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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

A Multi-Scale Sampling Strategy for Detecting Physiologically Significant Signals in AVIRIS Imagery

Models of photosynthetic production at ecosystem and global scales require multiple input parameters specifying physical and physiological surface features. While certain physical parameters (e.g., absorbed photosynthetically active radiation) can be derived from current satellite sensors, other physiologically relevant measures (e.g., vegetation type, water status, carboxylation capacity, or photosynthetic light-use efficiency), are not generally directly available from current satellite sensors at the appropriate geographic scale. Consequently, many model parameters must be assumed or derived from independent sources, often at an inappropriate scale. An abundance of ecophysiological studies at the leaf and canopy scales suggests strong physiological control of vegetation-atmosphere CO2 and water vapor fluxes, particularly in evergreen vegetation subjected to diurnal or seasonal stresses. For example hot, dry conditions can lead to stomatal closure, and associated "downregulation" of photosynthetic biochemical processes, a phenomenon often manifested as a "midday photosynthetic depression". A recent study with the revised simple biosphere (SiB2) model demonstrated that photosynthetic downregulation can significantly impact global climate. However, at the global scale, the exact significance of downregulation remains unclear, largely because appropriate physiological measures are generally unavailable at this scale. Clearly, there is a need to develop reliable ways of extracting physiologically relevant information from remote sensing. Narrow-band spectrometers offer many opportunities for deriving physiological parameters needed for ecosystem and global scale photosynthetic models. Experimental studies on the ground at the leaf- to stand-scale have indicated that several narrow-band features can be used to detect plant physiological status. One physiological signal is caused by xanthophyll cycle pigment activity, and is often expressed as the Photochemical Reflectance Index (PRI). Because the xanthophyll cycle pigments are photoregulatory pigments closely linked to photosynthetic function, this index can be used to derive relative photosynthetic rates. An additional signal with physiological significance is the 970 nm water absorption band, which provides a measure of liquid water content. This feature has been quantified both using a simple 2-band ratio (900/970 nm, here referred to as the "Water Band Index" or WBI;), and using the "continuum removal" method. Current atmospheric correction methods for AVIRIS imagery also obtain quantitative expressions of surface liquid water absorption based on the 970 nm water band and may be comparable to ground-based estimates of water content using this feature. However, physiological interpretations of both the PRI and the WBI are best understood at the leaf and canopy scales, where complications of atmospheric interference and complex stand and landscape features can be minimized, and where experimental manipulations can be readily applied. Currently it is not known whether these physiological indices can be used to derive meaningful physiological information from AVIRIS imagery. In addition to the problem of atmospheric interference, another challenge is that any simple physiological index can be confounded by multiple factors unrelated to physiology, and this problem can become more severe at progressively larger spatial scales. For example, previous work has suggested that both the PRI and the WBI, are strongly correlated with other optical measures of canopy structure (e.g., the Normalized Difference Vegetation Index or green vegetation fraction), indicating a confounding effect of structure on physiological signals at the larger, landscape scale. Furthermore, the normal operating mode of most imaging spectrometers does not allow simultaneous, ground truthing at a level of detail needed for physiological sampling. Additionally, manipulative experiments of physiology are difficult to apply at a geographic scale suitable for comparison with remote imagery, which often works at spatial scales that are several orders of magnitude larger than those typically used for physiological studies. These limitations require the consideration of alternative approaches to validating physiological information derived from AVIRIS data. In this report, we present a multi-scale sampling approach to detecting physiologically significant signals in narrow-band spectra. This approach explores the multi-dimensional data space provided by narrow-band spectrometry, and combines AVIRIS imagery at a large scale, with ground spectral sampling at an intermediate scale, and detailed ecophysiological measurements at a fine scale, to examine seasonally and spatially changing relationships between multiple structural and physiological variables. Examples of this approach are provided by simultaneous sampling of the Normalized Difference Vegetation Index (NDVI), an index of fractional PAR interception and green vegetation cover, the Water Band Index (WBI, an index of liquid water absorption, and the Photochemical Reflectance Index (PRI, an index of xanthophyll cycle pigment activity and photosynthetic light-use efficiency. By directly linking changing optical properties sampled on the ground with measurable physiological states, we hope to develop a basis for interpreting similar signals in AVIRIS imagery.

