Aboveground Biomass Estimation Models Research Articles

Uncertainty Analysis of Remote Sensing Estimation of Chinese Fir (Cunninghamia lanceolata) Aboveground Biomass in Southern China

Forest aboveground biomass (AGB) is not only the basis for forest carbon stock research, but also an important parameter for assessing the forest carbon cycle and ecological functions of forests. However, there are various uncertainties in the estimation process, limiting the accuracy of AGB estimation. Therefore, we extracted the spectral features, vegetation indices and texture factors from remote sensing images based on the field data and Landsat 8 OLI remote sensing images in Southern China to quantify the uncertainties. Then, we established three AGB estimation models, including K Nearest Neighbor Regression (KNN), Gradient Boosted Regression Tree (GBRT) and Random Forest (RF). Uncertainties at the plot scale and models were measured by using error equations to analyze the influences of uncertainties at different scales on AGB estimation. Results were as follows: (1) The R2 of the per-tree biomass model for Cunninghamia lanceolata was 0.970, while the uncertainty of the residual and parameters for per-tree biomass model was 4.62% and 4.81%, respectively; and the uncertainty transferred to the plot scale was 3.23%. (2) The estimation methods had the most significant effects on the remote sensing models. RF was more accurate than other two methods, and had the highest accuracy (R2 = 0.867, RMSE = 19.325 t/ha) and lowest uncertainty (5.93%), which outperformed both the KNN and GBRT models (KNN: R2 = 0.368, RMSE = 42.314 t/ha, uncertainty = 14.88%; GBRT: R2 = 0.636, RMSE = 32.056 t/ha, uncertainty = 6.3%). Compared to KNN and GBRT, the R2 of RF was enhanced by 0.499 and 0.231, while the uncertainty was decreased by 8.95% and 0.37%, respectively. The uncertainty associated with the scale of remote sensing models remains the primary source of uncertainty when compared to the plot scale. On the remote sensing scale, RF is the model with the best estimation effect. This study examines the impact of both plot-scale and remote sensing model-scale methodologies on the estimation of AGB for Cunninghamia lanceolata. The findings aim to offer valuable insights and considerations for enhancing the accuracy of AGB estimations.

Crop canopy volume weighted by color parameters from UAV-based RGB imagery to estimate above-ground biomass of potatoes

Current techniques to estimate crop aboveground biomass (AGB) across the multiple growth stages mainly used optical remote-sensing techniques. However, this technology was limited by saturation of the canopy spectrum. To meet this problem, this study used digital images obtained by an unmanned aerial vehicle to extract the spectral and structural indicators of the crop canopy in three key potato growth stages. We took the color parameters (CP) of assorted color space transformations as the canopy spectral information, and crop height (CH), crop coverage (CC), and crop canopy volume (CCV) as the canopy structural indicators. Based on the complementary advantages of CP and CCV, we proposed a new metric: the color parameter-weighted crop-canopy volume (CCVCP). Results showed that the CH, CCV, and CCVCP correlated more strongly with potato AGB during the multi-growth stages than do CP and CC. The hue-weighted crop-canopy volume (CCVH) correlated most strongly with the potato AGB among all structural indicators. Using CH was more accurate in estimating potato AGB compared to CP and CC. Combining indicators (CP + CC/CH, CP + CC + CH) improved the accuracy of potato AGB estimation over the multi-growth stages. Except for the CP + CC + CH model, other AGB estimation models produced inaccurate AGB estimation than the models based on CCV and CCVH. The AGB estimation accuracy produced by the univariate-based CCVH model (R2 = 0.65, RMSE = 281 kg/hm2, and NRMSE = 23.61 %) was comparable to that of the complex model [CP + CC + CH using random forest (RF) or multiple stepwise regression (MSR)]. Compared with CP + CC + CH using RF and MSR, the RMSE decreased and increased by 0.35 % and 4.24 %, respectively. Compared with CP, CP + CC, CP + CH, and CCV, the use of CCVH to estimate AGB decreased the RMSE by 10.24 %, 7.42 %, 6.36 %, and 6.33 %, respectively. Meanwhile, the performance of CCVH was verified at different stages and among varieties. Thus, this indicator can be used for monitoring potato growth to help guide field production management.

