Plot-level Aboveground Biomass Research Articles

Water level regime variation is a crucial driver for taxonomic, functional, and phylogenetic diversity in seasonally flooded tropical forests

Floodplains contribute significantly to terrestrial ecosystem service provision but are also among the most vulnerable and degraded ecosystems worldwide. Heterogeneity in floodplain properties arises from variations in river-specific flood regimes, watershed characteristics, and valley morphology, influencing seasonally flooded forests' taxonomic, functional, and phylogenetic diversity. This study addresses persisting knowledge gaps in floodplain ecology, focusing on the seasonally dry tropics. We explore the relationships between flood regime, environmental conditions, vegetation composition, functional and phylogenetic diversity, and the impact of environmental variables on above-ground biomass (AGB) and ecological strategies. The study spans six rivers in southeastern Brazil's main river basins: Rio Grande and São Francisco. We identified five eco-units in each floodplain based on flooding regimes and surveyed six plots per eco-unit. We measured trees with DBH > 5 cm and collected functional traits, along with detailed soil, climate, and water level data. We calculated plot-level floristic composition, taxonomic, functional, and phylogenetic diversity, wood density, and AGB. Functional and phylogenetic dissimilarity were analyzed, and the effects of climate, soil, and hydrological variables were quantified using generalized linear mixed models. We show how flood frequency and duration affect floristic composition across the floodplains. Taxonomic and phylogenetic diversity responded to climate, soil, and hydrological variables, while functional diversity responded primarily to hydrological variables, emphasizing the role of environmental filtering. Hydrological seasonality, soil fertility, and flood regime emerged as key factors shaping community structure and ecological strategies in the studied seasonally flooded tropical forests. Plot-level AGB responded to phosphorus but not to climate or hydrological variables. The study also highlights functional and phylogenetic dissimilarities among eco-units and basins, indicating potential climate change impacts.

Estimating aboveground biomass dynamics of wheat at small spatial scale by integrating crop growth and radiative transfer models with satellite remote sensing data

Aboveground biomass (AGB) of plants is an agroecological indicator that can be used to monitor crop growth status and quantify biomass carbon stock in agricultural ecosystems. Although satellite remote sensing data and crop models are widely employed to estimate AGB dynamics, their application in small-scale research plots is often constrained by the unavailability of freely and timely high-resolution satellite imageries and the challenge of acquiring reliable model inputs like initial soil water and nitrogen content. This study integrated a crop growth model (APSIM-Wheat) and radiative transfer model (PROSAIL) to estimate AGB dynamics at a small plot scale (ca. 5 to 20 m2) within variety trials (ca. 1 to 2 ha) by assimilating trial-level remote sensing data into the hybrid APSIM-PROSAIL model. The APSIM-PROSAIL integration, developed by deriving PROSAIL input parameters from APSIM-Wheat output variables, was verified by evaluating its capacity to reproduce trial-level Sentinel-2 (S2) NDVI dynamics across 30 variety trials in 2020. The comparison of the simulated and observed trial-level S2 NDVI dynamics yielded an overall RRMSE of 18.2% (n = 646 observations), suggesting the potential of using observed S2 NDVI dynamics to constrain APSIM-PROSAIL and estimate environment variables in cropping systems. This constraint was subsequently assessed in 28 variety trials in 2021, where APSIM-PROSAIL inversely estimated unknown trial-specific variables including initial soil water and nitrogen content across the trials. Using the estimated initial soil status and the plot information including genotypes and management as sown, we demonstrated that this method could also facilitate accurate retrievals of plot-level AGB dynamics with an overall RRMSE of 20.9% (n = 976 measurements). The proposed APSIM-PROSAIL demonstrated the capability to accurately retrieve plot-level AGB dynamics and reproduce trial-level S2 NDVI dynamics across a broad and diverse range of variety trials in the Australian Wheatbelt, indicating its potential utility to facilitate biomass carbon stock assessment. It holds the potential as a tool to explore the relationships between canopy spectral information and crop traits in response to genotype-environment-management interactions in agricultural ecosystems. The method enables the examinations of within-field variabilities for crop traits through retrieving them at small spatial scales from coarse remote sensing data.

