Variation In Aboveground Biomass Research Articles

Small Footprint Full-Waveform Metrics Contribution to the Prediction of Biomass in Tropical Forests

We tested metrics from full-waveform (FW) LiDAR (light detection and ranging) as predictors for forest basal area (BA) and aboveground biomass (AGB), in a tropical moist forest. Three levels of metrics are tested: (i) peak-level, based on each return echo; (ii) pulse-level, based on the whole return signal from each emitted pulse; and (iii) plot-level, simulating a large footprint LiDAR dataset. Several of the tested metrics have significant correlation, with two predictors, found by stepwise regression, in particular: median distribution of the height above ground (nZmedian) and fifth percentile of total pulse return intensity (i_tot5th). The former contained the most information and explained 58% and 62% of the variance in AGB and BA values; stepwise regression left us with two and four predictors, respectively, explaining 65% and 79% of the variance. For BA, the predictors were standard deviation, median and fifth percentile of total return pulse intensity (i_totstdDev, i_totmedian and i_tot5th) and nZmedian, whereas for AGB, only the last two were used. The plot-based metric showed that the median height of echo count (HOMTC) performs best, with very similar results as nZmedian, as expected. Cross-validation allowed the analysis of residuals and model robustness. We discuss our results considering our specific case scenario of a complex forest structure with a high degree of variability in terms of biomass.

Active canopy sensing of winter wheat nitrogen status: An evaluation of two sensor systems

Crop canopy sensor based in-season site-specific nitrogen (N) management is a promising approach to precision N management. GreenSeeker sensor has previously been evaluated in North China Plain (NCP) for improving winter wheat (Triticum aestivum L.) N management. The Crop Circle ACS-470 is an active canopy sensor with three user-configurable wavebands. This study identified important vegetation indices that can be calculated from Crop Circle green, red edge and near infrared (NIR) wavebands for estimating winter wheat N status and evaluated their potential improvements over GreenSeeker normalized difference vegetation index (NDVI) and ratio vegetation index (RVI). Six field experiments involving different N rates and varieties were conducted in the Quzhou Experiment Station of the China Agricultural University from 2009 to 2012. The results indicated that best Crop Circle ACS-470 sensor vegetation indices could explain similar amounts of aboveground biomass variability in comparison with GreenSeeker sensor NDVI, but Crop Circle normalized difference red edge/green optimized soil adjusted vegetation index (NDRE/GOSAVI) and red edge chlorophyll index (CIRE) were more sensitive to aboveground biomass (having lower noise equivalent) than GreenSeeker NDVI before and after biomass reached about 5000kgha−1, respectively. The Crop Circle green difference vegetation index (GDVI) (R2=0.60) and chlorophyll index (CIG) (R2=0.89) explained 53% and 7–11% more variability in plant N concentration and uptake than GreenSeeker indices, respectively. The Crop Circle green re-normalized difference vegetation index (GRDVI) (R2=0.78) and modified green soil adjusted vegetation index (MGSAVI) (R2=0.77) performed consistently better than GreenSeeker NDVI (R2=0.47) and RVI (R2=0.44) for estimating N nutrition index (NNI). We conclude that the three band user configurable Crop Circle ACS-470 sensor can improve the estimation of winter wheat N status as compared with two fixed band GreenSeeker sensor.

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.

A wood density and aboveground biomass variability assessment using pre-felling inventory data in Costa Rica

