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NASA NTRS2007

Noncoherent DTTLs for Symbol Synchronization

Noncoherent data-transition tracking loops (DTTLs) have been proposed for use as symbol synchronizers in digital communication receivers. [Communication- receiver subsystems that can perform their assigned functions in the absence of synchronization with the phases of their carrier signals ( carrier synchronization ) are denoted by the term noncoherent, while receiver subsystems that cannot function without carrier synchronization are said to be coherent. ] The proposal applies, more specifically, to receivers of binary phase-shift-keying (BPSK) signals generated by directly phase-modulating binary non-return-to-zero (NRZ) data streams onto carrier signals having known frequencies but unknown phases. The proposed noncoherent DTTLs would be modified versions of traditional DTTLs, which are coherent. The symbol-synchronization problem is essentially the problem of recovering symbol timing from a received signal. In the traditional, coherent approach to symbol synchronization, it is necessary to establish carrier synchronization in order to recover symbol timing. A traditional DTTL effects an iterative process in which it first generates an estimate of the carrier phase in the absence of symbol-synchronization information, then uses the carrier-phase estimate to obtain an estimate of the symbol-synchronization information, then feeds the symbol-synchronization estimate back to the carrier-phase-estimation subprocess. In a noncoherent symbol-synchronization process, there is no need for carrier synchronization and, hence, no need for iteration between carrier-synchronization and symbol- synchronization subprocesses. The proposed noncoherent symbolsynchronization process is justified theoretically by a mathematical derivation that starts from a maximum a posteriori (MAP) method of estimation of symbol timing utilized in traditional, coherent DTTLs. In that MAP method, one chooses the value of a variable of interest (in this case, the offset in the estimated symbol timing) that causes a likelihood function of symbol estimates over some number of symbol periods to assume a maximum value. In terms that are necessarily oversimplified to fit within the space available for this article, it can be said that the mathematical derivation involves a modified interpretation of the likelihood function that lends itself to noncoherent DTTLs. The proposal encompasses both linear and nonlinear noncoherent DTTLs. The performances of both have been computationally simulated; for comparison, the performances of linear and nonlinear coherent DTTLs have also been computationally simulated. The results of these simulations show that, among other things, the expected mean-square timing errors of coherent and noncoherent DTTLs are relatively insensitive to window width. The results also show that at high signal-to-noise ratios (SNRs), the performances of the noncoherent DTTLs approach those of their coherent counterparts at, while at low SNRs, the noncoherent DTTLs incur penalties of the order of 1.5 to 2 dB.

Simon, Marvin↗

NASA NTRS2010

Estimates of the Lightning NOx Profile in the Vicinity of the North Alabama Lightning Mapping Array

The NASA Marshall Space Flight Center Lightning Nitrogen Oxides Model (LNOM) is applied to August 2006 North Alabama Lightning Mapping Array (LMA) data to estimate the raw (i.e., unmixed and otherwise environmentally unmodified) vertical profile of lightning nitrogen oxides, NOx = NO + NO 2 . This is part of a larger effort aimed at building a more realistic lightning NOx emissions inventory for use by the U.S. Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) modeling system. Data from the National Lightning Detection Network TM (NLDN) is also employed. Overall, special attention is given to several important lightning variables including: the frequency and geographical distribution of lightning in the vicinity of the LMA network, lightning type (ground or cloud flash), lightning channel length, channel altitude, channel peak current, and the number of strokes per flash. Laboratory spark chamber results from the literature are used to convert 1-meter channel segments (that are located at a particular known altitude; i.e., air density) to NOx concentration. The resulting raw NOx profiles are discussed.

Koshak, William J.↗

NASA NTRS2010

Estimates of the Lightning NOx Profile in the Vicinity of the North Alabama Lightning Mapping Array

The NASA Marshall Space Flight Center Lightning Nitrogen Oxides Model (LNOM) is applied to August 2006 North Alabama Lightning Mapping Array (NALMA) data to estimate the (unmixed and otherwise environmentally unmodified) vertical source profile of lightning nitrogen oxides, NOx = NO + NO2. Data from the National Lightning Detection Network (Trademark) (NLDN) is also employed. This is part of a larger effort aimed at building a more realistic lightning NOx emissions inventory for use by the U.S. Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) modeling system. Overall, special attention is given to several important lightning variables including: the frequency and geographical distribution of lightning in the vicinity of the NALMA network, lightning type (ground or cloud flash), lightning channel length, channel altitude, channel peak current, and the number of strokes per flash. Laboratory spark chamber results from the literature are used to convert 1-meter channel segments (that are located at a particular known altitude; i.e., air density) to NOx concentration. The resulting lightning NOx source profiles are discussed.

