Feature Importance and Model Stability Data for Machine Learning-Based Mangrove Mapping in Sumbawa Regency, Indonesia (2025)
Description
This dataset contains feature importance scores and model stability results from a multi-sensor machine learning mangrove mapping study in Sumbawa Regency, West Nusa Tenggara, Indonesia. Files: 1. RF_importance.csv - Random Forest Gini importance scores for 15 spectral predictors - Columns: band (predictor name), importance (Gini score) 2. CART_importance.csv - CART Gini importance scores for 15 spectral predictors - Columns: band (predictor name), importance (Gini score) 3. SVM_importance.csv - SVM permutation-based importance (accuracy drop method) - Columns: band (predictor name), accuracy_drop 4. RF_stability_5seeds.csv - RF classification stability across 5 random seed configurations - Columns: seed, OA (overall accuracy), Kappa, Luas_Ha (mangrove area) Study Area: Sumbawa Regency, West Nusa Tenggara, Indonesia Satellite Data: Sentinel-1 (SAR) + Sentinel-2 (Multispectral) Platform: Google Earth Engine Year: 2025
Files
Steps to reproduce
1. Load Sentinel-1 and Sentinel-2 imagery for Sumbawa Regency (June-October 2025) in Google Earth Engine 2. Compute 15 spectral predictors including MVI, NDVI, EVI, LSWI, NDMI, CMRI, and SAR features 3. Generate 300 random training points per class from 6 land cover classes 4. Train RF, SVM, and CART classifiers 5. Extract feature importance using explain() for RF/CART and permutation method for SVM 6. Run RF with 5 random seeds (42, 123, 456, 789, 999) for stability assessment