Aboveground Biomass Density Research Articles

Changes in tree species diversity, composition and aboveground biomass in areas of fuelwood harvesting in miombo woodland ecosystems of southern Malawi

ABSTRACTFuelwood is an essential forest product for small-holder farmers in the tropics, but fuelwood harvesting may cause forest degradation and impact ecosystem services. Understanding tree species composition, diversity and biomass changes in forests with active fuelwood collection is important for informing sustainable forest harvesting. In the miombo woodlands of southern Malawi, using forest plots, we investigated: 1) if forests with fuelwood harvesting (n = 50) have different tree, sapling and seedling stem density, species diversity, species composition, and aboveground biomass (AGB) than forests with minimal use (n = 36); and 2) if forest product harvesting pressure and access are predictors of tree stem density, diversity and AGB. We found a significant reduction in tree diversity and AGB but not stem density, and different species composition in areas with fuelwood harvesting compared to reference sites. For saplings, stem density was higher and species composition was different in fuelwood harvesting sites. Seedling Shannon index and Simpson’s diversity were lower in fuelwood harvesting sites. Harvesting pressure and access were predictors of AGB and tree stem density. The reduced AGB and tree species diversity may hinder collection of fuelwood and other forest products, and may reduce ecosystem functioning. Exploring the possibility of forest landscape restoration in the area could be beneficial.

Biomass and carbon budgeting of sustainable agroforestry systems as ecosystem service in Indian Himalayas

ABSTRACTAdoption of agroforestry is paramount as a climate change mitigation and adaptation strategy. The assessment of plant biomass is crucial for understanding the vulnerability of biological systems to climate change. In the present study, agroforestry systems viz., agrisilviculture (AS), agrihorticulture (AH), agrihortisilviculture (AHS) and agrisilvihorticulture (ASH) were investigated for biomass production and carbon stock in vegetation as well as in soil in the Indian central Himalaya along the elevation i.e. E1 (<1100 m), E2 (1100–1400 m), E3 (1400–1700 m), E4 (1700–2000 m) and E5 (>2000 m). Mean aboveground and belowground biomass were 73.9% and 26.1%, respectively, of total biomass (64.4 t ha−1) in agroforestry systems. Fodder and/or timber trees accounted for 31% (in AHS) to 74% (in AS) of total biomass, while fruit trees accounted for 18% (in ASH) to 73% (in AH) of total biomass. The contribution of agriculture crops to total biomass fluctuated between 19% (in ASH) and 26% (in AH). Total vegetation biomass, soil carbon and total carbon density in agroforestry systems increased significantly along the elevation, with maximum biomass at elevation E5 (32.0 t ha−1, 64.7 t C ha−1 and 96.7 t C ha−1). Total biomass of vegetation among agroforestry systems differed significantly. Soil carbon stock was highest in AHS (59.5 t C ha−1) and total carbon density (vegetation + soil) was highest in ASH (93.0 t C ha−1). Thus, in Indian Himalayas, vegetation biomass, carbon stock, soil and total carbon (vegetation + soil) stock increased along the elevation.Abbrviations: AG: aboveground; BG: belowground; WD: wood density; VOB: volume over bark; BEF: biomass expansion factor; AS: agrisilviculture; AH: agrihorticulture; ASH: agrisilvihorticulture; AHS: agrihortisilviculture; E: elevation; C: carbon; CO2: carbon-di-oxide; IPCC: Intergovernmental Panel on Climate Change; DBH: diameter at breast height; AGBD: aboveground biomass density; BGBD: belowground biomass density; GSVD: growing stock volume density

Beyond MRV: high-resolution forest carbon modeling for climate mitigation planning over Maryland, USA

