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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Initial Evaluation of CropManage Decision-Support Model for Vineyard ET Estimation

The CropManage(CM) decision-support web application was originally developed by U.C. Cooperative Extension to support evapotranspiration (ET) based irrigation scheduling and nutrient management of cool-season vegetables. The model uses prescribed crop phenology curves to develop daily estimates of fractional green canopy cover (Fc) within the field. Periodic Fc observations acquired by ground-based methods or imported from NASA’s Satellite Irrigation Management Support (SIMS) can be used to adjust the prescribed timeseries for such factors as weather anomalies or non-standard agronomic practice, as needed. Fc is then converted to daily crop coefficient (fraction of reference ET) values. The crop coefficient is combined with reference evapotranspiration, collected by the California Irrigation Management Information System, to derive daily ET estimates for the given field. Irrigation runtime recommendations are issued for a given date based on total ET since the last irrigation event (less any rainfall), and corrected for distribution uniformity of the water delivery system. In this project, CM was adapted to vineyards by adding sub-models to account for early-season depletion of stored soil moisture, cover crop presence, and intentional water stress. An initial trial was performed on a Central Coast winegrape vineyard, where an eddy covariance tower measured daily ET during from May-Dec 2020. Total ET for the period showed strong agreement between the tower-based measurements (443 mm) and the CM model (431 mm). The model tended to overestimate cumulative ET during mid-June by up to 30 mm (about 15%) and later underestimated cumulative ET by as much as 65 mm (about 23%) in late September, suggesting that additional model calibration is needed to improve simulation of within-season variability. Results will be reported for trials on additional vineyard sites conducted during the 2021 season.

Evaluation↗
NASA NTRS2021

Estimating Bare Earth in Sparse Boreal Forests With WorldView Stereo Imagery

Circumboreal forests are currently experiencing rapid climate warming which is altering their structure, productivity, and status as a carbon sink. Very-high resolution (VHR, <2 m) stereo derived digital surface models are available to monitor these forests, but, a similar resolution digital terrain model (DTM) is required to extract information about tree height, which is often used to estimate carbon content. To our knowledge, no openly available VHR DTM currently exists. To address this need, we developed approaches to extract DTMs by filtering VHR stereo point clouds (PCs) in sparse canopies of Alaska. Our evaluation consisted of two stereo processing methods with three PC search radii, at six different tree canopy cover (TCC) intervals. We found VHR DTMs were robust for estimating bare ground at TCC intervals less than 40% with vertical errors <1.6 m using airborne small footprint light detection and ranging (LiDAR) as reference.

Christopher S R Neigh↗
NASA NTRSDate not supplied

Assessing Urban Heat in the Cincinnati and Covington Area to Inform Local Decision-Makers and Environmental Justice Initiatives

The Urban Heat Island effect is a phenomenon characterized by urban areas experiencing temperatures that are, on average, warmer than surrounding suburban and rural regions. Urban Heat Islands are fueled by expansive impervious surfaces, vehicle emissions, and insufficient urban green space. Densely populated urban centers like the Cincinnati, Ohio and Covington, Kentucky area can experience negative health impacts due to the Urban Heat Island. NASA DEVELOP partnered with Groundwork USA and Groundwork Ohio River Valley to combine environmental education and outreach with technical capacity building in NASA Earth observations. The DEVELOP team used Landsat 5 TM and ISS ECOSTRESS to calculate daytime and nighttime land surface temperature anomalies. The team found that the Cincinnati and Covington area was 8.32°F warmer during the day and 4.97°F warmer at night compared to non-urban areas. The team also ran the Natural Capital Project InVEST Urban Cooling Model to map a heat mitigation index for the study area. The heat mitigation index models the cooling capacity of each pixel and the effect of green spaces on the pixel’s ability to mitigate heat. Results show which communities are most vulnerable to impacts of increased urban heat. The team also assessed alternative tree canopy scenarios with the InVEST model to better understand the effectiveness of potential heat mitigation strategies. Increasing tree canopy cover by 25% in urbanized land cover types was found to reduce mean temperature across the study area by 0.87°F. Combining these results with work from Groundwork’s Climate Safe Neighborhoods program highlights the relationship between extreme heat and historical-race based housing practices in the region. Results provide partners at Groundwork with refined methodologies to support future education and outreach, as well as increase future capacity to NASA Earth observations.

