Accurate Estimation Of Forest Biomass Research Articles

Comparative Study of Single-Wood Biomass Model at Plot Level Based on Multi-Source LiDAR

Forests play an important role in promoting carbon cycling and mitigating the urban heat island effect as one of the world’s major carbon storages. Scientifically quantifying tree biomass is the basis for assessing tree carbon storage and other ecosystem functions. In this study, a sample plot of Populus tomentosa plantation in the Olympic Forest Park in Beijing was selected as the research object. Point cloud data from three types of laser scanners, including terrestrial laser scanner (TLS), backpack laser scanner (BLS), and handheld laser scanner (HLS), were used to estimate the biomass of single tree trunks, branches, leaves, and aboveground total biomass based on the Allometric Biomass Model (ABM) and Advanced Quantitative Structure Model (AdQSM). The following conclusions were drawn from the estimation results: (1) For the three types of laser scanner point clouds, the biomass estimation values obtained using the AdQSM model were generally higher than those obtained using the Allometric Biomass Model. However, the estimation values obtained using the two models were similar, especially for tree trunks and total biomass. (2) For total biomass and individual biomass components of single trees, the results obtained from handheld and terrestrial laser scanner point clouds are consistent; however, they show some differences from the results obtained from backpack-mounted point clouds. This study further enriches the methodological system for estimating forest biomass, providing a theoretical basis and reference for more accurate estimates of forest biomass and more sustainable forest management.

Construction and precision analysis of individual tree biomass model of Larix olgensis considering random effects

Accurate estimation of forest biomass in China is crucial for the study of carbon cycle and mechanisms underlying carbon storage in global terrestrial ecosystems. Based on the biomass data of 376 individuals of Larix olgensis in Heilongjiang Province, we used seemingly unrelated regression (SUR) method to build a univariate biomass SUR model with diameter at breast height as the independent variable and considering the random effect at the sampling site level. Then, a seemingly unrelated mixed effect (SURM) model was constructed. As the calculation of random effects of SURM model did not require the empirically measured values of all dependent variables, we analyzed the deviations from the following four types in detail: 1) SURM1, the random effect was calculated according to the measured biomass of stem, branch and foliage; 2) SURM2, the random effect was calculated according to the measured value of tree height (H); 3) SURM3, the random effect was calculated according to the measured crown length (CL); 4) SURM4, the random effect was calculated according to the measured values of H and CL. The results showed that the fitting effect of branch and foliage biomass models was improved significantly after considering the horizontal random effect of the sampling plot, with R2 being increased by more than 20%. The fitting effect of stem and root biomass models were improved slightly, with R2 being increased by 4.8% and 1.7%, respectively. When using five randomly selected trees to calculate the horizontal random effect of the sampling plot, the prediction performance of SURM model was better than that of SUR model and SURM model considering only fixed effects, especially SURM1 model (MAPE% of stem, branch, foliage and root was 10.4%, 29.7%, 32.1% and 19.5%, respectively). Except for SURM1 model, the deviation of SURM4 in predicting stem, branch, foliage and root biomass was smaller than that of SURM2 and SURM3 models. In actual prediction, although the prediction accuracy of SURM1 model was the highest, it needed to measure aboveground biomass of several trees, and the use cost was relatively high. Therefore, the SURM4 modelled on measured H and CL was recommended to predict the standing tree biomass of L. olgensis.

Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China

The accurate estimation of forest biomass is crucial for supporting climate change mitigation efforts such as sustainable forest management. Although traditional regression models have been widely used to link stand biomass with biotic and abiotic predictors, this approach has several disadvantages, including the difficulty in dealing with data autocorrelation, model selection, and convergence. While machine learning can overcome these challenges, the application remains limited, particularly at a large scale with consideration of climate variables. This study used the random forests (RF) algorithm to estimate stand aboveground biomass (AGB) and total biomass (TB) of larch (Larix spp.) plantations in north and northeast China and quantified the contributions of different predictors. The data for modelling biomass were collected from 445 sample plots of the National Forest Inventory (NFI). A total of 22 independent variables (6 stand and 16 climate variables) were used to develop and train climate-sensitive stand biomass models. Optimization of hyper parameters was implemented using grid search and 10-fold cross-validation. The coefficient of determination (R2) and root mean square error (RMSE) of the RF models were 0.9845 and 3.8008 t ha−1 for AGB, and 0.9836 and 5.1963 t ha−1 for TB. The cumulative contributions of stand and climate factors to stand biomass were >98% and <2%, respectively. The most crucial stand and climate variables were stand volume and annual heat-moisture index (AHM), with relative importance values of >60% and ~0.25%, respectively. The partial dependence plots illustrated the complicated relationships between climate factors and stand biomass. This study illustrated the power of RF for estimating stand biomass and understanding the effects of stand and climate factors on forest biomass. The application of RF can be useful for mapping of large-scale carbon stock.

Root Growth Was Enhanced in China Fir (Cunninghamia lanceolata) after Mechanical Disturbance by Ice Storm

Accurate estimation of forest biomass and its growth potential could be important in assessing the mitigation potential of forest for climate change. However, severe mechanical disturbance such as stem breakage imposed significant changes to tree individuals in biomass structure, which could bring new inaccuracy to biomass estimation. In order to investigate the influence of severe mechanical disturbance on tree biomass accumulation and to construct accurate models for biomass and carbon storage estimation, this paper analyzed the relationship between tree size and biomass for China fir (Cunninghamia lanceolata (Lamb.) Hook) which suffered stem breakage from, and survived, an ice storm. The performance of independent variables diameter (D) and height (H) of China fir, were also compared in biomass estimation. The results showed that D as an independent variable was adequate in biomass estimation for China fir, and tree height was not necessary in this case. Root growth was faster in China fir which had suffered breakage in the main stem by the ice storm, than China fir which were undamaged for at least 7 years after the mechanical disturbance, which, in addition to biomass loss in stem, caused changes in the allocation pattern of the damaged trees. This suggests biomass models constructed before severe mechanical disturbance would be less suitable in application for a subsequent period, and accurate estimations of biomass and forest carbon storage would take more effort.

Estimation of Forest Aboveground Biomass in Karst Areas Using Multi-Source Remote Sensing Data and the K-DBN Algorithm

Accurate estimation of forest biomass is the basis for monitoring forest productivity and carbon sink function, which is of great significance for the formulation of forest carbon neutralization strategy and forest quality improvement measures. Taking Guizhou, a typical karst region in China, as the research area, this study used Landsat 8 OLI, Sentinel-1A, and China national forest resources continuous inventory data (NFCI) in 2015 to build a deep belief network (DBN) model for aboveground biomass (AGB) estimation. Based on the introduction of forest canopy density (FCD), we improved the DBN model to design the K-DBN model with the highest estimation accuracy is selected for AGB inversion and spatial mapping. The results showed that: (1) The determination coefficients R2 of DBN is 0.602, which are 0.208, 0.101 higher than that of linear regression (LR) and random forest (RF) model. (2) The K-DBN algorithm was designed based on FCD to optimize the DBN model, which can alleviate the common problems of low-value overestimation and high-value underestimation in AGB estimation to a certain extent to improve the estimation accuracy. The maximum R2 of the model reached 0.848, and we mapped the forest AGB using the K-DBN model in the study area in 2015. The conclusion of this study: Based on multi-source optical and radar data, the retrieval accuracy of forest AGB can be improved by considering the FCD, and the deep learning algorithm K-DBN is excellent in forest AGB remote sensing estimation. These research results provide a new method and data support for the spatio-temporal dynamic remote sensing monitoring of forest AGB in karst areas.

