Aboveground Shrub Biomass Research Articles

Shrub aboveground biomass estimation considering bulk volume geometry: general and specific models for 14 species in the Mediterranean central Chile

Shrub aboveground biomass estimation considering bulk volume geometry: general and specific models for 14 species in the Mediterranean central Chile

Mercury mobilization in shrubland after a prescribed fire in NE Portugal: Insight on soil organic matter composition and different aggregate size

Soils constitute the major reservoir of mercury (Hg) in terrestrial ecosystems, whose stability may be threatened by wildfires. This research attempts to look at the effect of prescribed fire on the presence of Hg in a shrubland ecosystem from NE Portugal, delving into its relationship with soil aggregate size and the molecular composition of soil organic matter (SOM). During the prescribed fire, on average 347 mg Hg ha−1 were lost from the burnt aboveground biomass of shrubs and 263 mg Hg ha−1 from the combustion of the soil organic horizon. Overall, Hg concentration and pools in the mineral soil did not show significant changes due to burning, which highlights their role as long-term Hg reservoirs. The higher Hg concentrations found in smaller aggregates (<0.2 mm) compared to coarser ones (0.5–2 mm) are favored by the higher degree of organic matter decomposition (low C/N ratio), rather than by greater total organic C contents. The Hg-enriched finest fraction of soil (<0.2 mm) could be more prone to be mobilized by erosion, whose potential arrival to water bodies increases the environmental concern for the Hg present in fire-affected soils. The SOM quality (molecular composition) and the main organic families, analyzed by Fourier-transform infrared spectroscopy in combination with multivariate statistical analysis, significantly conditioned the retention/emission of Hg in the uppermost soil layers. Thus, before the fire, Hg was strongly linked to lipid and protein fractions, while Hg appeared to be linked to aromatic-like compounds in fire-affected SOM.

Dynamic Model for Caragana korshinskii Shrub Aboveground Biomass Based on Theoretical and Allometric Growth Equations

As one of the ways to achieve carbon neutralization, shrub biomass plays an important role for natural resource management decision making in arid regions. To investigate biomass dynamic variations of Caragana korshinskii, a typical shrub found in the arid desert area of Ningxia, northwest China, we combined a nonlinear simultaneous (NLS) equation system with theoretical growth (TG) and allometric growth (AG) equations. On the basis of a large biomass survey dataset and analytical data of shrub stems, four methods (NOLS, NSUR, 2SLS, and 3SLS) of the NLS equations system were combined with the TG and AG equations. A model was subsequently established to predict the AGB growth of C. korshinskii. The absolute mean residual (AMR), root mean system error (RMSE), and adjusted determination coefficient (adj-R2) were used to evaluate the performance of the equations. Results revealed that the NSUR method of the NLS equations had better performance than other methods and the independent equations for BD and H growth and AGB. Additionally, the NSUR method exhibited extremely significant differences (p &lt; 0.0001) when compared with the equations without heteroscedasticity on the basis of the likelihood ratio (LR) test, which used the power function (PF) as the variance function. The NSUR method of the NLS equations was an efficient method for predicting the dynamic growth of AGB by combining the TG and AG equations and could estimate the carbon storage for shrubs accurately, which was important for stand productivity and carbon sequestration capacity.

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

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

Quantifying the Aboveground Biomass (AGB) of Gobi Desert Shrub Communities in Northwestern China Based on Unmanned Aerial Vehicle (UAV) RGB Images