Celeste Gambino↗
NASA NTRS2022

Yonkers Urban Development II: Leveraging NASA Earth Observations to Support Modeling Urban Cooling Interventions and Urban Heat Vulnerability in Yonkers, New York

The City of Yonkers, New York, located in Westchester County, is experiencing rising temperatures which are a growing threat to the health and safety of its residents. Furthermore, the risk of heat-related illnesses and mortality disproportionately affects neighborhoods in Yonkers historically subjected to race-based housing segregation. To better understand these inequities, Groundwork Hudson Valley and NASA DEVELOP collaborated for a second term to evaluate community-level heat vulnerability, landcover distribution, street-level thermal comfort, and modeled urban cooling interventions. This team applied 2019 5-year American Community Survey (ACS) data and social and biophysical heat vulnerability variables established by the New York State Department of Health (NYSDOH), along with land surface temperature (LST) data collected from Landsat 8 Thermal Infrared Sensor (TIRS), and ISS ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) to identify communities in Yonkers in need of prioritized cooling intervention at the census tract level. Data from the Real-Time Mesoscale Analysis (RTMA) provided relevant meteorological data for the ENVI-met model to conduct street-level thermal observations and model tree canopy cooling interventions in the Yonkers neighborhoods of Kimball and Old 7th Ward. The project results will support the prioritization and equitable distribution of cooling infrastructure in identified neighborhoods. Additionally, Groundwork Hudson Valley will use the analyses as a heat literacy tool to improve advocacy efforts and inform both residents and officials about how investment in deliberate modification to tree canopy cover improves the city’s thermal environment and helps mitigate extreme heat.

Tamara Barbakova↗
NASA NTRS2021

Estimating Bare Earth in Sparse Boreal Forests With WorldView Stereo Imagery

Circumboreal forests are currently experiencing rapid climate warming which is altering their structure, productivity, and status as a carbon sink. Very-high resolution (VHR, <2 m) stereo derived digital surface models are available to monitor these forests, but, a similar resolution digital terrain model (DTM) is required to extract information about tree height, which is often used to estimate carbon content. To our knowledge, no openly available VHR DTM currently exists. To address this need, we developed approaches to extract DTMs by filtering VHR stereo point clouds (PCs) in sparse canopies of Alaska. Our evaluation consisted of two stereo processing methods with three PC search radii, at six different tree canopy cover (TCC) intervals. We found VHR DTMs were robust for estimating bare ground at TCC intervals less than 40% with vertical errors <1.6 m using airborne small footprint light detection and ranging (LiDAR) as reference.

Christopher S. R. Neigh↗
NASA NTRSDate not supplied

The Global Water Monitor: Wetland surface height dynamics for Ecology, Conservation, and Resource Management

The Global Water Monitor (GWM) offers surface water-related products for lakes, river reaches and wetland zones. The surface elevation change products are derived from satellite radar altimeters, such as the Jason/Sentinel series, and can span multiple decades. While the current instruments are profiling, they offer surface water height at specific locations, thus contributing to parallel observation of wetland surface water extent. GWM wetland products are “operational” with routine updates within 1-2days of satellite overpass. There is also assurance of product continuity to at least the end of 2030. Currently, the GWM holds products for a small selection (~40) of diverse wetlands for objectives that focus on conservation and water/fisheries resource monitoring. These sites have been requested by various organizations also looking to the effects of climate change and anthropogenic influences. Many of the current GWM wetland regions are in National Parks such as the Everglades (USA), the Wasur (Rep. Indonesia), and the Kakadu marshes (Australia). They also include coastal river deltas of the Danube, Mekong, Amazon, Orinoco and Ganges Rivers, and the internal Okavango Delta in Botswana. The preliminary set also ensures a diverse range of wetlands e.g., the shallow pot-hole lake region of the Sand Hills and the coastal Black Swamp of the USA, the peat fields of Malaysia and rice growing fields of Yilinzhen in China. Extensive wetlands in Brazil (the Pantanal), South Sudan (the Sudd Marshes) and Belarus (the Pinsk) are also featured, as are the mangrove regions of the Sundarbans (India) and the Laguna Terminos (Mexico). The Usangu wetland (Tanzania) is also included, a focus region where wetland ecology strongly competes with demands from irrigation, livestock, and the needs of downstream hydro-electric power. The nadir-pointing altimeter instruments record height variations regardless of vegetation or canopy cover, and height dynamics at 10-day or monthly resolution reveal seasonal and inter-annual variations as well as longer-term declining or re-charging trends. These products provide a unique, multi-decadal data set that can stand-alone or serve with cross-validation or image-fusion efforts.

