Aboveground Biomass Models Research Articles

How can UAV contribute in satellite-based Phragmites australis aboveground biomass estimating?

• Build an UAV-based reed aboveground biomass (AGB) model by introducing canopy height. • Propose a way to obtain ample reed AGB samples matched with satellite pixel. • Develop a reed AGB satellite-based model and map the AGB in Nandagang Wetland. Phragmites australis (common reed) is a widely distributed emergent aquatic vegetation species in many wetland ecosystems, and its aboveground biomass (AGB) is an important parameter for evaluating the carbon–nitrogen cycle in wetlands. Satellite remote sensing (RS) is a powerful tool used to monitor the spatio-temporal distribution of AGB within reedbeds over a large area. However, when building AGB models based on satellite data, especially medium resolution satellites, it is difficult to obtain ample and properly measured AGB samples which can be matched with image pixels due to the inaccessibility of the wetlands. In this study, we proposed a solution based on the unmanned aerial vehicle (UAV) and Sentinel-2 data, which allowed us to estimate and successfully map the AGB of Phragmites australis in the Nan Da Gang Wetland Reserve (NDG) in China’s Hebei Province close to the Bohai sea. First, in an experimental area (EA) of NDG, an AGB model (R 2 = 0.74, RMSE = 174 g/m 2 ) was built based on NDVI (534, 734) and canopy height derived from UAV data, and an AGB map was obtained of the EA. Second, the AGB map was resampled to the pixel of the Sentinel-2 image, and an AGB sample set was matched with the acquired spatial resolution of the Sentinel-2 image. Finally, based on the sample set, an AGB model (R 2 = 0.59, RMSE = 194 g/m 2 ) was built using RVI derived from the Sentinel-2 image, which allowed us to map the Phragmites australis AGB in the NDG wetland reedbed. The study illustrated well that a UAV can be proficient in obtaining enough AGB samples matched with satellite pixels to build satellite-based AGB estimation models.

Forest Aboveground Biomass Estimation and Response to Climate Change Based on Remote Sensing Data

As the largest and most important natural terrestrial ecosystem, forest plays a crucial role in reducing the concentrations of greenhouse gases in the atmosphere, mitigating global warming, maintaining the global ecological balance, and promoting global biological evolution and community succession. The accurate and rapid assessment of forest biomass is highly significant for estimating the regional carbon budget and monitoring forest change. In this study, Landsat images and China’s National Forest Continuous Inventory data of 1999, 2004, 2009, and 2014 were used to establish extreme gradient boosting (XGBoost) models for forest aboveground biomass (AGB) estimation based on forest type in the Xiangjiang River Basin, Hunan Province, China. Kriging interpolation of the AGB residuals was used to correct the error of AGB estimation. Then, a new XGBoost model was established using the final corrected AGB maps and climate data to estimate the AGB under different climate scenarios during the 2050s and 2070s. The results indicated that AGB estimation using the XGBoost model with correction via Kriging interpolation of the AGB residuals can significantly improve the accuracy of AGB estimation. The total AGB of the study area increased over time from 1999 to 2014, indicating that the forest quality improved in the study area. Under the different climate scenarios, the total AGB during the 2050s and 2070s was predicted to decline continuously with increasing of greenhouse gas emissions, indicating that greenhouse gas emissions have a negative impact on forest growth. The results of this study can provide data support for evaluating the ecological function and value of forest ecosystems, and for formulating reasonable forest management measures to mitigate the effects of climate change.

RETRACTED: Estimation of aboveground biomass of vegetation based on landsat 8 OLI images

Remote sensing estimation of aboveground biomass for desert oasis vegetation in arid area is an important means to monitor land desertification, it is of great significance to accurately evaluate the carbon sink change of desert oasis ecosystem, and maintain the stability of oasis ecosystem. The aboveground biomass information of vegetation, such as vegetation index and band factor in a delta oasis area is obtained by using Landsat 8 OLI image data; Based on the combination with the measured aboveground biomass data of vegetation, the optimal estimation model of aboveground biomass of four vegetation types (arbors, shrubs, herbs and crops) in this area is established, and the above ground biomass of vegetation was retrieved and verified. The results showed that: (1) there was a very significant correlation between the remote sensing factors of aboveground biomass of four vegetation types and the measured aboveground biomass, and the correlation coefficient ranged from 0.711 to 0.756 (P < 0.01); (2) multiple stepwise regression (MSR) model is the optimal estimation model of aboveground biomass of arbors and shrubs, and partial least squares regression (PLSR) model is the optimal estimation model of aboveground biomass of herbs and crops, the estimation results have a good linear fitting relationship with the measured results; (3) the order of aboveground biomass of vegetation in oasis area from low to high is: herbs < shrubs < arbors < crops. Among them, the aboveground biomass of grass is mainly below 280 g m-2, the aboveground biomass of shrub is mainly 280-950 g m-2, and the aboveground biomass of four vegetation is mainly distributed in 280-1450 g m-2. Based on the Landsat 8 OLI image data, the remote sensing estimation model can accurately estimate the aboveground biomass of four oasis vegetation types (arbors, shrubs, herbs and crops), and reveal the spatial distribution characteristics of aboveground biomass of oasis vegetation.

