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NASA NTRS2004

Mosquito Larval Habitats, Land Use, and Potential Malaria Risk in Northern Belize from Satellite Image Analyses

The distribution of Anopheles mosquito habitats and land use in northern Belize is examined with satellite data. -A land cover classification based on multispectral SPOT and multitemporal Radarsat images identified eleven land cover classes, including agricultural, forest, and marsh types. Two of the land cover types, Typha domingensis marsh and flooded forest, are Anopheles vestitipennis larval habitats. Eleocharis spp. marsh is the larval habitat for Anopheles albimanus. Geographic Information Systems (GIS) analyses of land cover demonstrate that the amount of T-ha domingensis in a marsh is positively correlated with the amount of agricultural land in the adjacent upland, and negatively correlated with the amount of adjacent forest. This finding is consistent with the hypothesis that nutrient (phosphorus) runoff from agricultural lands is causing an expansion of Typha domingensis in northern Belize. This expansion of Anopheles vestitipennis larval habitat may in turn cause an increase in malaria risk in the region.

Pope, Kevin↗

NASA NTRS2018

Automated Pneumothorax Diagnosis using Deep Neural Networks

Thoracic ultrasound can provide information leading to rapid diagnosis of pneumothorax with improved accuracy over the standard physical examination and with higher sensitivity than anteroposterior chest radiography. However, the clinical We have Furthermore, remote environments, such as the battlefield or deep-space exploration, may lack expertise for diagnosing developed an automated image interpretation pipeline for the analysis of thoracic ultrasound data and the classification of pneumothorax events to provide decision support in such situations. Our pipeline consists of image preprocessing, data augmentation, and deep learning architectures for medical diagnosis. In this work, we demonstrate that robust, accurate interpretation of chest images and video can be achieved using deep neural networks. A number of novel image processing techniques were employed to achieve this result. Affine transformations were applied for data augmentation. Hyperparameters were optimized for learning rate, dropout regularization, batch size, and epoch iteration by a sequential model-based Bayesian approach. In addition, we utilized pretrained architecturesinterpretation of a patient medical image is highly operator dependent. certain pathologies., applying transfer learning and fine-tuning techniques to fully connected layers. Our pipeline yielded binary classification validation accuracies of 98.3% for M-mode images and 99.8% with B-mode video frames.

US Army collaboration↗

NASA NTRS1992

Application of artificial neural networks to thermal detection of disbonds

A novel technique for processing thermal data is presented and applied to simulation as well as experimental data. Using a neural network of thermal data classification, good classification accuracies are obtained, and the resulting images exhibit very good contrast between bonded and disbonded locations. In order to minimize the preprocessing required before using the network of classification, the temperature values were directly employed to train a network using data from an on-site testing run of a commercial aircraft. Training was extremely fast, and the resulting classification also agreed reasonably well with an ultrasonic characterization of the panel. The results obtained using one sample show the partially disbonded vertical doubler. The vertical lines along the doubler correspond to the original extent of the doubler obtained using blueprints of the aircraft.

Prabhu, D. R.↗

NASA NTRS2024

Rhode Island Ecological Conservation: Methods for Monitoring Rhode Island Habitats: Contributing to a Framework for Targeted Conservation and Management

Global avian population decline since the 1970s is largely attributable to habitat loss and degradation from anthropogenic disturbances. NASA DEVELOP’s Rhode Island Ecological Conservation team partnered with the Audubon Society of Rhode Island to compute land use land cover (LULC) maps of Rhode Island to aid in the conservation of the state’s 140 bird species. This project aimed to support the partner’s land acquisition strategies with updated and specific LULC classifications showing potential bird-habitat locations across the state. We incorporated remotely sensed data from Landsat 8 and 9 Operational Land Imager (OLI) into LULC maps using unsupervised classification techniques in ArcGIS Pro and supervised classification in Google Earth Engine. We generated six land classifications for 2023, which showed land cover dominated by upland habitats (forests, scrub/shrub, and grasslands), followed by development. We used TerrSet’s Land Change Modeler to forecast LULC change through 2043, using 2011 and 2021 National Land Cover Database (NLCD) land cover maps derived from Landsat 8 and 9 imagery. Project results suggest that non-urban upland and wetland habitats will decrease over time, while development will continue to encroach on non-urban avian habitats. Our maps and associated data will allow for more efficient land acquisition and management efforts to support avian habitat conservation across Rhode Island. Our study shows that data acquisition and processing from open data sources is feasible and further analysis can be done through GIS classification tools. More analysis is needed beyond this study to obtain more detailed land cover maps, though Audubon can aid its targeted conservation efforts with our current, historic, and forecasted LULC maps.

