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Coastal areas remote sensing instrumentation and techniques, discussing data handling and analysis and ground verification of color and multispectral imagery
Commercial remote sensing uses satellite imagery to provide valuable information about the planet's features. By capturing light reflected from the Earth's surface with cameras or sensor systems, usually mounted on an orbiting satellite, data is obtained for business enterprises with an interest in land feature distribution. Remote sensing is practical when applied to large-area coverage, such as agricultural monitoring, regional mapping, environmental assessment, and infrastructure planning. For example, cellular service providers use satellite imagery to select the most ideal location for a communication tower. Crowsey Incorporated has the ability to use remote sensing capabilities to conduct spatial geographic visualizations and other remote-sensing services. Presently, the company has found a demand for these services in the area of litigation support. By using spatial information and analyses, Crowsey helps litigators understand and visualize complex issues and then to communicate a clear argument, with complete indisputable evidence. Crowsey Incorporated is a proud partner in NASA's Mississippi Space Commerce Initiative, with research offices at the John C. Stennis Space Center.
The International Space Station (ISS) became operational in November of 2000, and until recently remote sensing activities and operations have focused on handheld astronaut photography of the Earth. This effort builds from earlier NASA and Russian space programs (e.g. Evans et al. 2000; Glazovskiy and Dessinov 2000). To date, astronauts have taken more than 600,000 images of the Earth s land surface, oceans, and atmospheric phenomena from orbit using film and digital cameras as part two payloads: NASA s Crew Earth Observations experiment (http://eol.jsc.nasa.gov/) and Russia s Uragan experiment (Stefanov et al. 2012). Many of these images have unique attributes - varying look angles, ground resolutions, and illumination - that are not available from other remote sensing platforms. Despite this large volume of imagery and clear capability for Earth remote sensing, the ISS historically has not been perceived as an Earth observations platform by many remote sensing scientists. With the recent installation of new facilities and sophisticated sensor systems, and additional systems manifested and in development, that perception is changing to take advantage of the unique capabilities and viewing opportunities offered by the ISS.
Topics addressed include: multivariate spline method; normal mixture analysis applied to remote sensing; image data analysis; classifications in spatially correlated environments; probability density functions; graphical nonparametric methods; subpixel registration analysis; hypothesis integration in image understanding systems; rectification of satellite scanner imagery; spatial variation in remotely sensed images; smooth multidimensional interpolation; and optimal frequency domain textural edge detection filters.
To assist the U.S. Geological Survey in carrying out a Congressional mandate to investigate the use of side-looking airborne radar (SLAR) for resources exploration, a research program was conducted to define the contribution of SLAR imagery to structural geologic mapping and to compare this with contributions from other remote sensing systems. Imagery from two SLAR systems and from three other remote sensing systems was interpreted, and the resulting information was digitized, quantified and intercompared using a computer-assisted geographic information system (GIS). The study area covers approximately 10,000 square miles within the Naval Petroleum Reserve, Alaska, and is situated between the foothills of the Brooks Range and the North Slope. The principal objectives were: (1) to establish quantitatively, the total information contribution of each of the five remote sensing systems to the mapping of structural geology; (2) to determine the amount of information detected in common when the sensors are used in combination; and (3) to determine the amount of unique, incremental information detected by each sensor when used in combination with others. The remote sensor imagery that was investigated included real-aperture and synthetic-aperture radar imagery, standard and digitally enhanced LANDSAT MSS imagery, and aerial photos.
Some approaches are presented to the problem of labeling clusters using information from a given set of labeled and unlabeled aerospace imagery patterns. The assignment of class labels to the clusters is formulated as the determination of the best assignment over all possible ones with respect to some criterion. Cluster labeling is also viewed as the probability of correct labeling with a maximization of likelihood function. Results of the application of these techniques in the processing of remotely sensed multispectral scanner imagery data are presented.
