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NASA NTRS2016

Geologic Mapping of Ascraeus Mons, Mars

Ascraeus Mons (AM) is the northeastern most large shield volcano residing in the Tharsis province on Mars. We are funded by NASA's Mars Data Analysis Program to complete a digital geologic map based on the mapping style. Previous mapping of a limited area of these volcanoes using HRSC images (13-25 m/pixel) revealed a diverse distribution of volcanic landforms within the calderas, along the flanks, rift aprons, and surrounding plains. The general scientific objectives for which this mapping is based is to show the different lava flow morphologies across AM to better understand the evolution and geologic history.

Volcanoes↗

NASA NTRS2015

Quantifying Mapping Orbit Performance in the Vicinity of Primitive Bodies

Predicting and quantifying the capability of mapping orbits in the vicinity of primitive bodies is challenging given the complex orbit geometries that exist and the irregular shape of the bodies themselves. This paper employs various quantitative metrics to characterize the performance and relative effectiveness of various types of mapping orbits including terminator, quasi-terminator, hovering, pingpong, and conic-like trajectories. Metrics of interest include surface area coverage, lighting conditions, and the variety of viewing angles achieved. The metrics discussed in this investigation are intended to enable mission designers and project stakeholders to better characterize candidate mapping orbits during preliminary mission formulation activities.The goal of this investigation is to understand the trade space associated with carrying out remotesensing campaigns at small primitive bodies in the context of a robotic space mission. Specifically,this study seeks to understand the surface viewing geometries, ranges, etc. that are available fromseveral commonly proposed mapping orbits architectures.

Pavlak, Thomas A.↗

NASA NTRS2017

Daily Kilometer-Scale MODIS Satellite Maps of PM2.5 Describe Wintertime Episodes

The San Joaquin Valley (SJV) suffers from severe health-endangering episodes of PM2.5 aerosol loadings in wintertime; episodes last approximately 5 days and differ in geographical distribution and composition. PM2.5 stations are scattered; consequently the use of remote sensing to map variable regional patterns of these varying respirable aerosol concentrations is desirable. High-precision AOT retrievals can capture column particulate loading. However,PM2.5 mapping is challenging due to several reasons: particularly thin mixed layers (ML) and thus relatively low aerosol optical thickness (AOT) close to current measurement limits, variable and a typical composition of the aerosols, and complex surface bidirectional reflectance. However, the West does present some advantages in analysis. Air basins are isolated from long-distance transport, and experience predominant strong meteorological subsidence. Thus these Western basin regions have fewer problematic cases of overriding aerosol layers detached from the surface. To counter such local overriding, Chu et al. have described an approach for the Eastern US, and He et al have described a synoptic classification approach useful in Shanghai. The Bay Area Air Quality Management District (BAAQMD) expands our experience with the use of AOT, with lower PM2.5 and several isolated sub-basins. We have prepared daily maps of episodes in each region. We present also a sequence of increasingly detailed statistical models, AOT initially appears to contribute little information; however, inclusion of weather information reveals its utility. Lyapustin and Wang's MultiAngle Implementation of Atmospheric Correction (MAIAC) retrieval for AOT provided the most useful operational remote sensing information for these regions. It provides high (1-km) spatial resolution maps and a high percentage of availability. Empirical regression methods have found that random effects regression models (aka mixed effects models, ME) employing AOT provide good estimates of ground PM2.5 concentrations.Here, we attempt to extend these methods and evaluate the usefulness of AOT with greater physical analysis, based on DISCOVER-AQ4 experience.

Chatfield, Robert B.↗

NASA NTRS2016

Erasing the Milky Way: New Cleaning Technique Applied to GBT Intensity Mapping Data

We present the first application of a new foreground removal pipeline to the current leading HI intensity mapping dataset, obtained by the Green Bank Telescope (GBT). We study the 15- and 1-h field data of the GBT observations previously presented in Masui et al. (2013) and Switzer et al. (2013), covering about 41 square degrees at 0.6 less than z is less than 1.0, for which cross-correlations may be measured with the galaxy distribution of the WiggleZ Dark Energy Survey. In the presented pipeline, we subtract the Galactic foreground continuum and the point source contamination using an independent component analysis technique (fastica), and develop a Fourier-based optimal estimator to compute the temperature power spectrum of the intensity maps and cross-correlation with the galaxy survey data. We show that fastica is a reliable tool to subtract diffuse and point-source emission through the non-Gaussian nature of their probability distributions. The temperature power spectra of the intensity maps is dominated by instrumental noise on small scales which fastica, as a conservative sub-traction technique of non-Gaussian signals, can not mitigate. However, we determine similar GBT-WiggleZ cross-correlation measurements to those obtained by the Singular Value Decomposition (SVD) method, and confirm that foreground subtraction with fastica is robust against 21cm signal loss, as seen by the converged amplitude of these cross-correlation measurements. We conclude that SVD and fastica are complementary methods to investigate the foregrounds and noise systematics present in intensity mapping datasets.

