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At least 19 records

Assessing soybean leaf area and leaf biomass by spectral measurements

Red and photographic infrared spectral radiances were correlated with soybean total leaf area index, green leaf area index, chlorotic leaf area index, green leaf biomass, chlorotic leaf biomass, and total biomass. The most significant correlations were found to exist between the IR/red radiance ratio data and green leaf area index and/or green leaf biomass (r squared equals 0.85 and 0.86, respectively). These findings demonstrate that remote sensing data can supply information basic to soybean canopy growth, development, and status by nondestructive determination of the green leaf area or green leaf biomass.

Holben, B. N.↗

Spectral assessment of soybean leaf area and leaf biomass

Red and photographic infrared spectral radiance have been correlated with soybean total leaf area index, green leaf area index, chlorotic area index, green leaf biomass, chlorotic leaf biomass, and total biomass. The most significant correlations were found to exist between the spectral data and green leaf area index and/or green leaf biomass. These findings demonstrate that ground based remote sensing data can supply information basic to soybean canopy growth, development, and status by non-destructive determination of the green leaf area or green leaf biomass.

Holben, B. N.↗

Spectral radiance estimates of leaf area and leaf phytomass of small grains and native vegetation

Similarities and/or dissimilarities in radiance characteristics were studied among barley (Hordeum vulgare L.), oats (Avena fatua L.), spring and winter wheat (Triticum aestivum L.), and short-grass prairie vegetation. The site was a Williams loam soil (fine-loamy mixed, Typic Argiborolls) near Sidney, Montana. Radiances were measured with a truck-mounted radiometer. The radiometer was equipped with four wavelength bands: 0.45 to 0.52, 0.52 to 0.60, 0.63 to 0.69, and 0.76 to 0.90 micron. Airborne scanner measurements were made at an altitude of 600 m four times during the season under clear sky conditions. The airborne scanner was equipped with the same four bands as the truck-mounted radiometer plus the following: 1.00 to 1.30, 1.55 to 1.75, 2.08 to 2.35, and 10.4 to 12.5 microns. Comparisons using individual wave bands, the near IR/red, (0.76 to 0.90 micron)/(0.63 to 0.69 micron) ratio and the normalized difference vegetation index, ND = (IR - red)/(IR + red), showed that only during limited times during the growing season were some of the small grains distinguishable from one another and from native rangeland vegetation. There was a common relation for all small grains between leaf area index and green leaf phytomass and between leaf area index or green leaf phytomass and the IR/red ratio.

Aase, J. K.↗

Vegetation Warming Experiment: Leaf Mass Area, Leaf Carbon and Nitrogen Content, Utqiagvik (Barrow), Alaska, 2017

Leaf mass per area (LMA), leaf carbon and nitrogen content of vegetation (Petasites frigidus) within warming chambers and paired control plots. See related datasets for plant physiology, phenology and environmental conditions. The files included in this data package are in .csv format, and include 2 data files and 4 metadata files.These data were collected in 2017 as part of a series of single-season warming experiments on tundra vegetation on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. A different plant species was targeted each year, over four experimental years from 2017–2021. Each year, five warming chambers and paired ambient control plots were deployed from around the time of snowmelt in mid-June through to mid-September. Average seasonal warming of 3–4°C was achieved using Zero Power Warming (ZPW) chambers (Lewin et al, 2017). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

Vegetation Warming Experiment: Leaf Mass Area, Leaf Carbon and Nitrogen Content, Utqiagvik (Barrow), Alaska, 2018

Leaf mass per area (LMA), leaf carbon and nitrogen content of vegetation (Arctagrostis latifolia, Petasites frigidus) within warming chambers and paired control plots. Samples include gas exchange measurement leaves and an end of season bulk harvest. The files included in this data package are in .csv format, and include 2 data files and 4 metadata files. See related datasets for plant physiology, phenology and environmental conditions of these samples. These data were collected in 2018 as part of a series of single-season warming experiments on tundra vegetation on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. A different plant species was targeted each year, over four experimental years from 2017–2021. Each year, five warming chambers and paired ambient control plots were deployed from around the time of snowmelt in mid-June through to mid-September. Average seasonal warming of 3–4°C was achieved using Zero Power Warming (ZPW) chambers (Lewin et al, 2017).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

