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NASA NTRS2005

Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data

NASA's Goddard Space Flight Center has developed a fast and effective method for generating image segmentation hierarchies. These segmentation hierarchies organize image data in a manner that makes their information content more accessible for analysis. Image segmentation enables analysis through the examination of image regions rather than individual image pixels. In addition, the segmentation hierarchy provides additional analysis clues through the tracing of the behavior of image region characteristics at several levels of segmentation detail. The potential for extracting the information content from imagery data based on segmentation hierarchies has not been fully explored for the benefit of the Earth and space science communities. This paper explores the potential of exploiting these segmentation hierarchies for the analysis of multi-date data sets, and for the particular application of change monitoring.

Tilton, James C.↗

NASA NTRS2006

Utilizing Hierarchical Segmentation to Generate Water and Snow Masks to Facilitate Monitoring Change with Remotely Sensed Image Data

The hierarchical segmentation (HSEG) algorithm is a hybrid of hierarchical step-wise optimization and constrained spectral clustering that produces a hierarchical set of image segmentations. This segmentation hierarchy organizes image data in a manner that makes the image's information content more accessible for analysis by enabling region-based analysis. This paper discusses data analysis with HSEG and describes several measures of region characteristics that may be useful analyzing segmentation hierarchies for various applications. Segmentation hierarchy analysis for generating landwater and snow/ice masks from MODIS (Moderate Resolution Imaging Spectroradiometer) data was demonstrated and compared with the corresponding MODIS standard products. The masks based on HSEG segmentation hierarchies compare very favorably to the MODIS standard products. Further, the HSEG based landwater mask was specifically tailored to the MODIS data and the HSEG snow/ice mask did not require the setting of a critical threshold as required in the production of the corresponding MODIS standard product.

Tilton, James C.↗

NASA NTRS2022

The Mars 2020 Ground Data System Architecture

The Mars 2020 Mission’s primary objective is to collect 20 geographically unique samples during its prime mission of one and a quarter Martian years, or just over 2 Earth years. Mission planners determined the project needed to develop a system that would enable the operations team to analyze engineering and science data, make science decisions, select viable rover targets at a millimeter resolution and validate an uplink bundle for a car sized rover with more complex science instruments than any previous Mars surface mission. All this had to be done within a five hour time frame. Doing this with a small team would be a challenge, but this had to be accomplished by a large team of engineers and scientists located across North America and Europe. Achieving this level of operational efficiency was unheard of in the prime mission. In addition, the mission had another set of requirements that had nothing to do with surface operations; the Mars 2020 Ground Data System (GDS) was also expected to comply with a new set of security requirements to keep up with the ever changing cybersecurity landscape. The Mars 2020 Ground Data System (GDS) is a re-architected version of the Mars Science Laboratory GDS. The primary goal was to integrate the lessons learned from previous Mars surface missions, accommodate a set of new requirements and capabilities required to ensure mission success, and comply with a new set of cybersecurity controls. The new architecture includes several unique qualities including a data lake, language-agnostic system-wide event-based operations, containerization, automated deployment, network segmentation, infrastructure-as-code, API-driven interfaces, and the first Mars surface GDS to operate primarily in the cloud. The new architecture enabled greater access to the system’s data, tighter integration with the operations team, and a higher level of traceability. The availability of the data also enabled a new set of capabilities previously not possible on surface missions. These new capabilities include an autonomous data to information, pipeline for downlink analysis, horizontal scaling of science data processing capabilities, autonomous round trip data tracking of science and engineering data, integration of flight system state into the tactical planning cycle, high fidelity targeting utilizing kinematic data, and hierarchical image and 3d meshes data representations. This paper will introduce the requirements for the Mars 2020 Mission, the heritage architecture, and the rationale for the changes to achieve the new architecture. The paper will continue to describe the fundamental changes made to the GDS architecture, how these changes enabled a more tightly integrated GDS, and the new capabilities that were enabled by the new architecture. The paper will conclude with the lessons learned from the process of rearchitecting a heritage GDS system and from the first 200 days of operations supporting over 800 users from around the world.

