Aboveground Biomass Map Research Articles

Small Field Plots Can Cause Substantial Uncertainty in Gridded Aboveground Biomass Products from Airborne Lidar Data

Emerging satellite radar and lidar platforms are being developed to produce gridded aboveground biomass (AGB) predictions that are poised to expand our understanding of global carbon stocks and changes. However, the spatial resolution of AGB map products from these platforms is often larger than the available field plot data underpinning model calibration and validation efforts. Intermediate-resolution/extent remotely sensed data, like airborne lidar, can serve as a bridge between small plots and map resolution, but methods are needed to estimate and propagate uncertainties with multiple layers of data. Here, we introduce a workflow to estimate the pixel-level mean and variance in AGB maps by propagating uncertainty from a lidar-based model using small plots, taking into account prediction uncertainty, residual uncertainty, and residual spatial autocorrelation. We apply this workflow to estimate AGB uncertainty at a 100 m map resolution (1 ha pixels) using 0.04 ha field plots from 11 sites across four ecoregions. We compare uncertainty estimates using site-specific models, ecoregion-specific models, and a general model using all sites. The estimated AGB uncertainty for 1 ha pixels increased with mean AGB, reaching 7.8–33.3 Mg ha−1 for site-specific models (one standard deviation), 11.1–28.2 Mg ha−1 for ecoregion-specific models, and 21.1–22.1 Mg ha−1 for the general model for pixels in the AGB range of 80–100 Mg ha−1. Only 3 of 11 site-specific models had a total uncertainty of <15 Mg ha−1 in this biomass range, suitable for the calibration or validation of AGB map products. Using two additional sites with larger field plots, we show that lidar-based models calibrated with larger field plots can substantially reduce 1 ha pixel AGB uncertainty for the same range from 18.2 Mg ha−1 using 0.04 ha plots to 10.9 Mg ha−1 using 0.25 ha plots and 10.1 Mg ha−1 using 1 ha plots. We conclude that the estimated AGB uncertainty from models estimated from small field plots may be unacceptably large, and we recommend coordinated efforts to measure larger field plots as reference data for the calibration or validation of satellite-based map products at landscape scales (≥0.25 ha).

Improved estimation of aboveground biomass of regional coniferous forests integrating UAV-LiDAR strip data, Sentinel-1 and Sentinel-2 imageries

BackgroundForest aboveground biomass (AGB) is not only the basis for estimating forest carbon storage, but also an important parameter for evaluating forest carbon cycle contribution and forest ecological function. Data saturation and fewer field plots limit the accuracy of AGB estimation. In response to these questions, we constructed a point-line-polygon framework for regional coniferous forests AGB mapping using field survey data, UAV-LiDAR strip data, Sentinel-1 and Sentinel-2 imageries in this study. Under this framework, we explored the feasibility of acquiring the LiDAR sampling plots using the LiDAR sampling strategy consistent with the field survey, and analyzed the potentials of multi-scale wavelet transform (WT) textures and tree species stratification for improving AGB estimation accuracy of coniferous forests in North China.ResultsThe results showed that UAV-LiDAR strip data of high density point clouds could be used as a sampling tool to achieve sample amplification. Experimental comparison results showed that the Sentinel-based AGB estimation models incorporating the multi-scale WT textures and SAR data performed better, and the model based on coniferous forests tree species significantly improved the performance of AGB estimation. Additionally, the accuracy comparison using different validation sets indicated that the proposed LiDAR sampling strategy under the point-line-polygon framework was suitable for estimating coniferous forests AGB on a large area. The highest accuracy of AGB estimation of larch, Chinese pine and all coniferous forests was 74.55%, 78.96%, and 73.42%, respectively.ConclusionsThe proposed approach can successfully alleviate the data signal saturation issue and accurately produce a large-scale wall-to-wall high-resolution AGB map by integrating optical and SAR data with a relative small number of field plots.