Climate-based allometric biomass equations for five major Canadian timber species

Accurate estimation of forest biomass is essential to quantify the role forests play in balancing terrestrial carbon. Allometric equations based on tree size have been used for this purpose worldwide. There is little quantitative understanding on how environmental variation may affect tree allometries. Even less known is how to incorporate environmental factors into such equations to improve estimation. Here we tested the effects of climate on tree allometric equations and proposed to model forest biomass by explicitly incorporating climatic factors. For the five major Canadian timber species tested, the incorporation of climate was not found to improve the allometric models. For trembling aspen (Populus tremuloides Michx.) and tamarack (Larix laricina (Du Roi) K.Koch), the residuals of their conventional allometric models were found strongly related to frost-free period and mean annual temperature, respectively. The predictions of the two best climate-based models were significantly improved, which indicates that trembling aspen and tamarack store more aboveground biomass when growing in warmer than in colder regions. We showed that, under the RCP4.5 modest climate change scenario, there would be a 10% underestimation of aboveground biomass for these two species if the conventional non-climate models would still be in use in 2030. This study suggests the necessity to proactively develop climate-based allometric equations for more accurate and reliable forest biomass estimation.

Synergistic evaluation of Sentinel 1 and 2 for biomass estimation in a tropical forest of India

Spatially explicit measurement of Above Ground Biomass (AGB) is crucial for the quantification of forest carbon stock and fluxes. To achieve this, an integration of Optical and Synthetic Aperture Radar (SAR) satellite datasets could provide an accurate estimation of forest biomass. This will also help in removing the uncertainties associated with the single sensor-based estimation approaches. Therefore, the present study attempts to integrate Sentinel-2 optical data with Sentinel-1 SAR dataset to estimate AGB in the Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. In this study, two non-parametric machine learning algorithms viz Support Vector Machines (SVMs) with different kernel functions—linear, sigmoidal, radial and polynomial and Random Forest (RF) were employed for the prediction of AGB using different combinations of VV, VH, Normalized Difference Vegetation Index (NDVI) and Incidence Angle (IA). Ground based AGB was estimated through allometric equation at 35 sampling sites with the help of tree height and Diameter at Breast’s Height (DBH). Standalone collinearity analysis among different parameters resulted in poor correlation of AGB with VH (r = 0.05) and IA (r = 0.015), whereas a significantly good correlation with NDVI (r = 0.80) and VV (r = 0.74) were observed. Inclusion of NDVI with VV and VH together also resulted in a better correlation (r = 0.85) than other combinations. The SVM with linear kernel utilizing parametric the combinations of VV + VH + NDVI and VV + VH + NDVI + IA were found to be best performing on the basis of evaluation metrics. The outcome of this study highlighted the significance of machine learning techniques and synergistic use of different remote sensing data for an improved AGB quantification in tropical forests.

Developing Additive Systems of Biomass Equations for Robinia pseudoacacia L. in the Region of Loess Plateau of Western Shanxi Province, China

The accurate estimation of forest biomass is important to evaluate the structure and function of forest ecosystems, estimate carbon sinks in forests, and study matter cycle, energy flow, and the effects of climate change on forest ecosystems. Biomass additivity is a desirable characteristic to predict each component and the total biomass since it ensures consistency between the sum of the predicted values of components such as roots, stems, leaves, pods, and branches and the prediction for the total tree. In this study, 45 Robinia pseudoacacia L. trees were harvested to determine each component and the total biomass in the Loess Plateau of western Shanxi Province, China. Three additive systems of biomass equations of R. pseudoacacia L., based on the diameter at breast height (D) only and on the combination of D and tree height (H) with D2H and DbHc, were established. To ensure biomass model additivity, the additive system of biomass equations considers the correlation among different components using simultaneous equations and establishes constraints on the parameters of the equation. Seemingly uncorrelated regression (SUR) was used to estimate the parameters of the additive system of biomass equations, and the jackknifing technique was used to verify the accuracy of prediction of the additive system of biomass equations. The results showed that (1) the stem biomass contributed the most to the total biomass, comprising 51.82% of the total biomass, followed by the root biomass (24.63%) and by the pod and leaf biomass, which accounted for the smallest share, comprising 1.82% and 2.22%, respectively; (2) the three additive systems of biomass equations of R. pseudoacacia L. fit well with the models and were effective at making predictions, particularly for the root, stem, above-ground, and total biomass (R2adj > 0.812; root mean square error (RMSE) < 0.151). The mean absolute error (MAE) was less than 0.124, and the mean prediction error (MPE) was less than 0.037. (3) When the biomass model added the tree height predictor, the goodness of fit R2adj increased, RMSE decreased, and the accuracy of prediction was much improved. In particular, the additive system, which was developed based on DbHc combination prediction factors, was the most accurate. The additive system of biomass equations established in this study can provide a reliable and accurate estimation of the individual biomass of R. pseudoacacia L. in the Loess region of western Shanxi Province, China.

