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Ajouter le résultat dans votre panierQuantifying the influence of plot-level uncertainty in above ground biomass up scaling using remote sensing data in central Indian dry deciduous forest / Thangavelu Mayamanikandan in Geocarto international, vol 37 n° 12 ([01/07/2022])
[article]
Titre : Quantifying the influence of plot-level uncertainty in above ground biomass up scaling using remote sensing data in central Indian dry deciduous forest Type de document : Article/Communication Auteurs : Thangavelu Mayamanikandan, Auteur ; Suraj Reddy, Auteur ; Rakesh Fararoda, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 3489 - 3503 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] forêt de feuillus
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] incertitude des données
[Termes IGN] Inde
[Termes IGN] placette d'échantillonnage
[Termes IGN] superposition d'imagesRésumé : (auteur) Accurate and reliable estimation of Above Ground Biomass (AGB) in tropical forests is much needed for net carbon assessments. The aim of the study is to determine the uncertainty in biomass estimation in terms of field plot size, shape and location error using field plot and remote sensing data in tropical dry deciduous forests of India. Detailed tree measurements and location mapping are performed in 13 (1 ha) plots and 1 a very large permanent plot of 32 ha and AGB is estimated using local volume equations. Remote sensing-based AGB estimated using a multiple linear regression model between the reflectance (Sentinel-2) and backscatter (Sentinel-1) with field AGB. The result shows relative root mean square error of the model decreased by approximately 50% with a plot size increase from 0.01 ha (64%) to 0.64 ha (14%). Furthermore, we also observed that the effect of global positioning system location errors in AGB modelling would be negated by increasing plot size. Numéro de notice : A2022-587 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1864029 Date de publication en ligne : 29/12/2020 En ligne : https://doi.org/10.1080/10106049.2020.1864029 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101361
in Geocarto international > vol 37 n° 12 [01/07/2022] . - pp 3489 - 3503[article]Detection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks / Gensheng Hu in Geocarto international, vol 37 n° 12 ([01/07/2022])
[article]
Titre : Detection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks Type de document : Article/Communication Auteurs : Gensheng Hu, Auteur ; Yanqiu Zhu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 3520 - 3539 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image captée par drone
[Termes IGN] Pinus (genre)
[Termes IGN] santé des forêtsRésumé : (auteur) This study presents a method that uses high-resolution remote sensing images collected by an unmanned aerial vehicle (UAV) and combines MobileNet and Faster R-CNN for detecting diseased pine trees. MobileNet is used to remove backgrounds to reduce the interference of background information. Faster R-CNN is adopted to distinguish between diseased and healthy pine trees. The number of training samples is expanded due to the insufficient number of available UAV images. Experimental results show that the proposed method is better than traditional machine learning approaches, such as support vector machine and AdaBoost, and methods of DCNN, such as Alexnet, Inception and Faster R-CNN. Through sample expansion and background removal, the proposed method achieves effective detection of diseased pine trees in UAV images by using deep learning technology. Numéro de notice : A2022-588 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1864025 Date de publication en ligne : 06/01/2021 En ligne : https://doi.org/10.1080/10106049.2020.1864025 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101362
in Geocarto international > vol 37 n° 12 [01/07/2022] . - pp 3520 - 3539[article]