Shrubs are an important part of the Gobi Desert ecosystem, and their aboveground biomass (AGB) is an important manifestation of the productivity of the Gobi Desert ecosystem. Characterizing the biophysical properties of low-stature vegetation such as shrubs in the Gobi Desert via conventional field surveys and satellite remote sensing images is challenging. The AGB of shrubs had been estimated from spectral variables taken from high-resolution images obtained by unmanned aerial vehicle (UAV) in the Gobi Desert, Xinjiang, China, using vegetation feature metrics. The main results were as follows: (1) Based on the UAV images, several RGB vegetation indices (RGB VIs) were selected to extract the vegetation coverage, and it was found that the excess green index (EXG) had the highest accuracy and the overall extraction accuracy of vegetation coverage reached 97.00%. (2) According to field sample plot surveys, the AGB and shrub crown area of single shrubs in the Gobi Desert were in line with a power model. From the bottom of the alluvial fan to the top of the alluvial fan, as the altitude increased, the AGB of the vegetation communities showed an increasing trend: the AGB of the vegetation communities at the bottom of the alluvial fan was 2–90 g/m2, while that at the top of the alluvial fan was 60–201 g/m2. (3) Vegetation coverage (based on the UAV image EXG index) and AGB showed a good correlation. The two conform to the relationship model (R2 = 0.897) and the expression is Y = 1167.341 x0.946, where Y is the AGB of the sample plots in units g/m2 and x is the vegetation coverage extracted by the VI. (4) The predicted AGB values of Gobi Desert shrubs using UAV RGB images based on a power model were closer to the actual observed AGB values. The study findings provide a more efficient, accurate, and low-cost method for estimating vegetation coverage and AGB of Gobi Desert shrubs.

Allometric models to predict the individual aboveground biomass of shrubs of Malagasy xerophytic thickets

Xerophytic thicket (XT) is a shrubby vegetation found in the semi-arid southern and south-western Madagascar. Above ground biomass (AGB) is a key element to understand how this ecosystem is operating and then to design and implement any sustainable management strategy. This study aimed to build AGB allometric models for Malagasy XT shrubs. One hundred and ninety-eight individuals belonging to 20 XT common species were harvested. The models linked AGB to height, diameter at breast height (DBH), basal diameter, stem number and wood specific gravity. Empirical models taking into account the shrub architecture (multistemmed with single basal stem and first branches below a height of 1.3 m, multistemmed with multiple basal stems and shrubs with first branches over a height of 1.3 m) are more efficient to predict XT shrub AGB than the pantropical tree AGB model.

Temporal dynamics of carbon storage in a Mediterranean mountain scrubland managed by prescribed fire

Farmland abandonment and reduction of grazing activity, mainly in mountain areas with remote access and ageing population, have been contributing to shrub encroachment in such territories and, consequently, to increase fuel load available for triggering wildfires. Accordingly, it is necessary to use vegetation management practices in order to reduce wildfire risk, prescribed fire being one of the most common techniques used in the Mediterranean region. This research focused in the effects of a prescribed fire (PF) applied in Montesinho Natural Park (PNM), NE Portugal, on the temporal dynamics of carbon storage in mineral soil, litter layer (organic horizon), and shrub biomass. Before PF and thirty-six months after PF, aboveground shrub biomass was collected in areas of 1 m2 in 11 plots randomly distributed in the experimental shrub area. Also, in the same plots, litter thickness was measured and soil samples were collected before, two, six and thirty-six months after PF, in order to assess carbon concentration, bulk density and coarse elements content. Despite low to moderate fire intensity, carbon storage changes were observed in all compartments evaluated. Thirty-six months after PF, carbon storage in aboveground biomass of shrub species (7.4 Mg C ha−1) was roughly two-thirds of that recorded prior to PF, and in litter layer (1.6 Mg C ha−1) it was about half of that in the original situation (before PF). In contrast, the mineral soil showed a 10% carbon increase (6.4 Mg C ha−1). Based on the balance between losses (shrub species and litter layer) and gains (mineral soil), at the end of the monitoring period (36 months), there was an annual positive rate of carbon storage, equivalent to 0.2 Mg C ha−1 year−1. Even after anthropogenic disturbances, such as prescribed fire, shrub communities constitute important terrestrial carbon pools; hence, these ecosystems might play an important role in mitigating climate change.

