Areas Of High Biomass Research Articles

Global Estimates of Particulate Organic Carbon Concentration From the Surface Ocean to the Base of the Mesopelagic

AbstractThe gravitational settling of organic particles from the surface to the deep ocean is an important export pathway and one of the largest components of the ocean carbon pump. The strength and efficiency of the gravitational pump are often measured using metrics reliant on reference depths and empirical formulations that parameterize the relationship between depth and the flux or concentration of particulate organic carbon (POC). Here, BGC‐Argo profiles were used to identify the isolume where POC concentration, [POC], starts to decline, revealing attenuation trends below this isolume that are remarkably consistent across the global ocean. We developed a simple empirical approach that uses observations from the first optical depth to predict [POC] from the surface ocean to the base of the mesopelagic (1,000 m), allowing assessments of spatial and temporal variability in gravitational pump efficiencies. We find that rates of [POC] attenuation are high in areas of high biomass and low in areas of low biomass, supporting the view that bloom events sometimes result in a relatively weak deep biological pump that is characterized by low transfer efficiency to the base of the mesopelagic. Our isolume‐based attenuation model was applied to satellite data to yield the first remote sensing‐based estimate of integrated global POC stock of 3.02 Pg C over the top 1,000 m, with an uncertainty of 0.69 Pg C. Of this total stock, approximately 1.02 Pg was located above the reference isolume where [POC] begins to attenuate.

A spatially varying model for small area estimates of biomass density across the contiguous United States

The USDA Forest Service, Forest Inventory and Analysis (FIA) program constructs area estimates of forest attributes through a quasi-systematic sample of field plots distributed across the contiguous US. The program is under increasing pressure to provide estimates over small areas, for which the precision of FIA estimates is limited by small sample sizes. Small area estimation (SAE) models can improve the precision of estimates by leveraging relationships between forest attributes and auxiliary data across multiple small areas of interest. SAE models with large model domains are challenged by requiring auxiliary data encompassing the large domain and by spatially varying relationships between the auxiliary data and forest attributes. We implement a Fay–Herriot SAE model that accounts for spatial variation to make estimates of forest above-ground biomass density (AGBD) within 64,000 hectare hexagons across the contiguous US. Model inference is conducted through a Bayesian paradigm, providing AGBD estimates and quantification of uncertainty through posterior standard deviations and credible intervals. We compare the utility of data from the Global Ecosystem Dynamics Investigation (GEDI) and the National Land Cover Database 2016 Tree Canopy Cover (TCC) product as auxiliary data within the model. Results show that models using GEDI data provide more precise estimates of AGBD than direct estimates using FIA plot data alone while giving accurate quantification of uncertainty. The model using only the TCC product as auxiliary data also reported more precise estimates, but a cross-validation study revealed the reported uncertainty to be overly optimistic for high-biomass areas. We demonstrate that accounting for spatial variation in the model is crucial, and that doing otherwise leads to poor quantification of uncertainty and locally biased estimates. This study not only provides an effective model for small area estimates of AGBD with a large model domain, but also emphasizes the importance of validating models and their reported errors.

Fusing GEDI with earth observation data for large area aboveground biomass mapping

