Descripteur
Termes IGN > mathématiques > statistique mathématique
statistique mathématique
Commentaire :
>>
biométrie,
échantillonnage (statistique), probabilité, statistique. >>Terme(s) spécifique(s) : analyse de régression, analyse de variance, analyse des données, analyse multivariée, analyse séquentielle, calcul d'erreur, carré latin, corrélation (statistique), efficacité asymptotique (statistique), fonction pseudo-aléatoire, loi des grands nombres, modèle linéaire (statistique), modèle non linéaire (statistique), moindre carré, physique statistique, plan d'expérience, rang et sélection (statistique), rupture (statistique), SAS (logiciel), série chronologique, statistique non paramétrique, statistique robuste, tableau de contingence, test d'hypothèses (statistique), statistique stellaire. Equiv. LCSH : Mathematical statistics. Domaine(s) : 510. |
Documents disponibles dans cette catégorie (6658)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
Etendre la recherche sur niveau(x) vers le bas
DEM- and GIS-based analysis of soil erosion depth using machine learning / Kieu Anh Nguyen in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)
![]()
[article]
Titre : DEM- and GIS-based analysis of soil erosion depth using machine learning Type de document : Article/Communication Auteurs : Kieu Anh Nguyen, Auteur ; Walter Chen, Auteur Année de publication : 2021 Article en page(s) : n° 452 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] apprentissage automatique
[Termes IGN] bassin hydrographique
[Termes IGN] carte de profondeur
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] érosion
[Termes IGN] Extreme Gradient Machine
[Termes IGN] modèle de simulation
[Termes IGN] modèle numérique de surface
[Termes IGN] morphométrie
[Termes IGN] système d'information géographiqueRésumé : (auteur) Soil erosion is a form of land degradation. It is the process of moving surface soil with the action of external forces such as wind or water. Tillage also causes soil erosion. As outlined by the United Nations Sustainable Development Goal (UN SDG) #15, it is a global challenge to “combat desertification, and halt and reverse land degradation and halt biodiversity loss.” In order to advance this goal, we studied and modeled the soil erosion depth of a typical watershed in Taiwan using 26 morphometric factors derived from a digital elevation model (DEM) and 10 environmental factors. Feature selection was performed using the Boruta algorithm to determine 15 factors with confirmed importance and one tentative factor. Then, machine learning models, including the random forest (RF) and gradient boosting machine (GBM), were used to create prediction models validated by erosion pin measurements. The results show that GBM, coupled with 15 important factors (confirmed), achieved the best result in the context of root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE). Finally, we present the maps of soil erosion depth using the two machine learning models. The maps are useful for conservation planning and mitigating future soil erosion. Numéro de notice : A2021-551 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10070452 Date de publication en ligne : 01/07/2021 En ligne : https://doi.org/10.3390/ijgi10070452 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98074
in ISPRS International journal of geo-information > vol 10 n° 7 (July 2021) . - n° 452[article]Detecting high-temperature anomalies from Sentinel-2 MSI images / Yongxue Liu in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)
![]()
[article]
Titre : Detecting high-temperature anomalies from Sentinel-2 MSI images Type de document : Article/Communication Auteurs : Yongxue Liu, Auteur ; Zhi Weifeng, Auteur ; Bihua Xu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 174 - 193 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] anomalie thermique
[Termes IGN] éruption volcanique
[Termes IGN] image aérienne
[Termes IGN] image Landsat-OLI
[Termes IGN] image proche infrarouge
[Termes IGN] image Sentinel-MSI
[Termes IGN] image thermique
[Termes IGN] incendie
[Termes IGN] réflectance spectrale
[Termes IGN] risque technologique
[Termes IGN] série temporelle