Wolz, L.↗

NASA NTRS2015

Quantifying Mapping Orbit Performance in the Vicinity of Primitive Bodies

Predicting and quantifying the capability of mapping orbits in the vicinity of primitive bodies is challenging given the complex orbit geometries that exist and the irregular shape of the bodies themselves. This paper employs various quantitative metrics to characterize the performance and relative effectiveness of various types of mapping orbits including terminator, quasi-terminator, hovering, ping pong, and conic-like trajectories. Metrics of interest include surface area coverage, lighting conditions, and the variety of viewing angles achieved. The metrics discussed in this investigation are intended to enable mission designers and project stakeholders to better characterize candidate mapping orbits during preliminary mission formulation activities. The goal of this investigation is to understand the trade space associated with carrying out remote sensing campaigns at small primitive bodies in the context of a robotic space mission. Specifically, this study seeks to understand the surface viewing geometries, ranges, etc. that are available from several commonly proposed mapping orbits architectures

Pavlak, Thomas A.↗

NASA NTRS2017

HOTEX: An Approach for Global Mapping of Human Built-Up and Settlement Extent

Understanding the impacts of urbanization requires accurate and updatable urban extent maps. Here we present an algorithm for mapping urban extent at global scale using Landsat data. An innovative hierarchical object-based texture (HOTex) classification approach was designed to overcome spectral confusion between urban and nonurban land cover types. VIIRS nightlights data and MODIS vegetation index datasets are integrated as high-level features under an object-based framework. We applied the HOTex method to the GLS-2010 Landsat images to produce a global map of human built-up and settlement extent. As shown by visual assessments, our method could effectively map urban extent and generate consistent results using images with inconsistent acquisition time and vegetation phenology. Using scene-level cross validation for results in Europe, we assessed the performance of HOTex and achieved a kappa coefficient of 0.91, compared to 0.74 from a baseline method using per-pixel classification using spectral information.

Wang, Panshi↗

NASA NTRS2018

Quantitative Daily Maps of PM 2.5 Episodes for California and Other Regions: Satellite Column Water and Optical Depth as Allied Tracers of Dilution

The Western US and many regions globally present daunting difficulties in understanding PM 2.5 episodes. We evaluate extensions of a method independent of modeled source-description and transport/transformation and using several satellite remote sensing products from imaging spectrometers. The San Joaquin Valley (SJV) especially suffers few-day episodes due to shallow mixing; PM 2.5 retrieval suffers low satellite AOT (Aerosol Optical Thickness) and bright surfaces.Nevertheless, we find residual errors in our maps of of typically 5-8 micrograms per cubic meter. Episodes in the Valley reaching 60-100 micrograms per cubic meter. These maps detail pollution from Interstate 5 at the scale of a few kilometers. The maps are based on NASA's MODerate resolution Imaging Spectrometer (MODIS) data at circa 1 kilometer as processed with the Multi-Angle Implementation of Atmospheric Correction. The Bay Area Air Quality Management District has requested that we test our methods in their challenging environment characterized by multiple sub-basins defined by complex topography. Our tests suggest that nearly similar precision may be expected for wintertime conditions with high PM 2.5 . We note difficulties when measured PM 2.5 is less than 8-10 micrograms per cubic meter, but good relative precision when PM 2.5 rises above 20; i.e. in episodes of concern for morbidity and mortality. Our method stresses physically meaningful functions of MODIS-MAIAC (Multi-Angle Implementation of Atmospheric Correction)-derived AOD (Aerosol Optical Depth) and total water vapor column. A mixed-effects statistical model exploiting existing station data works powerfully to allow us daily AOT-to-PM 2.5 relationships that allow a calibration of the map. In those cases where water vapor and particles have generally similar surface sources, using the ratio of AOT / Column_water can improve the daily calibrations so as to reach our quoted precision. We briefly present some cartoon idealizations that explain this success and also the likely reasons that our mixed effects model (or "daily calibration") works; also when it should not work. The combined satellite/mixed-effects model works best for wintertime San Joaquin Valley episodes, where the meteorology of particle and H2O(v) dilution is quite appropriate. We extended and tested the methodology (a) for the Bay Area wintertime situations and (b) for smoke plume events (e.g. the October 2017 fire events of the Sonoma area). Our SJV work was evaluated using NASA's DISCOVER-AQ (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality) airborne measurements, and by season- long measurements in Fresno. If the composition and size distribution of the aerosols can be assessed for the regions we describe, retrievals should have improved accuracy.

