Centre de documentation
scientifique

Comparison of methods for the automatic classification of forest habitat types in the Southern Alps : Application to ecological data from the French national forest inventory / Charlotte Labit in Biodiversity & Conservation, vol 31 n° 13-14 (December 2022)  

Public [article]  inBiodiversity & Conservation > vol 31 n° 13-14 (December 2022) . - pp 3257 - 3283 Titre : Comparison of methods for the automatic classification of forest habitat types in the Southern Alps : Application to ecological data from the French national forest inventory Type de document : Article/Communication Auteurs : Charlotte Labit, Auteur ; Ingrid Bonhême , Auteur ; Sébastien Delhaye , Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : pp 3257 - 3283 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Alpes-de-haute-provence (04) [Termes IGN] Alpes-maritimes (06) [Termes IGN] analyse comparative [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] Drôme (26) [Termes IGN] habitat (nature) [Termes IGN] habitat forestier [Termes IGN] incertitude des données [Termes IGN] inventaire forestier national (données France) [Termes IGN] surveillance écologique [Vedettes matières IGN] Inventaire forestier Mots-clés libres : algorithm inspired by the habitat identification key used in the field Résumé : (auteur) The monitoring of habitats at plant association level, has been developed by the French-National Forest Inventory (NFI) progressively since 2011, whereas ecological and floristic data exist since the mid-1980s. The NFI habitat monitoring is the French tool of surveillance of forest habitats decreed by Natura 2000 Directive (article 11). Determination of plant association in NFI plots concerns all the habitats, whether they are of community interest or not. The objective of this study is to compare different methods of automatic classification of floristic and ecological surveys into forest habitat groups. Indeed, enriching the old surveys, which contain only ecological, floristic and trees data, with information on habitats would increase the accuracy of the calculated statistical results on habitats. The uncertainty of the attribution of a habitat outside the field (ex-situ) by experts was quantified by comparison with the determination in the field (in situ). This result was used as a benchmark to compare to the error rates obtained by two methods of automatic classification: an algorithm inspired by the habitat identification key used in the field and Random forest, a learning classification method. The classification performance was evaluated for three levels of habitat groupings. The results showed that the lower the level of clustering, the higher the error rate. Depending on the classification method used and the level of aggregation, the error rates varied between 5 and 15%. In all cases, the error rates were below the estimated uncertainty of the expert attribution of ex-situ habitat. Numéro de notice : A2022-696 Affiliation des auteurs : IGN+Ext (2020- ) Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10531-022-02487-6 Date de publication en ligne : 25/10/2022 En ligne : https://doi.org/10.1007/s10531-022-02487-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101980 [article]

A data-driven framework to manage uncertainty due to limited transferability in urban growth models / Jingyan Yu in Computers, Environment and Urban Systems, vol 98 (December 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 98 (December 2022) . - n° 101892 Titre : A data-driven framework to manage uncertainty due to limited transferability in urban growth models Type de document : Article/Communication Auteurs : Jingyan Yu, Auteur ; Alex Hagen-Zanker, Auteur ; Naratip Santitissadeekorn, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101892 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] automate cellulaire [Termes IGN] changement d'utilisation du sol [Termes IGN] croissance urbaine [Termes IGN] estimation bayesienne [Termes IGN] étalement urbain [Termes IGN] Europe (géographie politique) [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] modèle stochastique [Termes IGN] simulation dynamique Résumé : (auteur) The processes of urban growth vary in space and time. There is a lack of model transferability, which means that models estimated for a particular study area and period are not necessarily applicable for other periods and areas. This problem is often addressed through scenario analysis, where scenarios reflect different plausible model realisations based typically on expert consultation. This study proposes a novel framework for data-driven scenario development which, consists of three components - (i) multi-area, multi-period calibration, (ii) growth mode clustering, and (iii) cross-application. The framework finds clusters of parameters, referred to as growth modes: within the clusters, parameters represent similar spatial development trajectories; between the clusters, parameters represent substantially different spatial development trajectories. The framework is tested with a stochastic dynamic urban growth model across European functional urban areas over multiple time periods, estimated using a Bayesian method on an open global urban settlement dataset covering the period 1975–2014. The results confirm a lack of transferability, with reduced confidence in the model over the validation period, compared to the calibration period. Over the calibration period the probability that parameters estimated specifically for an area outperforms those for other areas is 96%. However, over an independent validation period, this probability drops to 72%. Four growth modes are identified along a gradient from compact to dispersed spatial developments. For most training areas, spatial development in the later period is better characterized by one of the four modes than their own historical parameters. The results provide strong support for using identified parameter clusters as a tool for data-driven and quantitative scenario development, to reflect part of the uncertainty of future spatial development trajectories. Numéro de notice : A2022-799 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101892 Date de publication en ligne : 08/10/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101966 [article]

