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Termes IGN > mathématiques > statistique mathématique > analyse de données > classification > classification par arbre de décision > classification et arbre de régression
classification et arbre de régressionSynonyme(s)CART (algorithme) |
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Titre : Bridging the gap : The measure of urban resilience Type de document : Monographie Auteurs : Grazia Brunetta, Éditeur scientifique ; Alessandra Faggian, Éditeur scientifique ; Ombretta Caldarice, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 266 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-3-0365-0767-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Urbanisme
[Termes IGN] changement climatique
[Termes IGN] classification et arbre de régression
[Termes IGN] données localisées
[Termes IGN] espace vert
[Termes IGN] géovisualisation
[Termes IGN] ilot thermique urbain
[Termes IGN] Italie
[Termes IGN] logement
[Termes IGN] modèle de simulation
[Termes IGN] modèle dynamique
[Termes IGN] planification urbaine
[Termes IGN] prévention des risques
[Termes IGN] système d'information géographique
[Termes IGN] utilisation du sol
[Termes IGN] ville durableRésumé : (auteur) The concept of resilience has arisen as a “new way of thinking”, becoming a response to both the causes and effects of ongoing global challenges. As it strongly stresses cities’ transformative potential, resilience’s final purpose is to prevent and manage unforeseen events and improve communities’ environmental and social quality. Although the resilience theory has been investigated in depth, several methodological challenges remain, mainly related to the concept’s practical sphere. As a matter of fact, resilience is commonly criticised for being too ambiguous and empty of meaning. At the same time, turning resilience into practice is not easy to do. This will arguably be one of the most impactful global issues for future research on resilience. The Special Issue “Bridging the Gap: The Measure of Urban Resilience” falls under this heading, and it seeks to synthesise state-of-the-art knowledge of theories and practices on measuring resilience. The Special Issue collected 11 papers that address the following questions: “What are the theoretical perspectives of measuring urban resilience? What are the existing methods for measuring urban resilience? What are the main features that a technique for measuring urban resilience needs to have? What is the role of measuring urban resilience in operationalising cities’ ability to adapt, recover and benefit from shocks?” Note de contenu : 1- Modelling, measuring, and visualising community resilience: A systematic review
2- Indicators for monitoring urban climate change resilience and adaptation
3- The Multi-risk assessment approach as a basis for the territorial resilience
4- Mapping urban resilience for spatial slanning-A first attempt to measure the vulnerability of the system
5- Breaking the black-box of regional resilience: A taxonomy using a dynamic cumulative shift-share occupational approach
6- Dynamic models for exploring the resilience in territorial scenarios
7- Energy consumption models at urban scale to measure energy resilience
8- Resilience and sectoral composition change of Italian inner areas in response to the great recession
9- Mainstreaming energetic resilience by morphological assessment in ordinary land use planning. The case study of Moncalieri, Turin (Italy)
10- Supporting resilient urban planning through walkability assessment
11- Evaluating and planning green infrastructure: A strategic perspective for sustainability and resilienceNuméro de notice : 28676 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-0767-5 En ligne : https://doi.org/10.3390/books978-3-0365-0767-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99959 Combination of Landsat 8 OLI and Sentinel-1 SAR time-series data for mapping paddy fields in parts of West and Central Java provinces, Indonesia / Sanjiwana Arjasakusuma in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)
[article]
Titre : Combination of Landsat 8 OLI and Sentinel-1 SAR time-series data for mapping paddy fields in parts of West and Central Java provinces, Indonesia Type de document : Article/Communication Auteurs : Sanjiwana Arjasakusuma, Auteur ; Sandiaga Swahyu Kusuma, Auteur ; Raihan Rafif, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 663 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] bande C
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image Landsat-OLI
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] Java (île de)
[Termes IGN] modèle numérique de surface
[Termes IGN] Normalized Difference Built-up Index
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Normalized Difference Water Index
[Termes IGN] polarisation
[Termes IGN] rizière