Generalized additive models reveal among-stand variation in live tree biomass equations

Accurate estimation of forest biomass is important for scientists and policymakers interested in carbon accounting, nutrient cycling, and forest resilience. Estimates often rely on the allometry of trees; however, limited datasets, uncertainty in model form, and unaccounted-for sources of variation warrant a reexamination of allometric relationships using modern statistical techniques. We asked the following questions: (i) Is there among-stand variation in allometric relationships? (ii) Is there nonlinearity in allometric relationships? (iii) Can among-stand variation or nonlinearities in allometric equations be attributed to differences in stand age? (iv) What are the implications for biomass estimation? To answer these questions, we synthesized a dataset of small trees from six different studies in the White Mountains of New Hampshire. We compared the performance of generalized additive models (GAMs) and linear models and found that GAMs consistently outperform linear models. The best-fitting model indicates that allometries vary among both stands and species and contain subtle nonlinearities that are themselves variable by species. Using a planned contrasts analysis, we were able to attribute some of the observed among-stand heterogeneity to differences in stand age. However, variability in these results points to additional sources of stand-level heterogeneity, which if identified could improve the accuracy of live tree biomass estimation.

Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data

Forest biomass reflects the material cycle of forest ecosystems and is an important index to measure changes in forest structure and function. The accurate estimation of forest biomass is the research basis for measuring carbon storage in forest systems, and it is important to better understand the carbon cycle and improve the efficiency of forest policy and management activities. In this study, to achieve an accurate estimation of meso-scale (regional) forest biomass, we used Ninth Beijing Forest Inventory data (FID), Landsat 8 OLI Image data and ALOS-2 PALSAR-2 data to establish different forest types (coniferous forest, mixed forest, and broadleaf forest) of biomass models in Beijing. We assessed the potential of forest inventory, optical (Landsat 8 OLI) and radar (ALOS-2 PALSAR-2) data in estimating and mapping forest biomass. From these data, a wide range of parameters related to forest structure were obtained. Random forest (RF) models were established using these parameters and compared with traditional multiple linear regression (MLR) models. Forest biomass in Beijing was then estimated. The results showed the following: (1) forest inventory data combined with multisource remote sensing data can better fit forest biomass than forest inventory data alone. Among the three forest types, mixed forest has the best fitting model. Forest inventory variables and multisource remote sensing variables can match each other in time and space, capturing almost all spatial variability. (2) The 2016 forest biomass density in Beijing was estimated to be 52.26 Mg ha−1 and ranged from 19.1381–195.66 Mg ha−1. The areas with high biomass were mainly distributed in the north and southwest of Beijing, while the areas with low biomass were mainly distributed in the southeast and central areas of Beijing. (3) The estimates from the RF model are better than those from the MLR model, showing a high R 2 and a low root mean square error (RMSE). The R 2 values of the MLR models of three forest types were greater than 0.5, and RMSEs were less than 15.5 Mg ha−1, The R 2 values of the RF models were higher than 0.6, and the RMSEs were lower than 13.5 Mg ha−1. We conclude that the methods in this paper can help improve the accurate estimation of regional biomass and provide a basis for the planning of relevant forestry decision-making departments.