Vegetation fuel characterization using machine learning approach over southern Portugal

Understanding the role of fire in the water and carbon cycles is crucial for understanding the Earth's system. Remote sensing is a valuable tool for this purpose as it covers large areas consistently over time. Furthermore, fire propagation models use vegetation parameters to gather information about wildfire conditions, thus reinforcing the need for vegetation dynamics comprehension. Hence, the study aims to improve vegetation representation of fuel load and moisture content, through remote sensing and in-situ data in Southern Portugal. In this study, three above-ground biomass (AGB) datasets and, for live fuel moisture content (LFMC), biweekly samples over two field sites (Herdade da Mitra and Serra de Ossa) were collected during the period between April and October 2022, counting 246 field samples. These samples combined with satellite data information derived from Sentinel-2 (spectral bands and indices) were used in a machine learning approach, the Random Forest (RF) classifier, considering 30 variables to predict the AGB and LFMC. Results showed reasonable agreement between predicted and observed values, with r2 and RSME values of 0.56 (0.69) and 17.56 ton ha−1 (6.47%) for AGB (LFMC). Finally, the RF model generated wall-to-wall dynamic AGB and LFMC maps. This study allowed a product derived from remote sensing data combined with an RF model to produce reliable information about vegetation conditions essential for wildfire risk assessment and atmosphere fire modelling in Southern Portugal.

Integration of UAV and GF-2 Optical Data for Estimating Aboveground Biomass in Spruce Plantations in Qinghai, China

More refined and economical aboveground biomass (AGB) monitoring techniques are needed because of the growing significance of spruce plantations in climate change mitigation programs. Due to the challenges of conducting field surveys, such as the potential inaccessibility and high cost, this study proposes a convenient and efficient alternative to traditional field surveys that integrates Gaofen-2 (GF-2) satellite optical images and unmanned aerial vehicle (UAV)-acquired optical and point cloud data to provide a reliable and refined estimation of the aboveground biomass (AGB) in spruce plantations. The feasibility of using data produced from the semiautomatic processing of UAV-based images and photogrammetric point clouds to replace conventional field surveys of sample plots in a young spruce plantation was evaluated. The AGB in 53 sample plots was estimated using data extracted from the UAV imagery. The UAV plot data and GF-2 optical data were used in four regression models to estimate the AGB in the study area. The coefficient of determination (R2), root-mean-square error (RMSE), mean percent standard error (MPSE), and Lin’s concordance correlation coefficient (LCCC) were calculated through five-fold cross-validation and stratified random sampling to evaluate the models’ efficacies. In the end, the most accurate model was used to generate the spatial distribution map of the AGB. The results revealed the following: (1) the individual-tree height (R2 = 0.90) and crown diameter (R2 = 0.74) extracted from UAV data were accurate enough to replace field surveys used to obtain the AGB at the plot levels; (2) the random forest (RF) model (R2 = 0.86; RMSE = 1.75 t/ha; MPSE = 15.75%; LCCC = 0.91) outperformed the ordinary least-squares (OLS) model (R2 = 0.68; RMSE = 2.49 t/ha; MPSE = 22.94%; LCCC = 0.81), artificial neural network (ANN) model (R2 = 0.67; RMSE = 2.54 t/ha; MPSE = 21.48%; LCCC = 0.80), and support vector machine (SVM) model (R2 = 0.60; RMSE = 2.84 t/ha; MPSE = 31.73%; LCCC = 0.76) in terms of the estimation accuracy; (3) an AGB map generated by the random forest model was in good agreement with field surveys and the age of the spruce plantations. Therefore, the method proposed in this study can be used as a refined and cost-effective way to estimate the AGB in young spruce plantations.

Selective logging and shifting agriculture may help maintain forest biomass on the Yucatán peninsula

AbstractEvaluation and monitoring of forest biomass are important to help combat global warming and conserve biodiversity. Above ground biomass (AGB) mapping has been effectively used to assess forest loss, degradation, and recovery in the tropics. In this study, temporal (2007, 2010, and 2015) AGB maps were developed for the Mayan Zone of the Yucatan Peninsula, México integrating vegetation data from the National Forest Inventory (NFI) with synthetic aperture radar (SAR) image data (ALOS PALSAR). We assess if degradation could be attributed to the land use categories present in the study area: selectively logged forest, shifting agriculture, permanent commercial cultivation, and conservation forest. Spatial autoregressive models are applied to determine differences in AGB dynamics between land use categories, compared to baselines of mature conserved forest. We find that forest biomass remains stable in the study area. AGB does not differ in selectively logged areas compared to conserved mature forests. Biomass losses are observed due to deforestation for commercial cropping and pasture. AGB in shifting agriculture areas, however, fluctuates and shows a slight gain from 2007 to 2015. AGB mapping using NFI data and SAR imagery has the potential for monitoring forest loss and degradation on the Yucatan Peninsula.