Leaf Nitrogen, Leaf Mass Area, Leaf Water Content, Seward Peninsula, Alaska, 2017

Nitrogen and carbon content, leaf mass per area and leaf water content of leaves sampled from the NGEE Arctic Teller study site, Seward Peninsula, Alaska in 2017. Data is included for 13 species from the deciduous shrub, forb and graminoid plant functional types. See related dataset for leaf reflective spectra, sample photographs and dGPS locations. This data package includes leaf sample information and trait data (*.csv). Note that leaf nitrogen content analysis was performed on a subset of samples. Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA103_flmd.csv. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

Leaf mass area, leaf carbon and nitrogen content, Seward Peninsula and Utqiagvik (Barrow), Alaska, 2022

Leaf mass per area (LMA), and leaf carbon and nitrogen content of Arctic vegetation species from three sites in Alaska, the Barrow Environmental Observatory (BEO), in Utqiagvik, and, two sites on the Seward Peninsula, Kougarok Mile 64 and Teller Mile 27. The plants were sampled in July 2022 as part of an ongoing project to improve the understanding of stomatal conductance in the Arctic. Species sampled from the BEO (from a 1 km2 area centered around 71.275°N, 156.641°W) were Arctagrostis latifolia, Arctophila fulva, Carex aquatilis, Eriophorum angustifolium, Petasites frigidus and Salix pulchra. Alnus viridis, Salix glauca and Salix pulchra were collected at the Seward Peninsula sites. All sampled leaves were used for gas exchange measurements prior to analysis of LMA and leaf carbon and nitrogen content. The data and metadata files included in this data package are in .csv format. See related datasets for stomatal response measurements.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

Leaf mass area, leaf carbon and nitrogen content of 18 plant species, Seward Peninsula, Alaska, 2023

Leaf mass per area (LMA), and leaf carbon and nitrogen content of Arctic vegetation species from three sites on the Seward Peninsula, Alaska. The plants were sampled in July 2023 from the Kougarok Mile 64, Teller Mile 27 and Council sites as part of an ongoing project to improve the understanding of stomatal conductance and photosynthetic parameterization of Arctic plant functional types (PFTs). Species sampled included deciduous tall and dwarf shrubs, forbs, and graminoids, for a total of 18 species. All sampled leaves were used for gas exchange measurements prior to analysis of LMA and leaf carbon and nitrogen content. The data and metadata files included in this data package are in .csv format. See related dataset NGA509 for leaf-level gas exchange measurements and shrub traits (size, thaw depth, soil moisture).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

Vegetation Warming Experiment: Leaf mass area, leaf carbon and nitrogen content, Utqiagvik, Alaska, 2019

Leaf mass per area (LMA), and leaf carbon and nitrogen content of two Arctic vegetation species (Eriophorum angustifolium Honck., Arctagrostis latifolia (R.Br.) Griseb.) from within warming chambers and paired control plots. The plants were sampled in July 2019 as part of the Zero Power Warming (ZPW) vegetation warming experiment conducted on the Barrow Environmental Observatory, Utqiagvik (formerly Barrow), Alaska. Samples include leaves used for gas exchange measurements and a bulk harvest from each warming chamber and paired ambient plot. The files included in this data package are in .csv format, and include 2 data files and 4 metadata files. See related datasets for plant physiology, phenology and environmental conditions of the plots and warming chambers.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

Vegetation Warming Experiment: Leaf mass area, leaf carbon and nitrogen content, Utqiagvik, Alaska, 2021-2022

Leaf mass per area (LMA), and leaf carbon and nitrogen content of an Arctic graminoid, Carex aquatilis, from within warming chambers and paired control plots. The plants were sampled in July 2021 and July 2022 as part of the Zero Power Warming (ZPW) vegetation warming experiment conducted on the Barrow Environmental Observatory (BEO), Utqiagvik (formerly Barrow), Alaska. Samples include leaves used for gas exchange measurements and bulk harvests from each warming chamber and paired ambient plot. The files included in this data package are in .csv format, and include 3 data files and 4 metadata files. See related datasets for plant physiology, phenology, thaw depth and environmental conditions of the plots and warming chambers. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