Koshak, William J.↗

NASA NTRS1973

Remote sensing applied to land-use studies in Wyoming

Impending development of Wyoming's vast fuel resources requires a quick and efficient method of land use inventory and evaluation. Preliminary evaluations of ERTS-1 imagery have shown that physiographic and land use inventory maps can be compiled by using a combination of visual and automated interpretation techniques. Test studies in the Powder River Basin showed that ERTS image interpretations can provide much of the needed physiographic and land use information. Water impoundments as small as one acre were detected and water bodies larger than five acres could be mapped and their acreage estimated. Flood plains and irrigated lands were successfully mapped, and some individual crops were identified and mapped. Coniferous and deciduous trees were mapped separately using color additive analysis on the ERTS multispectral imagery. Gross soil distinctions were made with the ERTS imagery, and were found to be closely related to the bedrock geology. Several broad unstable areas were identified. These were related to specific geologic and slope conditions and generally extended through large regions. Some new oil fields and all large open-cut coal mines were mapped. The most difficult task accomplished was that of mapping urban areas. Work in the urban areas provides a striking example of snow enhancement and the detail available from a snow enhanced image.

Breckenridge, R. M.↗

NASA NTRS2019

Rapid Generation of Flood Maps Using Dual-Polarimetric Synthetic Aperture Radar Imagery

Rapid generation of synthetic aperture radar (SAR) based flood extent and flood depth maps provide valuable data in disaster response efforts. We present a simple but powerful method using dual polarimetric SAR imagery. A RGB false-color map is generated using pre- and post-flooding imagery, allowing operators to distinguish between existing standing water in pre-flooding data and recently flooded areas. This method works very well in areas of standing water, while large omission errors can be seen in urban areas due to the double-bounce effect. A flood depth map is also estimated by using an external DEM. Compared with FEMA flood product, flood water depth from the proposed method showed low bias with small dispersion. This automatic flood mapping system will contribute to the rapid assessment for disaster relief efforts.

Jo, MinJeong↗

NASA NTRS2019

Rapid Generation of Flood Maps Using Dual-Polarimetric Synthetic Aperture Radar Imagery

Rapid generation of synthetic aperture radar (SAR) based flood extent and flood depth maps provide valuable data in disaster response efforts. We present a simple but powerful method using dual-polarimetric SAR imagery. A RGB false-color map is generated using pre- and post-flooding imagery, allowing operators to distinguish between existing standing water in pre-flooding data and recently flooded areas. This method works very well in areas of standing water, while large omission errors can be seen in urban areas due to the double-bounce effect. A flood depth map is also estimated by using an external DEM. Compared with FEMA flood product, flood water depth from the proposed method showed low bias with small dispersion. This automatic flood mapping system will contribute to the rapid assessment for disaster relief efforts.

Jo, MinJeong↗

NASA NTRS1977

The large area crop inventory experiment - A major demonstration of space remote sensing

The NASA-U.S. Department of Agriculture Large Area Crop Inventory Experiment (LACIE), aimed at using multispectral remote sensing data from Landsat 1 and 2 to generate accurate annual global crop production forecasts, is discussed. The forecasts take into account meteorological conditions as well as yield and acreage, and may be used to increase the discrimination of U.S. harvest estimates down to regional levels and to provide more accurate early-season predictions. Sample problems involving the determination of wheat harvests and the monitoring of drought conditions are described. Difficulties related to misidentification of abnormally-developing plantations, the automatic classification of homogeneous spectral groups, the computerized generation of colored maps, and the estimation of yields during years when exceptional meteorological conditions prevail are also considered. Samples of Landsat-generated classification maps for Western U.S. and for the Saratov, U.S.S.R. crop regions are given.

