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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Assessment of Natural and Anthropogenic Aerosol Air Pollution In the Middle East Using MERRA-2, CAMS Data Assimilation Products, and High-Resolution WRF-Chem Model Simulations

Modern-Era Retrospective analysis for Research and Applications v.2 (MERRA-2), Copernicus Atmosphere Monitoring Service Operational Analysis (CAMS-OA), and a high-resolution regional Weather Research and Forecasting model coupled with chemistry (WRF-Chem) were used to evaluate natural and anthropogenic particulate matter (PM) air pollution in the Middle East (ME) during 2015–2016. Two Moderate Resolution Imaging Spectrometer (MODIS) retrievals – combined product Deep Blue and Deep Target (MODIS-DB&DT) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) – and Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) observations as well as in situ PM measurements for 2016 were used for validation of the WRF-Chem output and both assimilation products. MERRA-2 and CAMS-OA assimilate AOD observations. WRF-Chem is a free-running model, but dust emission in WRF-Chem is tuned to fit AOD and aerosol volume size distributions obtained from AERONET. MERRA-2 was used to construct WRF-Chem initial and boundary conditions both for meteorology and chemical and aerosol species. SO2 emissions in WRF-Chem are based on the novel OMI-HTAP SO2 emission dataset. The correlation with the AERONET AOD is highest for MERRA-2 (0.72–0.91), MAIAC (0.63–0.96), and CAMSOA (0.65–0.87), followed by MODIS-DB&DT (0.56–0.84) and WRF-Chem (0.43–0.85). However, CAMS-OA has a relatively high positive mean bias with respect to AERONET AOD. The spatial distributions of seasonally averaged AODs from WRF-Chem, assimilation products, and MAIAC are well correlated with MODIS-DB&DT AOD product. MAIAC has the highest correlation (R = 0.8), followed by MERRA-2 (R = 0.66), CAMS-OA (R = 0.65), and WRF-Chem (R = 0.61). WRF-Chem, MERRA-2, and MAIAC underestimate and CAMS-OA overestimates MODIS-DB&DT AOD. The simulated and observed PM concentrations might differ by a factor of 2 because it is more challenging for the model and the assimilation products to reproduce PM concentration measured within the city. Although aerosol fields in WRF-Chem and assimilation products are entirely consistent, WRF-Chem is preferable for analysis of regional air quality over the ME due to its higher spatial resolution and better SO2 emissions. The WRF-Chem’s PM background concentrations exceed the World Health Organization (WHO) guidelines over the entire ME. Mineral dust is the major contributor to PM (≈ 75%–95%) compared to other aerosol types. Near and downwind from the SO2 emission sources, non-dust aerosols (primarily sulfate) contribute up to 30% to PM(sub 2.5). The contribution of sea salt to PM in coastal regions can reach 5%. The contributions of organic matter, black carbon and organic carbon to PM over the Middle East are insignificant. In the major cities over the Arabian Peninsula, the 90th percentile of PM(sub 10) and PM(sub 2.5) (particles with diameters less than 10 and 2.5 μm, respectively) daily mean surface concentrations exceed the corresponding Kingdom of Saudi Arabia air quality limits. The contribution of the non-dust component to PM(sub 2.5) is < 25%, which limits the emission control effect on air quality. The mitigation of the dust effect on air quality requires the development of environment-based approaches like growing tree belts around the cities and enhancing in-city vegetation cover. The WRF-Chem configuration presented in this study could be a prototype of a future air quality forecast system that warns the population against air pollution hazards.

