Aboveground Biomass Models Research Articles

Development and Evaluation of Species-Specific Biomass Models for Most Common Timber and Fuelwood Species of Bangladesh

Allometric biomass models are efficient tools to estimate biomass of trees and forest stands in a non-destructive way. Development of species-specific allometric biomass models requires extensive fieldwork and time. Our study aimed to generate species-specific allometric biomass models for the most common fuelwood and timber species of Bangladesh. We also wanted to evaluate the performances of our models relative to the performances of regional and commonly used pan-tropical biomass models. We used semi-destructive method that incorporates tree-level volume, species-specific biomass expansion factor (BEF), and wood density. We considered four base models, 1) Ln (biomass) = a + bLn (D); 2) Ln (biomass) = a + bLn (H); 3) Ln (Biomass) = a + bLn (D^2H); 4) Ln (Biomass) = a + bLn (D) + cLn (H) to develop species-specific best-fitted models for Total Above-Ground Biomass (TAGB) and stem biomass. The best-fitted model for each species was selected by the lowest value of Akaike Information Criterion (AIC), Residual Standard Error (RSE) and Root Mean Square Error (RMSE). The derived best-fitted models were then evaluated with respect to regional and pan-tropical models using a separate set of observed data. This evaluation was conducted by computing ME (Model Efficiency) and MPE (Model Prediction Error). The best-fitted allometric biomass models have shown higher model efficiency (0.85 to 0.99 at scale 1) and the lowest model prediction error (-8.94% to 5.27%) compared to the regional and pan-tropical models. All the examined regional and pan-tropical biomass models showed different magnitude of ME and MPE. Some models showed higher level (>0.90 at scale 1) of ME compared to the best-fitted specific species biomass model.

Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms

Forest biomass is a major store of carbon and plays a crucial role in the regional and global carbon cycle. Accurate forest biomass assessment is important for monitoring and mapping the status of and changes in forests. However, while remote sensing-based forest biomass estimation in general is well developed and extensively used, improving the accuracy of biomass estimation remains challenging. In this paper, we used China’s National Forest Continuous Inventory data and Landsat 8 Operational Land Imager data in combination with three algorithms, either the linear regression (LR), random forest (RF), or extreme gradient boosting (XGBoost), to establish biomass estimation models based on forest type. In the modeling process, two methods of variable selection, e.g., stepwise regression and variable importance-base method, were used to select optimal variable subsets for LR and machine learning algorithms (e.g., RF and XGBoost), respectively. Comfortingly, the accuracy of models was significantly improved, and thus the following conclusions were drawn: (1) Variable selection is very important for improving the performance of models, especially for machine learning algorithms, and the influence of variable selection on XGBoost is significantly greater than that of RF. (2) Machine learning algorithms have advantages in aboveground biomass (AGB) estimation, and the XGBoost and RF models significantly improved the estimation accuracy compared with the LR models. Despite that the problems of overestimation and underestimation were not fully eliminated, the XGBoost algorithm worked well and reduced these problems to a certain extent. (3) The approach of AGB modeling based on forest type is a very advantageous method for improving the performance at the lower and higher values of AGB. Some conclusions in this paper were probably different as the study area changed. The methods used in this paper provide an optional and useful approach for improving the accuracy of AGB estimation based on remote sensing data, and the estimation of AGB was a reference basis for monitoring the forest ecosystem of the study area.

Delineation of management zones in agricultural fields using cover–crop biomass estimates from PlanetScope data

