Forest Aboveground Biomass Research Articles

Extracting above-ground biomass in areas corresponding to FAO’s definition of “Forest” using open geospatial data: Results for ASEAN

Abstract. In this study, we extracted above-ground biomass (AGB) information for forests in different ecological zones of ASEAN (Association of Southeast Asian Nations) by integrating freely-available global AGB, forest, and ecological zone map products. Our objective was to assess the suitability of the data and proposed approach for national reporting of forest carbon stocks. We compared the satellite-derived AGB values of forests in ASEAN countries with the corresponding AGB values provided by the Intergovernmental Panel for Climate Change (IPCC) in their Guidelines for National Greenhouse Gas Inventories (which are derived from ground-based measurements of forest AGB). For this, we used a map integration approach that ensures that AGB data is extracted from areas corresponding to the Food and Agricultural Association (FAO) of the UN’s definitions of “forest” and “ecological zones”, as recommended by the relevant IPCC Guidelines. We found that the average satellite-derived AGB values extracted for each ecological zone were generally lower than the values for natural forests (but higher than the values for plantation forests) provided in the IPCC Guidelines. Further investigation showed that this was partly due to the presence of many erroneously low AGB values for forests in the extracted results, caused by errors in the data masks applied to the original global AGB map, including masks of cropland, urban areas, bare soil, and water bodies. Our findings suggest that further processing is necessary before using satellite-derived data for national reporting of forest carbon stocks in ASEAN countries, and we give a few possible options for this.

Application of Bayesian Causal Inference in the Study of the Relationship Between Biodiversity and Aboveground Biomass of Subtropical Forest in Eastern China

The relationship between biodiversity and ecosystem function is crucial for understanding the structure and processes of subtropical forest ecosystems. However, the extent to which biodiversity influences subtropical forest biomass remains unclear. This study applies Bayesian causal inference to explore causal relationships between forest Aboveground Biomass (AGB) and its potential driving factors (biodiversity factors, biotic factors and abiotic factors) based on Huangshan Forest Dynamics Plots. Furthermore, hypothetical interventions are introduced to these driving factors within the causal network to estimate their potential impact on AGB. The causal relationship network reveals that species diversity and functional diversity are the most direct factors influencing AGB, whereas phylogenetic diversity exerts only an indirect effect. Biotic and abiotic factors also contribute indirect effects on AGB, potentially by influencing other mediating indexes. Intervention analysis shows that with low-level interventions on direct influencing factors, the probability of low AGB is as high as 84%. As the intervention level increases to high, the probability of low AGB decreases by 36%. Moreover, AGB demonstrates a particularly sensitive response to changes in Rao’s quadratic entropy (RaoQ) intervention levels, more so than to other factors, highlighting its critical role in maintaining forest biomass. Therefore, we contend that functional diversity, due to its direct reflection of species’ roles in ecosystem processes, is a more accurate measure of the impact of biodiversity on biomass compared to species or phylogenetic diversity and the interplay between abiotic and biotic factors and biodiversity should not be overlooked. This approach offers a powerful tool for exploring causal relationships, thereby providing a more nuanced and accurate understanding of the relationship between biodiversity and forest ecosystem function.

Elevation, Soil and Environmental Factors Determine the Spatial and Quantitative Distribution of Qinghai Spruce Recruitment Biomass in Mountainous (Alpine) Watersheds

