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imagerie
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Terme regroupant photographies et images issues de différents capteurs.
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Smartphone digital photography for fractional vegetation cover estimation / Gaofei Yin in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 5 (May 2022)
[article]
Titre : Smartphone digital photography for fractional vegetation cover estimation Type de document : Article/Communication Auteurs : Gaofei Yin, Auteur ; Yonghua Qu, Auteur ; Aleixandre Verger, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 303 - 310 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] analyse comparative
[Termes IGN] champ visuel
[Termes IGN] couvert végétal
[Termes IGN] erreur moyenne quadratique
[Termes IGN] forêt alpestre
[Termes IGN] image à haute résolution
[Termes IGN] image hémisphérique
[Termes IGN] objectif grand angulaire
[Termes IGN] téléphone intelligentRésumé : (Auteur) Accurate ground measurements of fractional vegetation cover (FVC) are key for characterizing ecosystem functions and evaluating remote sensing products. The increasing performance of cameras equipped in smartphones opens new opportunities for extensive FVC measurement through citizen science initiatives. However, the wide field of view (FOV) of smartphone cameras constitutes a key source of uncertainty in the estimation of vegetation parameters, which has been largely ignored. We designed a practical method to characterize the FOV of smartphones and improve the FVC estimation. The method was assessed in a mountainous forest based on the comparison with in situ fisheye photographs. After the FOV correction, the agreement of smart-phone and fisheye FVC estimates highly improved: root-mean-square error (RMSE) of 0.103 compared to 0.242 of the original smartphone FVC estimates without considering the FOV effect, mean difference of 0.074 versus 0.213, and coefficient of determination R 2 of 0.719 versus 0.353. Smartphone cameras outperform traditional fisheye cameras: the overexposure and low vertical resolution of fisheye photographs introduced uncertainties in FVCestimation while the insensitivity to exposure and high spatial resolution of smartphone cameras make photograph acquisition and analysis more automatic and accurate. The smartphone FVCestimates highly agree with the GF-1 satellite product: RMSE = 0.066, bias = 0.007, and R 2 = 0.745. This study opens new perspectives for the validation of satellite products. Numéro de notice : A2022-527 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00038R2 Date de publication en ligne : 01/05/2022 En ligne : https://doi.org/10.14358/PERS.21-00038R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101375
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 5 (May 2022) . - pp 303 - 310[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2022052 SL Revue Centre de documentation Revues en salle Disponible 105-2022051 SL Revue Centre de documentation Revues en salle Disponible Unmixing-based spatiotemporal image fusion accounting for complex land cover changes / Xiaolu Jiang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 5 (May 2022)
[article]
Titre : Unmixing-based spatiotemporal image fusion accounting for complex land cover changes Type de document : Article/Communication Auteurs : Xiaolu Jiang, Auteur ; Bo Huang, Auteur Année de publication : 2022 Article en page(s) : n° 5623010 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] changement d'occupation du sol
[Termes IGN] données spatiotemporelles
[Termes IGN] fusion d'images
[Termes IGN] image Landsat
[Termes IGN] image Terra-MODIS
[Termes IGN] réflectance spectrale
[Termes IGN] régression géographiquement pondéréeRésumé : (auteur) Spatiotemporal reflectance fusion has received considerable attention in recent decades. However, various challenges remain despite varying levels of success, especially regarding the recovery of spatial details with complex land cover changes. Taking the blending of Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) images as an example, this article presents a locally weighted unmixing-based spatiotemporal image fusion model (LWU-STFM) that focuses on recovering complex land cover changes. The core idea is to redefine the land use class of each pixel featuring land cover change at the prediction date. The spatial unmixing process is enhanced using a proposed geographically spectrum-weighted regression (GSWR), and then, we optimize similar neighboring pixels for the final weighted-based prediction. Experiments are conducted using semisimulated and actual time-series Landsat–MODIS datasets to demonstrate the performance of the proposed LWU-STFM compared with the classic spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), two enhanced FSDAF models (SFSDAF and FSDAF 2.0), and a virtual image pair-based spatiotemporal fusion model for spatial weighting (VIPSTF-SW). The results reveal that the proposed LWU-STFM outperforms the other five models with the best quantitative accuracy. In terms of the relative dimensionless global error (ERGAS) index, the errors of Landsat-like images generated using LWU-STFM are 2.8%–63.4% lower than those of other models. From visual comparisons, LWU-STFM predictions illustrate encouraging improvements in recovering spatial details of pixels with complex land cover changes in heterogeneous landscapes and, thus, advancing applications of spatiotemporal image fusion for continuous and fine-scale land surface monitoring. Numéro de notice : A2022-409 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3173172 Date de publication en ligne : 05/05/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3173172 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100744
