Viable end-member selection scheme for spectral unmixing of multispectral satellite imagery data

1999 ◽  
Author(s):  
John A. Saghri ◽  
Andrew G. Tescher ◽  
Farid Jaradi
Polar Record ◽  
2011 ◽  
Vol 48 (1) ◽  
pp. 63-74 ◽  
Author(s):  
Anna Mikheeva ◽  
Anton Novichikhin ◽  
Olga Tutubalina

ABSTRACTAn experimental linear mixture modelling using ground spectroradiometric measurements in the Kola Peninsula, Russia has been carried out to create a basis for mapping vegetation and non-vegetation components in the tundra-taiga ecotone using satellite imagery. We concentrated on the ground level experiment with the goal to use it further for the classification of multispectral satellite imagery through spectral unmixing. This experiment was performed on the most detailed level of remote sensing research which is free from atmospheric effects and easy to understand. We have measured typical ecotone components, including Cetraria nivalis, Betula tortuosa, Empetrum nigrum, Betula nana, Picea abies and rocks (nepheline syenite). The result of the experiment shows that the spectral mixture is indeed formed linearly but different components have different influence. Typical spectral thresholds for each component were found which are significant for vegetation mapping. Spectral unmixing of ground level data was performed and accuracy was estimated. The results add new information on typical spectral thresholds which can potentially be applied for multispectral satellite imagery when upscaling from high resolution to coarser resolution.


2016 ◽  
Vol 33 ◽  
pp. 36-43 ◽  
Author(s):  
Sri Malahayati Yusuf ◽  
Kukuh Murtilaksono ◽  
Mahendra Harjianto ◽  
Endah Herlina

2021 ◽  
Author(s):  
Edy Irwansyah ◽  
Alexander A. Santoso. Gunawan ◽  
Calvin Surya ◽  
Dewa Ayu Defina Audrey Nathania

Author(s):  
Claudia Canedo-Rosso ◽  
Stefan Hochrainer-Stigler ◽  
Georg Pflug ◽  
Bruno Condori ◽  
Ronny Berndtsson

Abstract. Drought is a major natural hazard in the Bolivian Altiplano that causes large losses to farmers, especially during positive ENSO phases. However, empirical data for drought risk estimation purposes are scarce and spatially uneven distributed. Due to these limitations, similar to many other regions in the world, we tested the performance of satellite imagery data for providing precipitation and temperature data. The results show that droughts can be better predicted using a combination of satellite imagery and ground-based available data. Consequently, the satellite climate data were associated with the Normalized Difference Vegetation Index (NDVI) in order to evaluate the crop production variability. Moreover, NDVI was used to target specific drought hotspot regions. Furthermore, during positive ENSO phase (El Niño years), a significant decrease in crop yields can be expected and we indicate areas where losses will be most pronounced. The results can be used for emergency response operations and enable a pro-active approach to disaster risk management against droughts. This includes economic-related and risk reduction strategies such as insurance and irrigation.


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