Evaluating forest aboveground biomass estimation model using simulated ALS point cloud from an individual-based forest model and 3D radiative transfer model across continents

Area-based approach (ABA) has been widely employed for estimating forest aboveground biomass (AGB) using airborne laser scanning (ALS) data. However, its scalability is limited due to challenges in model generalization across different forest types and regions. The selection of sensitive variables from ALS data is crucial for constructing robust forest AGB estimation models, yet this selection varies significantly among forest types and regions. Traditionally, assessing the influence of variable selection is hindered by the lack of accurate reference forest AGB values. Computer simulation-based method provides a perspective for exploring these challenges. This study employs an individual-based forest growth process model, FORMIND, coupled with a 3D radiative transfer model (RTM), LESS, to evaluate the transferability of ABA-based forest AGB estimation models and the generalization of ALS-derived variables. We used six virtual 3D forest scenes and two real-world forest sites, representing a range of global forest types, along with their simulated ALS data, to develop a forest AGB estimation model using the random forest algorithm, which allowed us to analyze the importance of various variables. We assessed model transferability through cross-comparison. Additionally, we validated the model using field plots and ALS data collected from two distinct regions. The results showed that the canopy surface area and volume extracted using the α-shape algorithm and parameters fitted from the Weibull distribution are vital variables when using ALS for forest AGB estimation across forest types and regions. Incorporating these variables into the model significantly improves the accuracy of forest AGB estimation. The optimized model achieved a R2 of 0.945, a RMSE of 34.22 t/ha, and a MAE of 20.53 t/ha. Our study not only deepens the understanding of the relationship between forest vertical structural metrics and AGB but also highlights the potential of computer simulation as a tool for refining the estimation of forest structural parameters.

Above ground biomass estimation in the upper Blue Nile basin forests, North-Western Ethiopia

Forest ecosystems play a decisive role in the global climatic condition, as well as, provides a wide range of societal benefits, including fuel-wood, tourism, and ecosystem services are considered as one of the major sources of livelihood for the local people in the upper Blue Nile Basin. Therefore, rapid and accurate estimation of forest biomass is crucial for greatly reducing the uncertainty in carbon stock assessments, and for designing strategic forest management plans. Because, above-ground biomass (AGB) estimation is important in determining the management, environmental, and economic roles of forests in the Blue Nile basin. The study was aimed at estimating above-ground biomass in the Upper Blue Nile Basin forests by integrating field-measured data with predictors from Sentinel-2 image. The relationship between measured AGB and sentinel-2 derived vegetation indices and biophysical parameters showed a good correlation result (r value ranging from 0.67 to 0.74). A stepwise regression analysis was carried out in order to develop AGB estimation model by identifying the most important variable. The result demonstrated that, green normalized difference vegetation index, leaf area index, fraction of absorbed photosynthetic active radiation and fractional vegetation cover achieved good performance in predicting AGB with R2 value > 0.5. AGB was estimated with a coefficient of determination (R2) of 0.59 adjusted R2 of 0.618 and root mean square error of (RMSE) 38.36 t/ha in comparison to field observations. The maximum AGB value of 268.32 t/ha was estimated in the Alemsaga natural forest, which is a highly protected dense forest stand from any entrance and disturbance. Generally, integrating field data with optical remote sensing data provides more reliable result for AGB estimation. Moreover, it is also recommended to employ RADAR and LiDAR remote sensing data products together in order to attain more precise estimate results of AGB with great potential for forest resource monitoring and management.

Estimation of the Aboveground Biomass of Forests in Complex Mountainous Areas Using Regression Kriging

The forest area in China’s plateaus and mountainous regions accounts for as much as 43% of the country’s total forest area. Accurately estimating the aboveground biomass (AGB) in these plateau and mountain forests is significant for global carbon sink assessment and climate change. However, the complexity of the natural environment poses significant challenges to the accurate estimation of forests’ aboveground biomass (AGB), and the accuracy of both AGB estimation and spatial mapping needs further improvement. This study utilized support vector regression, backpropagation neural networks, and random forests to predict trends in AGB and establish an optimal original model for forest AGB estimation. Further calibration was performed using regression kriging on the optimal model. The results indicated that (1) random forests achieved the highest coefficient of determination (R2 for cypress = 0.63, R2 for fir = 0.66, R2 for cryptomeria = 0.64, and R2 for mixed forest = 0.54), showing greater potential in predicting AGB in complex mountainous mixed forests; (2) the residual kriging method significantly improved the estimation accuracy, increasing the R2 values of the original RF model by 25%, 24%, and 22%, and improving the accuracy of mixed plot estimates from 54% to 81%; and (3) the residual kriging method effectively addressed the underestimation of high values and overestimation of low values in AGB estimates, broadening the range of AGB values and allowing for a more detailed spatial distribution of forests’ aboveground biomass.

