Aboveground Biomass Estimation Models Research Articles

Comparative analysis of random forest, exploratory regression, and structural equation modeling for screening key environmental variables in evaluating rangeland above-ground biomass

Above-Ground Biomass (AGB) is a key indicator of rangeland health and productivity, as well as ecosystem conservation and resource sustainability. Estimating rangeland AGB under complex environmental conditions requires selecting appropriate predictors and models. In this research, we compared three methods to select the most important predictors of AGB from sixteen environmental variables: soil (pH, Nitrogen (N), Phosphorus (P), Potassium (K), Organic Matter (OM), clay, silt, sand), climate (Mean Temperature (MT), Mean Precipitation (MP), Actual Evapotranspiration (AET), Land Surface Temperature (LST)), and topography (elevation, slope, aspect, hillshade). The methods were Random Forest (RF), Exploratory Regression (ER), and Structural Equation Modeling (SEM). Based on the selected predictors from each method, we developed AGB estimation models using multiple linear regression (MLR). We evaluated the precision and accuracy of the models using the Ideal Point Error (IPE). The results showed that the three methods differed significantly in their selection of key predictors. RF identified sand, MT, MP, slope, silt, and N as the most important predictors based on %IncMSE criteria. ER selected sand, MT, P, and hillshade as the optimal combination of explanatory variables based on adjusted R2, JB, VIF, and SA (0.50, 0.9, 1.22, and 0.5, respectively). SEM revealed that N, P, K, pH, silt, MP, MT, slope, and hillshade were the most effective predictors based on CMIN/DF = 1.33, CFI = 0.96, GFI = 0.9, RMSEA = 0.06. The validation results based on IPE indicated that SEM performed the best (IPE = 0.17) compared to ER (IPE = 0.18) and RF (IPE = 0.19). Although the three models - RF, ER, and SEM - do not have the capability to directly predict and map the response variable from selected important drivers, SEM provides a direct understanding of the interrelationships among predictor and response variables through data summarization. This makes it more reliable than RF and ER.

Enhancing Wheat Above-Ground Biomass Estimation Using UAV RGB Images and Machine Learning: Multi-Feature Combinations, Flight Height, and Algorithm Implications

Above-ground biomass (AGB) serves as an indicator of crop growth status, and acquiring timely AGB information is crucial for estimating crop yield and determining appropriate water and fertilizer inputs. Unmanned Aerial Vehicles (UAVs) equipped with RGB cameras offer an affordable and practical solution for efficiently obtaining crop AGB. However, traditional vegetation indices (VIs) alone are insufficient in capturing crop canopy structure, leading to poor estimation accuracy. Moreover, different flight heights and machine learning algorithms can impact estimation accuracy. Therefore, this study aims to enhance wheat AGB estimation accuracy by combining VIs, crop height, and texture features while investigating the influence of flight height and machine learning algorithms on estimation. During the heading and grain-filling stages of wheat, wheat AGB data and UAV RGB images were collected at flight heights of 30 m, 60 m, and 90 m. Machine learning algorithms, including Random Forest Regression (RFR), Gradient Boosting Regression Trees (GBRT), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (Lasso) and Support Vector Regression (SVR), were utilized to construct wheat AGB estimation models. The research findings are as follows: (1) Estimation accuracy using VIs alone is relatively low, with R2 values ranging from 0.519 to 0.695. However, combining VIs with crop height and texture features improves estimation accuracy, with R2 values reaching 0.845 to 0.852. (2) Estimation accuracy gradually decreases with increasing flight height, resulting in R2 values of 0.519–0.852, 0.438–0.837, and 0.445–0.827 for flight heights of 30 m, 60 m, and 90 m, respectively. (3) The choice of machine learning algorithm significantly influences estimation accuracy, with RFR outperforming other machine learnings. In conclusion, UAV RGB images contain valuable crop canopy information, and effectively utilizing this information in conjunction with machine learning algorithms enables accurate wheat AGB estimation, providing a new approach for precision agriculture management using UAV remote sensing technology.