Lopez-Roig, Reynaldo↗

NASA NTRSDate not supplied

Keywords for All People: How Keyword Governance and Coordination with the NASA ESDIS Standards Coordination Office (ESCO) Improves GCMD Keywords for Discuvery and Use

The Global Change Master Directory (GCMD) Keywords, initiated over twenty years ago, are a hierarchical set of controlled Earth Science vocabularies that help ensure Earth science data, services, and variables are described in a consistent and comprehensive manner and allow for the precise searching of metadata and subsequent retrieval of data, services, and variables. GCMD keywords are periodically analyzed for relevancy and will continue to be refined and expanded in response to user needs. The periodic analysis is a result of successful coordination with the ESDIS Standards Coordination Office (ESCO), which is responsible for standards activities across ESDIS, and assists with providing valuable stakeholder and subject matter expert (SME) feedback on GCMD vocabularies. The ESCO is also turning to the GCMD to discuss how the keyword review process can be improved and more streamlined. In addition to the ESCO, keyword requests and feedback are also received through the GCMD Keyword Forum.

Tyler Stevens↗

NASA NTRS1973

Central Atlantic regional ecological test site: A prototype regional environmental information system

The author has identified the following significant results. A comparison of photomorphic regions from an uncontrolled ERTS-1 mosaic of CARETS to land use areas on a map published in the National Atlas revealed close correlations in non-urban regions. Such regional scale analysis of ERTS-1 data has the potential for providing an economical sampling strategy for selecting sites for more detailed field measurements if other environmental variables can be correlated with patterns on ERTS-1 imagery. ERTS-1 imagery has also revealed for the first time the appearance of CARETS during the winter months. Investigators have identified extensive areas of conifers, which have previously been indistinguishable from deciduous vegetation. Imagery has also shown very clearly the extent of snow cover at a particular time over the region. The evaluation of ERTS-1 imagery used for the land use mapping of the shore zone of CARETS, has shown that the presence or absence of elements of an hierarchal system of shoreline landforms can help identify areas of potential rapid change. Changes in land use class distributions on the Barrier Islands signify the environmental response to natural and man-caused processes. Both environmental vulnerability and sensitivity can be estimated from the repetitive ERTS-1 coverage of long reaches of the CARETS coast. Results indicate potential applications to land use planning, management, and regional environmental quality analysis.

Alexander, R. H.↗

NASA NTRS1983

Mapping project on land use changes in the carboniferous region of Santa Catarina

The utilization of remote sensing data for monitoring land use changes by means of digital image analysis is described. The following data were utilized: LANDSAT data from September 4, 1975, April 24, 1978, and September 8, 1981; LANDSAT paper photography data; area IV color photographs; IBGE topography maps, and auxiliary data about the Brazilian state of Santa Catarina. Three kinds of analyses of digital images were carried out. The project identified and mapped major classes of land use areas including urban areas, coal deposits, agricultural areas, forests, lakes, and flood plains. Five areas directly affected by coal exploration southeast of Santa Catarina are identified and described. In addition, the classification system used for organizing data about land cover in a hierarchical arrangement is presented. The project made use of two remote sensing data sources: data of MSS spectral (Mulitspectral Scanner System)/LANDSAT on a scale of 1:100,000 with approximately 80 m resolution, and infrared color aerial photographs on a scale of 1:45,000 with approximately 5 m resolution. Therefore, the classification system included three levels, two selected to be compatible with aerial photography data and the third to conform to the resolution of MSS/LANDSAT.