Alexander Ukhov↗
NASA NTRS2000

BOREAS AFM-12 1-km AVHRR Seasonal Land Cover Classification

The Boreal Ecosystem-Atmosphere Study (BOREAS) Airborne Fluxes and Meteorology (AFM)-12 team's efforts focused on regional scale Surface Vegetation and Atmosphere (SVAT) modeling to improve parameterization of the heterogeneous BOREAS landscape for use in larger scale Global Circulation Models (GCMs). This regional land cover data set was developed as part of a multitemporal one-kilometer Advanced Very High Resolution Radiometer (AVHRR) land cover analysis approach that was used as the basis for regional land cover mapping, fire disturbance-regeneration, and multiresolution land cover scaling studies in the boreal forest ecosystem of central Canada. This land cover classification was derived by using regional field observations from ground and low-level aircraft transits to analyze spectral-temporal clusters that were derived from an unsupervised cluster analysis of monthly Normalized Difference Vegetation Index (NDVI) image composites (April-September 1992). This regional data set was developed for use by BOREAS investigators, especially those involved in simulation modeling, remote sensing algorithm development, and aircraft flux studies. Based on regional field data verification, this multitemporal one-kilometer AVHRR land cover mapping approach was effective in characterizing the biome-level land cover structure, embedded spatially heterogeneous landscape patterns, and other types of key land cover information of interest to BOREAS modelers.The land cover mosaics in this classification include: (1) wet conifer mosaic (low, medium, and high tree stand density), (2) mixed coniferous-deciduous forest (80% coniferous, codominant, and 80% deciduous), (3) recent visible bum, vegetation regeneration, or rock outcrops-bare ground-sparsely vegetated slow regeneration bum (four classes), (4) open water and grassland marshes, and (5) general agricultural land use/ grasslands (three classes). This land cover mapping approach did not detect small subpixel-scale landscape features such as fens, bogs, and small water bodies. Field observations and comparisons with Landsat Thematic Mapper (TM) suggest a minimum effective resolution of these land cover classes in the range of three to four kilometers, in part, because of the daily to monthly compositing process. In general, potential accuracy limitations are mitigated by the use of conservative parameterization rules such as aggregation of predominant land cover classes within minimum horizontal grid cell sizes of ten kilometers. The AFM-12 one-kilometer AVHRR seasonal land cover classification data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Steyaert, Lou↗
NASA NTRS1978

Improving forest cover classification accuracy from Landsat by incorporating topographic information

The paper shows that accuracies of computer classification of species-specific forest cover types from Landsat imagery can be improved by 27% or more through the incorporation of topographic information from digital terrain tapes registered to multidate Landsat imagery. The topographic information improves classification accuracies because many common forest tree species have preferred elevation ranges and slope aspects. These preferences allow the separation of forest cover types which have similar spectral signatures but different species compositions. It is noted that the development of a classification system which uses prior probabilities and sets of prior probabilities conditioned by one or two external variables represents a significant increase in classification power.

Strahler, A. H.↗
NASA NTRS2020

Mark Twain National Forest Ecological Forecasting - Utilizing NASA Earth Observations to Classify Ground Cover Types in the Mark Twain National Forest

The Mark Twain National Forest (MTNF) encompasses 1.5 million acres of public land in the Ozarks region of southeastern Missouri. The industrial boom between the 1880s and 1920s had devastating effects on the shortleaf pine (Pinus echinata),Missouri’s only native pine species. The combination of fire suppression on this fire-dependent species and timber harvest of mature pine stands inhibited the development of pine seedlings and promoted the establishment of hardwood stands. Partners at the US Forest Service’s MTNF are currently involved in restoration efforts in two ranger districts, which includes removing invasive eastern red cedar (Juniperus virginiana) and prescribed burning. To expand spatial coverage for the MTNF beyond in situ observation sites, the NASA DEVELOP team analyzed land cover change from 1986 through 2019 and forecasted changes based on a ‘business-as-usual' scenario out to 2040. The team incorporated remotely sensed data from Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) into the random trees supervised classification tool in ArcGIS Pro. This tool spectrally separated pixels into five distinct land cover classes and produced classifications for 1986 and 2019, with kappa statistics of 0.87 and 0.81, respectively. Overall, there was a net decrease in conifer and meadow land cover between 1986 and 2019 along with a net increase in water, developed, and deciduous land cover. The team used TerrSet’s Land Change Modeler to forecast land cover through 2040. Results showed an increase in coniferous land cover and a decrease in deciduous cover, indicating a high probability that current restoration efforts will produce the intended effect.