Sundarbans Mangrove Mapping and Above Ground Biomass Estimation Using Earth Observation Techniques

The Sundarbans, the world's largest mangrove forest, plays a crucial role in Bangladesh's economy and environment. However, overexploitation, anthropogenic activities like tree cutting for development and agriculture, as well as natural disasters, have caused severe damage and changes to the Sundarbans. This research aims to detect changes in the mangrove forest areas and create an above-ground biomass map of the Sundarbans Forest. Previous studies relied on time-consuming and inaccurate traditional methods, while this study seeks to make a significant contribution to mangrove mapping and forest resource management. Landsat 5 and 8 images from 2007 to 2017 were used to generate the mangrove index (including CMRI and MI) for different years. ALOS-PALSAR 2 and JERS images from 1996, 2007, and 2017, with HV+HH polarization, were processed to calculate the backscatter ratio, enabling identification and estimation of vegetation loss and gain over the years. To estimate the above-ground biomass (AGB), tree height was derived from SRTM DEM, and an allometric equation was used to calculate AGB. The results indicate a continuous shrinkage of the mangrove forest. The 2017 maps of the two different indexes reveal significant mangrove vegetation loss compared to 1996 and 2007. Additionally, the study estimates the total above-ground biomass of the Sundarbans mangrove forest to be 329 million tons. The findings can assist relevant authorities in taking necessary action, formulating policies, and implementing plans for sustainable management of the Sundarbans mangrove forest resources.

Modeling and Mapping of Aboveground Biomass and Carbon Stock Using Sentinel-2 Imagery in Chure Region, Nepal

The concerns about climate change in recent decades have heightened the need for effective methods for assessing and reporting forest biomass and Carbon Stocks (CS) at local, national, continental, and global scales. Accurate assessment of Aboveground Biomass (AGB) is critical for the sustainable management of forests, especially in the Chure region, a fragile and young mountainous in the lesser Himalaya of Nepal. This paper presents the modeling and mapping approach and shows how medium-resolution Sentinel-2 multispectral instrument (MSI) data can be used instead of hyperspectral data in inaccessible areas of the Chure region. The data were collected and analyzed from 72 circular sample plots. 60% (43 random sample plots) were used to create the model, while the remaining 40% (29 plots) were used for model validation. This study involved calculating 12 different vegetation indices and correlating them with plot-level AGB. Five models, including linear, logarithmic, quadratic, power, and exponential, were created, but the best model was found to be the quadratic model using normalized difference vegetation indices (NDVIs) with an R2 value of 0.777 and a correlation coefficient of 0.881. The model’s AIC and BIC values were 313.60 and 320.65, respectively. The validity of the model was performed using observed and predicted AGB values, resulting in an r value of 0.9128, an R2 value of 0.8332, and an RMSE value of 10.7657 t·h−1. Finally, the developed regression equation was used to map AGB in the study area. The AGB per pixel ranges from 0 to 129.18 t·h−1, whereas the amount of CS ranges from 0 to 61.01 t·h−1. Among the different vegetation indices used in the study, NDVI was found to be more precise in estimating and mapping biomass and carbon stocks in this study. Therefore, the study recommends using the quadratic model of NDVI for accurate estimation of AGB and CS in the Chure region of Sainamaina municipality.

Improved Object-Based Mapping of Aboveground Biomass Using Geographic Stratification with GEDI Data and Multi-Sensor Imagery

Aboveground biomass (AGB) mapping using spaceborne LiDAR data and multi-sensor images is essential for efficient carbon monitoring and climate change mitigation actions in heterogeneous forests. The optimal predictors of remote sensing-based AGB vary greatly with geographic stratification, such as topography and forest type, while the way in which geographic stratification influences the contributions of predictor variables in object-based AGB mapping is insufficiently studied. To address the improvement of mapping forest AGB by geographic stratification in heterogeneous forests, satellite multisensory data from global ecosystem dynamics investigation (GEDI) and series of advanced land observing satellite (ALOS) and Sentinel were integrated. Multi-sensor predictors for the AGB modeling of different types of forests were selected using a correlation analysis of variables calculated from topographically stratified objects. Random forests models were built with GEDI-based AGB and geographically stratified predictors to acquire wall-to-wall biomass values. It was illustrated that the mapped biomass had a similar distribution and was approximate to the sampled forest AGB. Through an accuracy comparison using independent validation samples, it was determined that the geographic stratification approach improved the accuracy by 34.79% compared to the unstratified process. Stratification of forest type further increased the mapped AGB accuracy compared to that of topography. Topographical stratification greatly influenced the predictors’ contributions to AGB mapping in mixed broadleaf–conifer and broad-leaved forests, but only slightly impacted coniferous forests. Optical variables were predominant for deciduous forests, while for evergreen forests, SAR indices outweighed the other predictors. As a pioneering estimation of forest AGB with geographic stratification using satellite multisensory data, this study offers optimal predictors and an advanced method for obtaining carbon maps in heterogeneous regional landscapes.