• A method for biomass mapping using GEDI and Earth Observation data is proposed. • Biomass maps were produced for Australia and the United States in 2020. • The saturation effect is reduced in comparison to the previous product. • Data fusion led to improved accuracies driven by Sentinel-2 and land cover data. • Predictions were more reliable in forests as compared to grasslands and shrublands. An accurate and spatially explicit estimation of biomass is required for sustainable forest management, prevention of biodiversity loss, and carbon accounting for climate change mitigation. This study offers a methodology to generate wall-to-wall aboveground biomass density (AGBD) maps that exclusively relies on open access earth observation (EO) data. Specifically, spaceborne Global Ecosystem Dynamics Investigation (GEDI) LiDAR data were fused with Sentinel-1 synthetic-aperture radar, Sentinel-2 multispectral, elevation, and land cover data to produce biomass maps of Australia and the United States for 2020. The gradient boosting machine learning framework was applied to predict AGBD and its uncertainty at the resolutions of 100 m and 200 m. The performance of models based on (1) Sentinel-2 imagery and land cover and (2) a combination of Sentinel-2 and Sentinel-1 imagery with elevation and land cover data were compared. The most accurate gradient boosting model was identified using a Bayesian hyperparameter optimization with a 5-fold cross-validation. The Sentinel-2 imagery and land cover data analysis resulted in AGBD estimated with the coefficient of determination ( R 2 ) of 0.61 – 0.71, root-mean-square error ( RMSE ) of 59 – 86 Mg/ha, and relative root-mean-square error ( RMSE% ) of 45 – 80%. The accuracy of the models improved with the addition of Sentinel-1 and elevation data: AGBD estimation with R 2 of 0.66 – 0.74, RMSE of 55 – 81 Mg/ha, and RMSE% of 41 – 77%. It was found that Sentinel-2 and land cover-derived predictors were the most important in estimating annual AGBD. The proposed method also reduced the saturation effect, which is common in high biomass areas when predicting AGBD using satellite imagery. Prediction maps produced in this study could serve as a baseline for current AGB stocks of forested lands equal to 9.8 Pg and 37.1 Pg in Australia and the United States, respectively. Overall, this research highlights methodological opportunities for combining open access EO data to yield more accurate and globally applicable AGB maps through data fusion.

Improving above ground biomass estimates of Southern Africa dryland forests by combining Sentinel-1 SAR and Sentinel-2 multispectral imagery

Having the ability to make accurate assessments of above ground biomass (AGB) at high spatial resolution is invaluable for the management of dryland forest resources in areas at risk from deforestation, forest degradation pressure and climate change impacts. This study reports on the use of satellite-based synthetic-aperture radar (SAR) and multispectral imagery for estimating AGB by correlating satellite observations with ground truth data collected on forest plots from dryland forests in the Chobe National Park, Botswana. We undertook nineteen quantitative experiments with Sentinel-1 (S1), and Sentinel-2 (S2) and tested simple and multivariate regression including parametric (linear) and non-parametric (random forests) algorithms, to explore the optimal approaches for AGB estimation. The largest AGB value of 145 Mg/ha was found in northern Chobe while a large part of the study area (85%) is characterized by low AGB values (< 80 Mg/ha), with an average estimated at 51 Mg/ha. The results show that the AGB estimated using SAR backscatter values from vertical transmit receive (VV) polarization is more accurate than that based on horizontal receive (VH) polarization, accounting for 58% of the variance compared to 32%. Nevertheless, the combination of S1 SAR and S2 multispectral image data produced the best fit to the ground observations for dryland forests explaining 83% of the variance with an accuracy of 89%. Furthermore, the optimal AGB model performance was achieved with a random forest (RF) regression trees algorithm using S1 (SAR) and S2 (multispectral) image data (R2 = 0.95; RMSE = 0.25 Mg/ha). From the 11 vegetation indices tested, GNDVI, Normalized Difference Red Edge (NDRE1), and NDVI obtained the highest linear relationship with AGB (R2 = 0.71 and R2 = 0.56, p < 0.001), however, GNDVI and NDRE1 improved the AGB estimation at medium to high-density forests compared to NDVI. The GRVI and EVI were the least correlated with AGB (R2 = 0.09 and R2 = 0.31) at a significance level of p < 0.001, respectively. We show that NDVI saturates in areas with >80 Mg/ha AGB, whereas the inclusion of SAR backscatter and optical red edge bands (B5) significantly reduces saturation effects in areas of high biomass. GNDVI and red edge (B5) derived vegetation indices have more potential for estimating AGB in dryland forests than NDVI. Our results demonstrate that dryland AGB can be estimated with a reasonable level of precision from open access Earth observation data using multivariate random forest regression.

Seasonal bias in global ocean color observations.

