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Landslide susceptibility assessment considering spatial agglomeration and dispersion characteristics: A case study of Bijie City in Guizhou Province, China / Kezhen Yao in ISPRS International journal of geo-information, vol 11 n° 5 (May 2022)
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[article]
Titre : Landslide susceptibility assessment considering spatial agglomeration and dispersion characteristics: A case study of Bijie City in Guizhou Province, China Type de document : Article/Communication Auteurs : Kezhen Yao, Auteur ; Saini Yang, Auteur ; Shengnan Wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 269 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] cartographie des risques
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] dispersion
[Termes IGN] effondrement de terrain
[Termes IGN] Extreme Gradient Machine
[Termes IGN] modèle de simulation
[Termes IGN] régression linéaire
[Termes IGN] risque naturel
[Termes IGN] vulnérabilitéRésumé : (auteur) Landslide susceptibility assessment serves as a critical scientific reference for geohazard control, land use, and sustainable development planning. The existing research has not fully considered the potential impact of the spatial agglomeration and dispersion of landslides on assessments. This issue may cause a systematic evaluation bias when the field investigation data are insufficient, which is common due to limited human resources. Accordingly, this paper proposes two novel strategies, including a clustering algorithm and a preprocessing method, for these two ignored features to strengthen assessments, especially in high-susceptibility regions. Multiple machine learning models are compared in a case study of the city of Bijie (Guizhou Province, China). Then we generate the optimal susceptibility map and conduct two experiments to test the validity of the proposed methods. The primary conclusions of this study are as follows: (1) random forest (RF) was superior to other algorithms in the recognition of high-susceptibility areas and the portrayal of local spatial features; (2) the susceptibility map incorporating spatial feature messages showed a noticeable improvement over the spatial distribution and gradual change of susceptibility, as well as the accurate delineation of critical hazardous areas and the interpretation of historical hazards; and (3) the spatial distribution feature had a significant positive effect on modeling, as the accuracy increased by 5% and 10% after including the spatial agglomeration and dispersion consideration in the RF model, respectively. The benefit of the agglomeration is concentrated in high-susceptibility areas, and our work provides insight to improve the assessment accuracy in these areas, which is critical to risk assessment and prevention activities. Numéro de notice : A2022-371 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11050269 Date de publication en ligne : 19/04/2022 En ligne : https://doi.org/10.3390/ijgi11050269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100613
in ISPRS International journal of geo-information > vol 11 n° 5 (May 2022) . - n° 269[article]Detecting individuals' spatial familiarity with urban environments using eye movement data / Hua Liao in Computers, Environment and Urban Systems, vol 93 (April 2022)
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Titre : Detecting individuals' spatial familiarity with urban environments using eye movement data Type de document : Article/Communication Auteurs : Hua Liao, Auteur ; Wendi Zhao, Auteur ; Changbo Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101758 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse visuelle
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] navigation pédestre
[Termes IGN] oculométrie
[Termes IGN] service fondé sur la position
[Termes IGN] zone urbaine
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) The spatial familiarity of environments is an important high-level user context for location-based services (LBS). Knowing users' familiarity level of environments is helpful for enabling context-aware LBS that can automatically adapt information services according to users' familiarity with the environment. Unlike state-of-the-art studies that used questionnaires, sketch maps, mobile phone positioning (GPS) data, and social media data to measure spatial familiarity, this study explored the potential of a new type of sensory data - eye movement data - to infer users' spatial familiarity of environments using a machine learning approach. We collected 38 participants' eye movement data when they were performing map-based navigation tasks in familiar and unfamiliar urban environments. We trained and cross-validated a random forest classifier to infer whether the users were familiar or unfamiliar with the environments (i.e., binary classification). By combining basic statistical features and fixation semantic features, we achieved a best accuracy of 81% in a 10-fold classification and 70% in the leave-one-task-out (LOTO) classification. We found that the pupil diameter, fixation dispersion, saccade duration, fixation count and duration on the map were the most important features for detecting users' spatial familiarity. Our results indicate that detecting users' spatial familiarity from eye tracking data is feasible in map-based navigation and only a few seconds (e.g., 5 s) of eye movement data is sufficient for such detection. These results could be used to develop context-aware LBS that adapt their services to users' familiarity with the environments. Numéro de notice : A2022-121 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101758 Date de publication en ligne : 21/01/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101758 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99663
