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A GIS- and AHP-based approach to map fire risk: a case study of Kuan Kreng peat swamp forest, Thailand / Narissara Nuthammachot in Geocarto international, vol 36 n° 2 ([01/02/2021])
[article]
Titre : A GIS- and AHP-based approach to map fire risk: a case study of Kuan Kreng peat swamp forest, Thailand Type de document : Article/Communication Auteurs : Narissara Nuthammachot, Auteur ; Dimitris Stratoulias, Auteur Année de publication : 2021 Article en page(s) : pp 212 - 225 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] cartographie des risques
[Termes IGN] climat
[Termes IGN] forêt marécageuse
[Termes IGN] historique des données
[Termes IGN] incendie de forêt
[Termes IGN] outil d'aide à la décision
[Termes IGN] prévention des risques
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] Thaïlande
[Termes IGN] tourbièreRésumé : (auteur) Forest fires are abrupt transformations of the natural ecosystem and management authorities are required to take preventive measures to tackle fire events. Geographic information system (GIS) is a powerful tool for providing information with a spatial context and analytical hierarchy process (AHP) is a well-established technique for multiple criteria decision making. In this study, GIS and AHP are combined to analyse seven fire-related factors related to climate, topography and human influence. Fire risk for a peat swamp forested area in Kuan Kreng, Nakorn Sri Thammarat province, Thailand is estimated in five categories. 705 historic fire events from 2006 to 2017 are used to validate our approach. 82% of the historic fire incidents occurred within the highest fire risk class categories while only a few omission errors were recorded. The combined approach of GIS and AHP techniques can yield useful fire risk maps, which can consequently be used for future planning and management of fire prone areas. Numéro de notice : A2021-083 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1611946 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1611946 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96832
in Geocarto international > vol 36 n° 2 [01/02/2021] . - pp 212 - 225[article]A points of interest matching method using a multivariate weighting function with gradient descent optimization / Zhou Yang in Transactions in GIS, Vol 25 n° 1 (February 2021)
[article]
Titre : A points of interest matching method using a multivariate weighting function with gradient descent optimization Type de document : Article/Communication Auteurs : Zhou Yang, Auteur ; Mingjun Wang, Auteur ; Chen Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 359 - 381 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] algorithme du gradient
[Termes IGN] appariement automatique
[Termes IGN] appariement de données localisées
[Termes IGN] apprentissage automatique
[Termes IGN] données localisées des bénévoles
[Termes IGN] données multisources
[Termes IGN] exploration de données
[Termes IGN] intégration de données
[Termes IGN] point d'intérêt
[Termes IGN] pondération
[Termes IGN] qualité des donnéesRésumé : (Auteur) Volunteered geographic information contains abundant valuable data, which can be applied to various spatiotemporal geographical analyses. While the useful information may be distributed in different, low‐quality data sources, this issue can be solved by data integration. Generally, the primary task of integration is data matching. Unfortunately, due to the complexity and irregularities of multi‐source data, existing studies have found it difficult to efficiently establish the correspondence between different sources. Therefore, we present a multi‐stage method to match multi‐source data using points of interest. A spatial filter is constructed to obtain candidate sets for geographical entities. The weights of non‐spatial characteristics are examined by a machine learning‐related algorithm with artificially labeled random samples. A case study on Fuzhou reveals that an average of 95% of instances are accurately matched. Thus, our study provides a novel solution for researchers who are engaged in data mining and related work to accurately match multi‐source data via knowledge obtained by the idea and methods of machine learning. Numéro de notice : A2021-189 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12690 Date de publication en ligne : 05/10/2020 En ligne : https://doi.org/10.1111/tgis.12690 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97158
in Transactions in GIS > Vol 25 n° 1 (February 2021) . - pp 359 - 381[article]Stand-scale climate change impacts on forests over large areas: transient responses and projection uncertainties / NIca Huber in Ecological Applications, vol 31 ([01/02/2021])
[article]
Titre : Stand-scale climate change impacts on forests over large areas: transient responses and projection uncertainties Type de document : Article/Communication Auteurs : NIca Huber, Auteur ; Harald Bugmann, Auteur Année de publication : 2021 Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de sensibilité
[Termes IGN] changement climatique
[Termes IGN] croissance des arbres
[Termes IGN] forêt alpestre
[Termes IGN] incertitude des données
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle de simulation
[Termes IGN] modèle dynamique
[Termes IGN] modélisation de la forêt
[Termes IGN] Suisse