Macdonald, R. B.↗

NASA NTRSDate not supplied

Building Lunar Maps for Terrain Relative Navigation and Hazard Detection Applications

Terrain Relative Navigation (TRN) systems localize a spacecraft with respect to a map of the surface by comparing descent imagery to that reference map. The spacecraft position estimates can only be as accurate as the reference map itself. Accurate map products that are based on orbital reconnaissance data must be validated for navigation applications to ensure that all relevant error sources are minimized. Currently available map products have been generated for scientific applications, so the need for accurate TRN maps remains a gap to be filled for upcoming lunar lander missions, in particular missions to the South Pole region. Additionally, representative high-resolution maps that contain lander-scale features are needed for successful development and testing of Hazard Detection (HD) systems. This paper describes one of NASA’s current efforts to develop benchmark data sets that can be used for developing and testing TRN and HD algorithms as well as suggested processes and metrics for generating and validating lunar maps that can be used for navigation and hazard detection.

Lunar Maps↗

NASA NTRS2006

Assessing Hurricane Katrina Vegetation Damage at Stennis Space Center using IKONOS Image Classification Techniques

Hurricane Katrina inflicted widespread damage to vegetation in southwestern coastal Mississippi upon landfall on August 29, 2005. Storm damage to surface vegetation types at the NASA John C. Stennis Space Center (SSC) was mapped and quantified using IKONOS data originally acquired on September 2, 2005, and later obtained via a Department of Defense ClearView contract. NASA SSC management required an assessment of the hurricane s impact to the 125,000-acre buffer zone used to mitigate rocket engine testing noise and vibration impacts and to manage forestry and fire risk. This study employed ERDAS IMAGINE software to apply traditional classification techniques to the IKONOS data. Spectral signatures were collected from multiple ISODATA classifications of subset areas across the entire region and then appended to a master file representative of major targeted cover type conditions. The master file was subsequently used with the IKONOS data and with a maximum likelihood algorithm to produce a supervised classification later refined using GIS-based editing. The final results enabled mapped, quantitative areal estimates of hurricane-induced damage according to general surface cover type. The IKONOS classification accuracy was assessed using higher resolution aerial imagery and field survey data. In-situ data and GIS analysis indicate that the results compare well to FEMA maps of flooding extent. The IKONOS classification also mapped open areas with woody storm debris. The detection of such storm damage categories is potentially useful for government officials responsible for hurricane disaster mitigation.

Spruce, Joseph P.↗

NASA NTRS2020

Improved LOLA Elevation Maps for South Pole Landing Sites: Error Estimates and Their Impact on Illumination Conditions

We present new high-resolution topographic models of 4 high-priority lunar south pole landing sites based exclusively on the laser altimetry data acquired by the Lunar Orbiter Laser Altimeter (LOLA) onboard the Lunar Reconnaissance Orbiter. By iteratively adjusting the LOLA tracks to the LOLA-based digital elevation model (LDEM) in a self-consistent fashion, we reduce the orbital geolocation errors by over a factor of 10 such that the new ground track geolocation uncertainty is ~10–20 cm horizontally and ~2–4 cm vertically over each 16 × 16 km region. These new and improved 5 m/pix LDEMs will be useful to constrain higher-resolution topographic models derived from imagery, which are not as well controlled geodetically and which can be hindered by shadows. We developed a method to estimate surface height uncertainty in the new LDEMs, which accounts for the reduced orbital errors and interpolation errors by assuming a fractal behavior for the short-scale topography. The LDEM surface height and slope uncertainties have typical RMS values of ~0.30–0.50 m and ~1.5–2.5°, respectively. Finally, we examine how height uncertainties propagate to variations in horizon elevation and thus the predicted illumination conditions at these polar latitudes, and we show how this error characterization can inform landing site studies.

Michael K Barker↗

NASA NTRSDate not supplied

Dissemination of Global Surface Water Mapping from SAR and Optical Data to Global Stakeholders

Under NASA Solicitation A.37 “Earth Science Applications: Disaster Risk Reduction and Response,” NASA’s Disasters Program is funding projects focused on flood forecasting, post-event flood mapping, flood depth estimation and pre-event flood severity using Earth observation (EO) datasets (SAR and optical imagery) and derived flood products. A new initiative in the Disasters Program is underway to disseminate the flood products from different sensors to global stakeholders via the Pacific Disaster Center’s DisasterAWARE®, the NASA Disasters Mapping Portal and potentially other mechanisms. This initiative will also generate an integrated flood product(s) using an ensemble approach that will combine outputs from hydrologic models and EO data for different stakeholders globally for decision-making and response efforts.

remote sensing↗

NASA NTRS1993

Energy content of stormtime ring current from phase space mapping simulations

We perform a phase space mapping study to estimate the enhancement in energy content that results from stormtime particle transport in the equatorial magnetosphere. Our pre-storm phase space distribution is based on a steady-state transport model. Using results from guiding-center simulations of ion transport during model storms having main phases of 3 hr, 6 hr, and 12 hr, we map phase space distributions of ring current protons from the pre-storm distribution in accordance with Liouville's theorem. We find that transport can account for the entire ten to twenty-fold increase in magnetospheric particle energy content typical of a major storm if a realistic stormtime enhancement of the phase space density f is imposed at the nightside tail plasma sheet (represented by an enhancement of f at the neutral line in our model).

