Aboveground Biomass Models Research Articles

Aboveground Biomass Models in the Combretum-Terminalia Woodlands of Ethiopia: Testing Species and Site Variation Effects

The Combretum-Terminalia woodlands and wooded grasslands (CTW) are widely distributed in East Africa. While these landscapes may have the potential to act as key global carbon sinks, relatively little is known about their carbon storage capacity. Here we developed a set of novel aboveground biomass (AGB) models and tested for species and site variation effects to quantify the potential for CTW to store carbon. In total, 321 trees were sampled from 13 dominant tree species, across three sites in the Northwest lowlands of Ethiopia. Overall, fitted species-specific models performed the best, with diameter at breast height explaining 94–99% of the AGB variations. Interspecific tree allometry differences among species were more substantial than intraspecific tree allometry among sites. Incorporating wood density and height in the mixed-species models significantly improved the model performance relative mean absolute error (MAPE) of 2.4–8.0%, while site variation did not affect the model accuracy substantially. Large errors (MAPE%) were observed when using existing pantropical models, indicating that model selection remains an important source of uncertainty. Although the estimates of selected site-specific models were accurate for local sites, mixed-species and species-specific models performed better when validation data collated from different sites were incorporated together. We concluded that including site- and species-level data improved model estimates of AGB for the CTW of Ethiopia.

Above-Ground Biomass Estimation of Plantation with Different Tree Species Using Airborne LiDAR and Hyperspectral Data

Forest above-ground biomass (AGB) is an important index to evaluate forest carbon sequestration capacity, which is very important to maintain the stability of forest ecosystems. At present, the wide use of remote sensing technology makes it possible to estimate the large-scale forest AGB accurately and efficiently. Airborne hyperspectral remote sensing data can obtain rich spectral information and spatial structure information on the forest canopy with the characteristics of high spatial and hyperspectral resolution. Airborne LiDAR data can describe the three-dimensional structure characteristics of a forest and provide vertical structure information related to biomass. Based on the characteristics of the two data sources, this study takes Gaofeng forest farm in Nanning, Guangxi, as the study area, Chinese fir, pine tree, eucalyptus and other broadleaved trees as the research object, and constructs the AGB estimation models of different tree species by fusing airborne LiDAR and hyperspectral features. Firstly, spectral features, texture features, vegetation index, wavelet transform features and edge features are extracted from hyperspectral data. Canopy structure features, point cloud structure features, point cloud density features and terrain features are extracted from airborne LiDAR data. Secondly, the random forest (RF) method is used to screen the features of the two sets of data, and the features with the highest importance are selected. Finally, based on the optimal features of the two data sources, the forest AGB model is constructed using the multiple stepwise regression method. The results show that the texture features extracted by wavelet transform can be used for AGB modeling. The AGB of eucalyptus has high correlation with height features derived from airborne LiDAR, the AGB of other broadleaved trees mostly depends on the wavelet transform texture features from airborne hyperspectral data, while the AGB of Chinese fir and pine tree has high correlation with both height features and spectral features. Feature-fusion-based LiDAR and hyperspectral data can greatly improve the accuracy of the AGB models. The accuracy of the optimal AGB models of Chinese fir, pine tree, eucalyptus and other broadleaved trees is 0.78, 0.95, 0.72 and 0.89, respectively. In conclusion, more accurate estimation results can be obtained by combining active and passive remote sensing data for forest AGB estimation, which provides a solution for carbon storage assessment and forest ecosystem assessment.

Species-specific and generalized biomass models for estimating carbon stocks of young reforestations

Biomass models are key to the correct quantification of aboveground biomass and carbon stocks. Most of the current available sets of biomass models were developed for adult trees although very few have been employed to accurately estimate biomass during the first years of reforestations, despite the increasing number of reforestations over recent years.In this study, species-specific and generalized models for aboveground biomass of single trees have been developed for the main 14 species used in reforestations in Spain. A comparison with the existing biomass models at individual tree level for larger trees of these species was also conducted to confirm the hypothesis that specific models are required for the first years of the reforestation.A new set of biomass models was fitted simultaneously per species, based on three different independent variables – root-collar-diameter, total height and crown projection area- and their combinations. The fitted models provide more accurate and unbiased predictions of aboveground biomass in the first years after reforestation when compared with previously existing models for adult trees. The generalized models can be applied to reforestations when the main species is unknown or for mixed plantations where species are not individualized.The set of models provided can be used in a wide variety of surveys and monitoring conditions depending on the available data, from classical field inventories to more novel approaches such as airborne LiDAR data or aerial photographs.