Estimation of Coastal Wetland Vegetation Aboveground Biomass by Integrating UAV and Satellite Remote Sensing Data

Aboveground biomass (AGB) serves as a crucial indicator of the carbon sequestration capacity of coastal wetland ecosystems. Conducting extensive field surveys in coastal wetlands is both time-consuming and labor-intensive. Unmanned aerial vehicles (UAVs) and satellite remote sensing have been widely utilized to estimate regional AGB. However, the mixed pixel effects in satellite remote sensing hinder the precise estimation of AGB, while high-spatial resolution UAVs face challenges in estimating large-scale AGB. To fill this gap, this study proposed an integrated approach for estimating AGB using field sampling, a UAV, and Sentinel-2 satellite data. Firstly, based on multispectral data from the UAV, vegetation indices were computed and matched with field sampling data to develop the Field–UAV AGB estimation model, yielding AGB results at the UAV scale (1 m). Subsequently, these results were upscaled to the Sentinel-2 satellite scale (10 m). Vegetation indices from Sentinel-2 data were calculated and matched to establish the UAV–Satellite AGB model, enabling the estimation of AGB over large regional areas. Our findings revealed the AGB estimation model achieved an R2 value of 0.58 at the UAV scale and 0.74 at the satellite scale, significantly outperforming direct modeling from field data to satellite (R2 = −0.04). The AGB densities of the wetlands in Xieqian Bay, Meishan Bay, and Hangzhou Bay, Zhejiang Province, were 1440.27 g/m2, 1508.65 g/m2, and 1545.11 g/m2, respectively. The total AGB quantities were estimated to be 30,526.08 t, 34,219.97 t, and 296,382.91 t, respectively. This study underscores the potential of integrating UAV and satellite remote sensing for accurately assessing AGB in large coastal wetland regions, providing valuable support for the conservation and management of coastal wetland ecosystems.

Assessment of anthropogenic disturbances on mangrove aboveground biomass in Malaysian Borneo using airborne LiDAR data

Mangroves are known for their carbon storage capacity, yet they are under immense pressure from human activities. This study assessed anthropogenic disturbances on mangroves’ aboveground biomass (AGB) in northern Borneo, Malaysia, using airborne light detection and ranging (LiDAR) data. Three global or pantropical allometries were compared in the development of an AGB estimation model by regressing LiDAR metrics against the AGB. The best model predicted AGB from Saenger and Snedaker allometry with an R2 of 0.85 and a root mean square error (RMSE) of 14.59 Mg/ha (relative RMSE: 7.24 %). The high-resolution AGB map revealed a natural AGB gradient in intact mangroves from the coast to the interior. However, only a weak correlation between the distance from shoreline and AGB in disturbed mangroves was found. The LiDAR estimated AGBs were 196.36 Mg/ha and 157.27 Mg/ha for intact mangroves and disturbed mangroves, respectively. Relatively high AGB areas were abundant in the intact mangroves but scarce in the disturbed mangroves. The LiDAR-based AGB assessment is accurate and high-resolution, supporting carbon stock conservation and sustainable management activities under climate change mitigation programs such as REDD + .

Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages.

Unmanned aerial vehicles (UAVs) equipped with visible and multispectral cameras provide reliable and efficient methods for remote crop monitoring and above-ground biomass (AGB) estimation in rice fields. However, existing research predominantly focuses on AGB estimation based on canopy spectral features or by incorporating plant height (PH) as a parameter. Insufficient consideration has been given to the spatial structure and the phenological stages of rice in these studies. In this study, a novel method was introduced by fully considering the three-dimensional growth dynamics of rice, integrating both horizontal (canopy cover, CC) and vertical (PH) aspects of canopy development, and accounting for the growing days of rice. To investigate the synergistic effects of combining spectral, spatial and temporal parameters, both small-scale plot experiments and large-scale field testing were conducted in Jiangsu Province, China from 2021 to 2022. Twenty vegetation indices (VIs) were used as spectral features, PH and CC as spatial parameters, and days after transplanting (DAT) as a temporal parameter. AGB estimation models were built with five regression methods (MSR, ENet, PLSR, RF and SVR), using the derived data from six feature combinations (VIs, PH+CC, PH+CC+DAT, VIs+PH +CC, VIs+DAT, VIs+PH+CC+DAT). The results showed a strong correlation between extracted and ground-measured PH (R2 = 0.89, RMSE=5.08 cm). Furthermore, VIs, PH and CC exhibit strong correlations with AGB during the mid-tillering to flowering stages. The optimal AGB estimation results during the mid-tillering to flowering stages on plot data were from the PLSR model with VIs and DAT as inputs (R 2 = 0.88, RMSE=1111kg/ha, NRMSE=9.76%), and with VIs, PH, CC, and DAT all as inputs (R 2 = 0.88, RMSE=1131 kg/ha, NRMSE=9.94%). For the field sampling data, the ENet model combined with different feature inputs had the best estimation results (%error=0.6%-13.5%), demonstrating excellent practical applicability. Model evaluation and feature importance ranking demonstrated that augmenting VIs with temporal and spatial parameters significantly enhanced the AGB estimation accuracy. In summary, the fusion of spectral and spatio-temporal features enhanced the actual physical significance of the AGB estimation models and showed great potential for accurate rice AGB estimation during the main phenological stages.

Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm.

Aboveground biomass (AGB) is regarded as a critical variable in monitoring crop growth and yield. The use of hyperspectral remote sensing has emerged as a viable method for the rapid and precise monitoring of AGB. Due to the extensive dimensionality and volume of hyperspectral data, it is crucial to effectively reduce data dimensionality and select sensitive spectral features to enhance the accuracy of rice AGB estimation models. At present, derivative transform and feature selection algorithms have become important means to solve this problem. However, few studies have systematically evaluated the impact of derivative spectrum combined with feature selection algorithm on rice AGB estimation. To this end, at the Xiaogang Village (Chuzhou City, China) Experimental Base in 2020, this study used an ASD FieldSpec handheld 2 ground spectrometer (Analytical Spectroscopy Devices, Boulder, Colorado, USA) to obtain canopy spectral data at the critical growth stage (tillering, jointing, booting, heading, and maturity stages) of rice, and evaluated the performance of the recursive feature elimination (RFE) and Boruta feature selection algorithm through partial least squares regression (PLSR), principal component regression (PCR), support vector machine (SVM) and ridge regression (RR). Moreover, we analyzed the importance of the optimal derivative spectrum. The findings indicate that (1) as the growth stage progresses, the correlation between rice canopy spectrum and AGB shows a trend from high to low, among which the first derivative spectrum (FD) has the strongest correlation with AGB. (2) The number of feature bands selected by the Boruta algorithm is 19~35, which has a good dimensionality reduction effect. (3) The combination of FD-Boruta-PCR (FB-PCR) demonstrated the best performance in estimating rice AGB, with an increase in R² of approximately 10% ~ 20% and a decrease in RMSE of approximately 0.08% ~ 14%. (4) The best estimation stage is the booting stage, with R2 values between 0.60 and 0.74 and RMSE values between 1288.23 and 1554.82 kg/hm2. This study confirms the accuracy of hyperspectral remote sensing in estimating vegetation biomass and further explores the theoretical foundation and future direction for monitoring rice growth dynamics.

Estimating aboveground biomass of urban trees based on ICESat-2 LiDAR and Zhuhai-1 hyperspectral data

Accurately and quickly estimating the aboveground biomass (AGB) of urban trees is crucial for assessing their ecological benefits and providing a scientific basis for city managers. The spaceborne LiDAR (ICESat-2) data offers vital vertical structure information of trees for precise AGB estimation. However, its discrete footprints limit spatially continuous AGB estimation. This study pioneers the integration of ICESat-2 and hyperspectral (Zhuhai-1) images for the estimation of AGB of trees in Shenzhen city, south of China. The study comprises three main steps. First, land cover classification based on GaoFen-6 images was conducted to extract the tree areas. Second, an AGB estimation model based on ICESat-2 photon features yields AGB values for all ICESat-2 footprints. Finally, a Random Forest (RF) regression model linking the AGB of footprints with narrowband vegetation indices from the Zhuhai-1 data was built to produce a wall-to-wall AGB map of Shenzhen. The results indicated that the 70th percentile of vegetation photon height was the optimal feature for estimating AGB within ICESat-2 footprints (R2: 0.62, RMSE: 4.45 kg/m2). For wall-to-wall AGB estimation based on RF regression, narrowband vegetation indices like SIPI, PSI, MNDVI, ARI2, CI2, and ACI2 were crucial, achieving an R2 of 0.55 and an RMSE of 3.88 kg/m2. This suggests that the combination of ICESat-2 and Zhuhai-1 data has a good ability to estimate the AGB of urban trees on a large scale and provides a new approach for forest resource studies in urban areas.

Improving potato AGB estimation to mitigate phenological stage impacts through depth features from hyperspectral data

