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Multi-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images / Yiying Hua in Forests, vol 12 n° 12 (December 2021)  

Public [article]  inForests > vol 12 n° 12 (December 2021) . - n° 1768 Titre : Multi-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images Type de document : Article/Communication Auteurs : Yiying Hua, Auteur ; Xuesheng Zhao, Auteur Année de publication : 2021 Article en page(s) : n° 1768 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] bande infrarouge [Termes IGN] coefficient de corrélation [Termes IGN] couvert forestier [Termes IGN] détection de contours [Termes IGN] image multibande [Termes IGN] image Sentinel-MSI [Termes IGN] indice de végétation [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] modèle statistique [Termes IGN] Mongolie intérieure (Chine) [Termes IGN] régression [Termes IGN] surveillance de la végétation Résumé : (auteur) In remote sensing, red edge bands are important indicators for monitoring vegetation growth. To examine the application potential of red edge bands in forest canopy closure estimation, three types of commonly used models—empirical statistical models (multiple stepwise regression (MSR)), machine learning models (back propagation neural network (BPNN)) and physical models (Li–Strahler geometric-optical (Li–Strahler GO) models)—were constructed and verified based on Sentinel-2 data, DEM data and measured data. In addition, we set up a comparative experiment without red edge bands. The relative error (ER) values of the BPNN model, MSR model, and Li–Strahler GO model with red edge bands were 16.97%, 20.76% and 24.83%, respectively. The validation accuracy measures of these models were higher than those of comparison models. For comparative experiments, the ER values of the MSR, Li–Strahler GO and BPNN models were increased by 13.07%, 4% and 1.22%, respectively. The experimental results demonstrate that red edge bands can effectively improve the accuracy of forest canopy closure estimation models to varying degrees. These findings provide a reference for modeling and estimating forest canopy closure using red edge bands based on Sentinel-2 images. Numéro de notice : A2021-125 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f12121768 Date de publication en ligne : 14/12/2021 En ligne : https://doi.org/10.3390/f12121768 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99318 [article]

Modelling bark volume for six commercially important tree species in France: assessment of models and application at regional scale / Rodolphe Bauer in Annals of Forest Science [en ligne], vol 78 n° 4 (December 2021)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 78 n° 4 (December 2021) . - n° 104 Titre : Modelling bark volume for six commercially important tree species in France: assessment of models and application at regional scale Type de document : Article/Communication Auteurs : Rodolphe Bauer, Auteur ; Antoine Billard, Auteur ; Frédéric Mothe, Auteur ; Fleur Longuetaud, Auteur ; Mojtaba Houballah, Auteur ; Alain Bouvet, Auteur ; Henri E. Cuny , Auteur ; Antoine Colin , Auteur ; Francis Colin, Auteur Année de publication : 2021 Projets : ARBRE / AgroParisTech, EMERGE / Deleuze, Christine Article en page(s) : n° 104 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Abies alba [Termes IGN] biomasse forestière [Termes IGN] Bourgogne Franche-Comté (région 2016) [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] écorce [Termes IGN] Fagus sylvatica [Termes IGN] Grand Est (région 2016) [Termes IGN] inventaire forestier national (données France) [Termes IGN] modèle statistique [Termes IGN] Picea abies [Termes IGN] Pseudotsuga menziesii [Termes IGN] Quercus pedunculata [Termes IGN] Quercus sessiliflora [Termes IGN] volume (grandeur) [Vedettes matières IGN] Economie forestière Résumé : (auteur) Key message : A set of models of bark thickness at breast height and bark volume are now available for six species in France. A common model suitable for predicting bark volume was proposed for all species. A small but significant altitude effect on bark thickness at breast height was detected for three species. Context : The growing demand for wood energy and bio-molecules requires a thorough evaluation of forest biomass, particularly bark. Aims : The objective of this study is to have statistical models of bark volumes for the six main forest species present in North-Eastern France and to be able to estimate regional bark biomasses and quantities of chemical extractives at regional scale. Methods : A large databank gathering bark thickness measured at different heights in France was used for selecting literature or new alternative models of tree bark volume. These models were applied to the available forest inventory data from North-Eastern France to estimate the regional bark volume. Secondly, by multiplying these volumes by basic density data and extractive content recently obtained, bark biomasses and extractives quantities were deduced. Results : The first results consist in a set of species-specific models of bark thickness at breast height with R2 around 0.70 and a relative RMSE around 30% which is an improvement of 0.1 for R2 and of 1–2% for relative RMSE depending on the species compared to the best models from the literature. The second results consist in a set of species-specific models of tree bark volumes with R2 of 0.90 and a relative RMSE which varies between 22% when bark thickness at breast height is included and 40% when it is predicted. A significant relationship between bark thickness at breast height and altitude was also observed. The bark resources of Grand Est and Bourgogne-Franche-Comté regions were estimated at 558 000 m3/year and 611 000 m3/year respectively representing between 5.5% and 15% of the stem volume depending on the species. The propagation of the measurement error of bark gauge was estimated at 5% for model of bark thickness at breast height and 24% for bark volume model. Conclusion : These results constitute an important contribution for a better knowledge of the bark resource at a regional scale and may help to optimise bark valuation by the forest-wood sector. Numéro de notice : A2021-909 Affiliation des auteurs : IGN+Ext (2020- ) Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-021-01096-7 Date de publication en ligne : 02/01/2022 En ligne : https://doi.org/10.1007/s13595-021-01096-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99458 [article]

A geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection / Xi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 174 (April 2021) . - pp 87 - 104 Titre : A geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection Type de document : Article/Communication Auteurs : Xi Wu, Auteur ; Zhenwei Shi, Auteur ; Zhengxia Zou, Auteur Année de publication : 2021 Article en page(s) : pp 87 - 104 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] altitude [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection des nuages [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion de données [Termes IGN] image Gaofen [Termes IGN] information géographique [Termes IGN] latitude [Termes IGN] longitude [Termes IGN] modèle statistique [Termes IGN] neige [Termes IGN] Normalized Difference Snow Index Résumé : (auteur) Geographic information such as the altitude, latitude, and longitude are common but fundamental meta-records in remote sensing image products. In this paper, it is shown that such a group of records provides important priors for cloud and snow detection in remote sensing imagery. The intuition comes from some common geographical knowledge, where many of them are important but are often overlooked. For example, it is generally known that snow is less likely to exist in low-latitude or low-altitude areas, and clouds in different geographic may have various visual appearances. Previous cloud and snow detection methods simply ignore the use of such information, and perform detection solely based on the image data (band reflectance). Due to the neglect of such priors, most of these methods are difficult to obtain satisfactory performance in complex scenarios (e.g., cloud-snow coexistence). In this paper, a novel neural network called “Geographic Information-driven Network (GeoInfoNet)” is proposed for cloud and snow detection. In addition to the use of the image data, the model integrates the geographic information at both training and detection phases. A “geographic information encoder” is specially designed, which encodes the altitude, latitude, and longitude of imagery to a set of auxiliary maps and then feeds them to the detection network. The proposed network can be trained in an end-to-end fashion with dense robust features extracted and fused. A new dataset called “Levir_CS” for cloud and snow detection is built, which contains 4,168 Gaofen-1 satellite images and corresponding geographical records, and is over 20× larger than other datasets in this field. On “Levir_CS”, experiments show that the method achieves 90.74% intersection over union of cloud and 78.26% intersection over union of snow. It outperforms other state of the art cloud and snow detection methods with a large margin. Feature visualizations also show that the method learns some important priors which is close to the common sense. The proposed dataset and the code of GeoInfoNet are available in https://github.com/permanentCH5/GeoInfoNet. Numéro de notice : A2021-209 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.023 Date de publication en ligne : 22/02/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.023 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97187 [article]

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Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy / Vasil Yordanov in Applied geomatics, vol 12 n° 4 (December 2020)  