Influence of UAS Flight Altitude and Speed on Aboveground Biomass Prediction

The management of low-density savannah and woodland forests for carbon storage presents a mechanism to offset the expense of ecologically informed forest management strategies. However, existing carbon monitoring systems draw on vast amounts of either field observations or aerial light detection and ranging (LiDAR) collections, making them financially prohibitive in low productivity systems where forest management focuses on promoting resilience to disturbance and multiple uses. This study evaluates how UAS altitude and flight speed influence area-based aboveground forest biomass model predictions. The imagery was acquired across a range of UAS altitudes and flight speeds that influence the efficiency of data collection. Data were processed using common structures from motion photogrammetry algorithms and then modeled using Random Forest. These results are compared to LiDAR observations collected from fixed-wing manned aircraft and modeled using the same routine. Results show a strong positive relationship between flight altitude and plot-based aboveground biomass modeling accuracy. UAS predictions increasingly outperformed (2–24% increased variance explained) commercial airborne LiDAR strategies as acquisition altitude increased from 80–120 m. The reduced cost of UAS data collection and processing and improved biomass modeling accuracy over airborne LiDAR approaches could make carbon monitoring viable in low productivity forest systems.

Joint estimation of aboveground biomass using “Space-Air-Ground” data in the Qilian Mountains, China

Accurate monitoring of aboveground biomass (AGB) and its spatial distribution is essential for sustainable management and development. The most frequently used method for estimating AGB with remotely sensed data is based on the relationship between AGB measured data and vegetation indices. In the past, there were some shortcomings in analyzing the spatial heterogeneity and solving scale difference. Therefore, this paper realized the space-air-ground integrated based on Sentinel-2 Multispectral Instrument (MSI), Unmanned Aerial Vehicle (UAV) and measured data. The results indicated that the RFR models have excellent adaptability to invert AGB of three different coverage regions in study area, with R2 of 0.69 and root mean square error (RMSE) of 116.22 kg·hm−2 for low-coverage region, R2 of 0.65 and RMSE of 164.16 kg·hm−2 for medium-coverage region, R2 of 0.73 and RMSE of 149.99 kg·hm−2 for high-coverage region. We found that the main driving vegetation indices was different in each region during the process of model building and it was related to the characteristics of vegetation indices. Moreover, regions with different coverage have different responses to topography. The findings showed that the spatial distribution and driving mechanism of AGB in the study area could be more clearly analyzed based on the coverage division. The introduction of coverage division reasonably enhanced the accuracy of RFR modeling AGB. This study confirmed that using the Sentinel-2 MSI, UAV images, measured data and the RFR model to realize the space-air-ground integrated is an applicable method for monitoring and assessment of AGB in Qilian Mountains, which can offer scientific basis and theoretical support to rational development of land resources and maintaining the balance of ecosystem.

Interpreting the uncertainty of model-based and design-based estimation in downscaling estimates from NFI data: a case-study in Extremadura (Spain)

