Aboveground Biomass Prediction Research Articles

Allometric Biomass Models for European Beech and Silver Fir: Testing Approaches to Minimize the Demand for Site-Specific Biomass Observations

In this paper, site-specific allometric biomass models were developed for European beech (Fagus sylvatica L.) and silver fir (Abies alba Mill.) to estimate the aboveground biomass in Șinca virgin forest, Romania. Several approaches to minimize the demand for site-specific observations in allometric biomass model development were also investigated. Developing site-specific allometric biomass models requires new measurements of biomass for a sample of trees from that specific site. Yet, measuring biomass is laborious, time consuming, and requires extensive logistics, especially for very large trees. The allometric biomass models were developed for a wide range of diameters at breast height, D (6–86 cm for European beech and 6–93 cm for silver fir) using a logarithmic transformation approach. Two alternative approaches were applied, i.e., random intercept model (RIM) and a Bayesian model with strong informative priors, to enhance the information of the site-specific sample (of biomass observations) by supplementing with a generic biomass sample. The appropriateness of each model was evaluated based on the aboveground biomass prediction of a 1 ha sample plot in Șinca forest. The results showed that models based on both D and tree height (H) to predict tree aboveground biomass (AGB) were more accurate predictors of AGB and produced plot-level estimates with better precision, than models based on D only. Furthermore, both RIM and Bayesian approach performed similarly well when a small local sample (of seven smallest trees) was used to calibrate the allometric model. Therefore, the generic biomass observations may effectively be combined with a small local sample (of just a few small trees) to calibrate an allometric model to a certain site and to minimize the demand for site-specific biomass measurements. However, special attention should be given to the H-D ratio, since it can affect the allometry and the performance of the reduced local sample approach.

Prediction of aboveground biomass and carbon stock of Balanites aegyptiaca, a multipurpose species in Burkina Faso

Balanites aegyptiaca (L.) Delile is native to semi-arid regions in Africa where it is a well-known and conspicuous component of savannas. The species is highly preferred by local people because of its high socio-economic, cultural and ecological values. However, the species faces multiple environmental challenges such as desertification and human pressure. This study aimed to develop allometric models to predict aboveground biomass (AGB) of B. aegyptiaca in two climatic zones in Burkina Faso. Overall, thirty trees were sampled using destructive method in six study stands along two climatic zones. We assessed the biomass allocation to the different components of trees by computing its fraction. Furthermore, allometric models based on diameter at breast height (dbh) and basal diameter at 20 cm height (D20) were fitted separately as well as combined with crown diameter (CD) and/or tree total height (Ht). For each biomass component, non-linear allometric models were fitted. Branch biomass accounted for 64% of the AGB in the two climatic zones and increased with dbh. No significant difference in carbon content was found. However, biomass allotment (except leaves) varied across climatic zones. Although both dbh and D20 are typically used as independent variables for predicting AGB, the inclusion of the height in the equations did not significantly improve the statistical fits for B. aegyptica. However, adding CD to dbh improved significantly the equations only in the Sudano-Sahelian zone.The established allometric models can provide reliable and accurate estimation of individual tree biomass of the species in areas of similar conditions and may contribute to relevant ecological and economical biomass inventories.

Understanding forest biomass and net primary productivity in Himalayan ecosystem using geospatial approach