Gamon, John A.↗
NASA NTRS2005

Analysis of Upper Air, Ground and Remote Sensing Data for the Atlas Field Campaign in San Juan, Puerto Rico

The general climate of the island of Puerto Rico is dominated by the easterly trade winds from the Atlantic Ocean, and during synoptically calm days by the topographic and local land surface characteristics [1]. The urban canopy of the metropolitan area of San Juan, capital city of the Island, may introduce a new microclimate that changes the characteristics of the low atmosphere and interacts with the other microclimates already present in the island. The primitive land cover and land use (LCLU) of the metropolitan area of San Juan was composed by broadleaf trees, moist soils, and very dense vegetation in general. The urban LCLU changes the balance for the mass, momentum and energy between the bottom boundary and the lower atmosphere, creating different climate conditions over urban and rural regions. Some of these differences are low relative humidity and high temperatures observed in urban areas when compared to rural areas. These in turn produces a convective circulation over the urban areas, a phenomenon compared to the sea and land breezes, commonly known as heat islands (UHI). Factors that contribute to the formation of the UHI are anthropogenic heat sources, aerosols from pollutants, fast water canalization due to the presence of buildings and streets, among others. The comparison between urban and rural climates is the most common approach to analyze the UHI. These contrasts are larger in clear and calm conditions and tend to disappear in cloudy and windy weather. The UHI was recognized in the early 1950 s as closed isotherms that separates the city from the general temperature field [2]. The impact of the urban LCLU in San Juan, Puerto Rico, was quantified calculating the difference between historical data sets for the air temperature over an identified urban area and a rural area dT(U-R). The analysis of the climatological data revealed that a UHI exists in the metropolitan area of San Juan, Puerto Rico. The data reveals a permanent urban heat island effect present in the SJMA during the year, which is increasing at a rate of 0.41oC/decade. These findings encouraged the planning and execution of an intense field campaign in February 2004 referred as the ATLAS San Juan mission. The focus of the remaining of this report is the analysis of the data for this field campaign.

Gonzalez-Cruz, Jorge E.↗
DOE OSTI2024

Wildfire‐induced increases in photosynthesis in boreal forest ecosystems of North America

Abstract Observations of the annual cycle of atmospheric CO 2 in high northern latitudes provide evidence for an increase in terrestrial metabolism in Arctic tundra and boreal forest ecosystems. However, the mechanisms driving these changes are not yet fully understood. One proposed hypothesis is that ecological change from disturbance, such as wildfire, could increase the magnitude and change the phase of net ecosystem exchange through shifts in plant community composition. Yet, little quantitative work has evaluated this potential mechanism at a regional scale. Here we investigate how fire disturbance influences landscape‐level patterns of photosynthesis across western boreal North America. We use Alaska and Canadian large fire databases to identify the perimeters of wildfires, a Landsat‐derived land cover time series to characterize plant functional types (PFTs), and solar‐induced fluorescence (SIF) from the Orbiting Carbon Observatory‐2 (OCO‐2) as a proxy for photosynthesis. We analyze these datasets to characterize post‐fire changes in plant succession and photosynthetic activity using a space‐for‐time approach. We find that increases in herbaceous and sparse vegetation, shrub, and deciduous broadleaf forest PFTs during mid‐succession yield enhancements in SIF by 8–40% during June and July for 2‐ to 59‐year stands relative to pre‐fire controls. From the analysis of post‐fire land cover changes within individual ecoregions and modeling, we identify two mechanisms by which fires contribute to long‐term trends in SIF. First, increases in annual burning are shifting the stand age distribution, leading to increases in the abundance of shrubs and deciduous broadleaf forests that have considerably higher SIF during early‐ and mid‐summer. Second, fire appears to facilitate a long‐term shift from evergreen conifer to broadleaf deciduous forest in the Boreal Plain ecoregion. These findings suggest that increasing fire can contribute substantially to positive trends in seasonal CO 2 exchange without a close coupling to long‐term increases in carbon storage.