in Computers, Environment and Urban Systems > vol 93 (April 2022) . - n° 101758[article]Recent changes in the climate-growth response of European larch (Larix decidua Mill.) in the Polish Sudetes / Malgorzata Danek in Trees, vol 36 n° 2 (April 2022)
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Titre : Recent changes in the climate-growth response of European larch (Larix decidua Mill.) in the Polish Sudetes Type de document : Article/Communication Auteurs : Malgorzata Danek, Auteur ; Tomasz Danek, Auteur Année de publication : 2022 Article en page(s) : pp 803 - 817 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] altitude
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données dendrométriques
[Termes IGN] Larix decidua
[Termes IGN] modèle de croissance végétale
[Termes IGN] Pologne
[Termes IGN] stress hydrique
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Larches in the Sudetes are very sensitive to the currently changing climatic factors, and an extreme negative response to drought is observed. In this study, temporal changes in the climate-growth relationship of European larch were analyzed using moving-window correlation. Change-point detection analysis was performed to determine whether there is a temporal connection between tree-ring growth responses and changes in climatic factors trends. The Random Forest predictor importance determination method was used to establish the set of climatic factors that influence larch tree-ring growth the most and to show how this set changes over time. Additionally, cluster analysis was applied to find spatial growth patterns and to generalize the growth response of larch. The results indicate that the main clustering factor is altitude. Nevertheless, an increasing unification of the larch’s response to dominant climatic factors is observable throughout the whole study area. This unification is expressed in the increasingly positive and recently dominant effect of May temperature. A progressively negative influence of the temperature in the summer and late autumn of the year preceding growth was observed, as was an increasing influence of water availability in the summer months. The study indicates that there is a connection between the observed changes and the recent rapid rise in temperature, which has consequently had a negative influence on water availability. The growth of this tree species in the Sudetes is expected to be very limited in the future due to its sensitivity to drought, the predicted increase in temperatures and thermal extremes, and the decrease of the share of summer precipitation in the annual total. Numéro de notice : A2022-316 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s00468-021-02251-3 Date de publication en ligne : 09/12/2021 En ligne : http://dx.doi.org/10.1007/s00468-021-02251-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100420
in Trees > vol 36 n° 2 (April 2022) . - pp 803 - 817[article]Species level classification of Mediterranean sparse forests-maquis formations using Sentinel-2 imagery / Semiha Demirbaş Çağlayana in Geocarto international, vol 37 n° 6 ([01/04/2022])
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Titre : Species level classification of Mediterranean sparse forests-maquis formations using Sentinel-2 imagery Type de document : Article/Communication Auteurs : Semiha Demirbaş Çağlayana, Auteur ; Ugur Murat Leloglu, Auteur ; Christian Ginzler, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1587 - 1606 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] Arbutus unedo
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données multitemporelles
[Termes IGN] Erica (genre)
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] forêt méditerranéenne
[Termes IGN] Genista (genre)
[Termes IGN] gestion forestière durable
[Termes IGN] image Sentinel-MSI
[Termes IGN] maquis
[Termes IGN] Olea europaea
[Termes IGN] TurquieRésumé : (auteur) Essential forest ecosystem services can be assessed by better understanding the diversity of vegetation, specifically those of Mediterranean region. A species level classification of maquis would be useful in understanding vegetation structure and dynamics, which would be an indicator of degradation or succession in the region. Although remote sensing was regularly used for classification in the region, maquis are simply represented as one to three categories based on density or height. To fill this gap, we test the capability of Sentinel-2 imagery, together with selected ancillary variables, for an accurate mapping of the dominant maquis formations. We applied Recursive Feature Selection procedure and used a Random Forest classifier. The algorithm is tested using ground truth collected from site and reached 78% and 93% overall accuracy at species level and physiognomic level, respectively. Our results suggest species level characterization of dominant maquis is possible with Sentinel-2 spatial resolution. Numéro de notice : A2022-475 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1783581 Date de publication en ligne : 09/07/2020 En ligne : https://doi.org/10.1080/10106049.2020.1783581 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100822