Valeriano, D. D.↗

NASA NTRS1993

Power attenuation characteristics as switch-over criterion in personal satellite mobile communications

A third generation mobile system intends to support communications in all environments (i.e., outdoors, indoors at home or office and when moving). This system will integrate services that are now available in architectures such as cellular, cordless, mobile data networks, paging, including satellite services to rural areas. One way through which service integration will be made possible is by supporting a hierarchical cellular structure based on umbrella cells, macro cells, micro and pico cells. In this type of structure, satellites are part of the giant umbrella cells allowing continuous global coverage, the other cells belong to cities, neighborhoods, and buildings respectively. This does not necessarily imply that network operation of terrestrial and satellite segments interconnect to enable roaming and spectrum sharing. However, the cell concept does imply hand-off between different cell types, which may involve change of frequency. Within this propsective, the present work uses power attenuation characteristics to determine a dynamic criterion that allows smooth transition from space to terrestrial networks. The analysis includes a hybrid channel that combines Rician, Raleigh and Log Normal fading characteristics.

Castro, Jonathan P.↗

NASA NTRS2020

Dynamic IT Security Database and Analytics for Launch Control Systems Software

During the Summer 2020 session, I worked with intern Destani S. Van Arsdalen of EGS Software. Together, we co-created a tool to aid the dynamic investigation, updated over time,of the security compliance of LCS COTS and open source software. We originally planned touse spreadsheet software for management and analysis, but through this exploratoryproject, chose to use Python and JSON after receiving feedback on our project’s current anddesired capabilities at that time.At first, the project was solely designed to help on-board new COTS software, based on aquestionnaire that could be filled out for each software package. This, combined with usingthe spreadsheet application’s web-query capabilities to fetch information from the NVD,allowed presentation and analytics cells to automatically populate as elements of themanually-filled questionnaire changed. While this system was promising, we decided tochange technologies for a few reasons. In the spreadsheet, single cells could not hold complexdata like arrays and objects. The automatic population of cells and dynamic updates made itdifficult to manage and add new features. And finally, it had limited extensibility sinceadding new software required significant understanding of how both the spreadsheet wasconstructed, and the more obscure, proprietary scripting languages packaged with it.The pivot to a standard computer science database language of JSON, aided by thescripting capabilities of Python, greatly helped to improve the project’s functionality. First,and most importantly, the script’s import and analysis of database data is easilyreproducible. Additional data analysis can be modularly added without requiringmodification of the script and is capable of routine scheduling. The revised process can besplit into three parts. First, the conversion of LCS asset and software documentation into theJSON hierarchical database format. Second, the merging of this database with the NVD,forming a new data structure, using CPEs of the CVE object as a linking element betweenthem. And third, the automatically performed analytics and analysis of the combined data,in a modular and extensible format, to produce better informed business decisions. The outputted graphs, for example, are automatically generated by the Python script inconnection with the combined database. This allows updated graphs and any analytics to be re-rendered automatically following updates to the LCS’s initial asset documentation. Afinal report can then be programmatically and easily constructed from these sources to allow fully reproducible metrics for heavily evidenced risk management decisions.

it↗

NASA NTRS2005

Automated Image Registration Using Morphological Region of Interest Feature Extraction

With the recent explosion in the amount of remotely sensed imagery and the corresponding interest in temporal change detection and modeling, image registration has become increasingly important as a necessary first step in the integration of multi-temporal and multi-sensor data for applications such as the analysis of seasonal and annual global climate changes, as well as land use/cover changes. The task of image registration can be divided into two major components: (1) the extraction of control points or features from images; and (2) the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual control feature extraction can be subjective and extremely time consuming, and often results in few usable points. Automated feature extraction is a solution to this problem, where desired target features are invariant, and represent evenly distributed landmarks such as edges, corners and line intersections. In this paper, we develop a novel automated registration approach based on the following steps. First, a mathematical morphology (MM)-based method is used to obtain a scale-orientation morphological profile at each image pixel. Next, a spectral dissimilarity metric such as the spectral information divergence is applied for automated extraction of landmark chips, followed by an initial approximate matching. This initial condition is then refined using a hierarchical robust feature matching (RFM) procedure. Experimental results reveal that the proposed registration technique offers a robust solution in the presence of seasonal changes and other interfering factors. Keywords-Automated image registration, multi-temporal imagery, mathematical morphology, robust feature matching.

