Land cover and forest health indicator datasets for central india using very-high resolution satellite data

SUMMARY

The images were mosaiced and clipped (i.e. pre-processed) into 233 tiles in ArcMap (Supplementary Fig 1) and then uploaded into Google Earth Engine (GEE), which was the first step to testing algorithms, classifying land cover, and calculating the BGI. Scientific Data | Algorithm Highest overall accuracy Kappa Random Forest Support Vector Machine Boosted Decision Tree (AdaBoost) Kohonen`s Self Organizing Map Four of the Planet imagery tiles, covering the fieldwork region were classified using RF, Support Vector Machine15, Boosted Decision Tree with AdaBoost, adaptive boosting16, and Kohonen`s Self Organizing Map with k-means clustering17,18 . . .

 



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