What is it about?
The scenes of the area for different seasons over 4 years were classified using vegetation indices to classify the land cover into two classes vegetation or non-vegetation areas. Two kinds of vegetation indices were applied to make the final classification using a k-NN algorithm to decide the region into three classes (evergreen, seasonal plants, or non-vegetation).
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Why is it important?
The results showed that the k-NN algorithm is capable to classify satellite data into three vegetation indicators with high accuracy when determining the optimum threshold of vegetation index. The NDVSI index can be considered as a better indicator for studying vegetation cover in dry and humid areas and areas with low vegetation cover.
Perspectives
I hope this article is useful for the conservation of natural plant resources, and serves the decision makers and institutions concerned. I hope you find this article thought-provoking.
Reem Sh Hameed
University of Baghdad
Read the Original
This page is a summary of: Analysis time series of vegetation indices using Landsat-8 satellite imagery, January 2023, American Institute of Physics,
DOI: 10.1063/5.0129346.
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