Results for “resource regions” — Page 5

NASA NTRS1973

Evaluation of ERTS-1 imagery in mapping and managing soil and range resources in the Sand Hills Region of Nebraska

Interpretations of high altitude photography of test sites in the Sandhills of Nebraska permitted identification of subirrigated range sites as well as complexes of choppy sands and sands range sites, units composing approximately 85% of the Sandhills rangeland. These range sites form the basic units necessary for the interpretation of range condition classes used in grazing management. Analysis of ERTS-1 imagery acquired during August, September and October, 1972 indicated potential for the identification of gross differences in forage density within given range sites identified on early season aerial photography.

Seevers, P. M.↗

NASA NTRS1974

Proposal to evaluate the use of ERTS-1 imagery in mapping and managing soil and range resources in the Sand Hills region of Nebraska

The author has identified the following significant results. Increase in radiance values is directly related to decrease in vegetative biomass, though not in a linear manner. Should the relationship hold true over an entire growing season, this would allow an extremely rapid evaluation of range condition. Computer access by remote terminal would allow production of this type of range condition evaluation in near real time, which is essential if grazing practice decisions are to be made based on satellite imagery acquisition. Negating the manipulation of photographic products appears to be the logical way to provide satellite imagery data to the user in near real time. There appears to be a direct linear relationship between radiance values of bands 4 and 5 and increase in total inorganic ions (6 ions) of lakes in the Sand hills region. Consistent ion concentration of lakes during the year could allow their radiance values to serve as a means of equating radiance values from image to image.

Drew, J. V.↗

NASA NTRS1974

Remote sensing experiment in West Africa

There are substantial needs of the Sahelien Zone to detail the state of regional agricultural resources in the face of a sixth year of serious drought conditions. While most of the work has been done in the Republic of Niger, the principles which have emerged from the analysis seem to be applicable to much of the Sahel. The discussion relates to quite specific rehabilitation and development initiations under consideration in Niger which are based in part upon direct analysis of ERTS imagery of the country, in part on field surveys and on discussions with Nigerian officials and technicians. Again, because the entire Sahelien Zone (including Niger) has large zones of similar ecologic characteristics, modificiations of the approaches suggested for Niger are applicable to the solution of rehabilitation of the desert, the savannah and the woodlands of West Africa in general.

Macleod, N. H.↗

NASA NTRS1974

Evaluation of ERTS-1 imagery in mapping and managing soil and range resources in the Sand Hills region of Nebraska

There are no author-identified significant results in this report.

Drew, J. V.↗

NASA NTRS1983

Atmospheric correction analysis on LANDSAT data over the Amazon region

The Amazon Region natural resources were studied in two ways and compared. A LANDSAT scene and its attributes were selected, and a maximum likelihood classification was made. The scene was atmospherically corrected, taking into account Amazonic peculiarities revealed by (ground truth) of the same area, and the subsequent classification. Comparison shows that the classification improves with the atmospherically corrected images.

Parada, N. D. J.↗

NASA NTRS2000

The Use of Multi-Source Satellite and Geospatial Data to Study the Effect of Urbanization of Primary Productivity in the United States

Data from two different satellites, a digital land cover map, and digital census data were analyzed and combined in a geographic information system to study the effect of urbanization on photosynthetic vegetation productivity in the United States. Results show that urbanization can have a measurable but variable impact on the primary productivity of the land surface. Annual productivity can be reduced by as much as 20 days in some areas, but in resource limited regions, photosynthetic production can be enhanced by human activity. Overall, urban development reduces the productivity of the land surface and those areas with the highest productivity are directly in the path of urban sprawl.

Imhoff, M. L.↗

NASA NTRS2007

Forecasting Plant Productivity and Health Using Diffuse-to-Global Irradiance Ratios Extracted from the OMI Aerosol Product

Atmospheric aerosols are a major contributor to diffuse irradiance. This Candidate Solution suggests using the OMI (Ozone Monitoring Instrument) aerosol product as input into a radiative transfer model, which would calculate the ratio of diffuse to global irradiance at the Earth s surface. This ratio can significantly influence the rate of photosynthesis in plants; increasing the ratio of diffuse to global irradiance can accelerate photosynthesis, resulting in greater plant productivity. Accurate values of this ratio could be useful in predicting crop productivity, thereby improving forecasts of regional food resources. However, disagreements exist between diffuse-to-global irradiance values measured by different satellites and ground sensors. OMI, with its unique combination of spectral bands, high resolution, and daily global coverage, may be able to provide more accurate aerosol measurements than other comparable sensors.

