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59 results for “biomass use”
Litterfall, Biomass and Productivity in CRUI Land Use Project at Harvard Forest 1996-1997
Prior agricultural land use often plays an important role in the present condition of forests. This research was conducted to find any productivity differences among previously plowed, pastured, and woodlot sites. There are six sites, two from each of the land use types. With this research we wanted to estimate the productivity for each of the six stands and test the hypothesis that rates of annual aboveground biomass and annual litter fall in recovering forests would decline in the order woodlot - pastured - plowed. Twenty-five trees, of various diameters and species, were cored at each site. The cores were analyzed and the radial growth was calculated for the most recent 5 and 10 year intervals. Biomass increments were also calculated for the same time span using published allometric equations. Aboveground woody productivity was calculated for diameter classes within species and semi-log relationships of productivity per diameter were used to calculate the productivity of all trees in the stand that were not cored. Site-specific and species-specific equations were used. Litter fall was estimated by collecting litter from ten traps each in the six stands. Litter was sorted by species and reported on a dry matter basis. Red oaks and red maples were the major contributors of biomass and litter fall in all six of the stands, accounting for about seventy percent of the total aboveground biomass. Although woodlots had higher total biomass there was not a significant difference in productivity across the six sites. The range of productivity including both litter fall and aboveground woody production was from 7524-9014 Kg/ha. This fell into the range for temperate forests according to results published by the International Biological Program. Production per unit biomass was greater in the plowed stand and least in the woodlots.
Spartina alterniflora aboveground biomass patterns from Landsat 5 TM imagery (1984-2011) and external driver data used in multivariate analysis.
We used Landsat 5 TM satellite imagery to derive aboveground biomass estimates for the three height classes (tall, medium, short) of Spartina alterniflora on the Centeral Georgia Coast. We used geospatial techniques to scale up in situ measurements of aboveground S. alterniflora aboveground biomass to landscape level estimates using 294 Landsat images acquired between 1984 to 2011. For each scene we extracted data from the same 63 sampling polygons, containing 1,222 pixels covering 1.1 million m^2. Using univariate and linear multiple regression tests, we compared Landsat derived biomass estimates for three S. alterniflora size classes against a suite of abiotic drivers. Drivers included monthly mean values for Altamaha River Discharge, Palmer Drought Severity Index, Standardized Precipitation Index, Mean Sea Level, Precipitation, and Temperature.
Simulation of the efficiency of a reversed supply chain of wood biomass using different types of transport units (NCN) DEC-2020/39/I/HS4/03533
<p>Data describing simulations related to the standardisation of loading units for the transport of wood biomass. The effectiveness of assumptions relating to the use of different types of packaging were verified from the perspective of the number of vehicles required and their emissions. The relationship between the size and specification of the wood biomass load and the packaging used was indicated. The study was funded by National Science Centre in Poland under agreement National Center of Science (NCN) through grant DEC-2020/39/I/HS4/0353</p>
Data for creating figures to the paper "Assessing Net Growth of Phytoplankton Biomass on Hourly to Annual Timescales Using the Geostationary Ocean Color Instrument."
