Aboveground Biomass Models Research Articles

Allometric biomass models for the most abundant fruit tree species of Bangladesh: A Non-destructive approach

Artocarpus heterophyllus Lam. and Mangifera indica L. are the common homestead species that are planted all over Bangladesh for fruit and commercial timber production to meet the local household demand. The allometric biomass model is a non-destructive mathematical tool to estimate the biomass and carbon storage in trees which are applied to estimate the biomass of Artocarpus heterophyllus and Mangifera indica in this study. The total above-ground biomass of sampled trees was calculated from stem volume, wood density, and biomass expansion factor (BEF). Frequently used four linear models (Ln transformed) were tested to derive the best fit allometric model for the total above-ground biomass (TAGB). The best fit model was selected considering the values of R2, RSE, AIC, AICw, and CF. The best fit TAGB allometric models of Artocarpus heterophyllus and Mangifera indica were TAGB = exp(−0.8971+ 1.9908*Ln (D)) and TAGB = exp(−0.2272865 + 1.8017*Ln(D)) respectively. In the case of both species, the best fit TAGB allometric models showed the highest efficiency in biomass estimation compared to the other frequently used regional and pan-tropical models.

Improving tree biomass models through crown ratio patterns and incomplete data sources

Aboveground biomass quantification is essential for determining carbon stocks in forests. Multiple tree biomass models are available, but estimations can be biased outside the fitting range. This is due to the lack of data for larger trees, mainly because of the cost and time required. This study proposed a methodology based on tree crown biomass ratio (crown biomass: total aboveground biomass) modelling. The original data used in the existing biomass models in Spain have been notably extended by the inclusion of stem data from First Spanish National Forest Inventory and other databases, covering better tree size variability. The analysis of the crown biomass ratio against tree size (d2h), allowed us to distinguish three different patterns: an increasing pattern, a constant one, and a decreasing pattern. A new system of biomass models was fitted simultaneously by species, including a model for crown biomass ratio according to the identified pattern, a stem biomass model, and a total aboveground biomass model. Using this methodology, models were fitted for the 29 most important species in Spain. The fitted models result in more accurate and unbiased predictions for stem biomass, and realistic estimations for the crown biomass. This methodology means more robust and flexible biomass estimations with the possibility of using different data sources. The absence of crown information is not an obstacle because this component is a percentage of total aboveground biomass. Moreover, determining the crown biomass ratio pattern allows improving the accuracy of tree biomass estimation beyond the range of tree sizes (2–70 cm) for which these models were fitted.

Aboveground biomass allocation patterns and model construction of Bambusa rigida

Aboveground biomass allocation patterns and model construction of Bambusa rigida

Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.

Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong

Seventy-percent of the terrestrial area of Hong Kong is covered by vegetation and 40% is protected as the Country Park. The above-ground biomass (AGB) acts as reliable source of carbon sink and while Hong Kong has recognized the importance of carbon sink in forest and urged for forest protection in the latest strategic plan, yet no study has been conducted on assessing the baseline of terrestrial AGB and its carbon storage. This study compared and estimated the AGB by the traditional allometric modeling and the Light Detection and Ranging (LiDAR) plot metrics at plot-level in a subtropical forest of Hong Kong. The study has tested five allometric models which were developed from pantropical regions, subtropical areas and locally. The best model was then selected as the dependent variable to develop the LiDAR-derived AGB model. The raw LiDAR point cloud was pre-processed to normalized height point cloud and hence generating the LiDAR metric as independent variables for the model development. Regression models were used to estimate AGB at various plot sizes (i.e., in 10-m, 5-m and 2.5-m radius). The models were then evaluated statistically and validated by bootstrapping and leave-one-out cross validation (LOOCV). The results indicated the LiDAR metric derived from larger plot size outperformed the smaller plot size, with model R2 of 0.864 and root-mean-square-error (RMSE) of 37.75 kg/ha. It also found that pantropical model was comparable to a site-specific model when including the bioclimatic variable in subtropical forests. This study provides the approach for delineating the baseline of terrestrial above-ground biomass and carbon stock in subtropical forests upon an appropriate plot size is being deployed.