Knowlton, Kelly↗

NASA NTRS2010

A Comparison of Satellite Based, Modeled Derived Daily Solar Radiation Data with Observed Data for the Continental US

Many applications of simulation models and related decision support tools for agriculture and natural resource management require daily meteorological data as inputs. Availability and quality of such data, however, often constrain research and decision support activities that require use of these tools. Daily solar radiation (SRAD) data are especially problematic because the instruments require electronic integrators, accurate sensors are expensive, and calibration standards are seldom available. The Prediction Of Worldwide Energy Resources (NASA/POWER; power.larc.nasa.gov) project at the NASA Langley Research Center estimates daily solar radiation based on data that are derived from satellite observations of outgoing visible radiances and atmospheric parameters based upon satellite observations and assimilation models. The solar data are available for a global 1 degree x 1 degree coordinate grid. SRAD can also be estimated based on attenuation of extraterrestrial radiation (Q0) using daily temperature and rainfall data to estimate the optical thickness of the atmosphere. This study compares daily solar radiation data from NASA/POWER (SRADNP) with instrument readings from 295 stations (SRADOB), as well as with values that were estimated with the WGENR solar generator. WGENR was used both with daily temperature and precipitation records from the stations reporting solar data and records from the NOAA Cooperative Observer Program (COOP), thus providing two additional sources of solar data, SRADWG and SRADCO. Values of SRADNP for different grid cells consistently showed higher correlations (typically 0.85 to 0.95) with SRADOB data than did SRADWG or SRADCO for sites within the corresponding cells. Mean values of SRADOB, SRADWG and SRADNP for sites within a grid cell usually were within 1 MJm-2d-1 of each other, but NASA/POWER values averaged 1.1 MJm-2d-1 lower than SRADOB. The magnitude of this bias was greater at lower latitudes and during summer months and may be at least partially explained by assumptions in ambient aerosol properties. Overall, the NASA/POWER solar radiation data are a promising resource for regional modeling studies where realistic accounting of historic variation is required.

White, Jeffrey W.↗

NASA NTRS2024

Using Commercial Satellite Imagery to Reconstruct 3 m and Daily Spring Snow Water Equivalent

Snow water equivalent (SWE) distribution at fine spatial scales (≤10 m) is difficult to estimate due to modeling and observational constraints. However, the distribution of SWE throughout the spring snowmelt season is often correlated to the timing of snow disappearance. Here, we show that snow cover maps generated from PlanetScope's constellation of Dove Satellites can resolve the 3 m date of snow disappearance across seven alpine domains in California and Colorado. Across a 5-year period (2019–2023), the average uncertainty in the date of snow disappearance, or the period of time between the last date of observed snow cover and the first date of observed snow absence, was 3 days. Using a simple shortwave-based snowmelt model calibrated at nearby snow pillows, the PlanetScope date of snow disappearance could be used to reconstruct spring SWE. Relative to lidar SWE estimates, the SWE reconstruction had a spatial coefficient of correlation of 0.75, and SWE spatial variability that was biased by 9%, on average. SWE reconstruction biases were then improved to within 0.04 m, on average, by calibrating snowmelt rates to track the spring temporal evolution of fractional snow cover observed by PlanetScope, including fractional snow cover over the full modeling domain, and across domain subsections where snowmelt rates may differ. This study demonstrates the utility of fine-scale and high-frequency optical observations of snow cover, and the simple and annually repeatable connections between snow cover and spring snow water resources in regions with seasonal snowpack.

Justin M Pflug↗

NASA NTRS1973

Research on the application of satellite remote sensing to local, state, regional, and national programs involved with resource management and environmental quality

Project summaries and project reports are presented in the area of satellite remote sensing as applied to local, regional, and national environmental programs. Projects reports include: (1) Douglas County applications program; (2) vegetation damage and heavy metal concentration in new lead belt; (3) evaluating reclamation of strip-mined land; (4) remote sensing applied to land use planning at Clinton Reservoir; and (5) detailed land use mapping in Kansas City, Kansas.