[Termes IGN] température au solRésumé : (Auteur) High-temperature anomalies (HTAs) of the earth's surface, such as fires, volcanic activities, and industrial heat sources, have a profound impact on Earth's system. Sentinel-2 Multispectral Instrument (MSI) provides spatially-specific information for precisely measuring the location and extent of HTAs at a fine scale. However, detecting HTAs from MSI images remains challenging because the emitted radiance of an HTA in the short-wave infrared (SWIR) bands can be easily mixed with the reflected solar radiance background in the daytime; and an increasing number of atypical cases in MSI images need to be treated with the enhanced spatial resolution. A generic HTA detection approach that handles both anthropogenic and natural HTAs will broaden the scope of MSI applications. In this study, (i) we highlight two spectral characteristics of HTAs in the far-SWIR, near-SWIR, and NIR bands (i.e., (ρfar-SWIR - ρnear-SWIR)/ρNIR ≥ 0.45 and (ρfar-SWIR -ρnear-SWIR) ≥ ρnear-SWIR - ρNIR) that can effectively enhance HTAs from background geo-features, based on the reflectance spectra in airborne imaging spectrometer data. (ii) We propose a tri-spectral thermal anomaly index (TAI) that jointly uses the two high-temperature-sensitive SWIR bands and the high-temperature-insensitive NIR band to enhance HTAs, based on the above characteristics and a comprehensive sampling of different types of HTAs from 1,974 MSI images. (iii) We develop a TAI-based approach for MSI images to detect HTAs in general. The proposed approach was applied to detect different types of HTAs, including different biomass burnings, active volcanoes, and industrial HTAs, over a wide range of land-cover scenarios. Validations and comparisons demonstrate the proposed approach is reliable and performs better than the existing state-of-the-art HTA detection approaches. Evaluations on two types of small industrial HTAs, including operating kilns and enclosed landfill gas flares, show that the HTA detection probability of the TAI-based approach from time-series MSI images is ~ 84.91% and 88.23%, respectively. Further investigations show that the TAI-based approach also has good transferability in detecting HTAs from multispectral images acquired by Landsat-family satellites. Numéro de notice : A2021-372 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.05.008 Date de publication en ligne : 23/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.05.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97808
in ISPRS Journal of photogrammetry and remote sensing > vol 177 (July 2021) . - pp 174 - 193[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021071 SL Revue Centre de documentation Revues en salle Disponible 081-2021073 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021072 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Estimation of biomass increase and CUE at a young temperate scots pine stand concerning drought occurrence by combining eddy covariance and biometric methods / Paulina Dukat in Forests, vol 12 n° 7 (July 2021)
![]()
[article]
Titre : Estimation of biomass increase and CUE at a young temperate scots pine stand concerning drought occurrence by combining eddy covariance and biometric methods Type de document : Article/Communication Auteurs : Paulina Dukat, Auteur ; Klaudia Ziemblińska, Auteur ; Janusz Olejnik, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 867 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse forestière
[Termes IGN] changement climatique
[Termes IGN] covariance
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] dioxyde de carbone
[Termes IGN] indice de végétation
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Pinus sylvestris
[Termes IGN] Pologne
[Termes IGN] production primaire brute
[Termes IGN] puits de carbone
[Termes IGN] sécheresse
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) The accurate estimation of an increase in forest stand biomass has remained a challenge. Traditionally, in situ measurements are done by inventorying a number of trees and their biometric parameters such as diameter at the breast height (DBH) and height; sometimes these are complemented by carbon (C) content studies. Here we present the estimation of net primary productivity (NPP) over a two years period (2019–2020) at a 25-year-old Scots pine stand. Research was based on allometric equations made by direct biomass analysis (tree extraction) and carbon content estimations in individual components of sampled trees, combined with a series of stem diameter increments recorded by a network of band dendrometers. Site-specific allometric equations were obtained using two different approaches: using the whole tree biomass vs DBH (M1), and total dry biomass-derived as a sum of the results from individual tree components’ biomass vs DBH (M2). Moreover, equations for similar forest stands from the literature were used for comparison. Gross primary productivity (GPP) estimated from the eddy-covariance measurements allowed the calculation of carbon use efficiency (CUE = NPP/GPP). The two investigated years differed in terms of the sum and patterns of precipitation distribution, with a moderately dry year of 2019 that followed the extremely dry 2018, and the relatively average year of 2020. As expected, a higher increase in biomass was recorded in 2020 