Kaitlyn Bretz↗
NASA NTRS2020

Mark Twain National Forest Ecological Forecasting: Utilizing NASA Earth Observations to Classify Ground Cover Types in the Mark Twain National Forest

The Mark Twain National Forest (MTNF) encompasses 1.5 million acres of public land in the Ozarks region of southeastern Missouri. The industrial boom between the 1880s and 1920s had devastating effects on the shortleaf pine (Pinus echinata),Missouri’s only native pine species. The combination of fire suppression on this fire-dependent species and timber harvest of mature pine stands inhibited the development of pine seedlings and promoted the establishment of hardwood stands. Partners at the US Forest Service’s MTNF are currently involved in restoration efforts in two ranger districts, which includes removing invasive eastern red cedar (Juniperus virginiana) and prescribed burning. To expand spatial coverage for the MTNF beyond in situ observation sites, the NASA DEVELOP team analyzed land cover change from 1986 through 2019 and forecasted changes based on a ‘business-as-usual' scenario out to 2040. The team incorporated remotely sensed data from Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) into the random trees supervised classification tool in ArcGIS Pro. This tool spectrally separated pixels into five distinct land cover classes and produced classifications for 1986 and 2019, with kappa statistics of 0.87 and 0.81, respectively. Overall, there was a net decrease in conifer and meadow land cover between 1986 and 2019 along with a net increase in water, developed, and deciduous land cover. The team used TerrSet’s Land Change Modeler to forecast land cover through 2040. Results showed an increase in coniferous land cover and a decrease in deciduous cover, indicating a high probability that current restoration efforts will produce the intended effect.

DEVELOP Tech Paper↗
NASA NTRSDate not supplied

Vegetation Heights for Landsat Land Cover Classes in the Interior Boreal Forest Region of Alaska

Accurate determination and mapping of vegetation height is a prerequisite for regional estimations of above ground biomass, carbon pool dynamics, and improved modeling of future climate-vegetation-soil interactions. This study combined land cover class maps (at 30-m pixel resolution) derived from Landsat image bands with canopy vegetation heights detected at 1-m resolution from NASA G-LiHT Lidar flights, both from the year 2014 for the Middle Tanana Valley near Fairbanks, Alaska. The statistical distributions and structural attributes from Lidar plant heights were derived and refined for all Landsat vegetation classes. Results showed that the tallest cover classes in this region were deciduous forests (dominated by birch and aspen), followed by evergreen spruce and wooded wetlands. Open shrubland and herbaceous cover areas were determined to be 2 - 5 m shorter, on average, than these boreal forest stands. Tall shrubland cover areas were slightly shorter than most spruce-dominated evergreen forest areas. Nearly 60% of all low shrubland and herbaceous wetland pixels were detected with at least one tree crown greater than 10 m tall. This was in contrast to evergreen and deciduous forest classes that showed 88% of stands with at least one tree crown greater than 10 m tall.

Vegetation↗
DOE OSTI2019

Northern forest winters have lost cold, snowy conditions that are important for ecosystems and human communities

Winter is an understudied but key period for the socioecological systems of northeastern North American forests. A growing awareness of the importance of the winter season to forest ecosystems and surrounding communities has inspired several decades of research, both across the northern forest and at other mid- and high-latitude ecosystems around the globe. Despite these efforts, we lack a synthetic understanding of how winter climate change may impact hydrological and biogeochemical processes and the social and economic activities they support. Here, we take advantage of 100 years of meteorological observations across the northern forest region of the northeastern United States and eastern Canada to develop a suite of indicators that enable a cross-cutting understanding of (1) how winter temperatures and snow cover have been changing and (2) how these shifts may impact both ecosystems and surrounding human communities. We show that cold and snow covered conditions have generally decreased over the past 100 years. These trends suggest positive outcomes for tree health as related to reduced fine root mortality and nutrient loss associated with winter frost but negative outcomes as related to the northward advancement and proliferation of forest insect pests. In addition to effects on vegetation, reductions in cold temperatures and snow cover are likely to have negative impacts on the ecology of the northern forest through impacts on water, soils, and wildlife. The overall loss of coldness and snow cover may also have negative consequences for logging and forest products, vector-borne diseases, and human health, recreation, and tourism, and cultural practices, which together represent important social and economic dimensions for the northern forest region. These findings advance our understanding of how our changing winters may transform the socioecological system of a region that has been defined by the contrasting rhythm of the seasons. Our research also identifies a trajectory of change that informs our expectations for the future as the climate continues to warm.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Thirty Years of Change in Subalpine Forest Cover from Landsat Image Analysis in the Sierra Nevada Mountains of California

