It is important to achieve estimates at the minimum cost, with no greater uncertainty than that which is appropriate for the objectives of the inventory. The aim of this study was to estimate the forest volume efficiently and accurately by sampling and analyzing the existing forest survey data, which is also a technical challenge. In this work, we used the spatial statistics tools in the ArcGIS software to analyze spatial autocorrelations with the data from the sixth to ninth continuous forest inventories (CFI) of Sichuan Province from 2002, 2007, 2012, and 2017. Based on the sampling framework of the CFI, we divided the sampling units into five groups using different methods to create the second-stage samples. Combined with the spatial autocorrelation analysis results, we selected certain samples from the collection of second-stage samples through stratified sampling to form the third-stage sampling units. We applied the sampling ratio, sampling accuracy, workload, and costs as the evaluation indexes for the sampling efficiency analysis. The main results are as follows: Before conversion, the forest volume density had a positively skewed distribution. There was substantial positive spatial autocorrelation, and its intensity was affected by the distance scale, especially at 187.3 km, where the spatial processes of clustering were most pronounced. At the significance level of α = 0.01, the high-volume stands were mainly concentrated in the Aba Prefecture, Garze Prefecture, and Liangshan Prefecture, while the low-volume stands were mainly concentrated in the Sichuan Basin region. The heterogeneous gatherings were staggered between the high-volume areas and low-volume areas, while the transition zone between the three prefecture regions and basin region was randomly distributed. With 95% reliability, the average estimation accuracy of the systematic sampling, random sampling, and cluster sampling in the second stage was 94.09%, which is less accurate than the CFI estimation accuracy. The mean correlation coefficients (R) between the estimated value of the forest volume and the observations of the systematic sampling, random sampling, and cluster sampling in the second stage were 0.95, 0.98, and 0.96, respectively. The relative differences (RD%) were −0.52, −0.39, and −0.36, respectively. The spatial stratified sampling in the third stage, which is based on spatial distribution pattern information, significantly reduced the sampling ratio to 1.68 per 10,000, compared with the average ratios of the CFI sampling and second-stage sampling, which were 13.73 per 10,000 and 2.75 per 10,000, respectively. With 95% reliability, the mean accuracy of the spatial stratified sampling in the third stage was 93.05%, the R was 0.94, and the RD% was −0.09. Spatial stratified sampling is more in line with the actual work conducted in annual surveys because it effectively reduces the sample size using prior spatial information, which can better meet the requirements of the annual output.

Improving the precision of aboveground biomass (AGB) estimation in lowland tropical forests is crucial to enhancing our understanding of carbon dynamics and formulating climate change mitigation strategies. This study proposes an AGB estimation method for lowland tropical forests in Xishuangbanna, which include various vegetation types, such as Pinus kesiya var. langbianensis, oak, Hevea brasiliensis, and other broadleaf trees. In this study, 2016 forest management inventory data are integrated with remote sensing variables from Landsat 8 OLI (L8) and Sentinel 2A (S2) imagery to estimate forest AGB. The forest age and aspect were utilized as stratified variables to construct the random forest (RF) models, which may improve the AGB estimation accuracy. The key findings are as follows: (1) through variable screening, elevation was identified as the main factor correlated with the AGB, with texture measures derived from a pixel window size of 7 × 7 perform best for AGB sensitivity, followed by 5 × 5, with 3 × 3 being the least effective. (2) A comparative analysis of imagery groups for the AGB estimation revealed that combining L8 and S2 imagery achieved superior performance over S2 imagery alone, which, in turn, surpassed the accuracy of L8 imagery. (3) Stratified models, which integrated aspect and age variables, consistently outperformed the unstratified models, offering a more refined fit for lowland tropical forest AGB estimation. (4) Among the analyzed forest types, the AGB of P. kesiya var. langbianensis forests was estimated with the highest accuracy, followed by H. brasiliensis, oak, and other broadleaf forests within the RF models. These findings highlight the importance of selecting appropriate variables and sensor combinations in addition to the potential of stratified modeling approaches to improve the precision of forest biomass estimation. Overall, incorporating stratification theory and multi-source data can enhance the AGB estimation accuracy in lowland tropical forests, thus offering crucial insights for refining forest management strategies.

