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Above-ground biomass estimation of arable crops using UAV-based SfM photogrammetry / Maria Luz Gil-Docampo in Geocarto international, vol 35 n° 7 ([15/05/2020])
[article]
Titre : Above-ground biomass estimation of arable crops using UAV-based SfM photogrammetry Type de document : Article/Communication Auteurs : Maria Luz Gil-Docampo, Auteur ; Marcos Arza-García, Auteur ; Juan Ortiz-Sanz, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 687 - 699 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] acquisition d'images
[Termes IGN] agronomie
[Termes IGN] biomasse
[Termes IGN] image à très haute résolution
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] photogrammétrie aérienne
[Termes IGN] sol arable
[Termes IGN] structure-from-motionRésumé : (Auteur) Methods of estimating the total amount of above-ground biomass (AGB) in crop fields are generally based on labourious, random, and destructive in situ sampling. This study proposes a methodology for estimating herbaceous crop biomass using conventional optical cameras and structure from motion (SfM) photogrammetry. The proposed method is based on the determination of volumes according to the difference between a digital terrain model (DTM) and digital surface model (DSM) of vegetative cover. A density factor was calibrated based on a subset of destructive random samples to relate the volume and biomass and efficiently quantify the total AGB. In all cases, RMSE Z values less than 0.23 m were obtained for the DTM-DSM coupling. Biomass field data confirmed the goodness of fit of the yield-biomass estimation (R2=0.88 and 1.12 kg/ha) mainly in plots with uniform vegetation coverage. Furthermore, the method was demonstrated to be scalable to multiple platform types and sensors. Numéro de notice : A2020-186 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1552322 Date de publication en ligne : 07/02/2019 En ligne : https://doi.org/10.1080/10106049.2018.1552322 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94993
in Geocarto international > vol 35 n° 7 [15/05/2020] . - pp 687 - 699[article]Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system / Minh Hai Pham in Plos one, vol 15 n° 5 (May 2020)
[article]
Titre : Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system Type de document : Article/Communication Auteurs : Minh Hai Pham, Auteur ; Thi Hoai Do, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 0233110 Note générale : biblographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne
[Termes IGN] biomasse forestière
[Termes IGN] changement d'occupation du sol
[Termes IGN] image Sentinel-SAR
[Termes IGN] image SPOT 6
[Termes IGN] Inférence floue
[Termes IGN] mangrove
[Termes IGN] Viet Nam
[Vedettes matières IGN] Ecologie forestièreRésumé : (auteur) Background : Advances in earth observation and machine learning techniques have created new options for forest monitoring, primarily because of the various possibilities that they provide for classifying forest cover and estimating aboveground biomass (AGB).
Methods : This study aimed to introduce a novel model that incorporates the atom search algorithm (ASO) and adaptive neuro-fuzzy inference system (ANFIS) into mangrove forest classification and AGB estimation. The Ca Mau coastal area was selected as a case study since it has been considered the most preserved mangrove forest area in Vietnam and is being investigated for the impacts of land-use change on forest quality. The model was trained and validated with a set of Sentinel-1A imagery with VH and VV polarizations, and multispectral information from the SPOT image. In addition, feature selection was also carried out to choose the optimal combination of predictor variables. The model performance was benchmarked against conventional methods, such as support vector regression, multilayer perceptron, random subspace, and random forest, by using statistical indicators, namely, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2).
Results : The results showed that all three indicators of the proposed model were statistically better than those from the benchmarked methods. Specifically, the hybrid model ended up at RMSE = 70.882, MAE = 55.458, R2 = 0.577 for AGB estimation.
Conclusion : From the experiments, such hybrid integration can be recommended for use as an alternative solution for biomass estimation. In a broader context, the fast growth of metaheuristic search algorithms has created new scientifically sound solutions for better analysis of forest cover.Numéro de notice : A2020-833 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE Nature : Article DOI : https://doi.org/10.1371/journal.pone.0233110 Date de publication en ligne : 21/05/2020 En ligne : https://doi.org/10.1371/journal.pone.0233110 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97667
in Plos one > vol 15 n° 5 (May 2020) . - n° 0233110[article]Modeling strawberry biomass and leaf area using object-based analysis of high-resolution images / Zhen Guan in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)
[article]
Titre : Modeling strawberry biomass and leaf area using object-based analysis of high-resolution images Type de document : Article/Communication Auteurs : Zhen Guan, Auteur ; Amr Abd-Elrahman, Auteur ; Zhen Fan, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 171 - 186 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse d'image orientée objet
[Termes IGN] biomasse
[Termes IGN] canopée
[Termes IGN] données spatiotemporelles
[Termes IGN] hauteur de la végétation
[Termes IGN] image à haute résolution
[Termes IGN] indice foliaire
[Termes IGN] orthophotoplan numérique
[Termes IGN] phénologie
[Termes IGN] semis de points
