Adjustment of Cadastral Network Using Least-Squares Variance Component Estimation

Abstract

Abstract. The role of the stochastic model very important in data processing of geodetic network since it describes the accuracy of the measurements and their correlation with each other. Knowledge of weights of the observables is required to provide a better understanding of the sources of errors and to model the error, hence the weights need to be determined correctly. This study concentrates on the estimation of variance components from different types of instruments used in the cadastral survey. The ideas are to combine the conventional and advanced instruments in a traverse network to enhance the estimated variance component in the stochastic model. Thus, Least Squares Variance Component Estimation (LS-VCE) method was used in this study because the method is simple, flexible and attractive due to the precision of variance estimators that can be directly obtained. Observation data come with several types of instruments such as chain measurement, Electronic Distance Measurement and total station were utilized. The findings showed that LS-VCE method was very reliable in cadastral network application. Besides, the results revealed that the estimated variance components for distance scale error, σp seem to become unrealistic for each data tested. It was found that the traverse network which included chain survey, showed the insignificant result to the precision of station coordinates when the measurements were combined.

Reliability of using LS-VCE computation in Deriving Variances for Multi-Classes Dataset

“…Involved with complex computation procedures (i.e., matrices computation), LSA has not become an option in the previous implementation due to the limitation of technology and computer processors. These equations have already been made automatic by the availability of modern computers and the least square technique has thus gained popularity [4]. On top of that, for any form of cadastral data, the LSA is relevant and has the advantage that observations of different accuracies can be correctly weighted in the computations [5].…”