[Termes IGN] série temporelleRésumé : (auteur) The rise of Google Earth Engine, a cloud computing platform for spatial data, has unlocked seamless integration for multi-sensor and multi-temporal analysis, which is useful for the identification of land-cover classes based on their temporal characteristics. Our study aims to employ temporal patterns from monthly-median Sentinel-1 (S1) C-band synthetic aperture radar data and cloud-filled monthly spectral indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI), from Landsat 8 (L8) OLI for mapping rice cropland areas in the northern part of Central Java Province, Indonesia. The harmonic function was used to fill the cloud and cloud-masked values in the spectral indices from Landsat 8 data, and smile Random Forests (RF) and Classification And Regression Trees (CART) algorithms were used to map rice cropland areas using a combination of monthly S1 and monthly harmonic L8 spectral indices. An additional terrain variable, Terrain Roughness Index (TRI) from the SRTM dataset, was also included in the analysis. Our results demonstrated that RF models with 50 (RF50) and 80 (RF80) trees yielded better accuracy for mapping the extent of paddy fields, with user accuracies of 85.65% (RF50) and 85.75% (RF80), and producer accuracies of 91.63% (RF80) and 93.48% (RF50) (overall accuracies of 92.10% (RF80) and 92.47% (RF50)), respectively, while CART yielded a user accuracy of only 84.83% and a producer accuracy of 80.86%. The model variable importance in both RF50 and RF80 models showed that vertical transmit and horizontal receive (VH) polarization and harmonic-fitted NDVI were identified as the top five important variables, and the variables representing February, April, June, and December contributed more to the RF model. The detection of VH and NDVI as the top variables which contributed up to 51% of the Random Forest model indicated the importance of the multi-sensor combination for the identification of paddy fields. Numéro de notice : A2020-733 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9110663 Date de publication en ligne : 04/11/2020 En ligne : https://doi.org/10.3390/ijgi9110663 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96346
in ISPRS International journal of geo-information > vol 9 n° 11 (November 2020) . - n° 663[article]Combining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods / Liheng Peng in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)
[article]
Titre : Combining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods Type de document : Article/Communication Auteurs : Liheng Peng, Auteur ; Kai Liu, Auteur ; Jingjing Cao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 813 - 838 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] boosting adapté
[Termes IGN] Chine, mer de
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] écosystème
[Termes IGN] extraction de la végétation
[Termes IGN] île
[Termes IGN] image Gaofen
[Termes IGN] image RapidEye
[Termes IGN] image satellite
[Termes IGN] mangrove
[Termes IGN] modèle numérique de surface
[Termes IGN] précision de la classification
[Termes IGN] Rotation Forest classificationRésumé : (auteur) Mangrove forests are important constitutions for sustainable development of coastal ecosystems, and they are often mapped and monitored with remote sensing approaches. Satellite images allow detailed studies of the distribution and composition of mangrove forests, and therefore facilitate the management and conservation of the ecosystems. The combination of multiple types of satellite images with different spatial and spectral resolutions is helpful in mangrove forests extraction and mangrove species discrimination as it reduces sampling workload and increases classification accuracies. In this study, the 1.0-m-resolution Gaofen-2 (GF-2) and the 5.0-m-resolution RapidEye-4 (RE-4) satellite images, acquired in February 2017 and November 2016 respectively, were used with ensemble machine-learning and object-oriented methods for mangroves mapping at both the community and species levels of the Qi’ao Island, Zhuhai, China. First, the mangroves on the island were segmented from the GF-2 image on a large scale, and then they were extracted combining with their digital elevation model (DEM) data. Second, the GF-2 image was further processed on a fine scale, in which object-oriented features from both the GF-2 and RE-4 images were extracted for each mangrove species. Third, it is followed by the mangrove species classification process which involves three ensemble machine-learning methods: the adaptive boosting (AdaBoost), the random forest (RF) and the rotation forest (RoF). These three methods employed a classification and regression tree (CART) as the base classifier. The results show that the overall accuracy (OA) of mangrove area extraction on the Qi’ao Island with the auxiliary data, DEM, achieves 98.76% (Kappa coefficient (κ) = 0.9289). The features extracted by the GF-2 and RE-4 images were shown to be beneficial for mangrove species discrimination. A maximum improvement in the OA of approximately 8% and a κκ of approximately 0.10 were achieved when employing RoF (OA = 92.01%, κ = 0.9016). Ensemble-learning methods can significantly improve the classification accuracy of CART, and the use of a bagging scheme (RF and RoF) is shown as a better way to map mangrove species than adaptive boosting (AdaBoost). In addition, RoF performed well in mangrove species classification but it was not as robust as the RF, whose average OA and κκ were 80.59% and 0.7608, respectively, while the RoF’s were 77.45% and 0.7214, respectively, in the 10-fold cross-validation. Numéro de notice : A2020-212 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01431161.2019.1648907 Date de publication en ligne : 30/07/2019 En ligne : https://doi.org/10.1080/01431161.2019.1648907 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94897