Biodiversity & Conservation↗
DOE OSTI2024

NOAA Arctic Report Card 2024 : Tundra Greenness

The Arctic tundra biome occupies Earth’s northernmost lands, covering a 5.1 million km 2 area that encircles the Arctic Ocean and is bound to the south by the boreal forest biome. Arctic tundra ecosystems are experiencing profound changes as vegetation and underlying permafrost soils are strongly influenced by rising air temperatures and the rapid decline of sea ice (see essays Surface Air Temperature and Sea Ice). By the late 1990s, an increase in the productivity of tundra vegetation became evident in global satellite observations, a phenomenon that continued and soon became known as “the greening of the Arctic.” Arctic greening is dynamically linked with Earth’s changing climate, seasonal snow, permafrost, and sea-ice cover, and remains a focus of multidisciplinary scientific research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Conservation management decreases surface runoff and soil erosion

Conservation management practices – including agroforestry, cover cropping, no-till, reduced tillage, and residue return – have been applied for decades to control surface runoff and soil erosion, yet results have not been integrated and evaluated across cropping systems. In this study we collected data comparing agricultural production with and without conservation management strategies. We used a bootstrap resampling analysis to explore interactions between practice type, soil texture, surface runoff, and soil erosion. We then used a correlation analysis to relate changes in surface runoff and soil erosion to 13 other soil health and agronomic indicators, including soil organic carbon, soil aggregation, infiltration, porosity, subsurface leaching, and cash crop yield. Across all conservation management practices, surface runoff and erosion had respective mean decreases of 67% and 80% compared with controls. Use of cover cropping provided the largest decreases in erosion and surface runoff, thus emphasizing the importance of maintaining continuous vegetative cover on soils. Coarse- and medium-textured soils had greater decreases in both erosion and runoff than fine-textured soils. Changes in surface runoff and soil erosion under conservation management were highly correlated with soil organic carbon, aggregation, porosity, infiltration, leaching, and yield, showing that conservation practices help drive important interactions between these different facets of soil health. This study offers the first large-scale comparison of how different conservation agriculture practices reduce surface runoff and soil erosion, and at the same time provides new insight into how these interactions influence the improvement or loss of soil health.

58 GEOSCIENCES↗
DOE OSTI2022

Classification of Wetland Vegetation Based on NDVI Time Series from the HLS Dataset

Natural wetlands are intrinsically heterogeneous and typically composed of a mosaic of ecosystem patches with different vegetation types. Hydrological and biogeochemical processes in wetlands vary strongly among these ecosystem patches. To date, most remote sensing classification approaches for wetland vegetation either rely on coarse images that cannot capture the spatial variability of wetland vegetation or rely on very-high-resolution multi-spectral images that are detailed but very sporadic in time (less than once per year). This study aimed to use NDVI time series, generated from NASA’s HLS dataset, to classify vegetation patches. We demonstrate our approach at a temperate, coastal lake, estuarine marsh. To classify vegetation patches, a standard time series library of the four land-cover patch types was built from referencing specific locations that were identified as “pure” pixels. These were identified using a single-time high-resolution image. We calculated the distance between the HLS-NDVI time series at each pixel and the “pure”-pixel standards for each land-cover type. The resulting true-positive classified rate was >73% for all patch types other than water lily. The classification accuracy was higher in pixels of a more uniform composition. A set of vegetation maps was created for the years 2016 to 2020 at our research site to identify the vegetation changes at the site as it is affected by rapid water elevation increases in Lake Erie. Our results reveal how changes in water elevation have changed the patch distribution in significant ways, leading to the local extinction of cattail by 2019 and a continuous increase in the area cover of water lily patches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Process-oriented analysis of dominant sources of uncertainty in the land carbon sink

The observed global net land carbon sink is captured by current land models. All models agree that atmospheric CO 2 and nitrogen deposition driven gains in carbon stocks are partially offset by climate and land-use and land-cover change (LULCC) losses. However, there is a lack of consensus in the partitioning of the sink between vegetation and soil, where models do not even agree on the direction of change in carbon stocks over the past 60 years. This uncertainty is driven by plant productivity, allocation, and turnover response to atmospheric CO 2 (and to a smaller extent to LULCC), and the response of soil to LULCC (and to a lesser extent climate). Overall, differences in turnover explain ~70% of model spread in both vegetation and soil carbon changes. Further analysis of internal plant and soil (individual pools) cycling is needed to reduce uncertainty in the controlling processes behind the global land carbon sink.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1977

Effect of vegetation on rock and soil type discrimination

The effect of naturally occurring vegetation on the spectral reflectance of earth materials in the wavelength region of 0.45 to 2.4 microns is determined by computer averaging of in situ acquired spectral data. The amount and type of vegetation and the spectral reflectance of the ground are considered. Low albedo materials may be altered beyond recognition with only ten per cent green vegetation cover. Dead or dry vegetation does not greatly alter the shape of the spectral reflectance curve and only changes the albedo with minimum wavelength dependency. With increasing amounts of vegetation the Landsat MSS band ratios 4/6, 4/7, 5/6, and 5/7 are significantly decreased whereas MSS ratios 4/5 and 6/7 remain entirely constant.