Forests are important ecosystems that are under increasing pressure from human use and environmental change, and have a significant ability to remove carbon dioxide from the atmosphere, and are therefore the focus of policy efforts aimed at reducing deforestation and degradation as well as increasing afforestation and reforestation for climate mitigation. Critical to these efforts is the accurate monitoring, reporting and verification of current forest cover and carbon stocks. For planning, the additional step of modeling is required to quantitatively estimate forest carbon sequestration potential in response to alternative land-use and management decisions. To be most useful and of decision-relevant quality, these model estimates must be at very high spatial resolution and with very high accuracy to capture important heterogeneity on the land surface and connect to monitoring efforts. Here, we present results from a new forest carbon monitoring and modeling system that combines high-resolution remote sensing, field data, and ecological modeling to estimate contemporary above-ground forest carbon stocks, and project future forest carbon sequestration potential for the state of Maryland at 90 m resolution. Statewide, the contemporary above-ground carbon stock was estimated to be 110.8 Tg C (100.3–125.8 Tg C), with a corresponding mean above-ground biomass density of 103.7 Mg ha−1 which was within 2% of independent empirically-based estimates. The forest above-ground carbon sequestration potential for the state was estimated to be much larger at 314.8 Tg C, and the forest above-ground carbon sequestration potential gap (i.e. potential-current) was estimated to be 204.1 Tg C, nearly double the current stock. These results imply a large statewide potential for future carbon sequestration from afforestation and reforestation activities. The high spatial resolution of the model estimates underpinning these totals demonstrate important heterogeneity across the state and can inform prioritization of actual afforestation/reforestation opportunities. With this approach, it is now possible to quantify both the forest carbon stock and future carbon sequestration potential over large policy relevant areas with sufficient accuracy and spatial resolution to significantly advance planning.

Spatiotemporal dynamics of the molluscan community associated with seagrass on the western equatorial Atlantic

AbstractThis study aimed to qualitatively and quantitatively analyse the molluscan assemblages associated with a Halodule wrightii seagrass bed in a rarely studied area within a conservation unit in north-eastern Brazil. Seasonal and spatial changes in several seagrass meadow characteristics, including sediment, were evaluated to explain temporal and spatial variations in the molluscs found there. The molluscan community differed in its structure among periods and meadows, as well as in the composition of its infaunal and epifaunal assemblages. The results of this study indicated that molluscs are affected by the particular characteristics of a seagrass meadow, especially by its location in the intertidal zone, more than by the area of the meadow. Molluscs were also affected by other characteristics of the seagrass meadow, such as above-ground biomass and shoot density. Changes in all molluscan assemblages were also mediated by differences among months and seasons in this region of the western equatorial Atlantic, but not by seasonal changes of the meadow. The studied meadow was found to be one of the densest in Brazil, which has considerable importance to its associated fauna.

The GEDI Simulator: A Large-Footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions.

NASA's Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar mission which will produce near global (51.6°S to 51.6°N) maps of forest structure and above‐ground biomass density during its 2‐year mission. GEDI uses a waveform simulator for calibration of algorithms and assessing mission accuracy. This paper implements a waveform simulator, using the method proposed in Blair and Hofton (1999; https://doi.org/10.1029/1999GL010484), and builds upon that work by adding instrument noise and by validating simulated waveforms across a range of forest types, airborne laser scanning (ALS) instruments, and survey configurations. The simulator was validated by comparing waveform metrics derived from simulated waveforms against those derived from observed large‐footprint, full‐waveform lidar data from NASA's airborne Land, Vegetation, and Ice Sensor (LVIS). The simulator was found to produce waveform metrics with a mean bias of less than 0.22 m and a root‐mean‐square error of less than 5.7 m, as long as the ALS data had sufficient pulse density. The minimum pulse density required depended upon the instrument. Measurement errors due to instrument noise predicted by the simulator were within 1.5 m of those from observed waveforms and 70–85% of variance in measurement error was explained. Changing the ALS survey configuration had no significant impact on simulated metrics, suggesting that the ALS pulse density is a sufficient metric of simulator accuracy across the range of conditions and instruments tested. These results give confidence in the use of the simulator for the pre‐launch calibration and performance assessment of the GEDI mission.