Public [article]  inApplied geomatics > vol 12 n° 4 (December 2020) . - 23 p. Titre : Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy Type de document : Article/Communication Auteurs : Vasil Yordanov, Auteur ; Maria Antonia Brovelli, Auteur Année de publication : 2020 Article en page(s) : 23 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse de sensibilité [Termes IGN] cartographie des risques [Termes IGN] cartographie géomorphologique [Termes IGN] classification par forêts aléatoires [Termes IGN] effondrement de terrain [Termes IGN] figuré linéaire [Termes IGN] indice de risque [Termes IGN] inventaire [Termes IGN] Lombardie [Termes IGN] modèle numérique de surface [Termes IGN] modèle numérique de terrain [Termes IGN] modèle statistique [Termes IGN] régression logistique Résumé : (auteur) Landslide susceptibility mapping is a crucial initial step in risk mitigation strategies. Landslide hazards are widely spread all over the world and, as such, mapping the relevant susceptibility levels is in constant research and development. As a result, numerous modelling techniques and approaches have been adopted by scholars, implementing these models at different scales and with different terrains, in search of the best-performing strategy. Nevertheless, a direct comparison is not possible unless the strategies are implemented under the same environmental conditions and scenarios. The aim of this work is to implement three statistical-based models (Statistical Index, Logistic Regression, and Random Forest) at the basin scale, using various scenarios for the input datasets (terrain variables), training samples and ratios, and validation metrics. A reassessment of the original input data was carried out to improve the model performance. In total, 79 maps were obtained using different combinations with some highly satisfactory outcomes and others that are barely acceptable. Random Forest achieved the highest scores in most of the cases, proving to be a reliable modelling approach. While Statistical Index passes the evaluation tests, most of the resulting maps were considered unreliable. This research highlighted the importance of a complete and up-to-date landslide inventory, the knowledge of local conditions, as well as the pre- and post-analysis evaluation of the input and output combinations. Numéro de notice : A2020-695 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1007/s12518-020-00344-1 Date de publication en ligne : 09/11/2020 En ligne : https://doi.org/10.1007/s12518-020-00344-1 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96244 [article]

Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya / Vijendra Kumar Pandey in Geocarto international, vol 35 n° 2 ([01/02/2020])  

Public [article]  inGeocarto international > vol 35 n° 2 [01/02/2020] . - pp 168 - 187 Titre : Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya Type de document : Article/Communication Auteurs : Vijendra Kumar Pandey, Auteur ; Hamid Reza Pourghasemi, Auteur ; Milap Chand Sharma, Auteur Année de publication : 2020 Article en page(s) : pp 168 - 187 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] autoroute [Termes IGN] classification dirigée [Termes IGN] effondrement de terrain [Termes IGN] entropie maximale [Termes IGN] Himalaya [Termes IGN] image IRS-LISS [Termes IGN] image Landsat-8 [Termes IGN] Linear Imaging Self-Scanning System [Termes IGN] modèle statistique [Termes IGN] mousson [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] séparateur à vaste marge [Termes IGN] test statistique Résumé : (Auteur) The main objective of this study to produce landslide susceptibility zones using maximum entropy (MaxEnt) and support vector machine (SVM) data-driven models along the Tipari to Ghuttu highway corridors in the Garhwal Himalaya. A landslide inventory has been prepared through field surveys and LISS-IV and Landsat 8 satellite images. The datasets of 85 landslides were categorised into training and test sets. In this study 11 landslide conditioning variables were used that are; altitude, slope angle, aspect, plan curvature, topographic wetness index, normalised difference vegetation index (NDVI), land use, soil texture, distance to rivers, distance to faults, and distance to the road. The result produced using MaxEnt and SVM model were subsequently validated using receiver operating characteristics curve (ROC) with test sets of landslide dataset. Both the models have good prediction capabilities. MaxEnt has ROC value of 0.78 while SVM has the highest prediction rate of 0.85. Numéro de notice : A2020-036 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1510038 Date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1510038 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94519 [article]

Detecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization / Si Song in International journal of geographical information science IJGIS, Vol 33 n° 1-2 (January - February 2019)  

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Nouvelle méthode en cascade pour la classification hiérarchique multi-temporelle ou multi-capteur d'images satellitaires haute résolution / Ihsen Hedhli in Revue Française de Photogrammétrie et de Télédétection, n° 216 (février 2018)  

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Learning aggregated features and optimizing model for semantic labeling / Jianhua Wang in The Visual Computer, vol 33 n° 12 (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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Silvicultural climatic turning point for European beech and sessile oak in Western Europe derived from national forest inventories / Klara Dolos in Forest ecology and management, vol 373 (1 August 2016)  

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Multifractal analysis for multivariate data with application to remote sensing / Sébastien Combrexelle (2016)  

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Prediction of traffic counts using statistical and neural network models / Abul Kalam Azad in Geomatica [en ligne], vol 69 n° 3 (september 2015)  

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Extraction de fragments forestiers et caractérisation de leurs évolutions spatio-temporelles pour évaluer l'effet de l'histoire sur la biodiversité : une approche multi-sources / Pierre-Alexis Herrault (2015)  

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Performance assessment of a recent change detection method for homogeneous and heterogeneous images / Jorge Prendes in Revue Française de Photogrammétrie et de Télédétection, n° 209 (Janvier 2015)  

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Estimation harmonisée du volume de tige à différentes découpes / Christine Deleuze in Rendez-vous techniques, n° 44 (printemps 2014) 

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