Plaza, Antonio↗

NASA NTRS1993

Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR).

Lure, Y. M. Fleming↗

NASA NTRS2004

Autonomous NanoTechnology Swarm (ANTS) Prospecting Asteroid Mission (PAM), Asteroid Proximity Operations

The Autonomous NanoTechnology Swarm (ANTS) is a generic mission architecture based on spatially distributed spacecraft, autonomous and redundant components, and hierarchical organization. The ANTS Prospecting Asteroid Mission (PAM) is an ANTS application which will nominally use a swarm of 1000 spacecraft. There would be 10 types of "specialists" with common spacecraft buses. There would be 10 subswarms of approximately 100 spacecraft each or approximately 10 of each specialist in each swarm. The ANTS PAM primary objective is the exploration of the asteroid belt in search of resources and material with astrobiologically relevant origins and signatures. The ANTS PAM spacecraft will nominally be released from a station in an Earth-Moon L1 libration point orbit, and they will use Solar sails for propulsion. The sail structure would be highly flexible, capable of changing morphology to change cross-section for capture of sunlight or to form effective "tip vanes" for attitude control. ANTS PAM sails would be capable of full to partial deployment, to change effective sail area and center of pressure, and thus allow attitude control. Results of analysis of a transfer trajectory from Earth to a sample target asteroid will be presented. ANTS PAM will require continuous coverage of different asteroid locations as close as one to two asteroid "diameters" from the surface of the asteroid for periods of science data collection during asteroid proximity operations. Hovering spacecraft could meet the science data collection objectives. The results of hovering analysis will be presented. There are locations for which hovering is not possible, for example on the illuminated side of the asteroid. For cases where hovering is not possible, the results of utilizing asteroid formations to orbit the asteroid and achieve the desired asteroid viewing will be presented for sample asteroids. The ability of ANTS PAM to reduce the area of the solar sail during asteroid proximity operations is critical to the maintenance of orbiting formations for a period of time. Results of analysis of potential "traffic" problems during asteroid proximity operations will be presented.

Marr, Greg↗

NASA NTRS2018

Understanding Climate-Vegetation Interactions in Global Rainforests Through a GP-Tree Analysis

The tropical rainforests are the largest reserves of terrestrial carbon and, therefore, the future of these rainforests is a question that is of immense importance in the geoscience research community. With the recent severe Amazonian droughts in 2005 and 2010 and on-going drought in the Congo region for more than two decades, there is growing concern that these forests could succumb to precipitation reduction, causing extensive carbon release and feedback to the carbon cycle. However, there is no single ecosystem model that quantifies the relationship between vegetation health in these rainforests and climatic factors. Small scale studies have used statistical correlation measure and simple linear regression to model climate-vegetation interactions, but suffer from the lack of comprehensive data representation as well as simplistic assumptions about dependency of the target on the covariates. In this paper we use genetic programming (GP) based symbolic regression for discovering equations that govern the vegetation climate dynamics in the rainforests. Expecting micro-regions within the rainforests to have unique characteristics compared to the overall general characteristics, we use a modified regression-tree based hierarchical partitioning of the space to build individual models for each partition. The discovery of these equations reveal very interesting characteristics about the Amazon and the Congo rainforests. Our method GP-tree shows that the rainforests exhibit tremendous resiliency in the face of extreme climatic events by adapting to changing conditions.

genetic programming↗

NASA NTRS2017

Nominal 30-M Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, is a starting point to develop high-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based Geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94, with a producers accuracy of 85.9 (or omission error of 14.1), and users accuracy of 68.5 (commission error of 31.5) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015.

Cropland mapping; cropland areas; 30-m; Landsat-8;↗