Improving plot-level above ground biomass estimation in tropical Indian forests

Accurate plot-level estimates of above ground biomass (AGB) are crucial for reducing uncertainty in spatial AGB quantification, a key requirement for Reducing Emissions from Deforestation and Forest Degradation framework (REDD+) and achieving Intended Nationally Determined Contributions (INDC) goals. This study explores the impact of plot size, selection and use of height-diameter (H–D) model, and allometric models on plot-level AGB estimation in tropical Indian forests. We analyze a dataset of 8179 tree measurements from 51 one-hectare plots across four tropical sites to investigate these factors and improve estimation accuracy. Our findings highlight the importance of locally derived H–D models for superior accuracy. We propose two H–D models for improved plot-level AGB estimation accuracy in tropical Indian forests: a general model with a residual standard error (RSE) of 4.18 m and a forest type-specific model with a RSE of 3.65 m. These models outperform global models, which significantly underestimate tree height by up to 51.3% and overestimate by 43.4% across our sites. The developed H–D models were evaluated using spatial cross-validation to ensure broader applicability across Indian tropical forests. Further, we evaluated the impact of the choice of the allometric model on plot-level AGB estimation using the prevalent models (local destructive models by the Forest Survey of India and global pantropical models). Our results indicate minimal variability between the local destructive models and the pantropical model when height measurements are derived from this study's developed H–D models. We have also investigated the effect of plot size on plot-level AGB estimation using synthetic plots generated from 1-ha plot data. Notably, the relative error diminishes from approximately 22% to about 5% as plot size increases from 0.04 ha (20x20m) to 0.49 ha (70x70m) across all sites, and we recommend field plots of size ≥0.5 ha for AGB mapping over Tropical Indian Forests. By making this data publicly available, this study paves the way for more accurate spatial AGB mapping in tropical Indian forests, ultimately contributing to better forest management.

Mapping historical forest biomass for stock-change assessments at parcel to landscape scales

Understanding historical forest dynamics, specifically changes in forest biomass and carbon stocks, has become critical for assessing current forest climate benefits and projecting future benefits under various policy, regulatory, and stewardship scenarios. Carbon accounting frameworks based exclusively on national forest inventories are limited to broad-scale estimates, but model-based approaches that combine these inventories with remotely sensed data can yield contiguous fine-resolution maps of forest biomass and carbon stocks across landscapes over time. Here we describe a fundamental step in building a map-based stock-change framework: mapping historical forest biomass at fine temporal and spatial resolution (annual, 30 m) across all of New York State (USA) from 1990 to 2019, using freely available data and open-source tools. Using Landsat imagery, US Forest Service Forest Inventory and Analysis (FIA) data, and off-the-shelf LiDAR collections we developed three modeling approaches for mapping historical forest aboveground biomass (AGB): training on FIA plot-level AGB estimates (direct), training on LiDAR-derived AGB maps (indirect), and an ensemble averaging predictions from the direct and indirect models. Model prediction surfaces (maps) were tested against FIA estimates at multiple scales. All three approaches produced viable outputs, yet tradeoffs were evident in terms of model complexity, map accuracy, saturation, and fine-scale pattern representation. The resulting map products can help identify where, when, and how forest carbon stocks are changing as a result of both anthropogenic and natural drivers alike. These products can thus serve as inputs to a wide range of applications including stock-change assessments, monitoring reporting and verification frameworks, and prioritizing parcels for protection or enrollment in improved management programs.

Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods

Aboveground biomass (AGB) of shrub communities in the desert is a basic quantitative characteristic of the desert ecosystem and an important index to measure ecosystem productivity and monitor desertification. An accurate and efficient method of predicting the AGB of a shrub community is essential for studying the spatial patterns and ecological functions of the desert region. Even though there are several entries in the literature on the AGB prediction of desert shrub communities using remote sensing data, the applicability and accuracy of airborne LiDAR data and prediction methods have not been well studied. We first extracted the elevation, density and intensity variables based on the airborne LiDAR, and then sample plot-level AGB prediction models were constructed using the parametric regression (nonlinear regression) and nonparametric methods (Random Forest, Support Vector Machine, K-Nearest Neighbor, Gradient Boosting Machine, and Multivariate adaptive regression splines). We evaluated accuracies of all the AGB prediction models we developed based on the fit statistics. Results showed that: (1) the elevation, density and intensity variables obtained from LiDAR point cloud data effectively predicted the AGB of the desert shrub community at a sample plot level, (2) the kappa coefficient of nonlinear mixed-effects (NLME) model obtained was 0.6977 with an improvement by 13% due to the random effects included into the model, and (3) the nonparametric model, such as Support Vector Machine showed the best fit statistics (R2 = 0.8992), which is 28% higher than the NLME-model, and effectively reduced the heteroscedasticity. The AGB prediction model presented in this paper, which is based on the airborne LiDAR data and machine learning algorithm, will provide a valuable tool to the managers and researchers for evaluating desert ecosystem productivity and monitoring desertification.