Forest operates as sink–source of the atmospheric CO2; hence, they form the integral part of terrestrial global carbon cycle. Biomass and primary productivity are the crucial dynamic biophysical parameters for understanding the ecosystem functioning in any forested landscape. The present study was performed in Aglar watershed situated in outer Indian Himalayan range. We performed geospatial modeling of plot-level field data on forest above ground biomass (AGB) by correlating it with textural, spectral and linearly transformed variables retrieved from Landsat 8 OLI data using of random forest (RF) machine learning algorithm. We also applied recursive feature elimination function (RFE) to obtain the variables contributing most in AGB prediction. A combination of 24 among 96 variables was identified as the most effective variables. Ground-based AGB varied from 62.54 to 470.98 Mg ha−1, whereas RF-modeled AGB ranged from 48.5 to 407.73 Mg ha−1. Results indicated that RFE selected variables were able to predict AGB with R2 of 0.84, RMSE of 42.03 Mg ha−1, MAE of 34.68 and %RMSE of 19.49 Mg ha−1 which was accepted considering the terrain complexity. Light use efficiency approach was used to model monthly NPP using Landsat 8 OLI data. The results indicated that Quercus mixed forest had highest carbon assimilation (95,148,073.9 gC) followed by Pinus roxburghii (1,863,187.7 gC), Cedrus deodara (5,752,954.1 gC) and mixed forest (2,634,737.1 gC). The seasonal pattern of NPP indicated that its strike peaked in October, whereas December and January were the lean months, suggesting that NPP is governed by climatic factors, viz. PAR, precipitation and temperature. Such watershed-level study in complex Himalayan terrain would help to understand complex biogeochemical processes in basins and ecosystem services provided by the forests.

Sampling trees to develop allometric biomass models: How does tree selection affect model prediction accuracy and precision?

Abstract Developing allometric biomass models is an important process because reliability of forest biomass and carbon estimations largely depend on the accuracy and precision of such models. The effects of tree sampling on tree aboveground biomass (AGB) prediction accuracy and precision are complex and can, therefore, be difficult to quantify. In this paper we use a Monte Carlo simulation to investigate how model prediction accuracy and precision are affected by tree sampling approaches. Because diameter at breast height (D, in cm) is the most common predictor of tree AGB (in kg dry weight), we focused our analysis on the AGB-D relationship. The following sample characteristics were investigated: (i) sample size; (ii) extent of the D-range (difference between the largest and the smallest D value); (iii) position of D-range (characterized by the starting point of D-range); and (iv) the size-distribution (distribution of D) of sample trees. We found that, although the natural variability of AGB-D relationship was a key driver for both prediction accuracy and precision, the above sample characteristics were important for improving prediction accuracy. Although having a negligible effect on precision, both sample size and size-distribution of sample trees, greatly influenced prediction accuracy. We demonstrate that selecting a constant number of trees for each D class (i.e. uniform distribution of the sample trees over the D-range) generally produced models that were more accurate predictors of AGB. The extent and position of D-range, although considerably affecting the goodness of fit and the standard errors of allometric model parameters, had only a marginal effect on AGB prediction accuracy and precision. Furthermore, we showed that R2 was a poor indicator of model prediction accuracy and precision, due to its sensitivity to changes in D-range. These findings inform certain practical recommendations we report for improving the accuracy and precision of biomass prediction.

Airborne Lidar Sampling Pivotal for Accurate Regional AGB Predictions from Multispectral Images in Forest-Savanna Landscapes

Precise accounting of carbon stocks and fluxes in tropical vegetation using remote sensing approaches remains a challenging exercise, as both signal saturation and ground sampling limitations contribute to inaccurate extrapolations. Airborne LiDAR Scanning (ALS) data can be used as an intermediate level to radically increase sampling and enhance model calibration. Here we tested the potential of using ALS data for upscaling vegetation aboveground biomass (AGB) from field plots to a forest-savanna transitional landscape in the Guineo–Congolian region in Cameroon, using either a design-based approach or a model-based approach leveraging multispectral satellite imagery. Two sets of reference data were used: (1) AGB values collected from 62 0.16-ha plots distributed both in forests and savannas; and (2) an AGB map generated form ALS data. In the model-based approach, we trained Random Forest models using predictors from recent sensors of varying spectral and spatial resolutions (Spot 6/7, Landsat 8, and Sentinel 2), along with biophysical predictors derived after pre-processing into the Overland processing chain, following a forward variable selection procedure with a spatial 4-folds cross validation. The models calibrated with field plots lead to a systematic overestimation in AGB density estimates and a root mean squared prediction error (RMSPE) of up to 65 Mg.ha−1 (90%), whereas calibration with ALS lead to low bias and a drop of ~30% in RMSPE (down to 43 Mg.ha−1, 58%) with little effect of the satellite sensor used. Decomposing bias along the AGB density range, we show that multispectral images can (in some specific cases) be used for unbiased prediction at landscape scale on the basis of ALS-calibrated statistical models. However, our results also confirm that, whatever the spectral indices used and attention paid to sensor quality and pre-processing, the signal is not sufficient to warrant accurate pixelwise predictions, because of large relative RMSPE, especially above (200–250 t/ha). The design-based approach, for which average AGB density values were attributed to mapped land cover classes, proved to be a simple and reliable alternative (for landscape to region level estimations), when trained with dense ALS samples.