in Geocarto international > vol 37 n° 6 [01/04/2022] . - pp 1587 - 1606[article]The integration of multi-source remotely sensed data with hierarchically based classification approaches in support of the classification of wetlands / Aaron Judah in Canadian journal of remote sensing, vol 48 n° 2 (April 2022)
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Titre : The integration of multi-source remotely sensed data with hierarchically based classification approaches in support of the classification of wetlands Type de document : Article/Communication Auteurs : Aaron Judah, Auteur ; Baoxin Hu, Auteur Année de publication : 2022 Article en page(s) : pp 158 - 181 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] classification bayesienne
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-TM
[Termes IGN] image Sentinel-SAR
[Termes IGN] intégration de données
[Termes IGN] modèle numérique de terrain
[Termes IGN] précision de la classification
[Termes IGN] tourbière
[Termes IGN] utilisation du sol
[Termes IGN] zone humideRésumé : (auteur) Methodologies were developed to classify wetlands (Open Bog, Treed Bog, Open Fen, Treed Fen, and Swamps) from remotely sensed data using advanced classification algorithms through two hierarchical approaches. The data utilized included multispectral optical and thermal data (Landsat-5, and Landsat-8), radar imagery (Sentinel-1), and a digital elevation model. Goals were to determine the best way to combine imagery to classify wetlands through hierarchically based classification approaches to produce more accurate and efficient maps compared to standard classification. Algorithms used were Random Forest (RF), and Naïve Bayes. A hierarchically based RF classification methodology produced the most accurate classification result (91.94%). The hierarchically based approaches also improved classification accuracies for low-quality data, as defined through feature analysis, when compared to a nonhierarchical classifier. The hierarchical approaches also produced a significant increase in classification accuracy for the Naïve Bayes classifier versus the standard approach (∼12% increase) while not significantly increasing computation time – comparable in accuracy to the RF tests for around 20% the computational effort. Preselection of spectral bands, polarizations and other input parameters (Normalized Difference Vegetation Index, Normalized Difference Water Index, albedo, slope, etc.) using log-normal or RF variable importance analysis was very effective at identifying low-quality features and features which were of higher quality. Numéro de notice : A2022-372 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/07038992.2021.1967732 Date de publication en ligne : 13/11/2021 En ligne : https://doi.org/10.1080/07038992.2021.1967732 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100614
in Canadian journal of remote sensing > vol 48 n° 2 (April 2022) . - pp 158 - 181[article]Urban land cover/use mapping and change detection analysis using multi-temporal Landsat OLI with Lidar-DEM and derived TPI / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 4 (April 2022)
PermalinkClassification of Eucalyptus plantation Site Index (SI) and Mean Annual Increment (MAI) prediction using DEM-based geomorphometric and climatic variables in Brazil / Aliny Aparecida Dos Reis in Geocarto international, vol 37 n° 5 ([01/03/2022])
PermalinkEvaluating Sentinel-1A datasets for rice leaf area index estimation based on machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 5 ([01/03/2022])
PermalinkLand surface phenology retrieval through spectral and angular harmonization of Landsat-8, Sentinel-2 and Gaofen-1 data / Jun Lu in Remote sensing, vol 14 n° 5 (March-1 2022)
PermalinkDynamic modelling of rice leaf area index with quad-source optical imagery and machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 3 ([01/02/2022])
PermalinkMapping abundance distributions of allergenic tree species in urbanized landscapes: A nation-wide study for Belgium using forest inventory and citizen science data / Sébastien Dujardin in Landscape and Urban Planning, vol 218 (February 2022)
PermalinkPlanning of commercial thinnings using machine learning and airborne Lidar data / Tauri Arumäe in Forests, vol 13 n° 2 (February 2022)
PermalinkSiamese Adversarial Network for image classification of heavy mineral grains / Huizhen Hao in Computers & geosciences, vol 159 (February 2022)
Permalink3D modeling of urban area based on oblique UAS images - An end-to-end pipeline / Valeria-Ersilia Oniga in Remote sensing, vol 14 n° 2 (January-2 2022)
PermalinkVariable selection for estimating individual tree height using genetic algorithm and random forest / Evandro Nunes Miranda in Forest ecology and management, vol 504 (January-15 2022)
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