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.

Innovative agroecological practices can restore degraded farmlands and revive crop yields

Land degradation is a major obstacle to agricultural development in Africa, where it's accentuated by poor agricultural practices and climate change effects. Restoration of degraded lands is crucial to prevent incursions into virgin and marginal lands. A field experiment was carried out over a four-year period on two degraded sites, to assess and compare the effect of the common practices of: (i) burning crop residues and weeds by resource-poor farmers (T1) and (ii) burning crop residues and weeds followed with application of manure and/or NPK as external inputs (T2) by resource-endowed farmers with (iii) an innovative agroecological package (T3) on soil physical, chemical and biological attributes, and crop yields. T3 consisted of crop rotation and/ intercropping with Mucuna pruriens cover crop, grasses (Pennisetum purpureum and Setaria sphacelate) and shrubs (Calliandra calothyrsus, and Leucaena diversifolia), and the application of manure and NPK. A randomized complete block design with 8 and 10 blocks, with each package appearing once in each block, was used respectively, at Mulungu and Mushinga. The agroecological package significantly improved soil and plant parameters compared to the common practices at both sites. The average plot-level aboveground biomass was significantly lower (p < 0.001) in T1 (11.3–17.4 t/ha) and T2 (10.1–21.9) than in T3 (39.9–60.4 t/ha). Similar trends were observed for bean and maize grain and banana bunch yields. When T1 is compared to T3, mean yields increased 2.6 times (848 kg/ha against 327 kg/ha), 2.8 times (2,201 kg/ha against 792 kg/ha) and 1.5 times (7.4 t/ha against 5.0 t/ha) for bean grains, maize grain, and banana bunches, respectively. Improvements were also observed for soil physical, biological, and chemical properties. A decrease in soil temperature; and increases in soil porosity, earthworm density/m2 (1,932 against 0), nodules/bean plant (28 against 21) and root length density (65 against 15.5 cm) were observed in T3. T3 (compared to T1), had a decrease in acidity and Al3+; an increase in soil organic matter, K+, Ca+, and aboveground carbon stock (26.5 t/ha against 5.6 t/ha). The innovative agroecological package is thus an approach that can be used to effectively restore degraded and abandoned farmlands.

Direct use of large-footprint lidar waveforms to estimate aboveground biomass

Many studies have established the strong connections between aboveground biomass and lidar height metrics; however, these relationships are site-specific. Field data required to derive these relationships are not readily available in many cases. We developed a model to estimate plot-level aboveground biomass density (AGBD) directly from large-footprint lidar waveform measurements. An individual tree-based aboveground biomass (AGB)-height allometric relationship was scaled up to the plot level using lidar-waveform sensed tree height and crown size distribution characteristics. The AGBD was estimated based on a waveform/foliage profile-weighted height-based allometric equation. The AGBD-height scaling exponent was then built on the allometric relationships of tree height with stem diameter and crown volume with tree height. Global vegetation structure data analysis demonstrated that one general model (scaling exponent ~ 1.6–1.8) works reasonably well across all global forest biomes except boreal forests (scaling exponent ~ 0.9). We applied the model to estimate aboveground biomass in two distinct geographic regions: temperate deciduous/conifer forests in the northeastern USA and a montane conifer forest in Sierra National Forest in California. Local vegetation structural data analysis leads to a consistent height scaling exponent for these two distinct biomes, slightly different from the global data analysis results. This model produced optimal AGBD estimates using the local height scaling exponent value. Adequate AGBD estimates with the general height scaling exponent value were also provided by our model. Our analysis suggests one general allometric relationship between plot-level AGBD and large-footprint lidar waveforms. Integrating local structure allometric relationships improve the predictive accuracy of the model. Our model outperformed the lidar height metrics-based approach for AGBD estimates and overcame the biomass underestimation problem using height metrics for high biomass regions. This model could potentially serve as a general and robust model for monitoring forest carbon stocks using large-footprint lidar waveform measurements such as the Global Ecosystem Dynamics Investigation (GEDI) mission at the continental and global scales. The model could be a framework for integrating a demography-based terrestrial ecosystem model and GEDI global mission measurements to improve global carbon stock and flux estimates.

