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Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches / S.M. Hamylton in International journal of applied Earth observation and geoinformation, vol 89 (July 2020)
[article]
Titre : Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches Type de document : Article/Communication Auteurs : S.M. Hamylton, Auteur ; R.H. Morris, Auteur ; R.C. Carvalho, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 102085 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] carte de la végétation
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification pixellaire
[Termes IGN] détection de changement
[Termes IGN] données de terrain
[Termes IGN] image captée par drone
[Termes IGN] Nouvelle-Galles du Sud
[Termes IGN] pesticide
[Termes IGN] réserve naturelle
[Termes IGN] série temporelle
[Termes IGN] surveillance de la végétationRésumé : (auteur) We evaluate three approaches to mapping vegetation using images collected by an unmanned aerial vehicle (UAV) to monitor rehabilitation activities in the Five Islands Nature Reserve, Wollongong (Australia). Between April 2017 and July 2018, four aerial surveys of Big Island were undertaken to map changes to island vegetation following helicopter herbicide sprays to eradicate weeds, including the creeper Coastal Morning Glory (Ipomoea cairica) and Kikuyu Grass (Cenchrus clandestinus). The spraying was followed by a large scale planting campaign to introduce native plants, such as tussocks of Spiny-headed Mat-rush (Lomandra longifolia). Three approaches to mapping vegetation were evaluated, including: (i) a pixel-based image classification algorithm applied to the composite spectral wavebands of the images collected, (ii) manual digitisation of vegetation directly from images based on visual interpretation, and (iii) the application of a machine learning algorithm, LeNet, based on a deep learning convolutional neural network (CNN) for detecting planted Lomandra tussocks. The uncertainty of each approach was assessed via comparison against an independently collected field dataset. Each of the vegetation mapping approaches had a comparable accuracy; for a selected weed management and planting area, the overall accuracies were 82 %, 91 % and 85 % respectively for the pixel based image classification, the visual interpretation / digitisation and the CNN machine learning algorithm. At the scale of the whole island, statistically significant differences in the performance of the three approaches to mapping Lomandra plants were detected via ANOVA. The manual digitisation took a longer time to perform than others. The three approaches resulted in markedly different vegetation maps characterised by different digital data formats, which offered fundamentally different types of information on vegetation character. We draw attention to the need to consider how different digital map products will be used for vegetation management (e.g. monitoring the health individual species or a broader profile of the community). Where individual plants are to be monitored over time, a feature-based approach that represents plants as vector points is appropriate. The CNN approach emerged as a promising technique in this regard as it leveraged spatial information from the UAV images within the architecture of the learning framework by enforcing a local connectivity pattern between neurons of adjacent layers to incorporate the spatial relationships between features that comprised the shape of the Lomandra tussocks detected. Numéro de notice : A2020-716 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2020.102085 Date de publication en ligne : 03/03/2020 En ligne : https://doi.org/10.1016/j.jag.2020.102085 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96287
in International journal of applied Earth observation and geoinformation > vol 89 (July 2020) . - n° 102085[article]Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery / Kasper Johansen in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)
[article]
Titre : Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery Type de document : Article/Communication Auteurs : Kasper Johansen, Auteur ; Qibin Duan, Auteur ; Yu-Hsuan Tu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 28 - 40 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Australie
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données multitemporelles
[Termes IGN] image captée par drone
[Termes IGN] image multibande
[Termes IGN] image Worldview
[Termes IGN] production agricole végétale
[Termes IGN] surveillance de la végétationRésumé : (auteur) Australia is one of the world’s largest producers of macadamia nuts. As macadamia trees can take up to 15 years to mature and produce maximum yield, it is important to optimize tree condition. Field based assessment of macadamia tree condition is time-consuming and often inconsistent. Using remotely sensed imagery may allow for faster, more extensive, and more consistent assessment of macadamia tree condition. To identify individual macadamia tree crowns, high spatial resolution imagery is required. Hence, the objective of this work was to develop and test an approach to map the condition of individual macadamia tree crowns using both multi-spectral Unmanned Aerial Vehicle (UAV) and WorldView-3 imagery for different macadamia varieties and three different sites located near Bundaberg, Australia. A random forest classifier, based on all available spectral bands and selected vegetation indices was used to predict five condition categories, ranging from excellent (category 1) to poor (category 5). Various combinations of the developed models were tested between the three sites and over time. The results showed that the multi-spectral WorldView-3 imagery produced the lowest out of bag (OOB) classification errors in most cases. However, for both the UAV and the WorldView-3 imagery, more than 98.5% of predicted macadamia condition categories were either correctly mapped or offset by a single category out of the five condition categories (excellent, good, moderate, fair and poor) for trees of the same variety and at one point in time. Multi-temporally, the WorldView-3 imagery performed better than the UAV data for predicting the condition of the same macadamia tree variety. Applying a model from one site to another site with the same macadamia tree variety produced OOB classification between 31.20 and 42.74%, but with >98.63% of trees predicted within a single condition category. Importantly, models trained based on one type of macadamia tree variety could not be successfully applied to a site with another variety. The developed classification models may be used as a decision and management support tool for the macadamia industry to inform management practices and improve on-demand irrigation, fertilization, and pest inspection at the individual tree level. Numéro de notice : A2020-277 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.01 Date de publication en ligne : 20/05/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.017 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95093