ABSTRACT Remotely sensed data are increasingly used together with National Forest Inventory (NFI) data to improve the spatial precision of forest variable estimates. In this study, we combined data from the 4th Spanish National Forest Inventory (SNFI-4) and from the 2nd nationwide Airborne Laser Scanning (ALS) survey to develop predictive forest inventory variables (total over bark volume (V), basal area (G), and annual increase in total volume (IAVC)) and aboveground biomass (AGB) models for the eight major forest strata in the region of Extremadura that are included in the Spanish Forest Map (SFM). We generated maps at 25 m resolution by applying an area‐based approach (ABA) and 758 sample plots measured with good positional accuracy within the SNFI-4 in Extremadura (Spain). Inventory performance is mainly influenced by spatial scale and vegetation structure. Therefore, in this study, we conducted a comparative analysis of statistical inference methods that can characterize forest inventory variables and AGB uncertainty across multiple spatial scales and types of vegetation structure. Predictions at pixel level were used to produce county, provincial, and regional model-based estimates, which were then compared with design-based estimates at different scales for different types of forest. We developed and tested both methods for forested area (cover, 19,744.15 km2), one province (9126.78 km2), and two counties (1594.42 km2 and 2076.76 km2, respectively) in Extremadura. The resulting relative standard error (SE) for regional level forest type-specific model-based estimates of V, G, IAVC, and AGB ranged from 3.34%–14.46%, 3.22%–12.50%, 4.46%–16.67%, and 3.63%–12.58%, respectively. The performance of the model-based approach, as assessed by the relative SE, was similar to that of the design-based approach at regional and provincial levels. However, the precision of SNFI model-based estimates was higher than that of estimates based on only the plot observations in small areas (e.g. at county level). The standard errors (SE) for model-based inferences were stable across the different scales, while SNFI design-based errors were higher due to the small sample sizes available for small areas. The findings indicate that SNFI-model based maps could be used directly to estimate forest inventory variables and AGB in the major forest strata included in the Spanish Forest Map, leading to potentially large economic savings.

Allometric models to predict the individual aboveground biomass of shrubs of Malagasy xerophytic thickets

Xerophytic thicket (XT) is a shrubby vegetation found in the semi-arid southern and south-western Madagascar. Above ground biomass (AGB) is a key element to understand how this ecosystem is operating and then to design and implement any sustainable management strategy. This study aimed to build AGB allometric models for Malagasy XT shrubs. One hundred and ninety-eight individuals belonging to 20 XT common species were harvested. The models linked AGB to height, diameter at breast height (DBH), basal diameter, stem number and wood specific gravity. Empirical models taking into account the shrub architecture (multistemmed with single basal stem and first branches below a height of 1.3 m, multistemmed with multiple basal stems and shrubs with first branches over a height of 1.3 m) are more efficient to predict XT shrub AGB than the pantropical tree AGB model.

Above-ground biomass models for dominant trees species in cacao agroforestry systems in Talamanca, Costa Rica

Above-ground biomass models for dominant trees species in cacao agroforestry systems in Talamanca, Costa Rica

Comparison and transferability of thermal, temporal and phenological-based in-season predictions of above-ground biomass in wheat crops from proximal crop reflectance data

Timely monitoring of above-ground biomass (AGB) is essential for indicating the crop growth status and predicting grain yield and carbon dynamics. Non-destructive remote sensing techniques with a large spatial coverage have become a promising method for crop biomass monitoring. However, most existing crop biomass models have only been tested at a single growth stage or only at a small number of growth stages at a single location. This has limited the ability of these AGB models to be transferred spatially, to other fields or regions, to predict AGB at any growth stage during the season, or to be potentially used with data from other sensing systems. Here, a new crop biomass algorithm (CBA-Wheat) was developed to estimate AGB over the entire growing season. It uses information on the crop growth stage, based on phenological scale observations (Zadoks scale or ZS), the day of the year or thermal indices (growing degree days), to correct AGB estimations from remotely sensed vegetation indices. The model transferability was evaluated across multiple regional test sites and different data sources (UAV and hand-held spectroscopic data). Results showed that the coefficient values [slope (k) and intercept (b)] of ordinary least squares regression (OLSR) of AGB with vegetation indices had a strong relationship with ZS. These k and b relationships were used to correct the OLSR model parameters based on the observed phenological stage (ZS value). The two-band enhanced vegetation index (EVI2) was the best vegetation index for predicting AGB with the new CBA-WheatZS model, with R2 and RMSE values of 0.83 and 2.07 t/ha for an experimental trial site, 0.78 and 2.05 t/ha for multiple independent regional test sites, and 0.69 and 1.87 t/ha when transferred to EVI2 derived from UAV. Model performance was lower with the day of the year and thermal index corrections; however, the use of relative growing degree-days (RGS; CBA-WheatRGS), instead of ZS information, to adjust the model parameters showed a high consistency with the CBA-WheatZS model, and a good potential for estimation of AGB at regional scales without the need for local phenological observations. The CBA-WheatRGS had validated R2 and RMSE values of 0.82 and 2.01 t/ha for the experimental trial site, 0.76 and 2.39 t/ha for multiple independent regional test sites, and 0.66 and 2.14 t/ha for UAV hyperspectral imagery. These results demonstrated a good potential to estimate biomass from remotely sensed imagery at varying spatio-temporal scales in winter wheat.