Combining Kriging Interpolation to Improve the Accuracy of Forest Aboveground Biomass Estimation Using Remote Sensing Data

The accurate estimation of forest biomass is very significant to the study of regional ecosystems. China’s National Forest Continuous Inventory data and Landsat 8 data were used to establish a linear regression (LR) model for the estimation of forest aboveground biomass (AGB) based on forest type. The original results of the LR yielded upon conducting AGB estimation were insufficient. Therefore, an interpolation map of the residuals of the observed and predicted AGB was used to correct the error of the original LR model. There is a highly positive result of the accuracy of the corrected AGB map. First, the AGB estimation based on forest type could effectively improve its accuracy. For example, significant improvements were made in the estimations of broadleaf, coniferous, and mixed forests compared to that of the total vegetation conducted using the original LR model. Second, semivariance analysis should be conducted before spatial interpolation using the Kriging method to determine optimal semivariogram models and parameters. The optimal semivariogram model for broadleaf and total forests was the exponential model, while that for the coniferous and mixed forests was the spherical model. Third, combining Kriging interpolation predicted the AGB map effectively and reduced the under- and overestimation of AGB, although it did not fully eliminate this limitation. The R2 values of broadleaf, coniferous, and mixed forests were improved to 0.897, 0.856, and 0.826, respectively. Overall, the methods used in this study provide an effective approach towards improving the accuracy of AGB estimations by reducing under- and overestimation based on remote sensing data and increasing the ability to monitor the forest ecosystem.

Combining allometry and landsat-derived disturbance history to estimate tree biomass in subtropical planted forests

Planted forests are a key component of sustainable forest management, as their fast-growing biomass can provide many important ecosystem goods and services essential to human well-being. Accurate estimation of forest biomass is important for global carbon accounting and afforestation policy making, yet saturated reflectance signals over vegetation and a shortage of cloud-free images during the growing season largely limit the utility of optical remote sensing for biomass estimation of planted forests in subtropical and tropical regions. Stand age is a crucial factor in determining forest growth and stand development but hasn't been widely used in remote sensing-based biomass estimation approaches. The main objective of this study was to investigate the applicability of an allometric analysis approach combined with Landsat-derived forest age structure to estimate stand biomass of fast-growing forest plantations in a subtropical landscape of China. We used the Vegetation Change Tracker (VCT) approach and Landsat time series data from 1986 to 2016 to detect annual stand-replacing disturbance and estimate forest stand age. Allometric equations and relative growth rate functions were combined to establish the linkage between tree biomass and forest stand age. We also modeled the spatial distribution of tree biomass using a conventional remote sensing (CRS) method that only utilizes spectral and textural variables and an extended CRS method that incorporates stand age (CRS_EX). We then compared the CRS and CRS_EX methods with the allometric analysis method to highlight the importance of stand age and post-disturbance forest regrowth for biomass estimation. The Landsat-derived disturbance history was significantly correlated with stand age (R2 = 0.82, RMSE = 3.7 year, p < 0.01). The CRS_EX and allometric analysis methods substantially improved tree biomass estimates (R2CRS_EX = 0.77, RMSECRS_EX = 37.7 t/ha; R2Allometric = 0.70, RMSEAllometric = 43.5 t/ha) compared to the CRS method (R2CRS = 0.53, RMSE CRS = 54.38 t/ha). Tree biomass estimated from the allometric analysis method aligned better with the allometric growth curves, while CRS_EX and CRS methods both exhibited notable overestimation for young and mid-aged forests. This study suggests that allometric models have the potential to be applied in subtropical forest plantations through the use of the existing forest stand development knowledge and increasingly open access remote sensing data.