Multi-source remote sensing data reveals complex topsoil organic carbon dynamics in coastal wetlands

• UAV data accurately predicted SOC concentrations (RMSE < 2.44 %) • Radar data characterized spatial patterns of flood frequency in coastal areas. • The effect of flood frequency on SOC was highest at pioneer and seaward communities. • AGB showed a larger explanatory power than flood frequency in SOC predictions. • UAV-SAR fusion unveils site-specific fine-scale patterns of SOC in coastal meadows. Coastal wetlands are considered important stores of blue carbon, containing some of the largest stores of pedologic and biotic carbon per unit area on the planet. These ecosystems are however highly sensitive to Climate Change and changes in the management practices. It is of utmost importance to address relevant ecosystem scales in order to fully understand carbon dynamics in coastal wetlands. In this regard, Unoccupied Aerial Vehicles (UAVs) can provide spatial scales detailed enough to address the fine scale patters of topsoil organic carbon accumulation in coastal wetlands. This study demonstrates the use of multispectral and photogrammetric data derived from UAVs to accurately map plant communities and topsoil organic carbon concentration in coastal wetlands. The overall accuracies from the classification of plant communities ranged from 88% to 97% whereas RMSE for topsoil organic carbon concentration ranged from 2.44% to 0.74%. By combining both models, site-specific variations in topsoil carbon concentrations among plant communities were unveiled. Open pioneer communities consistently showed the lowest topsoil organic carbon concentration, while the concentrations vary considerably across plant communities characterized by denser vegetation coverage. Furthermore, Sentinel-1 radar data was used to assess the spatial patterns of flood frequency. GAMs were used to combine flood frequency with the plant communities and topsoil organic carbon models, as well as an aboveground biomass (AGB) model from a previous study. GAMs revealed a stronger effect than flood frequency on topsoil organic carbon. Regarding flooding, increased flood frequency generally led decreased topsoil organic concentrations across communities and sites. However, the relative contribution of flood frequency to topsoil organic carbon concentration in Baltic coastal wetlands depends strongly on the location of the wetland and the nature of the floods. Higher flood frequencies could lead to increased topsoil organic carbon in wetlands subject to the input of estuarine sediments. Lastly, the integration of remote sensing platforms constitutes an effective tool for revealing spatial heterogeneity of carbon storage in coastal wetlands.

Improving above ground biomass estimates of Southern Africa dryland forests by combining Sentinel-1 SAR and Sentinel-2 multispectral imagery

Having the ability to make accurate assessments of above ground biomass (AGB) at high spatial resolution is invaluable for the management of dryland forest resources in areas at risk from deforestation, forest degradation pressure and climate change impacts. This study reports on the use of satellite-based synthetic-aperture radar (SAR) and multispectral imagery for estimating AGB by correlating satellite observations with ground truth data collected on forest plots from dryland forests in the Chobe National Park, Botswana. We undertook nineteen quantitative experiments with Sentinel-1 (S1), and Sentinel-2 (S2) and tested simple and multivariate regression including parametric (linear) and non-parametric (random forests) algorithms, to explore the optimal approaches for AGB estimation. The largest AGB value of 145 Mg/ha was found in northern Chobe while a large part of the study area (85%) is characterized by low AGB values (< 80 Mg/ha), with an average estimated at 51 Mg/ha. The results show that the AGB estimated using SAR backscatter values from vertical transmit receive (VV) polarization is more accurate than that based on horizontal receive (VH) polarization, accounting for 58% of the variance compared to 32%. Nevertheless, the combination of S1 SAR and S2 multispectral image data produced the best fit to the ground observations for dryland forests explaining 83% of the variance with an accuracy of 89%. Furthermore, the optimal AGB model performance was achieved with a random forest (RF) regression trees algorithm using S1 (SAR) and S2 (multispectral) image data (R2 = 0.95; RMSE = 0.25 Mg/ha). From the 11 vegetation indices tested, GNDVI, Normalized Difference Red Edge (NDRE1), and NDVI obtained the highest linear relationship with AGB (R2 = 0.71 and R2 = 0.56, p < 0.001), however, GNDVI and NDRE1 improved the AGB estimation at medium to high-density forests compared to NDVI. The GRVI and EVI were the least correlated with AGB (R2 = 0.09 and R2 = 0.31) at a significance level of p < 0.001, respectively. We show that NDVI saturates in areas with >80 Mg/ha AGB, whereas the inclusion of SAR backscatter and optical red edge bands (B5) significantly reduces saturation effects in areas of high biomass. GNDVI and red edge (B5) derived vegetation indices have more potential for estimating AGB in dryland forests than NDVI. Our results demonstrate that dryland AGB can be estimated with a reasonable level of precision from open access Earth observation data using multivariate random forest regression.