Estimating Aboveground Biomass of Two Different Forest Types in Myanmar from Sentinel-2 Data with Machine Learning and Geostatistical Algorithms

The accurate estimation of spatially explicit forest aboveground biomass (AGB) provides an essential basis for sustainable forest management and carbon sequestration accounting, especially in Myanmar, where there is a lack of data for forest conservation due to operational limitations. This study mapped the forest AGB using Sentinel-2 (S-2) images and Shuttle Radar Topographic Mission (SRTM) based on random forest (RF), stochastic gradient boosting (SGB) and Kriging algorithms in two forest reserves (Namhton and Yinmar) in Myanmar, and compared their performance against AGB measured by the traditional methods. Specifically, a suite of forest sample plots were deployed in the two forest reserves, and forest attributes were measured to calculate the plot-level AGB based on allometric equations. The spectral bands, vegetation indices (VIs) and textures derived from processed S-2 data and topographic parameters from SRTM were utilized to statistically link with field-based AGB by implementing random forest (RF) and stochastic gradient boosting (SGB) algorithms. Followed by an evaluation of the algorithmic performances, RF-based Kriging (RFK) models were employed to determine the spatial distribution of AGB as an improvement of accuracy against RF models. The study’s results showed that textural measures produced from wavelet analysis (WA) and vegetation indices (VIs) from Sentinel-2 were the strongest predictors for evergreen forest reserve (Namhton) AGB prediction and spectral bands and vegetation indices (VIs) showed the highest sensitivity to the deciduous forest reserve (Yinmar) AGB prediction. The fitted models were RF-based ordinary Kriging (RFOK) for Namhton forest reserve and RF-based co-Kriging (RFCK) for Yinmar forest reserve because their respective R2, whilst the RMSE values were validated as 0.47 and 24.91 AGB t/ha and 0.52 and 34.72 AGB t/ha, respectively. The proposed random forest Kriging framework provides robust AGB maps, which are essential to estimate the carbon sequestration potential in the context of REDD+. From this particular study, we suggest that the protection/disturbance status of forests affects AGB values directly in the study area; thus, community-participated or engaged forest utilization and conservation initiatives are recommended to promote sustainable forest management.

Upscaling aboveground biomass of larch (Larix olgensis Henry) plantations from field to satellite measurements: a comparison of individual tree-based and area-based approaches

ABSTRACT Currently, aboveground biomass (AGB) estimation primarily relies on an area-based approach (ABA), particularly at a large scale. With the advancement of individual tree detection techniques and the availability of multi-platform remotely sensed data, the individual tree-based approach (ITA) provides the potential of accurate and nondestructive fine AGB mapping. However, the performances of the two approaches on upscaling AGB from individual tree to stand level have not been thoroughly investigated. This study conducted the nondestructive AGB estimation using both ITA and ABA and compared the AGB estimates and their spatial uncertainties from an individual tree- to stand-level based on multi-platform LiDAR and Landsat 8 OLI imagery, taking Larix olgensis that is one of the most notable afforestation tree species in northeastern China as an example. Results showed that the point cloud segmentation (PCS) outperformed CHM-based individual tree crown delineation algorithms and obtained the highest accuracy of individual tree AGB estimate (R2 = 0.97, RMSE = 28.58 kg, rRMSE = 21.13%) for the dense larch plantations (about 1265 trees/ha). The plot-level AGB estimate aggregated by all detected trees and its uncertainty based on Monte Carlo simulation were 158.16 and 3.64 Mg·ha−1, respectively. The average pixel-level AGB of larch plantations in Maorshan Forest Farm estimated by ITA and ABA were similar (129.66 v.s 144.38 Mg·ha−1). ITA outperformed ABA in terms of pixel-level AGB accuracy and spatial uncertainty of pixel-level and stand-level AGB estimates. The overestimation of low AGB values, typical in ABA, was effectively eliminated by the ITA. The upscaling frameworks proposed in this study for AGB estimation and spatial uncertainty quantification based on ITA and ABA could be extended to other plantations or uncomplex forests. This study contributes to the accurate quantification of AGB and understanding of the uncertainties in the carbon stock of forest ecosystems at multi-scales.