Characterizing aboveground biomass and tree cover of regrowing forests in Brazil using multi‐source remote sensing data

AbstractCharacterization of regrowing forests is vital for understanding forest dynamics to assess the impacts on carbon stocks and to support sustainable forest management. Although remote sensing is a key tool for understanding and monitoring forest dynamics, the use of exclusively remotely sensed data to explore the effects of different variables on regrowing forests across all biomes in Brazil has rarely been investigated. Here, we analyzed how environmental and human factors affect regrowing forests. Based on Brazil's secondary forest age map, 3060 locations disturbed between 1984 and 2018 were sampled, interpreted and analyzed in different biomes. We interpreted the time since disturbance for the sampled pixels in Google Earth Engine. Elevation, slope, climatic water deficit (CWD), the total Nitrogen of soil, cation exchange capacity (CEC) of soil, surrounding tree cover, distance to roads, distance to settlements and fire frequency were analyzed in their importance for predicting aboveground biomass (AGB) and tree cover derived from global forest aboveground biomass map and tree cover map, respectively. Results show that time since disturbance interpreted from satellite time series is the most important predictor for characterizing AGB and tree cover of regrowing forests. AGB increased with increasing time since disturbance, surrounding tree cover, soil total N, slope, distance to roads, distance to settlements and decreased with larger fire frequency, CWD and CEC of soil. Tree cover increased with larger time since disturbance, soil total N, surrounding tree cover, distance to roads, distance to settlements, slope and decreased with increasing elevation and CWD. These results emphasize the importance of remotely sensing products as key opportunities to improve the characterization of forest regrowth and to reduce data gaps and uncertainties related to forest carbon sink estimation. Our results provide a better understanding of regional forest dynamics, toward developing and assessing effective forest‐related restoration and climatic mitigation strategies.

Mapping high-resolution forest aboveground biomass of China using multisource remote sensing data

ABSTRACT Forest aboveground biomass (AGB) estimation is crucial for carbon cycle studies and climate change mitigation actions. However, because of limitations in timely and reliable forestry surveys and high-resolution remote sensing data, producing a fine resolution and spatial continuous forest AGB map of China is challenging. Here, we combined 4789 ground-truth AGB measurements and multisource remote sensing data such as a recently released forest canopy-height product, optical spectral indexes, topographic data, climatological data, and soil properties to train a random forest regression model for forest AGB estimation of China at 30-m resolution. The accuracy of the estimated AGB can yield R2 = 0.67 and RMSE = 70.71 Mg/ha. The nationwide estimates show that the average forest AGB and total forest carbon storage were 97.57 ± 23.85 Mg/ha and 11.06 Pg C for the year 2019, respectively. The value of AGB uncertainty ranges from 0.68 Mg/ha to 37.80 Mg/ha, and the average AGB uncertainty was 4.32 ± 1.75 Mg/ha. The forest AGB estimates of China in this study correspond reasonably well with the AGB estimates derived from the forestry and grassland statistical yearbook at the provincial level (R2 = 0.61, RMSE = 30.15 Mg/ha). In addition, we found that previous AGB products generally underestimate the forest AGB compared with our estimated AGB at the pixel-level and ground-truth AGB measurements. The high-resolution forest AGB map provides an important alternative data source for forest carbon cycle studies and can be used as a baseline map for forest management and conservation practices.

