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Artificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey / Alkan Günlü in Geocarto international, Vol 35 n° 1 ([02/01/2020])  

Public [article]  inGeocarto international > Vol 35 n° 1 [02/01/2020] . - pp 17 - 28 Titre : Artificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey Type de document : Article/Communication Auteurs : Alkan Günlü, Auteur ; Ilker Erkanli, Auteur Année de publication : 2020 Article en page(s) : pp 17 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] carbone [Termes descripteurs IGN] Fagus (genre) [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] peuplement forestier [Termes descripteurs IGN] régression multiple [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] Turquie Résumé : (auteur) The goal of this study was to estimate aboveground stand carbon (AGSC) of pure beech stands in Turkey with ground measurements as well as topographic information and remote sensing data. For this purpose, 153 sample plots were collected from pure beech stands in study area. The AGSC of each sample plot was computed. Eight texture images (variance, dissimilarity, homogeneity, entropy, contrast, mean, second moment and correlation) with five window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9 and 11 × 11) generated from ALOS PALSAR L-band satellite image. The AGSC models predicting the relationships between ALOS PALSAR texture values and topographic information, and sample plot AGSC were developed by using multiple linear regressions (MLR). Also, artificial neural networks (ANNs) architectures were trained by comparing various numbers of neurons and activation functions in its network types. Our results revealed the ability of ANNs was better than MLR models to predict AGSC values. Numéro de notice : A2020-017 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1499817 date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1499817 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94410 [article]

Knowing is not enough: exploring the missing link between climate change knowledge and action of German forest owners and managers / Yvonne Hengst-Ehrhart in Annals of Forest Science [en ligne], Vol 76 n° 4 (December 2019)  

Public [article]  inAnnals of Forest Science [en ligne] > Vol 76 n° 4 (December 2019) Titre : Knowing is not enough: exploring the missing link between climate change knowledge and action of German forest owners and managers Type de document : Article/Communication Auteurs : Yvonne Hengst-Ehrhart, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] Allemagne [Termes descripteurs IGN] foresterie [Termes descripteurs IGN] gestion forestière [Termes descripteurs IGN] industrie forestière [Termes descripteurs IGN] politique forestière [Termes descripteurs IGN] propriétaire forestier [Termes descripteurs IGN] régression multiple [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Key message : Adaptation to climate change is a complex but urgent task in forest management; however, a lack of action is widely reported. This study shows that adaptive action on both stand and business levels is missing in forest management. Beyond the cognitive dimension, affective and conative aspects should be promoted through awareness-raising initiatives specific to different target groups. Context : Adaptation to climate change is a complex but urgent task in forest management. A lack of action is widely reported combined with a call for awareness-raising and better knowledge transfer to bridge the gap between knowledge and action. Aims : Based on an understanding of awareness encompassing cognitive, affective, and conative dimensions, the paper aims to clarify (1) what kind of adaptive measures are missing in forest management and (2) if there is a gap in climate change awareness of forest owners and managers hindering adaptive action. Methods : An online survey among German forest owners and managers was conducted. The theory of planned behavior was selected to examine variables which support the implementation of adaptive measures and to examine different awareness dimensions. Data was analyzed using descriptive statistics and multiple linear regression analysis. Results : Adaptive measures on stand level were more often implemented than those on business level. All awareness dimensions were influential for the intention to implement adaptive measures. Experience and attitude towards adaptive measures were most important while social norm and perceived behavioral control were influential in some groups. Conclusion : The potential of adaptive measures on stand level and particularly on business level is not fully exploited. Based on these findings, awareness-raising initiatives and forest consultancy can be adapted to consider the specific perspectives of target groups as a means of promoting the implementation of adaptive measures. Numéro de notice : A2019-532 Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-019-0878-z date de publication en ligne : 16/10/2019 En ligne : https://doi.org/10.1007/s13595-019-0878-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94120 [article]

Multi-sensor prediction of Eucalyptus stand volume: A support vector approach / Guilherme Silverio Aquino de Souza in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 156 (October 2019) . - pp 135 - 146 Titre : Multi-sensor prediction of Eucalyptus stand volume: A support vector approach Type de document : Article/Communication Auteurs : Guilherme Silverio Aquino de Souza, Auteur ; Vicente Paulo Soares, Auteur ; Helio Garcia Leite, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 135 - 146 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bande L [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] Eucalyptus (genre) [Termes descripteurs IGN] image ALOS-AVNIR2 [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] régression multiple [Termes descripteurs IGN] taux d'échantillonnage [Termes descripteurs IGN] volume en bois Résumé : (Auteur) Stem volume is a key attribute of Eucalyptus forest plantations upon which decision-making is based at diverse levels of planning. Quantifying volume through remote sensing can support a proper management of forests. Because of limitations on spaceborne optical and synthetic aperture radar sensors, this study integrated both types of datasets assembled using support vector regression (SVR) to retrieve the stand volume of Eucalyptus plantations. We assessed different combinations of sensors and a minimum number of plots to develop an SVR model. Finally, the best SVR performance was compared with other analytical methods already tested and in the literature: multilinear regression, artificial neural networks (ANN), and random forest (RF). Here, we introduce a test for comparative analysis of the performance of different methods. We found that SVR accurately predicted stem volume of Brazilian fast-growing Eucalyptus forest plantations. Gaussian radial basis was the most suitable kernel function. Integrating the optical and L-band backscatter data increased the predictive accuracy compared to a single sensor model. Combining NIR-band data from ALOS AVNIR-2 and backscatter of L-band horizontal emitted and vertical received (HV) electric fields from ALOS PALSAR produced the most accurate SVR model (with an R2 of 0.926 and root mean square error of 11.007 m3/ha). The number of field plots sufficient for model development with non-redundant explanatory variables was 77. Under this condition, SVR performed similarly to ANN and outperformed the multiple linear regression and random forest methods. Numéro de notice : A2019-319 Thématique : FORET/IMAGERIE Nature : Article DOI : doi.org/10.1016/j.isprsjprs.2019.08.002 date de publication en ligne : 20/08/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.08.002 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93357 [article]