Modeling wetland aboveground biomass in the Poyang Lake National Nature Reserve using machine learning algorithms and Landsat-8 imagery

Quantitative estimation of wetland aboveground biomass (AGB) is an essential aspect in evaluating the health and conservation of this valuable ecosystem. We combine AGB field measurements and remote sensing data to establish a suitable model for estimating wetland AGB in the Poyang Lake National Nature Reserve (PLNNR), which is included in the Ramsar Convention’s List of Wetlands of International Importance. All field sampling points cover four dominant vegetation communities (Carex cinerascen, Phalaris arundinacea, Artemisia selengensis, and Miscanthus sacchariflorus) in the PLNNR. Wetland AGB is retrieved from the Landsat-8 OLI image. To improve the accuracy of wetland AGB estimation, we compare the performances of three machine learning algorithms, namely, random forest (RF), back-propagation neural network (BPNN), and support vector regression (SVR), with linear regression (LR) in estimating the AGB in the PLNNR. Results are as follows: (1) the RF model with a root-mean-square error of 0.25 kg m − 2 performs better than BPNN (0.29 kg m − 2), SVR (0.27 kg m − 2), and LR (0.31 kg m − 2) in our testing dataset, and AGB density in the PLNNR is between 0 and 1.973 kg m − 2. (2) The four most important features for AGB modeling are near-infrared, short-wave infrared 1 band, enhanced vegetation index, and red band. Our study presents an effective and operational RF model that estimates wetland AGB from Landsat data, providing a scientific basis for floodplain wetland carbon accounting and possible future studies, such as the linkage between wetland AGB and the great water level fluctuations.

Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China

Quantification of forest aboveground biomass density (AGB) is useful in forest carbon cycle studies, biodiversity protection and climate-change mitigation actions. However, a finer resolution and spatially continuous forest AGB map is inaccessible at national level in China. In this study, we developed forest type- and ecozone-specific allometric models based on 1607 field plots. The allometric models were applied to Geoscience Laser Altimeter System (GLAS) data to calculate AGB at the footprint level. We then mapped a 30 m resolution national forest AGB by relating the GLAS footprint AGB to various variables derived from Landsat images and Phased Array L-band Synthetic Aperture Radar (PALSAR) data. We estimated the average forest AGB to be 69.88 Mg/ha with a standard deviation of 35.38 Mg/ha and the total AGB carbon stock to be 5.44 PgC in China. Our AGB estimates corresponded reasonably well with AGB inventories from the top ten provinces in the forested area, and the coefficient of determination and root mean square error were 0.73 and 20.65 Mg/ha, respectively. We found that the main uncertainties for AGB estimation could be attributed to errors in allometric models and in height measurements by the GLAS. We also found that Landsat-derived variables outperform PALSAR-derived variables and that the textural features of PALSAR better support forest AGB estimates than backscattered intensity.

Determinants of Above-Ground Biomass and Its Spatial Variability in a Temperate Forest Managed for Timber Production

The proper estimation of above-ground biomass (AGB) stocks of managed forests is a prerequisite to quantifying their role in climate change mitigation. The aim of this study was to analyze the spatial variability of AGB and its uncertainty between actively managed pine and unmanaged pine-oak reference forests in central Mexico. To investigate the determinants of AGB, we analyzed variables related to forest management, stand structure, topography, and climate. We developed linear (LM), generalized additive (GAM), and Random Forest (RF) empirical models to derive spatially explicit estimates and their uncertainty, and compared them. AGB was strongly influenced by forest management, as LiDAR-derived stand structure and stand age explained 80.9% to 89.8% of its spatial variability. The spatial heterogeneity of AGB varied positively with stand structural complexity and age in the managed forests. The type of predictive model had an impact on estimates of total AGB in our study site, which varied by as much as 19%. AGB densities varied from 0 to 492 ± 17 Mg ha−1 and the highest values were predicted by GAM. Uncertainty was not spatially homogeneously distributed and was higher with higher AGB values. Spatially explicit AGB estimates and their association with management and other variables in the study site can assist forest managers in planning thinning and harvesting schedules that would maximize carbon stocks on the landscape while continuing to provide timber and other ecosystem services. Our study represents an advancement toward the development of efficient strategies to spatially estimate AGB stocks and their uncertainty, as the GAM approach was used for the first time with improved results for such a purpose.