UAV-based individual shrub aboveground biomass estimation calibrated against terrestrial LiDAR in a shrub-encroached grassland

Shrub encroachment is an important ecological issue that is increasingly receiving global attention in arid and semiarid grasslands. Monitoring the spatial distribution of encroached shrub aboveground biomass (AGB) is critical for ecological conservation and adaptive ecosystem management. However, the low stature and fine spatial heterogeneity of encroached shrub communities increase difficulties for coarse spatial-resolution satellite images to adequately capture detailed characteristics of individual shrubs. Unmanned aerial vehicle (UAV) can acquire centimeter-level optical images or high-density LiDAR point cloud data, providing an effective means to map encroached shrub AGB spatially explicitly, even at the individual scale. In this study, we first extracted the individual shrubs based on thresholds in normalized difference vegetation index (NDVI) and canopy height model (CHM) using UAV-based multispectral and LiDAR data. For each shrub, we then derived and determined the dominant geometric, spectral, and textural features from the high-resolution multispectral image and the volumetric features from the LiDAR data as predictors of shrub AGB. Finally, we compared the capability of different data sources (UAV-based multispectral image, LiDAR, and their combination) and regression methods (multiple linear, random forest, and support vector regression) to estimate and map the individual shrub AGB in the study area. The volume-based approaches to individual shrub AGB, including global convex hull method, voxel method, and surface differencing method, were also employed using terrestrial laser scanning (TLS) to further calibrate the UAV-based estimation. Our results show that individual shrubs can be accurately extracted based on the threshold method with an overall classification accuracy of 91.8%. The UAV-based AGB estimation suggests that the textural feature, the sum of contrast metric within the individual shrub canopy, is the most important predictor of individual shrub AGB, followed by volumetric, geometric and spectral features. Moreover, the high-resolution multispectral image shows greater potential (R2 = 0.83, RMSE = 106.46 g) than LiDAR (R2 = 0.77, RMSE = 123.33 g) in the estimation of individual shrub AGB, and their combination can only slightly improve the estimation accuracy (R2 = 0.86, RMSE = 101.97 g). Our results also show that TLS-derived volume based on the surface differencing method obtained the best prediction accuracy of individual shrub AGB (R2 = 0.91, RMSE = 79.98 g), and can be used as an alternative of destructive harvesting. This study provides a new insight for quantifying and mapping individual shrub AGB using UAV-based optical sensors and TLS without destructive harvesting in arid and semiarid grasslands.

Aboveground Biomass Allocation of Boreal Shrubs and Short-Stature Trees in Northwestern Canada

In this follow-on study on aboveground biomass of shrubs and short-stature trees, we provide plant component aboveground biomass (herein ‘AGB’) as well as plant component AGB allometric models for five common boreal shrub and four common boreal short-stature tree genera/species. The analyzed plant components consist of stem, branch, and leaf organs. We found similar ratios of component biomass to total AGB for stems, branches, and leaves amongst shrubs and deciduous tree genera/species across the southern Northwest Territories, while the evergreen Picea genus differed in the biomass allocation to aboveground plant organs compared to the deciduous genera/species. Shrub component AGB allometric models were derived using the three-dimensional variable volume as predictor, determined as the sum of line-intercept cover, upper foliage width, and maximum height above ground. Tree component AGB was modeled using the cross-sectional area of the stem diameter as predictor variable, measured at 0.30 m along the stem length. For shrub component AGB, we achieved better model fits for stem biomass (60.33 g ≤ RMSE ≤ 163.59 g; 0.651 ≤ R2 ≤ 0.885) compared to leaf biomass (12.62 g ≤ RMSE ≤ 35.04 g; 0.380 ≤ R2 ≤ 0.735), as has been reported by others. For short-stature trees, leaf biomass predictions resulted in similar model fits (18.21 g ≤ RMSE ≤ 70.0 g; 0.702 ≤ R2 ≤ 0.882) compared to branch biomass (6.88 g ≤ RMSE ≤ 45.08 g; 0.736 ≤ R2 ≤ 0.923) and only slightly better model fits for stem biomass (30.87 g ≤ RMSE ≤ 11.72 g; 0.887 ≤ R2 ≤ 0.960), which suggests that leaf AGB of short-stature trees (&lt;4.5 m) can be more accurately predicted using cross-sectional area as opposed to diameter at breast height for tall-stature trees. Our multi-species shrub and short-stature tree allometric models showed promising results for predicting plant component AGB, which can be utilized for remote sensing applications where plant functional types cannot always be distinguished. This study provides critical information on plant AGB allocation as well as component AGB modeling, required for understanding boreal AGB and aboveground carbon pools within the dynamic and rapidly changing Taiga Plains and Taiga Shield ecozones. In addition, the structural information and component AGB equations are important for integrating shrubs and short-stature tree AGB into carbon accounting strategies in order to improve our understanding of the rapidly changing boreal ecosystem function.