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) The increasing impacts of climate change on forest ecosystems have triggered multiple model-based impact assessments for the future, which typically focused either on a small number of stand-scale case studies or on large scale analyses (i.e., continental to global). Therefore, substantial uncertainty remains regarding the local impacts over large areas (i.e., regions to countries), which is particularly problematic for forest management. We provide a comprehensive, high-resolution assessment of the climate change sensitivity of managed Swiss forests (~10,000 km2), which cover a wide range of environmental conditions. We used a dynamic vegetation model to project the development of typical forest stands derived from a stratification of the Third National Forest Inventory until the end of the 22nd century. Two types of simulations were conducted: one limited to using the extant local species, the other enabling immigration of potentially more climate-adapted species. Moreover, to assess the robustness of our projections, we quantified and decomposed the uncertainty in model projections resulting from the following sources: (1) climate change scenarios, (2) local site conditions, and (3) the dynamic vegetation model itself (i.e., represented by a set of model versions), an aspect hitherto rarely taken into account. The simulations showed substantial changes in basal area and species composition, with dissimilar sensitivity to climate change across and within elevation zones. Higher-elevation stands generally profited from increased temperature, but soil conditions strongly modulated this response. Low-elevation stands were increasingly subject to drought, with strong negative impacts on forest growth. Furthermore, current stand structure had a strong effect on the simulated response. The admixture of drought-tolerant species was found advisable across all elevations to mitigate future adverse climate-induced effects. The largest uncertainty in model projections was associated with climate change scenarios. Uncertainty induced by the model version was generally largest where overall simulated climate change impacts were small, thus corroborating the utility of the model for making projections into the future. Yet, the large influence of both site conditions and the model version on some of the projections indicates that uncertainty sources other than climate change scenarios need to be considered in climate change impact assessments. Numéro de notice : A2021-312 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1002/eap.2313 Date de publication en ligne : 25/02/2021 En ligne : https://doi.org/10.1002/eap.2313 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97811
in Ecological Applications > vol 31 [01/02/2021][article]Stochastic model reliability in GNSS baseline solution / Aviram Borko in Journal of geodesy, vol 95 n° 2 (February 2021)
[article]
Titre : Stochastic model reliability in GNSS baseline solution Type de document : Article/Communication Auteurs : Aviram Borko, Auteur ; Gilad Even-Tzur, Auteur Année de publication : 2021 Article en page(s) : n° 20 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données GNSS
[Termes IGN] double différence
[Termes IGN] fiabilité des données
[Termes IGN] ligne de base
[Termes IGN] matrice de covariance
[Termes IGN] modèle stochastique
[Termes IGN] résolution d'ambiguïté
[Termes IGN] test statistique
[Vedettes matières IGN] Traitement de données GNSSRésumé : (auteur) GNSS observations stochastic model influences all subsequent stages of data processing, from the possibility to reach the optimal parameters estimation, to the reliability and quality control of the solution. Nowadays, an uncontrolled use of GNSS stochastic models is common for both data processing and simulation missions, especially in commercial GNSS software packages. As a result, the variance–covariance matrices that are derived in the processing are inadequate and cause incorrect interpretations of the results. A proper method to evaluate the reliability of the stochastic model is needed to reflect the confidence level in statistic testing and simulation mission efforts. In this contribution, a novel method for evaluating the statistical nature of GNSS stochastic model is presented. The method relies on the deterministic nature of the integer ambiguity variable to examine and express the expected multinormal distribution of the double-difference adjustment results. The suggested method was used with a controlled experiment and 24 h of observations data to investigate how the statistical nature of the stochastic model is affected by different baseline lengths. The results indicate that as the baseline length increases, the stochastic model is less predictable and exposed to irregularities in the observation’s precision. Additionally, the reliability of the integer ambiguity resolution success rate (SR) was tested as part of the stochastic model evaluation. The results show a dramatic degradation in the SR prediction level when using an inadequate stochastic model, which suggests using extra caution when handling this parameter unless high-confidence reliable stochastic model is available. Numéro de notice : A2021-136 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01472-1 Date de publication en ligne : 31/01/2021 En ligne : https://doi.org/10.1007/s00190-021-01472-1 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97009
