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Fuzzy modelling of growth potential in forest development simulation / Damjan Strnad in Ecological Informatics, vol 48 (November 2018)
[article]
Titre : Fuzzy modelling of growth potential in forest development simulation Type de document : Article/Communication Auteurs : Damjan Strnad, Auteur ; Štefan Kohek, Auteur ; Simon Kolmanič, Auteur Année de publication : 2018 Article en page(s) : pp 80 - 88 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Végétation
[Termes IGN] analyse de sensibilité
[Termes IGN] biodiversité
[Termes IGN] composition floristique
[Termes IGN] croissance des arbres
[Termes IGN] écosystème forestier
[Termes IGN] modèle de croissance végétale
[Termes IGN] modèle de simulation
[Termes IGN] Slovénie
[Termes IGN] sous ensemble flou
[Termes IGN] surveillance écologiqueRésumé : (Auteur) In the paper, we introduce a new fuzzy-based model for calculation of plant growth potential in the context of forest development simulation, which is an important tool for prediction and monitoring of forest biodiversity. When modelling a forest ecosystem, one needs to account for a significant amount of ambiguity in the specification of plant requirements and environmental conditions, whose overlap determines the competitive potential of co-occurring species. The proposed fuzzy model addresses the imprecision and uncertainty about proper interpretation of numerically estimated growth conditions with respect to linguistically specified plant requirements. Individual requirement levels are represented as fuzzy sets to which estimated growth conditions are mapped, while plant needs are modelled as fuzzy numbers with adjustable tolerance radii. The growth potential with respect to a particular resource is then calculated as a membership of condition mean in a fuzzy set of plant demand. We validate the model operation within the ForestMAS simulator on real data obtained from six decades of observations registered at a forest fire recovery site in northern Slovenia. We show that the enhanced expressiveness about the tolerance of tree species to deviations of growth conditions allows the fuzzy model to improve the accuracy of forest composition prediction with respect to the crisp model. Sensitivity analysis also shows that, in many cases, the fuzzy model increases simulation robustness with respect to vaguely defined plant needs and estimated site conditions. Numéro de notice : A2019-229 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ecoinf.2018.08.002 Date de publication en ligne : 11/08/2018 En ligne : https://doi.org/10.1016/j.ecoinf.2018.08.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92744
in Ecological Informatics > vol 48 (November 2018) . - pp 80 - 88[article]Estimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery / Lin Chen in Forests, vol 9 n° 10 (October 2018)
[article]
Titre : Estimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery Type de document : Article/Communication Auteurs : Lin Chen, Auteur ; Chunying Ren, Auteur ; Bai Zhang, Auteur ; Zongming Wang, Auteur ; Yanbiao Xi, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arbre caducifolié
[Termes IGN] biomasse aérienne
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] modèle de simulation
[Termes IGN] montagne
[Termes IGN] régression géographiquement pondérée
[Termes IGN] surveillance forestière
[Termes IGN] texture d'image
[Termes IGN] variable biophysique (végétation)Résumé : (Auteur) Accurate forest above-ground biomass (AGB) is crucial for sustaining forest management and mitigating climate change to support REDD+ (reducing emissions from deforestation and forest degradation, plus the sustainable management of forests, and the conservation and enhancement of forest carbon stocks) processes. Recently launched Sentinel imagery offers a new opportunity for forest AGB mapping and monitoring. In this study, texture characteristics and backscatter coefficients of Sentinel-1, in addition to multispectral bands, vegetation indices, and biophysical variables of Sentinal-2, based on 56 measured AGB samples in the center of the Changbai Mountains, China, were used to develop biomass prediction models through geographically weighted regression (GWR) and machine learning (ML) algorithms, such as the artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that texture characteristics and vegetation biophysical variables were the most important predictors. SVR was the best method for predicting and mapping the patterns of AGB in the study site with limited samples, whose mean error, mean absolute error, root mean square error, and correlation coefficient were 4 × 10−3, 0.07, 0.08 Mg·ha−1, and 1, respectively. Predicted values of AGB from four models ranged from 11.80 to 324.12 Mg·ha−1, and those for broadleaved deciduous forests were the most accurate, while those for AGB above 160 Mg·ha−1 were the least accurate. The study demonstrated encouraging results in forest AGB mapping of the normal vegetated area using the freely accessible and high-resolution Sentinel imagery, based on ML techniques. Numéro de notice : A2018-478 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f9100582 Date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.3390/f9100582 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91180
in Forests > vol 9 n° 10 (October 2018)[article]A new algorithm predicting the end of growth at five evergreen conifer forests based on nighttime temperature and the enhanced vegetation index / Huanhuan Yuan in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)
[article]
Titre : A new algorithm predicting the end of growth at five evergreen conifer forests based on nighttime temperature and the enhanced vegetation index Type de document : Article/Communication Auteurs : Huanhuan Yuan, Auteur ; Chaoyang Wu, Auteur ; Linlin Lu, Auteur ; Xiaoyue Wang, Auteur Année de publication : 2018 Article en page(s) : pp 390 - 399 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Canada
[Termes IGN] croissance des arbres
[Termes IGN] Enhanced vegetation index
[Termes IGN] forêt
[Termes IGN] modèle de simulation
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] nuit
[Termes IGN] Pinophyta
[Termes IGN] production primaire brute
[Termes IGN] simulation numérique
