Aboveground Biomass Research Articles

Predicting forest parameters through generalized linear mixed models using GEDI metrics in a temperate forest in Oaxaca, Mexico

ABSTRACT Lidar sensors are active remote sensing instruments that provide data on forest structure, which serve as inputs in the prediction of forest parameters. The objective of this study was to evaluate the performance of mixed-effects models to predict basal area (BA), aboveground biomass (AGB), and wood volume (VOL) from GEDI (Global Ecosystem Dynamics Investigation) predictor variables. Linear mixed and generalized linear mixed models (LMM and GLMM) were fitted with a clustering structure as a random effect between forest parameters and GEDI metrics. LMM and GLMM performed similarly regarding root mean square error (RMSE) and correlation coefficients (r) between observed and predicted data. Our results showed moderate correlations between RH50 and BA (r = 0.53), AGB (r = 0.54), and RH80 with VOL (r = 0.62). Another finding was the correlation between GEDI predictions of the AGB density and the biomass predicted in this study, with an agreement of 79% and 77% and a RMSE of 76.95 and 82.34 Mg ha−1 for LMM and GLMM, respectively. We conclude that the use of mixed models allowed for the inclusion of both fixed and random effects that capture the systematic and random variation in the data, and they provide a flexible framework for modelling forest parameters.

Water and nitrogen coupling regulates the present and future restoration trends under Spartina alterniflora invasion in a coastal salt marsh

Global change can easily cause the wetland ecosystem structure and function to be damaged by alien species. Former studies on Spartina alterniflora invasion only focused on the effect of aboveground communities, ignoring the potential regeneration of soil seed banks. Therefore, the study aimed to find the key resources that limit the S. alterniflora invasion and the regulation mechanism for S. alterniflora regeneration. Through investigating the S. alterniflora communities with different invasion stages, we studied the structure and composition of the aboveground communities and the soil seed banks, in response to the soil properties and water and nitrogen addition. The dominant competitive advantage of S. alterniflora was mainly affected by the aboveground biomass, which was regulated by soil NH4+-N and moisture content. Although the richness was same in the soil seed banks under the S. alterniflora communities with different coverage, S. alterniflora seeds maintained its specific competitive dominance. The niche breadth of S. alterniflora and the niche overlap between S. alterniflora and Tripolium pannonicum was the highest under low aboveground coverage. The soil seed bank germination experiments showed that the S. alterniflora density decreased when the soil nitrogen concentration exceeded 1 g/kg, while the density of native species E. crusgalli and T. pannonicum decreased when the water depth above the soil surface exceeded 2 cm. The successful naturalization of S. alterniflora invasion regulated by nitrogen-water coupling is a bet-hedging of the niche and fitness differences between invasive and native species in the coastal salt marsh of eastern China.

Canopy height uniformity: a new 3D phenotypic indicator linking individual plant to canopy

Canopy height uniformity (CHU) is a key indicator linking individual plants to populations. Determining CHU by the manual measurement of the height of individual plants is inefficient and subjective, making meeting the demand for a high-throughput assessment of CHU in high-yield crop management and variety selection challenging. Therefore, a high-throughput CHU estimation approach using unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data is proposed. First, individual plant point clouds were segmented by incorporating planting density, and the CHU was estimated based on the extracted plant heights (PHs). The CHU was then applied to quantify the effects of different cropping practices on maize canopy structure. In addition, 18 canopy structural parameters (SPs) were extracted from the canopy point cloud, and aboveground biomass (AGB) was estimated by combining these SPs with Pelican Optimization Algorithm (POA) machine learning models (POA-MLs). Finally, as an indicator of individual variability within the canopy, CHU was integrated into the training process to evaluate the accuracy of AGB estimation for different models and datasets. The results showed that the plant height extracted by the canopy population-individual plant segmentation was accurate, with R2 ranging from 0.85 to 0.93. The CHU was able to accurately quantify the effects of different cropping practices on canopy structure. An increase in applied nitrogen fertilizer and irrigation could significantly contribute to an increase in CHU and the formation of a clean and homogeneous canopy structure. Meanwhile, marginal effects can be accurately quantified through PHs estimation to further quantify the intra-canopy differences. In addition, the accuracy of the AGB estimation can be effectively improved by merging SPs, PH, and CHU. In this study, we demonstrated the efficacy of CHU as a phenotypic indicator for representing differences in canopy structure, thus providing practical phenotypic identification information for breeders and field managers.