Mapping tropical forest aboveground biomass using airborne SAR tomography

Mapping tropical forest aboveground biomass (AGB) is important for quantifying emissions from land use change and evaluating climate mitigation strategies but remains a challenging problem for remote sensing observations. Here, we evaluate the capability of mapping AGB across a dense tropical forest using tomographic Synthetic Aperture Radar (TomoSAR) measurements at P-band frequency that will be available from the European Space Agency’s BIOMASS mission in 2024. To retrieve AGB, we compare three different TomoSAR reconstruction algorithms, back-projection (BP), Capon, and MUltiple SIgnal Classification (MUSIC), and validate AGB estimation from models using TomoSAR variables: backscattered power at 30 m height, forest height (FH), backscatter power metric (Q), and their combination. TropiSAR airborne campaign data in French Guiana, inventory plots, and airborne LiDAR measurements are used as reference data to develop models and calculate the AGB estimation uncertainty. We used univariate and multivariate regression models to estimate AGB at 4-ha grid cells, the nominal resolution of the BIOMASS mission. Our results show that the BP-based variables produced better AGB estimates compared to their counterparts, suggesting a more straightforward TomoSAR processing for the mission. The tomographic FH and AGB estimation have an average relative uncertainty of less than 10% with negligible systematic error across the entire biomass range (~ 200–500 Mg ha−1). We show that the backscattered power at 30 m height at HV polarization is the best single measurement to estimate AGB with significantly better accuracy than the LiDAR height metrics, and combining it with FH improved the accuracy of AGB estimation to less than 7% of the mean. Our study implies that using multiple information from P-band TomoSAR data from the BIOMASS mission provides a new capability to map tropical forest biomass and its changes accurately.

Improving the accuracy of models to map alpine grassland above‐ground biomass using Google earth engine

AbstractAccurate modelling and mapping of alpine grassland aboveground biomass (AGB) are crucial for pastoral agriculture planning and management on the Qinghai Tibet Plateau (QTP). This study assessed the effectiveness of four popular models (traditional multiple linear regression (MLR), support vector machine (SVM), artificial neural network (ANN), and deep neural network (DNN)) with various input combinations (geospatial variables [GV], vegetation types [VT], field measurements [FM], meteorological variables [MV] and observation time [OT]) for AGB estimation based on a new framework for AGB modelling and mapping using Google Earth Engine. The results showed that the input feature of GV had a poor performance in AGB estimation (0.121 < R2 < 0.591). FM improved the accuracy the most when incorporated with GV (0.815 < R2 < 0.833). Although MV, VT and OT improved the accuracy (R2) only by 0.112–0.216 with an importance rank order of MV > VT > OT for machine learning models, their outputs could be used to map AGB. Grass AGB was less accurately predicted than shrub AGB, but the pooling of both VTs improved estimation accuracy (R2) by 0.171–0.269. The performance of the models followed the ranked order of DNN > ANN > SVM > MLR. DNN had the highest accuracy (R2 = 0.818) using all non‐field measured variables (excluding FM) as the inputs, and it was successfully applied to a new dataset (not associated with the data used in the training and testing) with aR2of 0.676. This study presents an effective and operational framework for modelling and mapping grassland AGB. Accordingly, it provides the scientific foundations to determine of sustainable grazing carrying capacity in alpine grasslands.

Modelling Above-ground Biomass Using Machine Learning Algorithm: Case Study Miombo Woodlands of Malawi

Malawi’s Miombo Woodlands provide critical wood products and ecosystem services, including carbon storage through above ground-biomass (AGB). However, accurate and efficient estimation of AGB and C remains a huge challenge due to the complexity of heterogeneous natural and anthropogenic environmental factors exacerbated by choice of data collection and analysis techniques. A study was conducted to explore the potential of Machine Learning (ML) algorithmic on modelling AGB in Malawi’s Miombo Woodlands using remotely sensed data. A combination of AGB field measurements and Sentinel 2 Multi-Spectral Instrument (S2 MSI) imagery, and environmental data were employed to train and evaluate the performance of Random Forest (RF) regression ML model, thus against the backdrop of traditional simple linear and multiple linear regression models used in the past. The randomForest package in Rstudio R-3.6.1 software was used to model the S2 median seasonal composite band imagery datasets acquired in 2019 over Ntchisi Forest. Results demonstrate an outstanding performance of the RF with Mean Decrease in Accuracy (MDA) importance of >80 acquired from the Green, Blue and Near-Infra-Red (NIR) bands of the post-rainy season imagery while the dry season scene registered a MDA importance of >52 obtained from the Red, Blue and NIR bands. These findings underscore the superiority of the RF, revealing its ability to model AGB using different spectral bands, and more importantly, in different seasons. The outcomes have also revealed the better accuracy that the RF render in generating AGB maps (providing a minimum range of 25-33 tCha-1, which is in line with the ground reference estimates). Overall, the study has shown that RF Regression is a better technique to estimate AGB in Miombo Woodlands using the S2 MSI imagery under the given conditions. In conclusion, the research has further shown that the optical band combination of NIR and the Red regions of the Electro-Magnetic Spectrum are indispensable variables for successful modelling of AGB as well as delineating forest cover from non-forest attributes in the Miombo Woodlands. However, the study recommends exploring modelling the AGB using averaged vegetation indices i.e., NDVI, SAVI, and MSAVI, for robust results.

