Landsat time-series (LTS) archived the multi-temporal 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.

In forest resource surveys, using sampling methods to estimate aboveground carbon stock (ACS) can significantly reduce survey costs. This study improves the accuracy of ACS estimation by optimizing the stratified sampling design. The sampling process was divided into two stages: stratification and intra-stratum sampling. For stratification, remote sensing features were used as stratification variables, and a spatial clustering stratification method was introduced. For intra-stratum sampling, a composite method, Spatially Correlated Poisson Disk Sampling (SCPDS), was proposed. Using Random Forest (RF) and the sample points selected by SCPDS, the ACS was estimated and compared with traditional sampling methods for Pinus densata in Shangri-La, Yunnan, China. The results showed that (1) by selecting effective stratification variables (e.g., texture features), the required sample size was reduced by up to 19.35% compared to that of simple random sampling; (2) the Ward clustering method greatly improved stratification heterogeneity; (3) for intra-stratum sampling, the SCPDS method ensured spatial independence within strata, particularly at low sampling rates (1%–5%), where its error was significantly lower than that of other methods, indicating greater stability and improved accuracy; (4) the SCPDS-based model achieved the best fitting accuracy, with R² = 0.886. The total carbon stock of Pinus densata using RF was 7,872,787.5 t, closely matching forest management inventory (FMI) data. Through sampling, even with a relatively small sample size, the representative plots can still accurately reflect ACS estimates that are consistent with those derived from large-scale plot surveys. Thus, the optimized stratified sampling method effectively reduced sampling costs while significantly enhancing the stability and accuracy of the results.