Chatfield, Robert B.↗

NASA NTRS2018

Quantifying the Early Snowmelt Event of 2015 in the Cascade Mountains, USA by Developing and Validating MODIS-Based Snowmelt Timing Maps

Spring snowmelt serves as the major hydrological contribution to many watersheds of the US West. Since the 1970s the conterminous western USA has seen an earlier arrival of spring snowmelt. The extremely low snowpack and early melt of 2015 in the Cascade Mountains may be a harbinger of winters to come, underscoring the interest in advancements in spring snowmelt monitoring. Target-of-opportunity and point measurements of snowmelt using meteorological stations or stream gauges are common sources of these data, however, there have been few attempts to identify snowmelt timing using remote sensing. In this study, we describe the creation of snowmelt timing maps (STMs) which identify the day of year that each pixel of a remotely sensed image transitions from "snow-covered" to "no snow" during the spring melt season, controlling for cloud coverage and ephemeral spring snow storms. Derived from the 500 meter MODerate-resolution Imaging Spectroradiometer (MODIS) standard snow map, MOD10A2, this new dataset provides annual maps of snowmelt timing, with corresponding maps of cloud interference and interannual variability in snow coverage from 2001-2015. We first show that the STMs agree strongly with in-situ snow telemetry (SNOTEL) meteorological station measurements in terms of snowmelt timing. We then use the STMs to investigate the early snowmelt event of 2015 in the Cascade Mountains, USA, highlighting the protected areas of Mt. Rainier, Crater Lake, and Lassen Volcanic National Parks. In 2015 the Cascade Mountains experienced snowmelt 41 days earlier than the 2001-2015 average, with 25 percent of its land area melting more than 65 days earlier than average. The upper elevations of the Cascade Mountains experienced the greatest snowmelt anomaly. Our results are relevant to land managers and biologists as they plan adaptation strategies for mitigating the effects of climate change throughout temperate mountains.

snowmelt timing↗

NASA NTRS2018

Landslide Mapping Along the Karnali Highway, Nepal using High-Resolution Imagery

The Karnali highway (Figure 1) is the only major transportation link that connects the remote Karnali region to the provincial capital in Province 6 of Nepal. This area becomes inaccessible by roads during every rainy season due to landslides. Despite the known landslide frequency, there have been no systematic landslide inventories conducted along this highway to date. Recent advancements in remote-sensing technologies have significantly increased our ability to map landslides of various sizes rapidly with less in situ surveys or human interaction. Landslide susceptibility, hazard and risk studies require a complete landslide inventory, which might only be possible from very high-resolution (VHR) and high-resolution (HR) imagery. Recent launch of Sentinel-2 in 2015 has provided free access to HR imagery enabling landslide detection at finer scales then what was possible with previous open source satellite imagery obtained from Landsat and ASTER. Satellites providing VHR imagery are commercially owned, expensive and not freely available expect for when disasters charter is activated. NextView licensing agreement, a partnership between the US government and US commercial vendors provides access to VHR imagery to federal agencies in support of scientific research [1]. This partnership provides access to VHR imagery obtained from the DigitalGlobe (DG) constellation which enables mapping of small landslides (< 100 m2). In this study, VHR imagery from DG and HR imagery from Sentinel-2 will be used to map landslides along the Karnali highway using a semi automatic method based on object-oriented analysis (OOA) to create most recent and up-to-date landslide inventory. The effectiveness of this remote sensing based landslide inventory to produce a susceptibility map and its predictive capacity will be tested.

landslide↗

NASA NTRS2019

Simultaneous Localization and Mapping for Satellite Rendezvous and Proximity Operations Using Random Finite Sets

Future space missions require that spacecraft have the capability to autonomously navigate non-cooperative environments for rendezvous and proximity operations (RPO). Current relative navigation filters can have difficulty in these situations, diverging due to complications with data association, high measurement uncertainty, and clutter, particularly when detailed a priori maps of the target object or spacecraft do not exist. The goal of this work is to demonstrate the feasibility of random finite set (RFS) filters for spacecraft relative navigation and pose estimation. The approach is to formulate satellite relative navigation and pose estimation as a simultaneous localization and mapping (SLAM) problem, in which an observer spacecraft seeks to simultaneously estimate the location of features on a target object or spacecraft as well as its relative position, velocity and attitude. This work utilizes a filter developed using the framework of RFS which are well suited to multi-target SLAM operations, avoiding data association entirely. Relevant RPO scenarios with simulated flash LIDAR measurements are tested with a Probability Hypothesis Density (PHD) RFS filter embedded in a particle filter to obtain a feature map of a target and a relative pose estimate between the target and observer. Preliminary results show that an RFS-based filter can successfully perform SLAM in a spacecraft relative navigation scenario with no a priori map of the target. These results demonstrate the feasibility of RFS filtering for spacecraft relative navigation and motivate future studies which may expand to tracking space objects for space situational awareness, as well as relative navigation around small bodies.