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 5 (May 2022) . - n° 5623010[article]Unveiling the complex canopy spatial structure of a Mediterranean old-growth beech (Fagus sylvatica L.) forest from UAV observations / Francesco Solano in Ecological indicators, vol 138 (May 2022)
[article]
Titre : Unveiling the complex canopy spatial structure of a Mediterranean old-growth beech (Fagus sylvatica L.) forest from UAV observations Type de document : Article/Communication Auteurs : Francesco Solano, Auteur ; Giuseppe Modica, Auteur ; Salvatore Praticò, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108807 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Calabre
[Termes IGN] écosystème forestier
[Termes IGN] Fagus sylvatica
[Termes IGN] forêt ancienne
[Termes IGN] forêt méditerranéenne
[Termes IGN] forêt primaire
[Termes IGN] image à très haute résolution
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] orthophotoplan numérique
[Termes IGN] photogrammétrie aérienne
[Termes IGN] structure spatiale
[Termes IGN] structure-from-motion
[Termes IGN] surveillance forestièreRésumé : (auteur) In front of climate change scenarios and global loss of biodiversity, it is essential to monitor the structure of old-growth forests to study ecosystem status and dynamics to inform future conservation and restoration programmes. We propose an Unmanned Aerial Vehicle (UAV)-based framework to monitor fine-grained forest top canopy structure in a primary old-growth beech (Fagus sylvatica L.) forest in Pollino National Park, Italy, which belongs to the UNESCO World Heritage (UNESCO WH) serial site “Ancient and Primeval beech forests of the Carpathians and other regions of Europe”. Canopy profile, gap properties and their spatial distribution patterns were analysed using the canopy height model (CHM) derived from UAV surveys. Very high-resolution orthomosaic images coupled with direct field measurement data were used to assess gap detection accuracy and CHM validation. Forest canopy properties along with the vertical layering of the canopy were further explored using second-order statistics. The reconstructed canopy profile revealed a bimodal top height frequency distribution. The upper canopy layer (h > 14 m) was the most represented canopy height, with the remaining 50% split between the medium and lowest layer; 551 gaps were identified within 11.5 ha. Gap size varied between 2 m2 and 353 m2, and 19 m2was the mean gap size; the gap size-frequency relationship reflected a power-law probability distribution. About 97 % of the gaps were Numéro de notice : A2022-369 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ecolind.2022.108807 Date de publication en ligne : 01/04/2022 En ligne : https://doi.org/10.1016/j.ecolind.2022.108807 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100598
in Ecological indicators > vol 138 (May 2022) . - n° 108807[article]Automated inventory of broadleaf tree plantations with UAS imagery / Aishwarya Chandrasekaran in Remote sensing, vol 14 n° 8 (April-2 2022)
[article]
Titre : Automated inventory of broadleaf tree plantations with UAS imagery Type de document : Article/Communication Auteurs : Aishwarya Chandrasekaran, Auteur ; Guofan Shao, Auteur ; Songlin Fei, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1931 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] feuillu
[Termes IGN] hauteur à la base du houppier
[Termes IGN] hauteur des arbres
[Termes IGN] image captée par drone
[Termes IGN] Indiana (Etats-Unis)
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] orthophotoplan numérique
[Termes IGN] plantation forestière
[Termes IGN] R (langage)
[Termes IGN] semis de points
[Termes IGN] structure-from-motionRésumé : (auteur) With the increased availability of unmanned aerial systems (UAS) imagery, digitalized forest inventory has gained prominence in recent years. This paper presents a methodology for automated measurement of tree height and crown area in two broadleaf tree plantations of different species and ages using two different UAS platforms. Using structure from motion (SfM), we generated canopy height models (CHMs) for each broadleaf plantation in Indiana, USA. From the CHMs, we calculated individual tree parameters automatically through an open-source web tool developed using the Shiny R package and assessed the accuracy against field measurements. Our analysis shows higher tree measurement accuracy with the datasets derived from multi-rotor platform (M600) than with the fixed wing platform (Bramor). The results show that our automated method could identify individual trees (F-score > 90%) and tree biometrics (root mean square error Numéro de notice : A2022-351 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs14081931 Date de publication en ligne : 16/04/2022 En ligne : https://doi.org/10.3390/rs14081931 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100539