Aboveground biomass mapping by integrating ICESat-2, SENTINEL-1, SENTINEL-2, ALOS2/PALSAR2, and topographic information in Mediterranean forests

ABSTRACT The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides an extraordinary opportunity to support global large-scale forest carbon mapping, but further research is needed in order to obtain wall-to-wall forest aboveground biomass (AGB) maps with this technology. The effects of vegetation structure on the performance of canopy height and AGB modeling using ICESat-2 photon-counting light detection and ranging (LiDAR) data in Mediterranean forest areas have not been previously studied in the literature. In this study, we combined recent ICESat-2 vegetation (ATL08) data, Airborne Laser Scanning (ALS)- and field-based estimates, and a multi-sensor earth observation composite for extrapolation of AGB estimates and AGB mapping. A diverse gradient of forest Mediterranean ecosystems, distributed over 19,744.15 km2 of forest area in the region of Extremadura (Spain), with different species and structural complexity forming 5 different forest types (3 Quercus spp. dominated and 2 Pinus spp. dominated forests), was used to (i) evaluate the precision of ICESat-2 canopy height estimations, (ii) develop ICESat-2-based AGB models, and (iii) generate a spatially continuous prediction of AGB by using data from the satellite missions Sentinel-1 (S1), Sentinel-2 (S2), Phased Array L-band Synthetic Aperture Radar (ALOS2/PALSAR2), and Shuttle Radar Topography Mission (SRTM). First, ALS- and ICESat-2-derived metrics that best described canopy height (p98 and rh98, respectively) were compared at the ATL08 segment level. Second, ALS-based AGB values were derived at the ATL08 segment scale. Third, ALS-based AGB estimates at the ICESat-2 segment level were used as dependent variables to fit ICESat-2-based AGB models. Fourth, a multi-sensor approach was then implemented to predict ICESat-2-derived AGB, by means of a Random Forest (RF) modeling technique, with predictors retrieved from S1, S2, ALOS2/PALSAR2, and SRTM. Finally, RF was used to generate wall-to-wall AGB maps that were compared with field-, ALS- and ICESat-2-based observations. The agreement between the ALS- and ICESat-2-derived metrics related to the canopy height distribution was higher for Pinus spp. forest than for the Quercus spp-dominated forests. The ICESat-2-based AGB models yielded model efficiency (Mef) values between 0.56 and 0.80, with a RMSE ranging from 7.76 to 17.71 Mg ha−1 and rRMSE from 19.04 to 55.21%. The multi-sensor RF models provided the following results when compared with the ICESat-2- and ALS-based AGB observations: R 2 values of 0.63 and 0.64, and RMSE values of 11.10 Mg ha−1(rRMSE = 28.15%) and 12.28 Mg ha−1 (rRMSE = 31.45%), respectively, and an approximately unbiased result (0.03 Mg ha−1 and 0.09 Mg ha−1). When applied to the field-based validation data set (4th Spanish National Forest Inventory (SNFI-4) plots = 508), the RF-derived AGB model showed a relatively lower predictive capacity (R 2 = 0.45), a higher RMSE value (25.88 Mg ha−1) and slightly biased results (−1.47 Mg ha−1), especially for larger field-derived AGB intervals. The results of this study serve to provide an initial quantitative assessment of the ICESat-2 ATL08 data for large-scale AGB estimation. The findings suggest that a multi-sensor approach may be feasible for extrapolating ICESat-2-derived AGB estimates over areas where field or ALS reference data are not available.

How can UAV bridge the gap between ground and satellite observations for quantifying the biomass of desert shrub community?

