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At least 289 records · Page 16

Solar position confounds the relationship between ecosystem function and vegetation indices derived from solar and photosynthetically active radiation fluxes

Vegetation indices derived from solar and photosynthetically active radiation (PAR) sensors (i.e. radiation derived) have been under-utilized in inferring ecosystem function, despite measurement capability at hundreds of sites. This under-utilization may be attributed to reported mismatches among the seasonality of radiation- and satellite-derived vegetation indices and canopy photosynthesis; herein referred to as measurement biases. In this work, biases in radiation derived reflectance and vegetation indices were assessed using a decadal record of satellite and ground based spectroradiometer data, ecosystem phenology and CO 2 fluxes, and radiation derived vegetation indices (i.e. the Normalized Difference Vegetation Index [NDVI], the two band Enhanced Vegetation Index [EVI2]) from a high latitude tundra site (i.e. Imnaviat). At Imnaviat, we found poor correspondence between the three types of reflectance and vegetation indices, especially during the latter part of the growing season. Radiation derived vegetation indices resulted in incorrect estimates of phenological timing of up to a month and poor relationships with canopy photosynthesis (i.e. Gross Ecosystem Exchange (GEE)). These mismatches were attributed to solar position (i.e. solar zenith and azimuth angle) and a method, based on the diel visible and near-infrared albedo variation, was developed to improve the performance of the vegetation indices. The ability of radiation derived vegetation indices to infer GEE and phenological dates drastically improved once radiation derived vegetation indices were corrected for solar position associated biases at Imnaviat. Moreover, radiation derived vegetation indices became better aligned with MODerate resolution Imaging Spectroradiometer (MODIS) satellite estimates after solar position associated biases were corrected at Imnaviat and at 25 Fluxnet sites (~90 site years) across North America. Corrections developed here provide a way forward in understanding daily ecosystem function or filling large gaps in eddy covariance data at a significant number of Fluxnet sites.

54 ENVIRONMENTAL SCIENCES↗

Unisexual flower initiation in the monoecious Quercus suber L.: a molecular approach

Abstract Several plant species display a temporal separation of the male and female flower organ development to enhance outbreeding; however, little is known regarding the genetic mechanisms controlling this temporal separation. Quercus suber is a monoecious oak tree with accentuated protandry: in late winter, unisexual male flowers emerge adjacent to the swollen buds, whereas unisexual female flowers emerge in the axils of newly formed leaves formed during spring (4–8 weeks after male flowering). Here, a phylogenetic profiling has led to the identification of cork oak homologs of key floral regulatory genes. The role of these cork oak homologs during flower development was identified with functional studies in Arabidopsis thaliana. The expression profile throughout the year of flower regulators (inducers and repressors), in leaves and buds, suggests that the development of male and female flowers may be preceded by separated induction events. Female flowers are most likely induced during the vegetative flush occurring in spring, whereas male flowers may be induced in early summer. Male flowers stay enclosed within the pre-dormant buds, but complete their development before the vegetative flush of the following year, displaying a long period of anthesis that spans the dormant period. Our results portray a genetic mechanism that may explain similar reproductive habits in other monoecious tree species.

Sobral, Rómulo↗

Sustainability Trait Modeling of Field-Grown Switchgrass ( Panicum virgatum ) Using UAV-Based Imagery

Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.

60 APPLIED LIFE SCIENCES↗

A Near-Real-Time Model for Predicting Electricity Disruptions in Texas During Winter Storms

There has been an increase in extreme weather events, posing a threat to power grid systems, potentially influenced by factors such as population growth, changes in ecosystems, land cover, and land use in the service area, as well as the growth of certain vegetation types. This research seeks to develop a predictive model to mitigate potential damages caused by future winter storms. This research utilizes the Light Gradient Boosting Machine (LightGBM), incorporating the number of power outages experienced at the county level, geographic details, weather information, and lagged outage and lagged weather data. The developed models were broadly divided into two groups, with six models in each group - one group without optimization and another with optimization, totaling 12 trained models. For model optimization, Bayesian optimization was employed using Root Mean Squared Error (RMSE) as the objective function. In results, when comparing Group 2 (the optimized group) with Group 1 (the non-optimized group), it was found that optimization did not always lead to a reduction in RMSE and Mean Absolute Error (MAE). However, in terms of Mean Directional Accuracy (MDA), while all results in Group 1 were below the baseline accuracy of 0.33, all results in Group 2 exceeded 0.33, with some cases showing an increase of more than three times the baseline. The results indicated that, in the optimized model group, Population and Pressure were the most influential factors when using current weather data and geographical information. When using lagged data, lagged recorded outages and lagged Pressure emerged as the most significant factors. Among the 12 developed models, the L-1-2-O model showed the lowest RMSE and MAE, as well as the highest accuracy, with values of 390.62 households and 168.13 households, respectively. To normalize the RMSE and MAE values, each metric was divided by the average number of households among the counties in Texas. For the L-1-2-O model, the scaled RMSE was 0.88% and the scaled MAE was 0.38%. In terms of MDA, which indicates the accuracy of the prediction direction, the L-1-O model achieved the highest score of 0.41. Although this study focused on Texas, which suffered the greatest impact from the winter storms in 2021, with additional validation, the methodology used in this research could be applied to other regions.