Estimating aboveground biomass using Landsat 8 OLI satellite image in pure Crimean pine (Pinus nigra J.F. Arnold subsp. pallasiana (Lamb.) Holmboe) stands: a case from Turkey

The aim of this study was to estimate the aboveground biomass (AGB) using Landsat 8 Operational Land Imager (OLI) satellite image in Araç Forest Enterprise. The ground data obtained from ground measurements in 317 sample plots were used to accomplish this purpose. The AGB for each sample plot was calculated using this data. The original band brightness, vegetation indices and texture feature values of each sample plot were calculated from the coordinate values of each sample area that were transferred to the Landsat 8 OLI satellite image. The relationships between the AGB with the original band brightness, vegetation indices and texture features values generated from Landsat 8 OLI satellite image were determined by multiple regression analysis. The results revealed that the AGB model using Band2, Band4, Band6 and Band11 had an adjusted coefficient of determination (), of 0.445,37.1738 ton/ha−1, the AGB model using FII, DVI, EVI and IPVI vegetation indices had an adjusted of 0.387,=39.4221 ton/ha−1, the AGB model using Band1_5x5_COR, Band3_3x3_COR, Band3_9x9_COR, Band4_3x3_M, Band7_5x5_V, Band7_9x9_COR, Band8_7x7_COR, Band10_9x9_HOM, Band11_3x3_COR, Band11_5x5_M and Band11_7x7_COR had an adjusted of 0.552, = 33.7669 ton/ha−1. In conclusion, the texture features values were better for the prediction of AGB by using the Landsat 8 OLI satellite image.

Assessment of Bias in Pan-Tropical Biomass Predictions

Above-ground biomass (AGB) is an essential descriptor of forests, of use in ecological and climate-related research. At tree- and stand-scale, destructive but direct measurements of AGB are replaced with predictions from allometric models characterizing the correlational relationship between AGB, and predictor variables including stem diameter, tree height and wood density. These models are constructed from harvested calibration data, usually via linear regression. Here, we assess systematic error in out-of-sample predictions of AGB introduced during measurement, compilation and modeling of in-sample calibration data. Various conventional bivariate and multivariate models are constructed from open access data of tropical forests. Metadata analysis, fit diagnostics and cross-validation results suggest several model misspecifications: chiefly, unaccounted for inconsistent measurement error in predictor variables between in- and out-of-sample data. Simulations demonstrate conservative inconsistencies can introduce significant bias into tree- and stand-scale AGB predictions. When tree height and wood density are included as predictors, models should be modified to correct for bias. Finally, we explore a fundamental assumption of conventional allometry, that model parameters are independent of tree size. That is, the same model can provide predictions of consistent trueness irrespective of size-class. Most observations in current calibration datasets are from smaller trees, meaning the existence of a size dependency would bias predictions for larger trees. We determine that detecting the absence or presence of a size dependency is currently prevented by model misspecifications and calibration data imbalances. We call for the collection of additional harvest data, specifically under-represented larger trees.

A Novel Approach for Estimation of Above-Ground Biomass of Sugar Beet Based on Wavelength Selection and Optimized Support Vector Machine