Siegal, B. S.↗
NASA NTRS2015

Mapping Biophysical Parameters for Land Surface Modeling over the Continental US Using MODIS and Landsat

In terms of the space cities occupy, urbanization appears as a minor land transformation. However, it permanently modifies land's ecological functions, altering its carbon, energy, and water fluxes. It is therefore necessary to develop a land cover characterization at fine spatial and temporal scales to capture urbanization's effects on surface fluxes. We develop a series of biophysical vegetation parameters such as the fraction of photosynthetically active radiation, leaf area index, vegetation greenness fraction, and roughness length over the continental US using MODIS and Landsat products for 2001. A 13-class land cover map was developed at a climate modeling grid (CMG) merging the 500mMODIS land cover and the 30m impervious surface area from the National Land Cover Database. The landscape subgrid heterogeneity was preserved using fractions of each class from the 500 m and 30 m into the CMG. Biophysical parameters were computed using the 8-day composite Normalized Difference Vegetation Index produced by the North American Carbon Program. In addition to urban impact assessments, this dataset is useful for the computation of surface fluxes in land, vegetation, and urban models and is expected to be widely used in different land cover and land use change applications.

Biophysical↗
NASA NTRS2020

Global Carbon Cycle and Climate Feedbacks in the NASA GISS ModelE2.1

We present results from the NASA GISS ModelE2.1‐G‐CC Earth System Model with coupled climate‐carbon cycle simulations that were submitted to the sixth phase of the Coupled Model Intercomparison Project (CMIP6) Coupled Climate‐Carbon Cycle MIP (C4MIP). Atmospheric CO2 concentration and carbon budgets for the land and ocean in the historical simulations were generally consistent with observations. Low simulated atmospheric CO2 concentrations during 1850‐1950 were due to excess uptake from prescribed land cover change, which erroneously replaced arid shrublands with higher biomass crops, and assumed high 2004 LAI values in vegetated lands throughout the historical simulation. At the end of the historical period, slightly higher simulated CO2 than observed resulted from the land being an insufficient net carbon sink, despite the net effect of CO2 fertilization and warming‐induced increases to leaf photosynthetic capacity. The global ocean carbon uptake agreed well with the observations with the largest discrepancies in the low latitudes. Future climate projection at 2091‐2100 agreed with CMIP5 models in the northward shift, of temperate deciduous forest climate and expansion across Eurasia along 60 °N latitude, and dramatic regional biome shifts from drying and warming in continental Europe. Carbon feedback parameters were largely similar to the CMIP5 model ensemble. For our model, the variation of land feedback parameters within the uncertainty arises from the fertilization feedback being less sensitive due to lack of increased vegetation growth, and the comparably more negative ocean carbon‐climate feedback is due to the large slowdown of the Atlantic overturning circulation.

Carbon cycle↗
NASA NTRS2022

Process-Oriented Analysis of Dominant Sources of Uncertainty in the Land Carbon Sink

The observed global net land carbon sink is captured by current land models. All models agree that atmospheric CO 2 and nitrogen deposition driven gains in carbon stocks are partially offset by climate and land-use and land-cover change (LULCC) losses. However, there is a lack of consensus in the partitioning of the sink between vegetation and soil, where models do not even agree on the direction of change in carbon stocks over the past 60 years. This uncertainty is driven by plant productivity, allocation, and turnover response to atmospheric CO 2 (and to a smaller extent to LULCC), and the response of soil to LULCC (and to a lesser extent climate). Overall, differences in turnover explain ~70% of model spread in both vegetation and soil carbon changes. Further analysis of internal plant and soil (individual pools) cycling is needed to reduce uncertainty in the controlling processes behind the global land carbon sink.

