Spatial Variation In Aboveground Biomass Research Articles

Temporal and Spatial Variation of Aboveground Biomass of Pinus densata and Its Drivers in Shangri-La, CHINA.

Understanding the drivers of forest aboveground biomass (AGB) is essential to further understanding the forest carbon cycle. In the upper Yangtze River region, where ecosystems are incredibly fragile, the driving factors that make AGB changes differ from other regions. This study aims to investigate AGB’s spatial and temporal variation of Pinus densata in Shangri-La and decompose the direct and indirect effects of spatial attribute, climate, stand structure, and agricultural activity on AGB in Shangri-La to evaluate the degree of influence of each factor on AGB change. The continuous sample plots from National Forest Inventory (NFI) and Landsat time series were used to estimate the AGB in 1987, 1992, 1997, 2002, 2007, 2012, and 2017. The structural equation model (SEM) was used to analyze the different effects of the four factors on AGB based on five scales: entire, 1987–2002, 2007–2017, low population density, and high population density. The results are as follows: (1) The AGB of Pinus densata in Shangri-La decreased from 1987 to 2017, with the total amount falling from 9.52 million tons to 7.41 million tons, and the average AGB falling from 55.49 t/ha to 40.10 t/ha. (2) At different scales, stand structure and climate were the drivers that directly affect the AGB change. In contrast, the agricultural activity had a negative direct effect on the AGB change, and spatial attribute had a relatively small indirect effect on the AGB change. (3) Analyzing the SEM results at different scales, the change of the contribution of the agricultural activity indicates that human activity is the main negative driver of AGB change in Shangri-La, especially at the high population density region. In contrast, the change of the contribution of the stand structure and climate indicates that the loss of old trees has an important influence on the AGB change. Forest resources here and other ecologically fragile areas should be gradually restored by adhering to policies, such as strengthening forest protection, improving forest stand quality, and limiting agricultural production activities.

Quantifying the Spatial Heterogeneity and Driving Factors of Aboveground Forest Biomass in the Urban Area of Xi’an, China

Investigating the spatial distribution of urban forest biomass and its potential influencing factors would provide useful insights for configuring urban greenspace. Although China is experiencing an unprecedented scale of urbanization, the spatial pattern of the urban forest biomass distribution as a critical component in the urban landscape has not been fully examined. Using the geographic detector method, this research examines the impacts of four geographical factors (GFs)—dominant tree species, forest categories, land types, and age groups—on the aboveground biomass distribution of urban forests in 1480 plots in Xi’an, China. The results indicate that (1) the aboveground biomass and four GFs show obvious heterogeneity regarding their spatial distribution in Xi’an; (2) the dominant tree species and age group which impacts the patterns of aboveground biomass are the primary GFs, with the independent q value (a statistic metric used to quantify the impacts of GFs in this study) reaching 0.595 and 0.202, respectively, while the forest category and land type were weakly linked to the spatial variation of aboveground biomass, with a q value of 0.087 and 0.076, respectively; and (3) the interactions among these four GFs also tend to contribute to the distribution pattern of aboveground biomass. The interactions between GFs achieved a larger impact than the sum of impacts that were independently obtained from the factors. Our results showed that the method of using a geographical detector is a useful tool in the urban area, and can reveal the driver pattern of aboveground biomass and provide a reference for city planning and management.

What controls local-scale aboveground biomass variation in central Africa? Testing structural, composition and architectural attributes

Tropical forests play a key role in regulating the terrestrial carbon cycle and climate change by storing a large amount of carbon. Yet, there is considerable uncertainty about the amount and spatial variation of aboveground biomass (AGB), especially in the relatively less studied African tropical forests. In this study, we explore the local-scale variation and determinants of plot-level AGB, between and within two types of forests, the Celtis and Manilkara forests, growing under the same climate but on different geological substrates in the northern Republic of Congo. In each forest site, all trees ≥10 cm diameter were censured in 36 × 1-ha plots and we measured tree height and crown size using a subsample of 18 × 1-ha of these plots. We developed height-diameter and crown-diameter allometric relationships and tested whether they differed between the two sites. For each 1-ha plot, we further estimated the AGB and calculated structural attributes (stem density and basal area), composition attributes (wood density) and architectural attributes (tree height and crown size), the latter being derived from site-specific allometric relationships. We found strong between-site differences in height-diameter and crown-diameter allometries. For a given diameter, trees were taller in the Celtis forest while they had larger crown in the Manilkara forest. Similar trends were found for the sixteen species present in both forest sites, suggesting an environmental control of tree allometry. Although there were some between-site differences in forest structure, composition and architecture, we did not detect any significant difference in mean AGB between the Celtis and the Manilkara forests. The AGB variation was related to the heterogeneous distribution of large trees, and influenced by basal area, height and crown dimensions, and to a lesser extent wood density. These forest attributes have strong practical implications on emerging remote-sensing technologies for carbon monitoring in tropical forests.

