scholarly journals Missing data reconstruction and anomaly detection in crop development using agronomic indicators derived from multispectral satellite images

2017 ◽  
Author(s):  
Mohanad Albughdadi ◽  
Denis Kouame ◽  
Guillaume Rieu ◽  
Jean-Yves Tourneret
Keyword(s):  
Missing Data ◽  
Anomaly Detection ◽  
Satellite Images ◽  
Data Reconstruction ◽  
Crop Development ◽  
Multispectral Satellite Images

scholarly journals Forensic Geoarchaeology in the Search for Missing Persons

2021 ◽  
Vol 1 (1) ◽  
pp. 8-15
Author(s):  
Pier Matteo Barone ◽  
Rosa Maria Di Maggio ◽  
Silvia Mesturini
Keyword(s):  
Satellite Images ◽  
Social Phenomenon ◽  
Geographic Profiling ◽  
Missing Persons ◽  
The Social ◽  
Multispectral Satellite Images

Despite widespread concern over missing persons, there has always been little clarity on what the word “missing” means. Although the category of young runaways is, indeed, an important cluster, other popular concepts related to disappearances describe a portion of missing persons. Thus, the following question persists: What exactly does “missing” mean? In this brief communication, we would like to open a discussion about the social phenomenon of missing persons and the consequent deployment of people and techniques to find those persons. In particular, the benefits of some forensic geoarchaeological approaches that are not yet fully standardized will be highlighted, such as geographic profiling and the use of multispectral satellite images, in order to provide materials for future searching protocols.

Interactive processing of satellite images for structural and lithological mapping in northeast Africa

1986 ◽  
Vol 123 (4) ◽  
pp. 393-403 ◽  
Author(s):  
S. M. Berhe ◽  
D. A. Rothery
Keyword(s):  
Principal Components ◽  
Satellite Images ◽  
Spatial Filtering ◽  
Digital Processing ◽  
Horn Of Africa ◽  
Lithological Mapping ◽  
Late Precambrian ◽  
Late Proterozoic ◽  
Pan African ◽  
Multispectral Satellite Images

AbstractInteractive digital processing of multispectral satellite images (Landsat MSS) using principal components transformations and spatial filtering has clarified the position of continuous sutures linking apparently isolated Pan African (late Proterozoic) ophiolites. These have been field-checked and an arrangement of Pan African suture zones is proposed. Spatial filtering has also highlighted faults with various trends which can be related to the late Precambrian tectonics of the Horn of Africa region.

scholarly journals Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images

2018 ◽  
Vol 10 (10) ◽  
pp. 1555 ◽  
Author(s):  
Caio Fongaro ◽  
José Demattê ◽  
Rodnei Rizzo ◽  
José Lucas Safanelli ◽  
Wanderson Mendes ◽  
...  
Keyword(s):  
Model Calibration ◽  
Large Scale ◽  
Satellite Images ◽  
Satellite Image ◽  
Soil Mapping ◽  
Spectral Signatures ◽  
Novel Approach ◽  
Multispectral Satellite Images ◽  
Reduced Costs ◽  
Multispectral Satellite Image

Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0–20 cm depth, 919 points) from an area of 14,614 km2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R2 = 0.83; RMSE = 65.0 g kg−1) and sand (R2 = 0.86; RMSE = 79.9 g kg−1). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.

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