Urban Forest Above-Ground Biomass Estimation Based on UAV 3D Real Scene

(1) Background: A three-dimensional (3D) real scene is a digital representation of the multidimensional dynamic real-world structure that enables the realistic and stereoscopic expression of actual scenarios, and is an important technological tool for urban refinement management. The above-ground biomass (AGB) of urban forests is an important indicator that reflects the urban ecological environment; therefore, the accurate estimation of AGB is of great significance for evaluating urban ecological functions. (2) Methods: In this study, multiangle aerial photographs of urban street trees were obtained via an unmanned aerial vehicle (UAV) single-lens five-way flight, from 0°, 0°, 90°, 180°, 270°, and five other directions. The multiple view stereo (MVS) algorithm was used to construct three-dimensional realistic models of two tree species: ginkgo and camphor. Then, structural parameters such as tree height, crown diameter, and crown volume were estimated from the 3D real-scene models. Lastly, single-tree AGB models were developed based on structural parameters. (3) Results: The results of this study indicated the following: (A) The UAV visible-light realistic 3D model had clear texture and truly reflected the structural characteristics of two tree species, ginkgo and camphor. (B) There was a significant correlation between the reference tree height, crown diameter and crown volume obtained from the realistic 3D model and the measured values; the R2 for ginkgo height was 0.90, the R2 for camphor crown diameter was 0.87, and the R2 for ginkgo crown volume was 0.89. (C) The accuracy of the AGB estimation models constructed with tree height and canopy volume as variables was generally higher than that of models with tree height and canopy diameter; the model with the highest accuracy of AGB estimation for ginkgo was the linear model with a validation accuracy R2 of 0.96 and RMSE of 8.21 kg, while the model with the highest accuracy of AGB estimation for camphor was the quadratic polynomial model with a validation accuracy R2 of 0.92 and RMSE of 27.74 kg. (4) Conclusions: This study demonstrated that the UAV 3D real-scene model can achieve high accuracy in estimating single-wood biomass in urban forests. In addition, for both tree species, there was no significant difference between the AGB estimates based on the UAV 3D real scene and LiDAR and the measured AGB. These results of urban single-wood AGB estimation based on the UAV 3D real-scene model were consistent with those of LiDAR and even with the measured AGB. Therefore, based on the UAV 3D real-scene model, the single-wood biomass can be estimated with high accuracy. This represents a new technical approach to urban forest resource monitoring and ecological environment function evaluation.

Tropical forest AGB estimation based on structure parameters extracted by TomoSAR

Forests play a crucial role in quantifying global carbon storage and detecting climate change in the form of aboveground biomass (AGB), which introduces an approach to study carbon cycle, ecology, and biodiversity. The monitoring and estimation of forest AGB are considered very important and of practical value. As we know, forest AGB relates with height, density and diameter at breast height, and how to relate the ecophysical parameters with remote sensing images is vital for forest AGB estimation. In this paper, we aim to explore structure parameters about forest density and height, extracted by tomographic SAR (TomoSAR) techniques, for further improving the precision of AGB estimation models. Firstly, vertical structure profiles are constructed via TomoSAR, and the structure features are extracted. Secondly, the correlation between these features and the in-situ forest maximum height, tree density, and average AGB in plot scale is analyzed. Thirdly, the 8-fold cross-validation and step-wise regression methods were utilized to construct the tropical forest AGB models. Finally, the results of these models have been presented and analyzed. Based on the analysis, it indicates that “Model 7” is the most effective model, and its performance at both plot and pixel scales indicates a high level of accuracy for predicting forest AGB. These findings suggest that the proposed method can be effectively applied to tropical forested areas and has good scalability.

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.

Comparison of multi-source remote sensing data for estimating and mapping above-ground biomass in the West Usambara tropical montane forests

Above-ground biomass (AGB) estimation is important to better understand the carbon cycle and improve the efficiency of forest policy and management activities. AGB estimation models, using a combination of field data and remote sensing data, can largely replace traditional survey methods for measuring AGB. There are, however, critical steps for mapping AGB based on satellite data with an acceptable degree of accuracy, such as choice of remote sensing data, the proper statistical modelling method, and remote sensing predictor variables, at known field locations. This study sought to identify the optimal optical and synthetic aperture radar (SAR) remote sensing imagery from five sensors (PlanetScope, Sentinel-2, Landsat 8 OLI, ALOS-2/PALSAR-2, and Sentinel-1) to model 159 field-based AGB values from two montane forests under semiparametric (Generalized Additive Model; GAM) and non-parametric (eXtreme Gradient Boosting; XGB) approaches using information from four groups of predictor variables (spectral bands/polarizations, vegetation indices, textures, and a combination of all). The study's results showed that PlanetScope (rRMSE = 69.19%; R2 = 0.161) was the most precise optical sensor while ALOS-2/PALSAR-2 (rRMSE = 70.76; R2 = 0.165) was the most precise amongst the SAR sensors. XGB models generally resulted in those with lower prediction errors as compared to GAMs for the five sensors. Models having textures of vegetation indices and polarization bands achieved greater accuracy than models that incorporated spectral bands/polarizations and vegetation indices only. The study recommends that PlanetScope and ALOS-2/PALSAR-2 remote sensing data using the XGB-based technique is an appropriate approach for accurate local and regional estimation of tropical forest AGB particularly for complex montane forest ecosystems.