<p>Processed data to generate figures for the paper "Assessing Net Growth of Phytoplankton Biomass on Hourly to Annual Timescales Using the Geostationary Ocean Color Instrument."</p> <p>The rate at which microscopic ocean plants, or phytoplankton, consume carbon dioxide represents a gap in scientific knowledge that needs to be filled in order to better model the earth system. To aid in this understanding we use a novel technique that allows us to track the growth behavior of phytoplankton in the Yellow Sea and the East Sea-Japan Sea. This is enabled by using satellite data from the Geostationary Ocean Color Imager, which has the unprecedented ability to collect quality biological information from the ocean surface each daylight hour. We find that the results, while in agreement with local observations and other satellite studies, also contain information about how phytoplankton change over daily to annual cycles and how native communities adapt in response to the annual solar cycle. This information is useful to the ocean modeling community, that seeks to understand various ways in which phytoplankton communities affect the cycling of Earth’s carbon.</p>
Resource use strategies, resistance and tolerance to aerial biomass removal in Argentina mid-west native plants
<p>Dataset of the PhD Thesis from Lucas D. Gorné:<br> - Gorné LD. 2018. Estrategias de uso de recursos, resistencia y tolerancia a la remoción de biomasa aérea en plantas nativas del centro-oeste de Argentina. Tesis del Doctorado en Ciencias Biológicas. Facultad de Ciencias Exactas, Físicas y Naturales. Universidad Nacional de Córdoba. Córdoba, Argentina. https://ri.conicet.gov.ar/handle/11336/87925.</p>
PIE LTER plant biomass associated with marsh sites used in space for time sea level rise study, Rowley, MA.
This dataset contains the mass of dried grasses collected from clip plots in the PIE LTER Space for Time Sea Level Rise study. The space for time study uses an intensive and comprehensive approach to compare low elevation, Spartina alterniflora marsh areas to higher elevation Spartina patens marsh areas. Grasses are sampled in plots from 4 transects per site with five plots per site, arranged from the tidal creek's edge to no more than 150 meters back from the creek. Other related data files include: HTL-RO-ST-MAR-Sites, HTL-RO-ST-MAR-Birds, HTL-RO-ST-MAR-Quads, HTL-RO-ST-MAR-Sediments, HTL-RO-ST-MAR-Bites, HTL-RO-ST-MAR-Sticky, HTL-RO-ST-MAR-Decomp, HTL-RO-ST-MAR-Traps, HTL-RO-ST-MAR-Deep_pitfalls
Raw data used in Kumar et al. 2020: Barley shoot biomass responds strongly to N:P stoichiometry and intraspecific competition, whereas roots only alter their foraging
<p>Raw data used in Kumar et al. 2020: Barley shoot biomass responds strongly to N:P stoichiometry and intraspecific competition, whereas roots only alter their foraging</p>
Dataset for the manuscript entitled "Can vermicomposting be used to process hyperaccumulator biomass in nickel agromining?"
<p>Data generated during an experiment of decomposition of biomass of the nickel hyperaccumulator <i>Bornmuellera emarginata </i>by the earthworm <i>Eisenia andrei</i> and associated microoganisms.</p><p>Five files are included:</p><p>"<strong>readme.txt</strong>" is a file where we explain the meaning of each column (and in which units is expressed) in each of the other four files.</p><p>"<strong>biomass.csv</strong>" is a table with biomass of different fractions and the number of <i>Eisenia andrei</i> individuals in each experimental unit.</p><p><strong>"FTIR.csv" </strong>is a file with the raw spectral data we obtained from the litter by Fourier Transform Infrared spectroscopy combined with Attenuated Total Reflectance (FTIR-ATR). First column indicate the wavenumber (cm-1) and the other columns indicate the absorbance values of each biomass sample for each wavenumber.</p><p><strong>"composition.csv"</strong> is a file with the pH, electrical conductivity, basal respiration and concentrations of different chemical elements measured in different fractions of each experimental unit</p><p><strong>"nickel.csv"</strong> contains the pseudototal and DTPA-extractable concentrations of Ni in different compartments of each experimental unit, as well as the total amount of this element (mg of Ni) in each compartment of each experimental unit.</p>
Biomass production at 2085 horizon for the Maurienne valley (French Alps) estimated using a Bayesian Belief Network