Charon Birkett↗
NASA NTRS1972

Multispectral determination of vegetative cover in corn crop canopy

The relationship between different amounts of vegetative ground cover and the energy reflected by corn canopies was investigated. Low altitude photography and an airborne multispectral scanner were used to measure this reflected energy. Field plots were laid out, representing four growth stages of corn. Two plot locations were chosen-on a very dark and a very light surface soil. Color and color infrared photographs were taken from a vertical distance of 10 m. Estimates of ground cover were made from these photographs and were related to field measurements of leaf area index. Ground cover could be predicted from leaf area index measurements by a second order equation. Microdensitometry and digitzation of the three separated dye layers of color infrared film showed that the near infrared dye layer is most valuable in ground cover determinations. Computer analysis of the digitized photography provided an accurate method of determining precent ground cover.

Stoner, E. R.↗
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↗
DOE OSTI2024

Tree Canopy in Beacon Hill, Washington [Slides]

This technical assistance through the Communities LEAP program aims to provide the Solutions' Collaborative with information and geospatial analysis of tree canopy coverage in Beacon Hill, Seattle as a high priority environmental justice community with the goal of exploring the benefits of trees to improve air quality and mitigate extreme heat. It further provides information on tree-planting programs in Seattle and King County.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2023

Evaluating MODIS Snow Products Using an Extensive Wildlife Camera Network

Snow covers a maximum of 47 million km 2 of Earth's northern hemisphere each winter and is an important component of the planet's energy balance, hydrology cycles, and ecosystems. Monitoring regional and global snow cover has increased in urgency in recent years due to warming temperatures and declines in snow cover extent. Optical satellite instruments provide large-scale observations of snow cover, but cloud cover and dense forest canopy can reduce accuracy in mapping snow cover. Remote camera networks deployed for wildlife monitoring operate below cloud cover and in forests, representing a virtually untapped source of snow cover observations to supplement satellite observations. Using images from 1,181 wildlife cameras deployed by the Norwegian Institute for Nature Research (NINA), we compared snow cover extracted from camera images to Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products during winter months of 2018–2020. Ordinal snow classifications (scale = 0–4) from cameras were closely related to normalized difference snow index (NDSI) values from the MODIS Terra Snow Cover Daily L3 Global 500m (MOD10A1) Collection 6 product (R 2 = 0.70). Tree canopy cover, the normalized difference vegetation index (NDVI), and image color mode influenced agreement between camera images and MOD10A1 NDSI values. For MOD10A1F, MOD10A1's corresponding cloud-gap filled product, agreement with cloud-gap filled values decreased from 78.5% to 56.4% in the first three days of cloudy periods and stabilized thereafter. Using our camera data as validation, we derived a threshold to create daily binary maps of snow cover from the MOD10A1 product. The threshold corresponding to snow presence was an NDSI value of 40.50, which closely matched a previously defined global binary threshold of 40 using the MOD10A2 8-day product. These analyses demonstrate the utility of camera trap networks for validation of snow cover products from satellite remote sensing, as well as their potential to identify sources of inaccuracy.

Validation↗
DOE OSTI2024

Aridity and forest age mediate landscape scale patterns of tropical forest resistance to cyclonic storms