Attribution↗
DOE OSTI2024

Harmonizing direct and indirect anthropogenic land carbon fluxes indicates a substantial missing sink in the global carbon budget since the early 20th century

Inconsistencies in the calculation of the two anthropogenic land flux terms of the global carbon cycle are investigated. The two terms—the direct anthropogenic flux (caused by direct human disturbance in anthromes, currently a carbon source to the atmosphere) and the indirect anthropogenic flux (caused indirectly by human activities that lead to global change and affecting all biomes, currently an atmospheric carbon sink)—are typically calculated independently, resulting in inconsistent underlying assumptions. We harmonize the estimation of the two anthropogenic land flux terms by incorporating previous estimates of these inconsistencies. We recalculate the global carbon budget (GCB) and apply change-point analysis to the cumulative budget imbalance. Cumulative over 1850–2018 (1959–2018), harmonization results in a 13% lesser (4% greater) land use source from anthromes and a 20% (23%) lesser land sink. This recalculation yields a greater non-closure of the GCB, indicating a missing carbon sink averaging 0.65 Pg C year -1 since the early 20th century. The imbalance likely results from a combination of method discontinuity and structural errors in the assessment of the direct anthropogenic land use flux, greater ocean carbon uptake, structural errors in land models, and in how these land terms are quantified for the budget. We caution against overconfidence in considering the GCB a solved problem and recommend further study of methodological discontinuities in budget terms. We strongly recommend studies that quantify the direct and indirect anthropogenic land fluxes simultaneously to ensure consistency, with a deeper understanding of human disturbance and legacy effects in anthromes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Optimization of the information content of multitemporal Landsat TM data sets for monitoring forest cover disturbance

Procedures that were developed to optimize the information content of multitemporal thematic mapper (TM) data sets for forest cover disturbance monitoring in Minnesota are described. TM imagery from three different years was calibrated to exoatmospheric reflectance. An atmospheric correction routine was applied combining two major components, atmospheric normalization over time and transformation to ground reflectance. Atmospheric conditions were modeled over time using regression functions derived from five ground features known to be unchanged over the time interval of interest and spanning the entire image reflectance range. The correlation between digital data and the forest cover was subsequently maximized and irrelevant information content was reduced by converting the band-specific reflectances into seven vegetation indices that were assumed to carry unique information. The application of two change detection algorithms to these seven indices ultimately resulted in 14 change features for each time interval of interest. Results show that the preprocessing sequence is vital to forest cover monitoring methodology.

Coppin, Pol R.↗
NASA NTRS2009

Potential for Monitoring Snow Cover in Boreal Forests by Combining MODIS Snow Cover and AMSR-E SWE Maps

Monitoring of snow cover extent and snow water equivalent (SWE) in boreal forests is important for determining the amount of potential runoff and beginning date of snowmelt. The great expanse of the boreal forest necessitates the use of satellite measurements to monitor snow cover. Snow cover in the boreal forest can be mapped with either the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) microwave instrument. The extent of snow cover is estimated from the MODIS data and SWE is estimated from the AMSR-E. Environmental limitations affect both sensors in different ways to limit their ability to detect snow in some situations. Forest density, snow wetness, and snow depth are factors that limit the effectiveness of both sensors for snow detection. Cloud cover is a significant hindrance to monitoring snow cover extent Using MODIS but is not a hindrance to the use of the AMSR-E. These limitations could be mitigated by combining MODIS and AMSR-E data to allow for improved interpretation of snow cover extent and SWE on a daily basis and provide temporal continuity of snow mapping across the boreal forest regions in Canada. The purpose of this study is to investigate if temporal monitoring of snow cover using a combination of MODIS and AMSR-E data could yield a better interpretation of changing snow cover conditions. The MODIS snow mapping algorithm is based on snow detection using the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to enhance snow detection in dense vegetation. (Other spectral threshold tests are also used to map snow using MODIS.) Snow cover under a forest canopy may have an effect on the NDVI thus we use the NDVI in snow detection. A MODIS snow fraction product is also generated but not used in this study. In this study the NDSI and NDVI components of the snow mapping algorithm were calculated and analyzed to determine how they changed through the seasons. A blended snow product, the Air Force Weather Agency and NASA (ANSA) snow algorithm and product has recently been developed. The ANSA algorithm blends the MODIS snow cover and AMSR-E SWE products into a single snow product that has been shown to improve the performance of snow cover mapping. In this study components of the ANSA snow algorithm are used along with additional MODIS data to monitor daily changes in snow cover over the period of 1 February to 30 June 2008.