Indirect Field Measurement of Wine-Grape Vineyard Canopy Leaf Area Index

Leaf area index (LAI) indirect measurements were made at 12 study plots in California's Napa Valley commercial wine-grape vineyards with a LI-COR LI-2000 Plant Canopy Analyzer (PCA). The plots encompassed different trellis systems, biological varieties, and planting densities. LAI ranged from 0.5 - 2.25 sq m leaf area/ sq m ground area according to direct (defoliation) measurements. Indirect LAI reported by the PCA was significantly related to direct LAI (r(exp 2) = 0.78, p less than 001). However, the PCA tended to underestimate direct LAI by about a factor of two. Narrowing the instrument's conical field of view from 148 deg to 56 deg served to increase readings by approximately 30%. The PCA offers a convenient way to discern relative differences in vineyard canopy density. Calibration by direct measurement (defoliation) is recommended in cases where absolute LAI is desired. Calibration equations provided herein may be inverted to retrieve actual vineyard LAI from PCA readings.

Johnson, Lee F.↗

Leaf Area Influence on Surface Layer in a Deciduous Forest: Detecting Leaf Area and Surface Resistance During Transition Seasons - Part 2

Temperate deciduous forest exhibit dramatic seasonal changes in surface exchange properties following on the seasonal changes in leaf area index. The canopy resistance to water vapor transport r(sub c) decreased abruptly at leaf emergence in each year but then also continued to decrease slowly during the remaining growing season due to slowly increasing LAI. Canopy resistance and PAR-albedo (albedo from photosynthetically active radiation) began to increase about one month before leaf fall with the diminishment of CO2 gradient above the canopy as well. At this time evaporation begun to be controlled as if the canopy were leafless.

Sakai, Ricardo K.↗

Three-dimensional estimation of deciduous forest canopy structure and leaf area using multi-directional, leaf-on and leaf-off airborne lidar data

Airborne laser scanning (ALS) has been widely used to map gap probability and leaf area index (LAI) distribution at plot and landscape scales. As an indirect measurement, most ALS methods to estimate LAI combine waveform or point density information with supporting field measurements such as the leaf angle distribution, gap probability, or direct LAI measures. The development of a more independent estimation approach would facilitate more widespread use of existing ALS data to investigate patterns of forest structure and build realistic 3-D vegetation scenes to simulate remote sensing imagery and energy balance. Here, we develop a data processing workflow (named PVlad) using ALS point cloud apparent reflectance to estimate LAI and voxel-based leaf area density (LAD), aiming to reduce the need for associated field measurements such as the gap probability. The adaptation of the path volume (PV) concept derived from apparent reflectance integrates information from multi-directional ALS pulses, and quantifies the percentage exploration of each voxel for classification and occlusion correction, such that rigorous volumetric sampling approaches can be developed to derive LAI and LAD. The PVlad workflow was applied to discrete-return lidar data (Riegl VQ480i) acquired by NASA Goddard's LiDAR, Hyperspectral and Thermal Imager (G-LiHT) Airborne Imager during leaf-on (summer) and leaf-off (spring) conditions at the Smithsonian Environmental Research Center (SERC). The estimates of LAI and LAD captured structural differences between mature, logged, and intermediate-aged stands over eight deciduous forest plots. The derived LAI values were compared to field litter collection measurements, and the derived LAD vertical distribution was compared to the output of the VoxLAD model using terrestrial laser scan (TLS) field survey data. Using voxel sizes ranging from 0.5 m to 5 m, overall LAI estimation showed linear fitting coefficient bias and for 1 and 2 m voxel sizes, and vertical LAD distribution showed strong correlation with and for 0.5 and 1m voxel sizes. For every forest stand, upper-canopy LAD had a low variance for voxel sizes of ≤ . Application of PVlad to the G-LiHT and other similar ALS data archives enables the development of fine-resolution LAI map products, including voxelization of LAD for ecosystem science and radiative transfer simulations of remote sensing imagery or surface energy balance.

