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Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging / Bo Li in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 161 -1 72 Titre : Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging Type de document : Article/Communication Auteurs : Bo Li, Auteur ; Xiangming Xu, Auteur ; Li Zhang, Auteur Année de publication : 2020 Article en page(s) : pp 161 -1 72 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] couvert végétal [Termes descripteurs IGN] hauteur de la végétation [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] pomme de terre [Termes descripteurs IGN] régression des moindres carrés partiels [Termes descripteurs IGN] rendement agricole Résumé : (auteur) Rapid and accurate biomass and yield estimation facilitates efficient plant phenotyping and site-specific crop management. A low altitude unmanned aerial vehicle (UAV) was used to acquire RGB and hyperspectral imaging data for a potato crop canopy at two growth stages to estimate the above-ground biomass and predict crop yield. Field experiments included six cultivars and multiple treatments of nitrogen, potassium, and mixed compound fertilisers. Crop height was estimated using the difference between digital surface model and digital elevation models derived from RGB imagery. Combining with two narrow-band vegetation indices selected by the RReliefF feature selection algorithm. Random Forest regression models demonstrated high prediction accuracy for both fresh and dry above-ground biomass, with a coefficient of determination (r2) > 0.90. Crop yield was predicted using four narrow-band vegetation indices and crop height (r2 = 0.63) with imagery data obtained 90 days after planting. A Partial Least Squares regression model based on the full wavelength spectra demonstrated improved yield prediction (r2 = 0.81). This study demonstrated the merits of UAV-based RGB and hyperspectral imaging for estimating the above-ground biomass and yield of potato crops, which can be used to assist in site-specific crop management. Numéro de notice : A2020-125 Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.013 date de publication en ligne : 28/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.013 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94750 [article]

The impact of second-order ionospheric delays on the ZWD estimation with GPS and BDS measurements / Shaocheng Zhang in GPS solutions, vol 24 n° 2 (April 2020)  

Public [article]  inGPS solutions > vol 24 n° 2 (April 2020) Titre : The impact of second-order ionospheric delays on the ZWD estimation with GPS and BDS measurements Type de document : Article/Communication Auteurs : Shaocheng Zhang, Auteur ; Lei Fang, Auteur ; Guangxing Wang, Auteur ; Wei Li, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes descripteurs IGN] champ géomagnétique [Termes descripteurs IGN] décalage d'horloge [Termes descripteurs IGN] données BeiDou [Termes descripteurs IGN] données GPS [Termes descripteurs IGN] gradient d'ionosphère [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] positionnement ponctuel précis [Termes descripteurs IGN] retard ionosphèrique [Termes descripteurs IGN] retard troposphérique zénithal [Termes descripteurs IGN] teneur verticale totale en électrons Résumé : (auteur) Since millimeter accuracy is required in many GNSS applications such as real-time zenith wet delay (ZWD) estimation, the higher-order ionospheric delays on GNSS signals are no longer negligible. We calculated the second-order ionospheric delays (I2) and analyzed the impact on the ZWD estimation with GPS-only and combined GPS/BDS observations. The undifferenced PPP model with fixed coordinates was used to estimate the ZWD and horizontal gradients. The method of blockwise sequential least squares was utilized to eliminate the receiver clock biases and compute the I2 impact on the ZWDs. The I2 delays on each GNSS satellite observations were calculated with the CODE final TEC map and the 12th generation of the international geomagnetic reference field (IGRF-12) model. The statistical results with the actual observation geometry show that the I2 delays can reach over 10 mm during the daytime, and the corresponding impact on the estimated ZWD can reach up to several millimeters. At station HKWS, the maximum I2 impact with GPS only reaches up to 3.1 mm and is still 2.4 mm when both GPS and BDS observations are used. The simulated I2 impact on the ZWD could reach several millimeters, even though the TEC and geomagnetic values were calculated from relatively moderate background models. Compared with the 5–10 mm precision of real-time ZWD estimation, the I2 delays must not be ignored, especially during high VTEC periods. Numéro de notice : A2020-082 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-0954-8 date de publication en ligne : 04/02/2020 En ligne : https://doi.org/10.1007/s10291-020-0954-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94651 [article]

Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds / Zhou Guo in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 4 (April 2020) . - pp 661 - 680 Titre : Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds Type de document : Article/Communication Auteurs : Zhou Guo, Auteur ; Chen-Chieh Feng, Auteur Année de publication : 2020 Article en page(s) : pp 661 - 680 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse multiéchelle [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] approche hiérarchique [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] Oakland (Californie) [Termes descripteurs IGN] régression [Termes descripteurs IGN] semis de points Résumé : (auteur) Point cloud classification, which provides meaningful semantic labels to the points in a point cloud, is essential for generating three-dimensional (3D) models. Its automation, however, remains challenging due to varying point densities and irregular point distributions. Adapting existing deep-learning approaches for two-dimensional (2D) image classification to point cloud classification is inefficient and results in the loss of information valuable for point cloud classification. In this article, a new approach that classifies point cloud directly in 3D is proposed. The approach uses multi-scale features generated by deep learning. It comprises three steps: (1) extract single-scale deep features using 3D convolutional neural network (CNN); (2) subsample the input point cloud at multiple scales, with the point cloud at each scale being an input to the 3D CNN, and combine deep features at multiple scales to form multi-scale and hierarchical features; and (3) retrieve the probabilities that each point belongs to the intended semantic category using a softmax regression classifier. The proposed approach was tested against two publicly available point cloud datasets to demonstrate its performance and compared to the results produced by other existing approaches. The experiment results achieved 96.89% overall accuracy on the Oakland dataset and 91.89% overall accuracy on the Europe dataset, which are the highest among the considered methods. Numéro de notice : A2020-109 Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1552790 date de publication en ligne : 10/12/2018 En ligne : https://doi.org/10.1080/13658816.2018.1552790 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94711 [article]

The application of bidirectional reflectance distribution function data to recognize the spatial heterogeneity of mixed pixels in vegetation remote sensing: a simulation study / Yanan Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 161 - 167 Titre : The application of bidirectional reflectance distribution function data to recognize the spatial heterogeneity of mixed pixels in vegetation remote sensing: a simulation study Type de document : Article/Communication Auteurs : Yanan Yan, Auteur ; Lei Deng, Auteur ; L. Xian-Lin, Auteur Année de publication : 2020 Article en page(s) : pp 161 - 167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] agrégation spatiale [Termes descripteurs IGN] anisotropie [Termes descripteurs IGN] approche pixel [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] dispersion [Termes descripteurs IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] hétérogénéité spatiale [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modèle de transfert radiatif [Termes descripteurs IGN] réflectance [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] télédétection Résumé : (auteur) Spectral decomposition of mixed pixels can provide information about the abundance of end members but fails to indicate the spatial distribution of end members in vegetation remote sensing. This work is a significant attempt to use the bidirectional reflectance distribution function (BRDF) characteristics of mixed pixels in the prediction of spatial-heterogeneity metrics. Data sets from this function with different spatial distributions were constructed by the discrete anisotropic radiative transfer model, and three spatial aggregation and dispersion metrics were calculated: percentage of like adjacencies, spatial division index, and aggregation index. A simple linear regression method was used to construct the prediction model of spatial aggregation and dispersion metrics. The potential of multiangle remote sensing model for identifying spatial patterns well was demonstrated, and its importance was found to differ for different spatial aggregation and dispersion metrics. Specifically, the precision of the model based on multiangle reflectance used for predicting the spatial division index could meet a minimum root mean square of 5.95%. The reflectance features from backward observation on the principal plane play the leading role in recognizing the spatial heterogeneity of mixed pixels. The prediction model is sufficiently robust to distinguish the same vegetation with different growth trends, but also performs well when the ground objects have a smaller reflectance difference in the mixed pixels in a certain band. This study is expected to offer a new thought for spatial-heterogeneity identification of ground objects and thus promote the development of remote sensing technology in assessing spatial distribution. Numéro de notice : A2020-146 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.3.161 date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.3.161 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94775 [article]

Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods / Elisa Kamir in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 124 - 135 Titre : Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods Type de document : Article/Communication Auteurs : Elisa Kamir, Auteur ; François Waldner, Auteur ; Zvi Hochman, Auteur Année de publication : 2020 Article en page(s) : pp 124 - 135 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] Australie [Termes descripteurs IGN] blé (céréale) [Termes descripteurs IGN] carte agricole [Termes descripteurs IGN] climat [Termes descripteurs IGN] estimation de précision [Termes descripteurs IGN] fonction de base radiale [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] modèle de croissance [Termes descripteurs IGN] modèle non linéaire [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] régression [Termes descripteurs IGN] rendement agricole [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] variation saisonnière Résumé : (Auteur) Closing the yield gap between actual and potential wheat yields in Australia is important to meet the growing global demand for food. The identification of hotspots of the yield gap, where the potential for improvement is the greatest, is a necessary step towards this goal. While crop growth models are well suited to quantify potential yields, they lack the ability to provide accurate large-scale estimates of actual yields, owing to the sheer quantity of data they require for parameterisation. In this context, we sought to provide accurate estimates of actual wheat yields across the Australian wheat belt based on machine-learning regression methods, climate records and satellite image time series. Out of nine base learners and two ensembles, support vector regression with radial basis function emerged as the single best learner (root mean square error of 0.55 t ha−1 and R2 of 0.77 at the pixel level). At national scale, this model explained 73% of the yield variability observed across statistical units. Benchmark approaches based on peak Normalised Difference Vegetation Index (NDVI) and on a harvest index were largely outperformed by the machine-learning regression models (R2  Numéro de notice : A2020-046 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.008 date de publication en ligne : 20/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.008 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94556 [article]

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