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Open AccessArticle

Uncertainty Analysis of Remote Sensing Pretreatment for Biomass Estimation on Landsat OLI and Landsat ETM+

1
School of Environment and Resources Science, Zhejiang A & F University, Hangzhou 311300, China
2
School of Landscape Architecture, Zhejiang A & F University, Hangzhou 311300, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(1), 48; https://doi.org/10.3390/ijgi9010048
Received: 16 December 2019 / Revised: 8 January 2020 / Accepted: 13 January 2020 / Published: 15 January 2020
The accurate quantification of biomass helps to understand forest productivity and carbon cycling dynamics. Research on uncertainty during pretreatment is still lacking despite it being one of the major sources of uncertainty and an essential step in biomass estimation. In this study, we investigated pretreatment uncertainty and conducted a comparative study on the uncertainty of three optical imagery preprocessing stages (radiometric calibration, atmospheric and terrain correction) in biomass estimation. A combination of statistical models (random forest) and multisource data (Landsat enhanced thematic mapper plus (ETM+), Landsat operational land imager (OLI), national forest inventory (NFI)) was used to estimate forest biomass. Particularly, mean absolute error (MAE) and relative error (RE) were used to assess and quantify the uncertainty of each pretreatment, while the coefficient of determination (R2) was employed to evaluate the accuracy of the model. The results obtained show that random forest (RF) and 10-fold cross validation algorithms provided reliable accuracy for biomass estimation to better understand the uncertainty in pretreatments. In this study, there was a considerable uncertainty in biomass estimation using original OLI and ETM+ images from. Uncertainty was lower after data processing, emphasizing the importance of pretreatments for improving accuracy in biomass estimation. Further, the effects of three pretreatments on uncertainty of biomass estimation were objectively quantified. In this study (results of test sample), a 33.70% uncertainty was found in biomass estimation using original images from the OLI, and a 34.28% uncertainty in ETM+. Radiometric calibration slightly increased the uncertainty of biomass estimation (OLI increased by 1.38%, ETM+ increased by 2.08%). Moreover, atmospheric correction (5.56% for OLI, 4.41% for ETM+) and terrain correction (1.00% for OLI, 1.67% for ETM+) significantly reduced uncertainty for OLI and ETM+, respectively. This is an important development in the field of improving the accuracy of biomass estimation by remote sensing. Notably, the three pretreatments presented the same trend in uncertainty during biomass estimation using OLI and ETM+. This may exhibit the same effects in other optical images. This article aims to quantify uncertainty in pretreatment and to analyze the resultant effects to provide a theoretical basis for improving the accuracy of biomass estimation.
Keywords: biomass estimation; uncertainty; data preprocessing; 10-fold cross validation; random forest biomass estimation; uncertainty; data preprocessing; 10-fold cross validation; random forest
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Zhang, Q.; Xu, L.; Zhang, M.; Wang, Z.; Gu, Z.; Wu, Y.; Shi, Y.; Lu, Z. Uncertainty Analysis of Remote Sensing Pretreatment for Biomass Estimation on Landsat OLI and Landsat ETM+. ISPRS Int. J. Geo-Inf. 2020, 9, 48.

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