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Article analysis:

This article provides an overview of the use of RGB images from two different periods to estimate fractional vegetation cover (FVC) in desert regions. The authors discuss three pixel-based machine learning algorithms, namely gradient boosting decision tree (GBDT), k-nearest neighbor (KNN) and random forest (RF), which were used to classify the main vegetation and calculate coverage. The results show that RGB images are suitable for mapping FVC, and that there was a significant increase in grass coverage from 2006 to 2019.

The article is generally reliable and trustworthy, as it provides detailed information on the methods used for estimating FVC as well as the results obtained from them. Furthermore, the authors provide an independent data set for evaluating the accuracy of their algorithms, which adds credibility to their findings. However, there are some potential biases in the article that should be noted. For example, while the authors mention that satellite data can be used for estimating FVC with satisfactory accuracy, they do not provide any evidence or examples of this claim. Additionally, while they discuss UAVs as an alternative method for obtaining higher resolution images of deserts, they do not explore other possible methods such as aerial photography or ground surveys which may also be useful for estimating FVC in desert regions. Finally, while they note that determining the best spatial resolution for different vegetation features may make estimation of desert vegetation coverage more accurate, they do not provide any further details on how this could be achieved or what factors should be taken into consideration when doing so.