Aboveground Biomass In Tropical Forests Research Articles

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.

Development of L-band fully polarimetric SAR algorithm for forest biomass retrieval using 7SD and random forest regression

Forest biomass is essential for carbon budgeting, biodiversity health, and climate change research. This study developed a novel L-band fully polarimetric SAR method for estimating aboveground biomass (AGB) in a forested region in India. Biomass was estimated using fully polarimetric ALOS-2/PALSAR-2 data sets and field AGB measurements. To estimate forest AGB, the algorithm employs a seven-component scattering power decomposition (7SD) and a random forest regression (RFR) machine-learning approach. By implementing the 7SD model, the seven scattering powers were extracted from ALOS-2/PALSAR-2 inverted for aboveground biomass. The field data were used to validate the biomass estimated by the model. The 7SD model estimated AGB in Shivamogga was consistent with field measured biomass. The root mean squared error (RMSE) and relative RMSE with respect to mean AGB were 21.94 Mg/ha and 19.46, respectively, which are within acceptable ranges. The 7SD model was also used for cross-validation at Tundi Forest, where the relative RMSE with respect to the mean AGB was 22.9. The scattering powers generated from L-band fully polarimetric data can be useful in tropical forest AGB estimation.

Estimating forest aboveground biomass in tropical forests with Landsat time-series data and recurrent neural network

ABSTRACT The insensitivity and saturation of sensor signals significantly limit the application of Landsat data on estimating biomass in tropical forests with a high level of biomass. The previous studies with single-date Landsat data have lower accuracies in tropical forests compared to boreal and temperate forests. Landsat time-series data provide a promising opportunity to improve the accuracy by enhancing the relationship between Landsat spectral reflectance and forest aboveground biomass with disturbance and recovery dynamics. Compared to the single-date image, Landsat time-series data can capture abrupt spectral changes (e.g. harvesting and fire) and show the regrowth process in forested pixels. However, very limited studies take advantage of Landsat time-series data to estimate aboveground biomass in tropical forests. Recurrent neural networks (RNNs) are powerful deep learning techniques to capture time dependencies in sequence data. However, the application of RNNs in estimating forest biomass has not been explored yet. Therefore, we integrate the long short-term memory network (LSTM) and the fully connected neuron network (FNN) to establish an RNN-FNN model for estimating forest biomass with Landsat time-series imagery and airborne LiDAR data. We compared the proposed model with the commonly used Random Forest model and linear regression model which are implemented with single-date data. The results show that the RNN-FNN model can deal with Landsat time-series sequence data to enhance the relationship between Landsat spectral reflectance and forest aboveground biomass. The proposed model achieves the R2 of 0.63 and RMSE of 25.5 Mg/ha, which significantly outperformed the Random Forest model and linear regression model with Landsat single-date data. This study demonstrates the value of RNN and Landsat time-series imagery in estimating forest biomass for tropical forests.

More Accurately Estimating Aboveground Biomass in Tropical Forests With Complex Forest Structures and Regions of High‐Aboveground Biomass

AbstractAccurately estimating aboveground biomass (AGB) in tropical forests is vital for managing the threats posed by deforestation, degradation, and climate change. However, challenges persist in accurately estimating AGB in high AGB regions. This study aims to accurately estimate the AGB of regions with high AGB by using spatial statistical analyses based on AGB estimates made by machine‐learning fusion of multisource data. We hypothesize that incorporating dominant auxiliary factors in the analysis increases the estimation accuracy. This study focuses on tropical forests located in Longyan, Fujian Province, China, covering an area of 19,028 km2. Multisource data are used, including airborne laser scanning, the Shuttle Radar Topography Mission digital elevation model, the Landsat Operational Land Imager, and the National Forest Inventory. Based on GeogDetector's spatial covariance matrix and the spatial similarity principle, we identify key auxiliary factors (dominant tree species, canopy closure, and herbaceous cover) and investigated how auxiliary variables can improve estimation accuracy. Empirical Bayesian kriging regression prediction introduces the main auxiliary factors to refine AGB estimates. These refinements significantly enhance the accuracy of AGB estimates, particularly for high AGB, resulting in a 0.1 increase in R2, a 7.0% reduction in root mean square error, a 13.5% reduction in mean square error, and a 6.6% reduction in mean absolute error when compared with the AGB estimates obtained by using machine learning to fuse multisource data. Thus, incorporating spatial statistical analysis into the integration of multisource data and machine learning for AGB estimation can enhance the accuracy of high‐AGB estimates in intricate forest structures, resulting in precise AGB maps.

