attractiveness and potential of the sustainable development of urban blocks, and it can be reflected by the type and the frequency of people's pedestrian activities on the street. While it is recognized that street built environment features affect pedestrian behavior and street vitality, quantifying the impact of these characteristics remains inconclusive. This paper proposes an automated deep learning approach to quantitatively explore the association between the street built environment and street vitality. First, we established a deep learning model for street vitality classification for automatic evaluation of street vitality based on the volumes and activities of pedestrians in the street through multiple object tracking and scene classification. Then, we applied semantic segmentation to measure five selected vitality-related street built environment variables. Finally, a linear regression model was applied to evaluate the built environment variables’ significance and effects on street vitality. To verify our method's accuracy and applicability, we selected a commercial complex in Osaka as an illustrative example. The experimental results highlight that street width and transparency have significant positive effects on street vitality. Compared with traditional methods, our approach is feasible, reliable, transferable, and more efficient. Numéro de notice : A2022-266 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.scs.2021.103656 Date de publication en ligne : 10/01/2022 En ligne : https://doi.org/10.1016/j.scs.2021.103656 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100271
in Sustainable Cities and Society > vol 79 (April 2022) . - n° 103656[article]Problems with models assessing influences of tree size and inter-tree competitive processes on individual tree growth: a cautionary tale / P.W. West in Journal of Forestry Research, vol 33 n° 2 (April 2022)
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Titre : Problems with models assessing influences of tree size and inter-tree competitive processes on individual tree growth: a cautionary tale Type de document : Article/Communication Auteurs : P.W. West, Auteur ; D.A. Ratkowsky, Auteur Année de publication : 2022 Article en page(s) : pp 565 - 577 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] arbre (flore)
[Termes IGN] Australie
[Termes IGN] croissance végétale
[Termes IGN] Eucalyptus pilularis
[Termes IGN] forêt équienne
[Termes IGN] hauteur des arbres
[Termes IGN] modèle de croissance végétale
[Termes IGN] régression non linéaire
[Termes IGN] surface terrière
[Vedettes matières IGN] ForesterieRésumé : (auteur) In forest growing at any one site, the growth rate of an individual tree is determined principally by its size, which reflects its metabolic capacity, and by competition from neighboring trees. Competitive effects of a tree may be proportional to its size; such competition is termed ‘symmetric’ and generally involves competition below ground for nutrients and water from the soil. Competition may also be ‘asymmetric’, where its effects are disproportionate to the size of the tree; this generally involves competition above ground for sunlight, when larger trees shade smaller, but the reverse cannot occur. This work examines three model systems often seen as exemplars relating individual tree growth rates to tree size and both competitive processes. Data of tree stem basal area growth rates in plots of even-aged, monoculture forest of blackbutt (Eucalyptus pilularis Smith) growing in sub-tropical eastern Australia were used to test these systems. It was found that none could distinguish between size and competitive effects at any time in any one stand and, thus, allow quantification of the contribution of each to explaining tree growth rates. They were prevented from doing so both by collinearity between the terms used to describe each of the effects and technical problems involved in the use of nonlinear least-squares regression to fit the models to any one data set. It is concluded that quite new approaches need to be devised if the effects on tree growth of tree size and competitive processes are to be quantified and modelled successfully. Numéro de notice : A2022-335 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s11676-021-01395-9 Date de publication en ligne : 04/10/2021 En ligne : https://doi.org/10.1007/s11676-021-01395-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100673
in Journal of Forestry Research > vol 33 n° 2 (April 2022) . - pp 565 - 577[article]Regularized integer least-squares estimation: Tikhonov’s regularization in a weak GNSS model / Zemin Wu in Journal of geodesy, vol 96 n° 4 (April 2022)PermalinkEvaluation of the mixed-effects model and quantile regression approaches for predicting tree height in larch (Larix olgensis) plantations in northeastern China / Longfei Xie in Canadian Journal of Forest Research, Vol 52 n° 3 (March 2022)PermalinkInfluence of determinant factors towards soil erosion using ordinary least squared regression in GIS domain / Imran Ahmad in Applied geomatics, vol 14 n° 1 (March 2022)PermalinkA novel regression method for harmonic analysis of time series / Qiang Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 185 (March 2022)PermalinkObservational constraint on the climate sensitivity to atmospheric CO2 concentrations changes derived from the 1971-2017 global energy budget / Jonathan Chenal in Journal of climate, vol 2022 ([01/03/2022])PermalinkPartitions of normalised multiple regression equations for datum transformations / Andrew Carey Ruffhead in Boletim de Ciências Geodésicas, vol 28 n° 1 ([01/03/2022])PermalinkReBankment : un algorithme pour déplacer les talus sur les cartes par moindres carrés / Guillaume Touya in Cartes & Géomatique, n° 247-248 (mars-juin 2022)PermalinkAboveground biomass estimation of an agro-pastoral ecology in semi-arid Bundelkhand region of India from Landsat data: a comparison of support vector machine and traditional regression models / Dibyendu Deb in Geocarto international, vol 37 n° 4 ([15/02/2022])PermalinkComparing methods to extract crop height and estimate crop coefficient from UAV imagery using structure from motion / Nitzan Malachy in Remote sensing, vol 14 n° 4 (February-2 2022)PermalinkSimulating fire-safe cities using a machine learning-based algorithm for the complex urban forms of developing nations: a case of Mumbai India / Vaibhav Kumar in Geocarto international, vol 37 n° 4 ([15/02/2022])Permalink