Results, Analysis and Discussion

Abstract

This chapter deals essentially with the results of this thesis, that are then followed with analysis and discussions. This chapter presents the various LULC-features classification results (the wide major classes, the irrigated areas development mapping, and the small detailed agricultural classes), and their accuracies. Factors which influence the classification results are also discussed. This chapter illustrates the various LULC-change detection mapping results (pre-classification approach results and post-classification approach results), and discusses the successes and the limitations of applying the various remotely sensed data used in this study, to satisfy investigation into the objectives of the thesis. Statistical records do not contain all elements of the irrigation projects. The second step in this research involves employing remotely sensed data to obtain statistical numbers which represent the areas in over past periods. Here, again, emerges the integration between statistical data and remote sensing data in study land uses, distribution of natural coverage and its change across time. In the first step (see Chap. 5.10), statistical numbers have been useful in the spatial determination of the spread of the targeted needed classes, and are represented in the automated classification process in order to represent the spectral characteristics of all classes. Plus the use of the total statistical records in evaluation, the accuracy of the classification needs to be determined through comparison of the final results of the automated classification with the results of the traditional human-based survey. The second step, after obtaining the training-samples from the irrigation projects which have statistical records or by using the available GPS-points as training-samples, is to determine the statistics of the regions which have no governmental statistical data.