Dear Researchers,
I want to detect vegetation changes in the permafrost areas and I downloaded AVHRR GIMMS NDVI3g images from this website.
https://nex.nasa.gov/nex/projects/1349/
I tried to get some guide from google and youtube how to use these images for vegetation change detection and I did not find any helpful information.
Please share some healthy links and info in this context using the mentioned data sets.
Thanks in Advance.
Regards
Naveed Ahmed.
Dear Ahmed,
Check the links below :
https://www.sciencedirect.com/science/article/pii/S0303243411001504
https://link.springer.com/article/10.1007/s00254-007-0901-9
Wishes
Dear Saadi,
You might want to expatiate further on the difficulty you encountered.
However,
https://www.youtube.com/watch?v=9z4XtUfApyc
Monitoring NDVI change is an active topic in remote sensing. It maybe include the following concepts.
1. NDVI trend analysis to analyze the NDVI linear trend in the long-term period. Usally, it is represented by the yearly increasing rate.
Suggested method: Theil-Sen median
2. Significant test for trend analysis to determine whether the NDVI trend by Theil-Sen median is statistically significant or not.
Suggested method: Mann-Kendall Test
3. Continuity analysis of NDVI time series to take a measurement of long-term memory of NDVI time series. For example, if NDVI is increasing (by Theil-Sen median) significantly (by Mann-Kendall Test) during 2000-2018, does it keep increasing trend, or oppositely turn to decrease, or fluctuate unpredictably, in future?
Suggested method: Hurst exponent.
Three methods aforementioned can be integrated and get some types of combinations about NDVI status:
I. decreasing significantly (severely) & remaining in future
II. decreasing insignificantly (slightly) & remaining in future
III. unchanged stably & remaining in future
IV. increasing insignificantly (slightly) & remaining in future
V. increasing significantly (markedly) & remaining in future
VI. unpredictable in future
More methods about NDVI time series analysis can be found in the attached.
In addition, you can use more complex method such as EOF(Empirical Orthogonal Function) analysis, also called PCA(Principal components analysis), which determine the spatial and temporal characteristics of time series and often used in climate change research.
Article Changes in hydro-meteorological conditions over tropical Wes...
Good luck!
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