Tiangang Yin↗

Active layer depth and soil properties impact specific leaf area variation and ecosystem productivity in a boreal forest

Specific leaf area (SLA, leaf area per unit dry mass) is a key canopy structural characteristic, a measure of photosynthetic capacity, and an important input into many terrestrial process models. Although many studies have examined SLA variation, relatively few data exist from high latitude, climate-sensitive permafrost regions. We measured SLA and soil and topographic properties across a boreal forest permafrost transition, in which dominant tree species changed as permafrost deepened from 54 to >150 cm over 75 m hillslope transects in Caribou-Poker Creeks Research Watershed, Alaska. We characterized both linear and threshold relationships between topographic and edaphic variables and SLA and developed a conceptual model of these relationships. We found that the depth of the soil active layer above permafrost was significantly and positively correlated with SLA for both coniferous and deciduous boreal tree species. Intraspecific SLA variation was associated with a fivefold increase in net primary production, suggesting that changes in active layer depth due to permafrost thaw could strongly influence ecosystem productivity. While this is an exploratory study to begin understanding SLA variation in a non-contiguous permafrost system, our results indicate the need for more extensive evaluation across larger spatial domains. These empirical relationships and associated uncertainty can be incorporated into ecosystem models that use dynamic traits, improving our ability to predict ecosystem-level carbon cycling responses to ongoing climate change.

59 BASIC BIOLOGICAL SCIENCES↗

Investigating the Relationship Between Liquid Water and Leaf Area in Clonal Populus

Leaf Area Index (LAI) is one of the most commonly employed biophysical parameters used to characterize vegetation canopies and scale leaf physiological processes to larger scales. For example, LAI is a critical parameter used in regional scale estimates of evapotranspiration, photosynthesis, primary productivity, and carbon cycling (Running et al., 1989; Dorman and Sellers, 1989; Potter et al., 1993). LAI is typically estimated using ratio-based techniques, such as the Normalized Difference Vegetation Index (NDVI: e.g. Tucker 1979; Asrar et al., 1989; Sellers 1985, 1987). The physical basis behind this relationship depends on the high spectral contrast between scattered near-infrared (NIR) and absorbed red radiation in canopies. As the number of leaves present in a canopy increases over a unit area, NIR reflectance increases, while red reflectance decreases, resulting in an increase in the ratio. Through time series and image compositing, NDVI provides an additional temporal measure of how these parameters change, providing a means to monitor fluxes and productivity (Tucker et al., 1983). NDVI, while highly successful for agriculture and grassland ecosystems has been found to be less successful in evergreen chaparral and forested ecosystems (Badhwar et al., 1986; Gamon et al., 1993; Hall et al., 1995). Typically, the relationship between NDVI and LAI becomes progressively more asymptotic at LAI values above three (Sellers, 1985), although linear relationships have been observed in conifers at LAis as high as 13 (Spanner et al., 1990). In this paper, we explore an alternative approach for estimating LAI for remotely sensed data from AVIRIS based on estimates of canopy liquid water. Our primary objective is to test the hypothesis that the depth of the liquid water bands expressed in canopy reflectance spectra at 960, 1200, 1400 and 1900 nm increases with increasing LAI in canopies. This study builds from work by Roberts et al. (1997), in which liquid water was shown to increase following a gradient of increasing LAI ranging from grasslands to coniferous forests. In that study, it was observed that forests, which showed little variation in NDVI, showed significant variation in liquid water. In order to test this hypothesis, we analyzed field spectra measured over Populus resprouts of known LAI and monitored changes in liquid water in young Populus stands as they aged over a 4-year time span. The study was conducted in south-central Washington, in a clonal Populus fiber farm owned and operated by Boise-Cascade near the town of Wallula.

Roberts, Dar↗

Costs of measuring leaf area index of corn

The magnitude of plant-to-plant variability of leaf area of corn plants selected from uniform plots was examined and four representative methods for measuring leaf area index (LAI) were evaluated. The number of plants required and the relative costs for each sampling method were calculated to detect 10, 20, and 50% differences in LAI using 0.05 and 0.01 tests of significance and a 90% probability of success (beta = 0.1). The natural variability of leaf area per corn plant was nearly 10%. Additional variability or experimental error may be introduced by the measurement technique employed and by nonuniformity within the plot. Direct measurement of leaf area with an electronic area meter had the lowest CV, required that the fewest plants be sampled, but required approximately the same amount of time as the leaf area/weight ratio method to detect comparable differences. Indirect methods based on measurements of length and width of leaves required more plants but less total time than the direct method. Unless the coefficients for converting length and width to area are verified frequently, the indirect methods may be biased. When true differences in LAI among treatments exceed 50% of mean, all four methods are equal. The method of choice depends on the resources available, the differences to be detected, and what additional information, such as leaf weight or stalk weight, is also desired.

Daughtry, C. S. T.↗