Lee, Jangjae [Texas A & M Univ., College Station, ↗

Genetic Characterization of the Acidic and Neutral Glycosphingolipid Biosynthetic Pathways in Neurospora crassa

Fungal glycosphingolipids (GSLs) are important membrane components which play a key role in vesicle trafficking. To assess the importance of GSLs in the fungal life cycle, we performed a mutant phenotypic study of the acidic and neutral GSL biosynthetic pathways in Neurospora crassa. GSL biosynthesis begins with two reactions leading up to the formation of dihydrosphingosine. The first of these reactions is catalyzed by serine palmitoyltransferase and generates 3-keto dihydrosphinganine. In N. crassa, this reaction is catalyzed by GSL-1 and GSL-2 and is required for viability. The second reaction is carried out by GSL-3, a 3-keto dihydrosphinoganine reductase to generate dihydrosphingosine, which is used for the synthesis of neutral and acidic GSLs. We found that deletion mutations in the acidic GSL pathway leading up to the formation of mannosylinositol-phosphoceramide are lethal, indicating that acidic GSLs are essential for viability in N. crassa. Once mannosylinositol-phosphoceramide is made, it is further modified by GSL-5, an inositol-phosphoceramide-B C26 hydroxylase, which adds a hydroxyl group to the amide-linked fatty acid. GSL-5 is not required for viability but gives a clear mutant phenotype affecting all stages of the life cycle. Our results show that the synthesis of mannosylinositol-phosphoceramide is required for viability and that the modification of the amide-linked fatty acid is important for acidic GSL functionality. We also examined the neutral GSL biosynthetic pathway and identified the presence of glucosylceramide. The deletion of neutral GSL biosynthetic genes affected hyphal morphology, vegetative growth rate, conidiation, and female development. Our results indicate that the synthesis of neutral GSLs is essential for normal growth and development of N. crassa.

59 BASIC BIOLOGICAL SCIENCES↗

The use of color infrared photography for wetlands assessment

A study was undertaken of Pointe Mouillee Marsh, located on Lake Erie, to assess shoreline erosion and to inventory and evaluate adjacent land as potential replacement for areas lost to erosion, and to provide better data sources for management decisions. The results of the study were: (1) Evaluation of low altitude oblique photography was useful in determining specifications of operational mission requirements; (2) Accurate base map revisions, reflecting shoreline erosion, were made using aerial photography and a Zoom Transfer Scope; (3) An aerial land cover inventory provided data necessary for the selection of adjacent lands suitable for marshland development; (4) A detailed inventory of vegetative communities (mapped from CIR), was made for management decisions; and (5) A carefully selected and well laid-out transect was a key asset to photo interpretation and analysis of vegetation.

Enslin, W. R.↗

Mesoscale monitoring of the soil freeze/thaw boundary from orbital microwave radiometry

The fundamental objectives are to test the feasibility of delineating the lateral boundary between frozen and thawed condition in the surface layer of soil from orbital microwave radiometry and secondly to examine the sensitivity of general circulation models to an explicit parameterization of the boundary condition. Physical models were developed to relate emissivity to scene properties and a simulation package was developed to predict brightness temperature as a function of emissivity and physical temperature in order to address issues of heterogeneity, scaling, and scene dynamics. Radiative transfer models were develped for both bare soil surfaces and those obscured by an intervening layer of vegetation or snow. These models relate the emissivity to the physical properties of the soil and to those of the snow or vegetation cover. A SMMR simulation package was developed to evaluate the adequacy of the emission models and the limiting effects of scaling for realistic scenarios incorporating spatially heterogeneous scenes with dynamic moisture and temperature gradients at the pixel scale.