Riggs, George A.↗
NASA NTRS2001

Oleoresin, Chemistry and Spectral Reflectance in "Stressed" Lodgepole and White Bark Pine, Mammoth Mountain, California

Development of methods to identify the physical and chemical character of materials on the earth's surface is one of the foci of hyperspectral remote sensing activities. Enhancing the ability to elucidate changes in foliar chemistry that relate to the health of a plant is a benefit to plant physiologists, foresters, and plant ecologists, as well as geologist and environmental scientists. Vegetation covers the landscape throughout the temperate and tropical regions of the earth. The existence of vegetation in these areas presents special problems to remote sensing systems since geologic bedrock and alteration zones are masked. At times, however, alterations in the soil/sediment geochemical environment result in foliar chemical changes that are detectable via remote sensing. Examples include monitoring of chlorophyll reflectance/fluorescence and equivalent water thickness indices as indicators of drought-induced plant stress. Another processing and interpretation approach used with hyperspectral data has been principal components analysis (PCA). Rowan et al. used PCA to identify absorption feature patterns obtained from vegetated areas with discrete bedrock geology or mineralization as the substrate. Many researchers highlight the need to advance our ability for hyperspectral imaging in vegetated areas as a near-term priority.

Hickey, James C.↗
DOE OSTI2025

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, ↗
DOE OSTI2021

Predictability and feedbacks of the ocean-soil-plant-atmosphere water cycle: deep learning water conductance in Earth System Model

This white paper responds to Focal Area 2. We seek to build predictive models of leaf and surface conductance of water by implementing deep learning (DL) data assimilation techniques. These new models would then be implemented in existing Land Surface Models (LSMs) and Earth System models (ESMs), generating novel water cycle feedbacks. In doing so, we would improve predictability of expected changes in land precipitation, soil moisture, and vegetation dynamics in the long-term, and the role of land cover on the impacts and feedbacks of extreme weather events in the short-term

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

A New Approach to Monitoring Coastal Marshes for Persistent Flooding

Many areas in coastal Louisiana are below sea level and protected from flooding by a system of natural and man-made levees. Flooding is common when the levees are overtopped by storm surge or rising rivers. Many levees in this region are further stressed by erosion and subsidence. The floodwaters can become constricted by levees and trapped, causing prolonged inundation. Vegetative communities in coastal regions, from fresh swamp forest to saline marsh, can be negatively affected by inundation and changes in salinity. As saltwater persists, it can have a toxic effect upon marsh vegetation causing die off and conversion to open water types, destroying valuable species habitats. The length of time the water persists and the average annual salinity are important variables in modeling habitat switching (cover type change). Marsh type habitat switching affects fish, shellfish, and wildlife inhabitants, and can affect the regional ecosystem and economy. There are numerous restoration and revitalization projects underway in the coastal region, and their effects on the entire ecosystem need to be understood. For these reasons, monitoring persistent saltwater intrusion and inundation is important. For this study, persistent flooding in Louisiana coastal marshes was mapped using MODIS (Moderate Resolution Imaging Spectroradiometer) time series of a Normalized Difference Water Index (NDWI). The time series data were derived for 2000 through 2009, including flooding due to Hurricane Rita in 2005 and Hurricane Ike in 2008. Using the NDWI, duration and extent of flooding can be inferred. The Time Series Product Tool (TSPT), developed at NASA SSC, is a suite of software developed in MATLAB(R) that enables improved-quality time series images to be computed using advanced temporal processing techniques. This software has been used to compute time series for monitoring temporal changes in environmental phenomena, (e.g. NDVI times series from MODIS), and was modified and used to compute the NDWI indices and also the Normalized Difference Soil Index (NDSI). Coastwide Reference Monitoring System (CRMS) water levels from various hydrologic monitoring stations and aerial photography were used to optimize thresholds for MODIS-derived time series of NDWI and to validate resulting flood maps. In most of the profiles produced for post-hurricane assessment, the increase in the NDWI index (from storm surge) is accompanied by a decrease in the vegetation index (NDVI) and then a period of declining water. The NDSI index represents non-green or dead vegetation and increases after the hurricane s destruction of the marsh vegetation. Behavior of these indices over time is indicative of which areas remain flooded, which areas recover to their former levels of vegetative vigor, and which areas are stressed or in transition. Tracking these indices over time shows the recovery rate of vegetation and the relative behavior to inundation persistence. The results from this study demonstrated that identification of persistent marsh flooding, utilizing the tools developed in this study, provided an approximate 70-80 percent accuracy rate when compared to the actual days flooded at the CRMS stations.

Kalcic, M. T.↗