Landsat imagery was analyzed to understand changes in subalpine forest stands since the mid-1980s in the Sierra-Nevada region of California. At locations where long-term plot measurements have shown that stands are becoming denser in the number of small tree stems (compared to the early 1930s), the 30-year analysis of Landsat greenness index (NDVI) indicated that no consistent increases in canopy leaf cover have occurred at these same locations since the mid-1980s. Interannual variations in stand NDVI closely followed snow accumulation amounts recorded at nearby stations. In contrast, at eastern Sierra whitebark pine stand locations where it has been observed that widespread tree mortality has occurred, decreasing NDVI trends over the past 5-10 years were consistent with rapid loss of forest canopy cover. Landsat imagery was further analyzed to understand patterns of post-wildfire vegetation recovery, focusing on high burn severity (HBS) patches within burned areas dating from the late 1940s. Analysis of landscape metrics showed that the percentage of total HBS area comprised by the largest patch of recovered woody cover was relatively small in all fires that occurred since 1995, but increased rapidly with time since fire. Patch complexity of recovered woody cover decreased notably after more than 50 years of regrowth, but was not readily associated with time for fires that occurred since the mid 1990s. The aggregation level of patches with recovery of woody cover increased steadily with time since fire. The study approach using satellite remote sensing can be expanded to assess the consequences of stand-replacing wildfires in all forests of the region.

Fires↗
NASA NTRS1986

Canopy reflectance modeling in a tropical wooded grassland

Geometric/optical canopy reflectance modeling and spatial/spectral pattern recognition are used to study the form and structure of savanna in West Africa. An invertible plant canopy reflectance model is tested for its ability to estimate the amount of woody vegetation cover in areas of sparsely wooded grassland from remotely sensed data. Dry woodlands and wooded grasslands, commonly referred to as savannas, are important ecologically and economically in Africa, and cover approximately forty percent of the continent by some estimates. The Sahelian and Sudanian savanna make up the important and sensitive transition zone between the tropical forests and the arid Saharan region. The depletion of woody cover, used for fodder and fuel in these regions, has become a very severe problem for the people living there. LANDSAT Thematic Mapper (TM) data is used to stratify woodland and wooded grassland into areas of relatively homogeneous canopy cover, and then by applying an invertible forest canopy reflectance model to estimate directly the height and spacing of the trees in the stands. Since height and spacing are proportional to biomass in some cases, a successful application of the segmentation/modeling techniques will allow direct estimation of woody biomass, as well as cover density, over significant areas of these valuable and sensitive ecosystems. Sahelian savanna sites in the Gourma area of Mali being used by the NASA/GIMMS project (Global Inventory Modeling and Monitoring System, at Goddard Space Flight Center), in conjunction with CIPEA/Mali (Centre International pour l'Elevage en Afrique) will be used for testing the canopy model. The model will also be tested in a Sudanian zone crop/woodland area in the Region of Segou, Mali.