Schlenker, Lauren↗

NASA NTRS2019

Simultaneous Localization and Mapping for Satellite Rendezvous and Proximity Operations using Random Finite Sets

Schlenker, Lauren↗

NASA NTRS2019

Intensity Mapping in the Presence of Foregrounds and Correlated Continuum Emission

Intensity mapping has attracted significant interest as an approach to measuring the properties of the interstellar medium in typical galaxies at high redshift. Intensity mapping measures the statistics of surface brightness as a function of frequency, making it sensitive to not only all line emission of interest but also radiation from all other sources. Significant effort has gone into developing approaches that reject foreground contamination. Additionally, the target galaxies have multiple sources of emission that can complicate the interpretation of the line brightness.We describe the problem of jointly estimating correlated continuum emission and cleaning uncorrelated continuum emission, such as from the Milky Way. We apply these considerations to a cross-correlation of Planck data with BOSS quasars for a determination of [C II] for 2 < z < 3.2. Intensity mapping surveys with few bands have unique challenges for treating foregrounds and avoiding bias from correlated continuum emission. We show how a future intensity mapping survey with many bands can separate line from continuum emission in cross-correlation.

Switzer, E. R.↗

NASA NTRS2019

Diverse Photosynthetic Capacity of Global Ecosystems Mapped by Satellite Chlorophyll Fluorescence Measurements

Photosynthetic capacity is often quantified by the Rubisco-limited photosynthetic capacity (i.e. maximum carboxylation rate, V(sub cmax)). It is a key plant functional trait that is widely used in Earth System Models for simulation of the global carbon and water cycles. Measuring V(sub cmax) is time-consuming and laborious; therefore, the spatiotemporal distribution of V(sub cmax) is still poorly understood due to limited measurements of V(sub cmax). In this study, we used a data assimilation approach to map the spatial variation of V(sub cmax) for global terrestrial ecosystems from a 11-year-long satellite-observed solar-induced chlorophyll fluorescence (SIF) record. In this SIF-derived V(sub cmax) map, the mean V(sub cmax) value for each plant function type (PFT) is found to be comparable to a widely used N-derived V(sub cmax) dataset by Kattge et al. (2009). The gradient of V(sub cmax) along PFTs is clearly revealed even without land cover information as an input. Large seasonal and spatial variations of V(sub cmax) are found within each PFT, especially for diverse crop rotation systems. The distribution of major crop belts, characterized with high V(sub cmax) values, is highlighted in this V(sub cmax) map. Legume plants are characterized with high V(sub cmax) values. This V(sub cmax) map also clearly illustrates the emerging soybean revolution in South America where V(sub cmax) is the highest among the world. The gradient of V(sub cmax) in Amazon is found to follow the transition of soil types with different soil N and P contents. This study suggests that satellite-observed SIF is powerful in deriving the important plant functional trait, i.e. V(sub cmax), for global climate change studies.

Liming He↗

NASA NTRS2019

A Streamlined Approach to Spatial Mapping of Complex 3D Thermal Boundary Condition Data