[Termes IGN] structure-from-motionRésumé : (auteur) Quantifying canopy biophysical parameters is critical to agricultural research and farm management. In this study, strawberry dry biomass and leaf area were modeled statistically using high spatial and temporal resolution imagery. A mobile field data acquisition system was used to acquire thousands of very high resolution (~0.5 mm) close-range images seven times throughout the strawberry growing season. Ortho-mosaics and dense point clouds were generated through Structure from Motion (SfM) and used in Object-Based Image Analysis (OBIA) at the sub-leaf level to extract canopy structure variables such as planimetric canopy area, canopy average height, and canopy smoothness metric. Regression analysis was carried out using these image-derived canopy variables as predictors to model leaf area ( = 0.79; ten-fold cross-validation RMSE = 0.056 m2) and dry biomass ( = 0.84; ten-fold cross-validation RMSE = 7.72 g) obtained through destructive measurements. Results indicate consistent predictive power through the season and across 17 strawberry genotypes. The study showed that the canopy smoothness metric developed in this study as an indicator of canopy density could complement other variables (planimetric canopy area, canopy average height) that describe canopy geometric properties. Numéro de notice : A2020-139 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.021 Date de publication en ligne : 18/03/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.021 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94757
in ISPRS Journal of photogrammetry and remote sensing > vol 163 (May 2020) . - pp 171 - 186[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020051 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020053 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020052 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Shrub biomass estimates in former burnt areas using Sentinel 2 images processing and classification / Jose Aranha in Forests, vol 11 n° 5 (May 2020)
[article]
Titre : Shrub biomass estimates in former burnt areas using Sentinel 2 images processing and classification Type de document : Article/Communication Auteurs : Jose Aranha, Auteur ; Teresa Enes, Auteur ; Ana Calvão, Auteur ; Hélder Viana, Auteur Année de publication : 2020 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arbuste
[Termes IGN] biomasse
[Termes IGN] classification dirigée
[Termes IGN] gestion forestière
[Termes IGN] image proche infrarouge
[Termes IGN] image RVB
[Termes IGN] image Sentinel-MSI
[Termes IGN] incendie de forêt
[Termes IGN] modèle de croissance végétale
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Portugal
[Termes IGN] signature spectrale
[Termes IGN] sous-bois
[Termes IGN] système d'information géographique
[Termes IGN] zone sinistréeRésumé : (auteur) Shrubs growing in former burnt areas play two diametrically opposed roles. On the one hand, they protect the soil against erosion, promote rainwater infiltration, carbon sequestration and support animal life. On the other hand, after the shrubs’ density reaches a particular size for the canopy to touch and the shrubs’ biomass accumulates more than 10 Mg ha−1, they create the necessary conditions for severe wild fires to occur and spread. The creation of a methodology suitable to identify former burnt areas and to track shrubs’ regrowth within these areas in a regular and a multi temporal basis would be beneficial. The combined use of geographical information systems (GIS) and remote sensing (RS) supported by dedicated land survey and field work for data collection has been identified as a suitable method to manage these tasks. The free access to Sentinel images constitutes a valuable tool for updating the GIS project and for the monitoring of regular shrubs’ accumulated biomass. Sentinel 2 VIS-NIR images are suitable to classify rural areas (overall accuracy = 79.6% and Cohen’s K = 0.754) and to create normalized difference vegetation index (NDVI) images to be used in association to allometric equations for the shrubs’ biomass estimation (R2 = 0.8984, p-value Numéro de notice : A2020-654 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f11050555 Date de publication en ligne : 14/05/2020 En ligne : https://doi.org/10.3390/f11050555 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96116
in Forests > vol 11 n° 5 (May 2020) . - 19 p.[article]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)
[article]
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 IGN] biomasse aérienne
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] couvert végétal
[Termes IGN] hauteur de la végétation
[Termes IGN] image captée par drone
[Termes IGN] image hyperspectrale
[Termes IGN] image RVB
[Termes IGN] indice de végétation
[Termes IGN] pomme de terre
[Termes IGN] régression des moindres carrés partiels
[Termes IGN] rendement agricoleRésumé : (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 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE 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
in ISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 161 -1 72[article]Radar Vegetation Index for assessing cotton crop condition using RISAT-1 data / Dipanwita Haldar in Geocarto international, vol 35 n° 4 ([15/03/2020])PermalinkAssessing forest availability for wood supply in Europe / Iciar A. Alberdi in Forest policy and economics, vol 111 (February 2020)PermalinkCan Carbon Sequestration in Tasmanian “Wet” Eucalypt Forests Be Used to Mitigate Climate Change? Forest Succession, the Buffering Effects of Soils, and Landscape Processes Must Be Taken into Account / Peter D. McIntosh in International journal of forestry research, vol 2020 ([01/02/2020])PermalinkImpact of precipitation, air temperature and abiotic emissions on gross primary production in Mediterranean ecosystems in Europe / S. Bartsch in European Journal of Forest Research, vol 139 n° 1 (February 2020)PermalinkThe effects of different combinations of simulated climate change-related stressors on juveniles of seven forest tree species grown as mono-species and mixed cultures / Alfas Pliüra in Baltic forestry, vol 26 n° 1 ([01/02/2020])PermalinkArtificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey / Alkan Günlü in Geocarto international, Vol 35 n° 1 ([02/01/2020])PermalinkCombination of linear regression lines to understand the response of Sentinel-1 dual polarization SAR data with crop phenology - case study in Miyazaki, Japan / Emal Wali in Remote sensing, vol 12 n° 1 (January 2020)PermalinkEstimation et suivi de la ressource en bois en France métropolitaine par valorisation des séries multi-temporelles à haute résolution spatiale d'images optiques (Sentinel-2) et radar (Sentinel-1, ALOS-PALSAR) / David Morin (2020)PermalinkPermalinkInversion de données PolSAR en bande P pour l'estimation de la biomasse forestière / Colette Gelas (2020)Permalink