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Geoglam, l'agriculture par satellite / Laurent Polidori in Géomètre, n° 2194 (septembre 2021)
[article]
Titre : Geoglam, l'agriculture par satellite Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2021 Article en page(s) : pp 17 - 17 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] blé (céréale)
[Termes IGN] carte d'occupation du sol
[Termes IGN] Glycine max
[Termes IGN] image à haute résolution
[Termes IGN] Leaf Area Index
[Termes IGN] maïs (céréale)
[Termes IGN] modèle dynamique
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] observation de la Terre
[Termes IGN] Oryza (genre)
[Termes IGN] série temporelle
[Termes IGN] surface cultivéeRésumé : (Auteur) Des satellites pour la sécurité alimentaire et la transparence du marché agricole Numéro de notice : A2021-579 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 13/09/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98400
in Géomètre > n° 2194 (septembre 2021) . - pp 17 - 17[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 063-2021081 RAB Revue Centre de documentation En réserve L003 Disponible The real potential of current passive satellite data to map aboveground biomass in tropical forests / Nidhi Jha in Remote sensing in ecology and conservation, vol 7 n° 3 (September 2021)
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Titre : The real potential of current passive satellite data to map aboveground biomass in tropical forests Type de document : Article/Communication Auteurs : Nidhi Jha, Auteur ; Nitin Kumar Tripathi, Auteur ; Nicolas Barbier, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 504 - 520 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] biomasse aérienne
[Termes IGN] forêt tropicale
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Worldview
[Termes IGN] ThaïlandeRésumé : (auteur) Forest biomass estimation at large scale is challenging and generally entails large uncertainty in tropical regions. With their wall-to-wall coverage ability, passive remote sensing signals are frequently used to extrapolate field estimates of forest aboveground biomass (AGB). However, studies often use limited reference data and/or flawed validation schemes and thus report unreliable extrapolation error estimates. Here, we compared the ability of three medium- to high-resolution passive satellite sensors, Landsat-8 (L8), Sentinel-2B (S2) and Worldview-3 (WV3), to map AGB in a forest landscape of Thailand. We used a large airborne LiDAR-derived AGB dataset as a reference to train and validate a random forest algorithm and conducted robust error assessments and variable selection using spatialized cross-validations. Our results indicate that the selected predictors strongly varied among the three sensors and between analyses restricted to low (≤200 Mg ha−1) and high (>200 Mg ha−1) AGB areas. WV3 and S2 data outperformed L8 data to extrapolate AGB (RMSE of 68 and 72 against 84 Mg ha−1, respectively) due to the inclusion of the red-edge band and, probably, to their higher spatial and spectral resolution. Sensitivity to large AGB values was higher for WV3 than for S2 and L8 with saturation point of 247 Mg ha−1 against 204 and 192 Mg ha−1. AGB values above these saturation points remained poorly predictable, especially for L8, indicating that several tropical forest AGB maps should be interpreted with extreme caution. However, predicted gradients of lower AGB values (≤200 Mg ha−1), i.e., in early forest successional stages, were fairly consistent among sensors (r > 0.70), even if the mean absolute difference between estimates was large when AGB predictions were extrapolated out of the calibration area at regional level (34%). We finally showed that calibrating the model only within the sensitivity AGB domain (e.g., Numéro de notice : A2021-731 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1002/rse2.203 En ligne : https://doi.org/10.1002/rse2.203 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98676
in Remote sensing in ecology and conservation > vol 7 n° 3 (September 2021) . - pp 504 - 520[article]Using electrical resistivity tomography to detect wetwood and estimate moisture content in silver fir (Abies alba Mill.) / Ludovic Martin in Annals of Forest Science, vol 78 n° 3 (September 2021)
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Titre : Using electrical resistivity tomography to detect wetwood and estimate moisture content in silver fir (Abies alba Mill.) Type de document : Article/Communication Auteurs : Ludovic Martin, Auteur ; Sébastien Cochard, Auteur ; Stefan Mayr, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 65 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Abies alba
[Termes IGN] bois sur pied
[Termes IGN] filière bois - forêt
[Termes IGN] forêt humide
[Termes IGN] humidité du sol
[Termes IGN] Massif central (France)
[Termes IGN] résistivité
[Termes IGN] teneur en eau de la végétation
[Termes IGN] tomographieRésumé : (auteur) Key message : Using several experimental approaches, we have demonstrated that electrical resistivity tomography (ERT) is a reliable nondestructive tool for estimating the moisture content of heartwood in situ. ERT measurements show that water pockets in heartwood (wetwood) are present in a large majority (90%) of silver fir ( Abies alba Mill.) trunks.
Context : For wood professionals, the presence of wetwood in wood logs leads to an increase in costs, especially during the drying process. Assessing these internal properties in situ with a nondestructive method will provide reliable information for improved management of respective forests.
Aims : The objective of this study was to evaluate the efficiency of the electrical resistivity tomography (ERT) tool to detect wetwood in standing trees and to estimate the mean moisture content (MC) of silver fir trunks.
Methods : The study was carried out in 3 forests located in the region “Massif Central” in France. We selected 58 silver fir trees, visually healthy and without visible default. Each tree has been subject to regular ERT measurements for more than a year. At the same time, one to three cores were taken from each tree in order to measure the actual MC of the wood.