BackgroundThe high spatio-temporal variability of aboveground biomass (AGB) in tropical forests is a large source of uncertainty in forest carbon stock estimation. Due to their spatial distribution and sampling intensity, pre-felling inventories are a potential source of ground level data that could help reduce this uncertainty at larger spatial scales. Further, exploring the factors known to influence tropical forest biomass, such as wood density and large tree density, will improve our knowledge of biomass distribution across tropical regions. Here, we evaluate (1) the variability of wood density and (2) the variability of AGB across five ecosystems of Costa Rica.ResultsUsing forest management (pre-felling) inventories we found that, of the regions studied, Huetar Norte had the highest mean wood density of trees with a diameter at breast height (DBH) greater than or equal to 30 cm, 0.623 ± 0.182 g cm-3 (mean ± standard deviation). Although the greatest wood density was observed in Huetar Norte, the highest mean estimated AGB (EAGB) of trees with a DBH greater than or equal to 30 cm was observed in Osa peninsula (173.47 ± 60.23 Mg ha-1). The density of large trees explained approximately 50% of EAGB variability across the five ecosystems studied. Comparing our study's EAGB to published estimates reveals that, in the regions of Costa Rica where AGB has been previously sampled, our forest management data produced similar values.ConclusionsThis study presents the most spatially rich analysis of ground level AGB data in Costa Rica to date. Using forest management data, we found that EAGB within and among five Costa Rican ecosystems is highly variable. Combining commercial logging inventories with ecological plots will provide a more representative ground level dataset for the calibration of the models and remotely sensed data used to EAGB at regional and national scales. Additionally, because the non-protected areas of the tropics offer the greatest opportunity to reduce rates of deforestation and forest degradation, logging inventories offer a promising source of data to support mechanisms such as the United Nations REDD + (Reducing Emissions from Tropical Deforestation and Degradation) program.Electronic supplementary materialThe online version of this article (doi:10.1186/s13021-014-0009-y) contains supplementary material, which is available to authorized users.

Assessment and prediction of above-ground biomass in selectively logged forest concessions using field measurements and remote sensing data: Case study in South East Cameroon

Within the framework of the current REDD+initiative,1Reduced Emissions due to Deforestation and Forest Degradation and the role of sustainable management, conservation, and the enhancement of carbon stocks.1 there is an urgent need for information to guide the development and implementation of strategies for the reduction of GHG2Greenhouse gas.2 emissions from developing countries. Selective logging is one of the main sources of GHG emissions; but few studies in Cameroon have analyzed the impact of selective logging activities on above-ground biomass (AGB). This has resulted in a gap in essential information needed for the design of suitable forest management policies that can guarantee reductions in GHG emissions. In this study, we assessed the impact of selective logging on AGB in a forest concession in South-East Cameroon by quantifying AGB logged and AGB damaged by logging. We equally investigated through linear regression modeling whether the density of logging roads and NDVI3Normalized difference vegetation index.3 values (from MODIS 250m) can be used to predict the quantity of AGB logged. Allometric equations were used to estimate AGB of trees, while the surface area of logging infrastructures and the unit area value (ha−1) of AGB for the forest zone of Cameroon permitted the calculation of AGB damaged by logging. The study reveals that 0.78 trees ha−1; an equivalence of 6.97Mgha−1 of AGB was logged. Logging affected a surface area of 85.04ha; approximately 2% of the study area. This is equivalent to 0.02haha−1 and 5.65Mgha−1 of AGB damaged. The density of the logging roads explained 66% of the variation in AGB logged, while the density of the logging roads and NDVI values together explained 73% of the variation. This study concludes as follows: (i) selective logging reduces AGB stock of the forest and the magnitude of the impact varies with the different activities of selective logging, (ii) ground-based measurements facilitated by GIS permitted to quantify the impact of selective logging on AGB, (iii) logging roads and NDVI (which can either be field measured or captured remotely) can be used to indirectly determine AGB logged, hence can contribute in the measurement and monitoring of forest degradation caused by selective logging, and (iv) the findings from this study can usefully support the design of sustainable forest management policies, which are beneficiary to the REDD+process in Cameroon.

Estimating above-ground biomass of tropical rainforest of different degradation levels in Northern Borneo using airborne LiDAR