Several methods have been proposed to delineate management zones in agricultural fields, which can guide interventions of the farmers to increase crop yield. In this study, we propose a new approach using remote sensing data to delineate management zones at three farm sites located in southern Brazil. The approach is based on the hypothesis that the measured aboveground biomass (AGB) of the cover crops is correlated with the measured cash-crop yield and can be estimated from surface reflectance and/or vegetation indices (VIs). Therefore, we used seven different statistical models to estimate AGB of three cover crops (forage turnip, white oats, and rye) in the season prior to cash-crop planting. Surface reflectance and VIs were used as predictors to test the performance of the models. They were obtained from high spatial and temporal resolution data of the PlanetScope (PS) constellation of satellites. From the time series of 30 images acquired in 2017, we used the PS data that matched the dates of the field campaigns to build the models. The results showed that the satellite AGB estimates of the cover crops at the date of maximum VI response at the beginning of the flowering stage were useful to delineate the management zones. The cover-crop AGB models that presented the highest coefficient of determination (R2) and the lowest root mean square (RMSE) in the validation and test datasets were Support Vector Machine (SVM), Cubist (CUB) and Stochastic Gradient Boosting (SGB). For most models and cover crops, the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) were the two most important AGB predictors. At the date of maximum VI at the beginning of the flowering stage, the correlation coefficients (r) between the cover-crop AGB and the cash-crop yield (soybean and maize) ranged from +0.70 for forage turnip to +0.78 for rye. The fuzzy unsupervised classification of the cover-crop AGB estimates delineated two management zones, which were spatially consistent with those obtained from cash-crop yield. The comparison between both maps produced overall accuracies that ranged from 61.20% to 68.25% with zone 2 having higher cover-crop AGB and cash-crop yield than zone 1 over the three sites. We conclude that satellite AGB estimates of cover crops can be used as a proxy for generating management zone maps in agricultural fields. These maps can be further refined in the field with any other type of method and data, whenever necessary.

Transectos de datos LiDAR: una estrategia de muestreo para estimar biomasa aérea en áreas forestales

La estimación y el mapeo de la biomasa aérea sobre áreas extensas puede realizarse haciendo uso de las herramientas que ofrece la percepción remota. El objetivo de este estudio fue estimar la biomasa aérea de dos tipos de selva mediana: subperennifolia (SMSP) y subcaducifolia (SMSC) en la península de Yucatán, México, empleando métricas generadas a partir de datos Light Detection and Ranging (LiDAR). Se usaron datos de 365 unidades de muestreo del Inventario Nacional Forestal y de Suelos (INFyS) de México para calibrar modelos de biomasa aérea usando regresión lineal múltiple y Random Forest (RF). Con estos modelos se mapeó la biomasa aérea sobre franjas de datos LiDAR. El modelo de regresión transformado logró explicar la varianza en un 62% (RMSE = 41.44 Mg ha-1 para SMSP y 36.60 Mg ha-1 para SMSC) para ambos tipos de vegetación. Los modelos generados a través de RF lograron explicar la varianza en un 57% (RMSE = 40.73 Mg ha-1) para la SMSP y solo de 52% (RMSE = 35.10 Mg Ha-1) para la SMSC. El desfase entre la toma de datos en campo y LiDAR, así como el error en la precisión de las coordenadas de los sitios de inventario, son factores reconocidos que influyeron en los resultados. A pesar de lo anterior, las estimaciones obtenidas podrían servir de base para estimar el inventario completo de biomasa en el área de estudio incorporando datos espectrales derivados de un sensor remoto que cubra la totalidad de esta.

Integrating Imaging Spectrometer and Synthetic Aperture Radar Data for Estimating Wetland Vegetation Aboveground Biomass in Coastal Louisiana

Aboveground biomass (AGB) plays a critical functional role in coastal wetland ecosystem stability, with high biomass vegetation contributing to organic matter production, sediment accretion potential, and the surface elevation’s ability to keep pace with relative sea level rise. Many remote sensing studies have employed either imaging spectrometer or synthetic aperture radar (SAR) for AGB estimation in various environments for assessing ecosystem health and carbon storage. This study leverages airborne data from NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) to assess their unique capabilities in combination to estimate AGB in coastal deltaic wetlands. Here we develop AGB models for emergent herbaceous and forested wetland vegetation in coastal Louisiana. In addition to horizontally emitted, vertically received (HV) backscatter, SAR parameters are expressed by the Freeman–Durden polarimetric decomposition components representing volume and double-bounce scattering. The imaging spectrometer parameters include normalized difference vegetation index (NDVI), reflectance from 290 visible-shortwave infrared (VSWIR) bands, the first derivatives from those bands, or partial least squares (PLS) x-scores derived from those data. Model metrics and cross-validation indicate that the integrated models using the Freeman-Durden components and PLS x-scores improve AGB estimates for both wetland vegetation types. In our study domain over Louisiana’s Wax Lake Delta (WLD), we estimated a mean herbaceous wetland AGB of 3.58 Megagrams/hectare (Mg/ha) and a total of 3551.31 Mg over 9.92 km2, and a mean forested wetland AGB of 294.78 Mg/ha and a total of 27,499.14 Mg over 0.93 km2. While the addition of SAR-derived values to imaging spectrometer data provides a nominal error decrease for herbaceous wetland AGB, this combination significantly improves forested wetland AGB prediction. This integrative approach is particularly effective in forested wetlands as canopy-level biochemical characteristics are captured by the imaging spectrometer in addition to the variable structural information measured by the SAR.