Walters, R. L.↗

NASA NTRS1990

MEASURE: An integrated data-analysis and model identification facility

The first phase of the development of MEASURE, an integrated data analysis and model identification facility is described. The facility takes system activity data as input and produces as output representative behavioral models of the system in near real time. In addition a wide range of statistical characteristics of the measured system are also available. The usage of the system is illustrated on data collected via software instrumentation of a network of SUN workstations at the University of Illinois. Initially, statistical clustering is used to identify high density regions of resource-usage in a given environment. The identified regions form the states for building a state-transition model to evaluate system and program performance in real time. The model is then solved to obtain useful parameters such as the response-time distribution and the mean waiting time in each state. A graphical interface which displays the identified models and their characteristics (with real time updates) was also developed. The results provide an understanding of the resource-usage in the system under various workload conditions. This work is targeted for a testbed of UNIX workstations with the initial phase ported to SUN workstations on the NASA, Ames Research Center Advanced Automation Testbed.

Singh, Jaidip↗

NASA NTRS2017

Synthetic Scenarios from CMIP5 Model Simulations for Climate Change Impact Assessments in Managed Ecosystems and Water Resources: Case Study in South Asian Countries

Increasing population, urbanization, and associated demand for food production compounded by climate change and variability have important implications for the managed ecosystems and water resources of a region. This is particularly true for south Asia, which supports one quarter of the global population, half of whom live below the poverty line. This region is largely dependent on monsoon precipitation for water. Given the limited resources of the developing countries in this region, the objective of our study was to empirically explore climate change in south Asia up to the year 2099 using monthly simulations from 35 global climate models (GCMs) participating in the fifth phase of the Climate Model Inter-comparison Project (CMIP5) for two future emission scenarios (representative concentration pathways RCP4.5 and RCP8.5) and provide a wide range of potential climate change outcomes. This was carried out using a three-step procedure: calculating the mean annual, monsoon, and non-monsoon precipitation and temperatures; estimating the percent change from historical conditions; and developing scenario funnels and synthetic scenarios. This methodology was applied for the entire south Asia region; however, the percent change information generated at 1.5deg grid scale can be used to generate scenarios at finer spatial scales. Our results showed a high variability in the future change in precipitation (-23% to 52%, maximum in the non-monsoon season) and temperature (0.8% to 2.1%) in the region. Temperatures in the region consistently increased, especially in the Himalayan region, which could have impacts including a faster retreat of glaciers and increased floods. It could also change rivers from perennial to seasonal, leading to significant challenges in water management. Increasing temperatures could further stress groundwater reservoirs, leading to withdrawal rates that become even more unsustainable. The high precipitation variability (with higher propensity for localized intense rainfall events) observed in the region can be a key factor for managed ecosystems and water management and could also lead to more incidence of severe urban flooding. The results could be used to assess both mitigation and adaptation alternatives to reduce vulnerabilities in managed ecosystems (agricultural and urban) and water resources.

agriculture↗

NASA NTRS1975

Regional inventories and mapping of land resources and environmental geology using remotely sensed data

Black-and-white stereoscopic aerial photographs at a scale of 1:20,000, 1:40,000, and 1:65,000 and controlled aerial photo mosaics at a scale of 1:24,000 were the basic data utilized for most mapping. Color and color infrared aerial photography (1:20,000) was employed to man the barrier islands, and color infrared photography (1:120,000) was used to map sand and mud units of the coastal plain north of Houston. LANDSAT-1 imagery in Bands 4, 5, and 7 (1:250,000) was utilized to examine land use and certain resource units statewide. Side scanning radar and LANDSAT imagery were studied experimentally toward mapping land suitability units in carbonate terranes with high relief and certain other terranes. Large-scale color and color infrared aerial stereoscopic photographs supply the most information for regional mapping. black-and-white photography is the most practical data for mapping because of scale, complete coverage, availability, and relatively low cost.