Ulaby, Fawwaz T.↗

Remote sensing of biomass of salt marsh vegetation in France

Spectral data (gathered using a hand-held radiometer) and harvest data were collected from four salt marsh vegetation types in Brittany, France, to develop equations predicting live aerial biomass from spectral measurements. Remote sensing estimates of biomass of the general salt marsh community (GSM) and of Spartina alterniflora can be obtained throughout the growing season if separate biomass prediction equations are formulated for different species mixtures (for the GSM) and for different canopy types (for S. alterniflora). Results suggest that remote sensing will not be useful for predicting Halimione portulacoides biomass, but can be used to estimate Puccinellia maritima biomass early in the growing season.

Gross, M. F.↗

Remote sensing of solar radiation absorbed and reflected by vegetated land surfaces

1D and 3D radiative-transfer models have been used to investigate the problem of remotely sensed determination of vegetated land surface-absorbed and reflected solar radiation. Calculations were conducted for various illumination conditions to determine surface albedo, soil- and canopy-absorbed photosynthetically active and nonactive radiation, and normalized difference vegetation index. Simple predictive models are developed on the basis of the relationships among these parameters.

Myneni, Ranga B.↗

L-band radar scattering from grass

A radar system based on a network analyzer has been developed to study the backscatter from vegetation. The radar is operated at L-band. Radar measurements of a grass field were made in 1991. The radar returns from the grass were measured at three incidence angles. Ground truth and canopy parameters such as blade and stem dimensions, moisture content of the grass and the soil, and blade and stem density, were measured. These parameters are used in a distorted Born approximation model to compute the backscatter coefficients from the grass layer. The model results are compared with the radar data.

Chauhan, N.↗

Characterization of wetland, forest, and agricultural ecosystems in Belize with airborne radar (AIRSAR)

The Shuttle Imaging Radar-C/X-SAR (Synthetic Aperture Radar) Experiment includes the study of wetland dynamics in the seasonal tropics. In preparation for these wetland studies, airborne P, L, and C band radar (AIRSAR) data of Belize, Guatemala, and Mexico acquired by NASA and JPL in March 1990 were analyzed. The first phase of our study focuses on AIRSAR data from the Gallon Jug test site in northwestern Belize, for which ground data were also collected during the three days prior to the overflight. One of the main objectives of the Gallon Jug study is to develop a method for characterizing wetland vegetation types and their flooding status with multifrequency polarimetric radar data.

Pope, Kevin O.↗

Induced Abnormality In Mir- and Earth-Grown Super Dwarf Wheat

Super-dwarf wheat grown on the Mir space station using the Svet "Greenhouse" exhibited morphological, metabolic and reproductive abnormalities compared with normal wheat. Of prominent importance were the abnormalities associated with reproductive ontogeny and the total absence of seed formation on Mir. Changes in the apical meristem associated with transition from the vegetative phase to floral initiation and development of the reproductive spike were all typical of 'Super Dwarf' wheat up to the point of anthesis. Observation of ruptured anthers from the Mir-grown plants revealed what appeared to be normally developed pollen. These pollen grains however, contain only one nucleus, while normal viable pollen is trinucleate. A potentially important difference in the flight experiment, compared with ground reference studies, was identified - a high level of atmospheric ethylene (1200 ppb). Ground studies conducted exposing "Super-dwarf" wheat to ethylene at just prior to anthesis resulted in manifestation of the same abnormalities observed in the space flight samples.

Bubenheim, David L.↗

Induced abnormality in Mir- and Earth grown Super Dwarf wheat

Super-dwarf wheat grown on the Mir space station using the Svet "Greenhouse" exhibited morphological, metabolic and reproductive abnormalities compared with Earth-grown wheat. Of prominent importance were the abnormalities associated with reproductive ontogeny and the total absence of seed formation on Mir. Changes in the apical meristem associated with transition from the vegetative phase to floral initiation and development of the reproductive spike were all typical of 'Super-Dwarf' wheat up to the point of anthesis. Observation of ruptured anthers from the Mir-grown plants revealed what appeared to be normally developed pollen. These pollen gains, however, contained only one nuclei, while normal viable pollen is tri-nucleate. A potentially important difference in the flight experiment, compared with ground reference studies, was the presence of a high level of atmospheric ethylene (1,200 ppb). Ground studies conducted by exposing 'Super-Dwarf' wheat to ethylene just prior to anthesis resulted in manifestation of the same abnormalities observed in the space flight samples. c2002 Published by Elsevier Science Ltd on behalf of COSPAR.