Spatial Downscaling of Forest Above-Ground Biomass Distribution Patterns Based on Landsat 8 OLI Images and a Multiscale Geographically Weighted Regression Algorithm

Forest above-ground biomass (AGB) is an excellent indicator for the health status and carbon sink potential of forest ecosystems, as well as the effectiveness of sustainable forest management practices. However, due to the strong heterogeneity of forest structures, acquiring high-accuracy and high-resolution AGB distributions over wide regions is often prohibitively expensive. To fill the resulting gap, this paper uses part of Lishui city, Zhejiang province as the study area, based on 168 forest sample observations, and proposes a novel integrated framework that combines a multi-scale geographically weighted regression (MGWR) with the co-kriging algorithm to refine the spatial downscaling of AGB. Specifically, optimal predictor variable sets identified by random forest importance ranking, multiple stepwise regression, and Pearson VIF methods were first assessed based on their total explanatory power (R square), followed by reconfirmation of the optimal predictor variable set based on the non-stationarity impact of each variable’s action scale (bandwidth) on the output pattern of AGB downscaling. The AGB downscaling statistical algorithms included MGWR, GWR, random forest (RF), and the ordinary least square (OLS), and their downscaling performances were quantitatively compared to determine the best downscaling method. Ultimately, the downscaled AGB pattern was produced using the best method, which was further refined by considering the spatial autocorrelation in AGB samples by implementing a co-kriging interpolation analysis of the predicted AGB downscaling residuals. The results indicated that the variable set selected by random forest importance ranking had the strongest explanatory power, with a validation R square of 0.58. This was further confirmed by the MGWR analysis which showed that the set of variables produced a more spatially smooth downscaled AGB pattern. Among the set of optimal variables, elevation and aspect affected AGB at local scales, representing a strong spatial heterogeneity. Some textural features and spectral features showed a smooth action scale relative to AGB, showing insignificant spatial scale processes. In the study area with complex terrain, using aspect as a covariant, the co-kriging (CK) model achieved a higher simulation accuracy for the MGWR-predicted AGB residuals than the ordinary kriging model. Overall, the proposed MGWR-CK model, with a final validation R square value of 0.62, effectively improved the spatial distribution characteristics and textural details of AGB mapping without the additional costs of procuring finer satellite images and GIS-based features. This will contribute to the accurate assessment of carbon sinks and carbon stock changes in subtropical forest ecosystems globally.

Gaussian process regression-based forest above ground biomass retrieval from simulated L-band NISAR data

Accurate mapping of forest above ground biomass (AGB) is essential for understanding changes in the rate of ecosystem processes such as biomass productivity, litter productivity, actual litter decomposition, and potential litter decomposition during secondary succession. They also play a vital role in evaluating forest carbon pools. This study presents a Matérn kernel-based Gaussian process regression (GPR) approach for biomass estimation integrating Synthetic Aperture Radar (SAR) backscatter intensities with LiDAR measurements. We use the backscatter intensities from five scenes of L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) simulated NISAR data collected during the 2019 AM–PM NASA airborne campaign. The biomass map derived from LVIS (Land, Vegetation, and Ice Sensor) Lidar point clouds over the Lenoir landing site, Alabama, was used with these simulated NISAR data. We have utilized the GPR model to estimate the AGB of the entire forest alongside the major forest classes in the study area, namely deciduous, evergreen, and woody wetlands. To examine the dependency of the model on acquisition conditions, we calibrated and validated our proposed model utilizing scene combinations. The experiment shows that multiple-scene retrieval delivered improved AGB estimates compared to single-scene retrieval. We evaluated the efficacy of the GPR model for three AGB ranges, i.e., (i) 8to100Mgha−1, (ii) 8to230Mgha−1, and (iii) 8to470Mgha−1 at 20m, 30m, 50m and 100m spatial resolution, respectively. The results indicate that the RMSE incurred by the GPR model for all these AGB ranges seemed to reduce as we increased spatial extent or reduced spatial resolution from 20m to 100m. Further, we demonstrate the performance of the optimized GPR model to retrieve AGB within the biomass range of 8Mgha−1 to 100Mgha−1 for all the above-mentioned forest types. Finally, this research highlights the advantages of the Matérn kernel-based GPR model over other statistical regression models towards improved AGB mapping.

