Скачать или смотреть Veronica Andreo: Analyzing space-time satellite data with GRASS GIS for environmental monitoring

For this session we will use a time series of monthly MODIS NDVI data and we will demostrate the use of reliability bands included in satellite products, the process of gap-filling a time series, the estimation of different phenology indices such as the start, end and length of growing season and the rate of change. We'll do operations with time series and estimate a simple regression among them, too.

All the material for the session is hosted here: https://github.com/veroandreo/grass_o...

Slides:
https://gitpitch.com/veroandreo/grass...

Software required:
GRASS GIS 7.6.1 (stable version). Please see GRASS installation guide here: https://gitpitch.com/veroandreo/grass...

Data:
https://github.com/veroandreo/grass_o...

Script:
GRASS script: https://raw.githubusercontent.com/ver...

General info about GRASS GIS for time series analysis
GRASS GIS is a general purpose Free and Open Source geographic information system (GIS) that offers raster, 3D raster and vector data processing support. Since 2015, GRASS GIS has also oficially incorporated a powerful support for time series (TGRASS). Through this, GRASS GIS became the first open source temporal GIS with comprehensive spatio-temporal analysis, processing and visualization capabilities. This functionality makes it easy to manage, analyse and visualize for example climatic data, vegetation index time series, harvest data or landuse changes over time. Time series are handled through new data types called space time data sets (stds) which are used as input in TGRASS modules. In this way, TGRASS simplifies the processing and analysis of large time series of hundreds of thousands of maps. For example, users can aggregate a daily time series into a monthly time series in just one line; get the date per year in which a certain value is reached; select maps from a time series in time periods in which a different time series reaches a maximum value, perform different temporal as well as spatial operations among time series, and so much more.