long duration↗

The Relationship of Forest Fires Detected by MODIS and SRTM Derived Topographic Features in Central Siberia

Fires are a common occurrence in the Siberian boreal forest. The MOD14 Thermal anomalies product of the Terra MODIS Moderate Resolution Spectroradiometer) product set is designed to detect thermal anomalies (i.e. hotspots or fires) on the Earth's surface. Recent field studies showed a dependence of fire occurrence on topography. In this study MODIS thermal anomaly data and SRTM topography data were merged and analyzed to evaluate if forest fires are more likely to occur at certain combinations of elevation, slope and aspect. Using the satellite data over a large area can lead to better understanding how topography and forest fires are related. The study area covers a 2.5 Million krn(exp 2) portion of the Central Siberian southern taiga from 72 deg to 110 deg East and from 50 deg to 60 deg North. About 57% of the study area is forested and 80% of the forest grows between 200 and 1000 m. Forests with pine (Pinus sylvestris), larch (Larix sibirica, L. gmelinii), Siberian pine (Pinus sibirica), spruce (Picea obovata.) and fir (Abies sibirica) cover most of the landscape. Deciduous stands with birch (Betula pendula, B. pubescens) and aspen (Populus tremula) cover the areas of lower elevation in this region. The climate of this area is distinctly continental with long, cold winters and short hot summers. The tree line in this part of the world is around 1500 m in elevation with alpine tundra, snow and ice fields and rock outcrops extending up to over 3800 m. A 500 m resolution landcover map was developed using 2001 MODIS MOD13 Normalized Vegetation Index (NDVI) and Middle Infrared (MIR) products for seven 16-day periods. The classification accuracy was over 87%. The SRTM version 2 data, which is distributed in 1 degree by 1 degree tiles were mosaiced using the ENVI software. In this study, only those MODIS pixels were used that were flagged as "nominal or high confidence fire" by the MODIS fire product team. Using MODIS data from the years 2000 to 2005 along with the improved Shuttle Radar Topographic Mission (SRTM) version 2 data at 100 m resolution, the distribution of hot spots was examined by elevation, slope and aspect as well as by forest type. The results show that more forest area burns at lower elevations but a larger percentage of the available forest area burns at higher elevations. This is probably because steep slopes occur at higher elevations. Fires are only more common on slopes with a southern exposure if the slope is steeper than 15 degrees. The next step in this study will be to monitor areas where the risk of fire is high (steep slopes with a southern exposure) and to refine this method by incorporating anthropogenic features for more accurate fire disturbance monitoring.

Ranson, Jon K.↗

A First-Order Radiative Transfer Model for Microwave Radiometry of Forest Canopies at L-Band

In this study, a new first-order radiative transfer (RT) model is developed to more accurately account for vegetation canopy scattering by modifying the basic r-co model (the zero-order RT solution). In order to optimally utilize microwave radiometric data in soil moisture (SM) retrievals over moderately to densely vegetated landscapes, a quantitative understanding of the relationship between scattering mechanisms within vegetation canopies and the microwave brightness temperature is desirable. A first-order RT model is used to investigate this relationship and to perform a physical analysis of the scattered and emitted radiation from vegetated terrain. The new model is based on an iterative solution (successive orders of scattering) of the RT equations up to the first order. This formulation adds a new scattering term to the i-w model. The additional term represents emission by particles (vegetation components) in the vegetation layer and emission by the ground that is scattered once by particles in the layer. The new model is tested against 1.4 GHz brightness temperature measurements acquired over deciduous trees by a truck-mounted microwave instrument system called ComRAD in 2007. The model predictions are in good agreement with the data and they give quantitative understanding for the influence of first-order scattering within the canopy on the brightness temperature. The model results show that the scattering term is significant for trees and modifications are necessary to the T-w model when applied to dense vegetation. Numerical simulations also indicate that the scattering term has a negligible dependence on SM and is mainly a function of the angle and polarization of the microwave observation.