Soil heterogeneity observed in the alpine environment plays a very important role in the growth of forest recruitment. However, the mechanisms by which the biomass accumulation and allocation patterns of forest recruitment respond to such environmental differences are unclear, which hinders a thorough understanding of climate change’s impact on forest biomass. We hypothesized that soil heterogeneity influences the distribution of Qinghai spruce recruitment biomass along with elevation. In the frame of this study, carried out in the northern Tibetan Plateau, forest Qinghai spruce recruitment data were combined with soil data derived from 24 sample plots, while permutation multifactor ANOVA and multiple linear regression were utilized to reveal the characteristics of forest recruits’ above- and below-ground biomass and their allocation patterns in response to soil heterogeneity. According to the results, the soil heterogeneity mainly affected the distribution characteristics of recruits’ above- and below-ground biomass at different elevations, while the recruits’ root–shoot ratio variability was influenced by a combination of soil and other environmental factors. Soil organic carbon (SOC) had the greatest effect on the variability of the above- and below-ground biomass of spruce recruits, with R2 of 0.280 and 0.257, respectively. Soil organic carbon and soil moisture content (SMC) had a significant effect on the variability of the root–shoot ratio, with R2 of 0.168 and 0.165, respectively. Soil total nitrogen (TN) and soil organic carbon were the main influencing factors of the above-ground biomass of forest recruits, with contribution rates of 43.15% and 35.28%, respectively. Soil total nitrogen and soil organic carbon were also the main factors influencing the below-ground biomass of forest recruits, with contribution rates of 42.52% and 37.24%, respectively, and both of them had a positive effect on biomass accumulation, and the magnitude of the influence varied with the elevation gradient. Soil moisture content was the main influence factor of spruce recruits’ root–shoot ratio, with a contribution rate of 54.12%. Decreasing soil moisture content would significantly increase the root–shoot ratio of spruce recruits and promote plants to allocate more biomass to root growth. Changes in elevation not only affected the intensity of the effect of soil factors on spruce recruitment biomass and its allocation pattern but even led to a change in the positive and negative effects.

A Separable Bootstrap Variance Estimation Algorithm for Hierarchical Model‐Based Inference of Forest Aboveground Biomass Using Data From NASA's GEDI and Landsat Missions

ABSTRACTThe hierarchical model‐based (HMB) statistical method is currently applied in connection with NASA's Global Ecosystem Dynamics Investigation (GEDI) mission for assessing forest aboveground biomass (AGB) in areas lacking a sufficiently large number of GEDI footprints for employing hybrid inference. This study focuses on variance estimation using a bootstrap procedure that separates the computations into parts, thus considerably reducing the computational time required and making bootstrapping a viable option in this context. The procedure we propose uses a theoretical decomposition of the HMB variance into two parts. Through this decomposition, each variance component can be estimated separately and simultaneously. For demonstrating the proposed procedure, we applied a square‐root‐transformed ordinary least squares (OLS) model, and parametric bootstrapping, in the first modeling step of HMB. In the second step, we applied a random forest model and pairwise bootstrapping. Monte Carlo simulations showed that the proposed variance estimator is approximately unbiased. The study was performed on an artificial copula‐generated population that mimics forest conditions in Oregon, USA, using a dataset comprising AGB, GEDI, and Landsat variables.

Estimation of above-ground biomass in dry temperate forests using Sentinel-2 data and random forest: a case study of the Swat area of Pakistan

Accurate mapping of above-ground biomass (AGB) is essential for carbon stock quantification and climate change impact assessment, particularly in mountainous areas. This study applies a random forest (RF) regression model to predict the spatial distribution of AGB in Usho (site A) and Utror (site B) forests located in the northern mountainous region of Pakistan. The predicted maps elucidate AGB variations across these sites, with non-forest areas excluded based on an normalized difference vegetation index (NDVI) threshold value of <0.4. Three different combinations of input datasets were used to predict the biomass, including spectral bands (SBs) only, vegetation indexes (VIs) only, and a combination of both spectral bands and vegetation indexes (SBVIs). Utilizing SBs, the biomass ranged between 150 and 286 mg/ha in site A and 99 and 376 mg/ha in site B. Meanwhile, using VIs indicated a biomass range of 163 Mg/ha–337 Mg/ha and 131–392 Mg/ha for sites A and B, respectively. The combination of spectral bands and vegetation indexes yielded AGB values of 145–290 Mg/ha in site A and 116–389 Mg/ha in site B. The northern and western regions of site A, characterized by higher altitudes and lower forest density, notably showed lower biomass values than other regions. Conversely, similar regions in site B, situated at lower latitudes, demonstrated different biomass ranges. The RF model exhibited robust accuracy, with R2 values of 0.74 and 0.83 for spectral bands and vegetation indexes, respectively. However, with a combination of both, an R2 of 0.79 was achieved. Furthermore, altitudinal gradients significantly influence the biomass distribution across both sites, with specific elevation ranges yielding optimal results. The AGB variation along the slope further corroborated these findings. In both sites, the western aspects showed the highest biomass across all combinations of input datasets. The variable importance analysis highlighted that ARVI8a, NDI45, Band12, Band11, TSAVI8, and ARVI8a are significant predictors in sites A and B. This comprehensive analysis enhances our understanding of AGB distribution in the mountainous forests of Pakistan, offering valuable insights for forest management and ecological studies.