Chen, Margaret W.↗

NASA NTRS1978

Soil resources and potential for agricultural development in Bahr El Jebel in southern Sudan, Jonglei Canal project area

The author has identified the following significant results. Fourteen LANDSAT scenes were used to produce mosaics of the 167, 474 sq km study area. These were black and white MSS 7 images and false color composite images. Five major soil-landscape units were delineated on the mosaics, and these were subdivided into a total of 40 soil mapping units. Aerial reconnaissance was useful in defining boundaries between mapping units and in estimating the proportion of the various soils which composed each mapping unit. Ground surveying permitted first-hand observation of major soils and sampling for quantitative laboratory analysis. Soil interpretations were made, including properties, potentials, and limitations.

Myers, V. I.↗

NASA NTRSDate not supplied

Dissemination of Global Flood Information Through Nasa Disasters Program Global Initiative for Flood Forecasting and Alerting (GIFFT)

The Global Initiative for Flood Forecasting and Alerting (GIFFT) is an initiative to disseminate flood products from different sensors to global stakeholders via the Pacific Disaster Center’s (PDC) DisasterAWARE®, the NASA Disasters Mapping Portal and potentially other mechanisms. GIFFT will also generate an integrated product(s) using ensembled hydrologic models and Earth observation derived flood outputs to stakeholders globally. This initiative combines the results of several Disasters Program funded projects that focus on flood forecasting, flood severity estimation, post-event flood mapping, flooddepth estimation and resulting flood severity using remote sensing-based flood information derived from SAR and optical imagery. PDC’s DisasterAWARE platform is a global multi-hazard alerting platform that currently incorporatesModel of Models (MoM) outputs as flood “incidents,” visually depicting potential floods in the context ofpopulation and infrastructure that may become affected. Automated procedures are being developed tocategorize MoM outputs as DisasterAWARE “hazards,” allowing for their dissemination to users alongwith other flood products that assess potential impacts. PDC’s stakeholders include UN, DoD, FEMA andtheir equivalents around the world. Additionally, the DisasterALERT mobile platform has 2 million usersworldwide and is a fully functional and sustained platform used to disseminate alerts and analyticalproducts for 18 different hazard types. GIFFT has incorporated HydroSAR products that will bedistributed via DisasterAWARE.

remote sensing↗

NASA NTRS2023

Satellite Data Reveals a Recent Increase in Shifting Cultivation and Associated Carbon Emissions in Laos

Although shifting cultivation is the major land use type in Laos, the spatial-temporal patterns and the associated carbon emissions of shifting cultivation in Laos are largely unknown. This study provides a nationwide analysis of the spatial-temporal patterns of shifting cultivation and estimations of the associated carbon emissions in Laos over the last three decades. This study found that shifting cultivation has been expanding and intensifying in Laos, especially in the last five years. The newly cultivated land from 2016-2020 accounted for 4.5% (±1.2%) of the total land area of Laos. Furthermore, the length of fallow periods has been continuously declining, indicating that shifting cultivation is becoming increasingly intensive. Combining biomass derived from GEDI (Global Ecosystem Dynamics Investigation) and shifting cultivation maps and area estimates, we found that the net carbon emissions from shifting cultivation declined in 2001-2015 but increased in 2016-2020. The largest carbon source is conversion from intact forests to shifting cultivation, which contributed to 89% of the total emissions from 2001 to 2020. In addition, there were increased emissions from intensified use of fallow land. This research provides useful information for policymakers in Laos to understand the changes in shifting cultivation and improve land use management. This study not only supports REDD+ (Reducing Emissions from Deforestation and forest Degradation) reporting for Laos but also provides a methodology for tracking carbon emissions and removals of shifting cultivation.