The accurate estimation of above ground biomass (AGB) is valuable in grasping the status of potato growth and assessing yield. Spectral analysis techniques play an important role in AGB estimation by virtue of its non-destructive and rapid advantages. However, the models constructed based on spectral features at the entire growth stages due to differences in phenological stages, such as vegetation indices (VIs) and locations at fixed wavelengths, can be not applicable to single growth stages. Therefore, the main objective of this study is to improve the applicability of the AGB estimation model to mitigate the impacts of phenological stages by mining depth features from hyperspectral data. The canopy hyperspectral and AGB data of potato tuber formation, tuber growth and starch accumulation stages were measured in 2017–2019. The acquired canopy hyperspectral reflectance was smoothed by Savitzky-Golay (SG) filter, and the green edge (GE), the red edge (RE), red edge chlorophyll index (CIred-edge), optimized soil adjusted vegetation index (OSAVI), green normalized difference vegetation index (GNDVI), and normalized difference vegetation index (NDVI) were extracted. The spectral changes were analyzed at different growth stages of each year. To obtain features in response to AGB changes, a new approach is proposed to analyze the hidden depth features between sensitive wavelengths by combining a successive projections algorithm (SPA) and a long-short-term memory network (LSTM). The AGB estimation models were constructed by partial least squares regression (PLSR) based on traditional VIs + GE + RE, full spectrum, sensitive wavelengths obtained by SPA, and depth features obtained by SPA-LSTM. The results showed that GE, RE and VIs alone explained only 24 %-36 % of the variation in AGB. The accuracy of estimating AGB at sensitive wavelengths obtained after SPA was higher (R2 = 0.70, RMSE = 406 kg/hm2, and NRMSE = 27.14 %) than that of conventional VIs + GE + RE (R2 = 0.51, RMSE = 536 kg/hm2, and NRMSE = 35.81 %) and full spectrum (R2 = 0.63, RMSE = 452 kg/hm2, and NRMSE = 30.19 %) in the three-year dataset. The depth features extracted by the SPA-LSTM method proposed in this study achieved the best AGB estimation (R2 = 0.82, RMSE = 318 kg/hm2, and NRMSE = 21.25 %). The applicability of the model was validated in different years and growth stages. The R2 of each year was in the range of 0.48–0.77, the RMSE was in the range of 290–367 kg/hm2, and the NRMSE was in the range of 16.69 %-25.81 %. The R2 of each growth stage within three year was in the range of 0.48–0.78, the RMSE was in the range of 184–386 kg/hm2, and the NRMSE was in the range of 14.91 %-29.45 %. The method proposed in this study mitigates the impacts of phenological stage and improves the robustness and accuracy of the AGB estimation model, which provides remote sensing technical support for realizing potato growth monitoring and yield assessment in the field.

High-density forest AGB estimation in tropical forest integrated with PolInSAR multidimensional features and optimized machine learning algorithms

Accurately estimating the above-ground biomass (AGB) of high-density tropical rainforests is a challenging issue. In this study, airborne multi-baseline PolInSAR data were used to estimate the tropical rainforest AGB in Gabon, Africa. The most suitable baseline combination of the PolInSAR data was initially determined through baseline selection, and the PolInSAR parameters related to forest height were obtained based on the forest canopy height estimation theory and microwave penetration depth theory. The height parameter, baseline parameter, and observation geometry parameter were then used as independent variables to construct the AGB regression model. Support vector regression (SVR) was chosen as the AGB estimation model, and the global best particle swarm algorithm (GLB-PSO) was used to optimize the SVR model’s parameters. The results show that the RFECV variable selection method is superior to the Pearson method. The GLB-PSO algorithm can also further improve the saturation point of the SVR model—the estimation results show that the saturation point of AGB estimation of PolInSAR multidimensional features combined with the SVR machine learning algorithm is up to 500 Mg/ha, while this saturation point can be increased to 650 Mg/ha when using the GLB-PSO-SVR algorithm.

Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral Images

Aboveground biomass (AGB) is an important indicator for characterizing crop growth conditions. A rapid and accurate estimation of AGB is critical for guiding the management of farmland and achieving production potential, and it can also provide vital data for ensuring food security. In this study, by applying different water and nitrogen treatments, an unmanned aerial vehicle (UAV) equipped with a multispectral imaging spectrometer was used to acquire images of winter wheat during critical growth stages. Then, the plant height (Hdsm) extracted from the digital surface model (DSM) information was used to establish and improve the estimation model of AGB, using the backpropagation (BP) neural network, a machine learning method. The results show that (1) the R2, root-mean-square error (RMSE), and relative predictive deviation (RPD) of the AGB estimation model, constructed directly using the Hdsm, are 0.58, 4528.23 kg/hm2, and 1.25, respectively. The estimated mean AGB (16,198.27 kg/hm2) is slightly smaller than the measured mean AGB (16,960.23 kg/hm2). (2) The R2, RMSE, and RPD of the improved AGB estimation model, based on AGB/Hdsm, are 0.88, 2291.90 kg/hm2, and 2.75, respectively, and the estimated mean AGB (17,478.21 kg/hm2) is more similar to the measured mean AGB (17,222.59 kg/hm2). The improved AGB estimation model boosts the accuracy by 51.72% compared with the AGB directly estimated using the Hdsm. Moreover, the improved AGB estimation model shows strong transferability in regard to different water treatments and different year scenarios, but there are differences in the transferability for different N-level scenarios. (3) Differences in the characteristics of the data are the key factors that lead to the different transferability of the AGB estimation model. This study provides an antecedent in regard to model construction and transferability estimation of AGB for winter wheat. We confirm that, when different datasets have similar histogram characteristics, the model is applicable to new scenarios.