Mapping individual tree and plot-level biomass using airborne and mobile lidar in piñon-juniper woodlands

Piñon-juniper (PJ) woodlands are a widespread dryland ecosystem in the US containing an immense but poorly-constrained amount of aboveground biomass (AGB). Found at the dry end of the climatic range within which trees can persist, PJ faces an uncertain future in a changing climate, giving unique importance to mapping its AGB, past, present, and future. Lidar remote sensing offers great potential towards that end with research in a range of tree-dominant ecosystems demonstrating a strong capacity for mapping AGB. However, studies applying lidar to the task of mapping AGB in PJ are few. Given the unique structural characteristics of PJ trees, which tend to be short in stature (<10 m), possess multiple stems often obscured by low crown base heights, and irregularly-shaped canopies, there remains a high degree of uncertainty surrounding tree- and stand-level structural mapping in these woodlands. To resolve our limited understanding of how to best quantify AGB in PJ using lidar, we conducted a field-validated analysis comparing lidar platform (airborne laser scanner, ALS vs. mobile laser scanner, MLS) and analytical framework (area-based modeling vs. individual tree-based modeling). We used a random forest machine learning approach to predicting tree- and stand-level structure with leave-one-out cross-validation to assess model performance. In an area-based context, MLS only marginally outperformed ALS, with both able to explain approximately 80 % of variance in AGB. However, in an individual tree-based context, MLS significantly outperforms ALS at several tasks, including: (1) delineating tree crowns (MLS delineated 100 % of trees; ALS delineated 69 %); (2) directly characterizing tree structural parameters including height (R2ALS = 0.52 vs. R2MLS = 0.74) and crown area (R2ALS = 0.26 vs. R2MLS = 0.68); and (3) modeling AGB (R2ALS = 0.38 vs. R2MLS = 0.62). We attribute this difference in performance to the vastly greater structural detail contained within the MLS point cloud data as compared to ALS, with the former possessing an average of approximately 30,000 pts/m2 and the latter 14 pts/m2. Based on our results, we make the following recommendation: if individual tree structure is needed, MLS is the far superior option; however, given the wider availability, ALS data are a suitable substitute when mapping PJ AGB, particularly under an area-based modeling analytical framework. This study represents the first time MLS data have been applied to tree structure mapping in PJ, and provides strong quantitative evidence towards best structural and AGB mapping practices in this unique, widespread, and important ecosystem. It serves as a solid foundation upon which future studies aiming to map AGB in PJ and other similar dryland woodland ecosystems can be built.

Continuous mapping of aboveground biomass using Landsat time series

Aboveground biomass (AGB) is an essential variable in the study of the carbon cycle, and remote sensing provides the only viable means for mapping and monitoring biomass at large scales in time and space. Still, inconsistencies in biomass estimates are large which necessitates methods to achieve consistent mapping of biomass over time and space. In this study, we combine Lidar-derived biomass-estimates from GLAS measurements, time series analysis of 20 years of Landsat data, and machine learning algorithms to map biomass for a study area in the Amazon basin. The results show that we are able to map AGB consistently over space and time at annual intervals between 1999 and 2019, with a root-mean-square error (RMSE) of 64 to 92 Mg AGB ha−1 (depending on the evaluation methodology used) for the best performing models, with AGB estimates ranging between 0 to 600 Mg AGB ha−1. Models run with the Extreme gradient boosting (XGBoost) resulted in the lowest errors. The presented methodological approach makes it possible to map biomass over time in large areas with wide biomass ranges, and without saturation below 400 Mg AGB ha−1.