Wermund, E. G.↗

NASA NTRSDate not supplied

VIPER Rover: Flight Build and Environmental Test Status

The NASA Artemis Program plans to return humans to the Moon to stay. Extended human stays on the Moon will require substantial resources to sustain human presence, requiring continuous supplies delivered from the Earth. However, if some of the resources were indigenously available, Earth logistical requirements could be substantially reduced by “living off the land” with in-situ lunar resources. Local volatiles could be processed into propellants and human life-supporting needs, reducing risk of maintaining a permanent human presence on the Moon. LCROSS, LRO and other missions have confirmed the presence of lunar volatiles resources in polar regions, so the next step is to understand the physical distribution of those resources, as well as the scientific basis for how water got there, and why it is still there. The Volatiles Investigating Polar Exploration Resource (VIPER) is a surface mobility scientific platform, designed to spend ~100 days mapping and surveying four different Ice Stability Regions to understand the scientific nature and distribution of water and other volatiles. VIPER will also provide scientific mineralogical context of the lunar regolith, such as the presence of silicon and light metals in lunar regolith, providing a composite picture of resource availability and sustainment. This paper will discuss VIPER’s completion of the flight rover build, as well as current progress in environmental testing, preparedness for mission operations, and overall readying for launch integration with our CLPS partner. VIPER is managed within NASA’s Science Mission Directorate (SMD), utilizing the Commercial Lunar Payload Services (CLPS) lunar delivery model.

VIPER↗

NASA NTRSDate not supplied

Viper Rover: Flight Build and Environmental Test Status

The NASA Artemis Program plans to return humans to the Moon to stay. Extended human stays on the Moon will require substantial resources to sustain human presence, requiring continuous supplies delivered from the Earth. However, if some of the resources were indigenously available, Earth logistical requirements could be substantially reduced by “living off the land” with in-situ lunar resources. Local volatiles could be processed into propellants and human life-supporting needs, reducing risk of maintaining a permanent human presence on the Moon. LCROSS, LRO and other missions have confirmed the presence of lunar volatiles resources in polar regions, so the next step is to understand the physical distribution of those resources, as well as the scientific basis for how water got there, and why it is still there. The Volatiles Investigating Polar Exploration Resource (VIPER) is a surface mobility scientific platform, designed to spend ~100 days mapping and surveying four different Ice Stability Regions to understand the scientific nature and distribution of water and other volatiles. VIPER will also provide scientific mineralogical context of the lunar regolith, such as the presence of silicon and light metals in lunar regolith, providing a composite picture of resource availability and sustainment. This paper will discuss VIPER’s completion of the flight rover build, as well as current progress in environmental testing, preparedness for mission operations, and overall readying for launch integration with our CLPS partner. VIPER is managed within NASA’s Science Mission Directorate (SMD), utilizing the Commercial Lunar Payload Services (CLPS) lunar delivery model.

rover↗

NASA NTRSDate not supplied

VIPER Rover Flight Build and Environmental Test Status

The NASA Artemis Program plans to return humans to the Moon to stay. Extended human stays on the Moon will require substantial resources to sustain human presence, requiring continuous supplies delivered from the Earth. However, if some of the resources were indigenously available, Earth logistical requirements could be substantially reduced by “living off the land” with in-situ lunar resources. Local volatiles could be processed into propellants and human life-supporting needs, reducing risk of maintaining a permanent human presence on the Moon. LCROSS, LRO and other missions have confirmed the presence of lunar volatiles resources in polar regions, so the next step is to understand the physical distribution of those resources, as well as the scientific basis for how water got there, and why it is still there. The Volatiles Investigating Polar Exploration Resource (VIPER) is a surface mobility scientific platform, designed to spend ~100 days mapping and surveying four different Ice Stability Regions to understand the scientific nature and distribution of water and other volatiles. VIPER will also provide scientific mineralogical context of the lunar regolith, such as the presence of silicon and light metals in lunar regolith, providing a composite picture of resource availability and sustainment. This paper will discuss VIPER’s completion of the flight rover build, as well as current progress in environmental testing, preparedness for mission operations, and overall readying for launch integration with our CLPS partner. VIPER is managed within NASA’s Science Mission Directorate (SMD), utilizing the Commercial Lunar Payload Services (CLPS) lunar delivery model.

rover↗

NASA NTRS2014

Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

Shukla, Shraddhanand↗

NASA NTRS2015

Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

Shukla, Shraddhanand↗