Aboveground biomass estimation and mapping using Sentinel-2 data in a dry afromontane forest

ABSTRACT Forest biomass estimates are required for many applications, including accounting for the role of forests in the global carbon cycle, supporting sustainable forest management and making informed decisions. For all these applications and others, accurate and reliable forest biomass estimates are required. This study evaluated the potential of Sentinel-2 data for predicting and mapping aboveground biomass (AGB) of a dry Afromontane Forest in Tigray, Northern Ethiopia. Multiple linear regression was employed to model the relationship between AGB and Sentinel-2-derived spectral variables. The evaluation criteria for best-fit model selection were based on the coefficient of determination (R2), root mean square error (RMSE, %), and bias (Bias, %). All the spectral variables evaluated here were significantly correlated with AGB (p < 0.01). The model that includes a fraction of photosynthetically active radiation (FAPAR), leaf area index (LAI), Band 2 and Band 3 as predictor variables provided the best predictive performance for AGB in the study area (R 2 = 0.38, RMSE = 13.62 Mg ha−1 and Bias = -3.10 Mg ha−1). The predicted AGB of the study area ranges from 0.1 to 141.8 Mg ha−1, with a mean value of 12.4 Mg ha−1. The results from this study suggested that Sentinel-2 data can be potentially applied for estimating and mapping AGB in dry Afromontane forests.

Above ground biomass estimation in the upper Blue Nile basin forests, North-Western Ethiopia

Forest ecosystems play a decisive role in the global climatic condition, as well as, provides a wide range of societal benefits, including fuel-wood, tourism, and ecosystem services are considered as one of the major sources of livelihood for the local people in the upper Blue Nile Basin. Therefore, rapid and accurate estimation of forest biomass is crucial for greatly reducing the uncertainty in carbon stock assessments, and for designing strategic forest management plans. Because, above-ground biomass (AGB) estimation is important in determining the management, environmental, and economic roles of forests in the Blue Nile basin. The study was aimed at estimating above-ground biomass in the Upper Blue Nile Basin forests by integrating field-measured data with predictors from Sentinel-2 image. The relationship between measured AGB and sentinel-2 derived vegetation indices and biophysical parameters showed a good correlation result (r value ranging from 0.67 to 0.74). A stepwise regression analysis was carried out in order to develop AGB estimation model by identifying the most important variable. The result demonstrated that, green normalized difference vegetation index, leaf area index, fraction of absorbed photosynthetic active radiation and fractional vegetation cover achieved good performance in predicting AGB with R2 value > 0.5. AGB was estimated with a coefficient of determination (R2) of 0.59 adjusted R2 of 0.618 and root mean square error of (RMSE) 38.36 t/ha in comparison to field observations. The maximum AGB value of 268.32 t/ha was estimated in the Alemsaga natural forest, which is a highly protected dense forest stand from any entrance and disturbance. Generally, integrating field data with optical remote sensing data provides more reliable result for AGB estimation. Moreover, it is also recommended to employ RADAR and LiDAR remote sensing data products together in order to attain more precise estimate results of AGB with great potential for forest resource monitoring and management.

Spatial and Temporal Patterns of Forest Biomass Carbon Sink in China from 1990 to 2021