In this study, we identify a seasonal bias in the ocean color satellite-derived remote sensing reflectances (Rrs(λ);sr-1) at the ocean color validation site, Marine Optical BuoY. The seasonal bias in Rrs(λ) is present to varying degrees in all ocean color satellites examined, including the Visible Infrared Imaging Radiometer Suite, Sea-Viewing Wide Field-of-View Sensor, and Moderate Resolution Imaging Spectrometer. The relative bias in Rrs has spectral dependence. Products derived from Rrs(λ) are affected by the bias to varying degrees, with particulate backscattering varying up to 50% over a year, chlorophyll varying up to 25% over a year, and absorption from phytoplankton or dissolved material varying by up to 15%. The propagation of Rrs(λ) bias into derived products is broadly confirmed on regional and global scales using Argo floats and data from the cloud-aerosol lidar with orthogonal polarization instrument aboard the cloud-aerosol lidar and infrared pathfinder satellite. The artifactual seasonality in ocean color is prominent in areas of low biomass (i.e., subtropical gyres) and is not easily discerned in areas of high biomass. While we have eliminated several candidates that could cause the biases in Rrs(λ), there are still outstanding questions regarding potential contributions from atmospheric corrections. Specifically, we provide evidence that the aquatic bidirectional reflectance distribution function may in part cause the observed seasonal bias, but this does not preclude an additional effect of the aerosol estimation. Our investigation highlights the contributions that atmospheric correction schemes can make in introducing biases in Rrs(λ), and we recommend more simulations to discern these influence Rrs(λ) biases. Community efforts are needed to find the root cause of the seasonal bias because all past, present, and future data are, or will be, affected until a solution is implemented.

Factors affecting the seasonal distribution and biomass of E. pacifica and T. spinifera along the Pacific coast of Canada: A spatiotemporal modelling approach.

Euphausiids are a keystone species in coastal food webs due to their high lipid content and seasonally high biomass. Understanding the habitat and environmental drivers that lead to areas of high biomass, or ‘hotspots’, and their seasonal persistence, will support the identification of important foraging regions for mid- and upper- trophic level predators. We quantify the distribution of hotspots of the two dominant species of euphausiid in the north-east Pacific Ocean: Euphausia pacifica and Thysanoessa spinifera, as well as euphausiid larvae (mixed species). The Canadian coast encompasses the northern California Current Ecosystem and the transition zone to the Alaska current, and is a highly productive region for fisheries, marine mammals, and seabirds. We used spatiotemporal modelling to predict the distribution of these three euphausiid groups in relation to geomorphic and environmental variables during the important spring-summer months (April through September) when euphausiid biomass is highest. We quantified the area, intensity, and persistence of biomass hotspots across months according to specific oceanographic ecosections developed for marine spatial planning purposes. Persistent hotspots of both adult species were predicted to occur along the 200 m depth contour of the continental slope; however, differences were predicted on the shallower Dixon shelf, which was a key area for T. spinifera, and within the Juan de Fuca Eddy system where E. pacifica hotspots occurred. The continental slope along the west coast of Vancouver Island was the only persistent hotspot region common between both adult species and euphausiid larvae. Larval distribution was more correlated with T. spinifera than E. pacifica biomass. Hotspots of adults were more persistent across months than hotspots of euphausiid larvae, which were seasonally patchy. The persistence of biomass hotspots of forage species through periods of low overall biomass could maintain trophic connectivity through perturbation events and increase ecosystem resilience to climate change.

New 3D measurements of large redwood trees for biomass and structure

Large trees are disproportionately important in terms of their above ground biomass (AGB) and carbon storage, as well as their wider impact on ecosystem structure. They are also very hard to measure and so tend to be underrepresented in measurements and models of AGB. We show the first detailed 3D terrestrial laser scanning (TLS) estimates of the volume and AGB of large coastal redwood Sequoia sempervirens trees from three sites in Northern California, representing some of the highest biomass ecosystems on Earth. Our TLS estimates agree to within 2% AGB with a species-specific model based on detailed manual crown mapping of 3D tree structure. However TLS-derived AGB was more than 30% higher compared to widely-used general (non species-specific) allometries. We derive an allometry from TLS that spans a much greater range of tree size than previous models and so is potentially better-suited for use with new Earth Observation data for these exceptionally high biomass areas. We suggest that where possible, TLS and crown mapping should be used to provide complementary, independent 3D structure measurements of these very large trees.

A fuzzy logic decision support model for climate-driven biomass loss risk in western Oregon and Washington.