Integrating multi-source remote sensing can improve the accuracy of forest aboveground biomass (AGB) estimation. However, the accuracy and stability of the forest AGB estimation results are affected by multiple remote sensing feature variables as well as parameter tuning of machine learning algorithms. To this end, this study employed six types of remote sensing data—Landsat 8 OLI, Sentinel-2A, GEDI, ICESat-2, ALOS-2, and SAOCOM. A dual-variable selection strategy based on SHapley Additive exPlanations (SHAP) was developed, and a genetic algorithm (GA) was used to optimize the parameters of five machine learning models—elastic net (EN), least absolute shrinkage and selection operator (Lasso), support vector regression (SVR), Random Forest (RF), and Categorical Boosting (CatBoost)—to estimate the AGB of Pinus kesiya var. langbianensis forest in Wuyi Village, Zhenyuan County. The dual-variable selection strategy integrates SHAP with the Pearson correlation coefficient (PC), RF, EN, and Lasso to enhance feature screening robustness and interpretability. The results of the study showed that Lasso-SHAP dual-variate screening was more stable than SHAP univariate screening. In particular, the Lasso-SHAP strategy improved the average R² from 0.59 (using SHAP alone) to above 0.70, achieving an enhancement of 11%. Among GA-optimized parametric machine learning models, the linear GA-Lasso achieved the best performance, with an R² of 0.91 and an RMSE of 12.94 Mg/ha, followed by the GA-EN model (R² = 0.89, RMSE = 14.46 Mg/ha). For nonlinear models, GA-SVR performed the best (R² = 0.74, RMSE = 22.07 Mg/ha), surpassing the GA-CatBoost model (R² = 0.64, RMSE = 25.88 Mg/ha). In summary, the Lasso-SHAP dual-variable selection strategy effectively improves the estimation accuracy of AGB for Pinus kesiya var. langbianensis forests, while GA-optimized machine learning models demonstrate excellent performance, providing strong support for regional-scale forest resource monitoring and carbon stock assessment.

Accurate estimation of small-scale forest biomass is a prerequisite and basis for trading forest carbon sinks and optimizing the allocation of forestry resources. This study aims to develop a plot-scale methodology for estimating aboveground biomass (AGB) that combines a biomass horizontal distribution model (HDM) and sampling techniques to improve efficiency, reduce costs and provide the reliability of estimation for biomass. Simao pine (Pinus kesiya var. langbianensis) from Pu’er City, Yunnan Province, was used as the research subject in this study. A canopy profile model (CPM) was constructed based on data from branch analysis and transformed into a canopy biomass HDM. The horizontal distribution of AGB within the sample plots was simulated using the HDM based on the data from the per-wood survey and compared with the results from the location distribution model (LDM) simulation. AGB sampling estimations were carried out separately by combining different sampling methods with the AGB distribution of sample plot simulated by different biomass distribution models. The sampling effectiveness of all sampling schemes was compared and analyzed, and the best plan for the sampling estimation of AGB in plot-scale forests was optimized. The results are as follows: the power function model is the best model for constructing the CPM of the Simao pine in this study; with visual comparison and the analysis of the coefficient of variation, the AGB simulated by HDM has a larger and more continuous distribution than that simulated by LDM, which is closer to the actual distribution; HDM-based sampling plans have smaller sample sizes and sampling ratios than LDM-based ones; and lastly, the stratified sampling method (STS)-HDM-6 plan has the best sampling efficiency with a minimum sample size of 10 and a minimum sampling ratio of 15%. The result illustrates the potential of the method for estimating plot-scale forest AGB by combining HDM with sampling techniques to reduce costs and increase estimation efficiency effectively.

The optical saturation problem is one of the main factors causing uncertainty in aboveground biomass (AGB) estimations using optical remote sensing data. It is critical for the improvement in AGB estimation accuracy to clarify the relationships between environmental factors and the variations in optical saturation values (OSVs). In this study, we obtained the OSVs for 20 districts and clarified the individual, interactive, and comprehensive effects of climate, soil, and topographical factors on the OSV variations. The results are as follows: (1) the range of the OSVs was from 104 t/hm² to 182 t/hm² for the 20 districts; (2) the soil factor had the greatest (−0.635) influence on the OSVs compared to climate and topography; (3) the highest interaction effect (−0.833) was between climate and soil; (4) the comprehensive effect of the three environmental factors on the OSVs was obvious, and the correlation coefficient was 0.436. Moreover, the mean temperature of the coldest quarter (MCQMean) had the highest effect on the OSVs. The results indicate that environmental factors significantly affect the variation in OSVs through their individual, interactive, and comprehensive effects. Our findings provide a valuable reference for reducing the uncertainty caused by spectral saturation in AGB estimations using optical remote sensing data.