Kurum, Mehmet↗

A First-Order Radiative Transfer Model for Microwave Radiometry of Forest Canopies at L-Band

In this study, a first-order radiative transfer (RT) model is developed to more accurately account for vegetation canopy scattering by modifying the basic Tau-Omega model (the zero-order RT solution). In order to optimally utilize microwave radiometric data in soil moisture (SM) retrievals over vegetated landscapes, a quantitative understanding of the relationship between scattering mechanisms within vegetation canopies and the microwave brightness temperature is desirable. The first-order RT model is used to investigate this relationship and to perform a physical analysis of the scattered and emitted radiation from vegetated terrain. This model is based on an iterative solution (successive orders of scattering) of the RT equations up to the first order. This formulation adds a new scattering term to the . model. The additional term represents emission by particles (vegetation components) in the vegetation layer and emission by the ground that is scattered once by particles in the layer. The model is tested against 1.4-GHz brightness temperature measurements acquired over deciduous trees by a truck-mounted microwave instrument system called ComRAD in 2007. The model predictions are in good agreement with the data, and they give quantitative understanding for the influence of first-order scattering within the canopy on the brightness temperature. The model results show that the scattering term is significant for trees and modifications are necessary to the . model when applied to dense vegetation. Numerical simulations also indicate that the scattering term has a negligible dependence on SM and is mainly a function of the incidence angle and polarization of the microwave observation. Index Terms Emission,microwave radiometry, scattering, soil, vegetation.

Kurum, Mehmet↗

Future Opportunities and Challenges in Remote Sensing of Drought

The value of satellite remote sensing for drought monitoring was first realized more than two decades ago with the application of Normalized Difference Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) for assessing the effect of drought on vegetation. Other indices such as the Vegetation Health Index (VHI) were also developed during this time period, and applied to AVHRR NDVI and brightness temperature data for routine global monitoring of drought conditions. These early efforts demonstrated the unique perspective that global imagers such as AVHRR could provide for operational drought monitoring through their near-daily, global observations of Earth's land surface. However, the advancement of satellite remote sensing of drought was limited by the relatively few spectral bands of operational global sensors such as AVHRR, along with a relatively short period of observational record. Remote sensing advancements are of paramount importance given the increasing demand for tools that can provide accurate, timely, and integrated information on drought conditions to facilitate proactive decision making (NIDIS, 2007). Satellite-based approaches are key to addressing significant gaps in the spatial and temporal coverage of current surface station instrument networks providing key moisture observations (e.g., rainfall, snow, soil moisture, ground water, and ET) over the United States and globally (NIDIS, 2007). Improved monitoring capabilities will be particularly important given increases in spatial extent, intensity, and duration of drought events observed in some regions of the world, as reported in the International Panel on Climate Change (IPCC) report (IPCC, 2007). The risk of drought is anticipated to further increase in some regions in response to climatic changes in the hydrologic cycle related to evaporation, precipitation, air temperature, and snow cover (Burke et al., 2006; IPCC, 2007; USGCRP, 2009). Numerous national, regional, and global efforts such as the Famine and Early Warning System (FEWS), National Integrated Drought Information System (NIDIS), and Group on Earth Observations (GEO), as well as the establishment of regional drought centers (e.g., European Drought Observatory) and geospatial visualization and monitoring systems (e.g, NASA SERVIR) have been undertaken to improve drought monitoring and early warning systems throughout the world. The suite of innovative remote sensing tools that have recently emerged will be looked upon to fill important data and knowledge gaps (NIDIS, 2007; NRC, 2007) to address a wide range of drought-related issues including food security, water scarcity, and human health.

Wardlow, Brian D.↗

The SMAP Level-4 ECO Project: Linking the Terrestrial Water and Carbon Cycles

The SMAP (Soil Moisture Active Passive) Level-4 projects aims to develop a fully coupled hydrology-vegetation data assimilation algorithm to generate improved estimates of modeled hydrological fields and carbon fluxes. This includes using the new NASA Catchment-CN (Catchment-Carbon-Nitrogen) model, which combines the Catchment land surface hydrology model with dynamic vegetation components from the Community Land Model version 4 (CLM4). As such, Catchment-CN allows a more realistic, fully coupled feedback between the land hydrology and the biosphere. The L4 ECO project further aims to inform the model through the assimilation of Soil Moisture Active Passive (SMAP) brightness temperature observations as well as observations of Moderate Resolution Imaging Spectroradiometer (MODIS) fraction of absorbed photosynthetically active radiation (FPAR). Preliminary results show that the assimilation of SMAP observations leads to consistent improvements in the model soil moisture skill. An evaluation of the Catchment-CN modeled vegetation characteristics showed that a calibration of the model's vegetation parameters is required before an assimilation of MODIS FPAR observations is feasible.

MODIS↗