Prediction of Forest Burned Area based on MODIS-EVI2 and Ensemble Learning
In forest fire rescue,predicting the final burning area based on the early stages of the fire can effective-ly guide fire rescue.However,previous studies have used Normalized Difference Vegetation Index(NDVI)as an input indicator,which is sensitive to soil reflectance and has high data noise.Therefore,the Two-band En-hanced Vegetation Index(EVI2)is used to accurately predict the area burned by wildfires.In addition,to ad-dress the issue of poor anti-interference ability of a single machine learning prediction algorithm,a Stacking-XRSK model based on stacking ensemble learning is proposed.The results showed that using EVI2 increased R2 by 6.05％compared to NDVI,while reducing MAE and MSE by 0.88％and 0.41％,respectively.Com-pared with the single model,the Stacking-XRSK model has the highest R2,ranging from 2.8％to 11.06％,and MAE,MSE,and AOC are the lowest.The feasibility and accuracy of using EVI2 instead of NDVI to pre-dict the area of burnt areas have been verified.At the same time,the Stacking model can improve its generaliza-tion ability while fully leveraging the advantages of a single base model.This study provides scientific reference for forest fire safety management and timely firefighting.
万方数据
维普
