Forest aboveground biomass (AGB) is not only the basis for forest carbon stock research, but also an important parameter for assessing the forest carbon cycle and ecological functions of forests. However, there are various uncertainties in the estimation process, limiting the accuracy of AGB estimation. Therefore, we extracted the spectral features, vegetation indices and texture factors from remote sensing images based on the field data and Landsat 8 OLI remote sensing images in Southern China to quantify the uncertainties. Then, we established three AGB estimation models, including K Nearest Neighbor Regression (KNN), Gradient Boosted Regression Tree (GBRT) and Random Forest (RF). Uncertainties at the plot scale and models were measured by using error equations to analyze the influences of uncertainties at different scales on AGB estimation. Results were as follows: (1) The R² of the per-tree biomass model for Cunninghamia lanceolata was 0.970, while the uncertainty of the residual and parameters for per-tree biomass model was 4.62% and 4.81%, respectively; and the uncertainty transferred to the plot scale was 3.23%. (2) The estimation methods had the most significant effects on the remote sensing models. RF was more accurate than other two methods, and had the highest accuracy (R² = 0.867, RMSE = 19.325 t/ha) and lowest uncertainty (5.93%), which outperformed both the KNN and GBRT models (KNN: R² = 0.368, RMSE = 42.314 t/ha, uncertainty = 14.88%; GBRT: R² = 0.636, RMSE = 32.056 t/ha, uncertainty = 6.3%). Compared to KNN and GBRT, the R² of RF was enhanced by 0.499 and 0.231, while the uncertainty was decreased by 8.95% and 0.37%, respectively. The uncertainty associated with the scale of remote sensing models remains the primary source of uncertainty when compared to the plot scale. On the remote sensing scale, RF is the model with the best estimation effect. This study examines the impact of both plot-scale and remote sensing model-scale methodologies on the estimation of AGB for Cunninghamia lanceolata. The findings aim to offer valuable insights and considerations for enhancing the accuracy of AGB estimations.

Forest above-ground carbon stock (AGCS) is one of the primary ecological evaluation indicators, so it is crucial to estimate the AGCS accurately. In this research, we added the climatic and topographic factors to the estimation process by a remote sensing approach to explore their impact and to achieve more precise estimations. We hope to develop a more accurate estimation method for AGCS based on remote sensing data and climate data. The random forest (RF) method has good robustness and wide applicability. Therefore, we modeled and predicted the AGCS by RF based on sixty field sample plots of Pinus densata pure forests in southwest China and the factors extracted from Landsat 8 OLI images (source I), Sentinel-2A images (source II), and combined Landsat 8 OLI and Sentinel-2A images (source III). We added the topographic and climatic factors to establish the AGCS estimation model and compared the results. The topographic factors contain elevation, slope, and aspect. Climatic factors contain mean annual temperature, annual precipitation, annual potential evapotranspiration, and monthly mean potential evapotranspiration. It was found that the R² and RMSE of the model based on source III were better than the R² and RMSE of the models based on source I and source II. Compared to the models based on source I and source II, the model based on source III improved R² by up to 0.08, reduced RMSE by up to 2.88 t/ha, and improved P by up to 4.29%. Among the models without adding factors, the model based on source III worked the best, with an R² of 0.87, an RMSE of 10.81 t/ha, an rRMSE of 23.19%, and a P of 79.71%. Among the models that added topographic factors, the model based on source III worked best after adding elevation, with an R² of 0.89, an RMSE of 10.01 t/ha, an rRMSE of 21.47%, and a P of 82.17%. Among the models that added climatic factors, the model that added the annual precipitation factor had the best modeling result, with an R² of 0.90, an RMSE of 9.53 t/ha, an rRMSE of 20.59%, and a P of 83.00%. The prediction result exhibited that the AGCS of the Pinus densata forest in 2021 was 9,737,487.52 t. The combination of Landsat 8 OLI and Sentinel-2A could improve the prediction accuracy of the AGCS. The addition of annual precipitation can effectively improve the accuracy of AGCS estimation. Higher resolution of climate data is needed to enhance the modeling in future work.