Assessment of Slope-Adaptive Metrics of GEDI Waveforms for Estimations of Forest Aboveground Biomass over Mountainous Areas

Waveform broadening effects of large-footprint lidar caused by terrain slopes are still a great challenge limiting the estimation accuracy of forest aboveground biomass (AGB) over mountainous areas. Slope-adaptive metrics of waveforms were proposed in our previous studies. However, its validation was limited by the unavailability of enough reference data. This study made full validation of slope-adaptive metrics using data acquired by the Global Ecosystem Dynamics Investigation (GEDI) mission, meanwhile exploring GEDI waveforms on estimations of forest AGB. Three types of waveform metrics were employed, including slope-adaptive metrics (RHT), typical height metrics relative to ground peaks (RH), and waveform parameters (WP). In addition to terrain slopes, two other factors were also explored including the geolocation issue and signal start and ending points of waveforms. Results showed that footprint geolocations in the first version GEDI data products were shifted to the left forward of nominal geolocations with a distance of about 24 m~30 m and were substantially corrected in the second version; the fourth and fifth groups of signal start and ending points of waveforms had worse performance than the rest of the four groups because they used the maximum and minimum signal thresholds, respectively. Taking airborne laser scanner (ALS) data as reference, the root mean square error (RMSE) of terrain slopes extracted from the digital elevation model of the shuttle radar topography mission (SRTM DEM) was about 3°. The coefficients of determination ( R 2 ) of estimation models of forest AGB based on RH metrics were improved from 0.48 to 0.68 with RMSE decreased from 19.7 Mg/ha to 15.4 Mg/ha by the second version geolocations. The RHT and WP metrics gave the best and the worst estimation accuracy, respectively. RHT further improved R 2 to 0.77 and decreased RMSE to 13.0 Mg/ha using terrain slopes extracted from SRTM DEM with a resolution of 1 arc second. R 2 of estimation models based on RHT was finally improved to 0.8 with RMSE decreased to 11.7 Mg/ha using exact terrain slopes from ALS data. This study demonstrated the great potential of slope-adaptive metrics of GEDI waveforms on estimations of forest aboveground biomass over mountainous areas.

甘肃南部7种高寒杜鹃生物量模拟

精确测定与模拟高山-亚高山灌丛生物量是了解陆地生态系统碳功能的重要基础工作。以甘肃南部高山-亚高山地区常见的7种高寒杜鹃（Rhododendron spp.）灌木为对象，通过标准植株收获法，建立易测因子与各器官生物量及总生物量的方程并检验拟合精度，筛选最优拟合方程。结果表明：（1）自变量和函数的类型对杜鹃生物量的模拟效果影响较大，700组方程中以D和D²H为自变量和以幂函数为模型拟合的R²相对集中、中位数都较高。（2）遴选出的35组单物种最优生物量模型的R²介于0.66-0.99之间、中位数为0.92，除山光杜鹃（Rh.oreodoxa）的茎、叶生物量和地上生物量模型为线性函数、麻花杜鹃（Rh.maculiferum）的所有模型为指数函数外，其余的生物量模型均为幂函数；D和D²H是单物种生物量模型的最佳预测变量，H仅是黄毛杜鹃（Rh.rufum）除根外、美容杜鹃（Rh.calophytum）叶生物量的最佳预测变量。（3）混合物种最优模型是以D²H为自变量的幂函数，除对叶生物量的模拟精度相对较低外，对其它生物量的模拟均较好。甘肃南部7种高寒杜鹃灌木生物量模型的建立为高寒地区灌丛生态系统碳汇功能的研究提供了支撑。;Rhododendron species are broad-leaved evergreen woody shrubs belonging to Ericaceae, and are an important constituent of alpine and subalpine ecosystems. Rh. species are endemic to Tibetan Plateau and surrounding areas. Unlike for trees, biomass estimation models are virtually lacking for Rh. species in natural communities. Therefore, accurate measurement and modeling of biomass for alpine-subalpine Rh. species is a fundamental work for quantifying carbon functions of terrestrial ecosystems. This study aimed to develop allometric models for the estimation of biomass storage of seven Rh. species' in the alpine-subalpine region of southern Gansu Province. Investigated species included Rh. rufum, Rh. przewalskii, Rh. alophytum, Rh. oreodoxa, Rh. taibaiense, Rh. capitatum and Rh. maculiferum. A total of 312 individuals were harvested for the measurements of above- and belowground biomass. Commonly used models, such as linear, logarithmic, power-law and exponential functions were used for estimating biomass, and basal diameter (D), height (H), canopy (C), crown volume (V) and square of basal diameter×plant height (D²H) from field measurements were used as independent variables, and leaf biomass, stem biomass, aboveground biomass, root biomass and total biomass were treated as dependent variables. Among a total number of 700 models tested, significant relationships were detected between biomass components and field measurements of predictors for the seven studied woody species (all P<0.01). Using D and D²H as independent variables and the power function resulted in a relatively narrow distribution and a high median of R² values. The R² values of the selected 35 optimal models for individual species from the 700 sets varied between 0.66 and 0.99, with a median of 0.92.The models for stem biomass, leaf biomass and aboveground biomass of Rh. oreodoxa were linear functions, for each of these biomass of Rh. maculiferum were exponential functions, while the rest of the biomass models were power functions. The plant height (H) was the best independent variable for the estimation of stem biomass, leaf biomass and aboveground biomass of Rh. rufum and leaf biomass of Rh. calophytum. The results also showed that the power function with D²H as the independent variable was the best model for mixed species, but the predictive power for leaf biomass was relatively low using the mixed-species' model. The establishment of seven alpine rhododendron shrub biomass models in southern Gansu Provide can serve as an important tool for the study of the carbon sink function of shrub ecosystems in alpine regions. In addition, these models may be applied to Rh. woodland elsewhere in eastern Tibetan Plateau.