China’s forests act as a large carbon sink and play a vital role in achieving the carbon neutrality goal by the 2060s. To achieve this goal, the magnitude and spatial patterns of forest carbon sinks must be accurately quantified. In this study, we aim to provide the longest estimate of forest biomass carbon storage and sinks in China at a 1 km spatial resolution from 1990 to 2021 by merging long-term observations from optical and microwave remote sensing datasets with a field-validated benchmark map. We explored the spatial characteristics of aboveground biomass (AGB) and belowground biomass (BGB) carbon in China’s forests, as well as variations in AGB carbon sinks. The average AGB and BGB carbon storage from 1990 to 2021 in China’s forests were 8.42 ± 0.96 Pg C and 1.9 ± 0.21 Pg C, respectively. The average annual AGB carbon sink during this period was approximately 0.083 ± 0.023 Pg C yr−1. Forests in the southwest region contributed 31.15% of the forest AGB carbon sink in China and contributed 41.01% of the forest AGB carbon storage. Our study presents an effective tool for assessing changes in forest biomass carbon by leveraging comprehensive multi-source remote sensing data and highlights the importance of obtaining large-scale, high-quality, consistent, and accessible plot survey data to validate the earth observation of biomass.

Geoinformatics and Modeling Approaches for Estimating Above-Ground Biomass in the Moist Deciduous Forests of Uttara Kannada, Karnataka, India

The present study aims to estimate Above-Ground Biomass (AGB) in the moist deciduous forests of Mundgod Taluk, Uttara Kannada district, Karnataka, India using a combination of field sampling and remote sensing techniques. AGB was assessed using Sentinel-2A satellite imagery with a spatial resolution of 10 meters. Field data were collected using the Point-Centered Quarter (PCQ) method, it includes measuring tree girth at breast height (GBH), tree height, and canopy density. AGB was calculated using artificial form factor and specific gravity of tree species and further modelled using the Normalized Difference Vegetation Index (NDVI) derived from satellite data. The results showed a significant correlation between canopy density and biomass, with very dense forests exhibiting the highest AGB values. The field-measured AGB was 436.96 t/ha for very dense forests, 259.89 t/ha for moderately dense forests, and 161.67 t/ha for open forests. A linear regression model developed between AGB and NDVI showed a high R² value of 0.91 for open forests, 0.87 for moderately dense forests, and 0.85 for very dense forests. The total area-weighted biomass for the study area was estimated at 8.87 million tons, with 4.79 million tons in very dense forests, 3.44 million tons in moderately dense forests, and 0.76 million tons in open forests. The regression model validation indicated low Root Mean Square Error (RMSE) values, with the moderately dense forests 8.81 t/ha and the in very dense forests 7.54 t/ha. This study highlights the effectiveness of integrating field measurements with remote sensing for rapid, reliable AGB estimation. The approach offers valuable insights into biomass distribution and carbon sequestration potential, providing a scalable method for carbon inventories at state and national levels, contributing to forest management and climate change mitigation efforts.

A radar backscatter simulation of a forest canopy using 3D physical structures derived from LiDAR scanning

ABSTRACT This study aims to explore the forest aboveground biomass relationship to C-band backscatter. A one-hectare area in Wytham Woods, located west of Oxford, was selected for this research. The area has a total of 525 trees of seven different tree species. The Michigan Microwave Canopy Scattering Model (MIMICS), a two-layer radiative transfer model, was applied to simulate canopy backscatter responses in this deciduous UK forest. Model parameters related to forest structure were derived from previously published terrestrial laser (LiDAR) data. Simulated backscatter was performed for co-polarized and cross-polarized modes at a C-band frequency range and incidence angles (20° to 45° at 5° increments). The research includes five objectives: i) backscatter sensitivity analysis from the variation of different model parameters; ii) backscatter seasonal effect under spring–summer (leaf-on) and autumn–winter (leaf-off) periods; iii) backscatter comparison between species as well as between simulated and satellite observations in a spatial pattern; iv) relationship between simulated backscatter and aboveground biomass; and v) relationship between MIMICS forest structure parameters and simulated backscatter. Sensitivity analysis results showed significant differences in backscatter from changes in leaf distribution, leaf thickness and water content (leaf, trunk and soil) for both polarization modes in leaf-on scenarios. Leaf-off scenarios presented significant differences from changes in branch distribution but only for the co-polarized mode. Seasonal variations presented significant backscatter differences between spring–summer and autumn–winter scenarios; additionally, backscatter differences among species for each seasonality in the co-polarized mode were observed. The computation of the grid resolution (20 m × 20 m) showed a range of backscatter values depending on the grid and incidence angle. Higher backscatter values were observed for the satellite data than for the simulated data. Finally, the aboveground biomass and the MIMICS input parameters presented high random variability and little systematic co-variation with the total backscatter on both simulated seasons, suggesting that more robust allometric equations for biomass estimation are required.

