CINECA IRIS Institutional Research Information System
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
EnglishThe rapid expansion of cities globally leads to new challenges related to quality of life and health. The presence and fractional distribution of vegetation within urban cities directly impact the life and health of urban dwellers. This paper presents an approach to map urban vegetation from Sentinel-2 data. The twin Sentinel-2 satellites offer a 5-day revisit time global coverage at unprecedented spatial and temporal resolution. The temporal resolution allows for seasonal aggregation of the input data, thus providing phenological information. By considering seasonally aggregated Normalized Difference Spectral Vector (NDSV), a classification was performed using Random Forest (RF) and compared with Classification and Regression Trees (CART) and Support Vector Machines (SVM).
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo
| Titolo: | Mapping vegetation in urban areas using Sentinel-2 | |
| Autori: | ||
| Data di pubblicazione: | 2019 | |
| Abstract: | The rapid expansion of cities globally leads to new challenges related to quality of life and health. The presence and fractional distribution of vegetation within urban cities directly impact the life and health of urban dwellers. This paper presents an approach to map urban vegetation from Sentinel-2 data. The twin Sentinel-2 satellites offer a 5-day revisit time global coverage at unprecedented spatial and temporal resolution. The temporal resolution allows for seasonal aggregation of the input data, thus providing phenological information. By considering seasonally aggregated Normalized Difference Spectral Vector (NDSV), a classification was performed using Random Forest (RF) and compared with Classification and Regression Trees (CART) and Support Vector Machines (SVM). | |
| Handle: | http://hdl.handle.net/11571/1347106 | |
| ISBN: | 978-1-7281-0009-8 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
http://hdl.handle.net/11571/1347106
Copyright © 2021