Soil organic carbon and total nitrogen predict large-scale distribution of soil fungal communities in temperate and alpine shrub ecosystems

Soil fungi play important roles in soil organic matter decomposition, nutrient cycling processes and maintaining close associations with aboveground plant communities. However, the large-scale distribution patterns and drivers of soil fungal communities in shrub ecosystems remain largely unknown. In this study, we examined soil fungal communities among different shrub sites, and the relative importance of plant communities, abiotic factors and spatial distance in shaping the variations of soil fungal communities over large scales. Herb plant richness and diversity, shrub aboveground biomass (AGB), and soil properties showed varying differences among different sites. Soil fungal community diversity was the highest in Huangyuan and Guide, whereas the lowest in Dulan. Soil fungal community structures were significantly different among temperate and alpine grasslands. Random forest analysis showed that soil fungal α diversity was mainly predicted by soil organic carbon (SOC) and total nitrogen (TN), followed by other parameters such as AGB and mean annual precipitation. Variation partition analysis and redundancy analysis showed that shifts in shrub fungal community structures were predominantly determined by SOC and TN, followed by plant and spatial factors such as herb plant diversity, latitude and longitude. Collectively, our results suggested that incorporation of SOC and TN into Earth System Model might improve the prediction of fungal communities in terrestrial shrub ecosystems.

Allometric Equations for Shrub and Short-Stature Tree Aboveground Biomass within Boreal Ecosystems of Northwestern Canada

Aboveground biomass (AGB) of short-stature shrubs and trees contain a substantial part of the total carbon pool within boreal ecosystems. These ecosystems, however, are changing rapidly due to climate-mediated atmospheric changes, with overall observed decline in woody plant AGB in boreal northwestern Canada. Allometric equations provide a means to quantify woody plant AGB and are useful to understand aboveground carbon stocks as well as changes through time in unmanaged boreal ecosystems. In this paper, we provide allometric equations, regression coefficients, and error statistics to quantify total AGB of shrubs and short-stature trees. We provide species- and genus-specific as well as multispecies allometric models for shrub and tree species commonly found in northwestern boreal forest and peatland ecosystems. We found that the three-dimensional field variable (volume) provided the most accurate prediction of shrub multispecies AGB (R2 = 0.79, p &lt; 0.001), as opposed to the commonly used one-dimensional variable (basal diameter) measured on the longest and thickest stem (R2 = 0.23, p &lt; 0.001). Short-stature tree AGB was most accurately predicted by stem diameter measured at 0.3 m along the stem length (R2 = 0.99, p &lt; 0.001) rather than stem length (R2 = 0.29, p &lt; 0.001). Via the two-dimensional variable cross-sectional area, small-stature shrub AGB was combined with small-stature tree AGB within one single allometric model (R2 = 0.78, p &lt; 0.001). The AGB models provided in this paper will improve our understanding of shrub and tree AGB within rapidly changing boreal environments.

Bayesian model predicts the aboveground biomass of Caragana microphylla in sandy lands better than OLS regression models

Abstract Aims In forest ecosystems, different types of regression models have been frequently used for the estimation of aboveground biomass, where Ordinary Least Squares (OLS) regression models are the most common prediction models. Yet, the relative performance of Bayesian and OLS models in predicting aboveground biomass of shrubs, especially multi-stem shrubs, has relatively been less studied in forests. Methods In this study, we developed the biomass prediction models for Caragana microphylla Lam. which is a widely distributed multi-stems shrub, and contributes to the decrease of wind erosion and the fixation of sand dunes in the Horqin Sand Land, one of the largest sand lands in China. We developed six types of formulations under the framework of the regression models, and then, selected the best model based on specific criteria. Consequently, we estimated the parameters of the best model with OLS and Bayesian methods with training and test data under different sample sizes with the bootstrap method. Lastly, we compared the performance of the OLS and Bayesian models in predicting the aboveground biomass of C. microphylla. Important Findings The performance of the allometric equation (power = 1) was best among six types of equations, even though all of those models were significant. The results showed that mean squared error of test data with non-informative prior Bayesian method and the informative prior Bayesian method was lower than with the OLS method. Among the tested predictors (i.e. plant height and basal diameter), we found that basal diameter was not a significant predictor either in OLS or Bayesian methods, indicating that suitable predictors and well-fitted models should be seriously considered. This study highlights that Bayesian methods, the bootstrap method and the type of allometric equation could help to improve the model accuracy in predicting shrub biomass in sandy lands.