Shifting cultivation↗

NASA NTRS2011

The Lightning Nitrogen Oxides Model (LNOM): Status and Recent Applications

Improvements to the NASA Marshall Space Flight Center Lightning Nitrogen Oxides Model (LNOM) are discussed. Recent results from an August 2006 run of the Community Multiscale Air Quality (CMAQ) modeling system that employs LNOM lightning NOx (= NO + NO2) estimates are provided. The LNOM analyzes Lightning Mapping Array (LMA) data to estimate the raw (i.e., unmixed and otherwise environmentally unmodified) vertical profile of lightning NOx. The latest LNOM estimates of (a) lightning channel length distributions, (b) lightning 1-m segment altitude distributions, and (c) the vertical profile of NOx are presented. The impact of including LNOM-estimates of lightning NOx on CMAQ output is discussed.

Koshak, William↗

NASA NTRS2022

Chile Wildfires: Utilizing NASA and NOAA Earth Observations to Determine Lightning-ignited Wildfire Risks in Central Chile

In recent years, Central Chile has experienced wildfires of increasing frequency and intensity which threaten natural resources and communities. The Corporación Nacional Forestal (CONAF) responds to wildfires caused by a variety of ignitions, including lightning, but it is difficult to determine the prevalence of lightning-ignited wildfires based solely on ground observations. In collaboration with CONAF and the Embassy of Chile, Agricultural Office, the team used Earth observations to map coincidence of lightning strikes and wildfire ignitions. The Active Fire Product of Suomi NPP Visible Infrared Imaging Radiometer Suite (VIIRS) identified wildfires as thermal anomalies, which the team compared to the lightning events detected by NOAA’s GOES-16 Geostationary Lightning Mapper (GLM). Next, the team mapped lightning strike frequency and lightning related wildfires across the study area. Finally, the team calculated and mapped a relative estimate of lightning-ignited wildfire vulnerability across the year, fire season (December – March), and off-season (April – November) by summing the following factors: lightning frequency, the Normalized Difference Moisture Index (NDMI) and land surface temperature (LST). These risks were then weighted by fuel availability. Preliminary analysis of the lightning fire relationship showed a spatiotemporal coincidence, primarily in the South-central region of study, near Temuco, and isolated areas on the Andean front. The team identified areas at risk of lightning-induced wildfires, predominantly in the northern third of the study area and along the Andean front. Adjusting the relative weight of risk factors and improving the lightning and fire coincidence map by clustering VIIRS thermal anomalies into fire events could reduce discrepancies and improve risk assessments for future work.

Christopher Matechik↗

NASA NTRS2020

Chrono-Validation of Near-Real-Time Landslide Susceptibility Models via Plugin Statistical Simulations

The idea behind any validation scheme in landslide susceptibility studies is to test whether a model calibrated on a certain data can predict an unknown dataset of the same nature (landslide presences/absences and covariates). Almost the entirety of landslide susceptibility studies are validated by subsetting a single dataset into a training and test sets. This dataset usually corresponds either to event-specific or to historical inventories. Very rarely, a multi-temporal inventory is available and, in the few cases where this condition is met, the validation practices involve training a model on a specific landslide inventory, deriving a single predictive equation and validating it on a subsequent landslide inventory. This commonly leads landslide predictive studies, even those with a strong statistical rigor, to neglect the uncertainty estimation in their modeling scheme. In statistics, validation can also be performed via statistical simulations. This means that after fitting a given model, one can generate any number of predictive functions and test their predictive skills on any type and number of unknown datasets. In this work, we take a similar direction and we apply it to model and validate three separate co-seismic inventories, including an uncertainty estimation phase. We mapped these inventories within the same area in Indonesia, for three earthquakes occurred in 2012, 2017 and 2018. Specifically, we build three event-specific Bayesian Generalize Additive Models of the binomial family. From each model we then simulate 1000 predictive realizations over the remaining two inventories, by using a plug-in scheme where all the morphometric covariates are kept fixed and only the ground motion is replaced according to the prediction target. By doing so, we introduce a new analytical tool for near-real-time landslide predictive purposes, which is able to produce a probabilistic model which stands in between the definitions of susceptibility and hazard. In fact, our model is able to accurately estimate “where” and “when” - although not “how frequently” - landslide have occurred by featuring the multitemporal information of the trigger. In our findings, the simulations are quite similar to the fitted models; and the nine combinations we analyse produce excellent performance. This result confirms the assumption that “the past is the key to the future”, as we show that the relative contribution of each variable and their interactions in each probabilistic model remains practically the same across temporal replicates. This information is not trivial because it supports the routines implemented in global near-real-time applications.

Temporal validation↗