in Remote sensing > vol 14 n° 8 (April-2 2022) . - n° 1931[article]Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information / Murali Krishna Gumma in Geocarto international, vol 37 n° 7 ([15/04/2022])
[article]
Titre : Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information Type de document : Article/Communication Auteurs : Murali Krishna Gumma, Auteur ; Kimeera Tummala, Auteur ; Sreenath Dixit, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1833 - 1849 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] appariement spectral
[Termes IGN] blé (céréale)
[Termes IGN] carte de la végétation
[Termes IGN] distribution spatiale
[Termes IGN] image Sentinel-MSI
[Termes IGN] Inde
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] série temporelle
[Termes IGN] surface cultivée
[Termes IGN] variation saisonnièreRésumé : (auteur) Accurate monitoring of croplands helps in making decisions (for insurance claims, crop management and contingency plans) at the macro-level, especially in drylands where variability in cropping is very high owing to erratic weather conditions. Dryland cereals and grain legumes are key to ensuring the food and nutritional security of a large number of vulnerable populations living in the drylands. Reliable information on area cultivated to such crops forms part of the national accounting of food production and supply in many Asian countries, many of which are employing remote sensing tools to improve the accuracy of assessments of cultivated areas. This paper assesses the capabilities and limitations of mapping cultivated areas in the Rabi (winter) season and corresponding cropping patterns in three districts characterized by small-plot agriculture. The study used Sentinel-2 Normalized Difference Vegetation Index (NDVI) 15-day time-series at 10 m resolution by employing a Spectral Matching Technique (SMT) approach. The use of SMT is based on the well-studied relationship between temporal NDVI signatures and crop phenology. The rabi season in India, dominated by non-rainy days, is best suited for the application of this method, as persistent cloud cover will hamper the availability of images necessary to generate clearly differentiating temporal signatures. Our study showed that the temporal signatures of wheat, chickpea and mustard are easily distinguishable, enabling an overall accuracy of 84%, with wheat and mustard achieving 86% and 94% accuracies, respectively. The most significant misclassifications were in irrigated areas for mustard and wheat, in small-plot mustard fields covered by trees and in fragmented chickpea areas. A comparison of district-wise national crop statistics and those obtained from this study revealed a correlation of 96%. Numéro de notice : A2022-497 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1805029 Date de publication en ligne : 18/08/2020 En ligne : https://doi.org/10.1080/10106049.2020.1805029 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100989
in Geocarto international > vol 37 n° 7 [15/04/2022] . - pp 1833 - 1849[article]Spectral-spatial classification method for hyperspectral images using stacked sparse autoencoder suitable in limited labelled samples situation / Seyyed Ali Ahmadi in Geocarto international, vol 37 n° 7 ([15/04/2022])PermalinkWood decay detection in Norway spruce forests based on airborne hyperspectral and ALS data / Michele Dalponte in Remote sensing, vol 14 n° 8 (April-2 2022)PermalinkAssessment of RTK quadcopter and structure-from-motion photogrammetry for fine-scale monitoring of coastal topographic complexity / Stéphane Bertin in Remote sensing, vol 14 n° 7 (April-1 2022)PermalinkDeep generative model for spatial–spectral unmixing with multiple endmember priors / Shuaikai Shi in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)PermalinkDetecting and mapping drought severity using multi-temporal Landsat data in the uMsinga region of KwaZulu-Natal, South Africa / Shenelle Lottering in Geocarto international, vol 37 n° 6 ([01/04/2022])PermalinkDetecting land use and land cover change on Barbuda before and after the Hurricane Irma with respect to potential land grabbing: A combined volunteered geographic information and multi sensor approach / Andreas Rienow in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)PermalinkDirect photogrammetry with multispectral imagery for UAV-based snow depth estimation / Kathrin Maier in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)PermalinkExploring the association between street built environment and street vitality using deep learning methods / Yunqin Li in Sustainable Cities and Society, vol 79 (April 2022)PermalinkFlood mapping using multi-temporal Sentinel-1 SAR images: A case study—Inaouene watershed from Northeast of Morocco / Brahim Benzougagh in Iranian Journal of Science and Technology - Transactions of Civil Engineering, vol 46 n° 2 (April 2022)PermalinkGeoRec: Geometry-enhanced semantic 3D reconstruction of RGB-D indoor scenes / Linxi Huan in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)Permalink