Remote sensing↗

NASA NTRS1980

Study of the atmospheric effects on the radiation detected by the sensor aboard orbiting platforms (ERTS/LANDSAT)

The author has identified the following significant results. Multispectral scanner data for Brasilia was corrected for atmospheric interference using the LOWTRAN-3 computer program and the analytical solution of the radiative transfer equation. This improved the contrast between two natural targets and the corrected images of two different dates were more similar than the original ones. Corrected images of MSS data for Ribeirao Preto gave a classification accuracy for sugar cane about 10% higher as compared to the original images.

Dejesusparada, N.↗

NASA NTRS2023

Oregon Coast Range Ecological Conservation: Mapping Recent Logging Within Drinking Watersheds of Oregon’s Coastal Range to Support Future Resource Management Policies

Logging operations are widespread across the Oregon Coast Range and conventional logging practices pose a risk of contamination to surface water quality. The NASA DEVELOP Oregon Coast Ecological Conservation team partnered with nonprofit Oregon Wild to quantify the extent of clearcutting and commercial thinning in 80 Coast Range drinking watersheds between 2000 and 2022. This project used all available Landsat data from 1997 through June 2023 in Google Earth Engine. Sensors used include Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper Plus, Landsat 8 Operational Land Imager, and Landsat 9 Operational Land Imager-2. The Continuous Change Detection and Classification (CCDC) algorithm was used with Landsat observations to identify clearcutting patches. Percent change in summer median Landsat Normalized Difference Vegetation Index (NDVI) images were used to identify areas of forest disturbance including commercial thinning. The team concluded that logging, including both clearcutting and commercial thinning, impacted 31% of forested area in drinking watersheds and the intensity of logging remained consistent from year to year. Clearcutting occurred primarily on private land while commercial thinning occurred primarily on state and federal lands. This study showed that CCDC effectively identifies clearcutting, and percent change in NDVI successfully identifies disturbances including commercial thinning. Key constraints included the lack of field validation data and the inability to attribute disturbances to logging with certainty. Ultimately, this study identified the drinking watersheds and communities most likely to be impacted by logging activity. These results can inform legislation aimed at balancing the commercial and environmental benefits of forestlands.

Logging↗

NASA NTRS1993

MAC Europe 1991 campaign: AIRSAR/AVIRIS data integration for agricultural test site classification

During summer 1991, multi-sensor data were acquired over the Italian test site 'Otrepo Pavese', an agricultural flat area in Northern Italy. This area has been the Telespazio pilot test site for experimental activities related to agriculture applications. The aim of the investigation described in the following paper is to assess the amount of information contained in the AIRSAR (Airborne Synthetic Aperture Radar) and AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) data, and to evaluate classification results obtained from each sensor data separately and from the combined dataset. All classifications are examined by means of the resulting confusion matrices and Khat coefficients. Improvements of the classification results obtained by using the integrated dataset are finally evaluated.

Sangiovanni, S.↗

NASA NTRS1974

Interpretation of ERTS-1 imagery aided by photographic enhancement

Agfacontour film can be used to produce relatively economical enhancements of density differences recorded on film. Image interpreters often ask whether enhancement really increases the information content of the original images. Examples of ERTS-1 imagery enhancements show that it is possible to separate density levels which are not discernible by the human eye. It is stressed that these techniques do not replace the photo interpreter, but rather they aid him in the interpretation process. Subtle density variations are made to stand out and on objective classification of the densities forming the image is produced.

Nielsen, U.↗

NASA NTRS2001

Low-Altitude AVIRIS Data for Mapping Land Cover in Yellowstone National Park: Use of Isodata Clustering Techniques

