A New Spatiotemporal Filtering Method to Reconstruct Landsat Time-Series for Improving Estimation Accuracy of Forest Aboveground Carbon Stock

Abstract

Landsat time-series (LTS) archived the multitemporal hyperspectral images, providing freely accessible and long-term optical data for estimating forest aboveground carbon stock (ACS). Due to LTS carrying noise, there were such issues as bias, outliers, and missing values in ACS estimation. Hence, a new filtering method named terrain-perceive spatiotemporal filtering (TP-STF) was developed to improve the estimation accuracy. In TP-STF, landforms were classified based on the terrain data. A computer-recognizable identifier was generated by perceiving each terrain unit. Combining the discriminative criteria with the spatiotemporal information, the TP-STF adaptively selected performant filtering to reconstruct LTS. Then, the random forests regression (RFR) was employed to estimate ACS of Pinus densata in Shangri-La, Yunnan, China. Compared with the other filtering, the TP-STF method's reconstructed LTS had the best modeling accuracy and the highest prediction accuracy, with R2 = 0.903, RMSE = 17.049 t/hm2, P = 81.080%, and rRMSE = 19.691%. The ACS results using TP-STF and RFR were: 6.56 million tons in 1987, 6.44 million tons in 1992, 6.33 million tons in 1997, 6.35 million tons in 2002, 6.72 million tons in 2007, 6.70 million tons in 2012, and 7.04 million tons in 2017. The TP-STF could effectively denoise the LTS images in high-altitude regions, providing a new approach to improve the accuracy of remote sensing-based forest ACS estimation.

Keywords

• Aboveground carbon stock (ACS)
• Landsat time-series (LTS)
• Shangri-La
• terrain-perceive spatiotemporal filtering (TP-STF)