Dynamic global vegetation model (DGVM) projections are often put forth to aid resource managers in climate change-related decision making. However, interpreting model results and understanding their uncertainty can be difficult. Sources of uncertainty include embedded assumptions about atmospheric CO2 levels, uncertain climate projections driving DGVMs, and DGVM algorithm selection. For western Oregon and Washington, we implemented an Environmental Evaluation Modeling System (EEMS) decision support model using MC2 DGVM results to characterize biomass loss risk. MC2 results were driven by climate projections from 20 General Circulation Models (GCMs) and Earth System Models (ESMs), under Representative Concentration Pathways (RCPs) 4.5 and 8.5, with and without assumed fire suppression, for three different time periods. We produced maps of mean, minimum, and maximum biomass loss risk and uncertainty for each RCP / +/- fire suppression / time period. We characterized the uncertainty due to RCP, fire suppression, and climate projection choice. Finally, we evaluated whether fire or climate maladaptation mortality was the dominant driver of risk for each model run. The risk of biomass loss generally increases in current high biomass areas within the study region through time. The pattern of increased risk is generally south to north and upslope into the Coast and Cascade mountain ranges and along the coast. Uncertainty from climate future choice is greater than that attributable to RCP or +/- fire suppression. Fire dominates as the driving factor for biomass loss risk in more model runs than mortality. This method of interpreting DGVM results and the associated uncertainty provides managers with data in a form directly applicable to their concerns and should prove helpful in adaptive management planning.

Temporal Variability of MODIS Phenological Indices in the Temperate Rainforest of Northern Patagonia

Western Patagonia harbors unique and sparsely studied terrestrial ecosystems that are threatened by land use changes and exposure to basin-scale climatic variability. We assessed the performance of two satellite vegetation indices derived from MODIS–Terra, EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index), over the northern and southern sectors of the Chiloé Island System (CIS) to advance our understanding of vegetation dynamics in the region. Then we examined their time-varying relationships with two climatic indices indicative of tropical and extratropical influence, the ENSO (El Niño–Southern Oscillation) and the Antarctic Oscillation (AAO) index, respectively. The 17-year time series showed that only EVI captured the seasonal pattern characteristic of temperate regions, with low (high) phenological activity during Autumn-Winter (Spring–Summer). NDVI saturated during the season of high productivity and failed to capture the seasonal cycle. Temporal patterns in productivity showed a weakened seasonal cycle during the past decade, particularly over the northern sector. We observed a non-stationary association between EVI and both climatic indices. Significant co-variation between EVI and the Niño–Southern Oscillation index in the annual band persisted from 2001 until 2008–2009; annual coherence with AAO prevailed from 2013 onwards and the 2009–2012 period was characterized by coherence between EVI and both climate indices over longer temporal scales. Our results suggest that the influence of large-scale climatic variability on local weather patterns drives phenological responses in the northern and southern regions of the CIS. The imprint of climatic variability on patterns of primary production across the CIS may be underpinned by spatial differences in the anthropogenic modification of this ecosystem, as the northern sector is strongly modified by forestry and agriculture. We highlight the need for field validation of satellite indices around areas of high biomass and high endemism, located in the southern sector of the island, in order to enhance the utility of satellite vegetation indices in the conservation and management of austral ecosystems.

Tropical forest dynamics in unstable terrain: a case study from New Guinea

Abstract:Long-term forest dynamics plots in the tropics tend to be situated on stable terrain. This study investigated forest dynamics on the north coast of New Guinea where active subduction zones are uplifting lowland basins and exposing relatively young sediments to rapid weathering. We examined forest dynamics in relation to disturbance history, topography and soil nutrients based on partial re-census of the 50-ha Wanang Forest Dynamics Plot in Papua New Guinea. The plot is relatively high in cations and phosphorus but low in nitrogen. Soil nutrients and topography accounted for 29% of variation in species composition but only 4% of variation in basal area. There were few areas of high biomass and most of the forest was comprised of small-diameter stems. Approximately 18% of the forest was less than 30 y old and the annual tree mortality rate of nearly 4% was higher than in other tropical forests in South-East Asia and the neotropics. These results support the reputation of New Guinea's forests as highly dynamic, with frequent natural disturbance. Empirical documentation of this hypothesis expands our understanding of tropical forest dynamics and suggests that geomorphology might be incorporated in models of global carbon storage especially in regions of unstable terrain.

