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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Geospatial Method for Computing Supplemental Multi-Decadal U.S. Coastal Land-Use and Land-Cover Classification Products, Using Landsat Data and C-CAP Products

This paper discusses the development and implementation of a geospatial data processing method and multi-decadal Landsat time series for computing general coastal U.S. land-use and land-cover (LULC) classifications and change products consisting of seven classes (water, barren, upland herbaceous, non-woody wetland, woody upland, woody wetland, and urban). Use of this approach extends the observational period of the NOAA-generated Coastal Change and Analysis Program (C-CAP) products by almost two decades, assuming the availability of one cloud free Landsat scene from any season for each targeted year. The Mobile Bay region in Alabama was used as a study area to develop, demonstrate, and validate the method that was applied to derive LULC products for nine dates at approximate five year intervals across a 34-year time span, using single dates of data for each classification in which forests were either leaf-on, leaf-off, or mixed senescent conditions. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and C-CAP value-added products. Each classification's overall accuracy was assessed by comparing stratified random locations to available reference data, including higher spatial resolution satellite and aerial imagery, field survey data, and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall Kappa statistics ranging from 0.78 to 0.89. The accuracies are comparable to those from similar, generalized LULC products derived from C-CAP data. The Landsat MSS-based LULC product accuracies are similar to those from Landsat TM or ETM+ data. Accurate classifications were computed for all nine dates, yielding effective results regardless of season. This classification method yielded products that were used to compute LULC change products via additive GIS overlay techniques.

Spruce, J. P.↗
NASA NTRS2013

NASA Tech Briefs, March 2013

Topics covered include: Remote Data Access with IDL Data Compression Algorithm Architecture for Large Depth-of-Field Particle Image Velocimeters Vectorized Rebinning Algorithm for Fast Data Down-Sampling Display Provides Pilots with Real-Time Sonic-Boom Information Onboard Algorithms for Data Prioritization and Summarization of Aerial Imagery Monitoring and Acquisition Real-time System (MARS) Analog Signal Correlating Using an Analog-Based Signal Conditioning Front End Micro-Textured Black Silicon Wick for Silicon Heat Pipe Array Robust Multivariable Optimization and Performance Simulation for ASIC Design; Castable Amorphous Metal Mirrors and Mirror Assemblies; Sandwich Core Heat-Pipe Radiator for Power and Propulsion Systems; Apparatus for Pumping a Fluid; Cobra Fiber-Optic Positioner Upgrade; Improved Wide Operating Temperature Range of Li-Ion Cells; Non-Toxic, Non-Flammable, -80 C Phase Change Materials; Soft-Bake Purification of SWCNTs Produced by Pulsed Laser Vaporization; Improved Cell Culture Method for Growing Contracting Skeletal Muscle Models; Hand-Based Biometric Analysis; The Next Generation of Cold Immersion Dry Suit Design Evolution for Hypothermia Prevention; Integrated Lunar Information Architecture for Decision Support Version 3.0 (ILIADS 3.0); Relay Forward-Link File Management Services (MaROS Phase 2); Two Mechanisms to Avoid Control Conflicts Resulting from Uncoordinated Intent; XTCE GOVSAT Tool Suite 1.0; Determining Temperature Differential to Prevent Hardware Cross-Contamination in a Vacuum Chamber; SequenceL: Automated Parallel Algorithms Derived from CSP-NT Computational Laws; Remote Data Exploration with the Interactive Data Language (IDL); Mixture-Tuned, Clutter Matched Filter for Remote Detection of Subpixel Spectral Signals; Partitioned-Interval Quantum Optical Communications Receiver; and Practical UAV Optical Sensor Bench with Minimal Adjustability.