New Metrics and the Combinations for Estimating Forest Biomass From GLAS Data

Geoscience laser altimeter system (GLAS) data have been widely used for forest aboveground biomass (AGB) estimation, but there is no consensus on the optimal metrics. To explore whether a few optimal GLAS metrics could generate accurate AGB estimates, we proposed five metrics and explored their combinations with ten existing ones. The importance of these metrics was measured according to their contributions to changes in the cross-validated mean-squared error. The two to eight most important metrics were then selected to develop AGB models, and their performances were evaluated using field AGB. The optimal combination of GLAS metrics was finally used for AGB estimation at GLAS footprints from 2004 to 2007 within a 2°×2° spatial extent in Tahe and Changbai Mountain, China. The results showed that four GLAS metrics, including our proposed metric CRH25 (25th percentile of canopy reflection heights) combined with Lead, quadratic mean canopy height, and H75, yield the best AGB estimates, with an R 2 of 0.61±0.15 and RMSE of 52.20±23.50 Mg/ha, and the inclusion of more GLAS metrics did not improve the results. The estimated forest AGB in Tahe was 89.03±19.16 Mg/ha and 103.07±23.42 Mg/ha in Changbai Mountain. In both regions, the annual average forest AGB estimates for 2005 were higher than the AGB estimates for 2004, 2006, and 2007. The results of this study suggested that a few waveform parameters could enable the accurate estimation of forest AGB. Moreover, this study indicated that GLAS data might be able to monitor forest AGB changes, which require further investigation.

Stem and Total Above-Ground Biomass Models for the Tree Species of Freshwater Wetlands Forest, Coastal Areas and Dry Areas of Bangladesh: Using a Non-Destructive Approach

Biomass and carbon stock in a forested areas are now prime important indicators of forest management and climate change mitigation measures. But the accurate estimation of biomass and carbon in trees of forests is now a challenging issue. In most cases, pantropical and regional biomass models are used frequently to estimate biomass and carbon stock in trees, but these estimations have some uncertainty compared to the species-specific allometric biomass model. Acacia nilotica, Casuarina equisetifolia and Melia azedarach have been planted in different areas of Bangladesh considering the species-specific site requirements. While Barringtonia acutangula and Pongamia pinnata are the dominant tree species of the freshwater swamp forest of Bangladesh. This study was aimed to develop species-specific allometric biomass models for estimating stem and above ground biomass (TAGB) of these species using the non-destructive method and to compare the efficiency of the derived biomass models with the frequently used regional and pantropical biomass models. Four Ln-based models with diameter at breast height (DBH) and total height (H) were tested to derive the best fit allometric model. Among the tested models, Ln (biomass) = a + b Ln (D) + c Ln (H) was the best-fit model for A. nilotica, M. azedarach, B. acutangula and P. pinnata and Ln (biomass) = a + b Ln (D2H) was best-fit for C. equisetifolia. Finally, the derived best-fit species-specific TAGB models have shown superiority over the other frequently used pantropical and regional biomass models in relation to model efficiency and model prediction error.

Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping

Accurate mapping of forest aboveground biomass (AGB) is critical for better understanding the role of forests in the global carbon cycle. NASA's current GEDI and ICESat-2 missions as well as the upcoming NISAR mission will collect synergistic data with different coverage and sensitivity to AGB. In this study, we present a multi-sensor data fusion approach leveraging the strength of each mission to produce wall-to-wall AGB maps that are more accurate and spatially comprehensive than what is achievable with any one sensor alone. Specifically, we calibrate a regional L-band radar AGB model using the sparse, simulated spaceborne lidar AGB estimates. We assess our data fusion framework using simulations of GEDI, ICESat-2 and NISAR data from airborne laser scanning (ALS) and UAVSAR data acquired over the temperate high AGB forest and complex terrain in Sonoma County, California, USA. For ICESat-2 and GEDI missions, we simulate two years of data coverage and AGB at footprint level are estimated using realistic AGB models. We compare the performance of our fusion framework when different combinations of the sparse simulated GEDI and ICEsat-2 AGB estimates are used to calibrate our regional L-band AGB models. In addition, we test our framework at Sonoma using (a) 1-ha square grid cells and (b) similarly sized irregularly shaped objects. We demonstrate that the estimated mean AGB across Sonoma is more accurately estimated using our fusion framework than using GEDI or ICESat-2 mission data alone, either with a regular grid or with irregular segments as mapping units. This research highlights methodological opportunities for fusing new and upcoming active remote sensing data streams toward improved AGB mapping through data fusion.

An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products

This study provided a comprehensive evaluation of eight machine learning regression algorithms for forest aboveground biomass (AGB) estimation from satellite data based on leaf area index, canopy height, net primary production, and tree cover data, as well as climatic and topographical data. Some of these algorithms have not been commonly used for forest AGB estimation such as the extremely randomized trees, stochastic gradient boosting, and categorical boosting (CatBoost) regression. For each algorithm, its hyperparameters were optimized using grid search with cross-validation, and the optimal AGB model was developed using the training dataset (80%) and AGB was predicted on the test dataset (20%). Performance metrics, feature importance as well as overestimation and underestimation were considered as indicators for evaluating the performance of an algorithm. To reduce the impacts of the random training-test data split and sampling method on the performance, the above procedures were repeated 50 times for each algorithm under the random sampling, the stratified sampling, and separate modeling scenarios. The results showed that five tree-based ensemble algorithms performed better than the three nonensemble algorithms (multivariate adaptive regression splines, support vector regression, and multilayer perceptron), and the CatBoost algorithm outperformed the other algorithms for AGB estimation. Compared with the random sampling scenario, the stratified sampling scenario and separate modeling did not significantly improve the AGB estimates, but modeling AGB for each forest type separately provided stable results in terms of the contributions of the predictor variables to the AGB estimates. All the algorithms showed forest AGB were underestimated when the AGB values were larger than 210 Mg/ha and overestimated when the AGB values were less than 120 Mg/ha. This study highlighted the capability of ensemble algorithms to improve AGB estimates and the necessity of improving AGB estimates for high and low AGB levels in future studies.