Estimating forest height and above-ground biomass in tropical forests using P-band TomoSAR and GEDI observations

ABSTRACT Knowledge about the vertical structure of forests, such as forest height, above-ground biomass (AGB), and the vertical biomass distribution is important for understanding carbon allocation, structural diversity, and succession and degradation dynamics in forest ecosystems. While the use of lidar (light detection and ranging) observations is well established to investigate the vertical structure of forests, the sensitivity of P-band synthetic aperture radar tomography (TomoSAR) observations to biomass and vertical forest structure is not yet well understood. Here we use lidar observations from NASA’s Global Ecosystem Dynamics Investigation (GEDI) to analyse the sensitivity of airborne P-band SAR tomography backscatter to forest height and AGB at two tropical forests in Lopé and Mondah, Gabon, Africa. We use GEDI observations to parametrize an empirical model for estimating forest height and we use a random forest model for estimating AGB from TomoSAR profiles. The validation with Land, Vegetation, and Ice Sensor (LVIS) airborne lidar data shows moderate performance for estimating forest height (RMSE = 8.2 m in Lopé and 9.8 m in Mondah) and moderate to good performance for total AGB (RMSE = 115.3 Mg/ha in Lopé and 117.8 Mg/ha in Mondah). We also estimated the vertical distribution of AGB using the corrected TomoSAR backscatter and compared it with AGB profiles derived from field observations in Mondah, which indicates potential to use TomoSAR observations for estimating vertical AGB distribution over tropical forests. However, our results demonstrate the need for targeted field observations of vertical biomass profiles in order to make full use of P-band TomoSAR to map the vertical structure of tropical forests.

LiDAR-based reference aboveground biomass maps for tropical forests of South Asia and Central Africa

Accurate mapping and monitoring of tropical forests aboveground biomass (AGB) is crucial to design effective carbon emission reduction strategies and improving our understanding of Earth’s carbon cycle. However, existing large-scale maps of tropical forest AGB generated through combinations of Earth Observation (EO) and forest inventory data show markedly divergent estimates, even after accounting for reported uncertainties. To address this, a network of high-quality reference data is needed to calibrate and validate mapping algorithms. This study aims to generate reference AGB datasets using field inventory plots and airborne LiDAR data for eight sites in Central Africa and five sites in South Asia, two regions largely underrepresented in global reference AGB datasets. The study provides access to these reference AGB maps, including uncertainty maps, at 100 m and 40 m spatial resolutions covering a total LiDAR footprint of 1,11,650 ha [ranging from 150 to 40,000 ha at site level]. These maps serve as calibration/validation datasets to improve the accuracy and reliability of AGB mapping for current and upcoming EO missions (viz., GEDI, BIOMASS, and NISAR).

Both the selection and complementarity effects underpin the effect of structural diversity on aboveground biomass in tropical forests

AbstractAimDespite mounting empirical evidence regarding the positive effects of forest structural diversity (STRDIV) on forest functioning, the underlying biotic mechanisms and controlling abiotic factors remain poorly understood. This study provides the first assessment of the interactive effects of STRDIV and diversity in species and functional traits on aboveground biomass (AGB) in natural forests in West and East Africa.LocationWest and East Africa.Time period2014–2020.Major taxa studiedWoody plants.MethodsUsing data from 276 plots and 7993 trees of 207 species distributed across various types of natural forests and major climatic zones of Africa, linear mixed‐effects and structural equation models, we have evaluated how alternative causal relationships between STRDIV and taxonomic and functional diversity attributes influence AGB, while accounting for the effects of environmental covariates. We also assessed the consistency of these relationships across floristically and environmentally homogenous forest types.ResultsWe found that the positive effects of STRDIV on AGB were underpinned by both the community‐weighted mean (CWM) of trait values (selection effects) and species richness (niche complementarity), but the relative importance of these effects varied depending on forest types. Across the forest types, STRDIV primarily mediated the effects of CWM of traits and species richness on AGB. We also found that STRDIV–AGB relationships were constrained by resource (water and nutrient) availability.Main conclusionsOur findings provide novel insights into the role of functional traits as key determinants of the effects of STRDIV on AGB in tropical forests. We suggest that forest management and climate change mitigation strategies aimed at conserving biodiversity, and fostering biomass storage through increased STRDIV should focus on maintaining high levels of functionally dominant species while also increasing tree species diversity.