Above‐ground biomass is driven by mass‐ratio effects and stand structural attributes in a temperate deciduous forest

Abstract Forest ecosystems are critical for the global regulation of carbon (C), a substantial portion of which is stored in above‐ground biomass (AGB). While it is well understood that taxonomic and functional composition, stand structure and environmental gradients influence spatial variation in AGB, the relative strengths of these drivers at landscape scales have not been investigated in temperate forests. Furthermore, when biodiversity enhances C storage, it is unclear whether it is through mass‐ratio effects (i.e. the dominant trait in communities regulates AGB) or through niche complementarity (i.e. increased AGB due to interspecific resource partitioning). To address these mechanisms, we analysed data from a census of 28,262 adult trees sampled across 900 ha of temperate deciduous forest in southwestern Pennsylvania. We used data on four key plant functional traits to determine if (1) there is a positive relationship between species diversity and AGB and (2) whether this is due to mass‐ratio effects or niche complementarity. We also sought to (3) identify the physical stand structural attributes and topographic variables that influence AGB across this landscape. We found AGB was positively related to species richness and negatively related to species evenness, albeit weakly, while functional diversity indices had neutral effects. Above‐ground biomass was enhanced in communities dominated by traits related to greater maximum tree height, deeper minimum rooting depths and larger seeds. Most importantly, areas with high AGB were dominated by Acer saccharum and Liriodendron tulipifera. Overall, these results support mass‐ratio effects, with little evidence for niche complementarity. Synthesis. Stand structure, topography, and species and functional composition, but not taxonomic or functional diversity, were found to be key drivers of above‐ground biomass at landscape scales (<900 ha) in this temperate deciduous forest. Our findings suggest that simultaneously managing for both high diversity and for above‐ground carbon storage may prove challenging in some forest systems. Our results further indicate that the impact of tree biodiversity loss on above‐ground carbon stocks will depend greatly on the identity of the species that are lost.

Spatial pattern of grassland aboveground biomass and its environmental controls in the Eurasian steppe

Vegetation biomass is an important component of terrestrial ecosystem carbon stocks. Grasslands are one of the most widespread biomes worldwide, playing an important role in global carbon cycling. Therefore, studying spatial patterns of biomass and their correlations to environment in grasslands is fundamental to quantifying terrestrial carbon budgets. The Eurasian steppe, an important part of global grasslands, is the largest and relatively well preserved grassland in the world. In this study, we analyzed the spatial pattern of aboveground biomass (AGB), and correlations of AGB to its environment in the Eurasian steppe by meta-analysis. AGB data used in this study were derived from the harvesting method and were obtained from three data sources (literature, global NPP database at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL), some data provided by other researchers). Our results demonstrated that: (1) as for the Eurasian steppe overall, the spatial variation in AGB exhibited significant horizontal and vertical zonality. In detail, AGB showed an inverted parabola curve with the latitude and with the elevation, while a parabola curve with the longitude. In addition, the spatial pattern of AGB had marked horizontal zonality in the Black Sea-Kazakhstan steppe subregion and the Mongolian Plateau steppe subregion, while horizontal and vertical zonality in the Tibetan Plateau alpine steppe subregion. (2) Of the examined environmental variables, the spatial variation of AGB was related to mean annual precipitation (MAP), mean annual temperature (MAT), mean annual solar radiation (MAR), soil Gravel content, soil pH and soil organic content (SOC) at the depth of 0–30 cm. Nevertheless, MAP dominated spatial patterns of AGB in the Eurasian steppe and its three subregions. (3) A Gaussian function was found between AGB and MAP in the Eurasian steppe overall, which was primarily determined by unique patterns of grasslands and environment in the Tibetan Plateau. AGB was significantly positively related to MAP in the Black Sea-Kazakhstan steppe subregion (elevation < 3000 m), the Mongolian Plateau steppe subregion (elevation < 3000 m) and the surface (elevation ≥ 4800 m) of the Tibetan Plateau. Nevertheless, the spatial variation in AGB exhibited a Gaussian function curve with the increasing MAP in the east and southeast margins (elevation < 4800 m) of the Tibetan Plateau. This study provided more knowledge of spatial patterns of AGB and their environmental controls in grasslands than previous studies only conducted in local regions like the Inner Mongolian temperate grassland, the Tibetan Plateau alpine grassland, etc.

Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models.

Understanding the processes that determine above‐ground biomass (AGB) in Amazonian forests is important for predicting the sensitivity of these ecosystems to environmental change and for designing and evaluating dynamic global vegetation models (DGVMs). AGB is determined by inputs from woody productivity [woody net primary productivity (NPP)] and the rate at which carbon is lost through tree mortality. Here, we test whether two direct metrics of tree mortality (the absolute rate of woody biomass loss and the rate of stem mortality) and/or woody NPP, control variation in AGB among 167 plots in intact forest across Amazonia. We then compare these relationships and the observed variation in AGB and woody NPP with the predictions of four DGVMs. The observations show that stem mortality rates, rather than absolute rates of woody biomass loss, are the most important predictor of AGB, which is consistent with the importance of stand size structure for determining spatial variation in AGB. The relationship between stem mortality rates and AGB varies among different regions of Amazonia, indicating that variation in wood density and height/diameter relationships also influences AGB. In contrast to previous findings, we find that woody NPP is not correlated with stem mortality rates and is weakly positively correlated with AGB. Across the four models, basin‐wide average AGB is similar to the mean of the observations. However, the models consistently overestimate woody NPP and poorly represent the spatial patterns of both AGB and woody NPP estimated using plot data. In marked contrast to the observations, DGVMs typically show strong positive relationships between woody NPP and AGB. Resolving these differences will require incorporating forest size structure, mechanistic models of stem mortality and variation in functional composition in DGVMs.

Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome.

Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate “wall-to-wall” remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution.

Analysis of biophysical and anthropogenic variables and their relation to the regional spatial variation of aboveground biomass illustrated for North and East Kalimantan, Borneo

BackgroundLand use and land cover change occurring in tropical forest landscapes contributes substantially to carbon emissions. Better insights into the spatial variation of aboveground biomass is therefore needed. By means of multiple statistical tests, including geographically weighted regression, we analysed the effects of eight variables on the regional spatial variation of aboveground biomass. North and East Kalimantan were selected as the case study region; the third largest carbon emitting Indonesian provinces.ResultsStrong positive relationships were found between aboveground biomass and the tested variables; altitude, slope, land allocation zoning, soil type, and distance to the nearest fire, road, river and city. Furthermore, the results suggest that the regional spatial variation of aboveground biomass can be largely attributed to altitude, distance to nearest fire and land allocation zoning.ConclusionsOur study showed that in this landscape, aboveground biomass could not be explained by one single variable; the variables were interrelated, with altitude as the dominant variable. Spatial analyses should therefore integrate a variety of biophysical and anthropogenic variables to provide a better understanding of spatial variation in aboveground biomass. Efforts to minimise carbon emissions should incorporate the identified factors, by 1) the maintenance of lands with high AGB or carbon stocks, namely in the identified zones at the higher altitudes; and 2) regeneration or sustainable utilisation of lands with low AGB or carbon stocks, dependent on the regeneration capacity of the vegetation. Low aboveground biomass densities can be found in the lowlands in burned areas, and in non-forest zones and production forests.Electronic supplementary materialThe online version of this article (doi:10.1186/s13021-014-0008-z) contains supplementary material, which is available to authorized users.

The spatial pattern of grassland aboveground biomass on Xizang Plateau and its climatic controls