compared to 2019, as determined by both allometric equations based on in situ and literature data. For the former approach, annual NPP estimates reached ca. 2.0–2.1 t C ha−1 in 2019 and 2.6–2.7 t C ha−1 in 2020 depending on the “in situ equations” (M1-M2) used, while literature-derived equations for the same site resulted in NPP values ca. 20–30% lower. CUE was higher in 2020, which resulted from a higher NPP total than in 2019, with lower summer and spring GPP in 2020. However, the CUE values were lower than those reported in the literature for comparable temperate forest stands. A thorough analysis of the low CUE value would require a full interpretation of interrelated physiological responses to extreme conditions. Numéro de notice : A2021-641 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f12070867 Date de publication en ligne : 30/06/2021 En ligne : https://doi.org/10.3390/f12070867 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98313
in Forests > vol 12 n° 7 (July 2021) . - n° 867[article]Estimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data / Yueting Wang in Ecological indicators, vol 126 (July 2021)
![]()
[article]
Titre : Estimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data Type de document : Article/Communication Auteurs : Yueting Wang, Auteur ; Xiaoli Zhang, Auteur ; Zhengqi Guo, Auteur Année de publication : 2021 Article en page(s) : n° 107645 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] biomasse aérienne
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] forêt
[Termes IGN] hauteur des arbres
[Termes IGN] image Sentinel-MSI
[Termes IGN] image ZiYuan-3
[Termes IGN] modèle numérique de surface
[Termes IGN] Pinophyta
[Termes IGN] surveillance forestièreRésumé : (auteur) The forest tree height and aboveground biomass (AGB) are important indicators for monitoring changes and trends in forest carbon storage and terrestrial carbon fluxes. Accurate large-scale wall-to-wall mapping of the forest tree height and AGB remain challenging due to the limited data availability for extraction tree height and the data signal saturation problem in AGB estimation. In this study, we explored the potential of forest tree height mapping using stereo imageries, and analyzed whether accounting for such information, in addition to optical sensor data, could improve the performance of AGB estimations of coniferous forests in a case study in North China. First, a spatially continuous tree height product was obtained using Ziyuan-3 satellite (ZY-3) stereo images combined with a digital elevation model (DEM) obtained from Advanced Land Observing Satellite (ALOS) data. Second, two AGB estimation models were established by combining the forest tree height with vegetation index, spectral, biophysical (from Sentinel-2 images), and topographic variables. A random forest algorithm was utilized to evaluate the effect of including the tree height variable in the AGB estimation. The results showed that the tree height estimation using the nadir and forward views of the ZY-3 stereo images was more accurate than that based on the nadir and backward views from the same images. The AGB estimation model incorporating the tree height variable with a coefficient of determination value of 0.7789, a root mean square error (RMSE) value of 29.815 Mg/ha and a relative RMSE of 23.42% was more robust and effective, thereby demonstrating that the tree height variable can be used to alleviate the data signal saturation issue successfully. The proposed approach can provide new insight into forest tree height mapping and AGB products obtained from satellite stereo images and freely accessible Sentinel-2 multispectral images. Numéro de notice : A2021-942 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ecolind.2021.107645 En ligne : https://doi.org/10.1016/j.ecolind.2021.107645 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99753
in Ecological indicators > vol 126 (July 2021) . - n° 107645[article]Extracting Shallow-Water Bathymetry from Lidar point clouds using pulse attribute data: Merging density-based and machine learning approaches / Kim Lowell in Marine geodesy, vol 44 n° 4 (July 2021)
![]()
[article]
Titre : Extracting Shallow-Water Bathymetry from Lidar point clouds using pulse attribute data: Merging density-based and machine learning approaches Type de document : Article/Communication Auteurs : Kim Lowell, Auteur ; Brian Calder, Auteur Année de publication : 2021 Article en page(s) : pp 259 - 286 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] angle d'incidence
[Termes IGN] apprentissage automatique
[Termes IGN] bathymétrie laser
[Termes IGN] classification barycentrique
[Termes IGN] données lidar
[Termes IGN] Extreme Gradient Machine
[Termes IGN] Floride (Etats-Unis)