Simonett, D.↗
DOE OSTI2023

Grounding our Understanding of the Impacts of Boreal Forest Expansion on Shallow Cumulus Clouds with a Simple Modeling Framework

Abstract The expansion of the boreal forest poleward is a potentially important driver of feedbacks between the land surface and Arctic climate. A growing body of work has highlighted the importance of differences in evaporative resistance between different possible future Arctic land covers, which in turn alters humidity and cloudiness in the boundary layer, for these feedbacks. While thus far this problem has been studied primarily with complex Earth system models, we turn to a locally focused, idealized model capable of diagnosing and testing the sensitivity of first-order processes connecting vegetation, the atmospheric boundary layer, and low clouds in this critical region. This allows us to benchmark the mechanisms and results at the center of predictions from larger-scale simulations. A surface dominated by broadleaf trees, characterized by higher albedo and lower surface evaporative resistance, drives cooling and moistening of the boundary layer relative to a surface of needleleaf trees, characterized by lower albedo and higher surface evaporative resistance. Differences in evaporative resistance between these hypothetical Arctic vegetation covers are of equal importance to changes in albedo for the initial response of the boundary layer to boreal expansion, even with our idealized approach. However, compensation between the elevation of the lifting condensation level (LCL) and more rapid growth of the mixed layer over higher evaporative resistance surfaces can minimize changes in the favorability of shallow clouds over different land cover types under some conditions. We then perform two tests on the sensitivity of this compensating effect, to changes in water availability, represented first by a reduction in boundary layer humidity and then by both a reduction in humidity and soil moisture available to our vegetation surface. Finally, given the importance of this potential LCL–mixed-layer height compensation in our idealized modeling results, we look to determine its relevance in observational data from a field campaign in boreal Finland. These observations do confirm that such a coupling plays an important role in cumulus-topped boundary layers over a needleleaf forest surface. While our results confirm some underlying mechanisms at the center of prior work with Earth system models, they also provide motivation for future work to constrain the impact of boreal forest expansion. This will include both large eddy simulations to examine the impact of processes and feedbacks not resolved by a mixed-layer model, as well as a more systematic evaluation and comparison of relevant observations at the site in Finland and sites from prior boreal field campaigns. Significance Statement Clouds and vegetation are both important components of the climate system that interact across a range of scales. These interactions are central to understanding how changes at the land surface feedback on climate. For example, if a forest expands or recedes, diagnosing how that will impact clouds will determine whether you predict warming or cooling temperatures from that shift in the forest area. These predictions are often made with complex Earth system models, but we look to a more idealized representation of the land–atmosphere system to diagnose how shallow clouds should respond to changes in surface properties with different scenarios of boreal forest expansion at a more foundational level. This both grounds our understanding of previous analysis and provides helpful direction for future studies of this relevant and impactful land cover change.

Meteorology & Atmospheric Sciences↗
DOE OSTI2016

Cosmological simulations of Milky Way-sized galaxies

We introduce a new set of eight Milky Way-sized cosmological simulations performed using the AMR code Adaptive Refinement Tree + Hydrodynamics in a ΛCDM cosmology. The set of zoom-in simulations covers present-day virial masses that range from 8.3×10{sup 11} M{sub ⊙} to 1.56×10{sup 12} M{sub ⊙} and is carried out with our simple but effective prescriptions for deterministic star formation (SF) and “explosive” stellar feedback. The work focuses on showing the goodness of the simulated set of “field” Milky Way-sized galaxies. To this end, we compare some of the predicted physical quantities with the corresponding observed ones. Our results are as follows. (a) In agreement with some previous works, we found curves of circular velocity that are flat or slightly peaked. (b) All simulated galaxies with a significant disk component are consistent with the observed Tully–Fisher, radius–mass, and cold gas–stellar mass correlations of late-type galaxies. (c) The disk-dominated galaxies have stellar specific angular momenta in agreement with those of late-type galaxies, with values around 10{sup 3} km s{sup −1} kpc{sup −1}. (d) The SF rates at z = 0 of all runs but one are comparable to those estimated for the star-forming galaxies. (e) The two most spheroid-dominated galaxies formed in halos with late active merger histories and late bursts of SF, but the other run that ends also dominated by a spheroid never had major mergers. (f) The simulated galaxies lie in the semi-empirical correlation of stellar to halo mass of local central galaxies, and those that end up as disk-dominated evolve mostly along the low-mass branch of this correlation. Moreover, the growth histories of baryonic and stellar mass of these galaxies are proportional to their growth histories of halo mass over the last 6.5–10 Gyr. (g) Within the virial radii of the simulations, ≈25%–50% of the baryons are missed; the amount of gas in the halo is similar to the amount in stars in the galaxy, and most of this gas is in the warm–hot phase. (h) The profiles of z∼0 vertical gas velocity dispersion, σ{sub z}(r), are nearly flat and can be mostly explained by the kinetic energy injected by stars. The average values of σ{sub z} increase at higher redshifts, following roughly the shape of the SF history.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1980

