1,721,950 research outputs found
From Spectral to Ecological Information
No full textThe interpretation of spectral information is at the very core of remote sensing data analysis. However, the spectral signal carries much more information about the land surface than just what is readily accessible to the human eye. In order to harness this information the original bands are often transformed into new synthetic bands, so-called spectral indices, by mathematical operations combining multiple bands. These spectral indices combine several advantages over just using the original reflectance. Firstly, they can dramatically enhance the separability of certain specific land cover types in visual or automated image interpretation, e.g. vegetation versus open soil. Secondly, many indices involve mathematical division of bands which has a normalizing effect on illumination variability within a single scene and also between scenes. This can reduce terrain or cloud-induced illumination effects, improve multi-temporal comparability in a time-series and may even reduce the need for precise atmospheric correction. Thirdly, spectral indices are usually geared towards describing actual physical measures of the land surface, such as the degree of vegetation cover or water stress within vegetation. These are measures which allow interpretation in an ecological context, while it is hard to reason what e.g. a reflectance of 20% in the green band actually means
Spatial Data and Software
No full textGeo-spatial data are information which can be pinpointed to spatially explicit locations on Earth. Most of the data you sample in ecology are of geo-spatial nature, regardless of whether you recorded the spatial coordinates during data collection or not. A geo-spatial data element consists in principle out of two parts: (1) spatial coordinates in a defined coordinate system, such as latitude and longitude and (2) one or more values such as a label, a physical measurement or a species observation associated with this location
Spatial land cover pattern analysis
No full textIn the previous chapters we introduced land cover classifications, fractional cover and time-series analysis. All these approaches aimed to extract ecological relevant information based on the spectral signal. However differentiating a tree plantation (spatially regularly planted trees of same species, age, height) from a natural forest based on the spectral signal only might be quite challenging since the spectral signals might be quite similar but their spatial heterogeneity is different. A tree plantation will not have a high spatial variation in its spectral signal due to the same age and height of the trees while a natural forest will have different tree heights with casting shadows or even tree fall gaps, hence such a forest will show up with a higher spatial variation. Such information can be retrieved using texture metrics based on remote sensing data sets e.g. the NDVI
University Education: Privilege and Responsibility
No full textCommencement address given by Winfred G. Leutner to the Winter 1948 graduating class of The Ohio State University, Men's Gymnasium, Columbus, Ohio, March 19, 1948
[The] Gipsy Polka
Full text link80.7568.412 – “[The] Gipsy Polka”: Leutner: Geo. P. Reed & Co.: Boston: Dance Music, Polka: n.d.: Solo Piano
a laboratory and field experimental study on aspects of the cognitive exploitation hypothesis
No full textSchülerinnen und Schüler in Deutschland erzielten beim „Programme for International Student Assessment“ (PISA) im Jahr 2003 überdurchschnittliche Ergebnisse in der Domäne (fächerübergreifendes) Problemlösen. Die Leistungen in den fachlichen Domänen blieben im Vergleich dazu jedoch hinter den Erwartungen zurück. Diese Diskrepanz wird insbesondere für die Domänen Mathematik und Naturwissenschaften als Zeichen mangelnder kognitiver Potenzialausschöpfung interpretiert (Leutner, Klieme, Meyer & Wirth, 2004; OECD, 2004). In einer Serie von drei Experimenten werden Aspekte dieser kognitiven Potenzialausschöpfungshypothese für die mathematische Domäne labor- und feldexperimentell untersucht. Dabei steht die Frage möglicher Trainings- und Transfereffekten (von Komponenten) fächerübergreifenden Problemlösens auf mathematisches Problemlösen bzw. Modellieren im Fokus. Die Ergebnisse der beiden Laborexperimente zeigten teilweise Trainingseffekte, jedoch keine Transfereffekte auf die mathematische Domäne. Das Feldexperiment zeigt v. a. für leistungsschwache SuS kleine Trainingseffekte und kleine Transfereffekte auf die mathematische Domäne. Die Experimente werden in ihren Limitationen und Konsequenzen für zukünftige Forschung anschließend kritisch diskutiert.Students in Germany achieved above-average results in the domain of cross-curricular problem solving at “The Programme for International Student Assessment” (PISA). The results in the subject-specific domains lag behind the expectations based on the problem solving results. This discrepancy is interpreted especially for the domains mathematics and science as an indication that there exists a lack of exploitation of cognitive potential (Leutner, Klieme, Meyer & Wirth, 2004; OECD, 2004). In a series of three experiments aspects of this cognitive exploitation hypothesis for the domain of mathematics are analyzed by means of labor and field experiments. Thereby, the question of possible training and transfer effects from (components of) cross-curricular problem solving to mathematical problem solving is particularly focused. The results of both labor experiments show in part effects of training, but no effects of transfer to the mathematical domain. The field experiment shows small effects of training and transfer effects to the mathematical domain for low-achieving students. The results are discussed with respect to limitations and consequences for future research
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