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A comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: the case of the Seoul metropolitan area / Myung-Jin Jun in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)
[article]
Titre : A comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: the case of the Seoul metropolitan area Type de document : Article/Communication Auteurs : Myung-Jin Jun, Auteur Année de publication : 2021 Article en page(s) : pp 2149 - 2167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse comparative
[Termes IGN] arbre de décision
[Termes IGN] changement d'utilisation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] Extreme Gradient Machine
[Termes IGN] modèle de simulation
[Termes IGN] réseau neuronal artificiel
[Termes IGN] Séoul
[Termes IGN] zone urbaineRésumé : (auteur) This study compares the performance of gradient boosting decision tree (GBDT), artificial neural networks (ANNs), and random forests (RF) methods in LUC modeling in the Seoul metropolitan area. The results of this study showed that GBDT and RF have higher predictive power than ANN, indicating that tree-based ensemble methods are an effective technique for LUC prediction. Along with the outstanding predictive performance, the DT-based ensemble models provide insights for understanding which factors drive LUCs in complex urban dynamics with the relative importance and nonlinear marginal effects of predictor variables. The GBDT results indicate that distance to the existing residential site has the highest contribution to urban land use conversion (30.4% of the relative importance), while other significant predictor variables were proximity to industrial and public sites (combined 32.3% of relative importance). New residential development is likely to be adjacent to existing residential sites, but nonresidential development occurs at a distance (about 600 m) from such sites. The distance to the central business district (CBD) had increasing marginal effects on residential land use conversion, while no significant pattern was found for nonresidential land use conversion, indicating that Seoul has experienced more population suburbanization than employment decentralization. Numéro de notice : A2021-756 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1887490 Date de publication en ligne : 01/03/2021 En ligne : https://doi.org/10.1080/13658816.2021.1887490 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98771
in International journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2149 - 2167[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2021111 SL Revue Centre de documentation Revues en salle Disponible Diffuse attenuation coefficient (Kd) from ICESat-2 ATLAS spaceborne Lidar using random-forest regression / Forrest Corcoran in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)
[article]
Titre : Diffuse attenuation coefficient (Kd) from ICESat-2 ATLAS spaceborne Lidar using random-forest regression Type de document : Article/Communication Auteurs : Forrest Corcoran, Auteur ; Christopher E. Parrish, Auteur Année de publication : 2021 Article en page(s) : pp 831 - 840 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage automatique
[Termes IGN] arbre de décision
[Termes IGN] capteur spatial
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données ICEsat
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forme d'onde
[Termes IGN] littoral
[Termes IGN] modèle de régression
[Termes IGN] semis de points
[Termes IGN] turbidité des eauxRésumé : (Auteur) This study investigates a new method for measuring water turbidity—specifically, the diffuse attenuation coefficient of downwelling irradiance Kd —using data from a spaceborne, green-wavelength lidar aboard the National Aeronautics and Space Administration's ICESat-2 satellite. The method enables us to fill nearshore data voids in existing Kd data sets and provides a more direct measurement approach than methods based on passive multispectral satellite imagery. Furthermore, in contrast to other lidar-based methods, it does not rely on extensive signal processing or the availability of the system impulse response function, and it is designed to be applied globally rather than at a specific geographic location. The model was tested using Kd measurements from the National Oceanic and Atmospheric Administration's Visible Infrared Imaging Radiometer Suite sensor at 94 coastal sites spanning the globe, with Kd values ranging from 0.05 to 3.6 m –1 . The results demonstrate the efficacy of the approach and serve as a benchmark for future machine-learning regression studies of turbidity using ICESat-2. Numéro de notice : A2021-896 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00013R2 Date de publication en ligne : 01/11/2021 En ligne : https://doi.org/10.14358/PERS.21-00013R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99272