Estimation of tropical forest aboveground biomass in Nepal using multiple remotely sensed data and deep learning

ABSTRACT This study assessed the prediction accuracy of the forest aboveground biomass (AGB) model using remotely sensed data sources (i.e. airborne laser scanning (ALS), RapidEye, Landsat), and the combination of ALS with RapidEye/Landsat using parametric weighted least squares (WLS) regression. We also analysed the AGB model using random forests, extremely randomized trees, and deep learning stacked autoencoder (SAE) network from nonparametric statistics to compare the performance with WLS regression. We also compared the widths of the 95% confidence intervals for estimates of the mean AGB per unit area using the model-based estimator. The study site in the Terai Arc Landscape, Nepal, comprised 14 protected areas extending from the southern part of Nepal to India and encompassed mosaics of continuous dense forest and tall grassland. The ALS data provided the largest prediction accuracy (0.30–0.35 relative root mean squared error (rRMSE)), whereas RapidEye and Landsat had smaller prediction accuracies (0.48‒0.54 and 0.47‒0.55 rRMSE, respectively) for the estimation of AGB. The combined use of ALS and RapidEye predictors in the AGB model reduced the rRMSE and narrowed the confidence interval compared with ALS alone, but the improvements were minor. The SAE prediction technique provided the largest prediction accuracy, with inputs of combined ALS and RapidEye predictors that yielded an R2 of 0.80, an rRMSE of 0.30, and a confidence interval of 176‒184 compared to other tested prediction techniques. The SAE prediction technique can become more powerful than other tested prediction techniques if properly adjusted and tuned for accurate forest AGB mapping applications. To our knowledge, this is the first study assessing the performance of the SAE in AGB modelling with a range of hyper-parameter values.

Structural diversity is a key driver of above-ground biomass in tropical forests

ABSTRACT Background A gamut of abiotic and biotic factors is related to the amount of above-ground biomass (AGB) produced in ecosystems. Some factors have direct and others indirect relationships with AGB. Detailed analyses in tropical forests are few but much needed for better understanding the potential impacts of global change drivers and for mitigating impacts. Aims Here, we examined the relationship between AGB and different predictor variables and quantitatively evaluated their relative importance in lowland to lower montane deciduous and lower montane – montane evergreen forest types. We hypothesised that the relationship between AGB and climate, topography, structural diversity, species diversity (alpha and beta) and phylogenetic diversity would differ between the two forest types. Methods We inventoried trees from 114 plots (each 0.1 ha) and used partial least square structural equation modelling to test the direct and indirect relationship between AGB and the predictor variables. Results We found that structural diversity variables, stem density and tree girth, were significantly and positively related to AGB in both forest types, displaying a stronger relationship in montane evergreen forests (w = 0.65 for density and 0.89 for tree girth). In the deciduous forest, alpha and phylogenetic diversity were also important factors, whereas beta and phylogenetic diversity were important in the evergreen forest. The effects of topography and climate varied between forest types, with elevation and precipitation being related to AGB directly and indirectly through their relationship with structural diversity. Conclusion Our results suggest that structural diversity is a key driver of tropical forest biomass, both directly and indirectly. This fundamental understanding can aid in the predictive efforts of biodiversity conservation and forest management.

Tropical forest AGB estimation based on structure parameters extracted by TomoSAR

Forests play a crucial role in quantifying global carbon storage and detecting climate change in the form of aboveground biomass (AGB), which introduces an approach to study carbon cycle, ecology, and biodiversity. The monitoring and estimation of forest AGB are considered very important and of practical value. As we know, forest AGB relates with height, density and diameter at breast height, and how to relate the ecophysical parameters with remote sensing images is vital for forest AGB estimation. In this paper, we aim to explore structure parameters about forest density and height, extracted by tomographic SAR (TomoSAR) techniques, for further improving the precision of AGB estimation models. Firstly, vertical structure profiles are constructed via TomoSAR, and the structure features are extracted. Secondly, the correlation between these features and the in-situ forest maximum height, tree density, and average AGB in plot scale is analyzed. Thirdly, the 8-fold cross-validation and step-wise regression methods were utilized to construct the tropical forest AGB models. Finally, the results of these models have been presented and analyzed. Based on the analysis, it indicates that “Model 7” is the most effective model, and its performance at both plot and pixel scales indicates a high level of accuracy for predicting forest AGB. These findings suggest that the proposed method can be effectively applied to tropical forested areas and has good scalability.