UAV-LiDAR Integration with Sentinel-2 Enhances Precision in AGB Estimation for Bamboo Forests

Moso bamboo forests, recognized as a distinctive and significant forest resource in subtropical China, contribute substantially to efficient carbon sequestration. The accurate assessment of the aboveground biomass (AGB) in Moso bamboo forests is crucial for evaluating their impact on the carbon balance within forest ecosystems at a regional scale. In this study, we focused on the Moso bamboo forest located in Shanchuan Township, Zhejiang Province, China. The primary objective was to utilize various data sources, namely UAV-LiDAR (UL), Sentinel-2 (ST), and a combination of UAV-LiDAR with Sentinel-2 (UL + ST). Employing the Boruta algorithm, we carefully selected characterization variables for analysis. Our investigation delved into establishing correlations between UAV-LiDAR characterization parameters, Sentinel-2 feature parameters, and the aboveground biomass (AGB) of the Moso bamboo forest. Ground survey data on Moso bamboo forest biomass served as the basis for our analysis. To enhance the accuracy of AGB estimation in the Moso bamboo forest, we employed three distinct modeling techniques: multivariate linear regression (MLR), support vector regression (SVR), and random forest (RF). Through this approach, we aimed to compare the impact of different data sources and modeling methods on the precision of AGB estimation in the studied bamboo forest. This study revealed that (1) the point cloud intensity of UL, the variables of canopy cover (CC), gap fraction (GF), and leaf area index (LAI) reflect the structure of Moso bamboo forests, and the variables indicating the height of the forest stand (AIH1, AIHiq, and Hiq) had a significant effect on the AGB of Moso bamboo forests, significantly impact Moso bamboo forest AGB. Vegetation indices such as DVI and SAVI in ST also exert a considerable effect on Moso bamboo forest AGB. (2) AGB estimation models constructed based on UL consistently demonstrated higher accuracy compared with ST, achieving R2 values exceeding 0.7. Regardless of the model used, UL consistently delivered superior accuracy in Moso bamboo forest AGB estimation, with RF achieving the highest precision at R2 = 0.88. (3) Integration of ST with UL substantially improved the accuracy of AGB estimation for Moso bamboo forests across all three models. Specifically, using RF, the accuracy of AGB estimation increased by 97.7%, with R2 reaching 0.89 and RMSE reduced by 124.4%. As a result, the incorporation of LiDAR data, which reflects the stand structure, has proven to enhance the accuracy of aboveground biomass (AGB) estimation in Moso bamboo forests when combined with multispectral remote sensing data. This integration serves as an effective solution to address the limitations of single optical remote sensing methods, which often suffer from signal saturation, leading to lower accuracy in estimating Moso bamboo forest biomass. This approach offers a novel perspective and opens up new possibilities for improving the precision of Moso bamboo forest biomass estimation through the utilization of multiple remote sensing sources.

Mapping Plant Nitrogen Concentration and Aboveground Biomass of Potato Crops from Sentinel-2 Data Using Ensemble Learning Models

Excessive nitrogen (N) fertilization poses environmental risks at regional and global levels. Satellite remote sensing provides a novel approach for large-scale N monitoring. In this study, we evaluated the performance of different types of spectral bands and indices (SIs) coupled with ensemble learning models (ELMs) at retrieving the plant N concentration (PNC) and plant aboveground biomass (AGB) of potato from Sentinel-2 images. Cloud-free Sentinel-2 imagery was acquired during the tuber-formation to starch-accumulation stages from 2020 to 2021. Fourteen optimal SIs were selected using the successive projections algorithm (SPA) and principal component analysis (PCA). The PNC and AGB estimation models were then built using an ELMs. The results showed that the SIs based on chlorophyll absorption bands were strongly related to potato PNC and AGB. Also, the N-correlated bands were mainly concentrated in the red-edge (705 nm) and short-wave infrared (1610 and 2190 nm) regions. The ELMs successfully predicted PNC and AGB (R2PNC = 0.74; R2AGB = 0.82). Compared with the other five base models (k-nearest neighbor (KNN), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and Gaussian process regression (GPR)), the ELMs provided higher PNC and AGB estimation accuracy and effectively reduced overfitting to training data. This study demonstrated that the promising solution of using SPA-PCA coupled with an ensemble learning model improves the estimation accuracy of potato PNC and AGB based on Sentinel-2 imagery data.