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 11 (November 2021) . - pp 831 - 840[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2021111 SL Revue Centre de documentation Revues en salle Disponible Multi-sensor aboveground biomass estimation in the broadleaved hyrcanian forest of Iran / Ghasem Ronoud in Canadian journal of remote sensing, vol 47 n° 6 ([01/11/2021])
[article]
Titre : Multi-sensor aboveground biomass estimation in the broadleaved hyrcanian forest of Iran Titre original : Estimation multi-capteurs de la biomasse aérienne de la forêt de feuillus hyrcanienne d’Iran Type de document : Article/Communication Auteurs : Ghasem Ronoud, Auteur ; Parviz Fatehi, Auteur ; Ali Asghar Darvishsefat, Auteur Année de publication : 2021 Article en page(s) : pp 818 - 834 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] estimation statistique
[Termes IGN] Fagus orientalis
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Iran
[Termes IGN] régression multiple
[Vedettes matières IGN] Inventaire forestierMots-clés libres : Support Vector Regression Résumé : (auteur) In this study, the capability of Landsat-8 (L8), Sentinel-2 (S2), Sentinel-1 (S1), and their combination was investigated for estimating aboveground biomass (AGB). A pure stand of Fagus Orientalis located in the Hyrcanian forest of Iran was selected as the study area. The performance of a parametric approach, i.e., Multiple Linear Regression (MLR) model and non-parametric approaches, i.e., k-Nearest Neighbor (k-NN), Random Forest (RF), and Support Vector Regression (SVR), were also evaluated for AGB estimations. Our results indicated that among S2 metrics, the FAPAR canopy biophysical index and NDVI index based on the red-edge band (NIR-b8a) have the highest correlation coefficient (r) of 0.420 and 0.417, respectively. The results of AGB estimation showed that a combination of S2 and S1 datasets using the k-NN algorithm had the best accuracy (R2 of 0.57 and rRMSE of 14.68%). The best rRMSE using L8, S2, and S1 datasets was 18.95, 16.99, and 19.17% using k-NN, k-NN, and MLR algorithms, respectively. The combination of L8 with S1 dataset also improved the rRMSE relative to L8 and S1 separately by 0.96 and 1.18%, respectively. We concluded that the combination of optical data (L8 or S2) with SAR data (S1) improves the broadleaved Hyrcanian AGB estimation. Numéro de notice : A2021-956 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article DOI : 10.1080/07038992.2021.1968811 Date de publication en ligne : 07/09/2021 En ligne : https://doi.org/10.1080/07038992.2021.1968811 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99982
in Canadian journal of remote sensing > vol 47 n° 6 [01/11/2021] . - pp 818 - 834[article]Using LiDAR and Random Forest to improve deer habitat models in a managed forest landscape / Colin S. Shanley in Forest ecology and management, vol 499 (November-1 2021)
[article]
Titre : Using LiDAR and Random Forest to improve deer habitat models in a managed forest landscape Type de document : Article/Communication Auteurs : Colin S. Shanley, Auteur ; Daniel R. Eacker, Auteur ; Connor P. Reynolds, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119580 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Alaska (Etats-Unis)
[Termes IGN] Cervidae
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] coefficient de corrélation
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt
[Termes IGN] géomorphométrie
[Termes IGN] habitat animal
[Termes IGN] habitat forestier
[Termes IGN] semis de pointsRésumé : (auteur) Conservation strategies are hindered by a lack of accurate maps of important habitat for many wildlife species, but especially for species inhabiting managed forest landscapes. Prioritizing restoration efforts on Alaska’s Tongass National Forest from past extensive clearcut logging is extremely challenging given the difficulty in accurately mapping its remote, rugged temperate rainforest landscapes. We tested the application of airborne light detection and ranging (LiDAR) technology to build a winter habitat model for Sitka black-tailed deer (Odocoileus hemionus sitkensis), the primary herbivore in the coastal temperate rainforest. We analyzed the importance of geomorphometric and forest structure characteristics as predictors of deer winter habitat selection using Random Forest applied to a 3-year GPS relocation dataset collected from 40 adult female deer. The LiDAR-based habitat model had a predictive performance of 94% (Out-of-bag error = 6%), a 10% lower model error compared to air-photo interpreted polygons and modeled plot data. Random Forest also outperformed analogous resource selection function models based