Comparison of multi-source remote sensing data for estimating and mapping above-ground biomass in the West Usambara tropical montane forests

Above-ground biomass (AGB) estimation is important to better understand the carbon cycle and improve the efficiency of forest policy and management activities. AGB estimation models, using a combination of field data and remote sensing data, can largely replace traditional survey methods for measuring AGB. There are, however, critical steps for mapping AGB based on satellite data with an acceptable degree of accuracy, such as choice of remote sensing data, the proper statistical modelling method, and remote sensing predictor variables, at known field locations. This study sought to identify the optimal optical and synthetic aperture radar (SAR) remote sensing imagery from five sensors (PlanetScope, Sentinel-2, Landsat 8 OLI, ALOS-2/PALSAR-2, and Sentinel-1) to model 159 field-based AGB values from two montane forests under semiparametric (Generalized Additive Model; GAM) and non-parametric (eXtreme Gradient Boosting; XGB) approaches using information from four groups of predictor variables (spectral bands/polarizations, vegetation indices, textures, and a combination of all). The study's results showed that PlanetScope (rRMSE = 69.19%; R2 = 0.161) was the most precise optical sensor while ALOS-2/PALSAR-2 (rRMSE = 70.76; R2 = 0.165) was the most precise amongst the SAR sensors. XGB models generally resulted in those with lower prediction errors as compared to GAMs for the five sensors. Models having textures of vegetation indices and polarization bands achieved greater accuracy than models that incorporated spectral bands/polarizations and vegetation indices only. The study recommends that PlanetScope and ALOS-2/PALSAR-2 remote sensing data using the XGB-based technique is an appropriate approach for accurate local and regional estimation of tropical forest AGB particularly for complex montane forest ecosystems.

Examination of the amount of GEDI data required to characterize central Africa tropical forest aboveground biomass at REDD+ project scale in Mai Ndombe province

The Global Ecosystem Dynamics Investigation (GEDI) is the first spaceborne LiDAR designed to improve quantification of vegetation structure and forest aboveground biomass (AGB) including in the tropics where forest AGB inventory data are limited. GEDI is a sampling instrument on the International Space Station (ISS) and does not provide data on a regular, systematic basis. Reducing Emissions from Deforestation and Degradation and enhancement of carbon stocks (REDD+) projects require forest AGB inventories to quantify avoided carbon emissions achieved by conserving forest biomass. Although there is high confidence that GEDI can retrieve measurements that allow estimation of AGB at scale, less is known about how well its operational deployment performs for measurement of AGB to support REDD+ projects. This includes an understanding of the appropriate time period required to collect sufficient GEDI observations for reliable forest AGB assessment. This paper describes the first study to examine the amount of GEDI data needed to characterize tropical forest AGB at REDD+ project scale. In tropical Africa, the average REDD+ project size documented by the Center for International Forestry Research is equivalent to a square area of approximately 50 × 50 km (250,000 ha). Recently available good quality GEDI footprint-level AGB product data acquired over a 31 month period over Mai Ndombe province in the west of the Democratic Republic of the Congo were considered. A global 30 m percent tree cover product, updated with contemporary mapped forest cover loss, was used to map the intact forest across the province. Fifteen 50 × 50 km test sites, representing example REDD+ project areas with >80% forest cover and good quality AGB forest footprint data distributed across each site, were selected. The sites were selected from five AGB stratum defined from the GEDI data, and with three sites selected per stratum that had low, medium and high semivariogram sill values that reflect increasing within-site AGB spatial variation. The overall mean GEDI AGB (OMGA) was derived from all the good quality forest GEDI footprint AGB values acquired over the 31 months of GEDI operation at each site. The expected minimum number of GEDI orbits (norbitsp) required to characterize the OMGA to within p = ±5%, ±10%, and ±20% was derived by considering different combinations of GEDI orbits randomly selected from the 31 months of GEDI data. The expected minimum number of days (ndaysp) required to characterize the AGB over each site was derived by multiplying the site norbitsp values with a scalar coefficient of 13.03 days. The scalar coefficient was found by counting the temporal intervals between successive GEDI orbits containing good quality forest AGB data and is equivalent to the average number of days required to obtain a GEDI orbit containing good quality forest AGB data at 50 × 50 km scale. Among the 15 sites, observation periods ranging from 65 to 221 days (0.18 – 0.61 years), 143 – 534 days (0.39 – 1.46 years), and 390 – 742 days (1.07 – 2.03 years) were required to characterize the AGB to within ±20%, ±10%, and ±5% of the site OMGA, respectively. The Intergovernmental Panel on Climate Change (IPCC) recommended accuracy requirement for forest AGB estimates is 10%. Thus, to meet this accuracy requirement the findings of this study indicate that at least 534 days (1.46 years) would be required for REDD+ site monitoring using GEDI in Mai Ndombe province. In other central African tropical forest localities these observations periods may be different depending on the forest AGB and spatial variation, cloud cover, ephemeral surface water presence, and GEDI AGB retrieval sensitivity to the forest conditions.

