Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 Site

1986 ◽  
Vol 91 (B8) ◽  
pp. 8098 ◽  
Author(s):  
John B. Adams ◽  
Milton O. Smith ◽  
Paul E. Johnson
Keyword(s):  
Mixture Modeling ◽  
Soil Types ◽  
Spectral Mixture ◽  
Rock And Soil

Nonlinear spectral mixture modeling to estimate water-ice abundance of martian regolith

Icarus ◽  
2019 ◽  
Vol 329 ◽  
pp. 79-87 ◽  
Author(s):  
Szilárd Gyalay ◽  
Eldar Z. Noe Dobrea ◽  
Kathryn Chu ◽  
Karly M. Pitman
Keyword(s):  
Mixture Modeling ◽  
Water Ice ◽  
Martian Regolith ◽  
Spectral Mixture

Isolation Mechanism and Method for Buried Segmental Pipeline under Earhquake-Induced Large Displacement

2011 ◽  
Vol 99-100 ◽  
pp. 410-413
Author(s):  
Ai Ping Tang ◽  
Ai Hua Wen ◽  
Li Hua Gai ◽  
Ru Hong Zhong
Keyword(s):  
Slope Failure ◽  
Soil Types ◽  
Response Analysis ◽  
Buried Pipeline ◽  
Soil Slope ◽  
Seismic Response Analysis ◽  
Ground Displacement ◽  
Isolation System ◽  
Reliable Isolation ◽  
Rock And Soil

This paper deals with an isolation design methods of buried segment pipeline for large ground displacement in earthquake. The buried pipeline is always damaged due to the strong shaking and earthquake-induced large or permant ground displacement and deformation (PGD), including liquefaction, rock and soil slope failure, fault and fissure. Based on the seismic response analysis for buried segment pipeline, the isolation mechanism and an equipment for resisting ground displacement trigged by liquefaction and fault is presented in this paper. Some related parameters to isolation system like stiffness and damp of isolator, soil types and its displacement, depth of pipeline, are discussed for developing a reliable isolation system fitting the buried segment pipeline.

scholarly journals Mapping alpine snow using a spectral mixture modeling technique

1993 ◽  
Vol 17 ◽  
pp. 121-124 ◽  
Author(s):  
Anne W. Nolin ◽  
Jeff Dozier ◽  
Leal A. K. Mertes
Keyword(s):  
Snow Cover ◽  
Sierra Nevada ◽  
Land Surface ◽  
Mixture Modeling ◽  
Modeling Technique ◽  
Imaging Spectrometer ◽  
Snow Covered Area ◽  
Covered Area ◽  
Spectral Mixture ◽  
Spectrometer Data

Remote sensing has provided a means of obtaining estimates of snow-covered area, yet traditional methods have had difficulty mapping snow in shaded and vegetated areas. Spectral mixture analysis is a linear mixture modeling technique that shows promise for mapping land surface covers, particularly when imaging spectrometer data are used. Applying this technique to AVIRIS data collected over the Sierra Nevada, California, we have estimated the fraction of snow cover in each pixel, even in areas that are shaded or forested. This modeling technique enables us to map snow cover at the sub-pixel level and provides a means of estimating the errors associated with the calculation.

scholarly journals Mapping alpine snow using a spectral mixture modeling technique

1993 ◽  
Vol 17 ◽  
pp. 121-124 ◽  
Author(s):  
Anne W. Nolin ◽  
Jeff Dozier ◽  
Leal A. K. Mertes
Keyword(s):  
Snow Cover ◽  
Sierra Nevada ◽  
Land Surface ◽  
Mixture Modeling ◽  
Modeling Technique ◽  
Imaging Spectrometer ◽  
Snow Covered Area ◽  
Covered Area ◽  
Spectral Mixture ◽  
Spectrometer Data

Remote sensing has provided a means of obtaining estimates of snow-covered area, yet traditional methods have had difficulty mapping snow in shaded and vegetated areas. Spectral mixture analysis is a linear mixture modeling technique that shows promise for mapping land surface covers, particularly when imaging spectrometer data are used. Applying this technique to AVIRIS data collected over the Sierra Nevada, California, we have estimated the fraction of snow cover in each pixel, even in areas that are shaded or forested. This modeling technique enables us to map snow cover at the sub-pixel level and provides a means of estimating the errors associated with the calculation.

Die Identifikation früher Veränderungsmuster in der ambulanten Psychotherapie

2007 ◽  
Vol 36 (2) ◽  
pp. 93-104 ◽  
Author(s):  
Wolfgang Lutz ◽  
Niklaus Stulz ◽  
David W. Smart ◽  
Michael J. Lambert
Keyword(s):  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Growth Mixture

Zusammenfassung. Theoretischer Hintergrund: Im Rahmen einer patientenorientierten Psychotherapieforschung werden Patientenausgangsmerkmale und Veränderungsmuster in einer frühen Therapiephase genutzt, um Behandlungsergebnisse und Behandlungsdauer vorherzusagen. Fragestellung: Lassen sich in frühen Therapiephasen verschiedene Muster der Veränderung (Verlaufscluster) identifizieren und durch Patientencharakteristika vorhersagen? Erlauben diese Verlaufscluster eine Vorhersage bezüglich Therapieergebnis und -dauer? Methode: Anhand des Growth Mixture Modeling Ansatzes wurden in einer Stichprobe von N = 2206 ambulanten Patienten einer US-amerikanischen Psychotherapieambulanz verschiedene latente Klassen des frühen Therapieverlaufs ermittelt und unter Berücksichtigung unterschiedlicher Patientenausgangscharakteristika als Prädiktoren der frühen Veränderungen mit dem Therapieergebnis und der Therapiedauer in Beziehung gesetzt. Ergebnisse: Für leicht, mittelschwer und schwer beeinträchtigte Patienten konnten je vier unterschiedliche Verlaufscluster mit jeweils spezifischen Prädiktoren identifiziert werden. Die Identifikation der frühen Verlaufsmuster ermöglichte weiterhin eine spezifische Vorhersage für die unterschiedlichen Verlaufscluster bezüglich des Therapieergebnisses und der Therapiedauer. Schlussfolgerungen: Frühe Psychotherapieverlaufsmuster können einen Beitrag zu einer frühzeitigen Identifikation günstiger sowie ungünstiger Therapieverläufe leisten.

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