Integration of environmental and spectral data for sunflower stress determination

Stress in sunflowers was assessed in western and northwestern Minnesota. Weekly ground observations (acquired in 1980 and 1981) were analyzed in concert with large scale aerial photography and concurrent LANDSAT data. Using multidate supervised and unsupervised classification procedures, it was found that all crops grown in association with sunflowers in the study area are spectrally separable from one another. Under conditions of extreme drought, severely stressed plants were differentiable from those not severely stressed, but between-crop separation was not possible. Initial regression analyses to estimate sunflower seed yield showed a sensitivity to environmental stress during the flowering and seed development stages. One of the most important biological factors related to sunflower production in the Red River Valley area was found to be the extent and severity of insect infestations

LANDSAT applications to wetlands classification in the upper Mississippi River Valley

A 25% improvement in average classification accuracy was realized by processing double-date vs. single-date data. Under the spectrally and spatially complex site conditions characterizing the geographical area used, further improvement in wetland classification accuracy is apparently precluded by the spectral and spatial resolution restrictions of the LANDSAT MSS. Full scene analysis of scanning densitometer data extracted from scale infrared photography failed to permit discrimination of many wetland and nonwetland cover types. When classification of photographic data was limited to wetland areas only, much more detailed and accurate classification could be made. The integration of conventional image interpretation (to simply delineate wetland boundaries) and machine assisted classification (to discriminate among cover types present within the wetland areas) appears to warrant further research to study the feasibility and cost of extending this methodology over a large area using LANDSAT and/or small scale photography

Photographic quantification of water quality in mixing zones

A method was developed to quantitatively delineate waste concentrations throughout waste effluent mixing zones on the basis of densitometric measurements extracted from aerial photography. A mixing zone is the extent of a receiving water body ultilized to dilute a waste discharge to a concentration characteristic of a totally mixed condition. Simultaneously-acquired color infrared photography and suspended solids water samples were used to quantitatively delineate the mixing zone resulting from the discharge of a paper mill effluent. Digital scanning microdensitometer data was used to estimate and delineate suspended solids concentrations on the basis of a semi-empirical model. Photographic photometry, when predicated on a limited amount of ground sampling, can measure and delineate mixing zone waste distributions in more detail then conventional surface measuring techniques. The method has direct application to: (1) the establishment of definite and rational water quality guidelines; (2) the development of sampling and surveillance programs for use by governmental and private agencies; and (3) the development of design and location criteria for industrial and municipal waste effluent outfalls

Remote sensing in the mixing zone

Characteristics of dispersion and diffusion as the mechanisms by which pollutants are transported in natural river courses were studied with the view of providing additional data for the establishment of water quality guidelines and effluent outfall design protocols. Work has been divided into four basic categories which are directed at the basic goal of developing relationships which will permit the estimation of the nature and extent of the mixing zone as a function of those variables which characterize the outfall structure, the effluent, and the river, as well as climatological conditions. The four basic categories of effort are: (1) the development of mathematical models; (2) laboratory studies of physical models; (3) field surveys involving ground and aerial sensing; and (4) correlation between aerial photographic imagery and mixing zone characteristics

Prestasjonsfremmende egenskaper ved teamarbeid.

Denne oppgaven er bygd rundt problemstillingen: Opplever prosjektmedarbeidere at kunnskapsoverføring er en kilde til læring som påvirker prestasjonene i en prosjektgruppe? Disse forskningsspørsmålene vært sentrale i prosessen: - Er teamarbeid en arbeidsmetode som er prestasjonsfremmende? - Finnes det noe som kan påvirke om kunnskapsoverføring fører til læring? - Er teamarbeid en godt egnet kontekst for utvikling av individet? Denne bacheloroppgaven handler om læring gjennom overføring av kunnskap, hvilke faktorer som spiller inn på dette og hvordan det videre påvirker prestasjoner i team. Motivasjonen som lå bak denne problemstillingen var interessen for personlig utvikling. Vi fant teamet som en interessant kontekst da sammensetning av ulike mennesker kan bidra til utvikling og læring gjennom felles arbeid og sosial interaksjon. Da teammedlemmer utfyller hverandre kan de i fellesskap produsere resultater som utgjør mer enn det enkelte individet. Sammensetningen av mennesker kan ha en stor innvirkning på samhandlingen, og videre de prosessene som skjer i teamet. Vi fikk muligheten til å gjennomføre vår forskning i FMC Technologies. De benytter seg av team- og prosjektarbeid, og hadde derfor medarbeidere som var passende for vår forskning. Vi har benyttet oss av kvalitativ metode, med et intensivt design. Dette på grunn av at vi ønsket å undersøke hver enkelt respondents mening og oppfattelse rundt fenomenet kunnskapsoverføring i team. Vi har samlet inn data ved hjelp av semistrukturerte dybdeintervju. Undersøkelsen var begrenset til 10 respondenter, der hvert intervju hadde en varighet på cirka en time. Både teori og empiri har vist at læring forekommer både bevisst og ubevisst, samt at dette vil ha en påvirkning på prestasjonene både individuelt og kollektivt i teamet