This project uses new satellite and airborne imagery in combination with remote sensing, GIS, and GPS technology to understand the dynamics of how the Maya successfully interacted with their karst topographic landscape for several centuries in the northern Peten region of Guatemala. The ancient Maya attained one of the greatest population densities in human history in the tropical forest of the Peten, Guatemala, and it was in this region that the Maya civilization began, flourished, and abruptly disappeared for unknown reasons around AD 800. How the Maya were able to successfully manage water and feed this dense population is not known at this time. However, a recent NASA-funded project was the first to investigate large seasonal swamps (bajos) that make up 40 percent of the landscape. Through the use of remote sensing, ancient Maya features such as cities, roadways, canals and water reservoirs have been detected and verified through ground reconnaissance. The results of this research cast new light on the adaptation of the ancient Maya to their environment. Micro-environmental variation within the wetlands was elucidated and the different vegetational associations identified in the satellite imagery. More than 70 new archeological sites within and at the edges of the bajo were mapped and tested. Modification of the landscape by the Maya in the form of dams and reservoirs in the Holmul River and its tributaries and possible drainage canals in bajos was demonstrated. The recent acquisition of one-meter IKONOS imagery and high resolution STAR-3i radar imagery (2.5m backscatter/ 10m DEM), opens new possibilities for understanding how a civilization was able to survive for centuries upon a karst topographic landscape and their human-induced effects upon the local climate. This understanding is critical for the current population that is presently experiencing rapid population growth and destroying the landscape through non-traditional farming and grazing techniques, resulting in socioeconomic problems.
Models of photosynthetic production at ecosystem and global scales require multiple input parameters specifying physical and physiological surface features. While certain physical parameters (e.g., absorbed photosynthetically active radiation) can be derived from current satellite sensors, other physiologically relevant measures (e.g., vegetation type, water status, carboxylation capacity, or photosynthetic light-use efficiency), are not generally directly available from current satellite sensors at the appropriate geographic scale. Consequently, many model parameters must be assumed or derived from independent sources, often at an inappropriate scale. An abundance of ecophysiological studies at the leaf and canopy scales suggests strong physiological control of vegetation-atmosphere CO2 and water vapor fluxes, particularly in evergreen vegetation subjected to diurnal or seasonal stresses. For example hot, dry conditions can lead to stomatal closure, and associated "downregulation" of photosynthetic biochemical processes, a phenomenon often manifested as a "midday photosynthetic depression". A recent study with the revised simple biosphere (SiB2) model demonstrated that photosynthetic downregulation can significantly impact global climate. However, at the global scale, the exact significance of downregulation remains unclear, largely because appropriate physiological measures are generally unavailable at this scale. Clearly, there is a need to develop reliable ways of extracting physiologically relevant information from remote sensing. Narrow-band spectrometers offer many opportunities for deriving physiological parameters needed for ecosystem and global scale photosynthetic models. Experimental studies on the ground at the leaf- to stand-scale have indicated that several narrow-band features can be used to detect plant physiological status. One physiological signal is caused by xanthophyll cycle pigment activity, and is often expressed as the Photochemical Reflectance Index (PRI). Because the xanthophyll cycle pigments are photoregulatory pigments closely linked to photosynthetic function, this index can be used to derive relative photosynthetic rates. An additional signal with physiological significance is the 970 nm water absorption band, which provides a measure of liquid water content. This feature has been quantified both using a simple 2-band ratio (900/970 nm, here referred to as the "Water Band Index" or WBI;), and using the "continuum removal" method. Current atmospheric correction methods for AVIRIS imagery also obtain quantitative expressions of surface liquid water absorption based on the 970 nm water band and may be comparable to ground-based estimates of water content using this feature. However, physiological interpretations of both the PRI and the WBI are best understood at the leaf and canopy scales, where complications of atmospheric interference and complex stand and landscape features can be minimized, and where experimental manipulations can be readily applied. Currently it is not known whether these physiological indices can be used to derive meaningful physiological information from AVIRIS imagery. In addition to the problem of atmospheric interference, another challenge is that any simple physiological index can be confounded by multiple factors unrelated to physiology, and this problem can become more severe at progressively larger spatial scales. For example, previous work has suggested that both the PRI and the WBI, are strongly correlated with other optical measures of canopy structure (e.g., the Normalized Difference Vegetation Index or green vegetation fraction), indicating a confounding effect of structure on physiological signals at the larger, landscape scale. Furthermore, the normal operating mode of most imaging spectrometers does not allow simultaneous, ground truthing at a level of detail needed for physiological sampling. Additionally, manipulative experiments of physiology are difficult to apply at a geographic scale suitable for comparison with remote imagery, which often works at spatial scales that are several orders of magnitude larger than those typically used for physiological studies. These limitations require the consideration of alternative approaches to validating physiological information derived from AVIRIS data. In this report, we present a multi-scale sampling approach to detecting physiologically significant signals in narrow-band spectra. This approach explores the multi-dimensional data space provided by narrow-band spectrometry, and combines AVIRIS imagery at a large scale, with ground spectral sampling at an intermediate scale, and detailed ecophysiological measurements at a fine scale, to examine seasonally and spatially changing relationships between multiple structural and physiological variables. Examples of this approach are provided by simultaneous sampling of the Normalized Difference Vegetation Index (NDVI), an index of fractional PAR interception and green vegetation cover, the Water Band Index (WBI, an index of liquid water absorption, and the Photochemical Reflectance Index (PRI, an index of xanthophyll cycle pigment activity and photosynthetic light-use efficiency. By directly linking changing optical properties sampled on the ground with measurable physiological states, we hope to develop a basis for interpreting similar signals in AVIRIS imagery.