in Journal of geodesy > vol 95 n° 2 (February 2021) . - n° 20[article]Assessing the accuracy of remotely sensed fire datasets across the southwestern Mediterranean Basin / Luis Felipe Galizia in Natural Hazards and Earth System Sciences, vol 21 n° 1 (January 2021)
[article]
Titre : Assessing the accuracy of remotely sensed fire datasets across the southwestern Mediterranean Basin Type de document : Article/Communication Auteurs : Luis Felipe Galizia, Auteur ; Thomas Curt, Auteur ; Renaud Barbero, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 73 - 86 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bassin méditerranéen
[Termes IGN] cartographie des risques
[Termes IGN] exactitude des données
[Termes IGN] image Terra-MODIS
[Termes IGN] incendie
[Termes IGN] incertitude des données
[Termes IGN] jeu de données localiséesRésumé : (auteur) Recently, many remote-sensing datasets providing features of individual fire events from gridded global burned area products have been released. Although very promising, these datasets still lack a quantitative estimate of their accuracy with respect to historical ground-based fire datasets. Here, we compared three state-of-the-art remote-sensing datasets (RSDs; Fire Atlas, FRY, and GlobFire) with a harmonized ground-based dataset (GBD) compiled by fire agencies monitoring systems across the southwestern Mediterranean Basin (2005–2015). We assessed the agreement between the RSDs and the GBD with respect to both burned area (BA) and number of fires (NF). RSDs and the GBD were aggregated at monthly and 0.25∘ resolutions, considering different individual fire size thresholds ranging from 1 to 500 ha. Our results show that all datasets were highly correlated in terms of monthly BA and NF, but RSDs severely underestimated both (by 38 % and 96 %, respectively) when considering all fires > 1 ha. The agreement between RSDs and the GBD was strongly dependent on individual fire size and strengthened when increasing the fire size threshold, with fires > 100 ha denoting a higher correlation and much lower error (BA 10 %; NF 35 %). The agreement was also higher during the warm season (May to October) in particular across the regions with greater fire activity such as the northern Iberian Peninsula. The Fire Atlas displayed a slightly better performance with a lower relative error, although uncertainty in the gridded BA product largely outpaced uncertainties across the RSDs. Overall, our findings suggest a reasonable agreement between RSDs and the GBD for fires larger than 100 ha, but care is needed when examining smaller fires at regional scales. Numéro de notice : A2021-134 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/nhess-21-73-2021 Date de publication en ligne : 11/01/2021 En ligne : https://doi.org/10.5194/nhess-21-73-2021 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96995
in Natural Hazards and Earth System Sciences > vol 21 n° 1 (January 2021) . - pp 73 - 86[article]Chinese tourists in Nordic countries: An analysis of spatio-temporal behavior using geo-located travel blog data / Yunhao Zheng in Computers, Environment and Urban Systems, vol 85 (January 2021)PermalinkPermalinkEvaluation of a neural network with uncertainty for detection of ice and water in SAR imagery / Nazanin Asadi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkInitialization methods of convolutional neural networks for detection of image manipulations / Ivan Castillo Camacho (2021)PermalinkPermalinkReal-time multimodal semantic scene understanding for autonomous UGV navigation / Yifei Zhang (2021)PermalinkReprésentation sémantique de données géospatiales au service de l'analyse de changements / Jordan Dorne (2021)PermalinkPermalinkA valuable model ? / Alexis Debray in GEO: Geoconnexion international, vol 20 n° 1 (January - February 2021)PermalinkDu drone LiDAR à un nuage de points précis et exact : une chaîne de traitement LiDAR adaptée et quasi automatique / Maxime Lafleur in XYZ, n° 165 (décembre 2020)PermalinkInfrastructure of the spatial information in the European Community (INSPIRE) based on examples of Italy and Poland / Marek Ogryzek in ISPRS International journal of geo-information, vol 9 n° 12 (December 2020)PermalinkLegal aspects of registration the time of cadastral data creation or modification / Joanna Reczyńska in Reports on geodesy and geoinformatics, vol 110 n° 1 (December 2020)PermalinkActive and incremental learning for semantic ALS point cloud segmentation / Yaping Lin in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)PermalinkMapping 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)PermalinkStreets of London: Using Flickr and OpenStreetMap to build an interactive image of the city / Azam Raha Bahrehdar in Computers, Environment and Urban Systems, vol 84 (November 2020)PermalinkCombined InSAR and terrestrial structural monitoring of bridges / Sivasakthy Selvakumaran in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)PermalinkJS4Geo: a canonical JSON Schema for geographic data suitable to NoSQL databases / Angeol A. Frozza in Geoinformatica, vol 24 n° 4 (October 2020)PermalinkOpenStreetMap quality assessment using unsupervised machine learning methods / Kent T. Jacobs in Transactions in GIS, Vol 24 n° 5 (October 2020)PermalinkUncertainty of forested wetland maps derived from aerial photography / Stephen P. Prisley in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 10 (October 2020)PermalinkUrban flooding in Britain: an approach to comparing ancient and contemporary flood exposure / T.E. O'Shea in Natural Hazards, Vol 104 n° 1 (October 2020)Permalink