Biomass estimation of artificial Robinia pseudoacacia forest in Yellow River Delta based on multi-source remote sensing data

doi:10.14088/j.cnki.issn0439-8114.2023.07.025

Abstract

Abstract: Using sentinel images, digital terrain data and forest field quadrat survey data, K-nearest neighbor （KNN） model, random forest （RF） model, extreme gradient enhancement （XGBboost） model and Stacking model were constructed respectively to estimate the biomass of artificial Robbin pseudoacacia forest in Yellow River Delta. The results showed that the integrated learning Stacking model significantly improved the accuracy of biomass estimation compared with K-nearest neighbor model, random forest model, and extreme gradient enhancement model （R²=0.61, RMSE=13.42 t/hm²）.

Key words: sentinel, Stacking model, Robbin pseudoacacia forest, biomass, Yellow River Delta

CLC Number:

WANG Yi-cong. Biomass estimation of artificial Robinia pseudoacacia forest in Yellow River Delta based on multi-source remote sensing data[J]. HUBEI AGRICULTURAL SCIENCES, 2023, 62(7): 143-148.

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