Evaluation of statistical and machine learning models using satellite data to estimate aboveground biomass: A study in Vietnam Tropical Forests

The combination of machine learning models with satellite imagery is becoming a popular data-modeling tool for biomass prediction, supporting land cover management. This study aims to select the most suitable model to estimate tropical forest aboveground biomass in Vietnam, helping to manage and monitor changes in biomass at regional and local scales. The study identified the optimal model for estimating forest aboveground biomass and minimizing the number of input variables while achieving satisfactory model performance. A total of 59 input variables, including topography, texture features, and vegetation indices, from satellite data were used in four non-parametric algorithms and a conventional parametric model, Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression (MLR) to predict biomass and evaluate changes aboveground biomass over 10 years in two tropical forests in Vietnam. The results indicated that all models had good estimation performance with R2 ranging from 0.615 to 0.754. For RF, MLR, and XGBoost, vegetation indices contributed the highest model weights, occupying 77.71% – 92.48%. For ANN and SVM, textural and topographic features were the majority of the model weights (73.74 – 96.36%). The RF model performed the best using 59 variables (R2 = 0.754, MAE = 78.5 Mg·ha−1, and %RMSE = 13.57%) and ten variables (R2 = 0.745, MAE = 85.8 Mg·ha−1, and %RMSE = 16.17%). The biomass map using the RF and ten variables achieved a good degree of fitting of 0.76, so it was suitable for managing and monitoring forest biomass in Vietnam. The results indicated a sharp decrease in the areas of dense and very dense forests from 2013 to 2021 and a gradual increase in 2023.

Estimation of the Aboveground Biomass of Forests in Complex Mountainous Areas Using Regression Kriging

The forest area in China’s plateaus and mountainous regions accounts for as much as 43% of the country’s total forest area. Accurately estimating the aboveground biomass (AGB) in these plateau and mountain forests is significant for global carbon sink assessment and climate change. However, the complexity of the natural environment poses significant challenges to the accurate estimation of forests’ aboveground biomass (AGB), and the accuracy of both AGB estimation and spatial mapping needs further improvement. This study utilized support vector regression, backpropagation neural networks, and random forests to predict trends in AGB and establish an optimal original model for forest AGB estimation. Further calibration was performed using regression kriging on the optimal model. The results indicated that (1) random forests achieved the highest coefficient of determination (R2 for cypress = 0.63, R2 for fir = 0.66, R2 for cryptomeria = 0.64, and R2 for mixed forest = 0.54), showing greater potential in predicting AGB in complex mountainous mixed forests; (2) the residual kriging method significantly improved the estimation accuracy, increasing the R2 values of the original RF model by 25%, 24%, and 22%, and improving the accuracy of mixed plot estimates from 54% to 81%; and (3) the residual kriging method effectively addressed the underestimation of high values and overestimation of low values in AGB estimates, broadening the range of AGB values and allowing for a more detailed spatial distribution of forests’ aboveground biomass.

Response of Hydrothermal Conditions to the Saturation Values of Forest Aboveground Biomass Estimation by Remote Sensing in Yunnan Province, China

Identifying the key climate variables affecting optical saturation values (OSVs) in forest aboveground biomass (AGB) estimation using optical remote sensing is crucial for analyzing OSV changes. This can improve AGB estimation accuracy by addressing the uncertainties associated with optical saturation. In this study, Pinus yunnanensis forests and Landsat 8 OLI imagery from Yunnan were used as case studies to explain this issue. The spherical model was applied to determine the OSVs using specific spectral bands (Blue, Green, Red, Near-Infrared (NIR), and Short-Wave Infrared Band 2 (SWIR2)) derived from Landsat 8 OLI imagery. Canonical correlation analysis (CCA) uncovered the intricate relationships between climatic variables and OSV variations. The results reveal the following: (1) All Landsat 8 OLI spectral bands showed a negative correlation with the Pinus yunnanensis forest AGB, with OSVs ranging from 104.42 t/ha to 209.11 t/ha, peaking in the southwestern region and declining to the lowest levels in the southeastern region. (2) CCA effectively explained 93.2% of the OSV variations, identifying annual mean temperature (AMT) as the most influential climatic factor. Additionally, the mean temperature of the wettest quarter (MTQ) and annual precipitation (ANP) were significant secondary determinants, with higher OSV values observed in warmer, more humid areas. These findings offer important insights into climate-driven OSV variations, reducing uncertainty in forest AGB estimation and enhancing the precision of AGB estimations in future research.