Deep learning detects invasive plant species across complex landscapes using Worldview-2 and Planetscope satellite imagery / Thomas A. Lake in Remote sensing in ecology and conservation, vol 8 n° 6 (December 2022)  

Public [article]  inRemote sensing in ecology and conservation > vol 8 n° 6 (December 2022) . - pp 875 - 889 Titre : Deep learning detects invasive plant species across complex landscapes using Worldview-2 and Planetscope satellite imagery Type de document : Article/Communication Auteurs : Thomas A. Lake, Auteur ; Ryan D. Briscoe Runquist, Auteur ; David A. Moeller, Auteur Année de publication : 2022 Article en page(s) : pp 875 - 889 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte d'occupation du sol [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] espèce exotique envahissante [Termes IGN] image Worldview [Termes IGN] PlanetScope [Termes IGN] série temporelle Résumé : (auteur) Effective management of invasive species requires rapid detection and dynamic monitoring. Remote sensing offers an efficient alternative to field surveys for invasive plants; however, distinguishing individual plant species can be challenging especially over geographic scales. Satellite imagery is the most practical source of data for developing predictive models over landscapes, but spatial resolution and spectral information can be limiting. We used two types of satellite imagery to detect the invasive plant, leafy spurge (Euphorbia virgata), across a heterogeneous landscape in Minnesota, USA. We developed convolutional neural networks (CNNs) with imagery from Worldview-2 and Planetscope satellites. Worldview-2 imagery has high spatial and spectral resolution, but images are not routinely taken in space or time. By contrast, Planetscope imagery has lower spatial and spectral resolution, but images are taken daily across Earth. The former had 96.1% accuracy in detecting leafy spurge, whereas the latter had 89.9% accuracy. Second, we modified the CNN for Planetscope with a long short-term memory (LSTM) layer that leverages information on phenology from a time series of images. The detection accuracy of the Planetscope LSTM model was 96.3%, on par with the high resolution, Worldview-2 model. Across models, most false-positive errors occurred near true populations, indicating that these errors are not consequential for management. We identified that early and mid-season phenological periods in the Planetscope time series were key to predicting leafy spurge. Additionally, green, red-edge and near-infrared spectral bands were important for differentiating leafy spurge from other vegetation. These findings suggest that deep learning models can accurately identify individual species over complex landscapes even with satellite imagery of modest spatial and spectral resolution if a temporal series of images is incorporated. Our results will help inform future management efforts using remote sensing to identify invasive plants, especially across large-scale, remote and data-sparse areas. Numéro de notice : A2023-033 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1002/rse2.288 En ligne : https://doi.org/10.1002/rse2.288 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102295 [article]

Discriminating pure Tamarix species and their putative hybrids using field spectrometer / Solomon G. Tesfamichael in Geocarto international, vol 37 n° 25 ([01/12/2022])  

Public [article]  inGeocarto international > vol 37 n° 25 [01/12/2022] . - pp 7733 - 7752 Titre : Discriminating pure Tamarix species and their putative hybrids using field spectrometer Type de document : Article/Communication Auteurs : Solomon G. Tesfamichael, Auteur ; Solomon W. Newete, Auteur ; Elhadi Adam, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 7733 - 7752 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Afrique du sud (état) [Termes IGN] apprentissage automatique [Termes IGN] canopée [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] espèce exotique envahissante [Termes IGN] essence indigène [Termes IGN] Extreme Gradient Machine [Termes IGN] feuille (végétation) [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] image SPOT 6 [Termes IGN] image Worldview [Termes IGN] spectroradiomètre [Termes IGN] Tamarix (genre) Résumé : (auteur) South Africa is home to a native Tamarix species, while two were introduced in the early 1900s to mitigate the effects of mining on soil. The introduced species have spread to other ecosystems resulting in ecological deteriorations. The problem is compounded by hybridization of the species making identification between the native and exotic species difficult. This study investigated the potential of remote sensing in identifying native, non-native and hybrid Tamarix species recorded in South Africa. Leaf- and canopy-level classifications of the species were conducted using field spectroradiometer data that provided two inputs: original hyperspectral data and bands simulated according to Landsat-8, Sentinel-2, SPOT-6 and WorldView-3. The original hyperspectral data yielded high accuracies for leaf- and plot-level discriminations (>90%), while promising accuracies were also obtained using Landsat-8, Sentinel-2 and Worldview-3 simulations (>75%). These findings encourage for investigating the performance of actual space-borne multispectral data in classifying the species. Numéro de notice : A2022-928 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2021.1983033 Date de publication en ligne : 27/09/2021 En ligne : https://doi.org/10.1080/10106049.2021.1983033 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102661 [article]

Effect of climate on cork-ring width and density of Quercus suber L. in Southern Portugal / Augusta Costa in Trees, vol 36 n° 6 (December 2022)  

