DEM Production/Updating Based on Environmental Variables Modeling and Conflation of Data Sources

Availability of digital elevation models （DEMs） of a high quality is becoming more and more important in spatial studies. Standard methods for DEM creation use only intentionally acquired data sources. Two approaches which employ various types of data sets for DEM production are proposed： （1） Method of weighted sum of different data sources with morphological enhancement that conflates any additional data sources to principal DEM, and （2） DEM updating methods of modeling absolute and relative temporal changes, considering landslides, earthquakes, quarries, watererosion, building and highway constructions, etc. Spatial modeling of environmental variables concerning both approaches for （a） quality control of data sources, considering regions, （b） pre-processing of data sources, and （c） processing of the final DEM, have been applied. The variables are called rate of karst, morphologic roughness （modeled from slope, profile curvature and elevation）, characteristic features, rate of forestation, hydrological network, and rate of urbanization. Only the variables evidenced as significant were used in spatial modeling to generate homogeneous regions in spatial modeling a-c. The production process uses different regions to define high quality conflation of data sources to the final DEM. The methodology had been confirmed by case studies. The result is an overall high quality DEM with various well-known parameters.

Thoughts of a travelling ecologist 14. Structure-function conflation in environmental risk assessment and monitoring

Recently,I attended a conference organised by the European Food Safety Authority in the beautiful and prosperous Italian city of Parma.The overall topic of the conference was risk assessment,and the program included a section on aspects of environmental risk assessment.In various areas,including the evalution of the effects of pesticide applications,invasive organisms or genetically modified plants(Arpaia et al.,2014)preparing an environmental risk assessment is an obviously relevant exercise.

Error matching detection and robust estimation adjustment approach for map conflation

The conflation of geographic datasets is one of the key technologies in the realm of spatial data capture and integration in geographic information system (GIS). Map conflation is a complex process of matching and merging spatial data. Due to various reasons such as errors in original data related to map data discrepancies,a great amount of uncertainties exists during the process and this will result in errors in featuring matching,especially point feature. Thus,it is vital to develop the method to detect the errors in feature matching and further the conflation results will not be affected. In this paper,error matching detection and robust estimation adjustment methods are proposed for map conflation. The characteristics of errors in feature matching are first analyzed,then a new approach for map conflation based on the least-squares adjustment is presented,and a robust estimation adjustment method is further proposed to detect and process matching errors. The results of the map conflation test show that the proposed method not only determines the errors in feature matching,but also obtains the optimal merging results in map conflation.

Automatic Road Change Detection and GIS Updating from High Spatial Remotely-Sensed Imagery

This paper presents a framework for road network change detection in order to update the Canadian National Topographic DataBase (NTDB). The methodology has been developed on the basis of road extraction from IRS\|pan images (with a 5.8 m spatial resolution) by using a wavelet approach. The feature matching and conflation techniques are used to road change detection and updating. Elementary experiments have showed that the proposed framework could be used for developing an operational road database updating system.

＂Long Live King Albany!＂： The History of King Lear （1608） and the Revolution in Shakespeare Studies

A revolution in Shakespeare studies over the past three decades asks that we take earlier quarto versions of his plays as fully independent texts. In no case can such a change in our outlook yield more illuminating results than a serious look at the 1608 Quarto version of Shakespeare＇s King Lear. The 1623 Folio version and the ＂conflated＂ modem editions, which combine the ＂best＂ readings of the Quarto and Folio versions, both anoint his godson Edgar, the Earl of Gloucester, as the new king at the end. However, the 1608 Quarto version elevates, not Edgar, but Lear＇s son-in-law, the Duke of Albany, to the kingship at the end. Shakespeare may be telling the same story in texts that seem strikingly similar, but the difference in the endings shifts the center of gravity in both plays, requiring an interpretation that makes sense out this difference. The title page of the 1608 Quarto version of King Lear describes it as a ＂history＂; yet this ＂same＂ King Lear is listed as a ＂tragedy＂ in the 1623 Folio. The difference between those two words is the key to why Shakespeare＇s two versions could have startlingly different outcomes.