Robustness of Planar Fourier Capture Arrays to Colour Changes and Lost Pixels

Planar Fourier capture arrays (PFCAs) are optical sensors built entirely in standard microchip manufacturing flows. PFCAs are composed of ensembles of angle sensitive pixels (ASPs) that each report a single coefficient of the Fourier transform of the far-away scene. Here we characterize the performance of PFCAs under the following three non-optimal conditions. First, we show that PFCAs can operate while sensing light of a wavelength other than the design point. Second, if only a randomly-selected subset of 10% of the ASPs are functional, we can nonetheless reconstruct the entire far-away scene using compressed sensing. Third, if the wavelength of the imaged light is unknown, it can be inferred by demanding self-consistency of the outputs.Comment: 15 pages including cover page, 12 figures, associated with the 9th International Conference on Position Sensitive Detector

Remote sensing and image interpretation

A textbook prepared primarily for use in introductory courses in remote sensing is presented. Topics covered include concepts and foundations of remote sensing; elements of photographic systems; introduction to airphoto interpretation; airphoto interpretation for terrain evaluation; photogrammetry; radiometric characteristics of aerial photographs; aerial thermography; multispectral scanning and spectral pattern recognition; microwave sensing; and remote sensing from space

ICESat/GLAS Data as a Measurement Tool for Peatland Topography and Peat Swamp Forest Biomass in Kalimantan, Indonesia

Indonesian peatlands are one of the largest near-surface pools of terrestrial organic carbon. Persistent logging, drainage and recurrent fires lead to huge emission of carbon each year. Since tropical peatlands are highly inaccessible, few measurements on peat depth and forest biomass are available. We assessed the applicability of quality filtered ICESat/GLAS (a spaceborne LiDAR system) data to measure peatland topography as a proxy for peat volume and to estimate peat swamp forest Above Ground Biomass (AGB) in a thoroughly investigated study site in Central Kalimantan, Indonesia. Mean Shuttle Radar Topography Mission (SRTM) elevation was correlated to the corresponding ICESat/GLAS elevation. The best results were obtained from the waveform centroid (R2 = 0.92; n = 4,186). ICESat/GLAS terrain elevation was correlated to three 3D peatland elevation models derived from SRTM data (R2 = 0.90; overall difference = −1.0 m, ±3.2 m; n = 4,045). Based on the correlation of in situ peat swamp forest AGB and airborne LiDAR data (R2 = 0.75, n = 36) an ICESat/GLAS AGB prediction model was developed (R2 = 0.61, n = 35). These results demonstrate that ICESat/GLAS data can be used to measure peat topography and to collect large numbers of forest biomass samples in remote and highly inaccessible peatland forests

Land cover classification using multi-temporal MERIS vegetation indices

The spectral, spatial, and temporal resolutions of Envisat's Medium Resolution Imaging Spectrometer (MERIS) data are attractive for regional- to global-scale land cover mapping. Moreover, two novel and operational vegetation indices derived from MERIS data have considerable potential as discriminating variables in land cover classification. Here, the potential of these two vegetation indices (the MERIS global vegetation index (MGVI), MERIS terrestrial chlorophyll index (MTCI)) was evaluated for mapping eleven broad land cover classes in Wisconsin. Data acquired in the high and low chlorophyll seasons were used to increase inter-class separability. The two vegetation indices provided a higher degree of inter-class separability than data acquired in many of the individual MERIS spectral wavebands. The most accurate landcover map (73.2%) was derived from a classification of vegetation index-derived data with a support vector machine (SVM), and was more accurate than the corresponding map derived from a classification using the data acquired in the original spectral wavebands