Estimation of forest above‐ground biomass in Guangxi, China, by integrating forest age and stack learning

AbstractWith the implementation of large‐scale ecological restoration projects, Southwest China has become one of the fastest forest growth areas in the world in terms of vegetation cover and above‐ground biomass (AGB). It is expected to be a potential area for achieving the carbon neutrality target in China. Accurate estimation of forest AGB is becoming an increasingly urgent necessity for carbon neutrality and forest management. However, due to the complex geological background, there is significant uncertainty in estimating forest AGB in the southwestern region, which generally results in underestimating carbon sinks from forest restoration. To address the issue, we propose a method by incorporating forest age information and stack learning technique to estimate forest AGB. Based on remote sensing, forest inventory and in situ forest biomass data, three fundamental methods (Multiple regression, Random forest and Support vector machine) are employed and compared to build the AGB estimation model with vegetation indices, texture feature factors and forest age. Optimal basic models are further enhanced by integration learning to improve the estimation performance and then applied to the study area Guangxi to obtain regional AGB information of different forest types. The results show that: (1) forest age plays a vital role in reducing the uncertainty of AGB estimation. By incorporating forest age information, R2 of AGB estimation is improved by 0.07–0.27 and RMSE is decreased by 16.35%–47.47% for different forest types; (2) with R2 value >0.78, random forest model outperforms support vector machine and multiple linear regression models. Compared with the single optimal model, integration model by stack learning further enhances R2 of estimation by 0.02–0.03 and decreases RMSE by 5.20%–14.89%. (3) The total forest AGB in Guangxi is 988.17 Tg and the average forest AGB level is 73.30 t/ha. Natural broadleaf forest has the highest AGB level (86.75 t/ha), followed by natural coniferous forest (81.19 t/ha), planted coniferous forest (63.23 t/ha) and planted eucalyptus forest (49.71 t/ha). AGB level in karst areas is lower than that in non‐karst areas due to soil and water constraints. The majority of plantation forests in Guangxi is in the early and middle stages of forest succession, with the rapid growth of forest AGB, and has hence significant potential as carbon sinks. This study indicates that stack learning and incorporation of forest age could significantly decrease the uncertainty of forest AGB estimation. Our study helps to provide more accurate AGB information for karst ecological project management and regional carbon neutrality assessment.

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.

Smartagb: Aboveground Biomass Estimation of Sorghum Based on Spatial Resolution, Machine Learning and Vegetation Index

This work aims to explore the feasibility of predicting and estimating the aboveground biomass (AGB) of sorghum using multispectral images captured by UAVs, and clarify the quantitative relationship between vegetation index and sorghum AGB based on different spatial resolutions, and build an AGB estimation model based on UAV multispectral images and vegetation index under different spatial resolutions. Combining spatial resolution, vegetation index, and machine learning, a training set is used to train the model, and a verification set is used to verify the model to select the best prediction model corresponding to different spatial resolutions. The three best prediction models under three spatial resolutions are classic machine learning models. 1) when the spatial resolution is 0.017m, the model precision obtained from the random forest is R2=0.8961, MAE=26.4340, and RMSE=32.2459. 2) when the spatial resolution is 0.024m, the model accuracy obtained by the Lasso algorithm is R2=0.8826, MAE=31.106, and RMSE=40.2937; 3) when the spatial resolution is 0.030m, the model accuracy obtained by the decision tree algorithm is R2=0.8568, MAE=30.3373, and RMSE=40.8082; and 4) the model's accuracy decreases with the decrease of spatial resolution. The results show that the combination of spatial resolution, vegetation index, and machine learning algorithm is an effective, fast, and accurate prediction method.

