FUSION OF SENTINEL-2 AND PLANETSCOPE IMAGERY FOR VEGETATION DETECTION AND MONITORING
- 1Chair of Photogrammetry and Remote Sensing, Faculty of Geodesy, University of Zagreb, Kačićeva 26, Zagreb, Croatia
- 2Chair of Geoinformatics, Faculty of Geodesy, University of Zagreb, Kačićeva 26, Zagreb, Croatia
- 3Division for Forest Ecology, Croatian Forest Research Institute, Cvjetno naselje 41, Jastrebarsko, Croatia
- 4Division for Forest Management and Forestry Economics, Croatian Forest Research Institute, Cvjetno naselje 41, Jastrebarsko, Croatia
Keywords: Sentinel-2, PlanetScope, Fusion, Vegetation, Remote Sensing
Abstract. 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.