Aims Grassland is the most widely distributed vegetation type on the Xizang Plateau. Accurate remote sensing estimation of the grassland aboveground biomass (AGB) in this region is influenced by the types of vegetation indexes (VIs) used, the grain size (resolution) of the remote sensing data and the targeted ecosystem features. This study attempts to answer the following questions: (i) Which VI can most accurately reflect the grassland AGB distribution on the Xizang Plateau? (ii) How does the grain size of remote sensing imagery affect AGB reflection? (iii) What is the spatial distribution pattern of the grassland AGB on the plateau and its relationship with the climate? Methods We investigated 90 sample sites and measured site-specific AGBs using the harvest method for three grassland types (alpine meadow, alpine steppe and desert steppe). For each sample site, four VIs, namely, Normalized Difference VI (NDVI), Enhanced VI, Normalized Difference Water Index (NDWI) and Modified Soil-Adjusted VI (MSAVI) were extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) products with grain sizes of 250 m and 1 km. Linear regression models were employed to identify the best estimator of the AGB for the entire grassland and the three individual grassland types. Paired Wilcoxon tests were applied to assess the grain size effect on the AGB estimation. General linear models were used to quantify the relationships between the spatial distribution of the grassland AGB and climatic factors. Important Findings The results showed that the best estimator for the entire grassland AGB on the Xizang Plateau was MSAVI at a 250 m grain size (MSAVI(250) (m)). For each individual grassland type, the best estimator was MSAVI at a grain size of 250 m for alpine meadow, NDWI at a grain size of 1 km for alpine steppe and NDVI at a grain size of 1 km for desert steppe. The explanation ability of each VI for the grassland AGB did not significantly differ for the two grain sizes. Based on the best fit model (AGB = -10.80 + 139.13 MSAVI(250 m)), the spatial pattern of the grassland AGB on the plateau was characterized. The AGB varied from 1 to 136 g m(-2). Approximately 59% of total spatial variation in the AGB for the entire grassland was explained by the combination of the mean annual precipitation (MAP) and mean annual temperature. The explanatory power of MAP was weaker for each individual grassland type than that for the entire grassland. This study illustrated the high efficiency of the VIs derived from MODIS data in the grassland AGB estimation on the Xizang Plateau due to the vegetation homogeneity within a 1 x 1 km pixel in this region. Furthermore, MAP is a primary driver on the spatial variation of AGB at a regional scale.

Effects of land-use and hydroperiod on aboveground biomass and productivity of secondary Amazonian floodplain forests

Tropical floodplain forests are productive and diverse ecosystems for which there is scant data on biomass, carbon sequestration, and potential abiotic and human drivers. We used a regression approach to test the effects of seasonal flood duration, forest age, and livestock activity on aboveground biomass (AGB) and annual biomass accumulation (ABA) in 49 plots of 0.1ha in secondary floodplain forests of the Lower Amazon over nine years. AGB averaged 226±87Mgha−1 among forests 30–120years old. An intermediate peak model explained spatial variation in AGB, peaking in moderately flooded (70–140dy−1) forests. Flood duration was the key explanatory factor for AGB across all plots. In contrast, forest age and its interaction with flood duration affected net ABA, which declined from 10.3±4.3 to −6.2±11.1Mgha−1y−1 with increasing age. Tree diameter growth comprised 95±4% of total ABA, which declined with increasing flood duration and increasing forest age. Overall, forests had a high capacity to capture carbon, accumulating 16.4±7.1Mgha−1y−1 in AGB, but had high turnover of biomass at 76±81% of AGB per year. There was no strong evidence for differences in biomass accumulation due to livestock activity. We fill a major geographical gap for ground-based data on biomass of flooded forests, which comprise 11% of the Amazon Basin, and also provide an example of community-based monitoring for carbon storage in human-dominated tropical floodplain ecosystems.

Disentangling stand and environmental correlates of aboveground biomass in Amazonian forests

Tropical forests contain an important proportion of the carbon stored in terrestrial vegetation, but estimated aboveground biomass (AGB) in tropical forests varies two-fold, with little consensus on the relative importance of climate, soil and forest structure in explaining spatial patterns. Here, we present analyses from a plot network designed to examine differences among contrasting forest habitats (terra firme, seasonally flooded, and white-sand forests) that span the gradient of climate and soil conditions of the Amazon basin. We installed 0.5-ha plots in 74 sites representing the three lowland forest habitats in both Loreto, Peru and French Guiana, and we integrated data describing climate, soil physical and chemical characteristics and stand variables, including local measures of wood specific gravity (WSG). We use a hierarchical model to separate the contributions of stand variables from climate and soil variables in explaining spatial variation in AGB. AGB differed among both habitats and regions, varying from 78 Mg ha � 1 in white-sand forest in Peru to 605 Mg ha � 1 in terra firme clay forest of French Guiana. Stand variables including tree size and basal area, and to a lesser extent WSG, were strong predictors of spatial variation in AGB. In contrast, soil and climate variables explained little overall variation in AGB, though they did co-vary to a limited extent with stand parameters that explained AGB. Our results suggest that positive feedbacks in forest structure and turnover control AGB in Amazonian forests, with richer soils (Peruvian terra firme and all seasonally flooded habitats) supporting smaller trees with lower wood density and moderate soils (French Guianan terra firme) supporting many larger trees with high wood density. The weak direct relationships we observed between soil and climate variables and AGB suggest that the most appropriate approaches to landscape scale modeling of AGB in the Amazon would be based on remote sensing methods to map stand structure.