An Improved Triple Collocation Analysis Algorithm for Decomposing Autocorrelated and White Soil Moisture Retrieval Errors

AbstractIf not properly account for, autocorrelated retrieval errors can lead to inaccurate results in soil moisture data analysis and reanalysis. Here we propose a more generalized form of the triple collocation analysis algorithm (GTC) capable of decomposing the total error variance of remotely sensed surface soil moisture retrievals into their autocorrelated and the serially white components. Synthetic tests demonstrate the robustness and accuracy of GTC—even in the presence of significant temporal data gaps. However, the accuracy of GTC error autoregressive parameter estimates is relatively more sensitive to temporal data availability. In addition, land surface model soil moisture predictions collected from phase 2 of the North American Land Data Assimilation System and remotely sensed surface soil moisture retrievals obtained from the European Space Agency Climate Change Initiative (ESA CCI) are applied for a real data demonstration. Despite expectations to the contrary, significant error autocorrelation is found in the remotely sensed‐based ESA CCI soil moisture data sets. In particular, ESA CCI‐Act (i.e., the subset of ESA CCI soil moisture retrievals based on active scattomotter data) demonstrates the largest autoregressive parameters over low biomass areas. Conversely, ESA CCI‐Pas retrievals (based on passive radiometer data) have larger error autoregressive parameters over high biomass areas. As such, results clarify circumstances in which errors in remotely sensed surface soil moisture retrievals cannot be considered serially white.

Implications of allometric model selection for county-level biomass mapping

BackgroundCarbon accounting in forests remains a large area of uncertainty in the global carbon cycle. Forest aboveground biomass is therefore an attribute of great interest for the forest management community, but the accuracy of aboveground biomass maps depends on the accuracy of the underlying field estimates used to calibrate models. These field estimates depend on the application of allometric models, which often have unknown and unreported uncertainties outside of the size class or environment in which they were developed.ResultsHere, we test three popular allometric approaches to field biomass estimation, and explore the implications of allometric model selection for county-level biomass mapping in Sonoma County, California. We test three allometric models: Jenkins et al. (For Sci 49(1): 12–35, 2003), Chojnacky et al. (Forestry 87(1): 129–151, 2014) and the US Forest Service’s Component Ratio Method (CRM). We found that Jenkins and Chojnacky models perform comparably, but that at both a field plot level and a total county level there was a ~ 20% difference between these estimates and the CRM estimates. Further, we show that discrepancies are greater in high biomass areas with high canopy covers and relatively moderate heights (25–45 m). The CRM models, although on average ~ 20% lower than Jenkins and Chojnacky, produce higher estimates in the tallest forests samples (> 60 m), while Jenkins generally produces higher estimates of biomass in forests < 50 m tall. Discrepancies do not continually increase with increasing forest height, suggesting that inclusion of height in allometric models is not primarily driving discrepancies. Models developed using all three allometric models underestimate high biomass and overestimate low biomass, as expected with random forest biomass modeling. However, these deviations were generally larger using the Jenkins and Chojnacky allometries, suggesting that the CRM approach may be more appropriate for biomass mapping with lidar.ConclusionsThese results confirm that allometric model selection considerably impacts biomass maps and estimates, and that allometric model errors remain poorly understood. Our findings that allometric model discrepancies are not explained by lidar heights suggests that allometric model form does not drive these discrepancies. A better understanding of the sources of allometric model errors, particularly in high biomass systems, is essential for improved forest biomass mapping.

Oceanic protists with different forms of acquired phototrophy display contrasting biogeographies and abundance.

This first comprehensive analysis of the global biogeography of marine protistan plankton with acquired phototrophy shows these mixotrophic organisms to be ubiquitous and abundant; however, their biogeography differs markedly between different functional groups. These mixotrophs, lacking a constitutive capacity for photosynthesis (i.e. non-constitutive mixotrophs, NCMs), acquire their phototrophic potential through either integration of prey-plastids or through endosymbiotic associations with photosynthetic microbes. Analysis of field data reveals that 40-60% of plankton traditionally labelled as (non-phototrophic) microzooplankton are actually NCMs, employing acquired phototrophy in addition to phagotrophy. Specialist NCMs acquire chloroplasts or endosymbionts from specific prey, while generalist NCMs obtain chloroplasts from a variety of prey. These contrasting functional types of NCMs exhibit distinct seasonal and spatial global distribution patterns. Mixotrophs reliant on 'stolen' chloroplasts, controlled by prey diversity and abundance, dominate in high-biomass areas. Mixotrophs harbouring intact symbionts are present in all waters and dominate particularly in oligotrophic open ocean systems. The contrasting temporal and spatial patterns of distribution of different mixotroph functional types across the oceanic provinces, as revealed in this study, challenges traditional interpretations of marine food web structures. Mixotrophs with acquired phototrophy (NCMs) warrant greater recognition in marine research.