Machine learning-based grassland aboveground biomass estimation and its response to climate variation in Southwest China

The demand for accurate estimation of aboveground biomass (AGB) at high spatial resolution is increasing in grassland-related research and management, especially for those regions with complex topography and fragmented landscapes, where grass and shrub are interspersed. In this study, based on 519 field AGB observations, integrating Synthetic Aperture Radar (SAR; Sentinel-1) and high-resolution (Sentinel-2) remote sensing images, environmental and topographical data, we estimated the AGB of mountain grassland in Southwest China (Yunnan Province and Guizhou Province) by using remote sensing algorithms ranging from traditional regression to cutting edge machine learning (ML) and deep learning (DL) models. Four models (i.e., multiple stepwise regression (MSR), random forest (RF), support vector machine (SVM) and convolutional neural network (CNN)) were developed and compared for AGB simulation purposes. The results indicated that the RF model performed the best among the four models (testing dataset: decision co-efficient (R2) was 0.80 for shrubland and 0.75 for grassland, respectively). Among all input variables in the RF model, the vegetation indices played the most important role in grassland AGB estimation, with 6 vegetation indices (EVI, EVI2, NDVI, NIRv, MSR and DVI) in the top 10 of input variables. For shrubland, however, topographical factors (elevation, 12.7% IncMSE (increase in mean squared error)) and SAR data (VH band, 11.3% IncMSE) were the variables which contributed the most in the AGB estimation model. By comparing the input variables to the RF model, we found that integrating SAR data has the potential to improve grassland AGB estimation, especially for shrubland (26.7% improvement in the estimation of shrubland AGB). Regional grassland AGB estimation showed a lower mean AGB in Yunnan Province (443.6 g/m2) than that in Guizhou Province (687.6 g/m2) in 2021. Moreover, the correlation between five consecutive years (2018–2022) of AGB data and climatic factors calculated by partial correlation analysis showed that regional AGB was positively related with mean annual precipitation in more than 70% of the grassland and 60% of the shrubland area, respectively. Also, we found a positive relationship with mean annual temperature in 62.8% of the grassland and 55.6% of the shrubland area, respectively. This study demonstrated that integrating SAR into grassland AGB estimation led to a remote sensing estimation model that greatly improved the accuracy of modeled mountain grassland AGB in southwest China, where the grassland consists of a complex mix of grass and shrubs.

Estimasi Simpanan Karbon Hutan Mangrove Menggunakan Citra (SAR) Synthetic Aperture Radar Sentinel -1 di Taman Hutan Raya Ngurah Rai,Bali


 
 
 
 Taman Hutan Raya (Tahura) Ngurah Rai, Bali, is recorded to have a mangrove forest covering an area of 1,373.5 ha, which plays a role in absorbing carbon from the atmosphere. Carbon is stored in the form of biomass. The bigger the biomass, the bigger the carbon stock in it. Direct measurement of biomass in the field requires an expensive method, a relatively long time, and narrow area coverage. Nowadays, the weakness of measuring carbon stocks directly in the field can be overcome by remote sensing approaches, especially estimations from satellite imagery combined with geographic information systems (SIG). One of the images that can be used to estimate carbon stock is the Sentinel-1 Synthetic Aperture Radar (SAR) image. The aim of this research is to estimate biomass and carbon stocks based on SAR backscatter distribution. The method used is the analysis of satellite imagery combined with field data. Field data collection and above-ground biomass (AGB) calculations were carried out by taking 30 plots with a plot size of 10 x 10 m. The results showed that there was a correlation between the backscatter value of SAR images and AGB from field measurements (r = 0.59 and the AGB estimation model y = 57.42x + 1223.8, where y is biomass and x is backscatter), and the coefficient of determination R2 = 0.36. The estimation model developed next is used to calculate the overall AGB, which is then used to estimate the overall aboveground carbon storage.
 
