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Measurements of the Faraday rotation in YIG and TbIG have been made as a function of temperature in the range 100°–450°K at the helium-neon laser wavelength of 1.15 μ. This wavelength lies on the edge of the YIG window at the short-wavelength absorption limit. Measurements were made on single-crystal blocks of YIG and TbIG up to 7 mm thick in applied fields of up to 10 kOe. In YIG the major contribution to the Faraday rotation at this wavelength is the dispersive part associated with absorption bands in the visible and near ir. This contribution reaches a maximum of 188°/cm at 280°K and can be fitted to the sublattice magnetization data of Anderson. For TbIG we find αF = 448°/cm at 300°K and a very strong temperature dependence. In addition we observe a field-dependent Faraday rotation of −3.8°/cm/kOe at 300°K. Extrapolation of the observed rotations to zero internal field removes he discontinuity in | αF | which otherwise appears at the compensation temperature (245°K). The zero-field Faraday rotation may be analyzed in terms of three contributions: a nondispersive part arising from the exchange resonance, a dispersive part arising from the Fe3+ sublattices, and a dispersive contribution from the Tb3+ sublattice. Additional information on the Tb3+ contribution was obtained from Verdet constant measurements on TbGaG and TbAlG.

The Faraday Rotation of YIG, GdIG, and TbIG has been measured at 1.15 μ as a function of temperature between 100° and 450°K. The rotation has been analysed in terms of electric and magnetic dipole contributions from the various sublattices (Fe3+ octahedral and tetrahedral, and Re3+), and the contributions separated by a least-squares fit to magnetization data taken from published NMR results. (Both magnetic and electric contributions from an ion are proportional to the ionic magnetic moment). The electric dipole contribution from the two different Fe3+ sublattices were quite similar in YIG and GdIG, but those in TbIG showed a greater deviation. It is suggested that charge-transfer processes may account for this. The effect of a knowledge of the electric dipole contributions at 1.15 μ on published rotation results at longer wavelengths was investigated, and the assignment of the rotation entirely to magnetic-dipole effects was shown to be wrong. This affects deductions of magnetic g factors from long-wavelength rotation results.

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Background NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This double method uses Landsat 8 satellite images and daily rainfall data for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. Methods The SLIP algorithm is adapted, by integrating the inverse Normalized Difference Vegetation Index (NDVI) to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and their intersections are used to propose sets of dichotomous variables before (1948–2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlate landslide occurrence to rainfall, with the first known event in Cameroon as starting point, and using the Cox model. Results From the SLIP model, the Landslide Hazard Zonation (LHZ) map gives an overall accuracy of 96%. Further, the DRIP model states that 6/9 ranges of probability are rainfall-triggered landslides at 99.99%, between June and October, while 3/9 ranges show only 4.88% of risk for the same interval. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. Conclusions The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime analysis for a more flexibility in observation and prediction thresholding.

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