CNN-based transfer learning for forest aboveground biomass prediction from ALS point cloud tomography

ABSTRACT This study presents a new approach for predicting forest aboveground biomass (AGB) from airborne laser scanning (ALS) data: AGB is predicted from sequences of images depicting vertical cross-sections through the ALS point clouds. A 3D version of the VGG16 convolutional neural network (CNN) with initial weights transferred from pre-training on the ImageNet dataset was used. The approach was tested on datasets from Canada, Poland, and the Czech Republic. To analyse the effect of training sample size on model performance, different-sized samples ranging from 10 to 375 ground plots were used. The CNNs were compared with random forest models (RFs) trained on point cloud metrics. At the maximum number of training samples, the difference in RMSE between observed and predicted AGB of CNNs and RFs ranged from −2 t/ha to 5 t/ha, and the difference in squared Pearson correlation coefficient ranged from −0.05 to 0.06. Additional pre-training on synthetic data derived from virtual laser scanning of simulated forest stands could only improve the prediction performance of the CNNs when only a few real training samples (10–40) were available. While 3D CNNs trained on cross-section images derived from real data showed promising results, RFs remain a competitive alternative.

Spatial pattern of forest aboveground biomass and its environmental influencing factors in Qinling-Daba Mountains, central China

Accurate estimation of forest aboveground biomass (AGB) is crucial for understanding and managing forest ecosystems in the context of global environmental change, and also provides a scientific basis for national and regional ecological planning and carbon emission reduction policies. In order to investigate the regional pattern of forest AGB and its influencing factors in central China, a total of 469 sample plots were measured along four sample transects and on six mountains in field survey. The results showed that: 1) Two longitudinal distribution patterns of forest AGB were found, and one was that the AGB in the Qinling Mountains and the Daba Moutains gradually decreased from east to west, and the other was that the AGB in the areas between the two mountains gradually increased from east to west. The latitudinal distribution pattern of the forest AGB showed an "increasing–decreasing-increasing–decreasing" trend from south to north. The altitudinal distribution pattern showed a "first increasing-then decreasing" pattern with increasing altitude. 2) The influence of each factor on the spatial pattern of forest AGB was manifested as temperature > precipitation > HAI > topography, indicating that the spatial pattern of forest AGB in central China was the result of the interaction of climate, human activities and natural factors.

Modelling and Mapping of Aboveground Carbon of Oluwa Forest Reserve Using LandSat 8 TM and Forest Inventory Data

This research was carried out within the Oluwa Forest Reserve to evaluate and forecast its capacity for aboveground carbon sequestration using data from the Landsat Thematic Mapper. The Oluwa Forest Reserve, situated in Ondo State, Nigeria, is renowned for its abundant biodiversity and vast expanse. Assessing the forest's aboveground biomass and carbon traditionally involves intricate and expensive processes necessitating the expertise of diverse professionals and specialized equipment. Hence, this study investigated the utilization of Geographic Information System (GIS) and Remote Sensing (RS) technology, employing Landsat bands to calculate spectral indices and construct linear models for predicting the aboveground carbon sequestration potential of the tropical rainforest ecosystem within the Oluwa Forest Reserve. The measured aboveground carbon from sample plots, alongside the estimated spectral indices, was utilized to simulate the distribution of aboveground carbon across the Oluwa Forest Reserve. A positive linear correlation was identified between the observed data and the estimated spectral indices. Consequently, linear models were developed, and the most suitable model was determined through statistical analysis. The average aboveground carbon estimated from the sample plots was 150.70 tons per hectare (t/ha), closely aligning with the predicted value of 149.80 t/ha. Statistical analysis yielded a coefficient of determination of 94% and a Root Mean Square Error of 6.38E-16. These results indicate that the selected model accurately predicts the distribution of aboveground carbon within the Oluwa Forest Reserve. This study underscores the importance of spectral data, GIS, and RS in the efficient modelling and mapping of aboveground carbon in extensive forest ecosystems.