 
 

A 250 m annual alpine grassland AGB dataset over the Qinghai–Tibet Plateau (2000–2019) in China based on in situ measurements, UAV photos, and MODIS data

Abstract. The alpine grassland ecosystem accounts for 53 % of the Qinghai–Tibet Plateau (QTP) area and is an important ecological protection barrier, but it is fragile and vulnerable to climate change. Therefore, continuous monitoring of grassland aboveground biomass (AGB) is necessary. Although many studies have mapped the spatial distribution of AGB for the QTP, the results vary widely due to the limited ground samples and mismatches with satellite pixel scales. This paper proposed a new algorithm using unmanned aerial vehicles (UAVs) as a bridge to estimate the grassland AGB on the QTP from 2000 to 2019. The innovations were as follows: (1) in terms of ground data acquisition, spatial-scale matching among the traditional ground samples, UAV photos, and MODIS pixels was considered. A total of 906 pairs between field-harvested AGB and UAV sub-photos and 2602 sets of MODIS pixel-scale UAV data (over 37 000 UAV photos) were collected during 2015–2019. Therefore, the ground validation samples were sufficient and scale-matched. (2) In terms of model construction, the traditional quadrat scale (0.25 m2) was successfully upscaled to the MODIS pixel scale (62 500 m2) based on the random forest and stepwise upscaling methods. Compared with previous studies, the scale matching of independent and dependent variables was achieved, effectively reducing the impact of spatial-scale mismatch. The results showed that the correlation between the AGB values estimated by UAV and MODIS vegetation indices was higher than that between field-measured AGB and MODIS vegetation indices at the MODIS pixel scale. The multi-year validation results showed that the constructed MODIS pixel-scale AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m−2. Our dataset provides an important input parameter for a comprehensive understanding of the role of the QTP under global climate change. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Terre.tpdc.272587; H. Zhang et al., 2022).

Development of Estimation Models for Individual Tree Aboveground Biomass Based on TLS-Derived Parameters

Forest biomass is a foundation for evaluating the contribution to the carbon cycle of forests, and improving biomass estimation accuracy is an urgent problem to be addressed. Terrestrial laser scanning (TLS) enables the accurate restoration of the real 3D structure of forests and provides valuable information about individual trees; therefore, using TLS to accurately estimate aboveground biomass (AGB) has become a vital technical approach. In this study, we developed individual tree AGB estimation models based on TLS-derived parameters, which are not available using traditional methods. The height parameters and crown parameters were extracted from the point cloud data of 1104 trees. Then, a stepwise regression method was used to select variables for developing the models. The results showed that the inclusion of height parameters and crown parameters in the model provided an additional 3.76% improvement in model estimation accuracy compared to a DBH-only model. The optimal linear model included the following variables: diameter at breast height (DBH), minimum contact height (Hcmin), standard deviation of height (Hstd), 1% height percentile (Hp1), crown volume above the minimum contact height (CVhcmin), and crown radius at the minimum contact height (CRhcmin). Comparing the performance of the models on the test set, the ranking is as follows: artificial neural network (ANN) model > random forest (RF) model > linear mixed-effects (LME) model > linear (LN) model. Our results suggest that TLS has substantial potential for enhancing the accuracy of individual-tree AGB estimation and can reduce the workload in the field and greatly improve the efficiency of estimation. In addition, the model developed in this paper is applicable to airborne laser scanning data and provides a novel approach for estimating forest biomass at large scales.

Estimating Above-Ground Biomass from Land Surface Temperature and Evapotranspiration Data at the Temperate Forests of Durango, Mexico

The study of above-ground biomass (AGB) is important for monitoring the dynamics of the carbon cycle in forest ecosystems. The emergence of remote sensing has made it possible to analyze vegetation using land surface temperature (LST), Vegetation Temperature Condition Index (VTCI) and evapotranspiration (ET) information. However, relatively few studies have evaluated the ability of these variables to estimate AGB in temperate forests. The aim of the present study was to evaluate the relationship of LST, VTCI and ET with AGB in temperate forests of Durango, Mexico, regarding each season of the year and to develop a AGB estimation model using as predictors LST, VCTI and ET, together with topographic, reflectance and Gray-Level Co-Occurrence Matrix (GLCM) texture variables. A semi-parametric model was generated to analyze the linear and non-linear responses of the predictive variables of AGB using a generalized linear model (GAM). The results show that the best predictors of AGB were longitude, latitude, spring LST, ET, elevation VTCI, NDVI (Normalized Difference Vegetation Index), slope and GLCM mean (R2 = 0.61; RMSE = 28.33 Mgha−1). The developed GAM model was evaluated with an independent dataset (R2 = 0.58; RMSE = 31.21 Mgha−1), suggesting the potential of this modeling approach to predict AGB for the analyzed temperate forest ecosystems.