Aboveground Biomass Of Mongolian Larch (Larix Sibirica Ledeb.) Forests In The Eurasian Region

We used our database of tree biomass with a number of 433 sample trees of Larix from different ecoregions of Eurasia, involving 61 trees from Mongolia for developing an additive model of biomass tree components. Our approach solved the combined problem of additivity and regionality of the model. Our additive model of tree aboveground biomass was harmonized in two ways: first, it eliminated the internal contradictions of the component and of the total biomass equations, secondly, it took into account regional (and correspondingly species-specific) differences of trees in its component structure. A significant excess of larch biomass in the forest-tundra is found that may be explained by permafrost conditions, by tree growth in low-yielding stands with a high basic density of stem wood and relatively high developed tree crown in open stands. The aboveground biomass of larch trees in Mongolia does not stand out against the background of the most ecoregions of Eurasia. Based on our results, we conclude that the growing conditions of larch in Mongolia are not as tough as it was suggested earlier by other scientists. Biomass relations between regions may be explained by unknown and unaccounted factors and errors of measurements in all their phases (assessment of age, diameter, height of a tree, the selection of supposedly representative samples of component biomass, their drying, weighing, etc.). The question what explains the regional differences in the structure of biomass of trees with the same linear dimensions of their stems, remains open. Undoubtedly, the differences in tree age here play an important role. Also, important factor is the variation in the morphological structure of stands, which, in turn, is determined by both climatic and edaphic factors. The obtained models allow the determination of larch forest biomass in different ecoregions of Eurasia with the help of height and diameter data.

Comparing independent climate-sensitive models of aboveground biomass and diameter growth with their compatible simultaneous model system for three larch species in China

Accurate estimate of tree biomass is essential for forest management. In recent years, several climate-sensitive allometric biomass models with diameter at breast height [Formula: see text] as a predictor have been proposed for various tree species and climate zones to estimate tree aboveground biomass (AGB). But the allometric models only account for the potential effects of climate on tree biomass and do not simultaneously explain the influence of climate on [Formula: see text] growth. In this study, based on the AGB data from 256 destructively sampled trees of three larch species randomly distributed across the five secondary climate zones in northeastern and northern China, we first developed a climate-sensitive AGB base model and a climate-sensitive [Formula: see text] growth base model using a nonlinear least square regression separately. A compatible simultaneous model system was then developed with the climate-sensitive AGB and [Formula: see text] growth models using a nonlinear seemingly unrelated regression. The potential effects of several temperature and precipitation variables on AGB and [Formula: see text] growth were evaluated. The fitting results of climatic sensitive base models were compared against those of their compatible simultaneous model system. It was found that a decreased isothermality ([mean of monthly (maximum temperature-minimum temperature)]/(Maximum temperature of the warmest month-Minimum temperature of the coldest month)) and total growing season precipitation, and increased annual precipitation significantly increased the values of AGB; an increase of temperature seasonality (a standard deviation of the mean monthly temperature) and precipitation seasonality (a standard deviation of the mean monthly precipitation) could lead to the increase of [Formula: see text]. The differences of the model fitting results between the compatible simultaneous system with the consideration of climate effects on both AGB and [Formula: see text] growth and its corresponding climate-sensitive AGB and [Formula: see text] growth base models were very small and insignificant [Formula: see text]. Compared to the base models, the inherent correlation of AGB with [Formula: see text] was taken into account effectively by the proposed compatible model system developed with the climate-sensitive AGB and [Formula: see text] growth models. In addition, the compatible properties of the estimated AGB and [Formula: see text] were also addressed substantially in the proposed model system.