Source record↗
NASA NTRS2015

FINESSE: Field Investigations to Enable Solar System Science and Exploration

The FINESSE (Field Investigations to Enable Solar System Science and Exploration) team is focused on a science and exploration field-based research program aimed at generating strategic knowledge in preparation for the human and robotic exploration of the Moon, near-Earth asteroids (NEAs) and Phobos and Deimos. We follow the philosophy that "science enables exploration and exploration enables science." 1) FINESSE Science: Understand the effects of volcanism and impacts as dominant planetary processes on the Moon, NEAs, and Phobos & Deimos. 2) FINESSE Exploration: Understand which exploration concepts of operations (ConOps) and capabilities enable and enhance scientific return. To accomplish these objectives, we are conducting an integrated research program focused on scientifically-driven field exploration at Craters of the Moon National Monument and Preserve in Idaho and at the West Clearwater Lake Impact Structure in northern Canada. Field deployments aimed at reconnaissance geology and data acquisition were conducted in 2014 at Craters of the Moon National Monument and Preserve. Targets for data acquisition included selected sites at Kings Bowl eruptive fissure, lava field and blowout crater, Inferno Chasm vent and outflow channel, North Crater lava flow and Highway lava flow. Field investigation included (1) differential GPS (dGPS) measurements of lava flows, channels (and ejecta block at Kings Bowl); (2) LiDAR imaging of lava flow margins, surfaces and other selected features; (3) digital photographic documentation; (4) sampling for geochemical and petrographic analysis; (5) UAV aerial imagery of Kings Bowl and Inferno Chasm features; and (6) geologic assessment of targets and potential new targets. Over the course of the 5-week field FINESSE campaign to the West Clearwater Impact Structure (WCIS) in 2014, the team focused on several WCIS research topics, including impactites, central uplift formation, the impact-generated hydrothermal system, multichronometer dating of impact products, and using WCIS as an analog test site for crew studies of sampling protocols. The FINESSE team visited and mapped all of the major islands within West Clearwater Lake. Excellent cliff exposures around the coasts of many of the islands allowed a general stratigraphy of impactites to be defined. Notable differences to previous work includes the discovery of a monomict lithic breccia and a medium to coarse grained impact melt rock. In addition, ample rock samples were returned from West Clearwater for geochronology study. Geochronology work centers around laboratory analyses of these samples (and samples collected in the future or obtained from archives housed at the Canadian Geological Survey). Samples returned from the FINESSE field season have been evaluated for suitability for geochronologic analysis, and selected samples have been crushed for mineral separation and/or sawed for the preparation of polished petrologic thin sections. Heavy minerals (e.g., zircon, titanite, and apatite) will be separated from the crushed material for (U-Th)/He geochronology. The sections will be used for laser ablation 40Ar/39Ar research after neutron irradiation. This presentation will highlight the exciting science and exploration work conducted by FINESSE, as well as future plans for continued research.

Phobos & Deimos↗
NASA NTRSDate not supplied

Generation of Continental Scale Percent Tree Cover Product Using Deep-learning and Multi-scale Remote Sensing Data

Spatially explicit percent tree cover (TC) estimation is critical for mapping forest aboveground biomass and its dynamics. While various TC products have been developed, there has not been a generalized framework that can be applied to diverse terrestrial ecosystems due to underlain extreme complexities. Deep learning algorithms can learn a spatial pattern and radiometric characteristics of tree canopy as a robust approximation of physical or empirical models, and thus have emerged as promising and efficient tools for large-scale TC mapping. In this study, we synergistically use very high-resolution aerial imageries (National Agriculture Imagery Program, NAIP) and medium resolution Landsat data to map continental-scale TC (CONUS and Mexico) through a hierarchical deep learning approach (Convolutional Neural Network), i.e., NAIP TC generated from a NAIP model is utilized to train a Landsat model. The produced TC product (hereafter, NEX-TC) is able to capture the spatial pattern of TC distribution and its changes driven by natural disturbance and human land management. We further explore and analyze the reliability and potential uncertainty of the NEX-TC by comparing it to lidar- (lidar-TC), National Land Cover Database (NLCD-TC), and MODIS Vegetation Continuous Field (MODIS-TC). This evaluation practice reveals that TC products based on passive optical sensors tend to underestimate TC across all land cover types while Landsat-based TCs (i.e., NEX-TC & NLCD-TC) perform better than the coarser MODIS TC estimate. Our results show that the NEX-TC is generally comparable to NLCD-TC but it particularly outperforms NLCD-TC and MODIS-TC over the dense forests where lidar-TC indicates >80% TC. These results indicate that our hierarchical deep learning approach and TC product will be effective and useful for characterizing large-scale tree cover and possibly associated carbon dynamics.