Public [article]  inTrees > vol 36 n° 6 (December 2022) . - pp 1711 - 1720 Titre : Effect of climate on cork-ring width and density of Quercus suber L. in Southern Portugal Type de document : Article/Communication Auteurs : Augusta Costa, Auteur ; José Graça, Auteur ; Inês Barbosa, Auteur Année de publication : 2022 Article en page(s) : pp 1711 - 1720 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse diachronique [Termes IGN] anneau [Termes IGN] climat méditerranéen [Termes IGN] croissance des arbres [Termes IGN] dendroécologie [Termes IGN] Portugal [Termes IGN] Quercus suber [Termes IGN] volume en bois [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Cork rings have been extensively used in dendroecological studies of the Mediterranean evergreen cork oak (Quercus suber L.). Through measurements of cork-ring width only, strong relationships have been found between cork-ring widths and climate parameters. To our knowledge, cork-ring density, which is an important cork quality attribute, has never been used in any dendroecological study to explore physiological responses of the cork oak to climate change. In this study, we measured cork-ring width and density over 50 years (1962–2013), corresponding to five consecutive cork harvests, and analyzed their inter-annual fluctuations in eight trees from two different sites, a wetter peneplain area (Benavente) and a drier mountainous area (Grândola). Our results revealed a statistically significant correlation between cork-ring width and density (p  Numéro de notice : A2022-915 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s00468-022-02321-0 Date de publication en ligne : 29/06/2022 En ligne : https://doi.org/10.1007/s00468-022-02321-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102351 [article]

Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale / Shengwu Qin in Natural Hazards, vol 114 n° 3 (December 2022)  

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Fast calculation of gravitational effects using tesseroids with a polynomial density of arbitrary degree in depth / Fang Ouyang in Journal of geodesy, vol 96 n° 12 (December 2022)  

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Fusion of SAR and multi-spectral time series for determination of water table depth and lake area in peatlands / Katrin Krzepek in PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, vol 90 n° 6 (December 2022)  

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Geographic named entity recognition by employing natural language processing and an improved BERT model / Liufeng Tao in ISPRS International journal of geo-information, vol 11 n° 12 (December 2022)  

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High-precision positioning using plane-constrained RTK method in urban environments / Chen Zhuang in Navigation : journal of the Institute of navigation, vol 69 n° 4 (Fall 2022)  

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Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling / Saeid Janizadeh in Geocarto international, vol 37 n° 25 ([01/12/2022])  

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Identifying spurious cycle slips based on iterative filtering under disturbed ionospheric conditions for undifferenced GNSS observations / Yan Xiang in Advances in space research, vol 70 n° 11 (December 2022)  

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Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning / Aboubakar Sani-Mohammed in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)  

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Integration of geospatial technologies with multiple regression model for urban land use land cover change analysis and its impact on land surface temperature in Jimma City, southwestern Ethiopia / Mitiku Badasa Moisa in Applied geomatics, vol 14 n° 4 (December 2022)  

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Integration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) / Vahid Nasiri in Arabian Journal of Geosciences, vol 15 n° 24 (December 2022)  

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Modelling evacuation preparation time prior to floods: A machine learning approach / R. Sreejith in Sustainable Cities and Society, vol 87 (December 2022)  

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Multi-frequency simulation of ionospheric scintillation using a phase-screen model / Fernando D. Nunes in Navigation : journal of the Institute of navigation, vol 69 n° 4 (Fall 2022)  

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Navigation and Ionosphere Characterization Using High-Frequency Signals: A Performance Analysis / Yoav Baumgarten in Navigation : journal of the Institute of navigation, vol 69 n° 4 (Fall 2022)  

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A new data-adaptive network design methodology based on the k-means clustering and modified ISODATA algorithm for regional gravity field modeling via spherical radial basis functions / Rasit Ulug in Journal of geodesy, vol 96 n° 12 (December 2022)  

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A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds / Xiaoqiang Liu in Remote sensing of environment, vol 282 (December 2022)  

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On study of the Earth topography correction for the GRACE surface mass estimation / Fan Yang in Journal of geodesy, vol 96 n° 12 (December 2022)  

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Progressive collapse of dual-line rivers based on river segmentation considering cartographic generalization rules / Fubing Zhang in ISPRS International journal of geo-information, vol 11 n° 12 (December 2022)  

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Reconstructing compact building models from point clouds using deep implicit fields / Zhaiyu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 194 (December 2022)  

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Robust modeling of GNSS orbit and clock error dynamics / Elisa Gallon in Navigation : journal of the Institute of navigation, vol 69 n° 4 (Fall 2022)  

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Sea surface temperature prediction model for the Black Sea by employing time-series satellite data: a machine learning approach / Hakan Oktay Aydınlı in Applied geomatics, vol 14 n° 4 (December 2022)  

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