Accurate estimation of the shrub above-ground biomass (AGB) is an essential basis for determining carbon storage and monitoring desertification risk in arid ecosystems. However, significant uncertainties and biases exist in large-scale monitoring of desert AGB due to the scale mismatch and spatio-temporal topological errors between the coarse resolution of satellite images and the limited area that can be surveyed via ground measurements. The rapid development of unmanned aerial vehicle (UAV) technology has the prospective advantage of multi-scale vegetation observation. However, its potential to bridge the gap between satellites and the ground in desert AGB estimation has not yet been explored. This study developed a procedure to fill the gap between satellite and ground measurements based on low-cost and easy-to-use UAV visible-light technology in typical desert shrub communities in Inner Mongolia, China. First, canopy area (CA), canopy maximum height (CH), and canopy volume (CV) metrics derived from UAV-RGB (Red, Green, Blue) images, coupled with structure-from-motion photogrammetry, were used to invert the UAV-based AGB. Then, the UAV-based AGB data were aggregated to different scales to align with satellite data. Here, we focused on examining the performance of generalized additive models between the upscaled UAV-based AGB and vegetation indices (VIs) generated from PlanetScope (resolution: 3 m), Sentinel-2A MSI (resolution: 10 m, 20 m), and Landsat 8 OLI (resolution: 30 m). Finally, we investigated the effects of scale and spectra in the upscaling and modeling process. Results showed that the UAV-based AGB linear prediction models developed by the CV metric performed best for Reaumuria soongarica (R2 = 0.749, RMSE = 65.5 g) and Salsola passerina (R2 = 0.919, RMSE = 56.7 g), which allowed for accurately mapping the desert AGB at the individual plant level with ultra-high resolution (2 cm). The performance of satellite biomass models was excellent based on the upscaled UAV-based AGB and VIs (best-performing models for different satellites: adjusted R2 = 0.625–0.934, RMSE = 35.1–119.1 g/m2). As the resolution of satellite data increases, the increase in variance is a big scale-related challenge in AGB model performance evaluation. It is worth noting that the UAV-based AGB data are unmodifiable due to their area-independent nature, which can avoid the scale-related effect. Moreover, the satellite VIs most closely related to the desert AGB were associated with Red, NIR, and Red-Edge bands. Specifically, we recommend the Red-Edge band when using Sentinel-2A data. This study proves that UAV technology can not only provide exhaustive information on vegetation biomass but also can train and validate satellite data beyond individual UAV observations, which can significantly improve large-scale biomass monitoring if widely applied in the future.

Multi-Platform LiDAR for Non-Destructive Individual Aboveground Biomass Estimation for Changbai Larch (Larix olgensis Henry) Using a Hierarchical Bayesian Approach

Individual-tree aboveground biomass (AGB) estimation is vital for precision forestry and still worth exploring using multi-platform LiDAR data for high accuracy and efficiency. Based on the unmanned aerial vehicle and terrestrial LiDAR data, this study explores the feasibility of the individual tree AGB estimation of Changbai larch (Larix olgensis Henry) of eight plots from three different regions in Maoershan Forest Farm of Heilongjiang, China, using nonlinear mixed effect model with hierarchical Bayesian approach. Results showed that the fused LiDAR data estimated the individual tree parameters (i.e., diameter at breast height (DBH), tree height (TH), and crown projection area (CPA)) with high accuracies (all R2 &gt; 0.9 and relatively low RMSE and rRMSE) using region-based hierarchical cross-section analysis (RHCSA) algorithm. Considering regions as random variables, the nonlinear mixed-effects AGB model with three predictor variables (i.e., DBH, TH, and CPA) performed better than its corresponding nonlinear model. In addition, the hierarchical Bayesian method provided better model-fitting performances and more stable parameter estimates than the classical method (i.e., nonlinear mixed-effect model), especially for small sample sizes (e.g., &lt;50). This methodology (i.e., multi-platform LiDAR data and the hierarchical Bayesian method) provides a potential solution for non-destructive individual-tree AGB modeling with small sample size and high accuracy in both forestry and remote sensing communities.

Integrating spaceborne LiDAR and Sentinel-2 images to estimate forest aboveground biomass in Northern China

BackgroundFast and accurate forest aboveground biomass (AGB) estimation and mapping is the basic work of forest management and ecosystem dynamic investigation, which is of great significance to evaluate forest quality, resource assessment, and carbon cycle and management. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), as one of the latest launched spaceborne light detection and ranging (LiDAR) sensors, can penetrate the forest canopy and has the potential to obtain accurate forest vertical structure parameters on a large scale. However, the along-track segments of canopy height provided by ICESat-2 cannot be used to obtain comprehensive AGB spatial distribution. To make up for the deficiency of spaceborne LiDAR, the Sentinel-2 images provided by google earth engine (GEE) were used as the medium to integrate with ICESat-2 for continuous AGB mapping in our study. Ensemble learning can summarize the advantages of estimation models and achieve better estimation results. A stacking algorithm consisting of four non-parametric base models which are the backpropagation (BP) neural network, k-nearest neighbor (kNN), support vector machine (SVM), and random forest (RF) was proposed for AGB modeling and estimating in Saihanba forest farm, northern China.ResultsThe results show that stacking achieved the best AGB estimation accuracy among the models, with an R2 of 0.71 and a root mean square error (RMSE) of 45.67 Mg/ha. The stacking resulted in the lowest estimation error with the decreases of RMSE by 22.6%, 27.7%, 23.4%, and 19.0% compared with those from the BP, kNN, SVM, and RF, respectively.ConclusionCompared with using Sentinel-2 alone, the estimation errors of all models have been significantly reduced after adding the LiDAR variables of ICESat-2 in AGB estimation. The research demonstrated that ICESat-2 has the potential to improve the accuracy of AGB estimation and provides a reference for dynamic forest resources management and monitoring.