Allometric models for aboveground biomass estimation of small trees and shrubs in African savanna ecosystems

Quantification of plant biomass and carbon in ecosystems is critical for establishing climate change mitigation potential. For large trees in various ecosystems, allometric models for estimating biomass have been developed but few biomass equations exist for small trees and shrubby vegetation. Allometric above-ground biomass (AGB) models are needed for small trees and shrubs in order to improve the quantification of biomass, particularly for savanna ecosystems, where small trees and shrubs comprise a significant portion of the biomass. In this study we have developed species-specific and multi-species allometric models for biomass estimation of small tree species and shrubs in the savanna ecosystem of Lake Mburo National Park in South Western Uganda. For our models we selected 27 small tree species (N = 403 individuals) and 12 shrub species (N = 177) common in savanna ecosystems for destructive sampling. We developed species-specific and multi-species allometric AGB models to provide estimates of AGB using specific biometric variables recorded for the small trees (i.e. species, DBH, height and crown area), and shrubs (species, height and crown area). We found that crown area was the best single predictor of species-specific AGB for small trees and for species-specific and multi-species models for shrubs. Species-specific models had the best predictive capacity of AGB compared to multi-species biomass models for small trees and shrubs. Multiple-variable models had the best predictive capacity of AGB in both species-specific and multi-species modeling compared to single-variable models. Based on these findings we conclude that the evaluation of carbon stocks of tropical savanna ecosystems should use multi-variable species-specific models for AGB estimation at the individual level, and multi-species models for AGB at the ecosystem level.

Spatiotemporal fusion of multi-source remote sensing data for estimating aboveground biomass of grassland

Accurate estimation of aboveground biomass of grasslands is key to sustainable grassland utilization. However, most satellites cannot provide high temporal and spatial resolution data. Patterns of grassland dynamics associated with variability in climate conditions across spatiotemporal scales are yet to be adequately quantified. A spatiotemporal fusion model offers the opportunity to combine the resolution advantages of different remote sensing data to achieve a high frequency and high precision monitoring of vegetation. We test a flexible spatiotemporal data fusion (FSDAF) methodology to generate synthetic normalized difference vegetation index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat data sets. The methodology is tested for the semi-arid grassland of the Xilin River Basin, China. Based on NDVI data fusion and field measured aboveground biomass an aboveground biomass estimation model is established for the watershed. Exploring the temporal and spatial changes of biomass and its relationship with environmental factors. The results show that: (1) The FSDAF model performs well (R2 = 0.75) and has clear textural features. (2) The established Support Vector Machine Aboveground Biomass model not only ensured the accuracy of estimation (R2 = 0.78, RMSE = 15.43 g/m2), but also generated spatiotemporal maps of biomass with higher spatial (30 m) and temporal resolution (8 days). (3) The grassland aboveground biomass in this area decreases from southeast to northwest, and the grassland biomass reaches its peak at the end of July. The average biomass of different grasslands decreases in the order of meadow grassland > typical grassland > desert grassland. (4) Aboveground biomass increased linearly with increasing water content, organic carbon and total nitrogen, and was most sensitive to soil water content. During the early growing and rapid growing period, aboveground biomass is mainly affected by both air temperature and precipitation, while the effects of temperature and human activities gradually dominate in the middle and late growing periods. This study helps to improve the spatial and temporal resolution of dynamic monitoring of grassland biomass, and provides a scientific basis for grassland protection and management in arid and semi-arid regions.

A new strategy for improving the accuracy of forest aboveground biomass estimates in an alpine region based on multi-source remote sensing