The NASA Hurricane and Severe Storm Sentinel (HS3) mission is an aircraft field measurements program using NASA's unmanned Global Hawk aircraft system for remote sensing and in situ observations of Atlantic and Caribbean Sea hurricanes. One of the principal microwave instruments is the Hurricane Imaging Radiometer (HIRAD), which measures surface wind speeds and rain rates. For validation of the HIRAD wind speed measurement in hurricanes, there exists a comprehensive set of comparisons with the Stepped Frequency Microwave Radiometer (SFMR) with in situ GPS dropwindsondes [1]. However, for rain rate measurements, there are only indirect correlations with rain imagery from other HS3 remote sensors (e.g., the dual-frequency Ka- & Ku-band doppler radar, HIWRAP), which is only qualitative in nature. However, this paper presents results from an unplanned rain rate measurement validation opportunity that occurred in 2013, when HIRAD flew over an intense tropical squall line that was simultaneously observed by the Tampa NEXRAD meteorological radar (Fig. 1). During this experiment, Global Hawk flying at an altitude of 18 km made 3 passes over the rapidly propagating thunderstorm, while the TAMPA NEXRAD perform volume scans on a 5-minute interval. Using the well-documented NEXRAD Z-R relationship, 2D images of rain rate (mm/hr) were obtained at two altitudes (3 km & 6 km), which serve as surface truth for the HIRAD rain rate retrievals. A preliminary comparison of HIRAD rain rate retrievals (image) for the first pass and the corresponding closest NEXRAD rain image is presented in Fig. 2 & 3. This paper describes the HIRAD instrument, which 1D synthetic-aperture thinned array radiometer (STAR) developed by NASA Marshall Space Flight Center [2]. The rain rate retrieval algorithm, developed by Amarin et al. [3], is based on the maximum likelihood estimation (MLE) technique, which compares the observed Tb's at the HIRAD operating frequencies of 4, 5, 6 and 6.6 GHz with corresponding theoretical Tb values from a forward radiative transfer model (RTM). The optimum solution is the integrated rain rate that minimizes the difference between RTM and observed values. Because the excess Tb from rain comes from the direct upwelling and the indirect reflected downwelling paths through the atmosphere, there are several assumptions made for the 2D rain distribution in the antenna incident plane (crosstrack to flight direction). The opportunity to knowing 2D rain surface truth from NEXRAD at two different altitudes will enable a comprehensive evaluation to be preformed and reported in this paper.
The problem of obtaining the probabilities of class labels for the clusters using spectral and spatial information from a given set of labeled patterns and their neighbors is considered. A relationship is developed between class and clusters conditional densities in terms of probabilities of class labels for the clusters. Expressions are presented for updating the a posteriori probabilities of the classes of a pixel using information from its local neighborhood. Fixed-point iteration schemes are developed for obtaining the optimal probabilities of class labels for the clusters. These schemes utilize spatial information and also the probabilities of label imperfections. Experimental results from the processing of remotely sensed multispectral scanner imagery data are presented.