A Mixture of Summer Legume and Nonlegume Cover Crops Enhances Winter Wheat Yield, Nitrogen Uptake, and Nitrogen Balance

Cover crops protecting soil erosion during the summer fallow in the monsoon weather may enhance dryland winter wheat yield and N relations. We examined the effects of four summer cover crops (soybean (Glycine max L., SB), sudangrass (Sorghum sudanense {Piper} Stapf, SG), soybean and sudangrass mixture (SS), and no cover crop (CK)) and three N fertilization rates (0, 60, and 120 kg N ha−1) on winter wheat yield, quality, and N relations from 2017–2018 to 2020–2021 in the Loess Plateau of China. Cover crop biomass and N accumulation, soil mineral N, and winter wheat yield, protein concentration, and N uptake were greater for SB and SS than other cover crops at most N fertilization rates and years. The N fertilization rate had variable effects on these parameters. Winter wheat aboveground biomass and grain N productivities were greater for CK than other cover crops at all N fertilization rates and years. Nitrogen balance was greater for SS than other cover crops at 60 and 120 kg N ha−1 in all years. The SS with 120 kg N ha−1 can enhance soil mineral N, winter wheat yield and quality, and N balance compared to CK and SG with or without N fertilization rates.

Relationship Between Evolutionary Diversity and Aboveground Biomass During 150 Years of Natural Vegetation Regeneration in Temperate China.

While the link between plant species diversity and biomass has been well-studied, the impact of evolutionary diversity on community biomass across long timescales or ongoing change remains a subject of debate. We elucidated the association between evolutionary diversity and community aboveground biomass (AGB) using an ideal experimental system with over 150-year history of natural vegetation regeneration. Higher phylogenetic diversity facilitated the sampling effect under the influence of environmental filtering, and caused an increase in AGB. Phylogenetic structure varied from aggregation to dispersion during the later period of vegetation recovery (70-150 years), which was correlated with increases in niche complementarity and increasing AGB. Woody plant evolutionary diversity was used as a key to predict the relationship between vegetation recovery and AGB, with a total explanatory power of ~84.7%. Mixed forests composed of evergreen conifers and deciduous broadleaf forests had higher carbon sequestration potential than that of pure forests, which is advantageous for increasing top-stage AGB. This research expands our knowledge of the causes and effects of biodiversity and ecosystem function dynamics over time and space, which is important for accurately predicting future climate change effects.

Lower grass stomatal conductance under elevated CO2 can decrease transpiration and evapotranspiration rates despite carbon fertilization.

Anthropogenic increase in carbon dioxide (CO2) affects plant physiology. Plant responses to elevated CO2 typically include: (1) enhanced photosynthesis and increased primary productivity due to carbon fertilization and (2) suppression of leaf transpiration due to CO2-driven decrease in stomatal conductance. The combined effect of these responses on the total plant transpiration and on evapotranspiration (ET) has a wide range of implications on local, regional, and global hydrological cycles, and thus needs to be better understood. Here, we investigated the net effect of CO2-driven perennial ryegrass (Lolium perenne) physiological responses on transpiration and evapotranspiration by integrating physiological and hydrological (water budget) methods, under a controlled environment. Measurements of the net photosynthetic rate, stomatal conductance, transpiration rate, leaf mass per area, aboveground biomass, and water balance components were recorded. Measured variables under elevated CO2 were compared with those of ambient CO2. As expected, our results show that elevated CO2 significantly decreases whole-plant transpiration rates (38% lower in the final week) which is a result of lower stomatal conductance (57% lower in the final week) despite a slight increase in aboveground biomass. Additionally, there was an overall decline in evapotranspiration (ET) under elevated CO2, indicating the impact of CO2-mediated suppression of transpiration on the overall water balance. Although studies with larger sample sizes are needed for more robust conclusions, our findings have significant implications for global environmental change. Reductions in ET from ryegrass-dominated grasslands and pastures could increase soil moisture and groundwater recharge, potentially leading to increased surface runoff and flooding.