Quantifying Aboveground Biomass of Shrubs Using Spectral and Structural Metrics Derived from UAS Imagery

Shrub-dominated ecosystems support biodiversity and play an important storage role in the global carbon cycle. However, it is challenging to characterize biophysical properties of low-stature vegetation like shrubs from conventional ground-based or remotely sensed data. We used spectral and structural variables derived from high-resolution unmanned aerial system (UAS) imagery to estimate the aboveground biomass of shrubs in the Betula and Salix genera in a montane meadow in Banff National Park, Canada using an area-based approach. In single-variable linear regression models, visible light (RGB) indices outperformed multispectral or structural data. A linear model based on the red ratio vegetation index (VI) accumulated over shrub area could model biomass (calibration R2 = 0.888; validation R2 = 0.774) nearly as well as the top multivariate linear regression models (calibration R2 = 0.896; validation R2 &gt; 0.750), which combined an accumulated RGB VI with a multispectral metric. The excellent performance of accumulated RGB VIs represents a novel approach to fine-scale vegetation biomass estimation, fusing spectral and spatial information into a single parsimonious metric that rivals the performance of more complex multivariate models. Methods developed in this study will be relevant to researchers interested in estimating fine-scale shrub aboveground biomass within a range of ecosystems.

Is the removal of aboveground shrub biomass an effective technique to restore a shrub‐encroached grassland?

Encroachment of woody plants into grasslands is a global phenomenon that has substantial impacts on pastoral productivity and ecosystem services. Over the past half century, pastoralists and land management agencies have explored various options to control woody plants in order to improve ecosystem services in shrub‐encroached grasslands. We examined the effectiveness of controlling the encroachment of the shrub Caragana microphylla into grassland in Inner Mongolia, China. We cut and removed all of the aboveground biomass from 450 shrubs, predicting that the effectiveness of this technique to control shrubs would depend on shrub morphology. Specifically, we expected that larger shrubs with more biomass would be more difficult to kill by cutting than smaller shrubs. A year after treatment, we found that cutting killed only 11% of the 450 treated shrubs, and of these, three‐quarters of the locations that they occupied reverted to grasses and one‐quarter to bare soil. Shrubs that survived the cutting treatment produced more stems and leaf biomass, and therefore had a greater leaf to stem ratio. Shrubs that died after cutting had a lower crown area and basal area, and less stem biomass than shrubs that resprouted within 12 months of cutting. There were no effects of shrub height on the fate of treated shrubs. Cutting had no effect on understory plant cover or richness, but reproductive plants were taller under shrubs that were not cut. Overall, our study showed that removing aboveground shrub biomass by cutting is an ineffective technique for “restoring” the original grassland community unless shrubs are very small. Strategic targeting of small shrubs would be a more effective technique for controlling the spread of C. microphylla in the long term.

Above-Ground Biomass Models of Caragana korshinskii and Sophora viciifolia in the Loess Plateau, China

The quantification of above-ground biomass is based on the calculation of carbon storage, which is important for the balance of carbon cycling. However, the allometric models of shrubs for calculating the above-ground biomass of shrubs in the Loess Plateau are scarce. In order to solve this issue, this study analyzed some highly correlated variables, including height (H), branch diameters (D), canopy volume (Cv), canopy area (Ca), and then built a regression model to predict the above-ground biomass in two common shrubs (Caragana korshinskii and Sophora viciifolia) in the Loess Plateau, China. The results show that the above-ground biomass of these two shrubs can be accurately predicted by H and D, and then we can use allometric model (y = axb) to calculate shrub above-ground biomass (including leaf biomass and branch biomass). Furthermore, the correlation between leaf biomass and branch biomass in Caragana korshinskii and Sophora viciifolia indicates that the components of above-ground biomass are closely related to each other. In addition, there is a strong linear relationship (p &lt; 0.01) between the observed and estimated biomass values, which confirms the data accuracy of the above-ground biomass estimation models. In summary, these two biomass estimation models provide an accurate way to estimate the quantification of carbon for shrubs in the Loess Plateau.

