Taking debris flow area as an example, this paper studied retrieval of soil Sodium content and pH by hyperspectral remote sensing, which provided a new method for estimating soil dispersion. Based on preprocessing, the authors extracted four spectral indices, including reflectance(R), inverse reflectance(1/R), inverse-log reflectance(log(1/R)) and band depth(BD), to establish the prediction model for Sodium content and pH using stepwise multiple regression method. Results indicated that reflectance spectra and inverse-log reflectance were the optimum parameters for inverting soil sodium ions content and pH, respectively. Determination coefficients R2 of prediction samples were 0.690 and 0.641 respectively, and R2 of test samples were 0.523 and 0.438, which showed that soil spectra with high spectral resolution had the potential for the rapid prediction of Sodium content and pH, thus, providing reliable detection method for soil dispersion using hyper-spectral technology.
With the development of Hyperspectra and the method of rock-mineral information extraction, several cores were
analyzed based on analytical spectral devices (ASD) and rock-mineral information extraction in Wushan-cooper deposit
area. Aiming at the low accuracy of mineral identification with hyperspectral data, the present study established regional
spectra library on the basis of the study area geological background, section noise filtering and fast Fourier transform
processing methods. Using the rapid quantificational identification model, the rock-mineral alternation information was
extracted to build core profile and 3D model to discuss the deep mineralization evaluation.
Combing with the regional metallogenic background, the alteration information indicated that the ore mineral was related
with multiple alteration assemblages and there may be rock mass in deep space. The Cu element contents and ore
mineral were closely related with the skarnization, silicification and chloritization. It also suggested that the deposit was
skarn type in less than 1000 m depth, which was affected by the sandstone. Meanwhile, in more than 1000 m depth, the
deposit was controlled by composite minerallzation types, which was associated with the previous geology and mineral
deposits studies. In summary,this study supported a two stage mineralization model for the Wushan-copper deposit
area,namely,the first stage of synsedimentary hydrothermal exhalative stage and the second stage of magmatichydrothermal
ore-forming stage.
Image segmentation is the basis of object-based information extraction from remote sensing imagery. Image
segmentation based on multiple features, multi-scale, and spatial context is one current research focus. The scale
parameters selected in the segmentation severely impact on the average size of segments obtained by multi-scale
segmentation method, such as the Fractal Network Evolution Approach (FNEA) employed in the eCognition software. It
is important for the FNEA method to select an appropriate scale parameter that causes no neither over- nor undersegmentation.
A method for scale parameter selection and segments refinement is proposed in this paper by modifying a
method proposed by Johnson. In a test on two images, the segmentation maps obtained using the proposed method
contain less under-segmentation and over-segmentation than that generated by the Johnson’s method. It was
demonstrated that the proposed method is effective in scale parameter selection and segment refinement for multi-scale
segmentation algorithms, such as the FNEA method.
The aim of data conflation is to synergise geospatial information from different sources into a common framework,
which can be realised using multivariate geostatistics. Recently, multiple-point geostatistics (MPG) has been proposed
for data conflation. Instead of the variogram, MPG borrows structures from the training image, so the spatial correlation
is characterised by multiple-point statistics. In pattern-based MPG, two sets of data can be integrated by utilising the
secondary data as a locally varying mean (LVM). The training image provides a spatial correlation model and is
incorporated to facilitate reproduction of similar local patterns in the predicted image. However, the current patternbased
MPG gathers similar patterns based on a prototype class, which extracts spatial structures in an arbitrary way. In
this paper, we proposed an improved pattern-based MPG for conflation of digital elevation models (DEMs). In this
approach, a new strategy for forming prototype class is applied, which is based on the residual surface, vector
ruggedness measure (VRM) and ridge valley class (RVC) of terrain data. The method was tested on the SRTM and
GMTED2010 data. SRTM data at the spatial resolution of 3 arc-second was simulated by conflating sparse elevation
point data and GMTED2010 data at a coarser spatial resolution of 7.5 arc-second. The proposed MPG method was
compared with the traditional pattern-based MPG simulation. Several kriging predictors were applied to provide LVMs
for MPG simulation. The result shows that the new method can achieve more precise prediction and retain more spatial
details than the benchmarks.
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