Optimizing biomass estimates of savanna woodland at different spatial scales in the Brazilian Cerrado: Re-evaluating allometric equations and environmental influences

Cerrado is the second largest biome in South America and accounted for the second largest contribution to carbon emissions in Brazil for the last 10 years, mainly due to land-use changes. It comprises approximately 2 million km2 and is divided into 22 ecoregions, based on environmental conditions and vegetation. The most dominant vegetation type is cerrado sensu stricto (cerrado ss), a savanna woodland. Quantifying variation of biomass density of this vegetation is crucial for climate change mitigation policies. Integrating remote sensing data with adequate allometric equations and field-based data sets can provide large-scale estimates of biomass. We developed individual-tree aboveground biomass (AGB) allometric models to compare different regression techniques and explanatory variables. We applied the model with the strongest fit to a comprehensive ground-based data set (77 sites, 893 plots, and 95,484 trees) to describe AGB density variation of cerrado ss. We also investigated the influence of physiographic and climatological variables on AGB density; this analysis was restricted to 68 sites because eight sites could not be classified into a specific ecoregion, and one site had no soil texture data. In addition, we developed two models to estimate plot AGB density based on plot basal area. Our data show that for individual-tree AGB models a) log-log linear models provided better estimates than nonlinear power models; b) including species as a random effect improved model fit; c) diameter at 30 cm above ground was a reliable predictor for individual-tree AGB, and although height significantly improved model fit, species wood density did not. Mean tree AGB density in cerrado ss was 22.9 tons ha-1 (95% confidence interval = ± 2.2) and varied widely between ecoregions (8.8 to 42.2 tons ha-1), within ecoregions (e.g. 4.8 to 39.5 tons ha-1), and even within sites (24.3 to 69.9 tons ha-1). Biomass density tended to be higher in sites close to the Amazon. Ecoregion explained 42% of biomass variation between the 68 sites (P < 0.01) and shows strong potential as a parameter for classifying regional biomass variation in the Cerrado.

FUKUSHIMA FALLOUT IN SAKHALIN REGION, RUSSIA, PART 2: 137Cs AND 134Cs IN GRASSLAND VEGETATION

Samples of vegetation (vascular plants) were collected at 14 grasslands on Kunashir, Iturup, Urup and Paramushir Islands in August–September 2012. All surveyed grasslands were virgin lands with respect to Fukushima fallout. Four plots were used as pastures for cattle in 2012. About 1 kg of green vegetation (mixed grass-forb crop) was taken from each of 13 plots. Seven samples of wormwood (Artemisia sp.) and three samples of Kuril dwarf bamboo (Sasa sp.) of the same mass were collected for comparison. A density of the above ground biomass was estimated at one of the plots. Activities of134Cs and137Cs radionuclides were determined by direct γ-ray spectrometry method using HP-germanium detectors. Inventories of134Cs and137Cs in the top 20 cm layer of soil were estimated by the authors earlier for each of these 14 plots based of the soil samples analysis. Caesium-134, a marker of Fukushima fallout, was determined in 18 of 24 vegetation samples. Caesium-137 activity was quantified in all of 24 samples. The activity concentration of the radionuclides in plants (wet weight) ranged from &lt;0.05 Bq kg−1to 1.6 Bq kg-1for134Cs and from 0.06 Bq kg−1to 5.8 Bq kg-1for137Cs. About 2/5 (median = 41%, n = 18) of the total activity of137Cs in plants was associated with the Fukushima accident. The soil-to-plant aggregated transfer coefficient (Tag) values in mixed grass-forb crop ranged from &lt;0.2 × 10–3m2kg-1to 11 × 10–3m2kg-1for134Cs and from 0.08 × 10–3m2kg-1to 3.3 × 10–3m2kg-1for137Cs. The Tagvalues for134Cs were statistically significantly higher compared to the Tag values for 137Cs. The median Tagfor134Cs in mixed grass-forb crop decreased in the 2011–2012 period by a factor of about two: from 12 × 10–3m2kg-1to 6.6 × 10–3m2kg-1. The ecological half-time, Teco, of134Cs in the plants was approximately one year. In 2011–2012, the median Tagfor pre-Fukushima137Cs in mixed grass-forb crop was estimated as 0.12 × 10–3m2kg-1. This value is lower by a factor of 100 and 50 compared to the median values of Tagthat were deduced for Fukushima-derived radiocaesium in 2011 and 2012, respectively. The radiocaesium Tagvalues for Sasa sp. and Artemisia sp. agreed with those for mixed grass-forb crop. At grasslands with the aboveground biomass density of 1 kg m–2and the Tagof 6 × 10–3m2kg-1 for134Cs, the contribution of the vegetation contamination to total inventory of the radionuclide did not exceed 1%. For137Cs, this contribution was less than 0.1%.