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Sea level variation around Australia and its relation to climate indices / Armin Agha Karimi in Marine geodesy, vol 42 n° 5 (September 2019)  

Public [article]  inMarine geodesy > vol 42 n° 5 (September 2019) . - pp 469 - 489 Titre : Sea level variation around Australia and its relation to climate indices Type de document : Article/Communication Auteurs : Armin Agha Karimi, Auteur ; Xiaoli Deng, Auteur ; Ole Baltazar Andersen, Auteur Année de publication : 2019 Article en page(s) : pp 469 - 489 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes descripteurs IGN] analyse spectrale [Termes descripteurs IGN] Australie [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] changement du niveau de la mer [Termes descripteurs IGN] données altimétriques [Termes descripteurs IGN] El Niño-Southern oscillation [Termes descripteurs IGN] Indien (océan) [Termes descripteurs IGN] Pacifique (océan) [Termes descripteurs IGN] régression multiple Résumé : (auteur) This study aims at investigating the intradecadal and decadal signals of the sea level using 25 years of altimetry data around Australia. We have used the multivariable spectral analysis to extract six periodic signals at the 95% confidence level from altimetry-derived sea-level time series in the study area. They are signals with periods of 1, 1.5, 3, 4.3, 5.7 and 11.17 years, which can also be detected in the estimated power spectra from climate indices of the Interdecadal Pacific Oscillation, Multivariate ENSO Index, and Pacific Decadal Oscillation. A parametric model including the detected periodic signals is used to estimate sea-level trends. The determined trends in the area are in a good agreement with recent studies that consider effects of climate indices through a multivariate regression model. The advantage of our model is to present more descriptive explanation of the sea level signals around Australia in terms of periodicity and spatial variability. Numéro de notice : A2019-299 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2019.1629131 date de publication en ligne : 26/06/2019 En ligne : https://doi.org/10.1080/01490419.2019.1629131 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93219 [article]

Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images / Jie Wang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 189 - 201 Titre : Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images Type de document : Article/Communication Auteurs : Jie Wang, Auteur ; Xiangming Xiao, Auteur ; Rajen Bajgain, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 189 - 201 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] Oklahoma (Etats-Unis) [Termes descripteurs IGN] paturage [Termes descripteurs IGN] phénologie [Termes descripteurs IGN] régression multiple Résumé : (Auteur) Grassland degradation has accelerated in recent decades in response to increased climate variability and human activity. Rangeland and grassland conditions directly affect forage quality, livestock production, and regional grassland resources. In this study, we examined the potential of integrating synthetic aperture radar (SAR, Sentinel-1) and optical remote sensing (Landsat-8 and Sentinel-2) data to monitor the conditions of a native pasture and an introduced pasture in Oklahoma, USA. Leaf area index (LAI) and aboveground biomass (AGB) were used as indicators of pasture conditions under varying climate and human activities. We estimated the seasonal dynamics of LAI and AGB using Sentinel-1 (S1), Landsat-8 (LC8), and Sentinel-2 (S2) data, both individually and integrally, applying three widely used algorithms: Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). Results indicated that integration of LC8 and S2 data provided sufficient data to capture the seasonal dynamics of grasslands at a 10–30-m spatial resolution and improved assessments of critical phenology stages in both pluvial and dry years. The satellite-based LAI and AGB models developed from ground measurements in 2015 reasonably predicted the seasonal dynamics and spatial heterogeneity of LAI and AGB in 2016. By comparison, the integration of S1, LC8, and S2 has the potential to improve the estimation of LAI and AGB more than 30% relative to the performance of S1 at low vegetation cover (LAI  2 m2/m2, AGB > 500 g/m2). These results demonstrate the potential of combining S1, LC8, and S2 monitoring grazing tallgrass prairie to provide timely and accurate data for grassland management. Numéro de notice : A2019-269 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.06.007 date de publication en ligne : 21/06/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93086 [article]

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Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data : A case study of Wuhan, Central China / Xin Huang in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)  

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Including Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data / Abdelhakim Amazirh in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)  

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Urban impervious surface estimation from remote sensing and social data / Yan Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 12 (December 2018)  

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Introduction to multiple regression equations in datum transformations and their reversibility / A.C. Ruffhead in Survey review, vol 50 n° 358 (January 2018)  

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Area-based estimation of growing stock volume in Scots pine stands using ALS and airborne image-based point clouds / Paweł Hawryło in Forestry, an international journal of forest research, vol 90 n° 5 (December 2017)  

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Fusing tree‐ring and forest inventory data to infer influences on tree growth / Margaret E.K. Evans in Ecosphere, vol 8 n° 7 (July 2017)  

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Data science par la pratique : fondamentaux avec Python / Joël Grus (2017)

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Space-time multiple regression model for grid-based population estimation in urban areas / Ko Ko Lwin in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)  

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Relationship between landform classification and vegetation (case study: southwest of Fars province, Iran) / Marzieh Mokarram in Open geosciences, vol 8 n° 1 (January - July 2016)  

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La ville à l’échelle de l’Europe : apports du couplage et de l’expertise de bases de données issues de l’imagerie satellitale / Anne Bretagnolle in Revue internationale de géomatique, vol 26 n° 1 (janvier - mars 2016)  

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