Timely diagnosis of sugar beet above-ground biomass (AGB) is critical for the prediction of yield and optimal precision crop management. This study established an optimal quantitative prediction model of AGB of sugar beet by using hyperspectral data. Three experiment campaigns in 2014, 2015 and 2018 were conducted to collect ground-based hyperspectral data at three different growth stages, across different sites, for different cultivars and nitrogen (N) application rates. A competitive adaptive reweighted sampling (CARS) algorithm was applied to select the most sensitive wavelengths to AGB. This was followed by developing a novel modified differential evolution grey wolf optimization algorithm (MDE–GWO) by introducing differential evolution algorithm (DE) and dynamic non-linear convergence factor to grey wolf optimization algorithm (GWO) to optimize the parameters c and γ of a support vector machine (SVM) model for the prediction of AGB. The prediction performance of SVM models under the three GWO, DE–GWO and MDE–GWO optimization methods for CARS selected wavelengths and whole spectral data was examined. Results showed that CARS resulted in a huge wavelength reduction of 97.4% for the rapid growth stage of leaf cluster, 97.2% for the sugar growth stage and 97.4% for the sugar accumulation stage. Models resulted after CARS wavelength selection were found to be more accurate than models developed using the entire spectral data. The best prediction accuracy was achieved after the MDE–GWO optimization of SVM model parameters for the prediction of AGB in sugar beet, independent of growing stage, years, sites and cultivars. The best coefficient of determination (R2), root mean square error (RMSE) and residual prediction deviation (RPD) ranged, respectively, from 0.74 to 0.80, 46.17 to 65.68 g/m2 and 1.42 to 1.97 for the rapid growth stage of leaf cluster, 0.78 to 0.80, 30.16 to 37.03 g/m2 and 1.69 to 2.03 for the sugar growth stage, and 0.69 to 0.74, 40.17 to 104.08 g/m2 and 1.61 to 1.95 for the sugar accumulation stage. It can be concluded that the methodology proposed can be implemented for the prediction of AGB of sugar beet using proximal hyperspectral sensors under a wide range of environmental conditions.

The effects of soil phosphorus on aboveground biomass are mediated by functional diversity in a tropical cloud forest

Soil phosphorus is a key driver of plant biodiversity and aboveground biomass (AGB) in tropical forests. A plant community’s ability to exploit such limiting resources may be better represented by functional diversity than species or phylogenetic diversity, and may therefore have the higher predictive power regarding how soil phosphorus influences AGB in tropical forests. However, nearly no studies have tested this in tropical high-altitude forest ecosystems. Here we aim to test: 1) the relative influence of three biodiversity metrics (functional diversity, species diversity, and phylogenetic diversity) on aboveground biomass in a tropical cloud forest and 2) the interrelationships among soil phosphorus, biodiversity, and AGB in this ecosystem. In a tropical cloud forest in Hainan Island, China, we measured 13 key functional traits for 195 species in 48 plots of size 20 × 20 m2 each. We also measured soil phosphorus in all plots and computed the community phylogeny. Using this dataset, we tested the interrelationships among soil phosphorus, species diversity, functional diversity, phylogenetic diversity, and AGB using Generalized Additive Modeling and Redundancy Analysis. Functional diversity was significantly positively related to AGB, whereas species diversity and phylogenetic diversity were not significantly related to AGB. Functional diversity explained 53% of AGB, while species diversity and phylogenetic diversity only explained 17% and 6%, respectively. Soil phosphorus explained 56% of the variation in functional diversity, but only explained 22%, 13% and 21% of the variation in species diversity, phylogenetic diversity and AGB, respectively. Functional diversity, rather than species and phylogenetic diversity, are the better predictors of AGB. The influence of functional diversity on AGB appears to be linked to how variation in soil phosphorus affects functional diversity. We suggest that functional diversity should be incorporated into models that are designed to test how soil resources influence ecosystem function in tropical forest ecosystems.

Development of Allometric Models for Estimating the Biomass of <i>Sclerocarya birrea</i> (A.Rich) Hoscht and <i>Boscia senegalensis</i> (Pers.) Lam. ex Poir.

