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Carbon absorption and storage in forests is one of the important ways to mitigate climate change. Therefore, it is essential to use a variety of remote-sensing resources to accurately estimate forest aboveground biomass (AGB) in dry-hot valley regions. In this study, satellite images from the Sentinel-1A, Sentinel-2A, and Gaofen-2 satellites were utilized to estimate the forest AGB in Yuanmou County, Yunnan Province, China. Different combinations of image data, based on selected variables of stepwise regression and their performance in constructing linear stepwise regression (LSR) and random forest (RF) models, were explored. The results showed that: (1) after adding the polarized values of the synthetic aperture radar backscatter coefficients, the combination fitting effect was significantly improved; (2) the fitting effect of the Sentinel-1A + Sentinel-2A + Gaofen-2 data combination was superior to the other combinations, indicating that the effective extraction of forest horizon and vertical information can improve the estimation effect of the forest AGB; and (3) the RF model exhibited superior fitting performance compared to the LSR model across all permutations of remotely sensed image datasets, with R2 values of 0.71 and 0.65, and RMSE values of 30.67 and 33.79 Mg/ha, respectively. These findings lay the groundwork for enhancing the precision of AGB estimation in dry-hot valley areas by integrating Sentinel-2A, Sentinel-1A, and GF-2 imagery, providing valuable insights for future research and applications. [ABSTRACT FROM AUTHOR]

Using various biomass factors, such as biomass expansion factor (BEF) and biomass conversion and expansion factor (BCEF), yields different results for estimating forest biomass. Therefore, ensuring compatibility between total biomass and its components when employing different biomass factors is crucial for developing a set of rapid and efficient models for large-scale biomass calculation. In this study, allometric equations were utilized to construct independent models and the proportional values (root-to-shoot ratio (Rra), crown-to-stem ratio (Rcs), bark-to-wood ratio (Rbw), foliage-to-bark ratio (Rfb), and wood biomass-to-wood volume (ρ)) by using the mean height (Hm) and the mean diameter at breast height (Dg) of 98 Pinus densata plots in Shangri-La, Yunnan province, China. The compatible methods were applied to reveal the compatibility between the total biomass and each component's biomass. The results showed the following: (1) Both the independent model and compatible model had a higher accuracy. The values were greater than 0.7 overall, but the foliage biomass accuracy was only 0.2. The total biomass and the component biomass showed compatibility. (2) The accuracy of BEF and BCEF exceeded 0.87 and the total error was less than 0.1 for most components. (3) The mean BEF (1.6) was greater than that of the Intergovernmental Panel on Climate Change (IPCC) (M = 1.3), and the mean BCEF was smaller than that of the IPCC; the values were 0.6 and 0.7, respectively. The range of BEF (1.4–2.1) and BCEF (0.44–0.89) were all within the range of the IPCC (1.15–3.2, 0.4–1.0). This study provides a more convenient and accurate method for calculating conversion coefficients (BEF and BCEF), especially when only Rcs data is available. [ABSTRACT FROM AUTHOR]

Comparing algorithms are crucial for enhancing the accuracy of remote sensing estimations of forest biomass in regions with high heterogeneity. Herein, Sentinel 2A, Sentinel 1A, Landsat 8 OLI, and Digital Elevation Model (DEM) were selected as data sources. A total of 12 algorithms, including 7 types of learners, were utilized for estimating the aboveground biomass (AGB) of Pinus yunnanensis forest. The results showed that: (1) The optimal algorithm (Extreme Gradient Boosting, XGBoost) was selected as the meta-model (referred to as XGBoost-stacking) of the stacking ensemble algorithm, which integrated 11 other algorithms. The R2 value was improved by 0.12 up to 0.61, and RMSE was decreased by 4.53 Mg/ha down to 39.34 Mg/ha compared to the XGBoost. All algorithms consistently showed severe underestimation of AGB in the Pinus yunnanensis forest of Yunnan Province when AGB exceeded 100 Mg/ha. (2) XGBoost-Stacking, XGBoost, BRNN (Bayesian Regularized Neural Network), RF (Random Forest), and QRF (Quantile Random Forest) have good sensitivity to forest AGB. QRNN (Quantile Regression Neural Network), GP (Gaussian Process), and EN (Elastic Network) have more outlier data and their robustness was poor. SVM-RBF (Radial Basis Function Kernel Support Vector Machine), k-NN (K Nearest Neighbors), and SGB (Stochastic Gradient Boosting) algorithms have good robustness, but their sensitivity was poor, and QRF algorithms and BRNN algorithm can estimate low values with higher accuracy. In conclusion, the XGBoost-stacking, XGBoost, and BRNN algorithms have shown promising application prospects in remote sensing estimation of forest biomass. This study could provide a reference for selecting the suitable algorithm for forest AGB estimation. [ABSTRACT FROM AUTHOR]