<p>In mountains, grasslands managed for livestock production sustain local economies, culture and identity. However, their future fodder production is highly uncertain under climate change: while an extended growing season may be beneficial, more frequent and intense summer droughts could also reduce fodder quantity and quality. Land use and land cover (LULC) changes are another major driver of regional grassland biomass production, but combined effects of future land use transitions and climate change are rarely quantified.</p> <p>We modelled combined climate and LULC scenarios for grassland production of the Maurienne Valley (French Alps) by 2100. We built a Bayesian Belief Network (BBN) from long-term grassland production monitoring data complemented with expert knowledge. We assessed the potential of two candidate adaptations, intensification as an incremental solution, and silvopastoralism as a transformative solution to compensate combined impacts of two climate scenarios and three land use change scenarios.</p> <p>Total biomass production was far more sensitive to LULC than to climate scenarios. Production losses were largest under the Conservation LULC scenario (-28% on average between 2020 and 2085), followed by the Tourism development scenario (-7%) and the Business-as-Usual scenario (+3%). Climate change under RCP 8.5 altered the seasonality of production by increasing potential production from May to July while decreasing summer regrowth. Intensification somewhat compensated effects of climate and LULC changes on biomass production, whereas silvopastoralism offered only marginal gains. The Bayesian network model explicitly captured a future increase in interannual variability in biomass production.</p> <p>Synthesis and application: Changes in LULC are more decisive for global biomass production than climate change. However, under the most extreme climate change scenario (RCP8.5), the seasonal shift in production and increased interannnual variability threaten the current grass-based Protected Designation of Origin production system. Only the intensification adaptation solution showed significant gains in total biomass production. Still, the silvopastoralism would require less investment compared to the intensification and have a similar efficiency when assessing the gains of biomass by the surface concerned with adaptation solutions. </p>
DATASET: Evaluation of sorbents for high temperature removal of tars, hydrogen sulphide, hydrogen chloride and ammonia from biomass-derived syngas by using Aspen Plus
<p>Effect of S/B ratio on syngas; Effect of temperature on syngas; sensitivity analysis of H2S concentration respect to steam content in syngas; sensitivity analysis of H2S concentration respect to temperature.</p>
Biomass estimation of oilseed rape using the light use efficiency model in Bavaria (Atlas) in LandKlif project
<p>This dataset shows the predicted biomass (g/m2) of oilseed rape (OSR) using the light use efficiency (LUE) model for Bavaria in 2019. The LUE model uses satellite data (Landsat-8, MODIS) and climate data (temperature and solar radiation) to calculate the plant biomass. The crop yield is predicted and validated at district level using LfStat data. The validation results show an R2 of 0.79 with an RMSE of 2.21 dt/ha. </p> <p>This dataset is conducted under LandKlif project. LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html"><strong>Bavarian State Ministry of Science and the Arts</strong></a> within the <a href="https://www.bayklif.de/"><strong>Bavarian Climate Research Network (bayklif)</strong></a><strong>. </strong> Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. <strong>LandKliF</strong>, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</p>
Biomass estimation of winter wheat using the Light Use Efficiency model in Bavaria (Atlas) in LandKlif project
<p>This dataset shows the predicted biomass (g/m2) of winter wheat (WW) using the Light Use Efficiency (LUE) model for Bavaria in 2019. The LUE model uses satellite data (Landsat-8, MODIS) and climate data (temperature and solar radiation) to calculate the plant biomass. The crop yield is predicted and validated at district level using LfStat data. The validation results show an R2 of 0.82 with an RMSE of 5.46 dt/ha. </p> <p>This dataset is conducted under LandKlif project. LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html"><strong>Bavarian State Ministry of Science and the Arts</strong></a> within the <a href="https://www.bayklif.de/"><strong>Bavarian Climate Research Network (bayklif)</strong></a><strong>. </strong> Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. <strong>LandKliF</strong>, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</p>
Fig. 4 in Environmental assessment in tropical streams by using abundance-biomass curves and W index in fish assemblages
Fig. 4. Spearman correlation (ρ = -0.68; p = 0.02) between W and PCoA1, Pirapó River basin, Maringá City, Paraná state, Brazil (White squares, urban streams; black Diamond, rural streams)(1, RibeirÃo Maringá; 2, Miosótis; 3, Mandacaru; 4, Roseira; 5, Granada; 6, Lombo; 7, ZaÚna; 8, Remo; 9, Romeira; 10, Água QueÇaba).
