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Analysis of structure from motion and airborne laser scanning features for the evaluation of forest structure / Alejandro Rodríguez-Vivancos in European Journal of Forest Research, vol 141 n° 3 (June 2022)
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Titre : Analysis of structure from motion and airborne laser scanning features for the evaluation of forest structure Type de document : Article/Communication Auteurs : Alejandro Rodríguez-Vivancos, Auteur ; José Antonio Manzanera, Auteur ; Susana Martín-Fernández, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 447 - 465 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse de variance
[Termes IGN] Bootstrap (statistique)
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] erreur d'échantillon
[Termes IGN] Espagne
[Termes IGN] forêt inéquienne
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] lasergrammétrie
[Termes IGN] modèle de régression
[Termes IGN] modèle numérique de terrain
[Termes IGN] Pinus sylvestris
[Termes IGN] régression linéaire
[Termes IGN] semis de points
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] structure-from-motionRésumé : (auteur) Airborne Laser Scanning (ALS) is widely extended in forest evaluation, although photogrammetry-based Structure from Motion (SfM) has recently emerged as a more affordable alternative. Return cloud metrics and their normalization using different typologies of Digital Terrain Models (DTM), either derived from SfM or from private or free access ALS, were evaluated. In addition, the influence of the return density (0.5–6.5 returns m-2) and the sampling intensity (0.3–3.4%) on the estimation of the most common stand structure variables were also analysed. The objective of this research is to gather all these questions in the same document, so that they serve as support for the planning of forest management. This study analyses the variables collected from 60 regularly distributed circular plots (r = 18 m) in a 150-ha of uneven-aged Scots pine stand. Results indicated that both ALS and SfM can be equally used to reduce the sampling error in the field inventories, but they showed differences when estimating the stand structure variables. ALS produced significantly better estimations than the SfM metrics for all the variables of interest, as well as the ALS-based normalization. However, the SfM point cloud produced better estimations when it was normalized with its own DTM, except for the dominant height. The return density did not have significant influence on the estimation of the stand structure variables in the range studied, while higher sampling intensities decreased the estimation errors. Nevertheless, these were stabilized at certain intensities depending on the variance of the stand structure variable. Numéro de notice : A2022-417 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1007/s10342-022-01447-7 Date de publication en ligne : 12/04/2022 En ligne : https://doi.org/10.1007/s10342-022-01447-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100780
in European Journal of Forest Research > vol 141 n° 3 (June 2022) . - pp 447 - 465[article]Mapping uncertain geographical attributes: incorporating robustness into choropleth classification design / Wangshu Mu in International journal of geographical information science IJGIS, vol 34 n° 11 (November 2020)
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Titre : Mapping uncertain geographical attributes: incorporating robustness into choropleth classification design Type de document : Article/Communication Auteurs : Wangshu Mu, Auteur ; Daoqin Tong, Auteur Année de publication : 2020 Article en page(s) : pp 2204 - 2224 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie
[Termes IGN] attribut géomètrique
[Termes IGN] carte choroplèthe
[Termes IGN] conception cartographique
[Termes IGN] erreur d'échantillon
[Termes IGN] incertitude d'attribut
[Termes IGN] incertitude des données
[Termes IGN] inférence statistique
[Termes IGN] méthode robuste
[Termes IGN] optimisation (mathématiques)Résumé : (auteur) Choropleth mapping provides a simple but effective visual presentation of geographical data. Traditional choropleth mapping methods assume that data to be displayed are certain. This may not be true for many real-world problems. For example, attributes generated based on surveys may contain sampling and non-sampling error, and results generated using statistical inferences often come with a certain level of uncertainty. In recent years, several studies have incorporated uncertain geographical attributes into choropleth mapping with a primary focus on identifying the most homogeneous classes. However, no studies have yet accounted for the possibility that an areal unit might be placed in a wrong class due to data uncertainty. This paper addresses this issue by proposing a robustness measure and incorporating it into the optimal design of choropleth maps. In particular, this study proposes a discretization method to solve the new optimization problem along with a novel theoretical bound to evaluate solution quality. The new approach is applied to map the American Community Survey data. Test results suggest a tradeoff between within-class homogeneity and robustness. The study provides an important perspective on addressing data uncertainty in choropleth map design and offers a new approach for spatial analysts and decision-makers to incorporate robustness into the mapmaking process. Numéro de notice : A2020-614 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1726921 Date de publication en ligne : 16/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1726921 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95987