Deforestation and degradation of forests have severely depleted carbon storage in tropical countries, whose forests have the most carbon-rich ecosystems in the world. Estimating above-ground biomass (AGB) with high accuracy is critical to quantifying carbon stocks in the tropics. We propose a model to estimate AGB in the tropical montane forests of northern Borneo with different disturbance histories using airborne LiDAR data. The level of forest degradation was determined from species composition and field-observed AGB. Of 50 sample plots established in forests with various levels of degradation, we categorized 20 as highly degraded (AGB: 52.18–229.11Mg/ha), 16 as moderately degraded (AGB: 136.00–382.59Mg/ha), and 14 as old-growth forest (AGB: 280.31–622.79Mg/ha). Height metrics and laser penetration rate (LP) at specific heights from the ground were derived from vertical point profiles of LiDAR data. After testing the performance of single variables, we used stepwise multiple regressions to select variables to include in the model for AGB estimation. The best model with a single variable used the mean height from the laser returns (R2=0.78, RMSE=65.54Mg/ha). All LP variables were sensitive to AGB (R2>0.60). The final model from stepwise analysis included the mean height of the canopy height model and LP at 7m height (adjusted R2=0.81, RMSE=61.26Mg/ha). The results confirm the suitability of LP variables for estimating AGB. We suggest that airborne LiDAR data can capture AGB variability at fine spatial scales, which correspond to deforestation and forest degradation caused by human activities and natural disturbances.

Seasonality of eelgrass biomass across gradients in temperature and latitude

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 506:71-85 (2014) - DOI: https://doi.org/10.3354/meps10800 Seasonality of eelgrass biomass across gradients in temperature and latitude Kevin Kuhlmann Clausen1,2,*, Dorte Krause-Jensen2,3, Birgit Olesen4, Nuria Marbà5 1Department of Bioscience, Aarhus University, Grenåvej 14, 8410 Rønde, Denmark 2Arctic Research Centre, Aarhus University, C.F. Møllers Allé 8, 8000 Aarhus C, Denmark 3Department of Bioscience, Aarhus University, Vejlsøvej 25, 8600 Silkeborg, Denmark 4Department of Bioscience, Aarhus University, Ole Worms Allé 1, Building 1135, 8000 Aarhus C, Denmark 5Department of Global Change Research, IMEDEA (CSIC-UIB), Institut Mediterrani d’Estudis Avançats, Miquel Marquès 21, 07190 Esporles, Illes Balears, Spain *Corresponding author: kc@dmu.dk ABSTRACT: Eelgrass Zostera marina L. meadows are major structural and trophic components of coastal ecosystems. The role of eelgrass in ecosystem functioning depends on biomass and production of the meadows, which can fluctuate greatly during an annual cycle and be major temporal drivers of changes in the coastal zone. We analysed magnitude and seasonality of eelgrass aboveground biomass, shoot density and production across temperature and latitude gradients over the majority of the species’ distributional range, and investigated to what extent temperature and/or light drive differences in these values. Eelgrass phenology (timing of peak biomass, start and end of the growing season) showed strong effects of temperature and latitude, indicating that seasonality was considerably advanced in warm areas at low latitudes compared to cold areas at high latitudes. Magnitude of peak aboveground biomass, length of the growing season, mean annual shoot density and aboveground production did not change significantly with either temperature or latitude, indicating that these parameters were controlled mainly by other factors. Annual variation in aboveground biomass and shoot density was significantly smaller in areas with low summer temperature, indicating that while warm-water populations may show substantial temporal variation in biomass, cold-water meadows are less dynamic. These findings were supported by cold-water populations having a larger mean annual biomass and a greater investment in belowground parts. In all significant regressions, temperature was a better predictor of population dynamics than latitude. This indicates that eelgrass phenology might advance considerably in response to global warming, and suggests that the distributional range of this species might be moving northwards. Given the key role of eelgrass in coastal ecosystems, these climate-induced changes might entail substantial impacts on waterbirds, fish, invertebrates and other organisms exploiting these meadows. KEY WORDS: Zostera marina · Climate change · Phenology · Waterbirds · Ecosystem functioning Full text in pdf format PreviousNextCite this article as: Clausen KK, Krause-Jensen D, Olesen B, Marbà N (2014) Seasonality of eelgrass biomass across gradients in temperature and latitude. Mar Ecol Prog Ser 506:71-85. https://doi.org/10.3354/meps10800 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 506. Online publication date: June 23, 2014 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2014 Inter-Research.