A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets

ABSTRACT Accurately quantifying the aboveground biomass (AGB) of forests is crucial for understanding global change-related issues such as the carbon cycle and climate change. Many studies have estimated AGB from multiple remotely sensed datasets using various algorithms, but substantial uncertainties remain in AGB predictions. In this study, we aim to explore whether diverse algorithms stacked together are able to improve the accuracy of AGB estimates. To build the stacking framework, five base learners were first selected from a series of algorithms, including multivariate adaptive regression splines (MARS), support vector regression (SVR), multilayer perceptron (MLP) model, random forests (RF), extremely randomized trees (ERT), stochastic gradient boosting (SGB), gradient-boosted regression tree (GBRT) algorithm, and categorical boosting (CatBoost), based on diversity and accuracy metrics. Ridge and RF were utilized as the meta learner to combine the outputs of base learners. In addition, six important features were selected according to the feature importance values provided by the CatBoost, ERT, GBRT, SGB, MARS and RF algorithms as inputs of the meta learner in the stacking process. We then used stacking models with 3–5 selected base learners and ridge or RF to estimate AGB. The AGB data compiled from plot-level forest AGB, high-resolution AGB data derived from field and lidar data and the corresponding predictor variables extracted from the satellite-derived leaf area index, net primary production, forest canopy height, tree cover data, and Global Multiresolution Terrain Elevation Data 2010, as well as climate data, were randomly split into groups of 80% for training the model and 20% for model evaluation. The evaluation results showed that stacking generally outweighed the optimal base learner and provided improved AGB estimations, mainly by decreasing the bias. All stacking models had relative improvement (RI) values in bias of at least 22.12%, even reaching more than 90% under some scenarios, except for deciduous broadleaf forests, where an optimal algorithm could provide low biased estimations. In contrast, the improvements of stacking in R2 and RMSE were not significant. The stacking of MARS, MLP, and SVR provided improved results compared with the optimal base learner, and the average RI in R2 was 3.54% when we used all data without separating forest types. Finally, the optimal stacking model was used to generate global forest AGB maps.

Improving Plot-Level Model of Forest Biomass: A Combined Approach Using Machine Learning with Spatial Statistics

Estimating the aboveground biomass (AGB) at the plot level plays a major role in connecting accurate single-tree AGB measurements to relatively difficult regional AGB estimates. However, AGB estimates at the plot level suffer from many uncertainties. The goal of this study is to determine whether combining machine learning with spatial statistics reduces the uncertainty of plot-level AGB estimates. To illustrate this issue, this study evaluates and compares the performance of different models for estimating plot-level forest AGB. These models include three different machine learning models [support vector machine (SVM), random forest (RF), and a radial basis function artificial neural network (RBF-ANN)], one spatial statistic model (P-BSHADE), and three combinations thereof (SVM & P-BSHADE, RF & P-BSHADE, and RBF-ANN & P-BSHADE). The results show that the root mean square error, mean absolute error, and mean relative error of all combined models are substantially smaller than those of any individual model, with the RF & P-BSHADE combined method generating the smallest values. These results indicate that a combined approach using machine learning with spatial statistics, especially the RF & P-BSHADE model, improves the accuracy of plot-level AGB models. These research results contribute to the development of accurate large-forested-landscape AGB maps.

A 3D approach to model the taper of irregular tree stems: making plots biomass estimates comparable in tropical forests.

In tropical forests, the high proportion of trees showing irregularities at the stem base complicates forest monitoring. For example, in the presence of buttresses, the height of the point of measurement (HPOM ) of the stem diameter (DPOM ) is raised from 1.3 m, the standard breast height, up to a regular part of the stem. While DPOM is the most important predictor for tree aboveground biomass (AGB) estimates, the lack of harmonized HPOM for irregular trees in forest inventory increases the uncertainty in plot-level AGB stock and stock change estimates. In this study, we gathered an original non-destructive three-dimensional (3D) data set collected with terrestrial laser scanning and close range terrestrial photogrammetry tools in three sites in central Africa. For the 228 irregularly shaped stems sampled, we developed a set of taper models to harmonize HPOM by predicting the equivalent diameter at breast height (DBH') from a DPOM measured at any height. We analyzed the effect of using DBH' on tree-level and plot-level AGB estimates. To do so, we used destructive AGB data for 140 trees and forest inventory data from eight 1-ha plots in the Republic of Congo. Our results showed that our best simple taper model predicts DBH' with a relative mean absolute error of 3.7% (R2 = 0.98) over a wide DPOM range of 17-249 cm. Based on destructive AGB data, we found that the AGB allometric model calibrated with harmonized HPOM data was more accurate than the conventional local and pantropical models. At the plot level, the comparison of AGB stock estimates with and without HPOM harmonization showed an increasing divergence with the increasing share of irregular stems (up to -15%). The harmonization procedure developed in this study could be implemented as a standard practice for AGB monitoring in tropical forests as no additional forest inventory measurements is required. This would probably lead to important revisions of the AGB stock estimates in regions having a large number of irregular tree stems and increase their carbon sink estimates. The growing use of three-dimensional (3D) data offers new opportunities to extend our approach and further develop general taper models in other tropical regions.

