Do not confuse trees and ensembles (which may be consist from models, that need to be scaled).
Trees do not need to scale features, because at each node, the entire set of observations is divided by the value of one of the features: relatively speaking, to the left everything is less than a certain value, and to the right - more. What difference then, what scale is chosen?
Recently, I've been interested in Data analysis.
So I researched about how to do machine-learning project and do it by myself.
I learned that scaling is important in handling features.
So I scaled every features while using Tree model like Decision Tree or LightGBM.
Then, the result when I scaled had worse result.
I searched on the Internet, but all I earned is that Tree and Ensemble algorithm are not sensitive to variance of the data. 
I also bought a book "Hands-on Machine-learning" by O'Relly But I couldn't get enough explanation.
Can I get more detailed explanation for this?
Why Does Tree and Ensemble based Algorithm don't need feature scaling?