Traits estimated when grown alone may underestimate the competitive advantage and invasiveness of exotic species.

Functional differences between native and exotic species, estimated when species are grown alone or in mixtures, are often used to predict the invasion risk of exotic species. However, it remains elusive whether the functional differences estimated by the two methods and their ability to predict species invasiveness (e.g. high abundance) are consistent. We compiled data from two common garden experiments, in which specific leaf area, height, and aboveground biomass of 64 common native and exotic invasive species in China were estimated when grown individually (pot) or in mixtures (field). Exotic species accumulated higher aboveground biomass than natives, but only when grown in field mixtures. Moreover, aboveground biomass and functional distinctiveness estimated in mixtures were more predictive of species persistence and relative abundance in the field mixtures in the second year than those estimated when grown alone. These findings suggest that assessing species traits while grown alone may underestimate the competitive advantage for some exotic species, highlighting the importance of trait-by-environment interactions in shaping species invasion. Therefore, we propose that integrating multi-site or multi-year field surveys and manipulative experiments is required to best identify the key trait(s) and environment(s) that interactively shape species invasion and community dynamics.

Monitoring the rewilding of the Brazilian Atlantic Forest on tree and mammal diversity: From a biodiversity hotspot to a biodiversity hopespot

The implementation of rewilding initiatives is crucial for biodiversity recovery, particularly in biodiversity hotspots such as the Brazilian Atlantic Forest. Long-term monitoring of rewilding initiatives is critical for assessing progress and informing management choices. This study assesses the efficiency of different reforestation practices for biodiversity recovery, including passive restoration, active reforestation with different native seedlings, and plantations of exotic species. We applied an ecosystem approach to estimate the tree and mammal diversity, collected through forest plots and camera traps. We selected differently managed areas and included the oldest forest patches as reference points for the assessment of biodiversity; a naturally disturbed montane forest in the area was included as a control category for the disturbance analysis. Moreover, we investigated the influence of ecological variables, such as biodiversity, aboveground biomass, and altitude, on rewilding processes. Our results suggest that Eucalyptus plantations, a simplified ecosystem made up of monocultures of exotic species, support lower biodiversity. On the contrary, active reforestation with different native species resulted in an ecological status comparable to naturally regrown secondary forests. We show that active reforestation can reduce the time required for rewilding. We also found a positive correlation between tree and mammal species richness. These results underscore the importance and potential of responsible restoration efforts in recovering local biodiversity, contributing to the vision of a future where the Brazilian Atlantic Forest can be acknowledged not only as a biodiversity hotspot but also as a “hopespot.”

Net primary productivity of Betula platyphylla woodland and the response of current-year shoots to vegetation degradation in Mongolian forest steppe