Temporal dynamics of above ground biomass of Kaimoor Wildlife Sanctuary, Uttar Pradesh, India: conjunctive use of field and Landsat data

The present study assesses the temporal dynamics of above ground biomass (AGB) in Kaimoor Wildlife Sanctuary (KWS), Uttar Pradesh, India using series of Landsat data for the years 1989, 2000, 2010 and 2018. The satellite images were preprocessed for surface reflectance, and subsequently we computed Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI). A forest density map was generated by slicing the computed NDVI and this was used as a basis for sampling strategy. A total of 30 sampling locations were randomly identified. Plot size of 30 m × 30 m was established. Tree species were enumerated at each plot and the girth at breast height and tree height measurements were recorded for the year 2018. Tree and plot level AGB (i.e. AGBF, t ha−1) was computed by multiplying the tree volume and specific gravity of wood. AGB (i.e. AGBP) prediction models were developed as linear regression equations for the year 2018 by assessing the vegetation indices and the AGBF. The significant AGBP models (R2 = 0.94; P = 0.0001) were applied for all study years after the data correction among the Landsat sensor series. The AGBP was over estimated (22.67 t ha−1; 7.75%) compared to AGBF (t ha−1). Moreover, EVI (R2 = 0.90) was found to be a better predictor for AGB compared to NDVI (R2 = 0.69) or SAVI (R2 = 0.77). The AGBP of KWS ranged between 289 ± 36 (in 1989) and 292 ± 40 (in 2018) with an average decadal positive change of 1.06%.

Improving precision of field inventory estimation of aboveground biomass through an alternative view on plot biomass

We contrast a new continuous approach (CA) for estimating plot-level above-ground biomass (AGB) in forest inventories with the current approach of estimating AGB exclusively from the tree-level AGB predicted for each tree in a plot, henceforth called DA (discrete approach). With the CA, the AGB in a forest is modelled as a continuous surface and the AGB estimate for a fixed-area plot is computed as the integral of the AGB surface taken over the plot area. Hence with the CA, the portion of the biomass of in-plot trees that extends across the plot perimeter is ignored while the biomass from trees outside of the plot reaching inside the plot is added. We use a sampling simulation with data from a fully mapped two hectare area to illustrate that important differences in plot-level AGB estimates can emerge. Ideally CA-based estimates of mean AGB should be less variable than those derived from the DA. If realized, this difference translates to a higher precision from field sampling, or a lower required sample size. In our case study with a target precision of 5% (i.e. relative standard error of the estimated mean AGB), the CA required a 27.1% lower sample size for small plots of 100 m2 and a 10.4% lower sample size for larger plots of 1700 m2. We examined sampling induced errors only and did not yet consider model errors. We discuss practical issues in implementing the CA in field inventories and the potential in applications that model biomass with remote sensing data. The CA is a variation on a plot design for above-ground forest biomass; as such it can be applied in combination with any forest inventory sampling design.

A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA

This paper presents a prototype Carbon Monitoring System (CMS) developed to produce regionally unbiased annual estimates of aboveground biomass (AGB). Our CMS employed a bottom-up, two-step modeling strategy beginning with a spatially and temporally biased sample: project datasets collected and contributed by US Forest Service (USFS) and other forestry stakeholders in 29 different project areas in the northwestern USA. Plot-level AGB estimates collected in the project areas served as the response variable for predicting AGB primarily from lidar metrics of canopy height and density (R2 = 0.8, RMSE = 115 Mg ha−1, Bias = 2 Mg ha−1). This landscape model was used to map AGB estimates at 30 m resolution where lidar data were available. A stratified random sample of AGB pixels from these landscape-level AGB maps then served as training data for predicting AGB regionally from Landsat image time series variables processed through LandTrendr. In addition, climate metrics calculated from downscaled 30 year climate normals were considered as predictors in both models, as were topographic metrics calculated from elevation data; these environmental predictors allowed AGB estimation over the full range of observations with the regional model (R2 = 0.8, RMSE = 152 Mg ha−1, Bias = 9 Mg ha−1), including higher AGB values (>400 Mg ha−1) where spectral predictors alone saturate. For both the landscape and regional models, the machine-learning algorithm Random Forests (RF) was consistently applied to select predictor variables and estimate AGB. We then calibrated the regional AGB maps using field plot data systematically collected without bias by the national Forest Inventory and Analysis (FIA) Program. We found both our project landscape and regional, annual AGB estimates to be unbiased with respect to FIA estimates (Biases of 1% and 0.7%, respectively) and conclude that they are well suited to inform forest management and planning decisions by our contributing stakeholders.Social media abstractLidar-based biomass estimates can be upscaled with Landsat data to regionally unbiased annual maps.