[Termes IGN] lever bathymétrique
[Termes IGN] profondeur
[Termes IGN] semis de pointsRésumé : (auteur) To automate extraction of bathymetric soundings from lidar point clouds, two machine learning (ML1) techniques were combined with a more conventional density-based algorithm. The study area was four data “tiles” near the Florida Keys. The density-based algorithm determined the most likely depth (MLD) for a grid of “estimation nodes” (ENs). Unsupervised k-means clustering determined which EN’s MLD depth and associated soundings represented ocean depth rather than ocean surface or noise to produce a preliminary classification. An extreme gradient boosting (XGB) model was fitted to pulse return metadata – e.g. return intensity, incidence angle – to produce a final Bathy/NotBathy classification. Compared to an operationally produced reference classification, the XGB model increased global accuracy and decreased the false negative rate (FNR) – i.e. undetected bathymetry – that are most important for nautical navigation for all but one tile. Agreement between the final XGB and operational reference classifications ranged from 0.84 to 0.999. Imbalance between Bathy and NotBathy was addressed using a probability decision threshold that equalizes the FNR and the true positive rate (TPR). Two methods are presented for visually evaluating differences between the two classifications spatially and in feature-space. Numéro de notice : A2021-525 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article DOI : 10.1080/01490419.2021.1925790 Date de publication en ligne : 25/05/2021 En ligne : https://doi.org/10.1080/01490419.2021.1925790 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97964
in Marine geodesy > vol 44 n° 4 (July 2021) . - pp 259 - 286[article]Flood depth mapping in street photos with image processing and deep neural networks / Bahareh Alizadeh Kharazi in Computers, Environment and Urban Systems, vol 88 (July 2021)
PermalinkGeographical and temporal huff model calibration using taxi trajectory data / Shuhui Gong in Geoinformatica, vol 25 n° 3 (July 2021)
PermalinkA hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases / Chun Yang in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)
PermalinkImplementing a mass valuation application on interoperable land valuation data model designed as an extension of the national GDI / Arif Cagdas Aydinoglu in Survey review, Vol 53 n° 379 (July 2021)
PermalinkIonospheric irregularity layer height and thickness estimation with a GNSS receiver array / Seebany Datta-Barua in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)
PermalinkJUST: MATLAB and python software for change detection and time series analysis / Ebrahim Ghaderpour in GPS solutions, vol 25 n° 3 (July 2021)
PermalinkLayout graph model for semantic façade reconstruction using laser point clouds / Hongchao Fan in Geo-spatial Information Science, vol 24 n° 3 (July 2021)
PermalinkMachine learning for inference: using gradient boosting decision tree to assess non-linear effects of bus rapid transit on house prices / Linchuan Yang in Annals of GIS, vol 27 n° 3 (July 2021)
PermalinkMulti-scale coal fire detection based on an improved active contour model from Landsat-8 satellite and UAV images / Yanyan Gao in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)
PermalinkRemote sensing image colorization using symmetrical multi-scale DCGAN in YUV color space / Min Wu in The Visual Computer, vol 37 n° 7 (July 2021)
PermalinkReview of spectral indices for urban remote sensing / Akib Javed in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 7 (July 2021)
PermalinkRole of maximum entropy and citizen science to study habitat suitability of jacobin cuckoo in different climate change scenarios / Priyinka Singh in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)
PermalinkA scalable method to construct compact road networks from GPS trajectories / Yuejun Guo in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)
PermalinkSemantic-aware label placement for augmented reality in street view / Jianqing Jia in The Visual Computer, vol 37 n° 7 (July 2021)
PermalinkSemantic unsupervised change detection of natural land cover with multitemporal object-based analysis on SAR images / Donato Amitrano in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)
PermalinkSemiCDNet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images / Daifeng Peng in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)
PermalinkThree-dimensional reconstruction of single input image based on point cloud / Yu Hou in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 7 (July 2021)
PermalinkTrajectory and image-based detection and identification of UAV / Yicheng Liu in The Visual Computer, vol 37 n° 7 (July 2021)