Optimization of Samples for Remote Sensing Estimation of Forest Aboveground Biomass at the Regional Scale

Accurately estimating forest aboveground biomass (AGB) based on remote sensing (RS) images at the regional level is challenging due to the uncertainty of the modeling sample size. In this study, a new optimizing method for the samples was suggested by integrating variance function in Geostatistics and value coefficient (VC) in Value Engineering. In order to evaluate the influence of the sample size for RS models, the random forest regression (RFR), nearest neighbor (K-NN) method, and partial least squares regression (PLSR) were conducted by combining Landsat8/OLI imagery in 2016 and 91 Pinus densata sample plots in Shangri-La City of China. The mean of the root mean square error (RMSE) of 200 random sampling tests was adopted as the accuracy evaluation index of the RS models and VC as a relative cost index of the modeling samples. The research results showed that: (1) the statistical values (mean, standard deviation, and coefficient of variation) for each group of samples based on 200 experiments were not significantly different from the sampling population (91 samples) by t-test (p = 0.01), and the sampling results were reliable for establishing RS models; (2) The reliable analysis on the RFR, K-NN, and PLSR models with sample groups showed that the VC decreases with increasing samples, and the decreasing trend of VC is consistent. The number of optimal samples for RFR, K-NN, and PLSR was 55, 54, and 56 based on the spherical model of variance function, respectively, and the optimal results were consistent. (3) Among the established models based on the optimal samples, the RFR model with the determination coefficient R2 = 0.8485, RMSE = 12.25 Mg/hm2, and the estimation accuracy P = 81.125% was better than K-NN and PLSR. Therefore, they could be used as models for estimating the aboveground biomass of Pinus densata in the study area. For the optimal sample size and sampling population, the RFR model of Pinus densata AGB was established, combining 26 variable factors in the study area. The total AGB with the optimal samples was 1.22 × 107 Mg, and the estimation result with the sampling population was 1.24 × 107 Mg based on Landsat8/OLI images. Respectively, the average AGB was 66.42 Mg/hm2 and 67.51 Mg/hm2, with a relative precision of 98.39%. The estimation results of the two sample groups were consistent.

Allometric models for estimating aboveground biomass of young Norway spruce trees in the Western Carpathians, Slovakia

Abstract In the period of climate change, it is necessary to have biomass models for trees of all sizes to make precise estimations of biomass forest stocks to quantify carbon sequestration by forest cover. Therefore, we created allometric models of aboveground biomass in young Norway spruce (Picea abies [L.] Karst.) trees including main components, i.e. stem, branches and needles. The models used 200 sampled trees from 10 sites located in the central part of the Western Carpathians in Slovakia. The models, i.e. allometric regression relations implemented stem base diameter (diameter d0) and/or tree height. Moreover, using the derived allometric relations and a constant annual diameter increment of 10 mm, we calculated quasi-annual aboveground biomass production with regard to diameter d0. While stem had the largest contribution to aboveground biomass, followed by needles and branches, a different situation was revealed for the annual aboveground biomass production with the largest share of needles followed by stem and branches. Finally, we implemented the allometric models in a specific forest stand, where repetitive measurements were performed within 14 consecutive years. The results showed for example nearly 650 kg of the aboveground biomass per 102 m2 at the stand age of 10 years. The new biomass models can be applied to estimate the aboveground biomass stock in Norway spruce dominating stands in the Western Carpathians. Since the models are based on both diameter d0 and tree height a user can choose which variable is more suitable for particular conditions.

