A novel approach to produce NDVI image series with enhanced spatial properties. Issue 1 (1st January 2016)
- Record Type:
- Journal Article
- Title:
- A novel approach to produce NDVI image series with enhanced spatial properties. Issue 1 (1st January 2016)
- Main Title:
- A novel approach to produce NDVI image series with enhanced spatial properties
- Authors:
- Maselli, Fabio
Chiesi, Marta
Pieri, Maurizio - Abstract:
- Abstract: A novel multi-step method is presented to improve the spatial properties of MODIS NDVI data series based on one or few single-date higher spatial resolution (HR) images. This method does not rely on the classification of the HR imagery, which is often inadequate in characterizing all main vegetation types that are present in the observed area. An unmixing strategy is instead applied to identify these vegetation types from the low spatial resolution (LR) MODIS imagery, which offers a more effective description of seasonal NDVI evolutions. In particular, an annual multitemporal MODIS NDVI data series is preliminarily decomposed by an automatic technique, which produces abundance images representative of the main vegetation types. These images are then used to extract spatially variable NDVI endmembers. Next, a statistical method is applied to improve the spatial features of the abundance images based on these endmembers and the available HR NDVI imagery. The final recombination of the spatially enhanced abundance images and NDVI endmbemers allows the production of synthetic imagery, which maintains the temporal information of the MODIS NDVI data and most spatial properties of the HR images. The new method is preliminarily tested using an annual MODIS NDVI data series and five Landsat 8 OLI images taken in a study area of Tuscany (Central Italy). The results obtained support the potential of the method and indicate some possibilities for future methodologicalAbstract: A novel multi-step method is presented to improve the spatial properties of MODIS NDVI data series based on one or few single-date higher spatial resolution (HR) images. This method does not rely on the classification of the HR imagery, which is often inadequate in characterizing all main vegetation types that are present in the observed area. An unmixing strategy is instead applied to identify these vegetation types from the low spatial resolution (LR) MODIS imagery, which offers a more effective description of seasonal NDVI evolutions. In particular, an annual multitemporal MODIS NDVI data series is preliminarily decomposed by an automatic technique, which produces abundance images representative of the main vegetation types. These images are then used to extract spatially variable NDVI endmembers. Next, a statistical method is applied to improve the spatial features of the abundance images based on these endmembers and the available HR NDVI imagery. The final recombination of the spatially enhanced abundance images and NDVI endmbemers allows the production of synthetic imagery, which maintains the temporal information of the MODIS NDVI data and most spatial properties of the HR images. The new method is preliminarily tested using an annual MODIS NDVI data series and five Landsat 8 OLI images taken in a study area of Tuscany (Central Italy). The results obtained support the potential of the method and indicate some possibilities for future methodological advancement. … (more)
- Is Part Of:
- European journal of remote sensing. Volume 49:Issue 1(2016)
- Journal:
- European journal of remote sensing
- Issue:
- Volume 49:Issue 1(2016)
- Issue Display:
- Volume 49, Issue 1 (2016)
- Year:
- 2016
- Volume:
- 49
- Issue:
- 1
- Issue Sort Value:
- 2016-0049-0001-0000
- Page Start:
- 171
- Page End:
- 184
- Publication Date:
- 2016-01-01
- Subjects:
- NDVI -- MODIS -- OLI -- Image enhancement
Remote sensing -- Periodicals
Remote sensing
Electronic journals
Periodicals
621.3678 - Journal URLs:
- https://www.tandfonline.com/toc/tejr20/current ↗
http://www.tandfonline.com/ ↗ - DOI:
- 10.5721/EuJRS20164910 ↗
- Languages:
- English
- ISSNs:
- 2279-7254
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 7093.xml