Experimental drought indirectly enhances the individual performance and the abundance of an invasive annual weed

During environmental change, invasive species may be favored by increased resource input or reduced resource use of the resident community. Plasticity in certain plant traits of invasive species may be one possible mechanism behind their ability to quickly exploit unused resources. We tested whether rainfall manipulations (severe drought, moderate drought, watering) alter the growth and reproductive success of the invasive annual Conyza canadensis, and if it translates into a change in the abundance of the species in a semiarid perennial grassland in Central Hungary. Overall, C. canadensis exhibited greater individual performance and higher abundance in drought plots than in control and watered plots. At individual level, plants showed the strongest response to moderate drought: they grew 2.5-times taller than in control and watered plots, and produced twice and 2.5-times more seeds than in watered and control plots, respectively. Reproductive phenology was advanced in response to rain exclusions. Although severe drought caused 40% mortality, the cumulative performance of C. canadensis, expressed as plot-level aboveground biomass, was consistently greater in severe drought plots than in control and watered plots throughout the 3 years of the study. The higher performance of C. canadensis in drought plots is most likely due to the decreased abundance and, thus, decreased competitive effect of previously dominant perennial grasses caused by the rain exclusions. We conclude that drier summers that suppress perennial grasses will favor this alien annual forb, and phenotypic plasticity in growth and reproduction may contribute to its invasion success.

Estimating biomass of winter oilseed rape using vegetation indices and texture metrics derived from UAV multispectral images

Monitoring the above ground biomass (AGB) in winter oilseed rape is important for improving the agronomic management efficiency and predicting yield to ensure edible oil and biofuel supplies. The Yangtze River Basin in China accounts for one-fifth of the rapeseed yield in the world. However, the fragmented farming lands in the Yangtze River Basin make it difficult to accurately monitor winter oilseed rape growth at large scale using the medium-resolution satellite data. The low-altitude unmanned aerial vehicle (UAV) provides a feasible way to accurately and non-destructively estimate AGB in winter oilseed rape at the plot level. In this study, we evaluated the contributions of vegetation indices (VIs) and texture metrics, derived from multispectral images captured by the camera mounted on a UAV, to predict the AGB in winter oilseed rape. The AGB was estimated with (1) multiple VIs and (2) multiple VIs and texture metrics. The partial least square regression (PLSR) and random forest (RF) regression models were trained with datasets of the experiment in 2016–2017 and 2017–2018, and then applied to predict AGB of the 2018–2019 growth season. Results demonstrated that the incorporation of texture metrics to both PLSR and RF models provided more accurate estimations of AGB in winter oilseed rape than the models based solely on VIs. The accuracy of the AGB predicted by the RF regression model using VIs and texture metrics (RMSE = 274.18 kg/ha for the validation dataset) was slightly higher than the results of the PLSR model (RMSE = 284.09 kg/ha for the validation dataset). According to the evaluation of the important variables, the red edge chlorophyll index (CIred edge) and ratio vegetation index (RVI) were selected as the most important input features by PLSR and RF regression models. The normalized difference vegetation index (NDVI) contrast was selected as an important texture metrics for the AGB prediction, indicating that NDVI contrast could be a sensitive indicator of the spatial distribution of shadow caused by the different amount of biomass. This study suggested the great potential of UAV in estimating the plot-level AGB by combining VIs and texture metrics.

In situ biomass estimation at tree and plot levels: What did data record and what did algorithms derive from terrestrial and aerial point clouds in boreal forest