Crown area predicts total biomass for Rhodomyrtus tomentosa, an invasive shrub in Florida

AbstractPredictive models of aboveground plant biomass derived from nondestructive measurements greatly assist in monitoring and surveying natural areas. Where invasive species are concerned, these models can provide insights to the impacts of invasions and efficacy of management strategies. Furthermore, tools that facilitate a rapid inventory allow for multiple assessments of impact over larger areas. Downy rose myrtle [Rhodomyrtus tomentosa (Aiton) Hassk.] is an invasive shrub in Florida and Hawaii that is native to southeastern Asia. Rhodomyrtus tomentosa was imported into Florida in the early 20th century through the ornamental plant trade and produces pink flowers and edible purple globe fruits. This woody shrub is particularly problematic in the understory of Florida’s mesic pine forests, where it forms dense, impenetrable thickets. To characterize the populations more accurately in Florida and build predictive equations for biomass that could be used to inform control methods, we established a network of sites from which we harvested individuals over 3 yr. Based on these measurements, we built a simple predictive equation for R. tomentosa dry biomass. Crown area strongly associates with biomass in a linear relationship (P < 0.001, R2 = 0.82). Fruit production is highly variable, but positively correlates to plant height in individuals that have reached reproductive size (plants below 1 m generally do not produce fruit), albeit weakly (P < 0.002, R2 = 0.27). We demonstrate here that two simple measurements—height and crown area—can accurately predict biomass and, to some degree, fruit production for R. tomentosa in Florida and may guide control methods by focusing on removing individuals larger than 1 m tall.

Spatiotemporal dynamics of grassland aboveground biomass and its driving factors in North China over the past 20 years

Although remote sensing has enabled rapid monitoring of grassland aboveground biomass (AGB) at a regional scale, it is still a difficult challenge to construct an accurate estimation model of grassland AGB in a vast region to support the AGB dynamics analysis over a long time series. In this study, extensive grassland AGB measurements (collected in North China during the grassland growing season of 2000–2019), MODIS data, and environmental factors (climate, topography and soil) were employed to construct the grassland AGB models using four machine learning algorithms (random forest, support vector machine, artificial neural network and extreme learning machine) combined with four variable selections. The spatial distributions of annual grassland AGB from 2000 to 2019 were simulated based on the optimal AGB model. The temporal change and future trend of AGB series from 2000 to 2019 were comprehensively analyzed by the slope model and Hurst exponent. The influences of natural and anthropogenic factors on grassland AGB dynamics were explored quantitatively using the Geodetector model. The results showed that (1) the random forest model constructed from the variables selected by the successive projections algorithm is the optimal grassland AGB model. (2) The 20-year average grassland AGB in North China showed an overall spatial distribution of being low in the central and western parts and high in the southeastern part. (3) The annual maximum grassland AGB in most regions (82.71%) showed an increasing trend during 2000–2019; and most of the grasslands with a decreasing trend of AGB were located in regions with low AGB values and arid climates. (4) The future trend of grassland AGB after the study period may be optimistic, as reflected by more grassland AGB was predicted to increase rather than decrease (70.38% vs. 29.62%). (5) The main driving factors of spatiotemporal dynamics of grassland AGB were precipitation, soil type, and livestock density; the interactive influence of two drivers on AGB showed mutual enhancement.