Investigating changes of forest aboveground biomass induced by Moso bamboo expansion with terrestrial laser scanner

As a typical clonal plant, Moso bamboo expands excessively worldwide, causing changes in various aspects of the native forest ecosystem. Among these aspects, aboveground biomass (AGB) is a key indicator characterizing forest productivity and carbon sequestration. However, it is difficult to track AGB changes of a fixed plot in a relatively short period. In this paper, we utilized terrestrial laser scanner (TLS) to investigate AGB changes resulting from the intrusion of Moso bamboo using the space-for-time substitution method. Three sample plots including a China fir stand, a mixed stand and a pure Moso bamboo stand were chosen at an ecotone to represent the different stages of bamboo expansion in Hutou Village, Chongqing, China. Their point clouds were first scanned using TLS, and then segmented into individual plants through refinedly processing the stem intersections. Subsequently, tree and bamboo classification was achieved via combining the structural features, stem texture features, and point distribution features of individual plants. Finally, the compatible biomass models were employed to estimate plant AGBs and analyze the changes. As a result, the overall classification accuracy of trees and bamboos was improved to 92.67 %. The AGB per unit area initially increased and subsequently decreased at three stages of Moso bamboo expansion (5.83 kg/m2, 6.04 kg/m2 and 5.36 kg/m2), and the AGB differences among individual plants showed the similar tendency. Notably, the average AGB of individual China firs in mixed stand (78.97 kg) was higher than that in the pure stand (70.41 kg), so did the average AGB of individual Moso bamboos (21.22 kg vs 18.70 kg). These results indicated that maintaining a certain degree of tree-bamboo mixture was beneficial for improving the gross forest AGB.

Aboveground Biomass Mapping in SemiArid Forests by Integrating Airborne LiDAR with Sentinel-1 and Sentinel-2 Time-Series Data

Aboveground biomass (AGB) is a vital indicator for studying carbon sinks in forest ecosystems. Semiarid forests harbor substantial carbon storage but received little attention due to the high spatial–temporal heterogeneity that complicates the modeling of AGB in this environment. This study assessed the performance of different data sources (annual monthly time-series radar was Sentinel-1 [S1]; annual monthly time series optical was Sentinel-2 [S2]; and single-temporal airborne light detection and ranging [LiDAR]) and seven prediction approaches to map AGB in the semiarid forests on the border between Gansu and Qinghai Provinces in China. Five experiments were conducted using different data configurations from synthetic aperture radar backscatter, multispectral reflectance, LiDAR point cloud, and their derivatives (polarimetric combination indices, texture information, vegetation indices, biophysical features, and tree height- and canopy-related indices). The results showed that S2 acquired better prediction (coefficient of determination [R2]: 0.62–0.75; root mean square error [RMSE]: 30.08–38.83 Mg/ha) than S1 (R2: 0.24–0.45; RMSE: 47.36–56.51 Mg/ha). However, their integration further improved the results (R2: 0.65–0.78; RMSE: 28.68–35.92 Mg/ha). The addition of single-temporal LiDAR highlighted its structural importance in semiarid forests. The best mapping accuracy was achieved by XGBoost, with the metrics from the S2 and S1 time series and the LiDAR-based canopy height information being combined (R2: 0.87; RMSE: 21.63 Mg/ha; relative RMSE: 14.45%). Images obtained during the dry season were effective for AGB prediction. Tree-based models generally outperformed other models in semiarid forests. Sequential variable importance analysis indicated that the most important S1 metric to estimate AGB was the polarimetric combination indices sum, and the S2 metrics were associated with red-edge spectral regions. Meanwhile, the most important LiDAR metrics were related to height percentiles. Our methodology advocates for an economical, extensive, and precise AGB retrieval tailored for semiarid forests.

