A spectral mixture model analysis of the Kuroshio variability and the water exchange between the Kuroshio and the East China Sea

For understanding more about the water exchange between the Kuroshio and the East China Sea,We studied the variability of the Kuroshio in the East China Sea(ECS) in the period of 1991 to 2008 using a three-dimensional circulation model,and calculated Kuroshio onshore volume transport in the ECS at the minimum of 0.48 Sv(1 Sv ;106 m3/s) in summer and the maximum of 1.69 Sv in winter.Based on the data of WOA05 and NCEP,The modeled result indicates that the Kuroshio transport east of Taiwan Island decreased since 2000.Lateral movements tended to be stronger at two ends of the Kuroshio in the ECS than that of the middle segment.In addition,we applied a spectral mixture model(SMM) to determine the exchange zone between the Kuroshio and the shelf water of the ECS.The result reveals a significantly negative correlation(coefficient of-0.78) between the area of exchange zone and the Kuroshio onshore transport at 200 m isobath in the ECS.This conclusion brings a new view for the water exchange between the Kuroshio and the East China Sea.Additional to annual and semi-annual signals,intra-seasonal signal of probably the Pacific origin may trigger the events of Kuroshio intrusion and exchange in the ECS.

Use of Linear Spectral Mixture Model to Estimate Rice Planted Area Based on MODIS Data

MODIS （Moderate Resolution Imaging Spectroradiometer） is a key instrument aboard the Terra （EOS AM） and Aqua （EOS PM） satellites. Linear spectral mixture models are applied to MOIDS data for the sub-pixel classification of land covers. Shaoxing county of Zhejiang Province in China was chosen to be the study site and early rice was selected as the study crop. The derived proportions of land covers from MODIS pixel using linear spectral mixture models were compared with unsupervised classification derived from TM data acquired on the same day, which implies that MODIS data could be used as satellite data source for rice cultivation area estimation, possibly rice growth monitoring and yield forecasting on the regional scale.

Definition of water exchange zone between the Bohai Sea and Yellow Sea and the effect of winter gale on it

The marine dynamic environment of the Bohai Sea and the Yellow Sea in the winter of 2006 is simulated by the Regional Ocean Modelling System（ROMS） marine numerical model. Using the simulated temperature and salinity, the water exchange zone between the Bohai Sea and Yellow Sea is defined through the Spectral Mixture Model（SMM）. The influence of winter gales on the water exchange is also discussed. It is found that the Yellow Sea water masses in winter are distributed in a ＂tongue＂ shape in the Bohai Strait region, the water exchange zone presents a zonal distribution along the margin of the ＂tongue＂, with a tendency of running from northwest to southeast, and the water exchange is intensified at the tip of the ＂tongue＂. Besides, the coastal area in the northernmost Yellow Sea does not participate in the water exchange between the Bohai Sea and Yellow Sea. The result shows that the winter gale events play a role in enhancing the water exchange. It is specifically shown by the facts： the Yellow Sea warm current is enhanced to intrude the Bohai Sea by the gale process; the water exchange zone extends into the Bohai Sea; the water exchange belt in the southern part becomes wider; the mixture zone of river runoff with the Bohai Sea water upon its entry is enlarged and shifts northwards. Within two days after the gale process, the exchange zone retreats toward the Yellow Sea and the exchange zone resulted from the Huanghe River（Yellow River） runoff also shrinks back shoreward.

Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images

Conventional change detection approaches are mainly based on per-pixel processing,which ignore the sub-pixel spectral variation resulted from spectral mixture.Especially for medium-resolution remote sensing images used in urban landcover change monitoring,land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial resolution.Thus,traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably,degrading the overall accuracy of change detection.In order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level,a novel change detection scheme is designed based on the spectral mixture analysis and decision-level fusion.Nonlinear spectral mixture model is selected for spectral unmixing,and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple composition evidences.The proposed method is tested on multi-temporal Landsat Thematic Mapper and China–Brazil Earth Resources Satellite remote sensing images for the land-cover change detection over urban areas.The effectiveness of the proposed approach is confirmed in terms of several accuracy indices in contrast with two pixel-based change detection methods(i.e.change vector analysis and principal component analysis-based method).In particular,the proposed sub-pixel change detection approach not only provides the binary change information,but also obtains the characterization about change direction and intensity,which greatly extends the semantic meaning of the detected change targets.

Change of impervious surface area and its impacts on urban landscape:an example of Shenyang between 2010 and 2017

Introduction:One of the most striking features of urbanization is the replacement of the original natural land cover type by artificial impervious surface area(ISA).However,the extent of the contribution of various environmental factors,especially the growth of 3D space to ISA expansion,and the scope and mechanism of their influences in dramatically expanding cities,are yet to be determined.The boosted regression tree(BRT)model was adopted to analyze the main influencing factors and driving mechanisms of ISA change in Shenyang,China between 2010 and 2017.Outcomes:The nearly complete-coverage ISA(≥0.7)increased from 42%in 2010 to 47%in 2017.The percentage of landscape with a high ISA fraction increased,while the landscape evenness and diversity of ISA decreased.The BRT analysis revealed that elevation,regional population density,and landscape class had the largest influences on the change of urban ISA,contributing 22.55%,18.16%,and 11.18%to the model,respectively.Conclusion:Overall,topographic and socioeconomic factors had the greatest influence on urban ISA change in Shenyang,followed by land use type and building pattern indices.The trend of high aggregation was strong in large commercial and residential areas.The 3D expansion of the city had an influence on its areal expansion.