Land cover mapping using Sentinel-1 SAR and Landsat 8 imageries of Lagos State for 2017

For several years, Landsat imageries have been used for land cover mapping analysis. However, cloud cover constitutes a major obstacle to land cover classification in coastal tropical regions including Lagos State. In this work, a land cover appearance for Lagos State is examined using Sentinel-1 synthetic aperture radar (SAR) and Land Satellite 8 (Landsat 8) imageries. To this aim, a Sentinel-1 SAR dual-pol (VV+VH) Interferometric Wide swath mode (IW) data orbit for 2017 and a Landsat 8 Operational Land Imager (OLI) for 2017 over Lagos State were acquired and analysed. The Sentinel-1 imagery was calibrated and terrain corrected using a SRTM 3Sec DEM. Maximum likelihood classification algorithm was performed. A supervised pixel-based imagery classification to classify the dataset using training points selected from RGB combination of VV and VH polarizations was applied. Accuracy assessment was performed using test data collected from high-resolution imagery of Google Earth to determine the overall classification accuracy and Kappa coefficient. The Landsat 8 was orthorectified and maximum likelihood classification algorithm also performed. The results for Sentinel-1 include an RGB composite of the imagery, classified imagery, with overall accuracy calculated as 0.757, while the kappa value was evaluated to be about 0.719. Also, the Landsat 8 includes a RBG composite of the imagery, classified imagery, but an overall accuracy of 0.908 and a kappa value of 0.876. It is concluded that Sentinel 1 SAR result has been effectively exploited for producing acceptable accurate land cover map of Lagos State with relevant advantages for areas with cloud cover. In addition,  the Landsat 8 result reported a high accuracy assessment values with finer visual land cover map appearance.

Estimation of the resting motor threshold (RMT) in transcranial magnetic stimulation using relative-frequency and threshold-hunting methods in brain tumor patients

BACKGROUND

Application of transcranial magnetic stimulation is often based on the resting motor threshold. The aim of this study was to validate recent findings on the advantage of resting motor threshold estimation using adaptive threshold-hunting algorithms over the Rossini-Rothwell method in a clinical sample and healthy subjects.

METHODS

Resting motor thresholds in 115 patients with a brain tumor and 10 healthy subjects were assessed using the Rossini-Rothwell method and compared to an adaptive threshold-hunting algorithm. In healthy subjects, this measurement was repeated twice to capture test-retest reliability of both methods. Efficiency of both methods was assessed by comparing the number of pulses needed for resting motor threshold estimation.

RESULTS

There was no significant difference between the Rossini-Rothwell method and the adaptive threshold-hunting algorithm in patients and healthy controls with limits of agreement between ± 12 V/m. There was a strong intraclass correlation and both methods showed a good test-retest reliability. However, the adaptive threshold-hunting algorithm was significantly faster.

CONCLUSIONS

The adaptive threshold-hunting algorithm was more efficient in assessing the resting motor threshold, while reaching comparable results as the Rossini-Rothwell method. Thus, our results support the advantage of adaptive threshold-hunting algorithms to determine the resting motor threshold also in a clinical sample.