Forest above-ground biomass estimation based on strongly collinear variables derived from airborne laser scanning data

Airborne laser scanning technique (ALS) is the most appealing remote sensing technique for the precise estimation of forest above-ground biomass (AGB). Significantly strong correlations (collinearity) among the independent variables derived from ALS data decrease the accuracy of developed AGB models. To address this issue, we propose a novel variable selection algorithm-an improved sure independence screening (SPV), which integrates the Pearson correlation coefficient, threshold μ, and variance inflation factor. We further compared the performance of SPV-based, stepwise feature selection (SFS)-based, and least absolute shrinkage and selection operator (LASSO)-based AGB models developed with different regression approaches that were sensitive to strong collinearity. Field-measured data and corresponding ALS data, acquired from 1002 sample plots distributed across four distinct forest types within the Guangxi Zhuang Autonomous Region in Southern China, were used to evaluate variable selection techniques and develop AGB models. Results indicated that ALS variables selected by SPV exhibited weaker collinearity compared to those selected by SFS and LASSO. SPV-based AGB models outperformed SFS-based AGB models with higher leave-one-out cross-validation adjusted R2 (LOOCV Radj2) by 0.1% − 27.8%. SPV-based AGB models outperformed LASSO-based AGB models with higher LOOCV Radj2 by 0.4% − 16.3%. Hence, for variable selection in constructing AGB models (linear regression model, log–log regression model, and generalized additive model (GAM)) based on strongly collinear ALS variables, SPV is most preferred, followed by SFS and LASSO. The smooth curves from our GAMs developed using SPV-selected variables revealed that five canopy height variables (whp40, hmean, hp25, hp70, hskew), one canopy density variable (whp85), three density-related variables (nVegPts, bcc, and mcc), and one vertical structural variable (LADmean) were positively correlated with AGB. The canopy height variables (whp40, hmean, hp25, and hp70) were identified as the most important variables in estimating AGB for four forest types. The canopy density variable wdp85 showed a strong effect on AGB of the coniferous forests, whereas it had almost no effect on the AGB of broadleaved forests. Overall, this manuscript proposes a novel variable selection algorithm named SPV, aimed at addressing collinearity of variables derived from ALS data, which has significant implications for the application of ALS in forest inventory and forest modeling.

Mapping aboveground biomass in Indonesian lowland forests using GEDI and hierarchical models

Spaceborne lidar (light detection and ranging) instruments such as the Global Ecosystem Dynamics Investigation (GEDI) provide a unique opportunity for global forest inventory by generating broad-scale measurements sensitive to the vertical arrangement of plant matter as a supplement to in situ measurements. Lidar measurables are not directly relatable to most physical attributes of interest, including biomass, and therefore must be related through statistical models. Further, GEDI observations are not spatially complete, necessitating methods to convert the incomplete samples to predictions of area averages/totals. Such methods can face challenges in equatorial and persistently cloudy areas, such as Indonesia, where the density of quality observations is diminished. We developed and implemented a hierarchical model to produce gap-free maps of aboveground biomass density (AGBD) at various resolutions within the lowlands of Jambi province, Indonesia. A biomass model was trained between local field plots and a metric from GEDI waveforms simulated with coincident airborne laser scanning (ALS) data. After selecting a locally suitable ground-finding algorithm setting, we trained an error model depicting the discrepancies between the simulated and GEDI-observed waveforms. Finally, a geostatistical model was used to model the spatial distribution of the on-orbit GEDI observations. These three models were nested into a single hierarchical model, relating the spatial distribution of GEDI observations to field-measured AGBD. The model allows spatially complete predictions at arbitrary resolutions while accounting for uncertainties at each stage of the relationship. The model uncertainties were low relative to the predicted biomass, with a median relative standard deviation of 8% at the 1 km resolution and 26% at the 100 m resolution. The spatially consistent information on AGBD provided by our model is beneficial in support of sustainable forest management, carbon sequestration initiatives and the mitigation of climate change. This is particularly relevant in a dynamic tropical landscape like Jambi, Indonesia in order to understand the impacts of land-use transformations from forests to cash crops like oil palm and rubber. More generally, we advocate for the use of hierarchical models as a framework to account for multiple stages of relationships between field and sensor data and to provide reliable uncertainty audits for final predictions.