At the southern margin of forest steppe vegetation in Mongolia, Betula platyphylla woodlands occur on north-facing slopes and hill tops, where congeneric shrub Betula fusca often coexists. Woodland degradation results in B. fusca scrub with scattered B. platyphylla trees. We studied structure and productivity of a B. platyphylla woodland and the current-year shoot allometry in the Hustai National Park of central Mongolia. For the B. platyphylla woodland examined, stand basal area and estimated aboveground biomass for stems ≥ 3 cm diameter were 3.9 m2 ha–1 and 13.3 Mg dry mass ha–1, respectively. Leaf area index was 0.93 indicating open canopy layer. Aboveground net primary productivity as the sum of coarse wood production and current-year shoot mass amounted to 1.89 Mg dry mass ha–1 year–1, which was high relative to small aboveground biomass. Analysis of current-year shoot allometry demonstrated that B. platyphylla had more xeromorphic shoots in the scrub, with smaller specific leaf area, lower mass ratio of leaf laminae to supporting parts, and smaller lamina mass against sum of petiole basal area, than in the woodland. Our results indicate that current-year shoot allometry characterizes tree response to woodland degradation at the dry margin of forest steppe.

Potential of cattle urine as an alternative fertilizer for maize (Zea mays L.) production in Ethiopia

Maize productivity has been increasing through the years due to increased use of inorganic fertilizers. However, the price and environmental impact has brought lack of interest among the farmers. Thus, this study was aimed at evaluating the growth, yield and nitrogen use efficiency (NUE) of maize varieties under cattle urine application. The field experiment was conducted in randomized complete block design with three replications. The experiment consisted of four treatments viz. control, 100 and 50 % cattle urine and recommended dose of inorganic fertilizer. The results demonstrated a comparable growth and grain yield increment due to 100 % cattle urine and recommended dose of inorganic fertilizer. The difference between the two varieties in growth, yield and related traits, and NUE indices due to fertilizer applications was insignificant. The leaf area of maize nourished with 100, 50 % cattle urine and recommended dose of inorganic fertilizer increased by 11.15, 9.73 and 9.78 folds, respectively, in two months period relative to the controls. Fertilized plants had significantly (p < 0.05) higher grain yield (GY). The use of 100, 50 % cattle urine and inorganic fertilizers resulted in 526.76, 326.12, 542.91 % and 47.83, 36.03, 49.78 % increment in average GY and aboveground biomass, respectively, compared to controls. Similarly, plants fertilized with 50 % cattle urine showed an average GY reduction of 39.87 and 38.10 % compared to the recommended dose of inorganic fertilizer and 100 % cattle urine, respectively. The use of 50 % cattle urine produced significantly higher NUE, N agronomic efficiency (NAE), N recovery efficiency (NRE) and internal efficiency (IE). Higher GY kg-1 of N applied was obtained from cattle urine applications. In a nutshell, the results confirmed the potential of cattle urine as an alternative to inorganic fertilizers in maize production. However, further studies on optimization of the cattle urine rate and scale up works are recommended.

Understory plant biodiversity is inversely related to carbon storage in a high carbon ecosystem.

Given that terrestrial ecosystems globally are facing the loss of biodiversity from land use conversion, invasive species, and climate change, effective management requires a better understanding of the drivers and correlates of biodiversity. Increasingly, biodiversity is co-managed with aboveground carbon storage because high biodiversity in animal species is observed to correlate with high aboveground carbon storage. Most previous investigations into the relationship of biodiversity and carbon co-management do not focus on the biodiversity of the species rich plant kingdom, which may have tradeoffs with carbon storage. To examine the relationships of plant species richness with aboveground tree biomass carbon storage, we used a series of generalized linear models with understory plant species richness and diversity data from the USDA Forest Service Forest Inventory and Analysis dataset and high-resolution modeled carbon maps for the Tongass National Forest. Functional trait data from the TRY database was used to understand the potential mechanisms that drive the response of understory plants. Understory species richness and community weighted mean leaf dry matter content decreased along an increasing gradient of tree biomass carbon storage, but understory diversity, community weighted mean specific leaf area, and plant height at maturity did not. Leaf dry matter content had little variance at the community level. The decline of understory plant species richness but not diversity to increases in aboveground biomass carbon storage suggests that rare species are excluded in aboveground biomass carbon dense areas. These decreases in understory species richness reflect a tradeoff between the understory plant community and aboveground carbon storage. The mechanisms that are associated with observed plant communities along a gradient of biomass carbon storage in this forest suggest that slower-growing plant strategies are less effective in the presence of high biomass carbon dense trees in the overstory.