ABSTRACT Spatially explicit information on the distribution of dominant tree species groups and aboveground biomass (AGB) in forested areas is essential for developing targeted forest management and biodiversity conservation measures, as well as assessing forest carbon sequestration capacity. There is a shortage of continuously updated 30-m spatial resolution products for mapping dominant tree species groups. The vast majority of remote sensing-based AGB estimation approaches have relatively low accuracy for dominant tree species groups or forest types and are unsuitable for AGB modeling. Therefore, this study aims to develop an integrated framework that considers the phenological characteristics of different tree species to improve the mapping accuracies of forest dominant tree groups and corresponding AGB estimates. Thirty-meter resolution maps of dominant tree species groups were created using machine learning algorithms and phenological parameters. Features extracted from optical and radar images and phenological characteristics were used to construct AGB estimation models in a temporally consistent manner to improve the AGB estimation accuracy and perform dynamic AGB monitoring. The proposed method accurately characterized the dynamic distribution of the dominant tree species groups in the study area. The traditional AGB model that does not consider different forest types or species had an R2 value of 0.52, whereas the proposed model that considers phenology and forest types had an R2 value of 0.67. This result indicates that incorporating information on phenology and dominant species improves the accuracy of AGB estimations. The AGB in most regions was 30–55 t/ha, showing that the majority of the forests were young or middle-aged stands, and the areal percentage of AGB greater than 30 t/ha increased during the study period, suggesting an improvement in forest quality. Furthermore, the oak AGB was the highest, indicating that oak afforestation should be encouraged to enhance the carbon sequestration capacity of future forest ecosystems. The results provide new insights for researchers and managers to understand the trends of forest development and forest health, as well as technical information and a database for formulating more rational forest management strategies.

Estimation of grain yield in wheat using source–sink datasets derived from RGB and thermal infrared imaging

AbstractTimely and efficient monitoring of crop aboveground biomass (AGB) and grain yield (GY) forecasting before harvesting are critical for improving crop yields and ensuring food security in precision agriculture. The purpose of this study is to explore the potential of fusing source–sink‐level color, texture, and temperature values extracted from RGB images and thermal images based on proximal sensing technology to improve grain yield prediction. High‐quality images of wheat from flowering to maturity under different treatments of nitrogen application were collected using proximal sensing technology over a 2‐year trial. Numerous variables based on source and sink organs were extracted from the acquired subsample images, including 30 color features, 10 texture features, and two temperature values. The principal component analysis (PCA), least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE) were used to screen variables. Support vector regression (SVR) and random forest (RF) were applied to establish AGB estimation models, and the GY prediction models were built by RF. The source dataset and sink dataset performed differently on AGB and GY estimation, but the combined source–sink dataset performed best for estimating both AGB and GY. Based on the source–sink dataset, the LASSO‐RF model was the best combination for predicting AGB and GY, with the coefficient of determination (R2) of 0.85 and 0.86, root mean square error (RMSE) of 1179.09 and 609.61 kg ha−1, and the ratio of performance to deviation (RPD) of 2.10 and 2.45, respectively. This study demonstrates that the multivariate eigenvalues of both source and sink organs have the potential to predict wheat yield and that the combination of machine learning models and variable selection methods can significantly affect the accuracy of yield prediction models and achieve effective monitoring of crop growth at late reproductive stages.

New two-step species-level AGB estimation model applied to urban parks

Aboveground biomass (AGB) estimation for urban parks has received less attention as an essential component of the global carbon cycle. Current studies focus on vast areas of natural or planted forests. The characteristics of these study areas make the use of homogenised vegetation grids (using remote sensing data) and plots (using field data) as the basic research unit a consensus. However, this data-level simplification can be significantly affected by buildings when applied to urban areas. Developing tree species identification methods based on remote sensing provides us with new ideas to explore urban AGB estimation methods at the species level. To this end, we developed a species-level AGB estimation model to address the AGB distribution in urban parks by combining multitemporal airborne light detection and ranging (LiDAR), optical remote sensing data, and field data from two urban parks in Hong Kong through a two-step strategy. First, we constructed optimal remote sensing feature-AGB mapping relationships for each sample species using sample data from the study area, the tropical allometric growth equation, and the five regression algorithms. We then explored a tree species identification method based on the annual vegetation phenological change index (AVPCI), which allowed us to quickly obtain species distribution maps for the study area. Combining these two steps allowed us to obtain AGB information for the study area based on species-level mapping relationships based on species distributions. In the model validation, the correlation between the estimated and true values of the remote sensing feature and AGB mapping relationship was 0.91, with a significantly lower normalised root mean square deviation (RMSE). The overall accuracy of the sample tree species identification was 87.5%, which was better than the results of existing studies. The final AGB obtained was also within the reasonable interval of existing studies. In addition, with the model proposed in this study, we noted that the super typhoon Mangkhut in 2018 reduced the AGB in the study area by 32.6% and demonstrated the significant underestimation of high-density urban areas in existing global biomass products. The model developed in this study addresses the problems of existing AGB estimation methods for urban vegetation represented by urban parks while effectively contributing to understanding AGB distribution and short-term carbon cycle dynamics in urban scenarios.

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.