The Development and Evaluation of a High-Resolution Above Ground Biomass Product for the Commonwealth of Puerto Rico (2000).

This study details the development of a U.S. Commonwealth of Puerto Rico above-ground forest biomass (agb) product (baseline 2000) developed by the United States Environmental, Protection Agency (epa) that was compared to another AGB product developed by the U.S. Forest Service (usfs) for the same area. The USEPA product tended to over-predict in areas of low biomass and under-predict in high biomass areas when compared to observed plot data, but compared favorably to a Forest Inventory Analysis (fia) assessment of structure and condition of Puerto Rico forests (72.6 Mg/ha versus 80.0 Mg/ ha, respectively). AGB estimates were highly correlated with reference FIA biomass for both maps at their native spatial resolutions (USEPA: r =0.93, USFS: r = 0.92). AGB mean difference between both products was 33.5 Mg/ha (USFS mean = 106.1 Mg/ha; USEPA mean = 72.6 Mg/ha), a difference not out-of- scope when compared to other biomass comparative studies.

Dry Matter Biomass Productivity and Composition of Grasses along Grazing Gradient in Fenced and Unfenced Grazing Areas of the Gaborone North,Botswana

Objective: The present study was aimed at assessing and comparing grass species composition and biomass productivity along fenced and unfenced grazing gradient. Methods: For each study area 2 × 1000 m transects radiating from the water points (borehole) were used and sampling plots of size a total of 15 quadrants of size 1 m2 were systematically placed along each transect at intervals of 50 m for the first 500 m and the last 500 m the spacing interval was increased by 100 m. The total biomass of the selected plots was clipped, weighed; oven dried at 65˚C for 48 h and weight again in order to express the weight by dry matter. Results: The grazing gradient in fenced area exhibited the highest dry matter biomass (P<0.05) for the grass species at 644.7 g/m2) as compared to the unfenced area at 155.9 g/m2. High-value species (341 g/m2) significantly dominated the dry matter biomass composition in the fenced gradients while in the unfenced it was dominated by medium value species (66.8 g/m2). Despite the fluctuation of biomass from one interval to another, logarithmic trend line estimations suggested an increasing plant biomass relative to the distance from the water point in both grazing gradients. Areas of high biomass were demonstrated at 900 m in both fenced (915 g/m2) and unfenced (433 g/m2) gradients. Dry matter biomass declined in areas close to and furthest from the watering points. The high biomass of the intermediate grass species dominated by E. rigidior suggests that it was highly unutilized. Conclusions: Our findings indicate that dry matter biomass productivity of fenced gradients was higher as compared to that of unfenced grazing area. Dry matter biomass in fenced was mainly composed of high value species especially U. trichopus Meanwhile E. rigidior mid value species contributed the largest share to the biomass in the unfenced gradient.

Assessing the transferability of statistical predictive models for leaf area index between two airborne discrete return LiDAR sensor designs within multiple intensely managed Loblolly pine forest locations in the south-eastern USA