Grassland biomass spatiotemporal patterns and response to climate change in eastern Inner Mongolia based on XGBoost model estimates

Aboveground biomass (AGB) is a key index that reflects grassland health and is of great importance for grassland conservation and sustainable utilization. Many previous studies on grassland AGB were based on the analysis of the whole study period, can’t reflect the change of grassland in the short term, which was not conducive to the implementation and revision of policies. And it is still a challenge to accurately describe the response of grassland AGB to climate factors. In this study, we used four machine learning algorithms (kNN, k-nearest neighbor; RF, Random Forest; SVR, Support Vactor Regression; and XGBoost, eXtreme Gradient Boosting) to construct the estimation model of grassland AGB in eastern Inner Mongolia based on grassland AGB measurement data, MODIS(MOD13Q1) data and environmental data (climate and topography). The average distribution of grassland AGB from 2003 to 2021 was calculated based on the optimal grassland AGB estimation model. The changes of grassland AGB from 2003 to 2021 and in every five-year period during the study series were comprehensively analyzed by the Theil–Sen Median trend analysis and the Mann–Kendall test. The response of grassland AGB to climatic factors was investigated using sensitivity analysis. The results showed that (1) the XGBoost model constructed by NDVI, longitude, latitude, temperature, precipitation, elevation, slope, and aspect (NXYTPESA) is the optimal grassland AGB model (R2 = 0.87, RMSE = 14.27 g/m2). (2) The 20-year average grassland AGB in Eastern Inner Mongolia showed heterogeneous, with higher values in the northeast and lower values in the southwest. (3) The inter-annual variation fluctuated substantially, and the overall trend was increasing. Compared with other time periods, most grassland AGBs were in an increasing trend during 2008–2012 (79 %) and 2018–2021 (84 %). (4) The grassland AGB in the study area was regulated by temperature and precipitation, and the influence of precipitation was greater than that of temperature. This study provides a new method for estimating grassland AGB and its changes and a scientific basis for the sustainable development of grassland ecosystems in eastern Inner Mongolia.

Estimation of above ground biomass, biophysical and quality parameters of spinach (Spinacia Oleracea L.) using Sentinel-2 to support the supply chain

The estimation of biophysical and biochemical parameters using Sentinel-2 satellite imagery provides crucial information to support agronomic management and logistics for the processing industry, yet this approach has been poorly explored for vegetable crops, such as spinach (Spinacia oleracea L.). For this reason, Sentinel-2 satellite images were used to estimate biophysical and biochemical parameters of open field spinach crops (Above Ground Biomass (AGB), Canopy Nitrogen Content (CNC), Leaf Area Index (LAI), Canopy Chlorophyll Content (CCC), Leaf Dry Matter Content (LDMC), Leaf Nitrogen Content (LNC), Leaf Nitrate Content (LNO3−C) and Leaf Chlorophyll Content (LCC)). Spinach samples were collected in northern Italy in two different growing seasons (2020 – 2021) to measure biophysical and biochemical parameters which were linearly regressed via linear regression k-fold to five vegetation indices retrieved from Sentinel-2 images (MCARI, NDRE, NDVI, NDWI and SR). The AGB estimation models were also validated at field scale using historical data, totally independent from those used for the model calibration, collected by the processing industry between 2018 and 2021. Overall, canopy-level parameters (AGB, LAI, CNC, CCC) were estimated more accurately than leaf-level parameters (LDMC, LNC, LNO3−C, LCC). The highest accuracy was observed for the estimation of the canopy-scale parameter AGB (nRMSE = 9.48 %, R2 = 0.87) using MCARI, while the lowest accuracy was observed for the estimation of the leaf-scale parameter LCC (nRMSE = 29.24 %, R2 = 0.01) using NDWI. At field level, the validation of the AGB estimation models showed the highest performance using SR (nRMSE = 19.69 %, R2 = 0.21). This work demonstrates that using Sentinel-2 satellite images, it is feasible to estimate biophysical and biochemical parameters useful for monitoring the health of spinach crops for both agronomic management and the industrial supply chain.

Improved potato AGB estimates based on UAV RGB and hyperspectral images

Crops' above-ground biomass (AGB) is a crucial indicator that reflects crop health and predicts crop yield. However, using only optical vegetation indices (VIs) can produce inaccurate AGB estimates due to differences in crop varieties, growth stages, and measurement environments. Given the advantages of unmanned aerial vehicle (UAV) RGB and hyperspectral image fusion, this study evaluated the performance of multi-source remote sensing data for estimating potato AGB at multiple growth stages. In 2019, this study conducted potato trials with different varieties, fertilization levels, and planting densities at the Xiaotangshan Experiment Base (Beijing). UAV image and AGB data of potato three main stages were obtained from ground survey work. High-frequency information of the potato canopy was extracted from RGB images using discrete wavelet transform (DWT). VIs and wavelet energy coefficients were extracted from hyperspectral images using continuous wavelet transform (CWT). The linear relationships between potato AGB with VIs, high-frequency information, and wavelet coefficients were analyzed. Potato AGB estimation models were constructed based on single and multiple types of variables using multiple stepwise regression (MSR) and random forest (RF) models, respectively. This work showed the following results: (i) High-frequency information and wavelet coefficients were more sensitive to potato multi-growth stage AGB than VIs, and the latter were the most sensitive. (ii) Using VIs, high-frequency information, or wavelet coefficients separately to estimate the potato multi-growth stage AGB resulted in higher error and lower model accuracy. (iii) Combining VIs with either high-frequency information or wavelet coefficients improved the accuracy of AGB estimation, which was further improved by combining high-frequency information with wavelet coefficients. (iv) Combining VIs with both high-frequency information and wavelet coefficients provided the highest estimation accuracy using the MSR method. This combined AGB estimation model reduced the RMSE by 27%, 21%, and 16%, respectively, relative to VIs, high-frequency information, or wavelet coefficients alone. This result shows that the complementary advantages of multi-source UAV data can solve the challenge of insufficient AGB estimation by optical remote sensing. The work in this study provides remote sensing technology support to achieve potato crop growth monitoring and improve yield predictions.