Results : 90% of the silver fir trees showed the presence of wetwood in their heartwood. Our results showed a significant correlation between the mean heartwood MC measured on cores and the mean electrical resistivity (ER) obtained with ERT.
Conclusion : The presence of wetwood occurs in a high proportion of the silver fir trees studied, and (ii) ERT can be used to estimate the average MC of the heartwood of standing trees. However, the data provided by ERT vary seasonally and do not allow the precise location of wetwood.Numéro de notice : A2021-622 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-021-01078-9 Date de publication en ligne : 27/07/2021 En ligne : https://doi.org/10.1007/s13595-021-01078-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98244
in Annals of Forest Science > vol 78 n° 3 (September 2021) . - n° 65[article]Monitoring forest disturbance using time-series MODIS NDVI in Michoacán, Mexico / Yao Gao in Geocarto international, vol 36 n° 15 ([15/08/2021])
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Titre : Monitoring forest disturbance using time-series MODIS NDVI in Michoacán, Mexico Type de document : Article/Communication Auteurs : Yao Gao, Auteur ; Alexander Quevedo, Auteur ; Zoltan Szantoi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1768 - 1784 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] détection de changement
[Termes IGN] fonction harmonique
[Termes IGN] image Terra-MODIS
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Mexique
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] série temporelle
[Termes IGN] surveillance forestière
[Termes IGN] variation saisonnièreRésumé : (auteur) MODIS-based NDVI time series (2000–2016) was applied to monitor sub-annual forest disturbance in the Mexican state of Michoacán, with an algorithm that decomposes the time-series data into a harmonic function and a trend. To detect change, a moving sum of residuals between the observed and predicted NDVI values was compared with that from the reference period. Magnitude of change was computed by subtracting the predicted NDVI from the observed one. By comparing the detected changes with reference data through visual interpretation, a threshold of |0.05| was established as the magnitude of change for forest disturbance detection. The method detected more forest gain than loss for 2013–2016, a result which is supported by recent findings from the national forest inventory. Forest loss decreases yearly for 2013–2016, and forest gain peaks at 2014 and 2015. We verified the findings with data from the global forest cover change project. Numéro de notice : A2021-580 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1661032 Date de publication en ligne : 09/09/2019 En ligne : https://doi.org/10.1080/10106049.2019.1661032 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98185
in Geocarto international > vol 36 n° 15 [15/08/2021] . - pp 1768 - 1784[article]Automated tree-crown and height detection in a young forest plantation using mask region-based convolutional neural network (Mask R-CNN) / Zhenbang Hao in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)
[article]
Titre : Automated tree-crown and height detection in a young forest plantation using mask region-based convolutional neural network (Mask R-CNN) Type de document : Article/Communication Auteurs : Zhenbang Hao, Auteur ; Lili Lin, Auteur ; Christopher J. Post, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 112 - 123 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Abies (genre)
[Termes IGN] Abies numidica
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection automatique
[Termes IGN] hauteur des arbres
[Termes IGN] houppier
[Termes IGN] image captée par drone
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] plantation forestièreRésumé : (auteur) Tree-crown and height are primary tree measurements in forest inventory. Convolutional neural networks (CNNs) are a class of neural networks, which can be used in forest inventory; however, no prior studies have developed a CNN model to detect tree crown and height simultaneously. This study is the first-of-its-kind that explored training a mask region-based convolutional neural network (Mask R-CNN) for automatically and concurrently detecting discontinuous tree crown and height of Chinese fir (Cunninghamia lanceolata (Lamb) Hook) in a plantation. A DJI Phantom4-Multispectral Unmanned Aerial Vehicle (UAV) was used to obtain high-resolution images of the study site, Shunchang County, China. Tree crown and height of Chinese fir was manually delineated and derived from this UAV imagery. A portion of the ground-truthed tree height values were used as a test set, and the remaining measurements were used as the model training data. Six different band combinations and derivations of the UAV imagery were used to detect tree crown and height, respectively (Multi band-DSM, RGB-DSM, NDVI-DSM, Multi band-CHM, RGB-CHM, and NDVI-CHM combination). The Mask R-CNN model with the NDVI-CHM combination achieved superior performance. The accuracy of Chinese fir’s individual tree-crown detection was considerable (F1 score = 84.68%), the Intersection over Union (IoU) of tree crown delineation was 91.27%, and tree height estimates were highly correlated with the height from UAV imagery (R2 = 0.97, RMSE = 0.11 m, rRMSE = 4.35%) and field measurement (R2 = 0.87, RMSE = 0.24 m, rRMSE = 9.67%). Results demonstrate that the input image with an CHM achieves higher accuracy of tree crown delineation and tree height assessment compared to an image with a DSM. The accuracy and efficiency of Mask R-CNN has a great potential to assist the application of remote sensing in forests. Numéro de notice : A2021-563 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2021.06.003 Date de publication en ligne : 18/06/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.06.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98128
in ISPRS Journal of photogrammetry and remote sensing > vol 178 (August 2021) . - pp 112 - 123[article]Réservation
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