China is one of the large coal mining countries in the world. Coal mining accelerates economic prosperity, as well as
engenders a series of environment problems either. One of the most obvious problems is that coal mining changes the
landforms around the mining areas. Abundant arable area, garden area, forest area and construction area have been
changed under the drive of this dynamic landform. The law that other environment elements change resulting from
transformation of one element can be analysed by location theory---Concentric-Circle Mode (or Circle Layer Mode)
proposed by professor E. W. Burgess of Chicago University. For the case of Longkou coal mining subsidence area in
Shandong province, based on the ground measurement elevation data of the years of 1978, 1989, 1995 and 2004, firstly,
this paper considers the DEM data of 1978 before subsidence as standard elevation, and calculates the difference value
DEM data of three periods through the difference operation of the other later three-period DEM data and the standard
elevation. The coal mining subsidence region and area can be figured out, which is grid region and the overall sum of the
grid area with z<0. Secondly, by choosing the digital remote sensing images which are the same period with the later
three-period DEM data, with operation of the classifier of BP Artificial Nerve Network (BPNN), the author classifies
these images by combining spectral information, texture information of remote sensing image with terrain index. Thirdly,
under the guidance of location theory, the author uses location index to make "location image". Lastly, with spatial
superposition of location image, three-period DEM data and land use classification result, the author figures out the area
and proportion of all the land use types in different locations and the transfer matrix of land use types, and analyses the
rule of space-time change of land use in different locations, in order to explain the location effect that coal mining
subsidence affect land-use change.
Image fusion based on a Brovey transform (BT) and wavelet transform (WT) is developed to merge SPOT-5 images. The
main objective of this research was to study the effects of BT and WT on the information capacity of panchromatic and
multispectral images. The results show that the spatial resolution of images merged by BT and WT is higher than that of
the original SPOT-5 images. The two transforms techniques merge the features of the panchromatic and multispectral
images very well. However, the hue of the WT merged image is very different from that of the original image, indicating
that WT led to obvious color distortion. And the hue of the BT merged image is approximately the same as for the
original image, with no image distortion. Furthermore, the discussion of the information capacity considers quality in
terms of hue and definition, and quantity in terms of entropy, average gradient and spectral authenticity. Experimental
results show that images merged by BT showed higher spatial resolution and better spectral features than the original
SPOT-5 imagery. Images merged by WT also showed higher spatial resolution, but lost some spectral information.
Therefore, BT is very efficient and highly accurate for merging SPOT-5 images.
Based on the correlation of scattered light intensity profile with self-affine fractal surface parameters of roughness <i>w</i>, the lateral correlation length ζ and roughness exponent α, we propose a new algorithm for the simultaneous extraction of three surface parameters from a single experimental scattered intensity profile data. With this algorithm, the fit of theoretical function to experimental data is used, and Levenberg-Marquardt method is introduced in finding the minimum of sum-squared error. In the iteration of fit process, the gradient and the curvature of sum-squared error function govern a jump of linear-descent to gradient-descent to guarantee the convergence and to accelerate the progress of parameters approaching their real values. In the experiment, we design precision system for the acquisition of scattered intensity data using the integration technique of Boxcar. All the actions in the experiment such as the stepped movement of surfaces, the sampling and the averaging of signals by Boxcar, the readout of the intensity data are also controlled by computer via an analog-to-digital converter. The results of the extracted surface parameters conform well with those by atomic force microscopy.