Leaf area is an important forest structural variable which serves as the primary means of mass and energy exchange within vegetated ecosystems. The objective of the current study was to determine if leaf area index (LAI) could be estimated accurately and consistently in five intensively managed pine plantation forests using two multiple-return airborne LiDAR datasets. Field measurements of LAI were made using the LiCOR LAI2000 and LAI2200 instruments within 116 plots were established of varying size and within a variety of stand conditions (i.e. stand age, nutrient regime and stem density) in North Carolina and Virginia in 2008 and 2013. A number of common LiDAR return height and intensity distribution metrics were calculated (e.g. average return height), in addition to ten indices, with two additional variants, utilized in the surrounding literature which have been used to estimate LAI and fractional cover, were calculated from return heights and intensity, for each plot extent. Each of the indices was assessed for correlation with each other, and was used as independent variables in linear regression analysis with field LAI as the dependent variable. All LiDAR derived metrics were also entered into a forward stepwise linear regression. The results from each of the indices varied from an R2 of 0.33 (S.E. 0.87) to 0.89 (S.E. 0.36). Those indices calculated using ratios of all returns produced the strongest correlations, such as the Above and Below Ratio Index (ABRI) and Laser Penetration Index 1 (LPI1). The regression model produced from a combination of three metrics did not improve correlations greatly (R2 0.90; S.E. 0.35). The results indicate that LAI can be predicted over a range of intensively managed pine plantation forest environments accurately when using different LiDAR sensor designs. Those indices which incorporated counts of specific return numbers (e.g. first returns) or return intensity correlated poorly with field measurements. There were disparities between the number of different types of returns and intensity values when comparing the results from two LiDAR sensors, indicating that predictive models developed using such metrics are not transferable between datasets with different acquisition parameters. Each of the indices were significantly correlated with one another, with one exception (LAI proxy), in particular those indices calculated from all returns, which indicates similarities in information content for those indices. It can then be argued that LiDAR indices have reached a similar stage in development to those calculated from optical-spectral sensors, but which offer a number of advantages, such as the reduction or removal of saturation issues in areas of high biomass.

Pigments, elemental composition (C, N, P, and Si), and stoichiometry of particulate matter in the naturally iron fertilized region of Kerguelen in the Southern Ocean

Abstract. The particulate matter distribution and phytoplankton community structure of the iron-fertilized Kerguelen region were investigated in early austral spring (October–November 2011) during the KEOPS2 cruise. The iron-fertilized region was characterized by a complex mesoscale circulation resulting in a patchy distribution of particulate matter. Integrated concentrations over 200 m ranged from 72.2 to 317.7 mg m−2 for chlorophyll a 314 to 744 mmol m−2 for biogenic silica (BSi), 1106 to 2268 mmol m−2 for particulate organic carbon, 215 to 436 mmol m−2 for particulate organic nitrogen, and 29.3 to 39.0 mmol m−2 for particulate organic phosphorus. Three distinct high biomass areas were identified: the coastal waters of Kerguelen Islands, the easternmost part of the study area in the polar front zone, and the southeastern Kerguelen Plateau. As expected from previous artificial and natural iron-fertilization experiments, the iron-fertilized areas were characterized by the development of large diatoms revealed by BSi size–fractionation and high performance liquid chromatography (HPLC) pigment signatures, whereas the iron-limited reference area was associated with a low biomass dominated by a mixed (nanoflagellates and diatoms) phytoplankton assemblage. A major difference from most previous artificial iron fertilization studies was the observation of much higher Si : C, Si : N, and Si : P ratios (0.31 ± 0.16, 1.6 ± 0.7 and 20.5 ± 7.9, respectively) in the iron-fertilized areas compared to the iron-limited reference station (0.13, 1.1, and 5.8, respectively). A second difference is the patchy response of the elemental composition of phytoplankton communities to large scale natural iron fertilization. Comparison to the previous KEOPS1 cruise also allowed to address the seasonal dynamics of phytoplankton bloom over the southeastern plateau. From particulate organic carbon (POC), particulate organic nitrogen (PON), and BSi evolutions, we showed that the elemental composition of the particulate matter also varies at the seasonal scale. This temporal evolution followed changes of the phytoplankton community structure as well as major changes in the nutrient stocks progressively leading to silicic acid exhaustion at the end of the productive season. Our observations suggest that the specific response of phytoplankton communities under natural iron fertilization is much more diverse than what has been regularly observed in artificial iron fertilization experiments and that the elemental composition of the bulk particulate matter reflects phytoplankton taxonomic structure rather than being a direct consequence of iron availability.