Combining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms

Accurate estimates of aboveground biomass (AGB) in tropical forests are critical for supporting strategies of ecosystem functioning conservation and climate change mitigation. However, such estimates at regional and local scales are still highly uncertain. Airborne Light Detection And Ranging (LiDAR) and Hyperspectral Imaging (HSI) can characterize the structural and functional diversity of forests with high accuracy at a sub-meter resolution, and potentially improve the AGB estimations. In this study, we compared the ability of different data sources (airborne LiDAR and HSI, and their combination) and regression methods (linear model - LM, linear model with ridge regularization - LMR, Support Vector Regression - SVR, Random Forest - RF, Stochastic Gradient Boosting - SGB, and Cubist - CB) to improve AGB predictions in the Brazilian Amazon. We used georeferenced inventory data from 132 sample plots to obtain a reference field AGB and calculated 333 metrics (45 from LiDAR and 288 from HSI) that could be used as predictors for statistical AGB models. We submitted the metrics to a correlation filtering followed by a feature selection procedure (recursive feature elimination) to optimize the performance of the models and to reduce their complexity. Results showed that both LiDAR and HSI data used alone provided relatively high accurate models if adequate metrics and algorithms are chosen (RMSE = 67.6 Mg.ha−1, RMSE% = 36%, R2 = 0.58, for the best LiDAR model; RMSE = 68.1 Mg.ha−1, RMSE% = 36%, R2 = 0.58, for the best HSI model). However, HSI-only models required more metrics (5–12) than LiDAR-only models (2–5). Models combining metrics from both datasets resulted in more accurate AGB estimates, regardless of the regression method (RMSE = 57.7 Mg.ha−1, RMSE% = 31%, R2 = 0.70, for the best model). The most important LiDAR metrics for estimating AGB were related to the upper canopy cover and tree height percentiles, while the most important HSI metrics were associated with the near infrared and shortwave infrared spectral regions, particularly the leaf/canopy water and lignin-cellulose absorption bands. Finally, an analysis of variance (ANOVA) showed that the remote sensing data source (LiDAR, HSI, or their combination) had a greater effect size than the regression algorithms. Thus, no single algorithm outperformed the others, although the LM method was less suitable when applied to the HSI and hybrid datasets. Results show that the synergistic use of LiDAR and hyperspectral data has great potential for improving the accuracy of the biomass estimates in the Brazilian Amazon.

Allometric Equations for Estimating the Above-Ground Biomass of Five Forest Tree Species in Khangai, Mongolia

Understanding the contribution of forest ecosystems to regulating greenhouse gas emissions and maintaining the atmospheric CO2 balance requires the accurate quantification of above-ground biomass (AGB) at the individual tree species level. The main objective of this study was to develop species-specific allometric equations for the total AGB and various biomass components, including stem, branch, and foliage biomass in Khangai region, northern Mongolia. We destructively sampled a total of 183 trees of five species (22–74 trees per species), including Siberian stone pine (Pinus sibirica Du Tour.), Asian white birch (Betula platyphylla Sukacz.), Mongolian poplar (Populus suaveolens Fisch.), Siberian spruce (Picea obovata Ldb.), and Siberian larch (Larix sibirica Ldb.), across this region. The results showed that for the five species, the average biomass proportion for the stems was 75%, followed by branches at 20% and foliage at 5%. The species-specific component and total AGB models for the Khangai region were developed using tree diameter at breast height (D) and D² and tree height (H) combined ( D 2 H ); and both D and H were used as independent variables. The best allometric model was lnŶ = lna + b × lnD + c × lnH for the various components and total AGB of B. platyphylla and L. sibirica, for the stems and total AGB of P. suaveolens, and for the stem and branch biomass of P. obovata. The equation lnŶ = lna + b × ln( D 2 × H ) was best for the various components and total AGB of P. sibirica, for the branch and foliage biomass of P. suaveolens, and for AGB of P. obovata. The equation lnŶ = lna + b × ln(D) was best only for the foliage biomass of P. obovata. Our results highlight that developing species-specific tree AGB models is very important for accurately estimating the biomass in the Khangai forest region of Mongolia. Our biomass models will be used at the tree level inventories with sample plots in the Khangai forest region.