Estimation of the total dry aboveground biomass in the tropical forests of Congo Basin using optical, LiDAR, and radar data

ABSTRACT In this investigation, optical (SPOT-7 NAOMI), airborne LiDAR, and PolInSAR L-band data, along with forest inventories, were employed to develop models for estimating total dry aboveground biomass (AGB) over the tropical forests in the Congo Basin (Gabon) of Central Africa. Remote sensing-based variables like texture (from SPOT), median canopy height (from LiDAR), and backscattering coefficient along with canopy surface heights (from PolInSAR) were used to estimate the AGB. These variables were used individually (or combined) to develop the AGB models based on the multivariate adaptive regression splines (MARS) approach. Validation indicated that in case of the single variable models, the LiDAR-based model yielded the lowest estimation root-mean-square error (RMSE = 28%). The error decreased further when the median canopy height was combined with the texture or with the radar variables (RMSE <25%). The texture derived from the Fourier transform textural ordination (FOTO) approach was more effective in improving the results as compared to the gray level co-occurrence matrices (GLCM) approach. Model validation indicated that the best performance in AGB estimation was achieved by combining optical, LiDAR, and radar data (R2 = 0.89 and RMSE = 24%).

Detecting the distribution of grass aboveground biomass on a rangeland using Sentinel-2 MSI vegetation indices

Quantifying the grass biomass content of rangelands is potentially useful to rangeland managers. This study assessed the potential of image-derived vegetation indices to assess in-situ spatial variations in grass aboveground biomass (AGB). Sampling was conducted at the peak growth stage, at widely distributed sampling sites whose grass cover homogeneity was wider than 20 m. Up to three AGB samples per site were collected, in 1 m quadrats. Dry biomass weights were determined and averaged per site. Rainy season Sentinel-2 MSI images of the rangeland that corresponded with the fieldwork dates were obtained. Atmospheric correction of the images was performed using the Sen2Cor algorithm, which yielded 20 m resolution visible (vis), red edge (RE), near infrared (NIR), and short wave infrared (SWIR) bands. Seven biomass-sensitive vegetation indices that utilised Sentinel-2 MSI vis-RE-NIR-SWIR spectral ranges were then tested using the images, to determine which correlated best with the field-derived AGB. They were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Red Edge index (NDRE), Red Edge Inflection Point (REIP), Aerosol Free Vegetation Index (AFRI 2.1μm ), and Normalized Difference Water Index (NDWI). In decreasing order of magnitude, the EVI, SAVI, NDVI, AFRI 2.1μm , and NDWI had statistically significant correlations with AGB ( p ≤ 0.05). The discontinuous canopies but high biomass of tall-grass species weakened the correlations. The EVI model of AGB was then used for depicting rangeland-scale, location-context spatial variations in AGB. Pixels with woody vegetation cover ≥ 50% were excluded using a binary mask that was developed through sub-pixel classification of a grass-senescence period Sentinel-2 MSI image. A statistically significant linear relationship ( F = 21.192, p = 0.000) between EVI-predicted and actual AGB was established ( r = 0.765, p < 0.001). The results from the studied savannah rangeland suggest higher vegetation index prediction accuracy at short-grass than tall-grass sites.

On the Potential of Sequential and Nonsequential Regression Models for Sentinel-1-Based Biomass Prediction in Tanzanian Miombo Forests

This study derives regression models for above-ground biomass (AGB) estimation in miombo woodlands of Tanzania that utilise the high availability and low cost of Sentinel-1 data. The limited forest canopy penetration of C-band SAR sensors along with the sparseness of available ground truth restrict their usefulness in traditional AGB regression models. Therefore, we propose to use AGB predictions based on airborne laser scanning (ALS) data as a surrogate response variable for SAR data. This dramatically increases the available training data and opens for flexible regression models that capture fine-scale AGB dynamics. This becomes a sequential modelling approach, where the first regression stage has linked in situ data to ALS data and produced the AGB prediction map; We perform the subsequent stage, where this map is related to Sentinel-1 data. We develop a traditional, parametric regression model and alternative non-parametric models for this stage. The latter uses a conditional generative adversarial network (cGAN) to translate Sentinel-1 images into ALS-based AGB prediction maps. The convolution filters in the neural networks make them contextual. We compare the sequential models to traditional, non-sequential regression models, all trained on limited AGB ground reference data. Results show that our newly proposed non-sequential Sentinel-1-based regression model performs better quantitatively than the sequential models, but achieves less sensitivity to fine-scale AGB dynamics. The contextual cGAN-based sequential models best reproduce the distribution of ALS-based AGB predictions. They also reach a lower RMSE against in situ AGB data than the parametric sequential model, indicating a potential for further development.