Filtering ground noise from LiDAR returns produces inferior models of forest aboveground biomass in heterogenous landscapes

ABSTRACT Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest aboveground biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering – that is, excluding returns from LiDAR point clouds based on simple height thresholds – is a common practice meant to improve the `signal’ content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. However, ground returns may be helpful for making accurate aboveground biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types. In this paper, we applied several ground noise filtering thresholds while mapping forest AGB across New York State (USA), a heterogenous landscape composed of both contiguously forested and highly fragmented areas with mixed land cover types. We fit random forest models to predictor sets derived from each filtering intensity threshold and compared model accuracies, paying attention to how changes in accuracy correlated with landscape structure. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling, with mean correlation (Spearman’s ) between variables increasing from 0.183 to 0.266. We found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds, with RMSE increasing by up to 2.2 Mg ha-1 statewide. Although we only modeled AGB for forest cover types, models fit to predictors derived from filtered point clouds performed worse as landscape heterogeneity (as measured by patch density and edge density) increased, suggesting ground returns are particularly useful when modeling edge forests. Our results suggest that ground filtering should be a carefully considered decision when mapping forest AGB, particularly when mapping heterogeneous and highly fragmented landscapes, as ground returns are more likely to represent useful `signal’ than extraneous `noise’ in these cases.

Aboveground biomass allometric models for large trees in southwestern Amazonia

Most of the basal area in Amazon forest is in large trees, many of which are species of interest for forest management. In forest management these trees are divided into the commercial bole that is harvested for wood production and the stump and crown that are left in the forest where they decompose and emit CO2 over a period of years. Part of the commercial bole is converted to wood products that store carbon according to their durability. The quantification of these components is difficult due to their size, especially in the case of the crown, which causes uncertainties in the estimates of biomass and carbon. Our study estimated the aboveground biomass and carbon of 223 trees and subsequently fit allometric equations to these estimates. Aboveground biomass was calculated from stem volume, wood density and a biomass expansion factor, while total carbon stock estimates used carbon content determined in the laboratory. Linear models (log-transformed) were tested to derive the best-fit allometric model for total aboveground biomass and carbon. The best-fit allometric models used squared tree diameter, tree height, and wood density for biomass, whereas the best carbon model also used carbon content. Our models were more efficient in estimating biomass than were frequently used regional and pan-tropical models. Our equations allow reducing the errors in estimates of forest biomass and carbon stocks, in addition to allowing estimation of the amount of carbon emitted after harvest, although the other models also had good fits and can be used according to the criteria of each researcher and the availability of data.

Estimation of Aboveground Biomass (ABG) Using Allometric Equation: Case Study of Colo-I-Suva Forest Park in Fiji

Forests play an important role in reducing greenhouse gases in the atmosphere, thereby mitigating the impact of climate change. Estimating the accumulated biomass in a forest ecosystem is important for assessing the productivity and sustainability of the forest. Allometric models for above ground biomass (AGB)are linear regression equations based on the relationships between biomass and diameter at breast height (DBH), tree height (H), and/or wood density. This study estimates AGB in the Colo-ISuva Forest Park by applying the allometry of Chave et al (2005&amp; 2015) and the diameter: height ratio derived from Payton &amp; Weaver (2011) for height estimation in a plot of 20𝑚 × 20𝑚. 116 trees of 15 different species were measured and AGB was predicted from different diameters (0.4 to 19.4 cm) and heights (1.1 to 16 m). The R² result for each species ranged from 0.504 𝑡𝑜 0.952, showing that the model fits the data and the spatial distribution of AGB shows a positive correlation between AGB and DBH. Above-ground carbon stocks calculated in the study area ranged from 0.03 to 3.5 t C per species with an average of 0.31 t C/species. The total above ground biomass estimated for 1 hectare is 3.7 t C/ha.

Estimation of Leaf Area Index and Above-Ground Biomass of Winter Wheat Based on Optimal Spectral Index

Leaf area index (LAI) and above-ground biomass are both vital indicators for evaluating crop growth and development, while rapid and non-destructive estimation of crop LAI and above-ground biomass is of considerable significance for crop field management. Owing to the advantages of repeatable and high-throughput observations, spectral technology provides a feasible method for obtaining LAI and above-ground biomass of crops. In the present study, the spectral, LAI and above-ground biomass data of winter wheat were collected, and 7 species (14 in total) were calculated based on the original and first-order differential spectrum correlation spectral indices with LAI. Then, the correlation matrix method was used for correlation with LAI. The optimal wavelength combination was extracted, and the results were calculated as the optimal spectral index related to LAI. The calculation process of the optimal spectral index related to above-ground biomass was the same as that aforementioned. Finally, the optimal spectral index was divided into three groups of model input variables, winter wheat LAI and above-ground biomass estimation models were constructed using support vector machine (SVM), random forest (RF) and a back propagation neural network (BPNN), and the models were verified. The results show that the correlation coefficient between the highest of the optimal spectral indices, the LAI, and the above-ground biomass of winter wheat exceeded 0.6, and the correlation was good. The methods for establishing the optimal estimation models for LAI and above-ground biomass of winter wheat are all modeling methods in which the input variables are the combination of the first-order differential spectral index (combination 2) and RF. The R2 of the LAI estimation model validation set was 0.830, the RMSE was 0.276, and the MRE was 6.920; the R2 of the above-ground biomass estimation model validation set was 0.682, RMSE was 235.016, MRE was 4.336, and the accuracies of both models were high. The present research results can provide a theoretical basis for crop monitoring based on spectral technology and provide an application reference for the rapid estimation of crop growth parameters.