Allometric Equations for Shrub and Short-Stature Tree Aboveground Biomass within Boreal Ecosystems of Northwestern Canada

Aboveground biomass (AGB) of short-stature shrubs and trees contain a substantial part of the total carbon pool within boreal ecosystems. These ecosystems, however, are changing rapidly due to climate-mediated atmospheric changes, with overall observed decline in woody plant AGB in boreal northwestern Canada. Allometric equations provide a means to quantify woody plant AGB and are useful to understand aboveground carbon stocks as well as changes through time in unmanaged boreal ecosystems. In this paper, we provide allometric equations, regression coefficients, and error statistics to quantify total AGB of shrubs and short-stature trees. We provide species- and genus-specific as well as multispecies allometric models for shrub and tree species commonly found in northwestern boreal forest and peatland ecosystems. We found that the three-dimensional field variable (volume) provided the most accurate prediction of shrub multispecies AGB (R2 = 0.79, p &lt; 0.001), as opposed to the commonly used one-dimensional variable (basal diameter) measured on the longest and thickest stem (R2 = 0.23, p &lt; 0.001). Short-stature tree AGB was most accurately predicted by stem diameter measured at 0.3 m along the stem length (R2 = 0.99, p &lt; 0.001) rather than stem length (R2 = 0.29, p &lt; 0.001). Via the two-dimensional variable cross-sectional area, small-stature shrub AGB was combined with small-stature tree AGB within one single allometric model (R2 = 0.78, p &lt; 0.001). The AGB models provided in this paper will improve our understanding of shrub and tree AGB within rapidly changing boreal environments.

Additive model of aboveground biomass of larch single-trees related to age, Dbh and height, sensitive to temperature and precipitation in Eurasia

The first attempt of modeling changes in the aboveground additive component composition of larch (genus Larix spp.) tree biomass, according to the Trans-Eurasian hydrothermal gradients of Eurasia on the database compiled for the structure of harvest biomass in a number of 510 sample trees is fulfilled. The adequacy of the obtained regularities is determined by the level of variability 87-99 % explained by the proposed regression models. For the central territory of European Russia, characterized by the mean annual temperature of January -10 °C and the mean annual precipitation of 400 mm, the increase in temperature by 1°C at the constant level of precipitation causes on Larix spp. trees of the equal age and sizes, the decrease in the aboveground, stem, needle and branches by 0.4, 0.3, 1.4 и 1.3 %, respectively. For the same region, in equal-sized trees, the increase in precipitation by 100 mm at a constant annual temperature in January causes the decrease of the aboveground and stem biomass by 1.2 and 1.7%, respectively, and the increase of needle and branches biomass by 4.0 and 6.0%, respectively. The development of such models for the main forest-forming species of Eurasia will make it possible to predict changes in the productivity of the forest cover of Eurasia in connection with climate change.&#x0D; Keywords: larch trees, genus Larix spp., tree biomass, allometric models

Uncertainties in above ground tree biomass estimation

Models of above-ground tree biomass have been widely used to estimate forest biomass using national forest inventory data. However, many sources of uncertainty affect above-ground biomass estimation and are challenging to assess. In this study, the uncertainties associated with the measurement error in independent variables (diameter at breast height, tree height), residual variability, variances of the parameter estimates, and the sampling variability of national inventory data are estimated for five above-ground biomass models. The results show sampling variability is the most significant source of uncertainty. The measurement error and residual variability have negligible effects on forests above-ground biomass estimations. Thus, a reduction in the uncertainty of the sampling variability has the greatest potential to decrease the overall uncertainty. The power model containing only the diameter at breast height has the smallest uncertainty. The findings of this study provide suggestions to achieve a trade-off between accuracy and cost for above-ground biomass estimation using field work.

Estimating Structure and Biomass of a Secondary Atlantic Forest in Brazil Using Fourier Transforms of Vertical Profiles Derived from UAV Photogrammetry Point Clouds