The objective of this study was to develop allometric models for estimating the biomass of Sclerocarya birrea (A.Rich) Hoscht and Boscia senegalensis (Pers.) Lam. ex Poir. The inventory of these ligneous was carried out at Widou Thiengoly (North of Senegal). The sample consists of 43 individuals of Boscia senegalensis and 15 individuals of Sclerocarya birrea. The selected individuals were dendrometrically characterized before being cut, compartmentalized (trunk, branches, and twigs) and weighed entirely. Simple regression tests were performed to examine the most explanatory dendrometric parameter (x) for biomass (y) according to two types of models: the linear model (y = ax + b) and the polynomial model of degrees 2 (y = ax2 + bx + c). The criteria for selection and validity of the models are based firstly on the tests of normality, nullity, heterogeneity and autocorrelation of the residues. The results showed that the most explanatory dendrometric parameter of the biomass was the crown surface for Boscia senegalensis and the 1.30 m diameter for Sclerocarya birrea of all the tests performed, the second-order Polynomial model is the best predictor of above ground biomass for these two species. Thus, the allometric models established to predict the biomass of these two species are: y = 0.0023x2 + 0.4851x - 0.0519 for Boscia senegalensis and y = 0.35x2 + 10.35x - 12.90 for S. birrea; with very significant correlation coefficients (R2) of 0.85 and 0.94 respectively. These results can be used for a sequestered carbon assessment study and will play a role in monitoring the carbon market in Africa.

Landsat Time-Series for Estimating Forest Aboveground Biomass and Its Dynamics across Space and Time: A Review

The free open access data policy instituted for the Landsat archive since 2008 has revolutionised the use of Landsat data for forest monitoring, especially for estimating forest aboveground biomass (AGB). This paper provides a comprehensive review of recent approaches utilising Landsat time-series (LTS) for estimating AGB and its dynamics across space and time. In particular, we focus on reviewing: (1) how LTS has been utilised to improve the estimation of AGB (for both single-date and over time) and (2) recent LTS-based approaches used for estimating AGB and its dynamics across space and time. In contrast to using single-date images, the use of LTS can benefit forest AGB estimation in two broad areas. First, using LTS allows for the filling of spatial and temporal data gaps in AGB predictions, improving the quality of AGB products and enabling the estimation of AGB across large areas and long time-periods. Second, studies have demonstrated that spectral information extracted from LTS analysis, including forest disturbance and recovery metrics, can significantly improve the accuracy of AGB models. Throughout the last decade, many innovative LTS-based approaches for estimating forest AGB dynamics across space and time have been demonstrated. A general trend is that methods have evolved as demonstrated through recent studies, becoming more advanced and robust. However, most of these methods have been developed and tested in areas that are either supported by established forest inventory programs and/or can rely on Lidar data across large forest areas. Further investigations should focus on tropical forest areas where inventory data are often not systematically available and/or out-of-date.

Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests

An accurate estimation of forests’ aboveground biomass (AGB) is required because of its relevance to the carbon cycle, and because of its economic and ecological importance. The selection of appropriate variables from satellite information and physical variables is important for precise AGB prediction mapping. Because of the complex relationships for AGB prediction, non-parametric machine-learning techniques represent potentially useful techniques for AGB estimation, but their use and comparison in forest remote-sensing applications is still relatively limited. The objective of the present study was to evaluate the performance of automatic learning techniques, support vector regression (SVR) and random forest (RF), to predict the observed AGB (from 318 permanent sampling plots) from the Landsat 8 Landsat 8 Operational Land Imager (OLI) sensor, spectral indexes, texture indexes and physical variables the Sierra Madre Occidental in Mexico. The result showed that the best SVR model explained 80% of the total variance (root mean square error (RMSE) = 8.20 Mg ha−1). The variables that best predicted AGB, in order of importance, were the bands that belong to the region of red and near and middle infrared, and the average temperature. The results show that the SVR technique has a good potential for the estimation of the AGB and that the selection of the model hyperparameters has important implications for optimizing the goodness of fit.

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.