Shrub biomass estimation in semi-arid sandland ecosystem based on remote sensing technology

Global land degradation and sustainable development has become a serious challenge for the terrestrial ecosystems. Shrub plays a crucial role in global ecosystem protection, ecological reconstruction, which is especially important in arid and semi-arid sandland ecosystem. Shrub above ground biomass (AGB) is a proxy of carbon sequestration capacity. Shrub AGB in Mu Us Sandland was estimated using different methods based on Landsat Thematic Mapper (TM) data, topography data, combined with in situ survey data. Linear regression model, multiple stepwise regression model, machine learning model and geometric optical model were used to estimate shrub biomass in combination with in situ data, respectively and their effects were validated and compared. Results showed that shrub AGB predicted from one multiple stepwise regression model with Ratio Vegetation Index (RVI) and Brightness from K-T transformation as input variables reached highest accuracy. For both high and low shrub coverage regions, shrub AGB distribution maps derived from this multiple stepwise regression model achieved higher precision. All these findings will provide a scientific support for ecological sustainable development in eco-vulnerable ecosystems.

Tundra plant above-ground biomass and shrub dominance mapped across the North Slope of Alaska

Arctic tundra is becoming greener and shrubbier due to recent warming. This is impacting climate feedbacks and wildlife, yet the spatial distribution of plant biomass in tundra ecosystems is uncertain. In this study, we mapped plant and shrub above-ground biomass (AGB; kg m−2) and shrub dominance (%; shrub AGB/plant AGB) across the North Slope of Alaska by linking biomass harvests at 28 field sites with 30 m resolution Landsat satellite imagery. We first developed regression models (p < 0.01) to predict plant AGB (r2 = 0.79) and shrub AGB (r2 = 0.82) based on the normalized difference vegetation index (NDVI) derived from imagery acquired by Landsat 5 and 7. We then predicted regional plant and shrub AGB by combining these regression models with a regional Landsat NDVI mosaic built from 1721 summer scenes acquired between 2007 and 2016. Our approach employed a Monte Carlo uncertainty analysis that propagated sampling and sensor calibration errors. We estimated that plant AGB averaged 0.74 (0.60, 0.88) kg m−2 (95% CI) and totaled 112 (91, 135) Tg across the region, with shrub AGB accounting for ~43% of regional plant AGB. The new maps capture landscape variation in plant AGB visible in high resolution satellite and aerial imagery, notably shrubby riparian corridors. Modeled shrub AGB was strongly correlated with field measurements of shrub canopy height at 25 sites (rs = 0.88) and with a regional map of shrub cover (rs = 0.76). Modeled plant AGB and shrub dominance were higher in shrub tundra than graminoid tundra and increased between areas with the coldest and warmest summer air temperatures, underscoring the fact that future warming has the potential to greatly increase plant AGB and shrub dominance in this region. These new biomass maps provide a unique source of ecological information for a region undergoing rapid environmental change.

Rapid Recovery of Gross Production and Respiration in a Mesic Mountain Big Sagebrush Ecosystem Following Prescribed Fire

The impact of land management actions such as prescribed fire remains a key uncertainty in understanding the spatiotemporal patterns of carbon cycling in the Western USA. We therefore quantified carbon exchange and aboveground carbon stocks following a prescribed fire in a mountain big sagebrush ecosystem located in the northern Great Basin, USA. Specifically, we examined the changes in plant functional type, leaf area index, standing aboveground carbon stocks, net ecosystem production (NEP), gross ecosystem production (GEP), and ecosystem-level respiration (Reco) for 2 years before and 7 of 9 years after a prescribed fire. Post-burn GEP and Reco exceeded pre-burn GEP and Reco within 2 years and remained elevated. The variation in GEP and Reco provided no evidence of a large and prolonged net efflux of carbon in the 9 years after the fire. Rather, NEP indicated the site was a sink before and after the fire, with little change in sink strength associated with the burn. Re-sprouting and recruitment of grasses and forbs drove the post-burn increase in GEP. Woody shrub growth was the dominant control on aboveground biomass accumulation after fire, with shrub aboveground biomass reaching ~ 11% of pre-burn biomass after 5 years. The rapid recovery of GEP and the growth of mid-successional shrubs suggest ecosystem-level carbon fluxes and stocks can recover rapidly after fire in mesic mountain big sagebrush ecosystems.