Artificial intelligence tools and vegetation indices combined to estimate aboveground biomass in tropical forests

Measurement of forest biomass is a time-, money-, and labor-consuming activity. Developing methodological approaches for quantifying biomass in natural ecosystems has motivated countless investigations in several vegetation types worldwide. In this study, we developed a model to estimate the above-ground biomass (AGB) in a study site spatially located in Western Pará state, Brazilian Amazon. We applied artificial neural networks and remotely sensed data for adjusting our model based on forest inventory data. We tested four vegetation indices retrieved from a Landsat-5 thematic mapper scene and assessed their correlations with the field measured AGB. We trained 286 artificial neural networks using the vegetation stratus (two phytophysiognomies: dense lowland ombrophilous forest and dense submontane ombrophilous forest) and the normalized difference vegetation index (NDVI) and aerosol-free vegetation index as predictors variables of the AGB. We selected the artificial neural network model showing higher correlation coefficient and concentration of residuals in the center classes, which indicates higher predictive capabilities of biomass estimation. The generated model architecture (4-13-1) was composed by 4 predictor neurons in the input layer, 13 neurons in the hidden layer, activated by logistic functions, and a single neuron (the AGB in ton · ha − 1) in the output layer, activated by an identity function. We observed that the NDVI and aerosol free vegetation index showed the best performance to estimate the AGB in the study area. Using artificial neural network and a Landsat-5 image combined, we were able to accurately predict (estimation error of ∼20 % ) AGB in tropical forest. This is a promising methodological approach that can be applied to assess ecosystem services related to carbon stock in tropical regions.

Aboveground biomass stock and change estimation in Amazon rainforest using airborne light detection and ranging, multispectral data, and machine learning algorithms

Developing an efficient method to accurately estimate aboveground biomass in tropical forests is critical to monitoring the carbon stock and implementing policies to reduce emissions caused by deforestation. Thus, the objective of the present study was to estimate aboveground biomass in areas of the Amazonian Forest with selective logging, using the random forest (RF), support vector machine (SVM), and artificial neural network (ANN) machine learning algorithms, using light detection and ranging (LiDAR) data and these combined with OLI/Landsat 8 variables, as well as mapping the biomass for the years 2014 and 2017, allowing one to analyze its dynamics between the years of analysis. The RF and SVM algorithms obtained the lowest error values in all datasets. The association of the variables increased the RF performance. Analyzing the dynamics of biomass, it was observed that the oldest exploration units (2006, 2007, and 2008) have lower biomass stocks. The highest biomass losses in 2017 came from units operated between 2012 and 2013 (the most recent record). Thus, with the method used in this study, it was possible to infer that the machine learning algorithms were efficient in estimating the biomass, emphasizing the RF and the SVM.