Landsat↗
NASA NTRS2020

Flood Mapping Using UAVSAR and Convolutional Neural Networks

We have mapped flooded areas in data collected by the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) using two convolutional neural network (CNN) image classifier architectures: U-Net and SegNet. Our study area was a region around Houston, TX, USA affected by widespread flooding in 2017 due to Hurricane Harvey. To train and test the classifiers, we manually labelled over 10000 image segments in two flight lines. Both U-Net and SegNet yielded higher accuracy than a previous non-machine learning classifier we used as a baseline. U-Net had slightly higher accuracy than SegNet. The classifiers performed better in areas with more homogeneous land cover. To independently validate the classifier accuracy we used NOAA aerial imagery, with overall accuracy around 80%. Future work includes assessing the classifier robustness in other study areas, assessing the classifier dependence on UAVSAR incidence angle, particularly for open water and bare ground, and collecting more training data, particularly in urban areas. This study demonstrates the potential of CNN image classifiers for mapping flooded areas in airborne polarimetric SAR imagery, and for land cover classification of polarimetric SAR imagery more generally.

Denbina, Michael W↗
NASA NTRSDate not supplied

Assessing Change in Aspen Extent in Northern Yellowstone National Park

The trophic cascade among wolves, elk, and aspen has influenced the landscape of Yellowstone National Park. Aspen promote greater biodiversity and have been indirectly affected by the 1926 removal and 1995 reintroduction of wolves in the park. Partnered with Yellowstone National Park, Utah State University, and the University of Wisconsin–Stevens Point, NASA DEVELOP analyzed the change in aspen stand extent from 1954 to 2021 over the elk wintering range that occurs in the northern part of Yellowstone and southern Montana. Focusing on 113 stands corresponding with belt transects monitored annually since 1999, the team georeferenced and digitized 1954 historical aerial imagery to determine aspen stand extent. From 1986 to 2019, the team processed Landsat 5 Thematic Mapper (TM) and Sentinel-2 Multispectral Instrument (MSI) imagery for the entire elk wintering range using a random forest model to classify landcover types. Outputs were refined using a phenological approach that distinguishes between deciduous and evergreen landcover by differencing summer and fall vegetation indices. Finally, the team conducted a similar analysis for 2021, utilizing both Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MSI imagery. The team again focused on the stands associated with belt transects for this analysis to compare the beginning and end of the study period. Preliminary results indicate a slight decline over time. These results expand the understanding of the role of wolves on aspen in the Yellowstone ecosystem and inform future rewilding decisions within the park and beyond.

Vanessa Bailey↗
NASA NTRS1973

Lake Superior water quality near Duluth from analysis of aerial photos and ERTS imagery

ERTS imagery of Lake Superior in the late summer of 1972 shows dirty water near the city of Duluth. Water samples and simultaneous photographs were taken on three separate days following a heavy storm which caused muddy runoff water. The water samples were analyzed for turbidity, color, and solids. Reflectance and transmittance characteristics of the water samples were determined with a spectrophotometer apparatus. This same apparatus attached to a microdensitometer was used to analyze the photographs for the approximate colors or wavelengths of reflected energy that caused the exposure. Although other parameters do correlate for any one particular day, it is only the water quality parameter of turbidity that correlates with the aerial imagery on all days, as the character of the dirty water changes due to settling and mixing.