Development and dominance of Douglas-fir in North American rainforests

One of the five tallest tree species, Pseudotsuga menziesii has enormous economic and ecological importance, but rainforests dominated by this species are not as well understood as their drier montane counterparts. We climbed and measured 30 trees up to 97 m tall growing in coastal forests of the Olympic Peninsula and northern California to quantify structural attributes—leaves, bark, cambium, sapwood, heartwood, deadwood, biomass, growth increments, and age—and combined these with an equal number of trees up to 85 m tall growing in forests of the Cascade Mountains to develop allometric equations based on ground-level predictors. After comparing new equations to those previously published, we applied the best available equations for tall forests to predict aboveground quantities of all vascular plant species in 12 ha of Olympic and Cascade forests. The largest (117 Mg) and one of the oldest (615 years) trees we studied had the highest biomass increment (305 kg yr−1), but age had a negative effect on current and long-term growth increments. After accounting for variation in tree size and aboveground vigor, older trees produced less wood annually and grew less efficiently than younger trees. Size of P. menziesii trees increased more rapidly, and the proportion of biomass and leaf area in P. menziesii decreased more slowly, in Olympic than Cascade forests over six centuries following stand-replacing fire. Maximum aboveground biomass (1999 Mg ha−1) and carbon density (994 Mg ha−1) occurred in a Cascade forest with high abundance of three conifer species (P. menziesii, Tsuga heterophylla, Thuja plicata), but maximum P. menziesii biomass (1289 Mg ha−1) occurred in an Olympic forest with 50 trees ha−1 up to 90 m tall. Vulnerability to wood decay fungi and dependence on fire for stand dominance limit P. menziesii biomass accumulation in rainforests.

Estimating urban above ground biomass with multi-scale LiDAR

BackgroundUrban trees have long been valued for providing ecosystem services (mitigation of the “heat island” effect, suppression of air pollution, etc.); more recently the potential of urban forests to store significant above ground biomass (AGB) has also be recognised. However, urban areas pose particular challenges when assessing AGB due to plasticity of tree form, high species diversity as well as heterogeneous and complex land cover. Remote sensing, in particular light detection and ranging (LiDAR), provide a unique opportunity to assess urban AGB by directly measuring tree structure. In this study, terrestrial LiDAR measurements were used to derive new allometry for the London Borough of Camden, that incorporates the wide range of tree structures typical of an urban setting. Using a wall-to-wall airborne LiDAR dataset, individual trees were then identified across the Borough with a new individual tree detection (ITD) method. The new allometry was subsequently applied to the identified trees, generating a Borough-wide estimate of AGB.ResultsCamden has an estimated median AGB density of 51.6 Mg ha–1 where maximum AGB density is found in pockets of woodland; terrestrial LiDAR-derived AGB estimates suggest these areas are comparable to temperate and tropical forest. Multiple linear regression of terrestrial LiDAR-derived maximum height and projected crown area explained 93% of variance in tree volume, highlighting the utility of these metrics to characterise diverse tree structure. Locally derived allometry provided accurate estimates of tree volume whereas a Borough-wide allometry tended to overestimate AGB in woodland areas. The new ITD method successfully identified individual trees; however, AGB was underestimated by ≤ 25% when compared to terrestrial LiDAR, owing to the inability of ITD to resolve crown overlap. A Monte Carlo uncertainty analysis identified assigning wood density values as the largest source of uncertainty when estimating AGB.ConclusionOver the coming century global populations are predicted to become increasingly urbanised, leading to an unprecedented expansion of urban land cover. Urban areas will become more important as carbon sinks and effective tools to assess carbon densities in these areas are therefore required. Using multi-scale LiDAR presents an opportunity to achieve this, providing a spatially explicit map of urban forest structure and AGB.

Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations

Lidar provides critical information on the three-dimensional structure of forests. However, collecting wall-to-wall laser altimetry data at regional and global scales is cost prohibitive. As a result, studies employing lidar for large area estimation typically collect data via strip sampling, leaving large swaths of the forest unmeasured by the instrument. The goal of this research was to develop and examine the performance of a coregionalization modeling approach for combining field measurements, strip samples of airborne lidar and Landsat-based remote sensing products to predict aboveground biomass (AGB) in interior Alaska's Tanana Valley. The proposed modeling strategy facilitates mapping of AGB density across the domain. Additionally, the coregionalization framework allows for estimation of total AGB for arbitrary areal units within the study area—a key advance to support diverse management objectives in interior Alaska. This research focuses on characterization of prediction uncertainty in the form of posterior predictive coverage intervals and standard deviations. Using the framework detailed here, it is possible to quantify estimation uncertainty for any spatial extent, ranging from point-level predictions of AGB density to estimates of AGB stocks for the full domain. The lidar-informed coregionalization models consistently outperformed their counterpart lidar-free models in terms of point-level predictive performance and total (mean) AGB precision. Additionally, including a Landsat-derived forest cover covariate further improved precision in regions with lower lidar sampling intensity. Findings also demonstrate that model-based approaches not explicitly accounting for residual spatial dependence can grossly underestimate uncertainty, resulting in falsely precise estimates of AGB. The inferential capabilities of AGB posterior predictive distribution (PPD) products extend beyond simply mapping AGB density. We show how PPD products can provide insight regarding drivers of AGB heterogeneity in boreal forests, including permafrost and fire, highlighting the range of potential applications for Bayesian geostatistical methods to integrate field, airborne and satellite data.

Lidar supported estimators of wood volume and aboveground biomass from the Danish national forest inventory (2012–2016)

National forest inventories (NFI) provide estimates of forest resources at the national and regional level but are also increasingly used as basis for mapping forest resources based on remotely sensed data. Such maps procure local estimates of forest resources but may also improve precision of national and regional estimates. Supported by a countrywide airborne laser scanning (circa 2014) and a national land-use map (circa 2014), direct (DI), model-assisted (MA), and model calibrated (MC) estimates of wood volume (V) and aboveground biomass (AGB) densities in forest areas derived from the Danish NFI (2012–2016) are presented. Nonlinear models with three LiDAR metrics are used to predict V and AGB in forested areas. According to these models, the predicted values of V and AGB in sample plots missed in the field inventory was lower than in those visited in the field; we therefore opted for estimation with multiple (stochastic) imputations. MA estimates for the country suggested a 2% lower level of both V and AGB densities with errors 45% lower than estimated errors in DI results. National MC estimates were close to the DI estimates with an error approximately 40% lower than errors in DI estimates yet 5% greater than the MA estimates of error. Multiple imputations had the strongest impact on DI estimates, but only a weak impact on MA and MC results.

Mapping of the spatial distribution of carbon storage of the Pinus kesiya Royle ex Gordon (Benguet pine) forest in Sagada, Mt. Province, Philippines

ABSTRACTThe objective of this study is to map the spatial distribution of the aboveground biomass (AGB, tC/ha) storage of the Pinus kesiya Royle ex Gordon (Benguet pine) forest of Sagada, Mt. Province, Philippines by integrating Landsat image and the forest cover map. The data was obtained from 66 plots that were established in the different Benguet pine stands in Sagada. The AGB was estimated using the Digital Numbers (DN) and Normalized Difference Vegetation Index (NDVI) values (with filter and with no filter). The estimated aboveground biomass (AGB) density of the Benguet pine was determined to be 249.66 tonnes/ha corresponding to 112.35 tonnesC/ha.