Accurate estimation of forest aboveground carbon stock (AGC) is essential for understanding carbon accounting and climate change. In previous studies, the extracted factors, such as spectral textures, vegetation indices, and textural features, were used to estimate the AGC. However, few studies examined how different factors affect estimation accuracy in detail. Meanwhile, there are also many uncertainties in the collection and processing of the field data. To quantify the various uncertainties in the process of AGC estimation, we used the random forest (RF) to establish estimation models based on field data and Sentinel-1/2 images in Shangri-La. The models included the band information model (BIM), the vegetation index model (VIM), the texture information model (TIM), the Sentinel-2 factor model (S-2M), and the Sentinel-1/2 factor model (S-1/2M). Then, uncertainties resulting from the plot scale and estimation models were calculated using error equations. Our goal is to analyze the influence of different factors on AGC estimation and to assess the uncertainty of plot scale and estimation models quantitatively. The results showed that (1) the uncertainty of the measurement was 3.02%, while the error of the monocarbon stock model was the main uncertainty at the plot scale, which was 9.09%; (2) the BIM had the lowest accuracy (R² = 0.551) and the highest total uncertainty (22.29%); by gradually introducing different factors in the process of modeling, the accuracies improved significantly (VIM: R² = 0.688, TIM: R² = 0.715, S-2M: R² = 0.826), and the total uncertainty decreased to some extent (VIM: 14.12%, TIM: 12.56%, S-2M: 10.79%); (3) the S-1/2M with the introduction of Sentinel-1 synthetic aperture radar (SAR) data has the highest accuracy (R² = 0.872) and the lowest total uncertainty (8.43%). The inaccuracy of spectral features is highest, followed by vegetation indices, while textural features have the lowest inaccuracy. Uncertainty in the remote-sensing-based estimation model remains a significant source of uncertainty compared to the plot scale. Even though the uncertainty at the plot scale is relatively small, this error should not be ignored. The uncertainty in the estimation process could be further reduced by improving the precision of the measurement and the fitting of the monocarbon stock estimation model.

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.

Accurate estimation of forest aboveground carbon stock (AGC) is essential for understanding carbon accounting and climate change. In previous studies, the extracted factors such as spectral textures, vegetation indices, and textural features were used to estimate the AGC. However, few studies examined how different factors affect estimation accuracy in detail. Meanwhile, uncertainties in measurement and the fitting of mono-carbon stock model lack calculation. To quantify the various uncertainties in the process of AGC estimation, we used the Random Forest (RF) to establish estimation models based on field plots and Sentinel-1/2 images in Shangri-La. The models included the Band Information Model (BIM), Vegetation Index Model (VIM), Texture Information Model (TIM), Sentinel-2 factor Model (S-2M), and Sentinel-1/2 factor Model (S-1/2M). Then, uncertainties resulted from the plot scale and estimation models were calculated using error equations. Our goal is to analyze the influence of different factors on AGC estimation and to assess the uncertainty of plot scale and estimation models quantitatively. The results showed that: (1) the uncertainty of measurement was 3.02%, while the error of mono-carbon stock model was the main uncertainty at the plot scale, which was 9.09 %; (2) the BIM has the lowest accuracy (R2 = 0.551) and the highest total uncertainty (22.29%); by gradually introducing different factors in the process of modeling, the accuracies improved significantly (VIM: R2 = 0.688, TIM: R2 = 0.715, S-2M: R2 = 0.826) and the total uncertainty decreased to some extent (VIM: 14.12%, TIM: 12.56%, S-2M: 10.79%); (3) the S-1/2M with the introduction of Sentinel-1 Synthetic Aperture Radar (SAR) data has the highest accuracy (R2 = 0.872) and the lowest total uncertainty (8.43%). The uncertainty of spectral features is highest, following by vegetation indices, while textural features have the lowest uncertainty. Uncertainty in the remote sensing-based estimation model remains a significant source of uncertainty compared with the plot scale. Although texture features of SAR data are effective in reducing uncertainty, but uncertainty is lower at the plot scale. The total estimation uncertainty could be reduced by improving the precision of measurement and the fitting of the mono-carbon stock estimation model.