Microclimatic effects of tree shelters on the early establishment and resilience of seeded acorns vs. outplanted seedlings

Little is known about the effects of tree shelters on the early response of oak seedlings produced by acorn seeding. In this paper, we explore the effects on holm oak (Quercus ilex L. subsp. ballota (Desf.) Samp.) seedlings of the microenvironment created by the tree shelters and the restoration method (seeding vs. outplanting) in terms of emergence, survival, growth, and resilience after harvesting. For this purpose, seedling height [H], root collar diameter [RCD], number of leaves, and aerial biomass were monitored. We made two sowings of acorns in February 2017 and February 2018, together with a seedling outplanting in February 2018 in a common garden site in semiarid SE Spain. In total, 600 acorns were randomly sowed and 300 nursery-grown seedlings were outplanted and studied until 2022. Mother tree and initial acorn mass were also monitored as additional variables in the analyses. Tree shelters consisted of closed plastic Tubes, Mixed tubes, Cork shelters, Tiles, and a Control with no shelter. Emergence rate was positively influenced by the Tube shelter (86%) as compared to the Control (64%), and especially by the initial acorn mass. By contrast, mother tree or year of sowing seemed to have no effect. The survival rate for the emerged acorns (88%) was statistically similar to that of outplanted seedlings (91%), and was unaffected by mother tree, tree shelter, or acorn mass. In terms of growth, the slenderness ratio (H:RCD) was considerably higher in seedlings from directly seeded acorns than for those that were outplanted. With the exception of Tile, all the shelters showed a higher slenderness ratio than the Control, especially the Tube shelter, which also showed a lower number of leaves and a lower aboveground dry biomass than the Control, Cork, and Tile shelters. Virtually no interactions were observed between the mother tree and the tree shelter. At harvesting, all the growth-related parameters were still strongly dependent on the acorn mass and the initial seedling features recorded after the first growing season. Resprouting rate and growth were also highly dependent on the acorn mass and the plant features at the beginning of the experiment and at harvesting. In summary, we did not find evidence to support tree shelters to improve the microclimate of holm oak seedlings both seeded or outplanted. Direct acorn seeding can be as successful as outplanting of nursery-grown seedlings. Selection of heavy acorns from mothers with a high germination and emergence rate is highly advisable.

Allometric Models for Estimating Above-Ground, Below-Ground and Total Biomass of Tea (&lt;em&gt;Camellia sinensis&lt;/em&gt; (L.) O. Kuntze) Plants Grown in Uva and Up Country, Sri Lanka

This study was conducted to identify the dendrometric variables highly correlating with above-ground (AGB), below-ground (BGB) and total (TB) biomass of tea grown in Uva and Up Country Sri Lanka, develop allometric models to estimate AGB, BGB and TB of tea plants, and to investigate the applicability of already developed allometric models in other countries to Sri Lanka. Twelve experimental plots from TRIORCON field experiment at Tea Research Institute of Sri Lanka, and 18 farmer fields in Uva region, Sri Lanka were selected as the study sites. Representative tea plants were uprooted from the selected sites and seven dendrometric variables were measured. Fresh and dry AGB and BGB of the tea plants were determined. The best allometric models to predict AGB, BGB and TB using dendrometric variables were developed using multiple linear and non-linear regression analyses. Non-linear allometric models fitted best when predicting AGB and TB. Over 64% of total variability of AGB and TB was explained by these models using collar diameter at the soil level. Linear allometric fitted best when predicting BGB. Over 92% of total variability of BGB was explained by linear regression models using collar circumference, main-stem circumference, mean length and circumference at end of the primary branches. Leave-one-out cross validation showed strong positive (AGB and TB) and moderate positive (BGB) correlations between observed and predicted biomass values. Developed linear allometric models in comparison to those in literature showed better fit to data collected under Sri Lankan conditions and precisely estimated AGB, BGB and TB.