Above-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data

Implementing a scheduled and reliable estimation of forest characteristics is important for the sustainable management of forests. This study aimed at evaluating the capability of Sentinel-2 satellite data to estimate above-ground biomass (AGB) in coppice forests of Persian oak (Quercus brantii var. persica) located in Western Iran. To estimate the AGB, field data collection was implemented in 80 square plots (40×40 m, area of 1600 m2). Two diameters of the crown were measured and used to calculate the AGB of each tree based on allometric equations. Then, the performance of satellite data in estimating the AGB was evaluated for the area of study using the field-based AGB (dependent variable) as well as the spectral band values, spectrally-derived vegetation indices (independent variables) and four machine learning (ML) algorithms: Multi-Layer Perceptron Artificial Neural Network (MLPNN), k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Regression (SVR). A five-fold cross-validation was used to verify the effectiveness of models. Examination of the Pearson’s correlation coefficient between AGB and the extracted ‎values showed that IPVI and NDVI vegetation indices had the highest correlation with AGB (r = 0.897). The results indicated that the MLPNN algorithm was the best ML option (RMSE = 1.71 t ha-1; MAE = 1.37 t ha-1; relative RMSE = 24.75%; R2 = 0.87) in estimating the AGB, providing new insights on the capability of remotely sensed-based AGB modeling of sparse Mediterranean forest ecosystems in an area with limited number of field sample plots.

Spatiotemporal Variation in Aboveground Biomass and Its Response to Climate Change in the Marsh of Sanjiang Plain

The Sanjiang Plain has the greatest concentration of freshwater marshes in China. Marshes in this area play a key role in adjusting the regional carbon cycle. As an important quality parameter of marsh ecosystems, vegetation aboveground biomass (AGB) is an important index for evaluating carbon stocks and carbon sequestration function. Due to a lack of in situ and long-term AGB records, the temporal and spatial changes in AGB and their contributing factors in the marsh of Sanjiang Plain remain unclear. Based on the measured AGB, normalized difference vegetation index (NDVI), and climate data, this study investigated the spatiotemporal changes in marsh AGB and the effects of climate variation on marsh AGB in the Sanjiang Plain from 2000 to 2020. Results showed that the marsh AGB density and annual maximum NDVI (NDVImax) had a strong correlation, and the AGB density could be accurately calculated from a power function equation between NDVImax and AGB density (AGB density = 643.57 × NDVI). According to the function equation, we found that the AGB density significantly increased at a rate of 2.47 g·C/m2/a during 2000–2020 in marshes of Sanjiang Plain, with the long-term average AGB density of about 282.05 g·C/m2. Spatially, the largest increasing trends of AGB were located in the north of the Sanjiang Plain, and decreasing trends were mainly found in the southeast of the study area. Regarding climate impacts, the increase in precipitation in winter could decrease the marsh AGB, and increased temperatures in July contributed to the increase in the marsh AGB in the Sanjiang Plain. This study demonstrated an effective approach for accurately estimating the marsh AGB in the Sanjiang Plain using ground-measured AGB and NDVI data. Moreover, our results highlight the importance of including monthly climate properties in modeling AGB in the marshes of the Sanjiang Plain.

Mapping Spatiotemporal Changes in Forest Type and Aboveground Biomass from Landsat Long-Term Time-Series Analysis—A Case Study from Yaoluoping National Nature Reserve, Anhui Province of Eastern China

