Comparing different methods for biomass modeling over tropical region based on Landsat data

Manyao Li

doi:10.1117/12.2667877

10 February 2023 Comparing different methods for biomass modeling over tropical region based on Landsat data

Manyao Li

Proceedings Volume 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022); 125521L (2023) https://doi.org/10.1117/12.2667877
Event: International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 2022, Kunming, China

Abstract

Forest biomass is an important ecological parameter for forest research, which is greatly important to the understanding and analysis of global carbon cycle. Landsat Time Series (LTS) data, as medium optical resolution data, have the merits of long time–span and appropriate spatial resolution. Forest biomass research based on this data can better guide human understanding of forest ecosystem. In this study, LTS data and sample biomass data were used to study the forest biomass modeling. This study explored the effects of forest biomass modeling based on features extracted from two LTS processing methods and three algorithms including random forest (RF), extremely randomized trees (ERT), and eXtreme Gradient Boosting (XGBoost). Among these models, the accuracy of the models based on the features extracted by Best Available Pixel (BAP) image composite and three algorithms is generally higher. Among the models constructed by combining feature groups extracted from BAP image composite and three algorithms, the models constructed by ERT were stable in most situations from the perspective of coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and bias. However, the XGBoost model that combines all variables performed best in perspective of R² and RMSE, where R² can reach 0.87, and the RMSE, MAE, and bias were 34.84 Mg/ha, 19.26 Mg/ha, and 2.29 Mg/ha, respectively. The textures and Tasseled Cap (TC) indices also show favorable performances in different models in the feature importance analysis.

Citation Download Citation

Manyao Li "Comparing different methods for biomass modeling over tropical region based on Landsat data", Proc. SPIE 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 125521L (10 February 2023); https://doi.org/10.1117/12.2667877

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KEYWORDS

Biomass

Composites

Landsat

Data modeling

Modeling

Feature extraction

Biological research

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