Prompted by laser scanning (LS), point clouds have been applied in forest biomass estimation for three decades. Previously reported evaluations focused on the accuracy of above-ground biomass (AGB) estimates but did not distinguish between the influences from the data and those from the algorithm of data processing. Therefore, insufficient information has been available for hardware and software developers to prioritize future developments. In the present study, we evaluated the amount of trees digitized in terrestrial and aerial point clouds by means of manual tree detection. On a plot-level (a fixed size of 32 m × 32 m), approximately 97%, 93%, and 75% of individual trees could be recorded in easy (ca. 700 stems/ha), medium (ca. 900 stems/ha), and difficult stands (ca. 2, 200 stems/ha), respectively, using five-scan terrestrial laser scanning (TLS). With aerial point cloud from unmanned aerial vehicle (UAV)-borne laser scanning (ULS) (ca. 450 points/m2), promising digitization can be expected for 87%, 69%, and 55% of individual trees in easy, medium and difficult stands, respectively. Plot-level AGB concentrates on big trees. The dominant and codominant trees combined accounted for 90.7%, 86.0%, and 69.7% of the plot-level AGB, and their population combined accounted for 73.2%, 55.3%, and 31.0%, respectively, in easy, medium, and difficult stands. Therefore, missing of dominant and codominant trees in data has a greater influence on plot-level AGB estimates than missing intermediate and suppressed trees. At a tree-level, when AGB was predicted from tree height and DBH, the relative root mean square error (RMSE%) of AGB estimates in easy, medium, and difficult plots were 10.1%, 14.6%, and 20.4%, respectively, based on manually measured tree parameters from TLS point clouds. The manual measurements from ULS point clouds provided RMSE%s of 30.4%, 51.1%, and 76.9% in easy, medium, and difficult plots, respectively. These results indicate the magnitude of errors introduced by the data, and for ULS, by the tree height–DBH allometry used. When using automated in-house algorithms of the Finnish Geospatial Research Institute (FGI), the RMSE%s of tree-level AGB estimates using TLS were 11.5%, 17.8%, and 43.9% in easy, medium, and difficult plots, respectively. When using ULS, the corresponding errors were 31.7%, 57.7%, and 73.3%, respectively. Such results suggest that the automated algorithms perform similarly to manual processing in the tree-level ABG estimation from terrestrial and aerial point clouds, specifically in easy and medium forest stands. The incomplete tree digitization in the data was the main factor affecting the accuracy of tree-centric AGB estimates. Fusion of the terrestrial and aerial observation perspectives provided promising results. When manual TLS-based DBH and manual ULS-based tree height were used, the RMSE% was reduced to 8.6%–12.7%, compared to 10.1%–20.4% when only TLS-manual tree parameters were used and 30.3%–76.9% when only ULS-manual tree parameters were used.

Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment

The Brazilian Atlantic Forest is a highly heterogeneous biome of global ecological significance with high levels of terrestrial carbon stocks and aboveground biomass (AGB). Accurate maps of AGB are required for monitoring, reporting, and modelling of forest resources and carbon stocks. Previous research has linked plot-level AGB with environmental and remotely sensed data using pixel-based approaches. However, few studies focused on investigating possible improvements via object-based image analysis (OBIA) including terrain related data to predict AGB in topographically variable and mountainous regions, such as Atlantic forest in Minas Gerais, Brazil. OBIA is expected to reduce known uncertainties related to the positional discrepancy between the image and field data and forest heterogeneity, while terrain derivatives are strong predictors in forest ecosystems driving forest biomass variability. In this research, we compare an object-based approach to a pixel-based method for modeling, mapping and quantifying AGB in the Rio Doce basin, within the Brazilian Atlantic Forest biome. We trained a random forest (RF) machine learning algorithm using environmental, terrain, and Landsat Thematic Mapper (TM) remotely sensed imagery. We aimed to: (i) increase the precision of the AGB estimates; (ii) identify optimal variables that fit the best model, with the lowest root mean square error (RMSE, Mg/ha); (iii) produce an accurate map of the AGB for the study area, and subsequently (iv) describing the AGB spatial distribution as a function of the selected variables. The RF object-based model notably improved the AGB prediction by reducing the mean absolute error (MAE) from 28.64 to 20.95%, and RMSE from 33.43 to 20.08 Mg/ha, and increasing the R² (from 0.57 to 0.86) by using a combination of selected remote sensing, environmental, and terrain variables. Object-based modelling is a promising alternative to common pixel-based approaches to reduce AGB variability in topographically diverse and heterogeneous environments. Investigation of mapped outcomes revealed a decreasing AGB from west towards the east region of the Rio Doce Basin. Over the entire study area, we map a total of 195,799,533 Mg of AGB, ranging from 25.52 to 238 Mg/ha, following seasonal precipitation patterns and anthropogenic disturbance effects. This study provided reliable AGB estimates for the Rio Doce basin, one of the most important watercourses of the globally important Brazilian Atlantic Forest. In conclusion, we highlight that OBIA is a better solution to map forest AGB than the pixel-based traditional method, increasing the precision of AGB estimates in a heterogeneous and mountain tropical environment.