ESTIMATING ABOVEGROUND BIOMASS IN ZAGROS FOREST, IRAN, USING SENTINEL-2 DATA

Abstract. Accurate and reliable assessment of above-ground biomass (AGB) is important for the sustainable forest management, especially in Zagros forests, in which a frangible forest ecosystem is being threatened by anthropogenic factors as well as climate change effects. This study presents a new method for AGB estimation and demonstrates the potential of Sentinel-2 Multi-Spectral Instrument (MSI) data as an alternative to other costly remotely sensed data, such as hyperspectral and LiDAR data in unapproachable regions. Sentinel-2 performance was evaluated for a forest in Kurdistan province, west of Iran, using in-situ measured AGB as a dependent variable and spectral band values and spectral-derived vegetation indices as independent variables in the Random Forest Regression (RFR) algorithm. The influence of the input variables number on AGB prediction was also investigated. The model using all spectral bands plus all derived spectral vegetation indices provided better AGB estimates (R2 = 0.87 and RMSE = 10.75 t ha−1). Including the optimal subset of key variables did not improve model variance but slightly reduced the error. This result is explained by the technically-advanced nature of Sentinel-2, which includes fine spatial resolution (10, 20 m) and strategically-positioned bands (red-edge), conducted in different topographical conditions with an advanced machine learning algorithm. However, assessing its transferability to other forest types with varying conditions would enable future performance and interpretability assessments of Sentinel-2.

Estimating biomass of winter oilseed rape using vegetation indices and texture metrics derived from UAV multispectral images

Monitoring the above ground biomass (AGB) in winter oilseed rape is important for improving the agronomic management efficiency and predicting yield to ensure edible oil and biofuel supplies. The Yangtze River Basin in China accounts for one-fifth of the rapeseed yield in the world. However, the fragmented farming lands in the Yangtze River Basin make it difficult to accurately monitor winter oilseed rape growth at large scale using the medium-resolution satellite data. The low-altitude unmanned aerial vehicle (UAV) provides a feasible way to accurately and non-destructively estimate AGB in winter oilseed rape at the plot level. In this study, we evaluated the contributions of vegetation indices (VIs) and texture metrics, derived from multispectral images captured by the camera mounted on a UAV, to predict the AGB in winter oilseed rape. The AGB was estimated with (1) multiple VIs and (2) multiple VIs and texture metrics. The partial least square regression (PLSR) and random forest (RF) regression models were trained with datasets of the experiment in 2016–2017 and 2017–2018, and then applied to predict AGB of the 2018–2019 growth season. Results demonstrated that the incorporation of texture metrics to both PLSR and RF models provided more accurate estimations of AGB in winter oilseed rape than the models based solely on VIs. The accuracy of the AGB predicted by the RF regression model using VIs and texture metrics (RMSE = 274.18 kg/ha for the validation dataset) was slightly higher than the results of the PLSR model (RMSE = 284.09 kg/ha for the validation dataset). According to the evaluation of the important variables, the red edge chlorophyll index (CIred edge) and ratio vegetation index (RVI) were selected as the most important input features by PLSR and RF regression models. The normalized difference vegetation index (NDVI) contrast was selected as an important texture metrics for the AGB prediction, indicating that NDVI contrast could be a sensitive indicator of the spatial distribution of shadow caused by the different amount of biomass. This study suggested the great potential of UAV in estimating the plot-level AGB by combining VIs and texture metrics.

Prediction of Aboveground Biomass from Low-Density LiDAR Data: Validation over P. radiata Data from a Region North of Spain

Estimation of forestry aboveground biomass (AGB) by means of aerial Light Detection and Ranging (LiDAR) data uses high-density point sampling data obtained in dedicated flights, which are often too costly for available research budgets. In this paper we exploit already existing public low-density LiDAR data obtained for other purposes, such as cartography. The challenge is to show that such low-density data allows accurate biomass estimation. We demonstrate the approach on data available from plantations of Pinus radiata in the Arratia-Nervión region, located in Biscay province located in the North of Spain. We use public data gathered from the low-density (0.5 pulse/m2) LiDAR flight conducted by the Basque Government in 2012 for cartographic production. We propose a linear regression model based on explanatory variables obtained from the LiDAR point cloud data. We calibrate the model using field data from the Fourth National Forest Inventory (NFI4), including the selection of the optimal model variables. The results revealed that the best model depends on two variables extracted from LiDAR data: One directly related with tree height and a second parameter with the canopy density. The model explained 80% of its variability with a standard error of 0.25 ton/ha in logarithmic units. We validate the predictions against the biomass measurements provided by the government institutions, obtaining a difference of 8%. The proposed approach would allow the exploitation of the periodic available low-density LiDAR data, collected with territorial and cartographic purposes, for a more frequent and less expensive control of the forestry biomass.