Effects of short-term nitrogen addition on the recovery of alpine grassland in the Tianshan Mountains of Xinjiang, China.

The restoration of alpine grasslands has garnered significant attention across various sectors. Historically, natural restoration has been the primary approach for grassland recovery, characterized by its prolonged duration. To expedite the recovery of degraded grasslands, it is essential to identify the limiting factors of restoration, enabling efficient and rapid recovery. Appropriate nitrogen (N) addition levels have been considered a potential strategy to enhance the recovery of grassland ecosystems and augment their ecological benefits. However, the effectiveness of N addition in alpine grassland restoration remains debated. This study investigated the impact of five N addition levels (CK: control [0 g/m2]; LN: low N [5 g/m2]; MN: medium N [10 g/m2]; HN: high N [15 g/m2]; SN: severe N [20 g/m2]) and two experimental approaches (N addition once per year [NPY] and three times per year [NTY] at the same dosages) on plant and soil properties and the maximum restoration capacity of alpine meadows. Our findings reveal three key insights: The level of N addition was the primary factor influencing aboveground plant biomass and coverage. Plant diversity decreased under the NTY regime and increased with NPY in the Bayinbruck grassland. N addition significantly altered soil properties, including pH, salinity, soil organic carbon (SOC), soil-available phosphorus (AP), and soil total phosphorus (TP). Notably, soil TP, total nitrogen (TN), and AP substantially impacted plant community structure and diversity. Based on structural equation model (SEM) and analysis of variance (ANOVA), optimal grassland restoration was achieved with the HN (15 g/m2) treatment under NPY and the MN and HN (10 and 15 g/m2) treatments under NTY. Overall, our study offers crucial insights into the conservation, management, and restoration of grassland ecosystems on the Bayinbruck Plateau. It underscores the significance of N addition effects on plant communities, vegetation restoration, and soil properties.

The spatiotemporal stability of plant diversity is disconnected from biomass stability in response to human activities in a South American temperate grassland

Human activities alter biomass, nutrient availability, and species dominance in grasslands, impacting their richness, composition, and biomass production. Stability (invariability in time or space) can inform the predictability of plant communities in response to human activities. However, this measure has been simplistically analyzed for temporal (interannual) changes in live biomass, disregarding their spatial stability and the temporal stability of other plant community attributes. Moreover, the simultaneous analysis of temporal and spatial stabilities of plant communities has been scarcely assessed. Here, we test how biomass removal and nutrient addition simultaneously modify the temporal and spatial stabilities of plant richness (α diversity), composition dissimilarity (β diversity), aboveground live biomass, and the role of plant species dominance in the stability responses. We conducted a factorial experiment of biomass removal (grazing, mowing, or intact -no removal-) and nutrient addition (unfertilized or fertilized with nitrogen, phosphorus, and potassium) in a temperate grassland of Argentina, South America. We replicated the experiment in 6 blocks over 10 years to estimate the temporal and spatial stabilities of the plant community. The spatiotemporal stability of plant richness and composition dissimilarity decreased in the intact grassland, while the temporal stability of live biomass increased, compared to the grazed and mowed grasslands. Nutrient addition reduced the spatiotemporal stability of live biomass and the spatial stability of plant richness. The stability of species richness as well as that of composition dissimilarity was negatively associated with plant dominance, while the live biomass stability was not. Our results suggest that simplifying the effect of biomass removal and nutrient addition on grassland stability is not feasible, as plant diversity stability responses are not surrogates for biomass stability. The contrasting spatiotemporal stability responses of plant diversity and biomass represent a step forward in predicting human activities' impact over time and across space in temperate grasslands.

Changes in Soil Bacterial and Fungal Community Composition and Functional Groups During the Artificial Restoration of Degraded Grassland of "Black-Soil Mountain".