in International journal of geographical information science IJGIS > vol 34 n° 11 (November 2020) . - pp 2204 - 2224[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2020111 RAB Revue Centre de documentation En réserve L003 Disponible How do species and data characteristics affect species distribution models and when to use environmental filtering? / Lukáš Gábor in International journal of geographical information science IJGIS, vol 34 n° 8 (August 2020)
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Titre : How do species and data characteristics affect species distribution models and when to use environmental filtering? Type de document : Article/Communication Auteurs : Lukáš Gábor, Auteur ; Vítězslav Moudrý, Auteur ; Vojtěch Barták, Auteur ; Vincent Lecours, Auteur Année de publication : 2020 Article en page(s) : pp 1567 - 1584 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] distribution spatiale
[Termes IGN] données environnementales
[Termes IGN] données localisées
[Termes IGN] échantillonnage (statistique)
[Termes IGN] entropie maximale
[Termes IGN] erreur d'échantillon
[Termes IGN] filtrage d'information
[Termes IGN] interaction spatialeRésumé : (auteur) Species distribution models (SDMs) are widely used in ecology and conservation. However, their performance is known to be affected by a variety of factors related to species occurrence characteristics. In this study, we used a virtual species approach to overcome the difficulties associated with testing of combined effects of those factors on performance of presence-only SDMs when using real data. We focused on the individual and combined roles of factors related to response variable (i.e. sample size, sampling bias, environmental filtering, species prevalence, and species response to environmental gradients). Results suggest that environmental filtering is not necessarily helpful and should not be performed blindly, without evidence of bias in species occurrences. The more gradual the species response to environmental gradients is, the greater is the model sensitivity to an inappropriate use of environmental filtering, although this sensitivity decreases with higher species prevalence. Results show that SDMs are affected to the greatest degree by the species response to environmental gradients, species prevalence, and sample size. Models’ accuracy decreased with sample size below 300 presences. Furthermore, a high level of interactions among individual factors was observed. Ignoring the combined effects of factors may lead to misleading outcomes and conclusions. Numéro de notice : A2020-414 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1615070 Date de publication en ligne : 14/05/2019 En ligne : https://doi.org/10.1080/13658816.2019.1615070 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95465
in International journal of geographical information science IJGIS > vol 34 n° 8 (August 2020) . - pp 1567 - 1584[article]Hyperspectral classification with noisy label detection via superpixel-to-pixel weighting distance / Bing Tu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)
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Titre : Hyperspectral classification with noisy label detection via superpixel-to-pixel weighting distance Type de document : Article/Communication Auteurs : Bing Tu, Auteur ; Chengle Zhou, Auteur ; Danbing He, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 4116 - 4131 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] classification barycentrique
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données étiquetées d'entrainement
[Termes IGN] erreur d'échantillon
[Termes IGN] image hyperspectrale
[Termes IGN] pondération
[Termes IGN] précision de la classification