It is important to improve the accuracy of models estimating aboveground biomass (AGB) in large areas with complex geography and high forest heterogeneity. In this study, k-nearest neighbors (k-NN), gradient boosting machine (GBM), random forest (RF), quantile random forest (QRF), regularized random forest (RRF), and Bayesian regularization neural network (BRNN) machine learning algorithms were constructed to estimate the AGB of four forest types based on environmental factors and the variables selected by the Boruta algorithm in Yunnan Province and using integrated Landsat 8 OLI and Sentinel 2A images. The results showed that (1) DEM was the most important variable for estimating the AGB of coniferous forests, evergreen broadleaved forests, deciduous broadleaved forests, and mixed forests; while the vegetation index was the most important variable for estimating deciduous broadleaved forests, the climatic factors had a higher variable importance for estimating coniferous and mixed forests, and texture features and vegetation index had a higher variable importance for estimating evergreen broadleaved forests. (2) In terms of specific model performance for the four forest types, RRF was the best model both in estimating the AGB of coniferous forests and mixed forests; the R2 and RMSE for coniferous forests were 0.63 and 43.23 Mg ha−1, respectively, and the R2 and RMSE for mixed forests were 0.56 and 47.79 Mg ha−1, respectively. BRNN performed the best in estimating the AGB of evergreen broadleaved forests; the R2 was 0.53 and the RMSE was 68.16 Mg ha−1. QRF was the best in estimating the AGB of deciduous broadleaved forests, with R2 of 0.43 and RMSE of 45.09 Mg ha−1. (3) RRF was the best model for the four forest types according to the mean values, with R2 and RMSE of 0.503 and 52.335 Mg ha−1, respectively. In conclusion, different variables and suitable models should be considered when estimating the AGB of different forest types. This study could provide a reference for the estimation of forest AGB based on remote sensing in complex terrain areas with a high degree of forest heterogeneity. [ABSTRACT FROM AUTHOR]

• Accurately mapping forest AGB for large areas using images is challenging due to topographic complexity. • Random forest (RF) methods were compared for mapping forest AGB using Sentinel-2 images for Chuxiong. • Quantile RF coupled with the smallest mean error of each quantile led to the most accurate estimates. • Texture measures with windows of 3 × 3 and 5 × 5 pixels were more sensitive than other spectral variables. • Red edge 1 (B5) and Near-infrared bands derived EVIs were more sensitive than other vegetation indices. A simpler, unbiased, and comprehensive random forest (RF) model is needed to improve the accuracy of aboveground biomass (AGB) estimation. In this study, data were obtained from 128 sample plots of Pinus yunnanensis forest located in Chuxiong prefecture, Yunnan province, China. Sentinel-2 imagery data were applied to extract the important predictors of forest AGB, which were screened using the Boruta algorithm. We compared the fitting performance of two modified random forest models − regularized random forest (RRF) and quantile random forest (QRF) − with the random forest model. Moreover, we combined the smallest mean error of each quantile model as the best QRF (QRFb). The result showed: (1) Window sizes of 3 × 3 pixels and 5 × 5 pixels demonstrated greater sensitivity and suitability for estimating AGB than the 7 × 7 pixels window size. Enhanced vegetation indices derived from Red Edge 1 (B5) and Near-Infrared bands (B8A) were strongly correlated with AGB, indicating the heightened sensitivity of B5 and B8A bands to biomass and their potential in AGB estimation. (2) The RRF model outperformed both the standard RF and QRF in fitting performance, with an R2 of 0.56 and RMSE 57.14 Mg/ha. (3) The QRFb model exhibited the highest R2 of 0.88 and lowest RMSE of 29.56 Mg/ha, significantly reducing overestimation and underestimation issues. The modified RF regression supplies new insights into improving forest AGB estimation, which will be helpful for future research addressing carbon cycling. [ABSTRACT FROM AUTHOR]

