We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau, High Asia, incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS), Global Land cover mapping at 30 m resolution (GlobeLand30), MODIS Land Cover Type product (MCD12Q1), Climate Change Initiative Land Cover (CCI-LC), Global Land Cover 2000 (GLC2000), University of Maryland (UMD), and GlobCover 2009 (Glob-Cover). We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources. We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation. The results of this study reveal that distinct differences, even inverse time series trends, in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015, with the exception of category areal discordance between the seven datasets. We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns, that is, high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions. The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%, and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts, yielding maximum overall accuracy (OA) values of 77.39% and 61.43%, respectively. Finally, 5.63% of this area is characterized by both high assessment and accuracy (HH) values, mainly located in central and eastern regions of the Qiangtang Plateau, while most low accuracy regions are found in northern, northeastern, and western regions.
In this paper, according to the method of electrical constraint inversion, we can improve the accuracy of inversion interpretation using the constraint of electrical logging data in the inversion process, which is conducive to obtain more abundant geoelectric information. On the basis of the conventional OCCAM inversion algorithm, the model roughness in the objective function is improved, and the formula of the electrical constraint inversion iterative algorithm is derived in detail based on the modified objective function. Taking the geothermal exploration in Changbai Mountain area as an example, the measured CSAMT data are used as the resistivity model for inversion, and the electrical property is constrained by the actual data from wells drilled in the section. It can be found that the method of electrical constraint inversion can improve the accuracy of inversion interpretation and guide the development of constrained inversion methods in different ways. 相似文献