in ISPRS Journal of photogrammetry and remote sensing > vol 165 (July 2020) . - pp 28 - 40[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2020071 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020073 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020072 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Roles of horizontal and vertical tree canopy structure in mitigating daytime and nighttime urban heat island effects / Jike Chen in International journal of applied Earth observation and geoinformation, vol 89 (July 2020)
[article]
Titre : Roles of horizontal and vertical tree canopy structure in mitigating daytime and nighttime urban heat island effects Type de document : Article/Communication Auteurs : Jike Chen, Auteur ; Shuanggen Jin, Auteur ; Peijun Du, Auteur Année de publication : 2020 Article en page(s) : n° 102060 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] arbre urbain
[Termes IGN] canopée
[Termes IGN] carte de la végétation
[Termes IGN] couvert forestier
[Termes IGN] données lidar
[Termes IGN] ilot thermique urbain
[Termes IGN] modèle numérique de terrain
[Termes IGN] Nankin (Kiangsou)
[Termes IGN] occupation du sol
[Termes IGN] régression linéaire
[Termes IGN] semis de points
[Termes IGN] température au solRésumé : (auteur) The urban heat island (UHI) is increasingly recognized as a serious, worldwide problem because of urbanization and climate change. Urban vegetation is capable of alleviating UHI and improving urban environment by shading together with evapotranspiration. While the impacts of abundance and spatial configuration of vegetation on land surface temperature (LST) have been widely examined, very little attention has been paid to the role of vertical structure of vegetation in regulating LST. In this study, we investigated the relationships between horizontal/vertical structure characteristics of urban tree canopy and LST as well as diurnal divergence in Nanjing City, China, with the help of high resolution vegetation map, Light Detection and Ranging (LiDAR) data and various statistical analysis methods. The results indicated that composition, configuration and vertical structure of tree canopy were all significantly related to both daytime LST and nighttime LST. Tree canopy showed stronger influence on LST during the day than at night. Note that the contribution of composition of tree canopy to explaining spatial heterogeneity of LST, regardless of day and night, was the highest, followed by vertical structure and configuration. Combining composition, configuration and vertical structure of tree canopy can take advantage of their respective advantages, and best explain variation in both daytime LST and nighttime LST. As for the independent importance of factors affecting spatial variation of LST, percent cover of tree canopy (PLAND), mean tree canopy height (TH_Mean), amplitude of tree canopy height (TA) and patch cohesion index (COHESION) were the most influential during the day, while the most important variables were PLAND, maximum height of tree canopy (TH_Max), variance of tree canopy height (TH_SD) and COHESION at night. This research extends our understanding of the impacts of urban trees on the UHI effect from the horizontal to three-dimensional space. In addition, it may offer sustainable and effective strategies for urban designers and planners to cope with increasing temperature. Numéro de notice : A2020-715 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2020.102060 Date de publication en ligne : 25/02/2020 En ligne : https://doi.org/10.1016/j.jag.2020.102060 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96285
in International journal of applied Earth observation and geoinformation > vol 89 (July 2020) . - n° 102060[article]Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data / Johannes Schumacher in Forest ecosystems, vol 7 (2020)
[article]
Titre : Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data Type de document : Article/Communication Auteurs : Johannes Schumacher, Auteur ; Marius Hauglin, Auteur ; Rasmus Astrup, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 60 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte forestière
[Termes IGN] dendrochronologie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] gestion forestière
[Termes IGN] hauteur des arbres
[Termes IGN] image Sentinel-MSI
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Norvège
[Termes IGN] peuplement forestier
[Termes IGN] Picea abies
[Termes IGN] régression linéaire
[Termes IGN] semis de pointsRésumé : (auteur) Background: The age of forest stands is critical information for forest management and conservation, for example for growth modelling, timing of management activities and harvesting, or decisions about protection areas. However, area-wide information about forest stand age often does not exist. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. For model development we used more than 4800 plots of the Norwegian National Forest Inventory (NFI) distributed over Norway between latitudes 58° and 65° N in an 18.2 Mha study area. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2, and existing public map data. We performed model validation on an independent data set consisting of 63 spruce stands with known age.
