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Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests / Sruthi M. Krishna Moorthy in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)
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Titre : Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests Type de document : Article/Communication Auteurs : Sruthi M. Krishna Moorthy, Auteur ; Kim Calders, Auteur ; Matheus B. Vicari, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3057 - 3070 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage dirigé
[Termes IGN] atmosphère terrestre
[Termes IGN] canopée
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] faisceau laser
[Termes IGN] feuille (végétation)
[Termes IGN] foresterie
[Termes IGN] forêt de feuillus
[Termes IGN] forêt tropicale
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] précision de la classification
[Termes IGN] Python (langage de programmation)
[Termes IGN] semis de points
[Termes IGN] transfert radiatifRésumé : (auteur) Accurately classifying 3-D point clouds into woody and leafy components has been an interest for applications in forestry and ecology including the better understanding of radiation transfer between canopy and atmosphere. The past decade has seen an increase in the methods attempting to classify leaves and wood in point clouds based on radiometric or geometric features. However, classification purely based on radiometric features is sensor-specific, and the method by which the local neighborhood of a point is defined affects the accuracy of classification based on geometric features. Here, we present a leaf-wood classification method combining geometrical features defined by radially bounded nearest neighbors at multiple spatial scales in a machine learning model. We compared the performance of three different machine learning models generated by the random forest (RF), XGBoost, and lightGBM algorithms. Using multiple spatial scales eliminates the need for an optimal neighborhood size selection and defining the local neighborhood by radially bounded nearest neighbors makes the method broadly applicable for point clouds of varying quality. We assessed the model performance at the individual tree- and plot-level on field data from tropical and deciduous forests, as well as on simulated point clouds. The method has an overall average accuracy of 94.2% on our data sets. For other data sets, the presented method outperformed the methods in literature in most cases without the need for additional postprocessing steps that are needed in most of the existing methods. We provide the entire framework as an open-source python package. Numéro de notice : A2020-232 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947198 Date de publication en ligne : 31/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947198 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94970
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3057 - 3070[article]Incorporating landscape character in cork oak forest expansion in Sardinia: constraint or opportunity? / I.N. Vogiatzakis in Forests, vol 11 n° 5 (May 2020)
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Titre : Incorporating landscape character in cork oak forest expansion in Sardinia: constraint or opportunity? Type de document : Article/Communication Auteurs : I.N. Vogiatzakis, Auteur ; Geoffrey H. Griffiths, Auteur ; Maria Zomeni, Auteur Année de publication : 2020 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] biodiversité végétale
[Termes IGN] changement d'utilisation du sol
[Termes IGN] habitat forestier
[Termes IGN] modélisation spatiale
[Termes IGN] occupation du sol
[Termes IGN] paysage
[Termes IGN] protection des forêts
[Termes IGN] Quercus suber
[Termes IGN] Sardaigne
[Termes IGN] site Natura 2000Résumé : (auteur) Cork oak (Quercus suber) is a declining woodland species across the island of Sardinia, despite its former economic importance for wine production and its significance for biodiversity. In particular, cork oak forests (COFs) on the island have seen a 29% decrease in the past 45 years. A spatial GIS model was developed to determine suitability for the expansion of cork oak forests on the island. The model uses a set of simple spatial decision rules based on principles of landscape ecology and expert opinion to assign a suitability score for pure cork oak forests to every land use parcel in Sardinia. These rules include the type of existing land parcel, its size, distance to existing cork oak forest, and the area of seminatural habitats in its neighborhood. This was coupled with a map of landscape types to assist with the development of policy for the protection of cork oak forests across Sardinia. The results show that there is an area of 116,785 ha potentially suitable for cork oak forest expansion in Sardinia, with the largest area of potential habitat on granitic mountains. There is a substantial overall agreement (Cohen’s kappa = 0.61) between the suitability map produced and the historical reference map. The model is flexible and can be rerun to reflect changes in policy relating to agri-environmental targets for habitats and species. Numéro de notice : A2020-653 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article DOI : 10.3390/f11050593 Date de publication en ligne : 24/05/2020 En ligne : https://doi.org/10.3390/f11050593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96113
in Forests > vol 11 n° 5 (May 2020) . - 18 p.[article]De 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)
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Titre : De 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) Type de document : Article/Communication Auteurs : Mathias Voirin, Auteur ; Eric Boucard, Auteur Année de publication : 2020 Article en page(s) : pp 91 - 114 Note générale : bibliographie Langues : Français (fre) Descripteur : [Termes IGN] carte de la végétation
[Termes IGN] phytosociologie
[Termes IGN] Saône (rivière)
[Termes IGN] site Natura 2000
[Termes IGN] unité phytosociologique
[Vedettes matières IGN] Ecologie forestièreRésumé : (auteur) En 2019 et 2020, la réalisation de deux cartographies de végétations (à l’association) sur deux sites Natura 2000 de la vallée de la Saône aval par les auteurs, sur une surface cumulée de 5 500 ha, et ce, dans le cadre de marchés publics, a permis de compiler un nombre important d’observations de plantes patrimoniales dont certaines très rares et inscrites sur liste rouge en Rhône-Alpes. Les auteurs dressent un catalogue des espèces recensées les plus patrimoniales par commune. Les associations phytosociologiques dans lesquelles ces espèces ont été observées, sont également indiquées. Les auteurs montrent aussi que, grâce à une pression d’observation importante et un parcours systématique de chaque parcelle, de nombreuses observations d’espèces patrimoniales ont pu être faites dont beaucoup inédites, permettant d’actualiser leur répartition, qu’il s’agisse d’espèces très menacées comme: Crypsis Alopecuroides, ... Quelques cartes de répartition à l’échelle du Val de Saône rhône-alpin illustrent ces informations. Numéro de notice : A2020-886 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : sans En ligne : https://cbnfc-ori.org/espace-documentation/de-l-interet-des-cartographies-de-veg [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103079
in Nouvelles Archives de la Flore jurassienne et du nord-est de la France > n° 18 (2020) . - pp 91 - 114[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)
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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)
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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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