Fig. 2 in Environmental assessment in tropical streams by using abundance-biomass curves and W index in fish assemblages
Fig. 2. Ordination of two aXes generated by the principal coordinates analysis (PCoA) applied to the environmental data of Urban (white squares) and Rural streams (black diamond) of Maringá, Paraná, Brazil (1, RibeirÃo Maringá; 2, Miosótis; 3, Mandacaru; 4, Roseira; 5, Granada; 6, Lombo; 7, ZaÚna; 8, Remo; 9, Romeira; 10, Água QueÇaba).
Fig. 3 in Environmental assessment in tropical streams by using abundance-biomass curves and W index in fish assemblages
Fig. 3. Accumulation curves of abundance (white circles) and biomass (black circles) to show the ABC method and values of W indeX for fish assemblages sampled in the streams of Maringá, Paraná, Brazil. Negative values of W suggest environmentally stressed (following WARWICK & CLARKE, 1994) ecosystems, and positive values indicate not stressed environments.
Fig. 1 in Environmental assessment in tropical streams by using abundance-biomass curves and W index in fish assemblages
Fig. 1. Streams sampled in Maringá (PR), South Brazil: 1, RibeirÃo Maringá (51°58'8.00"W/ 23°22'28.09"S); 2, Miosótis (51°55'52.29"W/23°20'39.82"S); 3, Mandacaru (51°56'49.16"W/ 23°23'5.01"S); 4, Roseira (51°54'50.45"W/ 23°21'2.26"S); 5, Granada (51°45'39.95"W/ 23°18'5.26"S); 6, Lombo (51°58'7.07"W/ 23°18'49.53"S); 7, ZaÚna (51°50'55.11"W/ 23°23'41.99"S); 8, Remo (52°1'0.75"W/ 23°21'27.09"S); 9, Romeira (51°58'9.55"W/ 23°21'40.40"S); 10, Água QueÇaba (51°53'28.78"W/ 23°19'22.24"S). White circles represent rural streams; black circles represent urban streams; gray area is the urban perimeter; and dark gray are forest fragments.
Spatial Feature Engineering Dataset for Forest Aboveground Biomass Estimation Using Landsat Imagery
<p><strong>Study Area:</strong><br>The dataset covers forested regions in Oregon, Washington, Idaho, and eastern Montana, characterized by diverse climatic conditions due to orographic effects. The forests in the Coast Range and western slopes of the Cascades, with high precipitation (800-3000 mm annually), contrast with the drier forests in Idaho and Montana, which receive over 400 mm annually. The dataset includes highly productive Douglas-fir and western hemlock forests, with aboveground biomass (AGB) densities exceeding 1200 Mg ha⁻¹, as well as fire-adapted lodgepole and ponderosa pine forests in the rainshadow regions.</p> <p><strong>LiDAR AGB Estimates:</strong><br>The dataset includes 176 lidar-derived AGB maps from 2002 to 2016, covering various regions in Oregon, Washington, Idaho, and Montana. A Random Forest (RF) model was used to estimate AGB at a 30m² resolution, utilizing lidar height features, DEM features, and climate data. Non-forested areas and buildings were masked using binary forest cover maps from the LCMS dataset and the Microsoft Building Footprints dataset.</p> <p><strong>Reference Dataset:</strong><br>A composited AGB map, derived from the 176 lidar maps, was created to develop Landsat-based AGB models, covering 9,361,622 ha of forested land. The AGB layer was stratified into 30 bins, and training, development, and testing sets were constructed for model validation. The dataset includes 7500 test samples and 300,000 training and development samples, with a 500m buffer around test set locations to prevent spatial autocorrelation.</p> <p><strong>Landsat Satellite Imagery:</strong><br>Landsat imagery from 1990 to 2022 was utilized, with three time series derived: all scenes, scenes from May to November, and annual medoid composites. The imagery was processed using the Google Earth Engine (GEE) platform, focusing on periods of maximum phenological activity.</p> <p><strong>Feature Engineering:</strong><br>Extensive feature engineering was performed, generating spectral, spatial, temporal, and topographic features from Landsat imagery and DEM data. Features were extracted over the reference AGB map's domain, synchronized with the lidar acquisition dates.