Ecoregion-wide, multi-sensor biomass mapping highlights a major underestimation of dry forests carbon stocks

Tropical dry forests harbor major carbon stocks but are disappearing rapidly across the globe as agriculture expands into them. Unfortunately, carbon emissions from deforestation in dry forests remain poorly understood as high spatial-temporal and vertical heterogeneity complicate biomass mapping. Here, we use a novel Gradient Boosted Regression framework to test the relative gains of combining optical (MODIS) and radar (Sentinel 1) time series, as well as lidar-based (GEDI) canopy-height information, to map biomass in tropical dry forests. We apply our approach across the entire Dry Chaco ecoregion (about 800,000 km2), using an extensive ground dataset of forest inventory plots for training and validation, to map above-ground biomass (AGB) for the year 2019. Our best AGB model had an r2 of 0.89 (RMSE = 15.1 t/ha) with an estimated AGB in remaining natural vegetation of 4.65 Gt (+/− 0.9 Gt). Seasonal metrics from EVI time-series, combined with seasonal Sentinel 1 metrics, had the highest predictive power, while adding GEDI-based canopy height did not improve models. Our resulting AGB maps had a much higher level of agreement with independent ground-data than global AGB products (agreements between r2 = 0.07–0.41), which all suffer from a huge, up to 14-fold, underestimation of AGB in the Chaco. Most of the remaining AGB stored in Chaco woodlands is found in Argentina (2.4 Gt AGB), followed by Paraguay (1.13 Gt AGB) and Bolivia (1.11 Gt AGB). Our results also highlight that 71% of the remaining AGB is located outside protected areas, and around half of the remaining AGB occurs on land utilized by traditional communities. Together, our analyses reveal substantial risk of continued high carbon emissions should agricultural expansion progress. Considerable co-benefits appear to exist between protecting traditional livelihoods and carbon stocks. Our map, the most accurate and fine-scale AGB map for this global deforestation hotspot, can serve as a basis for land-use and conservation planning aimed at leveraging such co-benefits. More broadly, our analyses reveal the considerable potential of combining time series of optical and radar data for a more reliable mapping of above-ground biomass in tropical dry forests and savannas.

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

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

Improving Estimation of Woody Aboveground Biomass of Sparse Mixed Forest over Dryland Ecosystem by Combining Landsat-8, GaoFen-2, and UAV Imagery

Sparse mixed forest with trees, shrubs, and green herbaceous vegetation is a typical landscape in the afforestation areas in northwestern China. It is a great challenge to accurately estimate the woody aboveground biomass (AGB) of a sparse mixed forest with heterogeneous woody vegetation types and background types. In this study, a novel woody AGB estimation methodology (VI-AGB model stratified based on herbaceous vegetation coverage) using a combination of Landsat-8, GaoFen-2, and unmanned aerial vehicle (UAV) images was developed. The results show the following: (1) the woody and herbaceous canopy can be accurately identified using the object-based support vector machine (SVM) classification method based on UAV red-green-blue (RGB) images, with an average overall accuracy and kappa coefficient of 93.44% and 0.91, respectively; (2) compared with the estimation uncertainties of the woody coverage-AGB models without considering the woody vegetation types (RMSE = 14.98 t∙ha−1 and rRMSE = 96.31%), the woody coverage-AGB models stratified based on five woody species (RMSE = 5.82 t∙ha−1 and rRMSE = 37.46%) were 61.1% lower; (3) of the six VIs used in this study, the near-infrared reflectance of pure vegetation (NIRv)-AGB model performed best (RMSE = 7.91 t∙ha−1 and rRMSE = 50.89%), but its performance was still seriously affected by the heterogeneity of the green herbaceous coverage. The normalized difference moisture index (NDMI)-AGB model was the least sensitive to the background. The stratification-based VI-AGB models considering the herbaceous vegetation coverage derived from GaoFen-2 and UAV images can significantly improve the accuracy of the woody AGB estimated using only Landsat VIs, with the RMSE and rRMSE of 6.6 t∙ha−1 and 42.43% for the stratification-based NIRv-AGB models. High spatial resolution information derived from UAV and satellite images has a great potential for improving the woody AGB estimated using only Landsat images in sparsely vegetated areas. This study presents a practical method of estimating woody AGB in sparse mixed forest in dryland areas.