Northeast Yellowstone National Park (YNP) has a diversity of forest, range, and wetland cover types. Several remote sensing studies have recently been done in this area, including the NASA Earth Observations Commercial Applications Program (EOCAP) hyperspectral project conducted by Yellowstone Ecosystems Studies (YES) on the use of hyperspectral imaging for assessing riparian and in-stream habitats. In 1999, YES and NASA's Commercial Remote Sensing Program Office began collaborative study of this area, assessing the potential of synergistic use of hyperspectral, synthetic aperture radar (SAR), and multiband thermal data for mapping forest, range, and wetland land cover. Since the beginning, a quality 'reference' land cover map has been desired as a tool for developing and validating other land cover maps produced during the project. This paper recounts an effort to produce such a reference land cover map using low-altitude Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data and unsupervised classification techniques. The main objective of this study is to assess ISODATA classification for mapping land cover in Northeast YNP using select bands of low-altitude AVIRIS data. A secondary, more long-term objective is to assess the potential for improving ISODATA-based classification of land cover through use of principal components analysis and minimum noise fraction (MNF) techniques. This paper will primarily report on work regarding the primary research objective. This study focuses on an AVIRIS cube acquired on July 23, 1999, by the confluence of Soda Butte Creek with the Lamar River. Range and wetland habitats dominate the image with forested habitats being a comparatively minor component of the scene. The scene generally tracks from southwest to northeast. Most of the scene is valley bottom with some lower side slopes occurring on the western portion. Elevations within the AVIRIS scene range from approximately 1998 to 2165 m above sea level, based on US Geological Survey (USGS) 30-m digital elevation model (DEM) data. Despain and the National Park Service (NPS) provide additional description of the study area.

Spruce, Joseph P.↗

NASA NTRS1977

Development and application of operational techniques for the inventory and monitoring of resources and uses for the Texas coastal zone

The author has identified the following significant results. Four LANDSAT scenes were analyzed for the Harbor Island area test sites to produce land cover and land use maps using both image interpretation and computer-assisted techniques. When evaluated against aerial photography, the mean accuracy for three scenes was 84% for the image interpretation product and 62% for the computer-assisted classification maps. Analysis of the fourth scene was not completed using the image interpretation technique, because of poor quality, false color composite, but was available from the computer technique. Preliminary results indicate that these LANDSAT products can be applied to a variety of planning and management activities in the Texas coastal zone.

Harwood, P.↗

NASA NTRS1981

Crop classification using airborne radar and LANDSAT data

Airborne radar data acquired with a 13.3 GHz scatterometer over a test-site near Colby, Kansas were used to investigate the statistical properties of the scattering coefficient of three types of vegetation cover and of bare soil. A statistical model for radar data was developed that incorporates signal-fading and natural within-field variabilities. Estimates of the within-field and between-field coefficients of variation were obtained for each cover-type and compared with similar quantities derived from LANDSAT images of the same fields. The classification accuracy provided by LANDSAT alone, radar alone, and both sensors combined was investigated. The results indicate that the addition of radar to LANDSAT improves the classification accuracy by about 10; percentage-points when the classification is performed on a pixel basis and by about 15 points when performed on a field-average basis.

Ulaby, F. T.↗

NASA NTRS1982

Cooperative processes in image segmentation

Research into the role of cooperative, or relaxation, processes in image segmentation is surveyed. Cooperative processes can be employed at several levels of the segmentation process as a preprocessing enhancement step, during supervised or unsupervised pixel classification and, finally, for the interpretation of image segments based on segment properties and relations.

Davis, L. S.↗

NASA NTRS1997

Foundations for Measuring Volume Rendering Quality

The goal of this paper is to provide a foundation for objectively comparing volume rendered images. The key elements of the foundation are: (1) a rigorous specification of all the parameters that need to be specified to define the conditions under which a volume rendered image is generated; (2) a methodology for difference classification, including a suite of functions or metrics to quantify and classify the difference between two volume rendered images that will support an analysis of the relative importance of particular differences. The results of this method can be used to study the changes caused by modifying particular parameter values, to compare and quantify changes between images of similar data sets rendered in the same way, and even to detect errors in the design, implementation or modification of a volume rendering system. If one has a benchmark image, for example one created by a high accuracy volume rendering system, the method can be used to evaluate the accuracy of a given image.

Williams, Peter L.↗

NASA NTRS1989

Imaging infrared: Scene simulation, modeling, and real image tracking; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

Various papers on scene simulation, modeling, and real image tracking using IR imaging are presented. Individual topics addressed include: tactical IR scene generator, dynamic FLIR simulation in flight training research, high-speed dynamic scene simulation in UV to IR spectra, development of an IR sensor calibration facility, IR celestial background scene description, transmission measurement of optical components at cryogenic temperatures, diffraction model for a point-source generator, silhouette-based tracking for tactical IR systems, use of knowledge in electrooptical trackers, detection and classification of target formations in IR image sequences, SMPRAD: simplified three-dimensional cloud radiance model, IR target generator, recent advances in testing of thermal imagers, generic IR system models with dynamic image generation, modeling realistic target acquisition using IR sensors in multiple-observer scenarios, and novel concept of scene generation and comprehensive dynamic sensor test.