Abstract Cyclonic storms, or hurricanes, are expected to intensify as ocean heat energy rises due to climate change. Ecological theory suggests that tropical forest resistance to hurricanes should increase with forest age and wood density. However, most data on hurricane effects on tropical forests come from a limited number of well‐studied long‐term monitoring sites, restricting our capacity to evaluate the resistance of tropical forests to hurricanes across broad environmental gradients. In this study, we assessed whether forest age and aridity mediate the effects of hurricanes Irma and Maria in Puerto Rico, Vieques and Culebra islands. We leveraged functional trait data for 410 tree species, remotely sensed measurements of canopy height and cover, along with data on forest stand characteristics of 180 of 338 forest monitoring plots, each covering an area of 0.067 ha. The plots represent a broad mean annual precipitation (MAP) gradient from 701 to 4598 mm and a complex mosaic of forest age from 5 to around 85 years since deforestation. Hurricanes resulted in a 25% increase in basal area mortality rates, a 45% decrease in canopy height and a 21% reduction in canopy cover. These effects intensified with forest age, even after considering proximity to the hurricane path. The links between forest age and hurricane disturbances were likely due the prevalence of tall canopies. Tall forest canopies were strongly linked with low community‐weighted wood density (WD). These characteristics were on average more common in moist and wet forests (MAP >1250 mm). Conversely, dry forests were dominated by short species with high wood density (WD > 0.6 g cm −3 ) and did not show significant increases in basal area mortality rates after the hurricanes. Synthesis . Our findings show that selection towards drought‐tolerant traits across aridity gradients, such as short stature and dense wood, enhances resistance to hurricanes. However, forest age modulated responses to hurricanes, with older forests being less resistant across the islands. This evidence highlights the importance of considering the intricate links between ecological succession and plant function when forecasting tropical forests’ responses to increasingly strong hurricanes.

Vargas G., German↗
NASA NTRS2003

Real-Time Imaging of Ground Cover: Relationships with Radiation Capture, Canopy Photosynthesis, and Daily Growth Rate

Cumulative absorbed radiation is highly correlated with crop biomass and yield. In this chapter we describe the use of a digital camera and commercial imaging software for estimating daily radiation capture, canopy photosynthesis, and relative growth rate. Digital images were used to determine percentage of ground cover of lettuce (Lactuca sativa L.) communities grown at five temperatures. Plants were grown in a steady-state, 10-chamber CO2 gas exchange system, which was used to measure canopy photosynthesis and daily carbon gain. Daily measurements of percentage of ground cover were highly correlated with daily measurements of both absorbed radiation (r(sup 2) = 0.99) and daily carbon gain (r(sup 2) = 0.99). Differences among temperature treatments indicated that these relationships were influenced by leaf angle, leaf area index, and chlorophyll content. An analysis of the daily images also provided good estimates of relative growth rates, which were verified by gas exchange measurements of daily carbon gain. In a separate study we found that images taken at hourly intervals were effective for monitoring real-time growth. Our data suggests that hourly images can be used for early detection of plant stress. Applications, limitations, and potential errors are discussed. We have long known that crop yield is determined by the efficiency of four component processes: (i) radiation capture, (ii) quantum yield, (iii) carbon use efficiency, and (iv) carbon partitioning efficiency (Charles-Edwards, 1982; Penning de Vries & van Laar, 1982; Thornley, 1976). More than one-half century ago, Watson (1947, 1952) showed that variation in radiation capture accounted for almost all of the variation in yield between sites in temperate regions, because the three other components are relatively constant when the crop is not severely stressed. More recently, Monteith (1977) reviewed the literature on the close correlation between radiation capture and yield. Bugbee and Monje (1992) demonstrated the close relationship between absorbed radiation and yield in an optimal environment.

Klassen, S. P.↗
NASA NTRS1995

Determining coniferous forest cover and forest fragmentation with NOAA-9 advanced very high resolution radiometer data

NOAA-9 satellite data from the Advanced Very High Resolution Radiometer (AVHRR) were used in conjunction with Landsat Multispectral Scanner (MSS) data to determine the proportion of closed canopy conifer forest cover in the Cascade Range of Oregon. A closed canopy conifer map, as determined from the MSS, was registered with AVHRR pixels. Regression was used to relate closed canopy conifer forest cover to AVHRR spectral data. A two-variable (band) regression model accounted for more variance in conifer cover than the Normalized Difference Vegetation Index (NDVI). The spectral signatures of various conifer successional stages were also examined. A map of Oregon was produced showing the proportion of closed canopy conifer cover for each AVHRR pixel. The AVHRR was responsive to both the percentage of closed canopy conifer cover and the successional stage in these temperate coniferous forests in this experiment.

Ripple, William J.↗
NASA NTRS1985

Magnetic canopies and models of the solar chromosphere

Magnetic canopies are the topologies formed by magnetic field lines as they spread from compact, nearly vertical concentrations of flux in the low photosphere into the large-scale organization of the corona and heliosphere. Analytical techniques for inferring the base-heights of canopies from magnetographic data are reviewed together with observational evidence that much of the sun is covered by canopies which lie two or more pressure scale heights below the level which is traditionally inferred from thin flux tube models. Implications of these results for modeling the structure and energetics of the chromosphere are discussed, and it is argued that future models should be based upon both spectroscopic and polarimetric data. Recent improvements in magnetostatic model atmospheres are reviewed, and new observational data, including C IV Doppler-grams from the Ultraviolet Spectrometer and Polarimeter on the Solar Maximum Mission, are considered. Directions for future research in MHD modeling of canopies, in simulating spectrographic and polarimetric data from such models, and in observational programs are discussed.

