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Auteur Sam Dennis |
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Exploring the addition of airborne Lidar-DEM and derived TPI for urban land cover and land use classification and mapping / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)
[article]
Titre : Exploring the addition of airborne Lidar-DEM and derived TPI for urban land cover and land use classification and mapping Type de document : Article/Communication Auteurs : Clement E. Akumu, Auteur ; Sam Dennis, Auteur Année de publication : 2023 Article en page(s) : pp19 - 26 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] carte d'occupation du sol
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données topographiques
[Termes IGN] image Landsat-OLI
[Termes IGN] milieu urbain
[Termes IGN] MNS lidar
[Termes IGN] semis de points
[Termes IGN] Tennessee (Etats-Unis)
[Termes IGN] utilisation du solRésumé : (auteur) The classification and mapping accuracy of urban land cover and land use has always been a critical topic and several auxiliary data have been used to improve the classification accuracy. However, to the best of our knowledge, there is limited knowledge of the addition of airborne Light Detection and Ranging (lidar)-Digital Elevation Model (DEM) and Topographic Position Index (TPI) for urban land cover and land use classification and mapping. The aim of this study was to explore the addition of airborne lidar-DEM and derived TPI to reflect data of Landsat Operational Land Imager (OLI) in improving the classification accuracy of urban land cover and land use map- ping. Specifically, this study explored the mapping accuracies of urban land cover and land use classifications derived using: 1) standalone Landsat OLI satellite data; 2) Landsat OLI with acquired airborne lidar-DEM ; 3) Landsat OLI with TPI ; and 4) Landsat OLI with airborne lidar-DEM and derived TPI. The results showed that the addition of airborne lidar-DEM and TPI yielded the best overall urban land cover and land use classification accuracy of about 88%. The findings in this study demonstrated that both lidar-DEM and TPI had a positive impact in improving urban land cover and land use classification. Numéro de notice : A2023-045 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00029R2 Date de publication en ligne : 01/01/2023 En ligne : https://doi.org/10.14358/PERS.21-00029R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102354
in Photogrammetric Engineering & Remote Sensing, PERS > vol 89 n° 1 (January 2023) . - pp19 - 26[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2023011 SL Revue Centre de documentation Revues en salle Disponible 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)
[article]
Titre : Urban land cover/use mapping and change detection analysis using multi-temporal Landsat OLI with Lidar-DEM and derived TPI Type de document : Article/Communication Auteurs : Clement E. Akumu, Auteur ; Sam Dennis, Auteur Année de publication : 2022 Article en page(s) : pp 243 - 253 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] carte d'occupation du sol
[Termes IGN] changement d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] détection de changement
[Termes IGN] données multitemporelles
[Termes IGN] données topographiques
[Termes IGN] image Landsat-OLI
[Termes IGN] milieu urbain
[Termes IGN] MNS lidar
[Termes IGN] Tennessee (Etats-Unis)
[Termes IGN] utilisation du solRésumé : (auteur) The mapping and change detection of land cover and land use are essential for urban management. The aim of this study was to map and monitor the spatial and temporal change in urban land cover and land use in Davidson County, Tennessee in the periods of 2013, 2016, and 2020. The urban land cover and land use categories were classified and mapped using Random Forest algorithm. A combination of Landsat Operational Land Imager (OLI) satellite data with Light Detection and Ranging (lidar)-Digital Elevation Model (DEM) and derived Topographic Position Index (TPI) were used in the classification and monitoring of urban land cover and land use change. The urban land cover and land use types were mapped with average overall accuracies of about 87% in 2020, 85% in 2016 and 2013. The overall accuracy increased by around 8%, 9%, and 6% in 2020, 2016, and 2013 classifications respectively when lidarDEMand derived TPIwere added to Landsat OLIsatellite data in the classification relative to standalone Landsat OLI. Total change occurred in about 63% of Davidson County between 2016 and 2020 with significant net gains and losses among land cover and land use types. This information could support land use planning. Numéro de notice : A2022-286 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00042R3 Date de publication en ligne : 04/04/2022 En ligne : https://doi.org/10.14358/PERS.21-00042R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100320
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 4 (April 2022) . - pp 243 - 253[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2022041 SL Revue Centre de documentation Revues en salle Disponible