Carbon storage estimation of tropical urban trees by an improved allometric model for aboveground biomass based on terrestrial laser scanning

Current estimations of the carbon stored by urban trees are usually based on allometric models developed for natural forests. However, urban trees grow in different conditions, and their architecture might differ, thus models developed specially for urban trees are needed. With the aim of improving estimations of carbon storage and sequestration in tropical cities, we developed a model for Singapore’s urban trees based on three-dimensional data acquired on standing trees using a terrestrial LiDAR. This model relates the aboveground biomass (AGB) with the trunk diameter, wood specific density and total height of the tree. Its performance was compared to the performance of selected models for tropical forests and urban trees. As expected, being locally derived, our model reduced the bias of both individual and plot level estimates. The AGB estimates compared to those retrieved from the LiDAR showed a deviation of < 3% at plot level and 36–57% at individual scale. We applied this model to trees of a neighbourhood, where our team has investigated the role of urban vegetation in offseting anthropogenic emissions, as an example of how it could assist Singapore’s efforts to verify climate change mitigation measures.

Which option best estimates the above-ground biomass of mangroves of Bangladesh: pantropical or site- and species-specific models?

Bangladesh has the single largest tract of naturally growing mangrove forest as well as the world’s largest manmade mangrove forest on newly accreted land in coastal areas. These mangrove forests provide significant support to the community as sources of renewable resources, shelter from natural calamities, and carbon sinks. The second nationwide forest inventory is now underway in Bangladesh. Biomass and carbon stock assessment of trees and forests is one of the objectives of this inventory. The present study aims to derive multi-species allometric biomass models for the Sundarbans mangrove forests and species-specific allometric biomass models for planted Sonneratia apetala Buch. Ham in the coastal zone of Bangladesh. A total of 342 individuals from 14 tree species from the Sundarbans and 73 individuals of planted S. apetala from the coastal zone were selected for the development and validation of the allometric model. A semi-destructive method was adopted to estimate the biomass of the sample trees. The best fit multi-species allometric model of Total Above-ground Biomass (TAGB) for the Sundarbans zone was Ln (TAGB) = − 6.7189 + 2.1634 * Ln(D) + 0.3752 * Ln(H) + 0.6895 * Ln(W). Moreover, relatively simple models with only DBH or DBH and H as predictive variables are also recommended for the Sundarbans zone. The best fit species-specific allometric model of TAGB for the planted S. apetala was Ln (TAGB) = − 1.7608 + 2.0077 * Ln(D) + 0.2981 * Ln(H), where D = diameter at breast height in cm, H = total height in m, and W = wood density (kg m−3). The derived best fit allometric models of TAGB for the Sundarbans and planted S. apetala were more efficient in biomass estimation than the frequently used regional and pan-tropical allometric models.

Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

Grassland degradation has accelerated in recent decades in response to increased climate variability and human activity. Rangeland and grassland conditions directly affect forage quality, livestock production, and regional grassland resources. In this study, we examined the potential of integrating synthetic aperture radar (SAR, Sentinel-1) and optical remote sensing (Landsat-8 and Sentinel-2) data to monitor the conditions of a native pasture and an introduced pasture in Oklahoma, USA. Leaf area index (LAI) and aboveground biomass (AGB) were used as indicators of pasture conditions under varying climate and human activities. We estimated the seasonal dynamics of LAI and AGB using Sentinel-1 (S1), Landsat-8 (LC8), and Sentinel-2 (S2) data, both individually and integrally, applying three widely used algorithms: Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF). Results indicated that integration of LC8 and S2 data provided sufficient data to capture the seasonal dynamics of grasslands at a 10–30-m spatial resolution and improved assessments of critical phenology stages in both pluvial and dry years. The satellite-based LAI and AGB models developed from ground measurements in 2015 reasonably predicted the seasonal dynamics and spatial heterogeneity of LAI and AGB in 2016. By comparison, the integration of S1, LC8, and S2 has the potential to improve the estimation of LAI and AGB more than 30% relative to the performance of S1 at low vegetation cover (LAI < 2 m2/m2, AGB < 500 g/m2) and optical data of LC8 and S2 at high vegetation cover (LAI > 2 m2/m2, AGB > 500 g/m2). These results demonstrate the potential of combining S1, LC8, and S2 monitoring grazing tallgrass prairie to provide timely and accurate data for grassland management.