Mapping tropical forest aboveground biomass using airborne SAR tomography

Mapping tropical forest aboveground biomass (AGB) is important for quantifying emissions from land use change and evaluating climate mitigation strategies but remains a challenging problem for remote sensing observations. Here, we evaluate the capability of mapping AGB across a dense tropical forest using tomographic Synthetic Aperture Radar (TomoSAR) measurements at P-band frequency that will be available from the European Space Agency’s BIOMASS mission in 2024. To retrieve AGB, we compare three different TomoSAR reconstruction algorithms, back-projection (BP), Capon, and MUltiple SIgnal Classification (MUSIC), and validate AGB estimation from models using TomoSAR variables: backscattered power at 30 m height, forest height (FH), backscatter power metric (Q), and their combination. TropiSAR airborne campaign data in French Guiana, inventory plots, and airborne LiDAR measurements are used as reference data to develop models and calculate the AGB estimation uncertainty. We used univariate and multivariate regression models to estimate AGB at 4-ha grid cells, the nominal resolution of the BIOMASS mission. Our results show that the BP-based variables produced better AGB estimates compared to their counterparts, suggesting a more straightforward TomoSAR processing for the mission. The tomographic FH and AGB estimation have an average relative uncertainty of less than 10% with negligible systematic error across the entire biomass range (~ 200–500 Mg ha−1). We show that the backscattered power at 30 m height at HV polarization is the best single measurement to estimate AGB with significantly better accuracy than the LiDAR height metrics, and combining it with FH improved the accuracy of AGB estimation to less than 7% of the mean. Our study implies that using multiple information from P-band TomoSAR data from the BIOMASS mission provides a new capability to map tropical forest biomass and its changes accurately.

Species α-diversity promotes but β-diversity restricts aboveground biomass in tropical forests, depending on stand structure and environmental factors

Forest plays a vital role in the global biogeochemical cycles through a high rate of carbon sequestration and harboring biodiversity. However, local species diversity is declining while also becoming increasingly homogenized across communities. Although effects of local biotic processes (e.g., species α-diversity and stand structural heterogeneity) and environmental factors on aboveground biomass (AGB) have been widely tested, there is a huge knowledge gap for the effect of regional biotic processes (i.e., taxonomic and functional β-diversity) in forests. Here, we hypothesized that regional and local environmental factors along with biotic processes jointly regulate AGB through species shifts in tropical forests. Using piecewise structural equation modeling (pSEM), we linked climatic water availability, soil fertility, stand structural heterogeneity (either tree DBH inequality, height inequality, or stand density), species α-diversity, taxonomic or functional β-diversity (and its two components; β-turnover and β-richness), and AGB across 189 inventory plots in tropical forests of Sri Lanka. Soil fertility and climatic water availability shaped local and regional biotic processes. Stand structural heterogeneity promoted species α-diversity but declined β-diversity (but increased β-taxonomic turnover). Species α-diversity and stand structural heterogeneity promoted AGB whereas taxonomic and functional β-diversity declined (but β-taxonomic turnover increased) AGB. The relationships of AGB with species α-diversity and β-diversity varied from significant to nonsignificant positive depending on the specific combinations of stand structural heterogeneity metrics used. This study shows that local biotic processes could increase AGB due to the local and regional niche complementarity effect whereas the regional biotic processes could restrict AGB due to the regional selection or functional redundancy effect under favorable environmental conditions. We argue that biotic homogenization, as well as drought conditions, may have strong divergent impacts on forest functions and that the impacts of tree diversity loss may greatly reduce carbon sequestration.

Species–size networks elucidate the effects of biodiversity on aboveground biomass in tropical forests

Although biodiversity has been shown to profoundly affect ecosystem function in forests, the processes which it impacts are limited understood. Various plant species with diverse sizes interact to form complex networks to complete resource use processes, but little is known about the role of species-size networks in influencing ecosystem function and biodiversity-ecosystem function relationships. Using a dataset encompassing 423 species and 32,067 individuals, we constructed species–tree diameter and species–tree height networks for two sampling areas (0.04 and 0.09 ha), and then calculated the network modularity for species and size interlinked specialization and nestedness, species or sizes with relatively few links are a subset of those with more network links. We analyzed the relationships between modularity, nestedness, and the aboveground biomass. The direct and indirect effects of species abundance and richness on the aboveground biomass through network structures were explored using structural equation modelling. Regardless of the species–tree diameter or height network, modularity was positively associated with the aboveground biomass, independent of the sampling area, while nestedness was negatively associated. Species abundance negatively affected the modularity, but positively affected nestedness, whereas species richness had the opposite effect. Species abundance and richness affected the aboveground biomass indirectly through modularity and nestedness, but the effect of modularity was greater than that of nestedness. Our study confirms that the importance of species–size networks in the context of the aboveground biomass has similar effects on species–diameter and height networks across sampling areas. It clarifies that modularity from interactions between species and individuals is a useful indicator to reveal the mechanism by which plant diversity acquires resources for biomass production. These results provide new insights into biodiversity–ecosystem function relationships from a network perspective.