Improving Plot-Level Model of Forest Biomass: A Combined Approach Using Machine Learning with Spatial Statistics

Estimating the aboveground biomass (AGB) at the plot level plays a major role in connecting accurate single-tree AGB measurements to relatively difficult regional AGB estimates. However, AGB estimates at the plot level suffer from many uncertainties. The goal of this study is to determine whether combining machine learning with spatial statistics reduces the uncertainty of plot-level AGB estimates. To illustrate this issue, this study evaluates and compares the performance of different models for estimating plot-level forest AGB. These models include three different machine learning models [support vector machine (SVM), random forest (RF), and a radial basis function artificial neural network (RBF-ANN)], one spatial statistic model (P-BSHADE), and three combinations thereof (SVM &amp; P-BSHADE, RF &amp; P-BSHADE, and RBF-ANN &amp; P-BSHADE). The results show that the root mean square error, mean absolute error, and mean relative error of all combined models are substantially smaller than those of any individual model, with the RF &amp; P-BSHADE combined method generating the smallest values. These results indicate that a combined approach using machine learning with spatial statistics, especially the RF &amp; P-BSHADE model, improves the accuracy of plot-level AGB models. These research results contribute to the development of accurate large-forested-landscape AGB maps.

Energy Stored in Above-Ground Biomass Fractions and Model Trees of the Main Coniferous Woody Plants

The paper considers energy stored in above-ground biomass fractions and in model trees of the main coniferous woody plants (Picea abies (L.) H. Karst., Abies alba Mill., Pinus sylvestris (L.), Larix decidua Mill.), sampled in 22 forest stands selected in different parts of Slovakia. A total of 43 trees were felled, of which there were 12 spruces, 11 firs, 10 pines, and 10 larches. Gross and net calorific values were determined in samples of wood, bark, small-wood, twigs, and needles. Our results show that these values significantly depend on the tree species, biomass fractions, and sampling point on the tree. The energy stored in the model trees calculated on the basis of volume production taken from yield tables increases as follows: spruce &lt; fir &lt; pine &lt; larch. Combustion of tree biomass releases an aliquot amount of a greenhouse gas—CO2, as well as an important plant nutrient, nitrogen—into the atmosphere. The obtained data must be taken into account in the case of the economic utilization of energy stored in the fractions of above-ground tree biomass and in whole trees. The achieved data can be used to assess forest ecosystems in terms of the flow of solar energy, its accumulation in the various components of tree biomass, and the risk of biomass combustion in relation to the release of greenhouse gases.

Assessing Black Locust Biomass Accumulation in Restoration Plantations

Forests (either natural or planted) play a key role in climate change mitigation due to their huge carbon-storing potential. In the 1980s, the Hellenic Public Power Corporation (HPPC) started the rehabilitation of lignite post-mining areas in Northwest Greece by planting mainly black locust (Robinia pseudoacacia L.). Today, these plantations occupy about 2570 ha, but the accumulation of Above Ground Biomass (AGB) and deadwood has not been assessed to date. Therefore, we aimed at estimating these biomass pools by calibrating an allometric model for AGB, performing an inventory for both pools and predicting the spatial distribution of AGB. 214 sample plots of 100 m2 each were set up through systematic sampling in a grid dimension of 500 × 500 m and tree dbh and height were recorded. AGB was estimated using an exponential allometric model and performing inventory measurements and was on average 57.6 t ha−1. Kriging analysis reliably estimated mean AGB, but produced errors in the prediction of high and low biomass values, related to the high fragmentation and heterogeneity of the studied area. Mean estimated AGB was low compared with European biomass yield tables for black locust. Similarly, standing deadwood was low (6–10%) and decay degrees were mostly 1 and 2, indicating recent deadwood formation. The overall low biomass accumulation in the studied black locust restoration plantations may be partially attributed to their young age (5–30 years old), but is comparable to that reported in black locust restoration plantation in extremely degraded sites. Thus, black locust successfully adapted to the studied depositions of former mines and its accumulated biomass has the potential to improve the carbon footprint of the region. However, the invasiveness of the species should be considered for future management planning of these restoration plantations.