Reflectance and emissivity spectra of geologic materials have been used to discriminate mineral compositions and produce geological maps from remotely-sensed data. This paper presents a new application of such spectral data and remote sensing technique to estimate weathering degree of rock masses. For this purpose, two field spectrometers with interference filters and FT-IR were used to obtain reflectance data within 0.485 to 2.5, um and emissivity data within 8 to 14, um, respectively, of several rock types. The weathering degrees of rock samples were evaluated by X-ray CT value or P-wave velocity (Vp). Four indexes were proposed by focusing on the change patterns of reflectance and emissivity spectra with the progress of weathering. Two are IFe and Iclay that are based on the absorption depths resulting from the occurrence and abundance of ferric oxide minerals and clay minerals, respectively. The others are related to the wavelength and depth of emissivity absorption, λmin and Δε, which result from the changes in mineral compositions and crystal structures of silicates. The weathering of rock samples could be characterized by increase of the reflectance spectra and flattening of the emissivity spectra. By comparing the four indexes with the CT and V, values of granite, sandstone, granodiorite, and diorite samples, Iclay was specified to have the highest correlation with the weathering degree, and AE was subordinate to it. We demonstrate that L, is applicable to field measurement data by selecting two test sites, an amphibolite site with a fracture zone and a weathered granite site, in northern Kumamoto Prefecture, southwest Japan. Interval Vp at these sites is used in the correlation analysis with Iclay. Homogenous mixture of clay minerals, which makes area ratio of clay minerals being similar to volume ratio, can be considered as a reason why Hay, is a suitable index for evaluating the weathering degree of rocks.
JERS-1 SAR images provide one of the best ways to monitor rivers in tropical forests, but digital detection of rivers from such images is made difficult because too many objects have levels of backscattering intensities similar to those of rivers. To solve this problem, we examined the appearances of the rivers in the SAR images and created feature detection models with which we performed spatial operations to evaluate the sizes of the rivers, their structural characteristics, their distribution patterns, and their association with other objects. Such operations were combined with more traditional intensity-based segmentation methods. The rivers so detected corresponded closely with those identified using JERS-1 VNIR data. We also compared the results with those obtained from SAR images observed in different seasons and found the results to be consistent with known water levels. We further applied the method to other tropical rain forests in the Amazon Basin, Congo Basin, Borneo, and New Guinea.
The necessity to denoisify thermal images dated from decades ago for ecological and scientific purposes, and reduce the hardware costs in the design of the thermal cameras, motivated the present study. We propose, describe and demonstrate the effectiveness of two techniques for reducing considerably different types of noise that thermal images could have, their possibilities for real-time processing, accuracy and easy implementation. The first technique uses the multi-observation for reducing the random noise and identifying the fixed-pattern noise and then, subtracts it from the image. The second technique consists in a neighborhood classification method for identifying the type and degree of the fixed-pattern noise for subtracting it in the correct proportion. Real thermal patterns are used for the analysis. The error analysis is carried out. A novel method for evaluate the quality of the proposed techniques is also presented. The easy practical implementation of these techniques reduces the restrictions to be imposed to the sensor's hardware, reducing the sensor's production costs.
The spatial correlation coefficient of the rainfall rate is an important parameter in the rain attenuation prediction method proposed by Morita and Higuti (M-H method). However, the value of this coefficient can not be used without regard to its dependence on regional differences in climate and rain type. Thus, when the M-H method is used for predicting rain attenuation, the spatial correlation coefficient of the rainfall rate determined in the relevant area should be used. The Tropical Rainfall Measuring Mission (TRMM) is a satellite project which observes the precipitation from space. Global distribution of the spatial correlation coefficient of the rainfall rate has been inferred from TRMM measurement.