Application of remote sensing techniques to the study of the Chesapeake Bay and the availability of the resulting information are discussed in terms of public awareness of the Chesapeake Bay, its total environment, and the need to protect that environment and to preserve the Bay. Recommendations given include: (1) continue the study of remote sensing technology and its use in the Chesapeake Bay region; (2) emphasize the importance of LANDSAT imagery to the evolution of remote sensing technological developments and the awareness of the environment and its changes; (3) increase dissemination of information of the environmental applications of remote sensing technology to the public; (4) design surveys of the Chesapeake Bay environment and its manmade changes; and (5) establish a coordinating regional institution to develop a management plan for the Chesapeake Bay.
ERTS-1 imagery of geological, geophysical, hydrologic, oceanographic, and agricultural phenomena of Iceland
A binational, multidisciplinary research effort in Iceland is directed at an analysis of MSS imagery from ERTS-1 to study a variety of geologic, hydrologic, oceanographic, and agricultural phenomena. A preliminary evaluation of available MSS imagery of Iceland has yielded several significant results - some of which may have direct importance to the Icelandic economy. Initial findings can be summarized as follows: (1) recent lava flows can be delineated from older flows at Askja and Hekla; (2) MSS imagery from ERTS-1 and VHRR visible and infrared imagery from NOAA-2 recorded the vocanic eruption on Heimaey, Vestmann Islands; (3) coastline changes, particularly changes in the position of bars and beaches along the south coast are mappable; and (4) areas covered with new and residual snow can be mapped, and the appearance of newly fallen snow on ERTS-1, MSS band 7 appears dark where it is melting. ERTS-1 imagery provides a means of updating various types of maps of Iceland and will permit the compilation of special maps specifically aimed at those dynamic environmental phenomena which impact on the Icelandic economy.
Initial experimental analysis of ERTS-1 imagery has demonstrated that remote sensing from space is a means of delineating and inventorying Ohio's strip-mined areas, detecting power plant smoke plumes, and proving the data necessary for periodically compiling land use maps for the entire state. The nature and extent of these problems throughout Ohio, how ERTS data can contribute to their solution, and estimates of the long term significance of these initial findings to overall resource management interests in Ohio are summarized.
The effects of river inputs on boundary waters were studied in partial support of the task to assess the significance of river inputs into receiving waters, dispersion of pollutants, and water quality. The effects of the spring runoff of the Maumee River on Lake Erie were assessed by a combination of ship survey and remote sensing techniques. The imagery obtained from a multispectral scanner of the west basin of Lake Erie is discussed: this clearly showed the distribution of particulates throughout the covered area. This synoptic view, in addition to its qualitative value, is very useful in selecting sampling stations for shipboard in situ measurements, and for extrapolating these quantitative results throughout the area of interest.
A procedure for manual acreage estimation from Landsat imagery is proposed which involves a stepwise sequence of area stratification, simple size estimation, acreage estimation, and acreage precision calculation. The sample design consists of a systematic point sample of the Landsat imagery for crop type presence calibrated by a double sample of several image points having crop season-coincident ground data. The sample point layout chosen for cost-efficiency in manual sampling is a doubly aligned point matrix. The significant advantage of this sampling approach is that crop information is available from throughout the agricultural reporting unit. For technique demonstration purposes three strata were selected for sampling. The proposed technique can complement more sophisticated computer-based acreage estimation methodologies, and refinements in the sampling techniques promise significant improvements for operational utility.
A 10 channel scanning radiometer, built as a prototype for the coastal zone color scanner on the Nimbus 7 satellite, was flown on a high altitude aircraft during a Gymnodium breve bloom along the west coast of Florida. The remotely measured ocean color imagery shows what is probably the patchy structure of a G. breve bloom extending over a 60 km by 100 km area. This conclusion is based on visual inspection of bathymetry to infer bottom reflection trends and on a single growth truth measurement of B G. breve obtained the previous day. The image shows coherent blooms which extend scales up to 60 km in length.
The decomposition of mixture density of the data into its normal component densities is considered. The densities are approximated with first order dependent feature trees using criteria of mutual information and distance measures. Expressions are presented for the criteria when the densities are Gaussian. By defining different typs of nodes in a general dependent feature tree, maximum likelihood equations are developed for the estimation of parameters using fixed point iterations. The field structure of the data is also taken into account in developing maximum likelihood equations. Experimental results from the processing of remotely sensed multispectral scanner imagery data are included.