A natural reserve’s forest is an important base for promoting natural education, scientific research, biodiversity conservation and carbon accounting. Dynamic monitoring of the forest type and forest aboveground biomass (AGB) in a nature reserve is an important foundation for assessing the forest succession stage and trend. Based on the Landsat images covering the National Nature Reserve of Yaoluoping in Anhui province spanning from 1987 to 2020, a total of 42 Landsat scenes, the forest cover product set was first developed by using the well-established vegetation change tracker (VCT) model. On this basis, a new vegetation index, NDVI_DR, which considers the phenological characteristics of different forest types, was proposed to distinguish coniferous forest from broad-leaved forest. Next, multiple modeling factors, including remote sensing spectral signatures, vegetation indices, textural measures derived from gray level co-occurrence matrix and wavelet analysis and topographic attributes, were compiled to model the AGB in 2011 by forest type separately by using the stochastic gradient boosting (SGB) algorithm. Then, using the 2011 Landsat image as the base, all the Landsat images in the other years involved in the modelling were relatively normalized by using the weighted invariant pixels (WIP) method, followed by an extrapolation of the 2011 AGB model to other years to create a time-series of AGB. The results showed that the overall accuracy of the VCT-based forest classification products was over 90%. The annual forest type classifications derived from NDVI_DR thresholding gained an overall accuracy above 92%, with a kappa coefficient above 0.8. The 2011 forest-type-dependent SGB-based AGB estimation model achieved an independent validation R2 at 0.63 and an RMSE at 11.18 t/ha for broad-leaved forest, and 0.61 and 14.26 t/ha for coniferous forest. The mapped time-series of AGB showed a gradual increasing trend over the past three decades. The driving factors responsible for the observed forest cover and AGB changes were analyzed to provide references for reasonable protection and development. The proposed methodology is a reliable tool for evaluating the management status, which can be extended to other similar regions.

Aboveground Carbon Stock Estimation Model Using Sentinel-2A Imagery in Mbeliling Lanscape in Nusa Tenggara Timur, Indonesia

To determine emission levels, information on carbon stocks and changes in each carbon pool is required. Aboveground biomass, particularly on dry land, is one carbon pool that contributes significantly to carbon storage. The goal of this study was to develop a model for estimating aboveground carbon stocks in the Mbeliling landscape, in Nusa Tenggara Timur, using a vegetation index that was correlated with field carbon stocks. The best model was then used to create a map of the distribution of carbon stocks as the final result. Simple linear regression analysis and multiple linear regression analysis were used in the study. Google Earth Engine was used to process the images on a cloud system. When comparing the RGI index for measuring field carbon stocks to other indexes, the correlation test revealed a perfect correlation. The linear regression model for aboveground biomass = 14.046 + 272.496 RGI (R-sq = 0.86) was found to be the best model for aboveground biomass. In the multiple linear regression model, there were signs of multicollinearity. With an overall accuracy of 68% and a cappa accuracy of 54.23%, the best model was able to be used to create a carbon stock map in Mbeliling landscape.&#x0D; Keywords: Carbon stock estimation model, Above Ground Biomass, Sentinel 2A

Estimating the maize above-ground biomass by constructing the tridimensional concept model based on UAV-based digital and multi-spectral images

Above-ground biomass (AGB) is an important basis for the formation of crop yield. The accurate estimation of maize AGB based on unmanned aerial vehicle (UAV) images is important for superior varieties selection, field management and maize yield prediction. The previous studies mainly focused on constructing empirical models of AGB by using spectral vegetation indices (VIs), plant height (PH), texture, and is lacked of universality. We conducted the field experiments of maize breeding materials for three years, and obtained UAV digital and multi-spectral images. Considering that the maize AGB before tasseling stage was composed of stem and leaf, we constructed a tridimensional concept model to predict maize AGB coordinated by integrating leaf area index (LAI) and PH, in order to improve the accuracy and universality of UAV data on monitoring maize AGB at multiple growth stages. Firstly, the maize PH was estimated based on the maize canopy height model constructed using the UAV digital images. Secondly, the maize LAI was estimated based on UAV multi-spectrum images and the modified Beer-Lambert law. Finally, the tridimensional concept model of maize AGB was constructed by integrating PH and LAI, and compared with the AGB regression model based on the normalized difference vegetation index (NDVI). The results showed that the maize PH could be estimated well, and the R ², RMSE and rRMSE of the measured and estimated PH were 0.87, 11.17 cm and 16.04% respectively. The LAI could be estimated effectively, and the R ², RMSE, and rRMSE of the sample set were 0.78, 0.49 and 30% respectively. Compared with the maize AGB estimation model based on NDVI ( R ² = 0.79, RMSE = 41.95 g/m², rRMSE = 31.79%), the tridimensional concept model could better estimate the maize AGB ( R ² = 0.82, RMSE = 38.53 g/m², rRMSE = 29.19%). Testing the tridimensional concept model by stand-alone data of 2019 and 2021 years, the accuracy of the AGB estimation model based on the tridimensional concept was much higher than that of the NDVI model. In conclusion，the tridimensional concept model of maize AGB proposed in this study effectively improved the accuracy, stability and universality, which could provide a reference for the estimation of maize AGB by UAV technology at plot scale of the breeding materials.