scholarly journals Satellite image analysis and archaeological fieldwork in El-Markha Plain (South Sinai)

Antiquity ◽  
2002 ◽  
Vol 76 (294) ◽  
pp. 953-954 ◽  
Author(s):  
Gregory Mumford ◽  
Sarah Parcak
Keyword(s):  
Image Analysis ◽  
Satellite Image ◽  
South Sinai ◽  
Satellite Image Analysis

scholarly journals Comparing workflow application designs for high resolution satellite image analysis

2021 ◽  
Author(s):  
Aymen Al-Saadi ◽  
Ioannis Paraskevakos ◽  
Bento Collares Gonçalves ◽  
Heather J. Lynch ◽  
Shantenu Jha ◽  
...  
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Satellite Image ◽  
High Resolution Satellite Image ◽  
Satellite Image Analysis ◽  
Workflow Application

scholarly journals Metagenomic and satellite analyses of red snow in the Russian Arctic

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1491 ◽  
Author(s):  
Nao Hisakawa ◽  
Steven D. Quistad ◽  
Eric R. Hester ◽  
Daria Martynova ◽  
Heather Maughan ◽  
...  
Keyword(s):  
Image Analysis ◽  
Dna Analysis ◽  
Satellite Image ◽  
Polar Regions ◽  
Russian Arctic ◽  
Metagenomic Dna ◽  
Satellite Imaging ◽  
Franz Josef Land ◽  
Satellite Image Analysis ◽  
Snow And Ice

Cryophilic algae thrive in liquid water within snow and ice in alpine and polar regions worldwide. Blooms of these algae lower albedo (reflection of sunlight), thereby altering melting patterns (Kohshima, Seko & Yoshimura, 1993; Lutz et al., 2014; Thomas & Duval, 1995). Here metagenomic DNA analysis and satellite imaging were used to investigate red snow in Franz Josef Land in the Russian Arctic. Franz Josef Land red snow metagenomes confirmed that the communities are composed of the autotrophChlamydomonas nivalisthat is supporting a complex viral and heterotrophic bacterial community. Comparisons with white snow communities from other sites suggest that white snow and ice are initially colonized by fungal-dominated communities and then succeeded by the more complexC. nivalis-heterotroph red snow. Satellite image analysis showed that red snow covers up to 80% of the surface of snow and ice fields in Franz Josef Land and globally. Together these results show thatC. nivalissupports a local food web that is on the rise as temperatures warm, with potential widespread impacts on alpine and polar environments worldwide.

scholarly journals Bacterial Leaf Blight Detection in Rice Crops Using Ground-Based Spectroradiometer Data and Multi-temporal Satellites Images

2020 ◽  
Vol 12 (2) ◽  
pp. 38
Author(s):  
Rani Yudarwati ◽  
Chiharu Hongo ◽  
Gunardi Sigit ◽  
Baba Barus ◽  
Budi Utoyo
Keyword(s):  
Image Analysis ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Leaf Blight ◽  
Rice Crop ◽  
Bacterial Leaf Blight ◽  
First Derivative ◽  
Red Edge ◽  
Multi Temporal ◽  
Satellite Image Analysis

This study presents a method for detecting rice crop damage due to bacterial leaf blight (BLB) infestation. Rice crop samples are first analyzed using a handheld spectroradiometer. Then, multi-temporal satellite image analysis is used to determine the most suitable vegetation indices for detecting BLB. The results showed that healthy plants have the highest first derivative value of spectral reflectance of the different categories of diseased plants. Significant difference can be found at approximately 690-770 nm (red edge region) which peak or maximum of the first derivative occurs in healthy crop whereas the highest percentage of BLB showed the lowest in that region. Moreover, visible bands such as blue, green, red, and red edge 1 band show variation of correlation in the early (vegetative) to generative stage then getting high especially in early of harvesting stage than the other bands; the NIR band exhibits a low correlation from the early stage of the growing season whereas the red and red edge bands reveal the highest correlations in the later stage of harvesting. Similarly, the satellite image analysis also reveals that disease incidence gradually increases with increasing age of the plant. The vegetation indices whose formulas consist of blue, green, red, and red edge bands (NGRDI, NPCI, and PSRI) exhibit the highest correlation with BLB infestation. NPCI and PSRI indices indicate that crop stress due to BLB is detected from ripening stage of NPCI then the senescence condition is then detected 12 days later. The coefficients of determination between these indices and BLB are 0.44, 0.63, and 0.67, respectively

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