Knowing the aboveground biomass (AGB) stock of tropical forests is one of the main requirements to guide programs for reducing emissions from deforestation and forest degradation (REDD+). Traditional 3D products generated with digital aerial photogrammetry (DAP) have shown great potential in estimating AGB, tree density, diameter at breast height, height, and basal area in forest ecosystems. However, these traditional products explore only a small part of the structural information contained in the 3D data, thus not leveraging the full potential of the data for inventory purposes. In this study, we tested the performance of 3D products derived from DAP and a technique based on Fourier transforms of vertical profiles of vegetation to estimate AGB, tree density, diameter at breast height, height, and basal area in a secondary fragment of Atlantic Forest located in northeast Brazil. Field measurements were taken in 30 permanent plots (0.25 ha each) to estimate AGB. At the time of the inventory, we also performed a digital aerial mapping of the entire forest fragment with an unmanned aerial vehicle (UAV). Based on the 3D point clouds and the digital terrain model (DTM) obtained by DAP, vertical vegetation profiles were produced for each plot. Using traditional structure metrics and metrics derived from Fourier transforms of profiles, regression models were fit to estimate AGB, tree density, diameter at breast height, height, and basal area. The 3D DAP point clouds represented the forest canopy with a high level of detail, regardless of the vegetation density. The metrics based on the Fourier transform of profiles were selected as predictors in all models produced. The best model for AGB explained 93% (R2 = 0.93) of the biomass variation at the plot level, with an RMS error of 9.3 Mg ha−1 (22.5%). Similar results were obtained in the models fit for the tree density, diameter at breast height, height, and basal area, with R2 values above 0.90 and RMS errors of less than 18%. The use of Fourier transforms of profiles with 3D products obtained by DAP demonstrated a high potential for estimating AGB and other forest variables of interest in secondary tropical forests, highlighting the value of UAV as a low-cost tool to assist the implementation of REDD+ projects in developing countries like Brazil.

New 3D measurements of large redwood trees for biomass and structure

Large trees are disproportionately important in terms of their above ground biomass (AGB) and carbon storage, as well as their wider impact on ecosystem structure. They are also very hard to measure and so tend to be underrepresented in measurements and models of AGB. We show the first detailed 3D terrestrial laser scanning (TLS) estimates of the volume and AGB of large coastal redwood Sequoia sempervirens trees from three sites in Northern California, representing some of the highest biomass ecosystems on Earth. Our TLS estimates agree to within 2% AGB with a species-specific model based on detailed manual crown mapping of 3D tree structure. However TLS-derived AGB was more than 30% higher compared to widely-used general (non species-specific) allometries. We derive an allometry from TLS that spans a much greater range of tree size than previous models and so is potentially better-suited for use with new Earth Observation data for these exceptionally high biomass areas. We suggest that where possible, TLS and crown mapping should be used to provide complementary, independent 3D structure measurements of these very large trees.

Evaluating P-Band TomoSAR for Biomass Retrieval in Boreal Forest

P-band synthetic aperture radar (SAR) is sensitive to above-ground biomass (AGB) but retrieval accuracy has been shown to deteriorate in topographic areas. In boreal forest, the signal penetrates through the canopy to interact with the ground producing variations in backscatter depending on ground topography, forest structure, and soil moisture. Tomographic processing of multiple SAR images Tomographic SAR (TomoSAR) provides information about the vertical backscatter distribution. This article evaluates the use of P-band TomoSAR data to improve AGB retrievals from backscattered intensity by suppressing the backscattered signal from the ground. This approach can be used even when the tomographic resolution is insufficient to resolve the vertical backscatter profile. The analysis is based on P-band data from two campaigns: BioSAR-1 (2007) in Remingstorp, southern Sweden, and BioSAR-2 (2008) in Krycklan (KR), northern Sweden. BioSAR airborne data were also processed to correspond as closely as possible to future BIOMASS TomoSAR acquisitions, with BioSAR-2-based results shown. A power law AGB model using volumetric HV polarized backscatter performs best in KR, with training residual root mean-squared error (RMSE) of 30%–36% (27–33 t/ha) for airborne data and 38%–39% for simulated BIOMASS data. Airborne TomoSAR data suggest that both vertical and horizontal tomographic resolution are of importance and that it is possible to greatly reduce AGB retrieval bias when compared with airborne P-band SAR backscatter intensity-based retrievals. A lack of significant ground slopes in Remningstorp reduces the benefit of using TomoSAR data which performs similar to retrievals based solely on P-band SAR backscatter intensity.