Landscape‐scale lidar analysis of aboveground biomass distribution in secondary Brazilian Atlantic Forest

AbstractSecondary forests account for more than half of tropical forests and represent a growing carbon sink, but rates of biomass accumulation vary by a factor of two or more even among plots in the same landscape. To better understand the drivers of this variability, we used airborne lidar to measure forest canopy height and estimate biomass over 4529 ha at Serra do Conduru Park in Southern Bahia, Brazil. We measured trees in 30 georeferenced field plots (0.25‐ha each) to estimate biomass using allometry. Then we estimated aboveground biomass density (ABD) across the lidar study area using a statistical model developed from our field plots. This model related the 95th percentile of the distribution of lidar return heights toABD. We overlaid this map ofABDon a Landsat‐derived forest age map to determine rates of biomass accumulation. We found rapid initial biomass regeneration (~6 Mg/ha yr), which slowed as forests aged. We also observed high variability in both height and biomass across the landscape within forests of similar age. Nevertheless, a regression model that accounted for spatial autocorrelation and included forest age, slope, and distance to roads or open areas explained 62 and 77 percent of the landscape variation inABDand canopy height, respectively. Thus, while there is high spatial heterogeneity in forest recovery, and the drivers of this heterogeneity warrant further investigation, we suggest that a relatively simple set of predictor variables is sufficient to explain the majority of variance in both height andABDin this landscape.

Big Geospatial Data Analytics for Global Mangrove Biomass and Carbon Estimation

The objective of this study is to estimate the biomass and carbon of global-level mangroves as a special type of wetland. Mangrove ecosystems play an important role in regulating carbon cycling, thus having a significant impact on global environmental change. Extensive studies have been conducted for the estimation of mangrove biomass and carbon stock. However, this estimation at a global level has been insufficiently investigated because the spatial scale of interest is large and most existing studies are based on physically challenging fieldwork surveys that are limited to local scales. Over the past few decades, high-resolution geospatial data related to mangroves have been increasingly collected and processed using remote sensing and Geographic Information Systems (GIS) technologies. While these geospatial data create potential for the estimation of mangrove biomass and carbon, the processing and analysis of these data represent a big data-driven challenge. In this study, we present a spatially explicit approach that integrates GIS-based geospatial analysis and high-performance parallel computing for the estimation of mangrove biomass and carbon at the global level. This integrated approach provides support for enabling and accelerating the global-level estimation of mangrove biomass and carbon from existing high-resolution geospatial data. With this integrated approach, the total area, biomass (including above- and below-ground), and associated carbon stock of global mangroves are estimated as 130,420 km2, 1.908 Pg, and 0.725 Pg C for the year of 2000. The averaged aboveground biomass density of global mangroves is estimated as 146.3 Mg ha−1. Our analysis results demonstrate that this integrated geospatial analysis approach is efficacious for the computationally challenging estimation of global mangrove metrics based on high-resolution data. This global-level estimation and associated results are of great assistance for promoting our understanding of complex geospatial dynamics in mangrove forests.

A statistical model for streambank erosion in the Northern Gulf of Mexico coastal plain