Jones, H. P.↗
NASA NTRS1986

Estimation of canopy parameters for inhomogeneous vegetation canopies from reflectance data. I - Two-dimensional row canopy

A canopy-reflectance (CR) model for row-planted vegetation is presented. Its use of an estimation of important biophysical variables like leaf-area index (LAI) and average leaf angle (ALA) from bidirectional CR data is discussed. Using field-measured CR data for a partially covered soybean canopy, it is shown that one can accurately estimate LAI, ALA and extent of percentage of ground cover from CR data.

Goel, N. S.↗
NASA NTRS2022

Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City

The surface urban heat island (SUHI) effect is among the major environmental issues encountered in urban regions. To better predict the dynamics of the SUHI and its impacts on extreme heat events, an accurate characterization of the surface energy balance in urban regions is needed. However, the ability to improve understanding of the surface energy balance is limited by the heterogeneity of surfaces in urban areas. This study aims to enhance the understanding of the urban surface energy budget through an innovation in the use of land surface temperature (LST) observations from remote sensing satellites. A LST database with 5–min temporal and 30–m spatial resolution is developed by spatial downscaling of the Geostationary Operational Environmental Satellites—R (GOES–R) series LST product over New York City (NYC). The new downscaling method, known as the Spatial Downscaling Method (SDM), benefits from the fine spatial resolution of Landsat–8 and high temporal resolution of GOES–R, and considers the temporal variation in LST for each land cover type separately. Preliminary results show that the SDM can reproduce the temporal and spatial variability of LST over NYC reasonably well and the downscaled LST has a spatial root mean square error (RMSE) of the order of 2 K as compared to the independent Landsat–8 observations. The SDM shows smaller RMSE of 1.93 K over the tree canopy land cover, whereas RMSE is 2.19 K for built–up areas. The overall results indicate that the SDM has potential to estimate LST at finer spatial and temporal scales over urban regions.

land surface temperature↗
DOE OSTI2024

Laboratory time series moisture manipulative experiment from sediment across the contiguous US: time series aerobic respiration and geochemistry (v2)

This dataset supports a broader study examining the effects of wetting and drying on hyporheic zone respiration across the contiguous United States (CONUS). The dataset provides data generated from a laboratory moisture manipulation experiment. The contents include time series aerobic respiration and moisture; dissolved oxygen; sediment geochemistry data; and field metadata (including qualitative information on instream and river corridor characteristics). Samples were collected as part of the WHONDRS CONUS-Scale Model-Sample Study (CM). This study was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the CONUS. The data package associated with the CM study is available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1923689. CM sampling began in April 2022 and ended in October 2023. This study uses subsamples from a subset of CM samples collected between June 2022 and June 2023. The original field samples were labeled as CM_###. Subsequent subsamples for this study were labeled as EC_###. The labels from the field samples and the EC subsamples can be mapped directly based on the digits following the prefix and underscore (i.e., EC_001 is a subsample from CM_001). See the critical details section below for more details on sample naming. This data package was originally published in August 2024. It was updated in February 2026 (v2; new and modified files). See the change history section in the readme for more details. For details on how to navigate this data package, see this infographic from the River Corridor SFA https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) field protocol; and a (6) a subfolder with sediment sample data from the incubation experiment. The sample data subfolder contains (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC); (2) total nitrogen (TN); (3) adenosine triphosphate (ATP); (4) percent carbon and nitrogen; (5) effect size; (6) iron (II); (7) gravimetric moisture; (8) respiration rates and raw dissolved oxygen values; (9) specific conductance; (10) pH; (11) temperature; (12) a summary containing median values of each data type for each treatment (wet and dry); (13) methods codes; (14) FTICR-MS methods; and (15) a subfolder of 9.4 Tesla FTICR-MS data. This folder contains three subfolders, one containing the sediment .xml data files, one containing the sediment CoreMS output files, the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .ref, or .xml.

54 ENVIRONMENTAL SCIENCES↗