The application of LANDSAT remote sensing technology to natural resources management. Section 1: Introduction to VICAR - Image classification module. Section 2: Forest resource assessment of Humboldt County.

A teaching module on image classification procedures using the VICAR computer software package was developed to optimize the training benefits for users of the VICAR programs. The field test of the module is discussed. An intensive forest land inventory strategy was developed for Humboldt County. The results indicate that LANDSAT data can be computer classified to yield site specific forest resource information with high accuracy (82%). The "Douglas-fir 80%" category was found to cover approximately 21% of the county and "Mixed Conifer 80%" covering about 13%. The "Redwood 80%" resource category, which represented dense old growth trees as well as large second growth, comprised 4.0% of the total vegetation mosaic. Furthermore, the "Brush" and "Brush-Regeneration" categories were found to be a significant part of the vegetative community, with area estimates of 9.4 and 10.0%.

Fox, L., III↗
NASA NTRS1989

Land-surface temperature measurement from space - Physical principles and inverse modeling

To apply the multiple-wavelength (split-window) method used for satellite measurement of sea-surface temperature from thermal-infrared data to land-surface temperatures, the authors statistically analyze simulations using an atmospheric radiative transfer model. The range of atmospheric conditions and surface temperatures simulated is wide enough to cover variations in clear atmospheric properties and surface temperatures, both of which are larger over land than over sea. Surface elevation is also included in the simulation as the most important topographic effect. Land covers characterized by measured or modeled spectral emissivities include snow, clay, sands, and tree leaf samples. The empirical inverse model can estimate the surface temperature with a standard deviation less than 0.3 K and a maximum error less than 1 K, for viewing angles up to 40 degrees from nadir under cloud-free conditions, given satellite measurements in three infrared channels. A band in the region from 10.2 to 11.0 microns will usually give the most reliable single-band estimate of surface temperature. In addition, a band in either the 3.5-4.0-micron region or in the 11.5-12.6-micron region must be included for accurate atmospheric correction, and a band below the ozone absorption feature at 9.6 microns (e.g., 8.2-8.8 microns) will increase the accuracy of the estimate of surface temperature.

Wan, Zhengming↗
NASA NTRS2011

Canopy Spectral Invariants: Application to Classification of Forest Types from Hyperspectral Data - Part 2

Many studies have been conducted to demonstrate the ability of hyperspectral data to discriminate plant dominant species. Most of them have employed the use of empirically based techniques, which are site specific, requires some initial training based on characteristics of known leaf and/or canopy spectra and therefore may not be extendable to operational use or adapted to changing or unknown land cover. In this paper we propose a physically based approach for separation of dominant forest type using hyperspectral data. The radiative transfer theory of canopy spectral invariants underlies the approach, which facilitates parameterization of the canopy reflectance in terms of the leaf spectral scattering and two spectrally invariant and structurally varying variables - recollision and directional escape probabilities. The methodology is based on the idea of retrieving spectrally invariant parameters from hyperspectral data first, and then relating their values to structural characteristics of three-dimensional canopy structure. Theoretical and empirical analyses of ground and airborne data acquired by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over two sites in New England, USA, suggest that the canopy spectral invariants convey information about canopy structure at both the macro- and micro-scales. The total escape probability (one minus recollision probability) varies as a power function with the exponent related to the number of nested hierarchical levels present in the pixel. Its base is a geometrical mean of the local total escape probabilities and accounts for the cumulative effect of canopy structure over a wide range of scales. The ratio of the directional to the total escape probability becomes independent of the number of hierarchical levels and is a function of the canopy structure at the macro-scale such as tree spatial distribution, crown shape and size, within-crown foliage density and ground cover. These properties allow for the natural separation of dominant forest classes based on the location of points on the total escape probability vs the ratio log-log plane.