ABOVEGROUND BIOMASS AND CARBON STOCK ESTIMATION OF FALCATA THROUGH THE SYNERGISTIC USE OF SENTINEL-1 AND SENTINEL-2 IMAGES

Abstract. Estimates of aboveground biomass (AGB) in forests have been made in the context of climate change mitigation. There were limited studies about Falcata aboveground biomass and carbon stock estimation using the traditional method; however, that is time-intensive and expensive. Hence, this study was executed to utilize remote sensing and assess the potential of the synergistic use of Sentinel-1 and Sentinel-2 images in estimating the AGB and carbon stock of Falcata. The methodology consists of boundary demarcation of Falcata plantations in Butuan City, development of aboveground biomass models, and estimation and mapping of aboveground biomass and carbon stock. Among the developed models, the model which is the combination of Sentinel-1 and Sentinel-2 has the highest coefficient of determination (R2) of 0.632 and lowest Root Mean Square Error (RMSE) of 1.94 ton/pixel and was found to perform best in predicting the AGB and carbon stock of 4-year-old Falcata and performed poorly in 1-year-old Falcata. Nevertheless, its overall R2 and RMSE have proven that the model is moderately good and acceptable in predicting AGB of all stand ages of Falcata and, indirectly, the carbon stock. This study demonstrates that combining satellites generates a robust and more accurate AGB and Carbon Stock model than the models derived from the individual satellites.

The inversion modeling and aboveground biomass mapping of withered grass changes in the western grassland of Northeast China

The aboveground biomass (AGB) of withered grass is an important early-warning indicator for grassland fire risk. Most grassland fires occur during the dry-grass season. In order to improve the fire-warning efficiency of withered AGB, it is essential to rapidly acquire the amount of withered-grass biomass. Remote-sensing data has been widely used in monitoring and estimating grassland yields during the growing season. However, applying remote sensing to the estimation of withered grass is still in need of exploration. The aim of this work was to try to establish a remote-sensing estimation model for withered AGB in the dry-grass season. The estimation of aboveground biomass can effectively prevent the occurrence of fire, protect the environment, facilitate local management and reduce economic losses. Our approach was to, first, calculate a dry-grass index based on Sentinel-2 image data and using ENVI, SNAP, and ArcGIS software. Second, a model to estimate the fuel quantity during the dry-grass season was established by regression analysis combined with field-measured data. Finally, the estimation model was used to predict the amount of fuel in different months of the dry-grass season, followed by the fire-defense elements, which were quantified and mapped in the Longzhao Marsh wetlands. It was found that: 1) the two indices were significantly correlated (0.678) with the amount of fuel; 2) the established model could accurately estimate the amount of fuel in the study area during the dry season, and accurate test results demonstrated that the correlation between the estimated results of the best model and the measured values was 0.863, indicating high accuracy; 3) the spatiotemporal variation of withered grass in the study area was obviously different, and the quantities of fuel predicted for the other months were more accurate, which may reflect monthly dynamic changes in actual fuel quantities; and 4) the establishment of a remote-sensing estimation model for fuel quantity in the Longzhao Marsh during the dry-grass season could provide important parameters for fire-risk warning in the western grassland of Jilin Province and Northeast China.

Integrating Sentinel-1 and 2 with LiDAR data to estimate aboveground biomass of subtropical forests in northeast Guangdong, China

ABSTRACT Accurate monitoring of forest aboveground biomass (AGB) is vital for sustainable forest management. Generally, the AGB is estimated by combining satellite images and field measurements. Nevertheless, field plots are inaccessible in complex forest areas. Due to the limitations related to either optical or SAR data alone, combining the two data types is indispensable. Here, we explored the LiDAR-derived biomass as training/testing samples for the combination of yearly time-series of Sentinel-1 and Sentinel-2 images to estimate AGB in north-eastern Conghua, Guangdong province, China. The AGB reference map derived from airborne LiDAR data and field plots could provide more samples for satellite-based AGB estimation with a limited amount of sampling plots. We designed four groups of experiments based on diverse Sentinel-1 and Sentinel-2 variables, including backscatter, backscatter indices, texture features, spectral bands, vegetation indices, and biophysical features. Four different prediction methods (support vector regression, multi-layer perceptron neural network, K-nearest neighbour, and random forest (RF)) were separately used to estimate AGB. Results showed that the RF model achieved the best accuracy for AGB mapping. All Sentinel-1 and Sentinel-2 experiment (R2: 0.72, RMSEr: 17.65%) performed better than the monthly complementary experiment (R2: 0.66, RMSEr: 19.01%). Additionally, the arid period images were observed to be sensitive for estimating AGB. The most contributing Sentinels predictors were determined via a sequential forward selection. Consequently, the proposed methodology has great potential for low-cost, large-scale, and high-precision AGB estimation.