Stand age and soils as drivers of plant functional traits and aboveground biomass in secondary tropical dry forest

The distribution of tropical forest biomass across the landscape is poorly understood, particularly in increasingly common secondary tropical forests. We studied the landscape-scale distribution of edaphic properties, plant community characteristics, and aboveground biomass (AGB) in secondary tropical dry forests in northwest Costa Rica. We used structural equation modeling to examine conceptual models of relationships among these factors, with data from 84 0.1 ha plots. Stand age and soils explained 33%–60% of the variation in community-weighted mean values of foliar traits including specific leaf area, foliar nitrogen, phosphorus, and δ13C. Aboveground biomass ranged from 1.7 to 409 Mg·ha−1 among plots between 5 and >100 years old. Stand age alone explained 46% of the variation in AGB among plots, while a model including age, soil pH, traits, and forest type explained 58%. Stand age was the most important variable explaining the distribution of AGB and community characteristics in secondary forests. We speculate that plot size, landscape heterogeneity, disturbance history, and stand dynamics contribute to the unexplained variation in AGB across the landscape.

The importance of crown dimensions to improve tropical tree biomass estimates.

Tropical forests play a vital role in the global carbon cycle, but the amount of carbon they contain and its spatial distribution remain uncertain. Recent studies suggest that once tree height is accounted for in biomass calculations, in addition to diameter and wood density, carbon stock estimates are reduced in many areas. However, it is possible that larger crown sizes might offset the reduction in biomass estimates in some forests where tree heights are lower because even comparatively short trees develop large, well-lit crowns in or above the forest canopy. While current allometric models and theory focus on diameter, wood density, and height, the influence of crown size and structure has not been well studied. To test the extent to which accounting for crown parameters can improve biomass estimates, we harvested and weighed 51 trees (11-169 cm diameter) in southwestern Amazonia where no direct biomass measurements have been made. The trees in our study had nearly half of total aboveground biomass in the branches (44% +/- 2% [mean +/- SE]), demonstrating the importance of accounting for tree crowns. Consistent with our predictions, key pantropical equations that include height, but do not account for crown dimensions, underestimated the sum total biomass of all 51 trees by 11% to 14%, primarily due to substantial underestimates of many of the largest trees. In our models, including crown radius greatly improves performance and reduces error, especially for the largest trees. In addition, over the full data set, crown radius explained more variation in aboveground biomass (10.5%) than height (6.0%). Crown form is also important: Trees with a monopodial architectural type are estimated to have 21-44% less mass than trees with other growth patterns. Our analysis suggests that accounting for crown allometry would substantially improve the accuracy of tropical estimates of tree biomass and its distribution in primary and degraded forests.

Hyperspectral remote sensing of aboveground biomass on a river meander bend using multivariate adaptive regression splines and stochastic gradient boosting

Research on aboveground biomass (AGB) retrieval via remote sensing in floodplain forests, in particular, is urgently needed for improved understanding of carbon cycling in such areas. AGB estimation is particularly challenging in floodplain forests, which are characterized by high spatial variability in AGB resulting from biogeomorphodynamic processes. In this study, we perform remote AGB retrieval for a deciduous riparian forest on a river meander bend based on hyperspectral/high-dimensional Hyperion bands and other input variables. We compare multivariate adaptive regression splines (MARS)-, stochastic gradient boosting (SGB)- and Cubist-based AGB estimates. Results show that MARS- and SGB-derived estimates are significantly more accurate than Cubist-based AGB. The most accurate MARS and SGB estimates have a coefficient of determination, R2, of 0.97 and 0.95, respectively, whereas the Cubist estimate with the lowest error has an R2 of 0.85. MARS and SGB AGB are not significantly different, however. These modelling approaches are applicable across scales and environments.

Effects of livestock exclusion and climate change on aboveground biomass accumulation in alpine pastures across the Northern Tibetan Plateau