[Termes IGN] température au solRésumé : (Auteur) Accurate estimation of vegetation phenology (the start/end of growing season, SOS/EOS) is important to understand the feedbacks of vegetation to meteorological circumstances. Because the evergreen forests have limited change in greenness, there are relatively less study to predict evergreen conifer forests phenology, especially for EOS in autumn. Using 11-year (2000–2010) records of MODIS normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), together with gross primary production (GPP) and temperature data at five evergreen conifer forests flux sites in Canada, we comprehensively evaluated the performances of several variables in modeling flux-derived EOS. Results showed that neither NDVI nor EVI can be used to predict EOS as they had no significant correlation with ground observations. In comparison, temperature had a better predictive strength for EOS, and R2 between EOS and mean temperature (Tmean), the maximum temperature (Tmax, daytime temperature) and the minimum temperature (Tmin, nighttime temperature) were 0.45 (RMSE = 5.1 days), 0.32 (RMSE = 5.7 days) and 0.58 (RMSE = 4.6 days), respectively. These results suggest an unreported role of nighttime temperature in regulating EOS of evergreen forests, in comparison with previous study showing leaf-out in spring by daytime temperature. Furthermore, we demonstrated that it may be because nighttime temperature has a higher relationship with soil temperature (Ts) (R2 = 0.67, p Numéro de notice : A2018-403 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.08.013 Date de publication en ligne : 17/08/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.08.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90855
in ISPRS Journal of photogrammetry and remote sensing > vol 144 (October 2018) . - pp 390 - 399[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2018101 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018103 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018102 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Fine-grained prediction of urban population using mobile phone location data / Jie Chen in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)
[article]
Titre : Fine-grained prediction of urban population using mobile phone location data Type de document : Article/Communication Auteurs : Jie Chen, Auteur ; Shih-Lung Shaw, Auteur ; Feng Lu, Auteur ; Mingxiao Li, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 1770 - 1786 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] classification par réseau neuronal
[Termes IGN] données spatiotemporelles
[Termes IGN] modèle de simulation
[Termes IGN] population urbaine
[Termes IGN] Shanghai (Chine)
[Termes IGN] trace numériqueRésumé : (Auteur) Fine-grained prediction of urban population is of great practical significance in many domains that require temporally and spatially detailed population information. However, fine-grained population modeling has been challenging because the urban population is highly dynamic and its mobility pattern is complex in space and time. In this study, we propose a method to predict the population at a large spatiotemporal scale in a city. This method models the temporal dependency of population by estimating the future inflow population with the current inflow pattern and models the spatial correlation of population using an artificial neural network. With a large dataset of mobile phone locations, the model’s prediction error is low and only increases gradually as the temporal prediction granularity increases, and this model is adaptive to sudden changes in population caused by special events. Numéro de notice : A2018-304 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1460753 Date de publication en ligne : 26/04/2018 En ligne : https://doi.org/10.1080/13658816.2018.1460753 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90445
in International journal of geographical information science IJGIS > vol 32 n° 9-10 (September - October 2018) . - pp 1770 - 1786[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2018051 RAB Revue Centre de documentation En réserve L003 Disponible Estimating storm damage with the help of low-altitude photographs and different sampling designs and estimators / Pekka Hyvönen in Silva fennica, vol 52 n° 3 ([01/08/2018])
[article]
Titre : Estimating storm damage with the help of low-altitude photographs and different sampling designs and estimators Type de document : Article/Communication Auteurs : Pekka Hyvönen, Auteur ; Jaakko Heinonen, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] dommage matériel
[Termes IGN] estimateur
[Termes IGN] Finlande
[Termes IGN] forêt
[Termes IGN] image aérienne
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle de simulation
[Termes IGN] plan de volMots-clés libres : Théorème central limite Résumé : (Auteur) Climate change has been estimated to increase the risk of storm damage in forests in Finland. There is a growing need for methods to obtain information on the extent and severity of storm damage after a storm occurrence. The first objective of this study was to test whether digital photographs taken from aircrafts flying at low-altitude can be utilized in locating storm-damaged areas and estimating the need for harvesting of wind-thrown trees. The second objective was to test the performance of selected estimators. Depending on distances between flight lines, plots on lines and the used estimator, the relative standard errors of storm area estimates varied between 7.7 and 48.7%. For the area for harvesting and volume of wind-thrown trees, the relative standard errors of estimates varied between 16.8 and 167.3%. Using forest area information from Multisource National Forest Inventory data improved the accuracy of the estimates. However, performance of a simple random sampling estimator and ratio estimator were quite similar. Lindeberg’s method for variance estimation based on adjacent lines was sensitive to line directions in relation to possible trends in storm-damaged area locations. Our results showed that the tested method could be used in estimating storm-damaged area provided that the network of flight lines and photographs on lines are sufficiently dense. The developed model for simulations can be utilized also with forthcoming storms as model’s parameters can be freely adjusted to meet, e.g., the intensity and extent of the storm. Numéro de notice : A2018-508 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14214/sf.7710 Date de publication en ligne : 05/06/2018 En ligne : https://doi.org/10.14214/sf.7710 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91189
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