About 35% of grassland in Sanjiangyuan area of China has degenerated into black-soil mountain. Artificial grassland is considered to be an effective measure to alleviate the severely degraded grassland in the alpine region of the three rivers and has been widely used. However, the pattern, potential function, and changes of carbon and nitrogen contents of soil microorganisms in degraded grassland in Heimushan by planting artificial grassland are still unclear. In this study, mixed-sown artificial alpine grassland (AG) was the focus of our study, whereas degraded black-soil mountain grassland (BG) and natural alpine grassland (NG) served as controls. Illumina 16S and ITS gene sequence analyses were used to analyze the community structure of the soil bacteria and fungi. The functional groups of NG, AG, and BG were predicted using the FAPROTAX and FUNGuild databases. In addition, the levels of soil carbon, nitrogen, and soil enzyme activities were evaluated. The results indicated a significant increase in the aboveground biomass of BG due to the planting artificial grassland. Moreover, the contents of total carbon (TC), total nitrogen (TN), ammonium nitrogen ( ), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and leucine aminopeptidase (LAP) increased in the soil. Planting artificial grasslands changed the composition of bacterial and fungal communities. Among these, the bacterial community was more sensitive to planting artificial grasslands. The relative abundance of bacterial functional groups involved in carbon and nitrogen cycling changed significantly, suggesting that bacteria may play a role in regulating nutrient cycling during artificial grassland planting. Soil TC, TN, LAP, and affected the microbial community structure related to carbon and nitrogen. and β-1,4-glucosidase were carbon and nitrogen factors, respectively, that affected functional changes in fungi. These results indicate that planting artificial grasslands can effectively enhance the productivity of degraded black-soil mountain and regulate soil microbial communities and soil physical and chemical properties.

Mapping aboveground biomass and carbon in salt marshes across the contiguous United States

Mapping aboveground biomass and carbon in salt marshes across the contiguous United States

Design and performance of the Climate Change Initiative Biomass global retrieval algorithm

The increase in Earth observations from space in recent years supports improved quantification of carbon storage by terrestrial vegetation and fosters studies that relate satellite measurements to biomass retrieval algorithms. However, satellite observations are only indirectly related to the carbon stored by vegetation. While ground surveys provide biomass stock measurements to act as reference for training the models, they are sparsely distributed. Here, we addressed this problem by designing an algorithm that harnesses the interplay of satellite observations, modeling frameworks and field measurements, and generated global estimates of above-ground biomass (AGB) density that meet the requirements of the scientific community in terms of accuracy, spatial and temporal resolution. The design was adapted to the amount, type and spatial distribution of satellite data available around the year 2020. The retrieval algorithm estimated AGB annually by merging estimates derived from C- and L-band Synthetic Aperture Radar (SAR) backscatter observations with a Water Cloud type of model and does not rely on AGB reference data at the same spatial scale as the SAR data. This model is integrated with functions relating to forest structural variables that were trained on spaceborne LiDAR observations and sub-national AGB statistics. The yearly estimates of AGB were successively harmonized using a cost function that minimizes spurious fluctuations arising from the moderate-to-weak sensitivity of the SAR backscatter to AGB. The spatial distribution of the AGB estimates was correctly reproduced when the retrieval model was correctly set. Over-predictions occasionally occurred in the low AGB range (<50 Mg ha−1) and under-predictions in the high AGB range (>300 Mg ha−1). These errors were a consequence of sometimes too strong generalizations made within the modeling framework to allow reliable retrieval worldwide at the expense of accuracy. The precision of the estimates was mostly between 30% and 80% relative to the estimated value. While the framework is well founded, it could be improved by incorporating additional satellite observations that capture structural properties of vegetation (e.g., from SAR interferometry, low-frequency SAR, or high-resolution observations), a dense network of regularly monitored high-quality forest biomass reference sites, and spatially more detailed characterization of all model parameters estimates to better reflect regional differences.

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.

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.