Precise aboveground biomass estimation of plantation forest trees using the novel allometric model and UAV-borne LiDAR

IntroductionPlantation forest is an important component of global forest resources. The accurate estimation of tree aboveground biomass (AGB) in plantation forest is of great significance for evaluating the carbon sequestration capacity. In recent years, UAV-borne LiDAR has been increasingly applied to forest survey, but the traditional allometric model for AGB estimation cannot be directly used without the diameter at breast height (DBH) of individual trees. Therefore, it is practicable to construct a novel allometric model incorporating the crown structure parameters, which can be precisely extracted from UAV LiDAR data. Additionally, the reduction effect of adjacent trees on crown area (Ac) should be taken into account.MethodsIn this study, we proposed an allometric model depending on the predictor variables of Ac and trunk height (H). The UAV-borne LiDAR was utilized to scan the sample plot of dawn redwood (DR) trees in the test site. The raw point cloud was first normalized and segmented into individual trees, whose Acs and Hs were sequentially extracted. To mitigate the effects of adjacent trees, the initial Acs were corrected to refer to the potential maximum Acs under undisturbed growth conditions. Finally, the corrected Acs (Acc) and Hs were input into the constructed allometric model to achieve the AGBs of DR trees.Results and discussionAccording to accuracy assessment, coefficient of determination (R2) and root mean square error (RMSE) of extracted Hs were 0.9688 and 0.51 m; R2 and RMSE of calculated AGBs were 0.9432 and 10.91 kg. The unrestricted growth parts of the tree crowns at the edge of a plantation forest could be used to derive the potential maximum Ac. Compared with the allometric models for AGB estimation relying only on trunk H or on initial Ac and H, the novel allometric model demonstrated superior performance in estimating the AGBs of trees in a plantation forest.

Estimating potato above-ground biomass by using integrated unmanned aerial system-based optical, structural, and textural canopy measurements

Rapid and non-destructive potato above ground biomass (AGB) monitoring is a crucial step in the development of smart agriculture because AGB is closely related to crop growth, yield, and quality. Compared to time-consuming and laborious field surveys, unmanned aerial vehicle (UAV) remote sensing provides a new direction for large-scale AGB monitoring. However, estimating AGB using an optical remote sensing technique usually does not work well because of spectral saturation, but multi-source remote sensing feature fusion (e.g., fusing spectral and structural features) can mitigate that problem. Due to potato crop canopy structure and AGB change greatly during growth, the potential of fusing optical, textural (TFs), and structural features (SFs) for calculating potato AGB at multiple growth stages was unknown. In addition, the ability of optical features, TFs, and SFs and their combinations to estimate potato AGB had not been examined. Vegetation indices (RGB-VIs), TFs, and SFs were extracted from ultra-high spatial resolution RGB images and compared their performances for estimating potato AGB with those of hyperspectral vegetation indices (H-VIs) obtained from UAV hyperspectral images. The results revealed that each type of feature had its own advantages and limitations for potato AGB estimation. Except for canopy volume (CV) in SFs, the best H-VI, RGB-VI, and TF for estimating AGB in both single growth stages and the entire growth period were inconsistent. When estimating AGB with only a single type of feature, the model accuracy in descending order was SFs, TFs, H-VIs, and RGB-VIs. The fusion of any two types of remote sensing features improved AGB estimation model accuracy. Among them, TFs combined with SFs provided the best estimation performance. The fusion of RGB-VIs, TFs, and SFs produced the best AGB estimates precision (R2 = 0.81, RMSE = 207 kg/hm2, NRMSE = 17.40%). Since AGB was effectively estimated under different treatments in the field, the model applicability was confirmed. Using different types of remote sensing features, the Gaussian process regression method produced better estimation results than the partial least squares regression method did. This study provides an economic and effective method for monitoring the potato growth in the field, and thus helps improve farmland production and guide fertilization management.