[Termes IGN] superpixelRésumé : (auteur) Classification is an important technique for remotely sensed hyperspectral image (HSI) exploitation. Often, the presence of wrong (noisy) labels presents a drawback for accurate supervised classification. In this article, we introduce a new framework for noisy label detection that combines a superpixel-to-pixel weighting distance (SPWD) and density peak clustering. The proposed method is able to accurately detect and remove noisy labels in the training set before HSI classification. It considers two weak assumptions when exploiting the spectral–spatial information contained in the HSI: 1) all the pixels in a superpixel belong to the same class and 2) close pixels in spectral space have the same label. The proposed method consists of the following steps. First, a superpixel segmentation step is used to obtain self-adaptive spatial information for each training sample. Then, a metric is utilized to measure the spectral distance information between each superpixel and pixel. Meanwhile, in order to overcome the first weak assumption, we use K nearest neighbors to obtain the closest neighborhoods of pixels around each superpixel, and a Gaussian weight is employed to mitigate the second weak assumption by adapting the original distance information. Next, the noisy labels in the original training set are removed by a density threshold-based decision function. Finally, the support vector machine (SVM) classifier is employed to evaluate the effectiveness of the proposed SPWD detection method in terms of classification accuracy. Experiments performed on several real HSI data sets demonstrate that the method can effectively improve the performance of classifiers trained with noisy training sets in terms of classification accuracy. Numéro de notice : A2020-283 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2961141 Date de publication en ligne : 13/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2961141 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95105
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) . - pp 4116 - 4131[article]A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena / Guiming Zhang in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)
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Titre : A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena Type de document : Article/Communication Auteurs : Guiming Zhang, Auteur ; A - Xing Zhu, Auteur Année de publication : 2019 Article en page(s) : pp 1873 - 1893 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Aves
[Termes IGN] carte thématique
[Termes IGN] distribution spatiale
[Termes IGN] données localisées des bénévoles
[Termes IGN] échantillon
[Termes IGN] erreur d'échantillon
[Termes IGN] erreur de positionnement
[Termes IGN] erreur systématique
[Termes IGN] habitat (nature)
[Termes IGN] modèle de simulation
[Termes IGN] phénomène géographique
[Termes IGN] pondération
[Termes IGN] précision de localisation
[Termes IGN] régression logistique
[Termes IGN] representativité
[Termes IGN] science citoyenne
[Termes IGN] Wisconsin (Etats-Unis)Résumé : (auteur) Volunteered geographic information (VGI) contains valuable field observations that represent the spatial distribution of geographic phenomena. As such, it has the potential to provide regularly updated low-cost field samples for predictively mapping the spatial variations of geographic phenomena. The predictive mapping of geographic phenomena often requires representative samples for high mapping accuracy, but samples consisting of VGI observations are often not representative as they concentrate on specific geographic areas (i.e. spatial bias) due to the opportunistic nature of voluntary observation efforts. In this article, we propose a representativeness-directed approach to mitigate spatial bias in VGI for predictive mapping. The proposed approach defines and quantifies sample representativeness by comparing the probability distributions of sample locations and the mapping area in the environmental covariate space. Spatial bias is mitigated by weighting the sample locations to maximize their representativeness. The approach is evaluated using species habit suitability mapping as a case study. The results show that the accuracy of predictive mapping using weighted sample locations is higher than using unweighted sample locations. A positive relationship between sample representativeness and mapping accuracy is also observed, suggesting that sample representativeness is a valid indicator of predictive mapping accuracy. This approach mitigates spatial bias in VGI to improve predictive mapping accuracy. Numéro de notice : A2019-392 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1615071 Date de publication en ligne : 10/05/2019 En ligne : https://doi.org/10.1080/13658816.2019.1615071 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93490
in International journal of geographical information science IJGIS > vol 33 n° 9 (September 2019) . - pp 1873 - 1893[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-2019091 RAB Revue Centre de documentation En réserve L003 Disponible 079-2019092 RAB Revue Centre de documentation En réserve L003 Disponible The accuracy of grid digital elevation models linearly constructed from scattered sample data / F. Aguilar in International journal of geographical information science IJGIS, vol 20 n° 2 (february 2006)
PermalinkRepresenting and reducing error in natural-resource classification using model combination / Zhi Huang in International journal of geographical information science IJGIS, vol 19 n° 5 (may 2005)
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