in International Journal of Remote Sensing IJRS > vol 41 n° 3 (15 - 22 janvier 2020) . - pp 813 - 838[article]High‐resolution national land use scenarios under a shrinking population in Japan / Haruka Ohashi in Transactions in GIS, vol 23 n° 4 (August 2019)
[article]
Titre : High‐resolution national land use scenarios under a shrinking population in Japan Type de document : Article/Communication Auteurs : Haruka Ohashi, Auteur ; Keita Fukasawa, Auteur ; Toshinori Ariga, Auteur Année de publication : 2019 Article en page(s) : pp 786 - 804 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] aménagement du territoire
[Termes IGN] apprentissage automatique
[Termes IGN] changement d'occupation du sol
[Termes IGN] classification et arbre de régression
[Termes IGN] décroissance urbaine
[Termes IGN] distribution spatiale
[Termes IGN] données démographiques
[Termes IGN] données topographiques
[Termes IGN] Japon
[Termes IGN] modèle de simulation
[Termes IGN] optimisation spatiale
[Termes IGN] population
[Termes IGN] service écosystémique
[Termes IGN] utilisation du solRésumé : (auteur) In sharp contrast with the global trend in population growth, certain developed countries are expected to experience rapid national population declines. Considering future land use scenarios that include depopulation is necessary to evaluate changes in ecosystem services that affect human well‐being and to facilitate comprehensive strategies for balancing rural and urban development. In this study, we applied a population‐projection‐assimilated predictive land use modeling (PPAP‐LM) approach, in which a spatially explicit population projection was incorporated as a predictor in a land use model. To analyze the effects of future population distributions on land use, we developed models for five land use types and generated projections for two scenarios (centralization and decentralization) under a shrinking population in Japan during 2015–2050. Our results suggested that population centralization promotes the compaction of built‐up areas and the expansion of forest and wastelands, while population decentralization contributes to the maintenance of a mixture of forest and cultivated land. Numéro de notice : A2019-418 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12525 Date de publication en ligne : 08/03/2019 En ligne : https://doi.org/10.1111/tgis.12525 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93545
in Transactions in GIS > vol 23 n° 4 (August 2019) . - pp 786 - 804[article]Evaluating metrics derived from Landsat 8 OLI imagery to map crop cover / Rei Sonobe in Geocarto international, vol 34 n° 8 ([15/06/2019])
[article]
Titre : Evaluating metrics derived from Landsat 8 OLI imagery to map crop cover Type de document : Article/Communication Auteurs : Rei Sonobe, Auteur ; Yuki Yamaya, Auteur ; Hiroshi Tani, Auteur ; Xiufeng Wang, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 839 - 855 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-OLI
[Termes IGN] rayonnement lumineux
[Termes IGN] rayonnement proche infrarouge
[Termes IGN] réflectance végétale
[Termes IGN] signature spectrale
[Termes IGN] surface cultivéeRésumé : (auteur) Developing techniques are required to generate agricultural land cover maps to monitor agricultural fields. Landsat 8 Operational Land Imager (OLI) offers reflectance data over the visible to shortwave-infrared range. OLI offers several advantages, such as adequate spatial and spectral resolution, and 16 day repeat coverage, furthermore, spectral indices derived from Landsat 8 OLI possess great potential for evaluating the status of vegetation. Additionally, classification algorithms are essential for generating accurate maps. Recently, multi-Grained Cascade Forest, which is also called deep forest, was proposed, and it was shown to give highly competitive performance for classification. However, the ability of this algorithm to generate crop maps with satellite data had not yet been evaluated. In this study, the reflectance at 7 bands and 57 spectral indices calculated from Landsat 8 OLI data were evaluated for its potential for crop type identification. Numéro de notice : A2019-514 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1425739 Date de publication en ligne : 19/01/2018 En ligne : https://doi.org/10.1080/10106049.2018.1425739 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93823
in Geocarto international > vol 34 n° 8 [15/06/2019] . - pp 839 - 855[article]A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions / Syed Adnan in Forest ecology and management, vol 433 (15 February 2019)PermalinkFactors affecting forest dynamics in the Iberian Peninsula from 1987 to 2012 : The role of topography and drought / Juan José Vidal-Macua in Forest ecology and management, vol 406 (15 December 2017)PermalinkDevelopment and Comparison of Species Distribution Models for Forest Inventories / Óscar Rodríguez de Rivera in ISPRS International journal of geo-information, vol 6 n° 6 (June 2017)PermalinkPermalinkSpectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)PermalinkComparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points / Y. Shao in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)Permalink