Spectral Mixture Analysis of Inland Tropical Amazon Floodplain Waters Using EO-1 Hyperion

2006 ◽  
Author(s):  
C. Rudorff ◽  
E. Novo ◽  
L. Galvao
Keyword(s):  
Spectral Mixture Analysis ◽  
Mixture Analysis ◽  
Amazon Floodplain ◽  
Spectral Mixture ◽  
Floodplain Waters

Mapping two Eucalyptus subgenera using multiple endmember spectral mixture analysis and continuum-removed imaging spectrometry data

2011 ◽  
Vol 115 (5) ◽  
pp. 1115-1128 ◽  
Author(s):  
Kara N. Youngentob ◽  
Dar A. Roberts ◽  
Alex A. Held ◽  
Philip E. Dennison ◽  
Xiuping Jia ◽  
...  
Keyword(s):  
Spectral Mixture Analysis ◽  
Mixture Analysis ◽  
Imaging Spectrometry ◽  
Spectral Mixture

scholarly journals Modelling avian biodiversity using raw, unclassified satellite imagery

2014 ◽  
Vol 369 (1643) ◽  
pp. 20130197 ◽  
Author(s):  
Véronique St-Louis ◽  
Anna M. Pidgeon ◽  
Tobias Kuemmerle ◽  
Ruth Sonnenschein ◽  
Volker C. Radeloff ◽  
...  
Keyword(s):  
Species Richness ◽  
Habitat Heterogeneity ◽  
Bird Species ◽  
Image Texture ◽  
Spectral Mixture Analysis ◽  
Mixture Analysis ◽  
Bird Species Richness ◽  
Habitat Composition ◽  
Avian Biodiversity ◽  
Spectral Mixture

Applications of remote sensing for biodiversity conservation typically rely on image classifications that do not capture variability within coarse land cover classes. Here, we compare two measures derived from unclassified remotely sensed data, a measure of habitat heterogeneity and a measure of habitat composition, for explaining bird species richness and the spatial distribution of 10 species in a semi-arid landscape of New Mexico. We surveyed bird abundance from 1996 to 1998 at 42 plots located in the McGregor Range of Fort Bliss Army Reserve. Normalized Difference Vegetation Index values of two May 1997 Landsat scenes were the basis for among-pixel habitat heterogeneity (image texture), and we used the raw imagery to decompose each pixel into different habitat components (spectral mixture analysis). We used model averaging to relate measures of avian biodiversity to measures of image texture and spectral mixture analysis fractions. Measures of habitat heterogeneity, particularly angular second moment and standard deviation, provide higher explanatory power for bird species richness and the abundance of most species than measures of habitat composition. Using image texture, alone or in combination with other classified imagery-based approaches, for monitoring statuses and trends in biological diversity can greatly improve conservation efforts and habitat management.

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