Monitoring natural and rural ecosystems using the NDVI anomaly: an application to the Umbria Region
- Published
- Accepted
- Subject Areas
- Spatial and Geographic Information Systems
- Keywords
- NDVI, NDVI monitoring, MODIS, gfoss, python
- Copyright
- © 2016 Massei et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. Monitoring natural and rural ecosystems using the NDVI anomaly: an application to the Umbria Region. PeerJ Preprints 4:e2191v1 https://doi.org/10.7287/peerj.preprints.2191v1
Abstract
The aim of this work is to present a semi-automatic system that, through the use of a plurality of software tools, is able to obtain data from the satellite MODIS / Terra Vegetation Indices 16-Day L3 250m SIN Global Grid (MOD13Q1), to process them and to provide results (in terms of DEVNDVI) almost in real time. The model is applied to the Umbrian territory, for analyzing the dynamics of the vegetation. The applications proposed are two: the former is on the whole regional vegetation, while the latter considers just one specific agrarian typology, the olive orchards. The analysis of anomalies is possible thanks to a database of data collected since January 1st 2001. The comparison between NDVIi and NDVIaverage (the latter taken from the database) allows to identify areas, and the corresponding habitats, which show significant anomalies. Information obtained from such an analysis can be used for constructing synthetic indicators, representing the response of vegetation to climate variability. These indicators can be very useful to identify suffering areas, within particular habitat or ground coverings, such as vineyard and olive groves for example, or to monitor areas sensitive to climate variability (Sykes,2009)
Author Comment
This is an article intended for the OGRS2016 Collection.