Stream restoration practitioners often rely upon empirical models to quantify annual streambank erosion rates and identify streambank erosion hotspots. Such models are designed to be widely applicable by incorporating readily available field measurements, but they must be calibrated to each hydrophysiographic region and may not reflect the dominant streambank erosion processes in a given region. Here, we present statistical models for streambank erosion using physical and environmental data collected at 53 locations throughout the northern Gulf of Mexico coastal plain. The data include channel geometry, bank characteristics, precipitation, above-ground biomass density, and root density, the latter two surveyed using techniques introduced here. We developed a statistical model selection process using Akaike's Information Criterion (AIC) and repeated cross-validation (CV). Models derived from the literature that were applied a priori were only weak predictors of erosion rate, but AIC-CV model selection identified 3 strong statistical models. The best model according to AIC showed a significant correlation to lateral streambank erosion rates (R2 = 0.54) and included the five strongest covariates of our dataset (bank slope, biomass density, curvature index, BEHI, and understory cover). When volumetric erosion rate (m2/year) was predicted, the fit of this model increased (R2 = 0.65). CV-based selection resulted in a more conservative model with the four strongest covariates and a lower fit (R2 = 0.47). The similarity of the AIC and CV models indicates the stability of the two-tier model selection approach, and suggests it has utility for modeling phenomena with many potential variables. Our models also showcase the ability of our biomass survey to quantify root reinforcement of streambanks. Our approach incorporates measurements familiar to the stream restoration community and can be applied throughout the northern Gulf of Mexico coastal plain, a region characterized by low relief fluvial valleys, unconsolidated alluvium and meandering single thread sand bed channels. The approach, which is based on field observations and robust statistical modeling, offers an alternative for stream restoration practitioners to more traditional streambank erosion prediction methods that underperform in the region, and may have applicability elsewhere.

Aboveground biomass of the alpine shrub ecosystems in Three-River Source Region of the Tibetan Plateau

Though aboveground biomass (AGB) has an important contribution to the global carbon cycle, the information about storage and climatic effects of AGB is scare in Three-River Source Region (TRSR) shrub ecosystems. This study investigated AGB storage and its climatic controls in the TRSR alpine shrub ecosystems using data collected from 23 sites on the Tibetan Plateau from 2011 to 2013. We estimated the AGB storage (both shrub layer biomass and grass layer biomass) in the alpine shrubs as 37.49 Tg, with an average density of 1447.31 g m-2. Biomass was primarily accumulated in the shrub layer, which accounted for 92% of AGB, while the grass layer accounted for only 8%. AGB significantly increased with the mean annual temperature (P < 0.05). The effects of the mean annual precipitation on AGB were not significant. These results suggest that temperature, rather than precipitation, has significantly effects on of aboveground vegetation growth in the TRSR alpine shrub ecosystems. The actual and potential increase in AGB density was different due to global warming varies among different regions of the TRSR. We conclude that long-term monitoring of dynamic changes is necessary to improve the accuracy estimations of potential AGB carbon sequestration across the TRSR alpine shrub ecosystems.

Carbon stocks in aboveground biomass for Colombian mangroves with associated uncertainties

Decision-making process, as well as the implementation of future climate change mitigation strategies for mangrove ecosystems, need the availability of reliable base information and tools to estimate the carbon stocks at regional and national levels. We estimated carbon (C) stocks in aboveground biomass (AGB) for mangroves in the Caribbean and Pacific coasts of Colombia. Using available data on AGB density and mangrove area for the whole country (excluding islands) and each coast independently, we estimated a national carbon stock in AGB for Colombian mangroves as 14.95 ± 2.72 TgC (mean ± SE), with 2.20 ± 0.86 TgC in the Caribbean coast and 9.61 ± 2.78 TgC in the Pacific coast. Uncertainty for total carbon in AGB in Colombian mangroves, reported as SE/mean in percentage, was 18% at the national level, 39% in the Caribbean coast, and 29% in the Pacific coast. This uncertainty was more influenced by uncertainties associated with the estimation of mangrove area for the Caribbean coast, while for the Pacific coast it was more influenced by the uncertainties associated with AGB density. This difference is the result of a contrasting availability of AGB density data for both coasts. Comparison between observed AGB density data and predictions from large-scale models showed that these models underestimate AGB density for Colombian mangroves. We reparameterized these models with our data, but found poor goodness-of-fit statistics for these model structures. We propose therefore three new statistical models to predict AGB density in Colombian mangroves based on climatic and vegetation data. In all cases, the best models included the enhanced vegetation index (EVI) and the mean temperature of driest quarter (BIO9).