Functional composition of tall-statured trees underpins aboveground biomass in tropical forests

The influences of trait diversity (i.e., the niche complementarity effect) and functional composition (i.e., the mass ratio effect) on aboveground biomass (AGB) is a highly debated topic in forest ecology. Therefore, further studies are needed to explore these mechanisms in unstudied forest ecosystems to enhance our understanding, and to provide guidelines for specific forest management. Here, we hypothesized that functional composition would drive AGB better than trait diversity and stem size inequality in the (sub-) tropical forests of Nepal. Using data from 101 forest plots, we tested 25 structural equation models (SEMs) to link elevation, stem DBH inequality, trait diversity (i.e., trait richness, evenness, dispersion and divergence), functional composition [i.e., community-weighted of maximum height mean (CWM of Hmax), specific leaf area (CWM of SLA), leaf dry matter content (CWM of LDMC), and wood density (CWM of WD)] and AGB. The best-fitted SEMs indicated that CWM of Hmax promoted AGB while overruling the impacts of trait diversity indices on AGB. However, low trait diversity indices were linked with higher AGB while overruling the effects of CWM of SLA, LDMC and WD on AGB. In addition, AGB decreased with increasing elevation, whereas stem size inequality did not influence AGB. Our results suggest that divergent species’ functional strategies could shape AGB along an altitudinal gradient in tropical forests. We argue that forest management practices should include plant functional traits in the management plan for the co-benefits of biodiversity conservation and carbon sequestration that underpins human wellbeing.

Variation in trunk taper of buttressed trees within and among five lowland tropical forests

AbstractAccurately projecting global carbon dynamics requires understanding controls over temporal and spatial tropical forest biomass variation. Changes in tropical forest aboveground biomass per area are most commonly estimated using repeat forest plot censuses, but errors in these estimates can be relatively large, limiting our ability to detect real changes in forest carbon. One source of error is nonstandard heights of diameter measurement, which are common in tropical forests because buttressed trees are usually measured above buttresses. Nonstandard and temporally variable measurement heights affect biomass change estimates because tree trunks taper—higher measurements result in smaller diameters and, thus, smaller estimated biomass. Modeling trunk taper is a potential way to correct for biases associated with nonstandard and changing measurement heights. We measured trunk taper for 260 buttressed trees in five lowland tropical forests to assess variation in taper within and among sites. Trunks taper more slowly in trees with larger diameters above buttress, taller buttresses, and higher wood density; these variables together explain 29% of taper variation. We also used plot data to quantify the distribution of measurement heights—and potential for bias in biomass estimates. We found significant variation in measurement heights among plots, and over time within plots. At the site level, our general taper model (taper predicted from diameter above buttress, buttress height, and wood density) produced site‐level biomass estimates within 3.4%, on average, of estimates from measured taper. We recommend using this model to reduce bias from nonstandard measurement heights in studies of tropical forest biomass variation.Abstract in Spanish is available with online material.

Retrieval of Tropical Forest Height and Above-Ground Biomass Using Airborne P- and L-Band SAR Tomography

Synthetic aperture radar tomography (TomoSAR) at different radar wavelength can be used to measure different structural elements of forests. In this letter, we compared the airborne P- and L-band synthetic aperture radar (SAR) tomograms and TomoSAR-measured canopy height model (CHM) and above-ground biomass (AGB) over a tropical forest in Lope, Gabon. The SAR data sets were acquired by German Aerospace Center (DLR)'s F-SAR system during the AfriSAR2016 campaign. First, the Weighted covariance fitting-based Iterative Spectral Estimator (WISE) was applied to obtain tomograms. CHM was then retrieved based on the canopy phase center derived from the tomograms. Finally, AGB was estimated via an empirical logarithmic model developed from field measurements and the tomographic backscatter power of vegetation layers between 40 and 50 m above ground. Compared with the classical approaches of Capon and Wavelet-based Compressed Sensing, the WISE method can achieve better resolution with higher computational efficiency and reduce the ambiguity level of L-band tomograms successfully. The experimental results also show that there is no substantial difference between P- and L-band TomoCHM, while P-band tomographic intensity is more sensitive than the L-band for the inversion of tropical forest AGB at a resolution of 50 m x 50 m.