Over-mature shale has undergone complex geological processes, e.g., diagenesis, deep thermolysis, epigenesis, and metamorphism. During this series of processes, the internal components of shale are transformed coordinatively, and the special pore structure with a complex genesis in the over-mature shale is formed. The study of the special pore structure of over-mature shale and its main controlling factors can provide theoretical support for shale gas exploration and development. In this study, the over-mature shale of the Longtan Formation in the Laochang area, eastern Yunnan Province, China, was tested and analyzed to determine the total organic carbon content, kerogen microscopic composition, and its vitrinite reflectance. Moreover, the mineral composition and pore characteristics of the shale were tested using X-ray diffraction, the argon ion polishing field emission scanning electron microscopy technique, and the low temperature nitrogen adsorption experiment. The results show that the compositions of Longtan Formation shales have a low rigid clastic particles content, high clay content, and abundant metasomatic ankerite, and that the organic matter, dominated by type III, is highly mature. At the same time, the shale pore structure is characterized by a large average pore size, well-developed macropores, and a high number of micropores and mesopores. By analyzing the transformation of the mineral composition of shale during thermal evolution, the controlling factors of pore structure are determined. During the evolution of shale, the intergranular pores (mainly large pores) between rigid minerals are destroyed in large quantities. After a short period, a large number of dissolved pores with the pore size of macropores are formed in the acid-resistant minerals under the action of organic acid. These dissolved pores, together with the remaining intergranular pores of rigid minerals and micro-fractures, form most of the macropores of shale. There is a large amount of ankerite in over-mature shale formed during metasomatism, which occludes some pores of shale. However, intragranular pores and cleavage-sheets develop in ankerite, contributing some porosity to the shale reservoir. Inter-clay pores and organic matter pores constitute most shale micro and mesopores, and between which inter-clay pores take dominance. [ABSTRACT FROM AUTHOR]

• Analysing spatial effects of trees is critical for improving forest management. • This study explored spatial effects of trees using biomass data in Yunnan, China. • The significant spatial autocorrelations vary with stands, components, and distances. • The stands' spatial heterogeneity increased with distance and stabilised at 10 m. The trees in a given stand compete with each other for light, water and nutrients, producing spatial effects. To understand spatial effects, it is critical to characterise the spatial distributions and patterns of the aboveground biomass (AGB) of individual trees and the biomass of their components (stem, bark, branches and foliage). This study investigated the spatial effects by examining the AGB of individual trees and the biomass of their components in the context of typical sub-tropical tree species in Yunnan, China, including a Pinus kesiya var. langbianensis natural forest (PN), a Pinus kesiya var. langbianensis plantation (PP), and a Eucalyptus spp. plantation (EP), using datasets for three clear-cutting plots. Ripley's L function was employed to identify the spatial distributions and patterns of the AGB of individual trees and the biomass of their components. Then, Global and Local Moran's I indices were utilised to analyse spatial autocorrelation, with Intra-group variance being calculated to quantify spatial heterogeneity. This led to following findings. Firstly, with increasing distance, the spatial heterogeneity of the three stands increased before stabilising at a distance of 10 m. Secondly, excluding bark, all components of the tree biomass in the PN stand exhibited significant dispersion. In contrast, all the components in the PP stand showed significant dispersion. The spatial distributions and patterns of trees and their biomass in the EP stand indicated the coexistence of clustering and dispersion. Moreover, the significant spatial autocorrelations of tree AGB and components biomass could be noticed but depending on the stands, components and distances. These findings provide theoretical support for sustainable forest management based on biomass and carbon stocks. [ABSTRACT FROM AUTHOR]