Triplett, Milton J.↗

NASA NTRS1978

Multispectral data acquisition and classification - Statistical models for system design

In this paper we relate the statistical processes that are involved in multispectral data acquisition and classification to a simple radiometric model of the earth surface and atmosphere. If generalized, these formulations could provide an analytical link between the steadily improving models of our environment and the performance characteristics of rapidly advancing device technology. This link is needed to bring system analysis tools to the task of optimizing remote sensing and (real-time) signal processing systems as a function of target and atmospheric properties, remote sensor spectral bands and system topology (e.g., image-plane processing), radiometric sensitivity and calibration accuracy, compensation for imaging conditions (e.g., atmospheric effects), and classification rates and errors.

Huck, F. O.↗

NASA NTRS1993

Venus small volcano classification and description

The high resolution and global coverage of the Magellan radar image data set allows detailed study of the smallest volcanoes on the planet. A modified classification scheme for volcanoes less than 20 km in diameter is shown and described. It is based on observations of all members of the 556 significant clusters or fields of small volcanoes located and described by this author during data collection for the Magellan Volcanic and Magmatic Feature Catalog. This global study of approximately 10 exp 4 volcanoes provides new information for refining small volcano classification based on individual characteristics. Total number of these volcanoes was estimated to be 10 exp 5 to 10 exp 6 planetwide based on pre-Magellan analysis of Venera 15/16, and during preparation of the global catalog, small volcanoes were identified individually or in clusters in every C1-MIDR mosaic of the Magellan data set. Basal diameter (based on 1000 measured edifices) generally ranges from 2 to 12 km with a mode of 34 km, and follows an exponential distribution similar to the size frequency distribution of seamounts as measured from GLORIA sonar images. This is a typical distribution for most size-limited natural phenomena unlike impact craters which follow a power law distribution and continue to infinitely increase in number with decreasing size. Using an exponential distribution calculated from measured small volcanoes selected globally at random, we can calculate total number possible given a minimum size. The paucity of edifice diameters less than 2 km may be due to inability to identify very small volcanic edifices in this data set; however, summit pits are recognizable at smaller diameters, and 2 km may represent a significant minimum diameter related to style of volcanic eruption. Guest, et al, discussed four general types of small volcanic edifices on Venus: (1) small lava shields; (2) small volcanic cones; (3) small volcanic domes; and (4) scalloped margin domes ('ticks'). Steep-sided domes or 'pancake domes', larger than 20 km in diameter, were included with the small volcanic domes. For the purposes of this study, only volcanic edifices less than 20 km in diameter are discussed. This forms a convenient cutoff since most of the steep-sided domes ('pancake domes') and scalloped margin domes ('ticks') are 20 to 100 km in diameter, are much less numerous globally than are the smaller diameter volcanic edifices (2 to 3 orders of magnitude lower in total global number), and do not commonly occur in large clusters or fields of large numbers of edifices.

Aubele, J. C.↗

NASA NTRS1992

Can a time-stratigraphic classification system be developed for Venus?

Magellan radar images reveal that Venus' exposed geologic record covers a relatively short and recent time span, as indicated by the low density of impact craters across the planet. Therefore, because impact cratering in itself will not be a useful tool to define geologic ages on Venus, it was questioned whether a useful stratigraphic scheme can be developed for the planet. We believe that a venusian stratigraphy is possible and that it can be based on the following: (1) an examination of the rationale and methods that have been used to develop such schemes for the other planets; and (2) what can be gleaned from Magellan and other datasets of Venus.

Tanaka, K. L.↗

NASA NTRS1984

Improvements in forest classification and inventory using remotely sensed data

A Forest Classification and Inventory System (Focis) has been developed for large area forest inventories on the basis of Landsat and digital terrain data. It is a potential advantage of Focis that it can provide timely inventories at a reduced cost which are easily updated. The Klamath National Forest in Northern California was employed as test area for the initial development of Focis. Focis is constantly being changed and improved. Two recent additions to the inventory system include a spatial filtering algorithm which improves the spatial coherence in the final classified image, and a modification to the classification procedure designed to reduce the adverse effects of local topography on classification accuracy. Attention is given to a Focis overview, spatial filtering, the interface with the forest service geographic information system, and efforts to reduce the influence of topography.

Woodcock, C. E.↗