PermalinkUnmanned aerial vehicles (UAV)-based canopy height modeling under leaf-on and leaf-off conditions for determining tree height and crown diameter (Case study: Hyrcanian mixed forest) / Vahid Nasiri in Canadian Journal of Forest Research, Vol 51 n° 7 (July 2021)
PermalinkUsing information entropy and a multi-layer neural network with trajectory data to identify transportation modes / Qingying Yu in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)
PermalinkUsing machine learning to map Western Australian landscapes for mineral exploration / Thomas Albrecht in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)
PermalinkVectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization / Jiali Han in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)
PermalinkFast weakly supervised detection of railway-related infrastructures in lidar acquisitions / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)
PermalinkForest cover mapping and Pinus species classification using very high-resolution satellite images and random forest / Laura Alonso-Martinez in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)
PermalinkA framework to manage uncertainty in the computation of waste collection routes after a flood / Arnaud Le Guilcher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2021 (July 2021)
PermalinkIndividual tree extraction from UAV lidar point clouds based on self-adaptive mean shift segmentation / Zhenyang Hui in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2021 (July 2021)
PermalinkMarrying deep learning and data fusion for accurate semantic labeling of Sentinel-2 images / Guillemette Fonteix in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)
PermalinkTowards efficient indoor/outdoor registration using planar polygons / Rahima Djahel in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)
PermalinkCoral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers / Mohammad Shawkat Hossain in Geocarto international, vol 36 n° 11 ([15/06/2021])
PermalinkA framework for classification of volunteered geographic data based on user’s need / Nazila Mohammadi in Geocarto international, vol 36 n° 11 ([15/06/2021])
PermalinkGroundwater vulnerability assessment of the chalk aquifer in the northern part of France / Lahcen Zouhri in Geocarto international, vol 36 n° 11 ([15/06/2021])
Permalink3D reconstruction of bridges from airborne laser scanning data and cadastral footprints / Steffen Goebbels in Journal of Geovisualization and Spatial Analysis, vol 5 n° 1 (June 2021)
PermalinkAn automatic workflow for orientation of historical images with large radiometric and geometric differences / Ferdinand Maiwald in Photogrammetric record, vol 36 n° 174 (June 2021)
PermalinkAn incremental isomap method for hyperspectral dimensionality reduction and classification / Yi Ma in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)
PermalinkApplication of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery / Sikdar M. M. Rasel in Geocarto international, vol 36 n° 10 ([01/06/2021])
PermalinkCloud-native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel-2 / Dimitris Poursanidis in Remote sensing in ecology and conservation, vol 7 n° 2 (June 2021)
PermalinkA combined drought monitoring index based on multi-sensor remote sensing data and machine learning / Hongzhu Han in Geocarto international, vol 36 n° 10 ([01/06/2021])
PermalinkComparison and evaluation of high-resolution marine gravity recovery via sea surface heights or sea surface slopes / Shengjun Zhang in Journal of geodesy, vol 95 n° 6 (June 2021)
PermalinkDeep learning in denoising of micro-computed tomography images of rock samples / Mikhail Sidorenko in Computers & geosciences, vol 151 (June 2021)
PermalinkDirect analysis in real-time (DART) time-of-flight mass spectrometry (TOFMS) of wood reveals distinct chemical signatures of two species of Afzelia / Peter Kitin in Annals of Forest Science, vol 78 n° 2 (June 2021)
PermalinkDomain adaptive transfer attack-based segmentation networks for building extraction from aerial images / Younghwan Na in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)
PermalinkEfficient image dataset classification difficulty estimation for predicting deep-learning accuracy / Florian Scheidegger in The Visual Computer, vol 37 n° 6 (June 2021)
PermalinkEvaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities / Jingjing Zhou in Remote sensing, vol 13 n° 11 (June-1 2021)
PermalinkFractional vegetation cover estimation algorithm for FY-3B reflectance data based on random forest regression method / Duanyang Liu in Remote sensing, vol 13 n° 11 (June-1 2021)
PermalinkA high-resolution satellite DEM filtering method assisted with building segmentation / Yihui Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)
Permalink