Developing Impervious Surface Estimates for Coastal New Hampshire

Future population growth and the corresponding increase in development in the coastal zone of NH are widely recognized as major threats to the integrity of coastal systems and their watersheds. The potential impacts associated with the expansion of developed land, and specifically with increasing amounts of impervious surfaces – rooftops, sidewalks, roads, and parking lots - may include significant changes in water quantity, degradation in water quality, and habitat loss. Because asphalt, concrete, stone, and other impenetrable materials effectively seal the ground surface, water is repelled and is prevented from infiltrating soils. Instead, stormwater runoff flows directly into our surface waters, depositing metals, excess nutrients, organics, and other pollutants into the receiving bodies. In addition to these environmental impacts, increasing levels of imperviousness can dramatically alter our landscapes, as forested and other natural settings are converted to urban/suburban uses. Many of the impacts associated with impervious surfaces had been well documented by studies in other areas of the country. However, comprehensive studies in coastal New Hampshire had not been undertaken. The primary goals of this project were to provide an accurate, current description of the extent of impervious surface coverage in this region, as well as an estimate of change in the amount of “imperviousness” over a recent, ten-year period

Modeling Land-Cover Types Using Multiple Endmember Spectral Mixture Analysis in a Desert City

Spectral mixture analysis is probably the most commonly used approach among sub-pixel analysis techniques. This method models pixel spectra as a linear combination of spectral signatures from two or more ground components. However, spectral mixture analysis does not account for the absence of one of the surface features or spectral variation within pure materials since it utilizes an invariable set of surface features. Multiple endmember spectral mixture analysis (MESMA), which addresses these issues by allowing endmembers to vary on a per pixel basis, was employed in this study to model Landsat ETM+ reflectance in the Phoenix metropolitan area. Image endmember spectra of vegetation, soils, and impervious surfaces were collected with the use of a fine resolution Quickbird image and the pixel purity index. This study employed 204 (=3x17x4) total four-endmember models for the urban subset and 96 (=6x6x2x4) total five-endmember models for the non-urban subset to identify fractions of soil, impervious surface, vegetation, and shade. The Pearson correlation between the fraction outputs from MESMA and reference data from Quickbird 60 cm resolution data for soil, impervious, and vegetation were 0.8030, 0.8632, and 0.8496 respectively. Results from this study suggest that the MESMA approach is effective in mapping urban land covers in desert cities at sub- pixel level.

Importance of charge capture in interphase regions during readout of charge-coupled devices

The current understanding of charge transfer dynamics in charge-coupled devices (CCDs) is that charge is moved so quickly from one phase to the next in a clocking sequence and with a density so low that trapping of charge in the interphase regions is negligible. However, simulation capabilities developed at the Centre for Electronic Imaging, which includes direct input of electron density simulations, have made it possible to investigate this assumption further. As part of the radiation testing campaign of the Euclid CCD273 devices, data have been obtained using the trap pumping method, a method that can be used to identify and characterize single defects within CCDs. Combining these data with simulations, we find that trapping during the transfer of charge among phases is indeed necessary to explain the results of the data analysis. This result could influence not only trap pumping theory and how trap pumping should be performed but also how a radiation-damaged CCD is readout in the most optimal way

Fundamental remote sensing science research program. Part 1: Status report of the mathematical pattern recognition and image analysis project

The Mathematical Pattern Recognition and Image Analysis (MPRIA) Project is concerned with basic research problems related to the study of the Earth from remotely sensed measurement of its surface characteristics. The program goal is to better understand how to analyze the digital image that represents the spatial, spectral, and temporal arrangement of these measurements for purposing of making selected inference about the Earth

Computer vision and optimization methods applied to the measurements of in-plane deformations

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A Neural Network Method for Mixture Estimation for Vegetation Mapping

While most forest maps identify only the dominant vegetation class in delineated stands, individual stands are often better characterized by a mix of vegetation types. Many land management applications, including wildlife habitat studies, can benefit from knowledge of mixes. This paper examines various algorithms that use data from the Landsat Thematic Mapper (TM) satellite to estimate mixtures of vegetation types within forest stands. Included in the study are maximum likelihood classification and linear mixture models as well as a new methodology based on the ARTMAP neural network. Two paradigms are considered: classification methods, which describe stand-level vegetation mixtures as mosaics of pixels, each identified with its primary vegetation class; and mixture methods, which treat samples as blends of vegetation, even at the pixel level. Comparative analysis of these mixture estimation methods, tested on data from the Plumas National Forest, yields the following conclusions: (1) accurate estimates of proportions of hardwood and conifer cover within stands can be obtained, particularly when brush is not present in the understory; (2) ARTMAP outperforms statistical methods and linear mixture models in both the classification and the mixture paradigms; (3) topographic correction fails to improve mapping accuracy; and (4) the new ARTMAP mixture system produces the most accurate overall results. The Plumas data set has been made available to other researchers for further development of new mapping methods and comparison with the quantitative studies presented here, which establish initial benchmark standards.National Science Foundation (IRI 94-0165, SBR 95-13889); Office of Naval Research (N00014-95-1-0409, N00014-95-0657); Region 5 Remote Sensing Laboratory of the U.S. Forest Servic

Scene-based nonuniformity correction with video sequences and registration

We describe a new, to our knowledge, scene-based nonuniformity correction algorithm for array detectors. The algorithm relies on the ability to register a sequence of observed frames in the presence of the fixed-pattern noise caused by pixel-to-pixel nonuniformity. In low-to-moderate levels of nonuniformity, sufficiently accurate registration may be possible with standard scene-based registration techniques. If the registration is accurate, and motion exists between the frames, then groups of independent detectors can be identified that observe the same irradiance (or true scene value). These detector outputs are averaged to generate estimates of the true scene values. With these scene estimates, and the corresponding observed values through a given detector, a curve-fitting procedure is used to estimate the individual detector response parameters. These can then be used to correct for detector nonuniformity. The strength of the algorithm lies in its simplicity and low computational complexity. Experimental results, to illustrate the performance of the algorithm, include the use of visible-range imagery with simulated nonuniformity and infrared imagery with real nonuniformity