Naslov
Fusion of Sentinel-2 and PlanetScope Imagery for Vegetation Detection and Monitoring

Autori
Gašparović, Mateo ; Medak, Damir ; Pilaš, Ivan ; Jurjević, Luka ; Balenović, Ivan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-1 / Jutzi, B. ; Weinmann, M. ; Hinz, S. - Karlsruhe : The International Society for Photogrammetry and Remote Sensing, 2018, 155-160

Skup
ISPRS TC I Midterm Symposium Innovative Sensing - From Sensors to Methods and Applications

Mjesto i datum
Karlsruhe, Njemačka, 10.10.2018. - 12.10.2018

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Sentinel-2 ; PlanetScope ; Fusion ; Vegetation ; Remote Sensing

Sažetak
Different spatial resolutions satellite imagery with global almost daily revisit time provide valuable information about the earth surface in a short time. Based on the remote sensing methods satellite imagery can have different applications like environmental development, urban monitoring, etc. For accurate vegetation detection and monitoring, especially in urban areas, spectral characteristics, as well as the spatial resolution of satellite imagery is important. In this research, 10-m and 20-m Sentinel-2 and 3.7-m PlanetScope satellite imagery were used. Although in nowadays research Sentinel-2 satellite imagery is often used for land-cover classification or vegetation detection and monitoring, we decided to test a fusion of Sentinel-2 imagery with PlanetScope because of its higher spatial resolution. The main goal of this research is a new method for Sentinel-2 and PlanetScope imagery fusion. The fusion method validation was provided based on the land-cover classification accuracy. Three land-cover classifications were made based on the Sentinel-2, PlanetScope and fused imagery. As expected, results show better accuracy for PS and fused imagery than the Sentinel-2 imagery. PlanetScope and fused imagery have almost the same accuracy. For the vegetation monitoring testing, the Normalized Difference Vegetation Index (NDVI) from Sentinel-2 and fused imagery was calculated and mutually compared. In this research, all methods and tests, image fusion and satellite imagery classification were made in the free and open source programs. The method developed and presented in this paper can easily be applied to other sciences, such as urbanism, forestry, agronomy, ecology and geology.

Izvorni jezik
Engleski

Znanstvena područja
Geodezija, Šumarstvo

POVEZANOST RADA