Bryophytes and Organic layers Control Uptake of Airborne Nitrogen in Low-N Environments.

The effects of atmospheric nitrogen (N) deposition on ecosystem functioning largely depend on the retention of N in different ecosystem compartments, but accumulation and partitioning processes have rarely been quantified in long-term field experiments. In the present study we analysed for the first time decadal-scale flows and allocation patterns of N in a heathland ecosystem that has been subject to airborne N inputs over decades. Using a long-term 15N tracer experiment, we quantified N retention and flows to and between ecosystem compartments (above-ground/below-ground vascular biomass, moss layer, soil horizons, leachate). After 9 years, about 60% of the added 15N-tracer remained in the N cycle of the ecosystem. The moss layer proved to be a crucial link between incoming N and its allocation to different ecosystem compartments (in terms of a short-term capture, but long-term release function). However, about 50% of the 15N captured and released by the moss layer was not compensated for by a corresponding increase in recovery rates in any other compartment, probably due to denitrification losses from the moss layer in the case of water saturation after rain events. The O-horizon proved to be the most important long-term sink for added 15N, as reflected by an increase in recovery rates from 18 to 40% within 8 years. Less than 2.1% of 15N were recovered in the podzol-B-horizon, suggesting that only negligible amounts of N were withdrawn from the N cycle of the ecosystem. Moreover, 15N recovery was low in the dwarf shrub above-ground biomass (<3.9% after 9 years) and in the leachate (about 0.03% within 1 year), indicating still conservative N cycles of the ecosystem, even after decades of N inputs beyond critical load thresholds. The continuous accumulation of reactive forms of airborne N suggests that critical load-estimates need to account for cumulative effects of N additions into ecosystems.

A reliable and non-destructive method for estimating forage shrub cover and biomass in arid environments using digital vegetation charting technique

Despite the importance of fodder shrubs to small ruminant diets and production in arid and semi-arid ecosystems, they are often not considered when quantifying grazing land potential. This oversight is mainly due to the time consuming and costly traditional techniques used to estimate shrub biomass. The shrub fodder component should be measured to avoid underestimation of the carrying capacity of rangelands. In this study, we present a fast, reliable and non-destructive method to estimate canopy vegetation cover to obtain aboveground shrub biomass. The experiment was conducted under field conditions in northwest Syria, where seedlings of seven shrub species were monitored for one year: Atriplex leucoclada (Moq.) Boiss., A. halimus L., A. lentiformis (Torr.) S. Watson, A. canescens (Pursh) Nutt., A. nummularia Lindl., Salsola vermiculata L. and Haloxylon aphyllum (C.A. Meyer) Bunge. The experimental layout was a randomized complete block design with five replications. We explored the effectiveness of digital vegetation charting technique (DVCT) for estimating shrub canopy cover. Aboveground shrub biomass was clipped to estimate the dry matter (DM) weight per species and to determine its relationship to canopy cover. In this study, an estimate of greenness (percent green vegetation cover) was extracted by way of greenness algorithms. Simple linear regressions between vegetation cover and biomass for 210 plots were performed. The cover of the seven species differed (P < 0.01): A. leucoclada had the highest vegetation cover (56%) and H. aphyllum the lowest (7%). Vegetation cover and DM biomass were positively correlated (P < 0.01) with R-squared ranging from 0.66 (H. aphyllum) to 0.84 (S. vermiculata). Our method provided reasonable estimations of canopy coverage which could predict aboveground phytomass. We conclude that DVCT offers a rapid, reliable and consistent measurement of shrub cover and biomass provided that shrubs have open architecture. This study shows the potential of digital cameras and image processing to determine cover/biomass in a non-destructive, timely and cost efficient way.