Assessment of the Carbon Stock in Pine Plantations in Southern Spain through ALS Data and K-Nearest Neighbor Algorithm Based Models

Accurate estimation of forest biomass to enable the mapping of forest C stocks over large areas is of considerable interest nowadays. Airborne laser scanning (ALS) systems bring a new perspective to forest inventories and subsequent biomass estimation. The objective of this research was to combine growth models used to update old inventory data to a reference year, low-density ALS data, and k-nearest neighbor (kNN) algorithm Random Forest to conduct biomass inventories aimed at estimating the C sequestration capacity in large Pinus plantations. We obtained a C stock in biomass (Wt-S) of 12.57 Mg·ha−1, ranging significantly from 19.93 Mg·ha−1 for P. halepensis to 49.05 Mg·ha−1 for P. nigra, and a soil organic C stock of the composite soil samples (0–40 cm) ranging from 20.41 Mg·ha−1 in P. sylvestris to 37.32 Mg·ha−1 in P. halepensis. When generalizing these data to the whole area, we obtained an overall C-stock value of 48.01 MgC·ha−1, ranging from 23.96 MgC·ha−1 for P. halepensis to 58.09 MgC·ha−1 for P. nigra. Considering the mean value of the on-site C stock, the study area sustains 1,289,604 Mg per hectare (corresponding to 4,732,869 Mg CO2), with a net increase of 4.79 Mg·ha−1·year−1. Such C cartography can help forest managers to improve forest silviculture with regard to C sequestration and, thus, climate change mitigation.

Deep Learning Based Retrieval of Forest Aboveground Biomass from Combined LiDAR and Landsat 8 Data

Estimation of forest aboveground biomass (AGB) is crucial for various technical and scientific applications, ranging from regional carbon and bioenergy policies to sustainable forest management. However, passive optical remote sensing, which is the most widely used remote sensing data for retrieving vegetation parameters, is constrained by spectral saturation problems and cloud cover. On the other hand, LiDAR data, which have been extensively used to estimate forest structure attributes, cannot provide sufficient spectral information of vegetation canopies. Thus, this study aimed to develop a novel synergistic approach to estimating biomass by integrating LiDAR data with Landsat 8 imagery through a deep learning-based workflow. First the relationships between biomass and spectral vegetation indices (SVIs) and LiDAR metrics were separately investigated. Next, two groups of combined optical and LiDAR indices (i.e., COLI1 and COLI2) were designed and explored to identify their performances in biomass estimation. Finally, five prediction models, including K-nearest Neighbor, Random Forest, Support Vector Regression, the deep learning model, i.e., Stacked Sparse Autoencoder network (SSAE), and multiple stepwise linear regressions, were individually used to estimate biomass with input variables of different scenarios, i.e., (i) all the COLI1 (ACOLI1), (ii) all the COLI2 (ACOLI2), (iii) ACOLI1 and all the optical (AO) and LiDAR variables (AL), and (iv) ACOLI2, AO and AL. Results showed that univariate models with the combined optical and LiDAR indices as explanatory variables presented better modeling performance than those with either optical or LiDAR data alone, regardless of the combination mode. The SSAE model obtained the best performance compared to the other tested prediction algorithms for the forest biomass estimation. The best predictive accuracy was achieved by the SSAE model with inputs of combined optical and LiDAR variables (i.e., ACOLI1, AO and AL) that yielded an R2 of 0.935, root mean squared error (RMSE) of 15.67 Mg/ha, and relative root mean squared error (RMSEr) of 11.407%. It was concluded that the presented combined indices were simple and effective by integrating LiDAR-derived structure information with Landsat 8 spectral data for estimating forest biomass. Overall, the SSAE model with inputs of Landsat 8 and LiDAR integrated information resulted in accurate estimation of forest biomass. The presented modeling workflow will greatly facilitate future forest biomass estimation and carbon stock assessments.