Regression-Based Integrated Bi-sensor SAR Data Model to Estimate Forest Carbon Stock

The objective of this study is to estimate the forest aboveground carbon (AGC) stock using integrated space-borne synthetic aperture radar (SAR) data from COSMO-Skymed (X band) and ALOS PALSAR (L band) with field inventory over a tropical deciduous mixed forest. Carbon acts as a vital constituent in the global decision making policy targeting the impact of reducing emissions from deforestation and forest degradation (REDD) and climate change. The study proposed an approach to develop regression models for assessing the forest AGC with synergistic use of SAR bi-sensor X and L band sigma nought data. The best-fit integrated aboveground biomass (AGB) model was validated with additional sample points that produced a model accuracy of 78.6%, adjusted R2 = 0.88, RMSE = 16.6 Mg/ha, standard error of estimates of 16.03 and Willmott’s index of agreement of 0.93. Resulting modeled AGB was converted to AGC using conversion factors. L band resulted in higher accuracy of estimates when compared to X band, while the estimation accuracy enhanced on integrating X- and L-band information. Hence, the study presents an approach using integrated SAR bi-sensor X and L bands that enhance the AGB and AGC estimation accuracy, which can contribute to the operational forestry and policy making related to forest conservation, REDD/REDD+ climate change, etc.

Estimation of aboveground biomass in conserved areas of Stipa tenacissima L. stands in the high steppes of western Algeria by mean of the Landsat 8 imagery‐based vegetation indices

AbstractThe main aim of this paper was to evaluate the use of OLI spectral data as a tool to assess the steppe vegetation in a conservation context. The field sampling was conducted for two specific areas of treatment (a) an exclosure area and (b) a free grazing area. After testing several vegetation indices (VIs), the optimal results were obtained for the Normalised Difference Vegetation Index (NDVI)‐based aboveground biomass model with r2 = 0.61 and r2 = 0.72 for total and perennial biomass, respectively. No difference between observed and predicted total and perennial biomass was found (p = 0.700 and p = 0.306, respectively). The comparison between the two treatments using the field sampling revealed a significant difference on total plant cover (p = 0.016) and total biomass (p = 0.005), with a plant cover of 50.6% and a biomass of 325.771 kg dry matter per hectare (kg DM ha−1) on average in grazed area and 66.9%, 1,407.869 kg DM ha−1 in exclosure. Finally, a concordance is noted between the results obtained by the NDVI‐based biomass model and the field sampling‐based biomass.

Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach

Conventional ground survey data are very accurate, but expensive. Airborne lidar data can reduce the costs and effort required to conduct large-scale forest surveys. It is critical to improve biomass estimation and evaluate carbon stock when we use lidar data. Bayesian methods integrate prior information about unknown parameters, reduce the parameter estimation uncertainty, and improve model performance. This study focused on predicting the independent tree aboveground biomass (AGB) with a hierarchical Bayesian model using airborne LIDAR data and comparing the hierarchical Bayesian model with classical methods (nonlinear mixed effect model, NLME). Firstly, we chose the best diameter at breast height (DBH) model from several widely used models through a hierarchical Bayesian method. Secondly, we used the DBH predictions together with the tree height (LH) and canopy projection area (CPA) derived by airborne lidar as independent variables to develop the AGB model through a hierarchical Bayesian method with parameter priors from the NLME method. We then compared the hierarchical Bayesian method with the NLME method. The results showed that the two methods performed similarly when pooling the data, while for small sample sizes, the Bayesian method was much better than the classical method. The results of this study imply that the Bayesian method has the potential to improve the estimations of both DBH and AGB using LIDAR data, which reduces costs compared with conventional measurements.

Developing allometric models to predict the individual aboveground biomass of shrubs worldwide