Scherz, J. P.↗
NASA NTRS1978

Commercial Fishing Port Development in North Florida

The author has identified the following significant results. Seven major counties were examined: Escambia, Bay, Gulf, Franklin, Wakulla, Nassau, and Duval. Population and economic activity were reviewed, along with commercial fishing and port facilities. Recommendations for five northwest Florida counties were based on interpretation of aerial photographs, satellite imagery, an aerial survey site visit, and published data. Major needs in Pensacola included docking, ice supply, and net and engine repair services. Costs for additional docks, an ice plant, and gear storage were estimated at $3,658,600. Port users in Panama City identified additional docking and gear storage as primary needs, along with gear repair and a marine railway. Estimated costs for dock and gear storage were $2,860,000. Added docking, gear storage, and ice supply, as well as gear electronics and diesel repair were needed in Port St. Joe. Costs were calculated at $1,231,500. Franklin County has three ports (Apalachicola - $1,107,000 for docks and gear storage, Eastpoint - $420,000 for additional docks, and Carrabella - $2,824,100 for docks, gear storage, and ice plant).

Mathis, K.↗
NASA NTRS2021

Trends in Barrier Island Geomorphology Under Continuous Sea Level Rise: Padre Island from 1940-2020

Barrier islands serve an important role in shielding coastal areas from storm surges and wave erosion. Monitoring changes in a barrier island system helps determine the combined effect of sediment supply, aeolian sand transport, storms and sea level rise (SLR) on long-term island survival. Here we present and discuss changes to the southern end of Padre Island (TX),including a back-barrier active dune field, across 7 decades from 1941 - 2020. We have used aerial photos, satellite imagery, and field monitoring to map the decadal and seasonal geomorphological changes. We produced facies maps for each decade from 1970 to present, complemented with qualitative observations for 1941-1970, when aerial imagery was incomplete. We also used supervised classification to monitor monthly changes in the availability of sand for aeolian transport over a full seasonal cycle, to determine the role of the fluctuating water table on the sand budget of the active dune field. Results indicate that the southern end of Padre Island experienced significant change over the study period, transitioning from unvegetated dune fields and sand flats to expansive vegetated dunes, a tidal flat with microbial mats and a shrinking active dune field. Vegetated dunes, absent in 1970, now cover 14% of the study area. The active dune field shrunk from 12%coverage to 6%, with sand available for transport varying from 3% to 21% as the water table fluctuates throughout the year. The infrequently flooded back-island sand flat covering 40% of the study area has transitioned to a lower-lying tidal flat. Furthermore, extensive microbial mats and crusts have developed within the wind-tidal flat, in washover fans, and in low-lying interdune areas. All these early signs are consistent with a progressive drowning of the barrier island, an event we hypothesize was triggered by the sudden spread of vegetation along the back-beach dunes. Plant colonization then cut off sand supply from the beach to the back-barrier, and thus access to the primary source of sand to the system. Our findings highlight the contradictory role of vegetation in barrier islands, as they stabilize dunes and promote sand accretion at the back-beach, while also isolating the back-barrier from aeolian sediment sources, thus amplifying the effects of sea level rise in the absence of salt marshes and/or mangrove platforms.

K R Fisher↗
NASA NTRS1975

Mapping islands, reefs and shoals in the oceans surrounding Australia

The author has identified the following significant results. Comparisons of the imagery with aerial photography of areas of reefs and island and with 1:250,000 maps of coastlines indicate that the MSS imagery depicts detail to an extent which is satisfactory for 1:250,000 mapping. As the imagery does not have some of the disadvantages of aerial photography, the former should be valuable for mapping reefs, islands, and shoals. The water discoloration problem is significant as the discolored water appears to occur near shallow depths, so that confusion could arise through the misinterpretation of discolored water, when it exists, as shallow water.

Turner, L. G.↗