on a comprehensive k-fold cross-validation. Deer habitat selection patterns in the LiDAR-based model were nonlinear across geomorphometric and forest structure predictive variables, and generally supported existing studies of deer habitat selection. Besides improving deer conservation and management on the Tongass National Forest, our approach could greatly enhance the accuracy and resolution of habitat maps used for conservation and restoration planning across large managed forest landscapes. Numéro de notice : A2021-696 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2021.119580 Date de publication en ligne : 26/08/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119580 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98529
in Forest ecology and management > vol 499 (November-1 2021) . - n° 119580[article]Deep-learning-based burned area mapping using the synergy of Sentinel-1&2 data / Qi Zhang in Remote sensing of environment, vol 264 (October 2021)
[article]
Titre : Deep-learning-based burned area mapping using the synergy of Sentinel-1&2 data Type de document : Article/Communication Auteurs : Qi Zhang, Auteur ; Linlin Ge, Auteur ; Ruiheng Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112575 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] cartographie thématique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] incendie
[Termes IGN] réflectance du sol
[Termes IGN] réseau neuronal siamoisRésumé : (auteur) Around 350 million hectares of land are affected by wildfires every year influencing the health of ecosystems and leaving a trail of destruction. Accurate information over burned areas (BA) is essential for governments and communities to prioritize recovery actions. Prior research over the past decades has established the potentials and limitations of space-borne earth observation for mapping BA over large geographic areas at various scales. The operational deployment of Sentinel-1 and Sentinel-2 constellations significantly improved the quality and quantity of the imagery from the microwave (C-band) and optical regions on the spectrum. Based on that, this study set to investigate whether the existing coarse BA products can be further improved by the synergy of optical surface reflectance (SR), radar backscatter coefficient (BS), and/or radar interferometric coherence (COR) data with higher spatial resolutions. A Siamese Self-Attention (SSA) classification strategy is proposed for the multi-sensor BA mapping and a multi-source dataset is constructed at the object level for the training and testing. Results are analyzed by test sites, feature sources, and classification strategies to appraise the improvements achieved by the proposed method. Numéro de notice : A2021-807 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112575 Date de publication en ligne : 06/07/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112575 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98866
in Remote sensing of environment > vol 264 (October 2021) . - n° 112575[article]Spatial interpolation of mobile positioning data for population statistics / Anto Aasa in Journal of location-based services, vol 15 n° 4 ([01/10/2021])PermalinkClassification of tree species in a heterogeneous urban environment using object-based ensemble analysis and World View-2 satellite imagery / Simbarashe Jombo in Applied geomatics, vol 13 n° 3 (September 2021)PermalinkTwo hidden layer neural network-based rotation forest ensemble for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 16 ([01/09/2021])PermalinkUnsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification / Divyesh Varade in Geocarto international, vol 36 n° 15 ([15/08/2021])PermalinkInvestigating the application of artificial intelligence for earthquake prediction in Terengganu / Suzlyana Marhain in Natural Hazards, vol 108 n° 1 (August 2021)PermalinkRandom forests with bagging and genetic algorithms coupled with least trimmed squares regression for soil moisture deficit using SMOS satellite soil moisture / Pashrant K. Srivastava in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)PermalinkSurface modelling of forest aboveground biomass based on remote sensing and forest inventory data / Xiaofang Sun in Geocarto international, vol 36 n° 14 ([01/08/2021])PermalinkDEM- and GIS-based analysis of soil erosion depth using machine learning / Kieu Anh Nguyen in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)PermalinkEstimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data / Yueting Wang in Ecological indicators, vol 126 (July 2021)PermalinkImplementing a mass valuation application on interoperable land valuation data model designed as an extension of the national GDI / Arif Cagdas Aydinoglu in Survey review, Vol 53 n° 379 (July 2021)Permalink