AbstractAimExisting global models to predict standing biomass are based on trees characterized by a single principal stem, well developed in height. However, their use in open woodlands and shrublands, characterized by multistemmed species with substantial crown development, generates a high level of uncertainty in biomass estimates. This limitation led us to (a) develop global models of shrub individual aboveground biomass based on simple allometric variables, (b) to compare the fit of these models with existing global biomass models, and (c) to assess whether models fit change when bioclimatic variables are considered.LocationGlobal.Time periodPresent.Major taxa studied118 species of shrubs.MethodsWe compile a database of 3,243 individuals across 49 sites distributed worldwide. Including stem basal diameter, height and crown diameter as predictor variables, we built potential models and compared their fit using generalized least squares. We used mixed effects models to determine if bioclimatic variables improved the accuracy of biomass models.ResultsAlthough the most important variable in terms of predictive capacity was stem basal diameter, crown diameter significantly improved the models’ fit, followed by height. Four models were finally chosen, with the best model combining all these variables in the same equation [R2 = 0.930, root mean square error (RMSE) = 0.476]. Selected models performed as well as established global biomass models. Including the individual bioform significantly improved the models’ fit.Main conclusionsStem basal diameter, crown diameter and height measures could be combined to provide robust aboveground biomass (AGB) estimates of individual shrub species. Our study supplements well‐established models developed for trees, allowing more accurate biomass estimation of multistemmed woody individuals. We further provide tools for a methodological standardization of individual biomass quantification in these species. We expect these results contribute to improve the quality of biomass estimates across ecosystems, but also to generate methodological consensus on field biomass assessments in shrubs.

Combining hyperspectral imagery and LiDAR pseudo-waveform for predicting crop LAI, canopy height and above-ground biomass

Crop Leaf area index (LAI), canopy height and above-ground biomass (AGB) are important structural parameters. Accurate predictions of these three parameters are required for improving the applications of crop growth monitoring, health status assessment and yield prediction. Airborne Light Detection and Ranging (LiDAR) system is the most reliable technique for accurately predicting vegetation structure parameters. LiDAR technique has been broadly applied to estimate vegetation LAI, height and biomass, and reliable prediction results have been obtained. However, LiDAR data lack the spectral information of vegetation. The combination of LiDAR data and hyperspectral imagery can achieve complementary advantages of two data sources and improve the prediction accuracies of vegetation parameters. In this research, we aim to estimate maize LAI, canopy height and AGB using the combined hyperspectral imagery and LiDAR pseudo-waveforms. We constructed the LiDAR pseudo-waveforms through discrete-return point clouds and extracted pseudo-waveform variables. The prediction models of maize LAI, canopy height and AGB were established with a random forest (RF) regression algorithm using the traditional statistical variables derived from discrete-return point clouds, the pseudo-waveform variables, the combined hyperspectral vegetation indices and pseudo-waveform variables, respectively. Moreover, the comparative analyses of the three prediction models were conducted to determine the optimal prediction model and explore the potential of the combined hyperspectral vegetation indices and pseudo-waveform variables for predicting maize crop structural parameters. The results showed the strong relationships between LiDAR pseudo-waveform variables and maize LAI, height, and biomass (R2 = 0.799, 0.832 and 0.871, respectively). Moreover, the pseudo-waveform variables produced better results than the results estimated from traditional statistical variables of discrete-return LiDAR (R2 = 0.772, 0.812 and 0.811, respectively). Therefore, it is a viable method for predicting maize LAI, canopy height and AGB using the LiDAR pseudo-waveforms created from discrete-return LiDAR data. Nevertheless, we found that the combined pseudo-waveform variables and vegetation indices derived from hyperspectral imagery produced a better prediction result (R2 = 0.829, 0.892 and 0.909, respectively) when compared to LiDAR pseudo-waveform data alone, and the prediction accuracies improved by 3.8%, 7.2% and 4.4%, respectively. The combined hyperspectral imagery and LiDAR pseudo-waveform data provided complementary information and therefore improved prediction accuracies of these parameters. Although small improvements were observed, the combined data have potential for improving predictions of crop parameters. Our study will provide valuable information for predicting vegetation LAI, canopy height and AGB using the combined hyperspectral imagery and pseudo-waveform constructed from discrete-return LiDAR data.