Results: The best modelling strategy was to fit independent linear regression models to each observed site index (SI) level and using a SI prediction map in the application of the models. The most important predictor variable was an upper percentile of the ALS heights, and root mean squared errors (RMSEs) ranged between 3 and 31 years (6% to 26%) for SI-specific models, and 21 years (25%) on average. Mean deviance (MD) ranged between − 1 and 3 years. The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years. Using a mapped SI, which is required for practical applications, RMSE and MD on plot level ranged from 19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%), respectively.
Conclusions: Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. Overall, we obtained good results, especially for stands with high SI. The models could be considered for practical applications, although we see considerable potential for improvements if better SI maps were available.Numéro de notice : A2020-811 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1186/s40663-020-00274-9 Date de publication en ligne : 10/11/2020 En ligne : https://doi.org/10.1186/s40663-020-00274-9 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96983
in Forest ecosystems > vol 7 (2020) . - n° 60[article]Footprint determination of a spectroradiometer mounted on an unmanned aircraft system / Deepak Gautam in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)
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Titre : Footprint determination of a spectroradiometer mounted on an unmanned aircraft system Type de document : Article/Communication Auteurs : Deepak Gautam, Auteur ; Arko Lucieer, Auteur ; Juliane Bendig, Auteur Année de publication : 2020 Article en page(s) : pp 3085 - 3096 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] canopée
[Termes IGN] capteur aérien
[Termes IGN] carte de la végétation
[Termes IGN] chlorophylle
[Termes IGN] classification pixellaire
[Termes IGN] drone
[Termes IGN] échantillonnage
[Termes IGN] empreinte
[Termes IGN] fluorescence
[Termes IGN] géoréférencement
[Termes IGN] photosynthèse
[Termes IGN] point d'appui
[Termes IGN] réflectance spectrale
[Termes IGN] signature spectrale
[Termes IGN] spectroradiomètreRésumé : (auteur) Unmanned aircraft system (UAS)-mounted spectroradiometers offer a new capability to measure spectral reflectance and solar-induced chlorophyll fluorescence at detailed canopy scales. This capability offers potential for upscaling and comparison with airborne and space-borne observations [e.g., the upcoming European Space Agency (ESA) Fluorescence Explorer (FLEX) satellite mission]. In this respect, the accurate spatial characterization and georeferencing of the UAS acquisition footprints are essential to unravel the origin and spatial variability of optical signals acquired within the extent of airborne/satellite pixels. In this article, we present and validate a novel algorithm to georeference the footprint extent of a nonimaging spectroradiometer mounted on a multirotor UAS platform. We used information about the spectroradiometer position and orientation during flight and about topography of observed terrain to calculate the footprint geolocation. In a recursive process, the field of view (FOV) of the spectroradiometer projected on the ground. Multiple FOV ground projections retrieved during a spectroradiometer reading (i.e., a single integration time) were aggregated to calculate the footprint extent. The spatial accuracy of the footprint geolocation was validated by applying the georeferencing algorithm on checkpoint pixels of image acquired with a comounted digital camera. Geolocations of the checkpoint pixels, which served as a proxy for the spectroradiometer footprint, were successfully compared with surveyed ground checkpoints. Finally, the spectral and radiometric quality of UAS-acquired reflectance signatures was compared with ground-measured reflectance of four natural targets (three different types of grass and a bare soil), and a strong agreement was observed. Numéro de notice : A2020-233 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947703 Date de publication en ligne : 06/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947703 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94978
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3085 - 3096[article]Incorporating Sentinel-1 SAR imagery with the MODIS MCD64A1 burned area product to improve burn date estimates and reduce burn date uncertainty in wildland fire mapping / Kristofer Lasko in Geocarto international, vol 35 n° 6 ([01/05/2020])PermalinkDe l’intérêt des cartographies de végétation pour l’apport de connaissance sur la f!ore menacée. L’exemple de la vallée de la Saône aval (01 et 69) / Mathias Voirin in Nouvelles Archives de la Flore jurassienne et du nord-est de la France, n° 18 (2020)PermalinkCombining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine / Thuan Sarzynski in Remote sensing, vol 12 n° 7 (April 2020)PermalinkAn original method for tree species classification using multitemporal multispectral and hyperspectral satellite data / Olga Grigorieva in Silva fennica, vol 54 n° 2 (March 2020)PermalinkAnalyse automatique du couvert végétal pour la gestion du risque végétation en milieu ferroviaire à partir d'imagerie aérienne / Hélène Rouillon (2020)PermalinkCartographie des essences forestières à partir de séries temporelles d’images satellitaires à hautes résolutions : stabilité des prédictions, autocorrélation spatiale et cohérence avec la phénologie observée in situ / Nicolas Karasiak (2020)PermalinkClassification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery / H. Tombul in Journal of geodetic science, vol 10 n° 1 (January 2020)PermalinkComparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 159 (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)PermalinkGéodésie, topographie, cartographie / Bernard Lamy (2020)Permalink