Allometric Models for Estimation of Forest Biomass in North East India

In tropical and sub-tropical regions, biomass carbon (C) losses through forest degradation are recognized as central to global terrestrial carbon cycles. Accurate estimation of forest biomass C is needed to provide information on C fluxes and balances in such systems. The objective of this study was to develop generalized biomass models using harvest data covering tropical semi-evergreen, tropical wet evergreen, sub-tropical broad leaved, and sub-tropical pine forest in North East India (NEI). Among the four biomass estimation models (BEMs) tested AGBest = 0.32(D2Hδ)0.75 × 1.34 and AGBest = 0.18D2.16 × 1.32 were found to be the first and second best models for the different forest types in NEI. The study also revealed that four commonly used generic models developed by Chambers (2001), Brown (1989), Chave (2005) and Chave (2014) overestimated biomass stocks by 300–591 kg tree−1, while our highest rated model overestimated biomass by 197 kg tree−1. We believe the BEMs we developed will be useful for practitioners involved in remote sensing, biomass estimation and in projects on climate change mitigation, and payment for ecosystem services. We recommend future studies to address country scale estimation of forest biomass covering different forest types.

Allometric equations for aboveground biomass estimation of Diospyros abyssinica (Hiern) F. White tree species

ABSTRACT Introduction: Quantifying forest biomass requires the application of allometric equations which is a fundamental step. Generalized allometric equations have been applied to quantify aboveground biomass (AGB) of forests. But, adopting generalized allometric equations to quantify AGB of different forests creates uncertainty. Therefore, developing species- and site-specific allometric equations is essential to accurately quantify the biomass. The study was aimed to develop species-specific allometric equations for Diospyros abyssinica (Hiern) F. White in Yayu Coffee Forest Biosphere Reserve using the Semi-destructive method. The vegetation types of Yayu Coffee Forest Biosphere Reserve is categorized to Moist Evergreen Montane Rainforest of Ethiopia. Results and discussion: Evaluating statistical relationships of AGB against predictor variables, eight allometric equations were formulated. AGB was regressed against trunk diameter (D), total height (H), and wood density (ρ) individually and in combination. Selection of allometric equations was employed using model performance statistics. Equations with a higher coefficient of determination (adjusted R 2), lower residual standard error, and Akaike information criterion (AIC) values were found best-fitted. Relationships of AGB and independent variables were found statistically significant (p < 0.000). Overall, formulating species- and site-specific allometric equations is significant for accurate estimation of forest biomass and carbon stock budget.

Comparison of satellite-based estimates of aboveground biomass in coppice oak forests using parametric, semiparametric, and nonparametric modeling methods

Accurate estimates of forest biomass are essential for several purposes, ranging from carbon accounting and ecological applications to sustainable forest management. There are, however, critical steps for mapping aboveground forest biomass (AGB) based on optical satellite data with an acceptable degree of accuracy, such as selecting the proper statistical modeling method and deriving spectral information from imagery, at known field locations. We compare nine modeling techniques including parametric, semiparametric, and nonparametric methods for remotely estimating AGB based on various spectral variables derived from Landsat 8 Operational Land Imager (OLI). We conduct this research in Zagros oak forests on two sites under different human disturbance levels: an undegraded (UD) forest site and a highly degraded (HD) forest site. Based on cross-validation statistics, the UD site exhibited better results than the HD site. Support vector machine (SVM) and Cubist regression (CR) were more precise in terms of coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE), though these approaches also result in more biased estimates compared to the other methods. Our findings reveal that if the degree of under- or over-estimation is not problematic, then SVM and CR are good modeling options (R2 = 0.73; RMSE = 31.5 % of the mean, and MAE = 3.93 ton / ha), otherwise, the other modeling methods such as linear model, k-nearest neighbor, boosted regression trees, generalized additive model, and random forest may be better choices. Overall, our work indicates that the use of freely available Landsat 8 OLI and proper statistical modeling methods is a time- and cost-effective approach for accurate AGB estimates in Zagros oak forests.

