We ask the question whether petrofabric data from anisotropy of magnetic susceptibility (AMS) analysis of deformed quartzites gives information about shape preferred orientation (SPO) or crystallographic preferred orientation (CPO) of quartz. Since quartz is diamagnetic and has a negative magnetic susceptibility, 11 samples of nearly pure quartzites with a negative magnetic susceptibility were chosen for this study. After performing AMS analysis, electron backscatter diffraction (EBSD) analysis was done in thin sections prepared parallel to the K1K3 plane of the AMS ellipsoid. Results show that in all the samples quartz SPO is sub-parallel to the orientation of the magnetic foliation. However, in most samples no clear correspondance is observed between quartz CPO and K1 (magnetic lineation) direction. This is contrary to the parallelism observed between K1 direction and orientation of quartz c-axis in the case of undeformed single quartz crystal. Pole figures of quartz indicate that quartz c-axis tends to be parallel to K1 direction only in the case where intracrystalline deformation of quartz is accommodated by prism <c> slip. It is therefore established that AMS investigation of quartz from deformed rocks gives information of SPO. Thus, it is concluded that petrofabric information of quartzite obtained from AMS is a manifestation of its shape anisotropy and not crystallographic preferred orientation. 相似文献
Different pixel-based, object-based and subpixel-based methods such as time-series analysis, decision-tree, and different supervised approaches have been proposed to conduct land use/cover classification. However, despite their proven advantages in small dataset tests, their performance is variable and less satisfactory while dealing with large datasets, particularly, for regional-scale mapping with high resolution data due to the complexity and diversity in landscapes and land cover patterns, and the unacceptably long processing time. The objective of this paper is to demonstrate the comparatively highest performance of an operational approach based on integration of multisource information ensuring high mapping accuracy in large areas with acceptable processing time. The information used includes phenologically contrasted multiseasonal and multispectral bands, vegetation index, land surface temperature, and topographic features. The performance of different conventional and machine learning classifiers namely Malahanobis Distance (MD), Maximum Likelihood (ML), Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Random Forests (RFs) was compared using the same datasets in the same IDL (Interactive Data Language) environment. An Eastern Mediterranean area with complex landscape and steep climate gradients was selected to test and develop the operational approach. The results showed that SVMs and RFs classifiers produced most accurate mapping at local-scale (up to 96.85% in Overall Accuracy), but were very time-consuming in whole-scene classification (more than five days per scene) whereas ML fulfilled the task rapidly (about 10 min per scene) with satisfying accuracy (94.2–96.4%). Thus, the approach composed of integration of seasonally contrasted multisource data and sampling at subclass level followed by a ML classification is a suitable candidate to become an operational and effective regional land cover mapping method. 相似文献
Reconstruction of the spatial pattern of regional habitat quality can revivify the ecological environment background at certain historical periods and provide scientific support for revealing the evolution of regional ecological environmental quality.In this study,we selected 10 driving factors of land use changes,including elevation,slope,aspect,GDP,population,temperature,precipitation,river distance,urban distance,and coastline distance,to construct the CA-Markov model parameters and acquired the land use spatial data for 1975,1980,1985,1990,and 1995 by simulation based on the land use status map for 2010.On this basis,we used the InVEST model to reconstruct the spatial pattern of habitat quality in the study area and conducted classification division and statistical analysis on the computed habitat degradation degree index and the habitat quality index.(1)The results showed that from 1975 to 2010,the habitat degradation degree gradually increased,and the habitat degradation grade spatially presented a layered progressive distribution.Habitat quality presented a constantly decreasing trend.The high-value zones were mainly distributed in the mountainous areas,while the low-value zones were mostly located in built-up areas.During the period of 1975-2010,low-value zones gradually expanded to their surrounding high-value zones,and the high-value zones of habitat quality tended to be fragmented.(2)The spatial-temporal variation characteristics of habitat quality from 1975 to 2010 showed that the regions with low habitat quality were difficult to be restored and mostly maintained their original state;the regions with poor habitat quality,which accounted for 6.40%of the total study area,continued to deteriorate,mainly around built-up areas;the regions with good and superior habitat quality,which accounted for 5.68%of the total study area,were easily converted to regions with bad or poor habitat quality,thus leading to the fragmentation of the regional habitat.(3)From 1975 to 2010,land use changes in the study area were significant and had a huge influence on habitat quality;the habitat quality in the study area decreased consistently,and the area of the regions with bad and poor habitat quality accounted for more than 60%of the total study area.Construction land was the largest factor threatening habitat quality. 相似文献
In this study, a database developed from existing literature about permeability of cracked rock was established. The performance of Support Vector Machine (SVM) combined with optimisation algorithms: Genetic Algorithm (GA) and Particle Swarm Optimisation Algorithm (PSO) in predicting the permeability of cracked rock masses (CRM) is evaluated. Also, the sensitivity analysis of the influence factors to the permeability of CRM is conducted. The results indicate that the hybrid GA–SVM and hybrid PSO–SVM models can accurately predict the permeability of CRM in terms of the statistical performance criteria: Coefficient of Determination R2, Regression Coefficient R and Mean Residual Error (MSE); Additionally, optimisation algorithms: PSO and GA can improve significantly the predictive performance of the SVM model. Based on the sensitivity analysis, crack angle is the most important factor to change the permeability of CRM, followed by confining pressure.