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.

Land Use Changes, Disturbances, and Their Interactions on Future Forest Aboveground Biomass Dynamics in the Northern US

Land use change (LUC), disturbances, and their interactions play an important role in regional forest carbon (C) dynamics. Here we quantified how these activities and events may influence future aboveground biomass (AGB) dynamics in forests using national forest inventory (NFI) and Landsat time series data in the Northern United States (US). Total forest AGB predictions were based on simulations of diameter growth, mortality, and recruitment using matrix growth models under varying levels of LUC and disturbance severity (low (L), medium (M), and high (H)) every five years from 2018 to 2098. Land use change included the integrated effects of deforestation and reforestation/afforestation (forest [F]→agriculture [A], settlements [S, urbanization/other], and A&S→F), specifically, conversion from F→A, F→S, F→A&S, A→F, S→F, and A&S→F. Disturbances included natural and anthropogenic disturbances such as wildfire, weather, insects and disease, and forest harvesting. Results revealed that, when simultaneously considering both medium LUC and disturbances, total forest AGB predictions of LUC + fire, LUC + weather, LUC + insect & disease, and LUC + harvest indicated substantial increases in regional C stocks (± standard deviation) from 1.88 (±0.13) to 3.29 (±0.28), 3.10 (±0.24), 2.91 (±0.19), and 2.68 (±0.17) Pg C, respectively, from 2018 to 2098. An uncertainty analysis with fuzzy sets suggested that medium LUC under disturbances would lead to greater forest AGB C uptake than undisturbed forest C uptake with high certainty, except for LUC + harvest. The matrix models in this study were parameterized using NFI and Landsat data from the past few decades. Thus, our results imply that if recent trends persist, LUC will remain an important driver of forest C uptake, while disturbances may result in C emissions rather than undisturbed forest C uptake by 2098. The combined effects of LUC and disturbances may serve as an important driver of C uptake and emissions in the Northern US well into the 21st century.

Prediction of stem diameter and biomass at individual tree crown level with advanced machine learning techniques

Knowledge about the aboveground biomass (AGB) and the diameters at breast height (DBH) distribution can lead to a precise estimation of carbon density and forest structure which can be very important for ecology studies especially for those concerning climate change. In this study, we propose to predict DBH and AGB of individual trees using tree height (H) and crown diameter (CD), and other metrics extracted from airborne laser scanning (ALS) data as input. In the proposed approach, regression methods, such us support vector machine for regression (SVR) and random forests (RF), were used to find a transformation or a transfer function that links the input parameters (H, CD, and other ALS metrics) with the output (DBH and AGB). The developed approach was tested on two datasets collected in southern Norway comprising 3970 and 9467 recorded trees, respectively. The results demonstrate that the developed approach provides better results compared to a state-of-the-art work (based on a linear model with the standard least-squares method) with RMSE equal to 81.4 kg and 92.0 kg, respectively (compared to 94.2 kg and 110.0 kg) for the prediction of AGB, and 5.16 cm and 4.93 cm, respectively (compared to 5.49 cm and 5.30 cm) for DBH.

Modelling aboveground biomass in forest remnants of the Brazilian Atlantic Forest using remote sensing, environmental and terrain-related data

The Brazilian Atlantic Forest, one of the most threatened tropical regions in the world, exhibits high levels of terrestrial aboveground biomass (AGB). We propose a random forest approach to model, map and assess whether public lands provide protection for AGB in the Rio Doce watershed, one of the most important watercourses of the Atlantic Forest biome. We used 188 field plots and individual and hybrid features from remote sensing, environmental and terrain-related data. The hybrid model improved the AGB prediction by reducing the root mean square error to 33.43 Mg/ha and increasing the coefficient of determination (R2) to 0.57. The total estimated AGB was 178,967,656.73 Mg, ranging from 20.40 to 167.72 Mg/ha following the seasonal precipitation pattern and anthropogenic disturbance effects. Only 5.76% of the total AGB was located on public protected lands, totalling 10,305,501 Mg, while most of the remaining AGB were located on private properties.