Estimating Biomass of Single Oak Trees Using Terrestrial Photogrammetry

Accurate estimates of forest biomass to assess the ability of forests to sequester atmospheric carbon of very important issues in the management of forests. The present study aimed to estimate the biomass of single base Quercus brantii trees were terrestrial photogrammetry using nondestructive methods. The study was conducted in the Chaharmahal va Bakhtiari, the first 32 single base Quercus branti trees in different class diameter selection and of each trees two photos in the direction perpendicular were taken. Then the scale was calculated for each photo and determined the Volume of different components of trees (trunk, main branches, branches and foliage (Crown)). With the sampling of the various components of tree, calculated density, and biomass measured for different components and estimated biomass of terrestrial photogrammetry method was compared with biomass obtained from cutting and weighing. Results showed a significant difference is not between the estimated biomass of terrestrial photogrammetry method with the most accurate method to estimate the biomass. This study, accuracy and efficiency terrestrial photogrammetry method shows in the above ground biomass estimation in growth form single base Quercus brantii.

Carbon in Mature Native Forests in Australia: The Role of Direct Weighing in the Derivation of Allometric Equations

Accurate estimates of forest biomass are essential to understand the contribution of forests to climate change mitigation efforts. In this manuscript, we report on biomass determinations for 586 directly weighed trees located in three important native forest areas in Australia. The sites were paired according to management strategy; i.e., managed for periodic cycles of harvest or conservation only. The key aim of the work was to test whether non-site specific available biomass relationships are reliable, especially in the estimation of the biomass of trees with a large diameter at breast height (DBH). The above-ground carbon (AGC) estimates for largely undisturbed forests ranged from approximately 200–400 t C ha−1. Existing allometric equations were generally poor at estimating biomass for mature trees, especially those of large DBH. Direct weighing of biomass ensured a degree of certainty in the results that cannot be associated with previous studies that relied on sub-sampling, or with studies that relied on existing allometric equations. Thus, caution should be exercised when interpreting the results of previous studies that did not rely on direct weighing of the biomass in the context of decisions around optimum forest management regimens, and the contribution of mature forest stands to the global carbon balance.

Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass

Timely, spatially complete, and reliable forest above-ground biomass (AGB) data are a prerequisite to support forest management and policy formulation. Traditionally, forest AGB is spatially estimated by integrating satellite images, in particular, optical data, with field plots from forest inventory programs. However, field data are limited in remote and unmanaged areas. In addition, optical reflectance usually saturates at high-density biomass level and is subject to cloud contaminations. Thus, this study aimed to develop a deep learning based workflow for mapping forest AGB by integrating Landsat 8 and Sentinel-1A images with airborne light detection and ranging (LiDAR) data. A reference AGB map was derived from the wall-to-wall LiDAR data and field measurements. The LiDAR plots-stratified random samples of forest biomass extracted from the LiDAR simulated strips in the reference map-were adopted as a surrogate for traditional field plots. In addition to the deep learning model, i.e., stacked sparse autoencoder network (SSAE), five different prediction techniques including multiple stepwise linear regressions, K-nearest neighbor, support vector machine, back propagation neural networks, and random forest were individually used to establish the relationship between LiDAR-derived forest biomass and the satellite predictors. Optical variables (Landsat 8 OLI), SAR variables (Sentinel-1A), and their combined variables were individually input to the six prediction models. Results showed that the SSAE model had the best performance for the forest biomass estimation. The combined optical and microwave dataset as explanatory variables improved the modeling performance compared to either the optical-only or microwave-only data, regardless of prediction algorithms. The best mapping accuracy was obtained by the SSAE model with inputs of optical and microwave integrated metrics that yielded R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.812, root mean squared error (RMSE) of 21.753 Mg/ha, and relative RMSE (RMSEr) of 14.457%. Overall, the SSAE model with inputs of combined Landsat 8 OLI and Sentinel-1A information could result in accurate estimation of forest biomass by using the stratification-sampled and LiDAR-derived AGB as ground reference data. The modeling workflow has the potential to promote future forest growth monitoring and carbon stock assessment across large areas.