Mapping forest aboveground biomass with a simulated ICESat-2 vegetation canopy product and Landsat data

The assessment of forest aboveground biomass (AGB) can contribute to reducing uncertainties associated with the amount and distribution of terrestrial carbon. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) was launched on September 15th, 2018 and will provide data which will offer the possibility of assessing AGB and forest carbon at multiple spatial scales. The primary goal of this study was to develop an approach for utilizing data similar to ICESat-2’s land-vegetation along track product (ATL08) to generate wall-to-wall AGB maps. Utilizing simulated daytime and nighttime ICESat-2 data from planned ICESat-2 tracks over vegetation conditions in south-east Texas, we investigated the integration of Landsat data and derived products for AGB model and map production. Linear regression models were first used to relate simulated photon-counting lidar (PCL) metrics for 100 m segments along ICESat-2 tracks to reference airborne lidar-estimated AGB over Sam Houston National Forest (SHNF) in south-east Texas. Random Forest (RF) was then used to create AGB maps from predicted AGB estimates and explanatory data consisting of spectral metrics derived from Landsat TM imagery and land cover and canopy cover data from the National Land Cover Database (NLCD). Using RF, AGB and AGB uncertainty maps produced at 30 m spatial resolution represented three data scenarios; (1) simulated ICESat-2 PCL vegetation product without the impact of noise (no noise scenario), (2) simulated ICESat-2 PCL vegetation product from data with noise levels associated with daytime operation of ICESat-2 (daytime scenario), and (3) simulated ICESat-2 PCL vegetation product from data with noise levels associated with nighttime operation of ICESat-2 (nighttime scenario). The RF models exhibited moderate accuracies (0.42 to 0.51) with RMSE values between 19 Mg/ha to 20 Mg/ha with a separate test set. The adoption of a combinatory approach of simulated ICESat-2 and Landsat data could be implemented at larger spatial scales and in doing so, ancillary data such as climatic and topographic variables may be examined for improving AGB predictions.

Comparison and integration of lidar and photogrammetric point clouds for mapping pre-fire forest structure

Lidar is an established tool for mapping forest structure, but its sparse spatial and temporal coverage often preclude its use in studying forest disturbance. In contrast, aerial imagery has been and continues to be regularly collected in many regions, and advances in stereo image matching have automated the creation of dense photogrammetric point clouds, which can also be used to map forest structure when paired with an accurate digital terrain model. As part of a study of the physical and ecological impacts of the 2012 High Park fire in Colorado, we generated a photogrammetric point cloud from pre-fire aerial imagery collected in 2008 and combined it with a digital terrain model generated from a 2013 post-fire lidar collection to produce canopy metrics commonly used in modelling of forest structure. We explore the correlation structure between the lidar and photogrammetry-derived canopy metrics, and the relationships between those metrics and forest structure attributes measured at unburned plots in the vicinity of the burn scar. Most corresponding lidar- and photogrammetry- derived canopy metrics had strong linear relationships between them (median r = 0.82), and metrics from both datasets yielded similar root mean square errors in multiple regression models of aboveground biomass (29.3% and 31.0%), basal area (29.8% and 27.7%), and several other forest structure attributes. We found the source of the canopy metrics (lidar or photogrammetry) to be a non-significant factor in some models of forest structure, suggesting that these datasets may be interchangeable in particular cases. Models derived from pre-fire aerial imagery were combined with burn severity information to quantify loss of biomass (2.31 ± 0.001 Tg) and examine possible relationships between forest structure and burn severity. These applications illustrate how the broad spatial and temporal coverage of aerial imagery and growing coverage of lidar can be utilized to improve understanding of changes in forest structure, including assessments of forest carbon flux.

A comparison of multiple methods for mapping local-scale mesquite tree aboveground biomass with remotely sensed data

A local-scale mesquite tree (Prosopis glandulosa Torr.) aboveground biomass map contribute to our understanding of the spatial distribution of woody plant aboveground biomass, and carbon stocks and fluxes in rangeland ecosystems. The objective of the study was examining a methodological approach to use airborne lidar data and multispectral imagery to create very high spatial resolution local-scale mesquite tree aboveground biomass maps by comparing three statistical methods and identifying significant prediction variables. The three statistical methods were the stepwise regression, the least absolute shrinkage and selection operator (LASSO), and the random forests. These methods were applied to establish the mesquite tree aboveground biomass equations and model from the in-situ mesquite tree aboveground biomass with the lidar metrics and multispectral data. The results showed the stepwise regression and LASSO had limited adj-R2 and MSE. However, the random forests method with combined multispectral imagery and lidar data presented acceptable MSE and R2 (1.08 Mg ha−1 and 0.37). In summary, the random forests method with combined multispectral imagery and lidar data offered the most reliable and reasonable combination to generate a very high spatial resolution local-scale mesquite tree aboveground biomass map.