Improving Species Diversity and Biomass Estimates of Tropical Dry Forests Using Airborne LiDAR

The spatial distribution of plant diversity and biomass informs management decisions to maintain biodiversity and carbon stocks in tropical forests. Optical remotely sensed data is often used for supporting such activities; however, it is difficult to estimate these variables in areas of high biomass. New technologies, such as airborne LiDAR, have been used to overcome such limitations. LiDAR has been increasingly used to map carbon stocks in tropical forests, but has rarely been used to estimate plant species diversity. In this study, we first evaluated the effect of using different plot sizes and plot designs on improving the prediction accuracy of species richness and biomass from LiDAR metrics using multiple linear regression. Second, we developed a general model to predict species richness and biomass from LiDAR metrics for two different types of tropical dry forest using regression analysis. Third, we evaluated the relative roles of vegetation structure and habitat heterogeneity in explaining the observed patterns of biodiversity and biomass, using variation partition analysis and LiDAR metrics. The results showed that with increasing plot size, there is an increase of the accuracy of biomass estimations. In contrast, for species richness, the inclusion of different habitat conditions (cluster of four plots over an area of 1.0 ha) provides better estimations. We also show that models of plant diversity and biomass can be derived from small footprint LiDAR at both local and regional scales. Finally, we found that a large portion of the variation in species richness can be exclusively attributed to habitat heterogeneity, while biomass was mainly explained by vegetation structure.

Using hyperspectral vegetation indices to estimate the fraction of photosynthetically active radiation absorbed by corn canopies

The fraction of photosynthetically active radiation (FPAR) absorbed by vegetation – a key parameter in crop biomass and yields as well as net primary productivity models – is critical to guiding crop management activities. However, accurate and reliable estimation of FPAR is often hindered by a paucity of good field-based spectral data, especially for corn crops. Here, we investigate the relationships between the FPAR of corn (Zea mays L.) canopies and vegetation indices (VIs) derived from concurrent in situ hyperspectral measurements in order to develop accurate FPAR estimates. FPAR is most strongly (positively) correlated to the green normalized difference vegetation index (GNDVI) and the scaled normalized difference vegetation index (NDVI*). Both GNDVI and NDVI* increase with FPAR, but GNDVI values stagnate as FPAR values increase beyond 0.75, as previously reported according to the saturation of VIs – such as NDVI – in high biomass areas, which is a major limitation of FPAR-VI models. However, NDVI* shows a declining trend when FPAR values are greater than 0.75. This peculiar VI–FPAR relationship is used to create a piecewise FPAR regression model – the regressor variable is GNDVI for FPAR values less than 0.75, and NDVI* for FPAR values greater than 0.75. Our analysis of model performance shows that the estimation accuracy is higher, by as much as 14%, compared with FPAR prediction models using a single VI. In conclusion, this study highlights the feasibility of utilizing VIs (GNDVI and NDVI*) derived from ground-based spectral data to estimate corn canopy FPAR, using an FPAR estimation model that overcomes limitations imposed by VI saturation at high FPAR values (i.e. in dense vegetation).

Microzooplankton community composition along the Western Antarctic Peninsula

Microzooplankton are an integral part of aquatic food webs, yet compared to macrozooplankton, are understudied in the Southern Ocean. The region along the Western Antarctic Peninsula (WAP) is experiencing rapid climate warming, resulting in declines in sea ice extent and duration, and affecting the marine food web. Microzooplankton community structure along the WAP was analyzed in January 2010 and 2011 as part of the Palmer Antarctica Long-Term Ecological Research project. Whole seawater samples were collected within the top 100m of the water column along both north-south and coastal-offshore gradients, and major taxa of microzooplankton were quantified using microscopy. Average chlorophyll-a concentrations and microzooplankton biomass were higher in 2011 compared to 2010. Athecate dinoflagellates and aloricate ciliates dominated microzooplankton biomass, and the biomass of most taxonomic groups was higher in the south compared to the north. Specifically, aloricate ciliate and tintinnid biomass increased with increasing latitude, and biomass peaked at several southern, inshore stations – including Marguerite Bay, which was an area of high biomass for some microzooplankton taxa. Biomass was higher in surface waters compared to 100m, and variability in microzooplankton biomass between years and with distance from shore was most likely due to sea ice dynamics. Microzooplankton biomass was positively correlated with chlorophyll-a and particulate organic carbon. These results are used to consider how microzooplankton populations may be adjusting to environmental changes along the WAP.