</p> <ul> <li><strong>LandTrendr Fitted Imagery:</strong> Spectral features were derived from LandTrendr-fitted imagery, smoothing variations in the time series.</li> <li><strong>LandTrendr Disturbance and Recovery Features:</strong> Temporal features were derived from LandTrendr models, characterizing disturbance and recovery events.</li> <li><strong>CCDC Disturbance and Recovery Features:</strong> CCDC algorithm-derived features characterized disturbances and recovery using harmonic models.</li> <li><strong>Buffer Features:</strong> Local variations were captured using buffer statistics around each pixel.</li> <li><strong>GLCM Features:</strong> GLCM texture features summarized the joint distribution of gray-tone values.</li> <li><strong>Edge Detectors:</strong> Various edge detection operators captured spatial derivatives and edges.</li> <li><strong>Morphological Operations:</strong> Morphological features were derived using multi-channel image processing techniques.</li> <li><strong>Neighborhood Vectorization:</strong> Direct vectorization of satellite measurements in pixel neighborhoods.</li> <li><strong>Neighborhood Similarity:</strong> Similarity features characterized the relationship between pixel neighborhoods and their centroids.</li> <li><strong>Topographic Features:</strong> Topography was characterized using elevation, slope, aspect embeddings, and hillshade layers from the NED DEM.</li> </ul> <p>This comprehensive dataset enables robust analysis of AGB models and their performance across diverse forested landscapes in the Pacific Northwest</p>
Biomass production at 2085 horizon for the Maurienne valley (French Alps) estimated using a Bayesian Belief Network
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Model data created by MANDIFORE using ED2 simulating structure and biomass under harvest legacies and climate change as the Morton Arboretum, IL, USA, 2007-2099"
The data in this archive was used to investigate the long-term impacts of harvest-based management strategies on structure and resistance to climate-induced biomass loss using a process-based ecosystem model and an ensemble of climate change drivers in a midwestern USA hardwood forest. The model used was a modified version of the Ecosystem Demography model version 2.1 (ED2) where trees could be grouped based on plant functional type and size allowing for different management regimes to be simulated. The simulation site was an actively managed mid-continental North American temperate deciduous forest at The Morton Arboretum (TMA) near Chicago, USA. The initial conditions (composition and structure) of the forest simulated were initialized using field survey data from 487, 8.92-m radius plots collected in 2018. Different levels of tree removal based on traditional forestry management techniques for temperate deciduous forests were simulated and evaluated for their effects on forest structure and resistance to drought induced mortality for the period of 2006 to 2100. Model initialization and spin up occurred from 2006-2020 at which point management was simulated for the period of 2020-2024 before running without management for 2024 to 2100. Fourteen CMIP5 GCMs were used for meteorological drivers for 2006 to 2100 for both RCP4.5 and RCP 8.5 warming scenarios. CO2 concentration was held constant at 380 ppm over the simulation period. Four management regimes were simulated for each of these GCM-RCP pairings leading to a total of 112 distinct model runs. The four management regimes were group selection, shelterwood, understory thinning and an additional no management scenario served as a control. Data in this archived is separated into the 112 distinct model runs.
Enhanced reforming of mixed biomass tar model compounds using a hybrid gliding arc plasma catalytic process
<p>Datasheet for the paper; DOI: 10.1016/j.cattod.2019.05.046</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.