Spatial mapping of 1D, 2D, and particularly 3D thermal boundary condition data is essential for accurate thermal simulation of spacecraft and launch vehicles. This information typically is available in simple ASCII file format but can be problematic to use for thermal simulations. Difficulties that can be encountered include the ability to directly use the available file format, transformation of the data into the desired reference coordinate system in 3D space, and accurate interpolation of the data onto the thermal calculation domain. This paper presents a streamlined approach to spatial mapping of complex unsymmetrical 3D cold wall heat flux data using the fields’ capability in Simcenter3D. A recent example of 3D thermal plume simulation at MSFC will be employed to illustrate the use of text, csv, and Excel files, specifying coordinate systems for transformation of data in 3D space, and use of the various interpolation schemes available for spatial mapping. Spatial boundary condition verification is also very important, and the spacecraft thermal analyst needs effective visualization tools to develop confidence in the boundary condition definitions. Visualization of the spacecraft plume raw field data, the calculated field data, and the interpolated field data onto the thermal calculation domain will be discussed, with a particular focus on real-time viewing of the interpolated data for available interpolation schemes. The objective of this paper is to outline the modeling techniques used with the integrated thermal analysis of a terrestrial lunar lander demonstrator. Specifically, the techniques outlined herein focus on spatial mapping of convective and radiative exhaust plume results from a computational fluid dynamics (CFD) analysis. Two cases were investigated, however, the modeling techniques that were utilized translate to both cases seamlessly. Those two cases were as follows: • Lunar lander demonstrator firing on-pad (worst case). • Lunar lander demonstrator hovering at an elevated position above the launch pad. For the purposes of information control, this paper will focus on the modeling techniques only, using altered or dimensionless data where necessary. BACKGROUND The thermal model examined in this paper is of the lunar lander demonstrator known as XL-1T (terrestrial), born from a collaborative effort between Masten Space Systems (MSS) and NASA, under NASA’s Lunar CATALYST (also known as Lunar Cargo Transportation and Landing by Soft Touchdown) initiative. The lander is a reusable vertical takeoff/vertical landing (VTVL) test bed which is controlled by four throttleable main engines utilizing green hypergolic propellants.

Hawkins, Robert F.↗

NASA NTRS2019

Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility Analysis Along the Karnali Highway, Nepal

The Karnali highway is a vital transport link and the only primary roadway that connects the remote Karnali region to the lowlands in Mid-Western Nepal. Every year there are reports of landslides blocking the road, making this area largely inaccessible. However, little effort has focused on systematically identifying landslides and landslide-prone areas along this highway. In this study, landslides were mapped with an object-based approach from very high-resolution optical satellite imagery obtained by the DigitalGlobe constellation in 2012 and PlanetScope in 2018. Landslides ranging from 10 to 30,496 sq.m were detected within a 3 km buffer along the highway. Most of the landslides were located at lower elevations (between 500–1500 m) and on steep south-facing slopes. Landslides tended to cluster closer to the highway, near drainage channels and away from faults. Landslides were also most prevalent within the Kuncha Formation geologic class, and the forested and agricultural land cover classes. A susceptibility map was then created using a logistic regression methodology to highlight patterns in landslide activity. The landslide susceptibility map showed a good prediction rate with an area under the curve (AUC) of 0.90. A total of 33% of the study arealies in high/very high susceptibility zones. The map highlighted the lower elevated areas between Bangesimal and Manma towns with the Kuncha Formation geologic class as being the most hazardous. The banks of the Karnali River, its tributaries and areas near the highway were also highly susceptible to landslides. The results highlight the potential of very high-resolution optical imagery for documenting detailed spatial information on landslide occurrence, which enables susceptibility assessment in remote and data scarce regions such as the Karnali highway.

Pukar Amatya↗

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 NTRS2020

Great Lakes Water Resources II: A Google Earth Engine Tool to Automate Wetland Mapping Using Optical and Radar Satellite Sensors in the Great Lakes Basin for Wetland Management and Monitoring

The Great Lakes Basin is one of the world’s largest freshwater ecosystems. The Basin harbors over 200,000 acres of wetlands. These wetlands provide a variety of environmental, ecological, and recreational functions to over 30 million people in the region. Some of these functions include improving water quality, mitigating flood impacts, providing wildlife habitat, and housing recreational activities. However, due to anthropogenic activities, habitat conversion and degradation threaten to disrupt or destroy remaining wetland ecosystems. Maps of wetland distribution based on ground surveys are costly and labor-intensive, prohibiting timely evaluations of wetland loss and gain. The Great Lakes Water Resources II team at the NASA Jet Propulsion Laboratory developed the Wetlands Extent Tool 2.0 (WET 2.0) in Google Earth Engine to automate mapping of wetland distribution in the Great Lakes Basin. The team partnered with the US Fish and Wildlife Service (USFWS), Environmental Protection Agency (EPA), Minnesota Department of Natural Resources (MDNR), the National Oceanic and Atmospheric Administration (NOAA), and Ducks Unlimited (DU). WET 2.0 incorporates Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI) satellite data. WET 2.0 is trained to classify anywhere in the Great Lakes Basin. Utilizing a Random Forest classifier, WET 2.0 is capable of automatically mapping wetland extent in the entire Great Lakes Basin, achieving a mean overall accuracy of 80.12% when tested in Michigan. Findings and maps produced in WET 2.0 will enable our partners to identify areas of ecosystem degradation and wetland destruction in order to enact environmental practices and policy initiatives to maintain environmental and economic health in the area.

Water Resources↗