To better understand the ecological and economic benefits of short-term grazing exclusion on the aboveground net primary productivity (ANPP) of alpine pastures, we conducted annual multi-site transect surveys in the summers from 2009 to 2011 and calculated the aboveground biomass discrepancy (ABD) between grazed and ungrazed pastures at plant community and economic group levels for three zonal alpine grassland types—meadow, steppe, and desert-steppe—across the northern Tibetan Plateau. Our results indicated that aboveground biomass (AGB) significantly differed among grassland types and declined northwesterly from 64.07 to 11.44 g m−2 with decreasing precipitation and increasing temperature. The mean ABD exhibited considerable community dependency, with meadow (12.47 g m−2) > steppe (6.91 g m−2) > desert steppe (2.54 g m−2), and it declined from 25.42 to 1.29 g m−2 with decreasing precipitation and increasing temperature. ‘Good forage’, i.e. grasses and sedges, benefited most from grazing exclusion, followed by edible forbs. With longer grazing exclusion durations (GEDs), the aboveground biomass of poisonous locoweeds initially decreased and then increased compared with the adjacent grazed sites. In the nested analysis of co-variances with a general linear model, growing season precipitation (GSP; from May to September) accounted for 52.67% of the observed variation in AGB, followed by AGT (9.77 %) and pasture management systems (PMSs; grazing or grazing-excluded, 5.31 %). The variation in ABD was explained primarily by AGT (16.52 %), GED (20.25 %), and the interaction of AGT × GED (19.58 %). Our results confirm that precipitation is the primary factor controlling the ANPP of alpine grasslands on the Northern Tibetan Plateau and that the ecological benefits arising from grazing exclusion are also partly dependent on grassland type and exclusion duration. Therefore, spatial and temporal variations in growing season precipitation and plant functional traits or economic group composition should be jointly considered when developing policies concerning the management and spatial layouts of grazing exclosures in this region.

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Estimation of forest aboveground biomass (AGB) is informative of the role of forest ecosystems in local and global carbon budgets. There is a need to retrospectively estimate biomass in order to establish a historical baseline and enable reporting of change. In this research, we used temporal spectral trajectories to inform on forest successional development status in support of modelling and mapping of historic AGB for Mediterranean pines in central Spain. AGB generated with ground plot data from the Spanish National Forest Inventory (NFI), representing two collection periods (1990 and 2000), are linked with static and dynamic spectral data as captured by Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors over a 25year period (1984–2009). The importance of forest structural complexity on the relationship between AGB and spectral vegetation indices is revealed by the analysis of wavelet transforms. Two-dimensional (2D) wavelet transforms support the identification of spectral trajectory patterns of forest stands that in turn, are associated with traits of individual NFI plots, using a flexible algorithm sensitive to capturing time series similarity. Single-date spectral indices, temporal trajectories, and temporal derivatives associated with succession are used as input variables to non-parametric decision trees for modelling, estimation, and mapping of AGB and carbon sinks over the entire study area. Results indicate that patterns of change found in Normalized Difference Vegetation Index (NDVI) values are associated and relate well to classes of forest AGB. The Tasseled Cap Angle (TCA) index was found to be strongly related with forest density, although the related patterns of change had little relation with variability in historic AGB. By scaling biomass models through small (∼2.5ha) spatial objects defined by spectral homogeneity, the AGB dynamics in the period 1990–2000 are mapped (70% accuracy when validated with plot values of change), revealing an increase of 18% in AGB irregularly distributed over 814km2 of pines. The accumulation of C calculated in AGB was on average 0.65tha−1y−1, equivalent to a fixation of 2.38tha−1y−1 of carbon dioxide.

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.

Variación de la biomasa forestal en el sur de Brasil: impacto de los árboles de Araucaria

A variety of environmental and biotic factors determine vegetation growth and affect plant biomass accumulation. From temperature to species composition, aboveground biomass storage in forest ecosystems is influenced by a number of variables and usually presents a high spatial variability. With this focus, the aim of the study was to evaluate the variables affecting live aboveground forest biomass (AGB) in Subtropical Moist Forests of Southern Brazil, and to analyze the spatial distribution of biomass estimates. Data from a forest inventory performed in the State of Rio Grande do Sul, Southern Brazil, was used in the present study. Thirty-eight 1-ha plots were sampled and all trees with DBH > or = 9.5cm were included for biomass estimation. Values for aboveground biomass were obtained using published allometric equations. Environmental and biotic variables (elevation, rainfall, temperature, soils, stem density and species diversity) were obtained from the literature or calculated from the dataset. For the total dataset, mean AGB was 195.2 Mg/ha. Estimates differed between Broadleaf and Mixed Coniferous-Broadleaf forests: mean AGB was lower in Broadleaf Forests (AGB(BF)=118.9 Mg/ha) when compared to Mixed Forests (AGB(MF)=250.3 Mg/ha). There was a high spatial and local variability in our dataset, even within forest types. This condition is normal in tropical forests and is usually attributed to the presence of large trees. The explanatory multiple regressions were influenced mainly by elevation and explained 50.7% of the variation in AGB. Stem density, diversity and organic matter also influenced biomass variation. The results from our study showed a positive relationship between aboveground biomass and elevation. Therefore, higher values of AGB are located at higher elevations and subjected to cooler temperatures and wetter climate. There seems to be an important contribution of the coniferous species Araucaria angustifolia in Mixed Forest plots, as it presented significantly higher biomass than angiosperm species. In Brazil, this endangered species is part of a high diversity forest (Araucaria Forest) and has the potential for biomass storage. The results of the present study show the spatial and local variability in aboveground biomass in subtropical forests and highlight the importance of these ecosystems in global carbon stock, stimulating the improvement of future biomass estimates.