Remote-sensing inversion method for aboveground biomass of typical steppe in Inner Mongolia, China

Aboveground biomass (AGB) is a fundamental parameter used in grassland-related research, for which remote sensing has become an effective inversion technology on a regional level. Using a typical steppe in Inner Mongolia, China as an example, this study constructed a single-factor parametric model, a multifactor parametric model, and a multifactor nonparametric model to estimate the grassland AGB from actual field measurement data. We compared the accuracy and stability of the various models using a ten-fold cross-validation method and established a remote-sensing inversion model for grassland AGB. On this basis, we simulated the grassland AGB from 2015 to 2019 in the study area, analyzed its spatial–temporal distribution characteristics, and discussed the effects of these climatic factors. The research results showed that the accuracy and stability of the various models ranked as follows: back-propagation artificial neural network (BP-ANN) inversion model > multifactor parametric model > single-factor parametric model. The simulation capability of the BP-ANN inversion model for grassland AGB was effectively improved by averaging the repeated modeling results. The grassland AGB of the study area from 2015 through 2019 showed a spatial distribution of gradual increase from the northwest to the southeast and an inter-annual trend of increasing over time. The spatial and temporal distribution characteristics were closely related to the amount of precipitation. The research results can provide a direct reference for remote-sensing monitoring of grassland AGB and provide accurate spatial data as well as a scientific basis for sustained development of regional grassland.

Allometric models for estimating aboveground biomass of selected homestead tree species in the plain land Narsingdi district of Bangladesh

Globally allometric equations based biomass estimation is a popular non-destructive method for estimating biomass and sequestered carbon. However, the destruction process is involved in the development of allometric models. This study developed allometric models of aboveground biomass (AGB) for five frequently planted homestead tree species in Bangladesh. The tree species were Aphanamixis polystachya, Ficus hispida, Mangifera indica, Melia azedarach, and Swietenia mahagoni. Fifteen individuals from each species were randomly selected and felled. The felled trees were cut into manageable sections and processed for calculation of fresh and dry biomass. The considered explanatory variables were diameter at breast height (DBH), total height (TH), DBH × TH, and DBH2 × TH. For each category of fresh and dry biomass of the five tree species, the study developed a total of 40 models. However, based on the goodness-of-fit statistics, the study primarily selected 17 models for fresh biomass and 22 models for dry biomass. Finally, the best-fit model for each species was selected based on the Akaike Information Criterion (AIC) and Residual Standard Error (RSE). The whole study shows that a combination of Diameter at Breast Height (DBH) at 1.3 m above the ground and the total height of the trees are the essential explanatory variable for most of the species. The relationship between the AGB and the selected dendrometric variables were significant at p < 0.05 levels. The allometric models developed by this study will be useful for better estimation of biomass and sequestered carbon in the plain land homestead forests of Bangladesh.

Integration of high-resolution optical and SAR satellite remote sensing datasets for aboveground biomass estimation in subtropical pine forest, Pakistan.

In this study, we investigate stand-alone and combined Pleiades high-resolution passive optical and ALOS PALSAR active Synthetic Aperture Radar (SAR) satellite imagery for aboveground biomass (AGB) estimation in subtropical mountainous Chir Pine (Pinus roxburghii) forest in Murree Forest Division, Punjab, Pakistan. Spectral vegetation indices (NDVI, SAVI, etc.) and sigma nought HV-polarization backscatter dB values are derived from processing optical and SAR datasets, respectively, and modeled against field-measured AGB values through various regression models (linear, nonlinear, multi-linear). For combination of multiple spectral indices, NDVI, TNDVI, and MSAVI2 performed the best with model R2/RMSE values of 0.86/47.3 tons/ha. AGB modeling with SAR sigma nought dB values gives low model R2 value of 0.39. The multi-linear combination of SAR sigma nought dB values with spectral indices exhibits more variability as compared with the combined spectral indices model. The Leave-One-Out-Cross-Validation (LOOCV) results follow closely the behavior of the model statistics. SAR data reaches AGB saturation at around 120-140 tons/ha, with the region of high sensitivity around 50-130 tons/ha; the SAR-derived AGB results show clear underestimation at higher AGB values. The models involving only spectral indices underestimate AGB at low values (< 60 tons/ha). This study presents biomass estimation maps of the Chir Pine forest in the study area and also the suitability of optical and SAR satellite imagery for estimating various biomass ranges. The results of this work can be utilized towards environmental monitoring and policy-level applications, including forest ecosystem management, environmental impact assessment, and performance-based REDD+ payment distribution.