Contents
How is trend analysis of MODIS NDVI data used?
Time series analysis was applied to an 11-year MODIS NDVI satellite data record, based on the hypothesis that the resulting NDVI residual trend vectors would enable successful detection of changes in photosynthetically active vegetation. We performed regression analysis, derived regression slope values, and generated a map of significant trends.
How are time series analysis used in NDVI?
Time series analysis was applied to an 11-year MODIS NDVI satellite data record, based on the hypothesis that the resulting NDVI residual trend vectors would enable successful detection of changes in photosynthetically active vegetation.
When does the MODIS NDVI times series start?
Time series animations of Earth observation imagery are captivating and engaging. In this tutorial, you’ll learn how to generate an animated GIF representing 20-year median NDVI for serial 16-day MODIS composites spanning January 1st through December 31st.
What is the median NDVI for each year?
The data making up each frame of the animation is the 20-year median NDVI for the same 16-day composite each year from 2000 to 2019. Generating these median composites is the crux of this tutorial.
Can a MODIS NDVI satellite detect land degradation?
This study examines whether MODIS NDVI satellite data time series can be used to detect land degradation and regeneration areas in Mongolia.
How is the MODIS vegetation index ( mod13 ) used?
1.1 The MODIS vegetation index (VI) products The MODIS VI products (MOD13) provide consistent, spatial and temporal time series comparisons of global vegetation conditions that can be used to monitor the Earth’s terrestrial photosynthetic vegetation activity in support of phenologic, change detection, and biophysical interpretations.
What kind of data is used in timesat?
The program is tested with the 8 km ×8 km pixel resolution Pathfinder AVHRR Land (PAL) data set generated by NASA/NOAA ( Townshend, 1994; James and Kalluri, 1994 ). 2. Data NDVI data ( Rouse et al., 1974) are organized in images (two-dimensional spatial arrays).