Schull, M. A.↗
NASA NTRS1997

Landscape characterization of peridomestic risk for Lyme disease using satellite imagery

Remotely sensed characterizations of landscape composition were evaluated for Lyme disease exposure risk on 337 residential properties in two communities of suburban Westchester County, New York. Properties were categorized as no, low, or high risk based on seasonally adjusted densities of Ixodes scapularis nymphs, determined by drag sampling during June and July 1990. Spectral indices based on Landsat Thematic Mapper data provided relative measures of vegetation structure and moisture (wetness), as well as vegetation abundance (greenness). A geographic information system (GIS) was used to spatially quantify and relate the remotely sensed landscape variables to risk category. A comparison of the two communities showed that Chappaqua, which had more high-risk properties (P < 0.001), was significantly greener and wetter than Armonk (P < 0.001). Furthermore, within Chappaqua, high-risk properties were significantly greener and wetter than lower-risk properties in this community (P < 0.01). The high-risk properties appeared to contain a greater proportion of broadleaf trees, while lower-risk properties were interpreted as having a greater proportion of nonvegetative cover and/or open lawn. The ability to distinguish these fine scale differences among communities and individual properties illustrates the efficiency of a remote sensing/GIS-based approach for identifying peridomestic risk of Lyme disease over large geographic areas.

Topography, Medical↗
NASA NTRSDate not supplied

Quantifying Post-fire Vegetation Recovery on the Colorado Front Range

Forest composition and structure in the Colorado Front Range has been altered by changing wildfire regimes, particularly in response to increased fire severity. Subsequent reductions in post-fire tree regeneration can result in chronic impacts to ecological function and water quality. This project partnered with the U.S. Forest Service to estimate tree canopy recovery from 15 to 21 years after four Colorado Front Range fires that burned between 1996 and 2002— the Bobcat, Buffalo Creek, Hayman, and High Meadows fires—using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI). First, we evaluated relationships between recovery metrics derived from spectral vegetation indices (i.e. post-fire vegetation, normalized burn ratio, percent unrecovered vegetation) and field-collected counts of post-fire tree seedling regeneration. Then, we used high-resolution imagery paired with Landsat timeseries spectral and geomorphometric data to map forest canopy cover 15-21 years post-fire. Finally, we assessed ecological drivers of recovery including topography, climate, soils, and fire severity using Random Forest. While we found that basic vegetation index metrics failed to distinguish field-measured tree regeneration, our data showed that both aspect and burn severity were important drivers of vegetation recovery. North-facing aspects tended to have higher pre-fire Normalized Difference Vegetation Index (NDVI) and thus higher burn severities. However, areas that burned at high severity experienced slower vegetation recovery, regardless of aspect suggesting that high severity fire can mute favorable site conditions on north-facing aspects (i.e. moister, more developed soils). While this work propels our understanding of variables that influence vegetative recovery, vegetation type, forest conversion, and watershed function, more work should focus on what vegetation indices identifying areas of recovery indicate across different forest types and burn severities.

NASA DEVELOP↗
DOE OSTI2022

AmeriFlux US-SSH Susquehanna Shale Hills Critical Zone Observatory

This is the AmeriFlux version of the carbon flux data for the site US-SSH Susquehanna Shale Hills Critical Zone Observatory. Site Description - The Susquehanna Shale Hills Critical Zone Observatory is comprised of one first-order catchment in the Susquehanna River basin. This catchment, known as Shale Hills, is about 8 hectares in total area. The stream that defines the Shale Hills catchment flows into Shavers Creek in the Juniata River sub-basin. The vegetation cover at Shale Hills is dominated by deciduous broadleaf forest, with some evergreen needleleaf trees along the stream.

Forsythe, Brandon R.↗