Spatio-temporal variation in vegetation biomass and its relationships with climate factors in the Xilingol grasslands, Northern China.

Knowledge about grassland biomass and its dynamics is critical for studying regional carbon cycles and for the sustainable use of grassland resources. In this study, we investigated the spatio-temporal variation of biomass in the Xilingol grasslands of northern China. Field-based biomass samples and MODIS time series data sets were used to establish two empirical models based on the relationship of the normalized difference vegetation index (NDVI) with above-ground biomass (AGB) as well as that of AGB with below-ground biomass (BGB). We further explored the climatic controls of these variations. Our results showed that the biomass averaged 99.01 Tg (1 Tg=1012 g) over a total area of 19.6×104 km2 and fluctuated with no significant trend from 2001 to 2012. The mean biomass density was 505.4 g/m2, with 62.6 g/m2 in AGB and 442.8 g/m2 in BGB, which generally decreased from northeast to southwest and exhibited a large spatial heterogeneity. The year-to-year AGB pattern was generally consistent with the inter-annual variation in the growing season precipitation (GSP), showing a robust positive correlation (R2=0.82, P<0.001), but an opposite coupled pattern was observed with the growing season temperature (GST) (R2=0.61, P=0.003). Climatic factors also affected the spatial distribution of AGB, which increased progressively with the GSP gradient (R2=0.76, P<0.0001) but decreased with an increasing GST (R2=0.70, P<0.0001). An improved moisture index that combined the effects of GST and GSP explained more variation in AGB than did precipitation alone (R2=0.81, P<0.0001). The relationship between AGB and GSP could be fit by a power function. This increasing slope of the GSP–AGB relationships along the GSP gradient may be partly explained by the GST–GSP spatial pattern in Xilingol. Our findings suggest that the relationships between climatic factors and AGB may be scale-dependent and that multi-scale studies and sufficient long-term field data are needed to examine the relationships between AGB and climatic factors.

Quantification of aboveground forest biomass using Quickbird imagery, topographic variables, and field data

Optical remote sensing is the most widely used method for obtaining forest biomass information. This research investigated the potential of using topographical and high-resolution optical data from Quickbird for measurement of black locust plantation aboveground biomass (AGB) grown in the hill-gully region of the Loess Plateau. Three different processing techniques, including spectral vegetation indices (SVIs), texture, and topography were evaluated, both individually and combined. Simple linear regression and stepwise multiple-linear regression models were developed to describe the relationship between image parameters obtained using these approaches and field measurements. SVI and topography-based approaches did not yield reliable AGB estimates, accounting for at best 23 and 19% of the observed variation in AGB. Texture-based methods were better, explaining up to 70% of the observed variation. A combination of SVIs, texture, and topography yielded an even better R 2 value of 0.74 with the lowest root mean square error (17.21 t/ha ) and bias (−1.85 t/ha ). The results suggest that texture information from high-resolution optical data was more effective than SVIs and topography to estimate AGB. The performance of AGB estimation can be improved by adding SVIs and topography results to texture data; the best results can be obtained using a combination of these three data types.

Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages

Normalized Difference Vegetation Index and Ratio Vegetation Index obtained with the fixed band GreenSeeker active multispectral canopy sensor (GS-NDVI and GS-RVI) have been commonly used to non-destructively estimate crop growth parameters and support precision crop management, but their performance has been influenced by soil and/or water backgrounds at early crop growth stages and saturation effects at moderate to high biomass conditions. Our objective is to improve estimation of rice (Oryza sativa L.) aboveground biomass (AGB) with hyperspectral canopy sensing by identifying more optimal measurements using one or more strategies: (a) soil adjusted Vegetation Indices (VIs); (b) optimized narrow band RVI and NDVI; and (c) Optimum Multiple Narrow Band Reflectance (OMNBR) models based on raw reflectance, and its first and second derivatives (FDR and SDR).Six rice nitrogen (N) rate experiments were conducted in Jiansanjiang, Heilongjiang province of Northeast China from 2007 to 2009 to create different biomass conditions. Hyperspectral field data and AGB samples were collected at four growth stages from tillering through heading from both experimental and farmers’ fields. The results indicate that six-band OMNBR models (R2=0.44–0.73) explained 21–35% more AGB variability relative to the best performing fixed band RVI or NDVI at different growth stages. The FDR-based 6-band OMNBR models explained 4%, 6% and 8% more variability of AGB than raw reflectance-based 6-band OMNBR models at the stem elongation (R2=0.77), booting (R2=0.50), and heading stages (R2=0.57), respectively. The SDR-based 6-band OMNBR models made no further improvements, except for the stem elongation stage. Optimized RVI and NDVI for each growth stage (R2=0.34–0.69) explained 18–26% more variability in AGB than the best performing fixed band RVI or NDVI. The FDR- and SDR-based optimized VIs made no further improvements. These results were consistent across different sites and years. It is concluded that with suitable band combinations, optimized narrow band RVI or NDVI could significantly improve estimation of rice AGB at different growth stages, without the need of derivative analysis. Six-band OMNBR models can further improve the estimation of AGB over optimized 2-band VIs, with the best performance using SDR at the stem elongation stage and FDR at other growth stages.

Predicting Global Patterns in Mangrove Forest Biomass

AbstractUnderstanding spatial variation in carbon storage in natural habitats is critical for climate change mitigation efforts such as REDD. Terrestrial forests are being mapped with increasing accuracy, but the distribution of ``blue carbon'' in marine ecosystems remains poorly understood. We reviewed the literature to obtain field data on carbon storage and fluxes in mangroves world‐wide. Using this material we developed a climate‐based model for potential mangrove above‐ground biomass (AGB) with almost four times the explanatory power of the only previous published model. From this model, we present the first ever global map of potential mangrove AGB and estimate a total global mangrove AGB of 2.83 Pg, with an average of 184.8 t ha−1. Data on other carbon stocks and fluxes confirm the importance of mangroves in carbon accounting. The map highlights the high variability in mangrove AGB and indicates areas that should be prioritised for mangrove conservation and restoration.

Wheat genotypes with high early vigour accumulate more nitrogen and have higher photosynthetic nitrogen use efficiency during early growth.

Genotypic differences in early growth and nitrogen (N) uptake among 24 wheat (Triticum aestivum L.) genotypes were assessed in a field trial. At late tillering, large genetic variation was observed for shoot biomass (23-56gm-2 ground area) and N uptake (1.1-1.8gm-2 ground area). A strong correlation between aboveground biomass and N uptake was observed. Variation around this relationship was also found, with some genotypes having similar N uptake but large differences in aboveground biomass. A controlled environment experiment was conducted to investigate the underlying mechanisms for this variation in aboveground biomass using three vigorous genotypes (38-19, 92-11 and CV97) and a non-vigorous commercial cultivar (Janz). Vigorous genotypes had lower specific leaf N in the youngest fully expanded leaf than Janz. However, there was no difference in chlorophyll content, maximum Rubisco activity or the rate of electron transport per unit area. This suggests that Janz invested more N in non-photosynthetic components than the vigorous lines, which could explain the higher photosynthetic N use efficiency of the vigorous genotypes. The results suggest that the utilisation of wheat genotypes with high early vigour could improve the efficiency of N use for biomass production in addition to improving N uptake during early growth.