Journal of Geographical Sciences - Lake ice phenology is considered a sensitive indicator of regional climate change. We utilized time series information of this kind extracted from a series of... 相似文献
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.
A change in the elevation of bare tidal flats outside a mangrove area is an indispensable factor for the sustainable development of mangroves. Waterline extraction, as an effective and economical tool used in reconstructing the terrain of an intertidal zone, has been widely applied to open-coast tidal flats by constructing a digital elevation model (DEM). However, mangrove wetlands are usually located in wave-sheltered sites, such as estuaries and bays that have narrow tidal channels flanked by tidal flats. Changes in water level are affected by the dry-wet processes of complex landforms caused by tides. This article takes as a study case the area of Yingluo Bay, which covers the core region of the Zhanjiang and Shankou National Mangrove National Nature Reserve in southwestern China. Waterline extraction based on seventeen multisource and multispectral satellite images obtained from December 2014 to April 2015, combining the finite-volume coastal ocean model (FVCOM) hydrodynamic model in an iterative process, was used to generate a topographical map of the bare tidal flat outside the mangrove area in Yingluo Bay. The quality of the iterative DEMs was evaluated via six transects of a ground-based survey using Real - time kinematic (RKT) GPS in May 2015. The mean absolute error (MAE) and root mean square error (RMSE) of the DEM decreased with an increase in the number of iterations. In this study, the DEM in the third iteration was used as the final output because the difference from the previous iterative DEM satisfied an inversion-stopping criterion. The MAE and RMSE of the final DEM with the measured data were 0.072 and 0.09?m, respectively, without considering small tidal creeks. The method used in this study can be an effective and highly precise approach for detecting and reconstructing the historical terrain of a bare tidal flat outside a mangrove area. This work also has great importance regarding intertidal resource management and the sustainable development of mangroves facing the vulnerable coastal ecological environment. 相似文献
The tracking control problem of AUV in six degrees-of-freedom (DOF) is addressed in this paper. In general, the velocities of the vehicles are very difficult to be accurately measured, which causes full state feedback scheme to be not feasible. Hence, an adaptive output feedback controller based on dynamic recurrent fuzzy neural network (DRFNN) is proposed, in which the location information is only needed for controller design. The DRFNN is used to online estimate the dynamic uncertain nonlinear mapping. Compared to the conventional neural network, DRFNN can clearly improve the tracking performance of AUV due to its less inputs and stronger memory features. The restricting condition for the estimation of the external disturbances and network's approximation errors, which is often given in the existing literatures, is broken in this paper. The stability analysis is given by Lyapunov theorem. Simulations illustrate the effectiveness of the proposed control scheme. 相似文献
