Remote Sensing (RS) data can assist in the classification of landscapes to identify landslides. Recognizing the relationship between landform/landscape and landslide areas is, however, complex. Soil properties, geomorphological, and groundwater conditions govern the instability of slopes. Previous study of Wan ( 2009 Wan, S. 2009. A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map. Engineering Geology, 108: 237–251. [Crossref], [Web of Science ®] [Google Scholar] ; A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map. Engineering Geology, 108, 237–251) used the maximum-likelihood classifier to classify the multi-category landslide image data. Unfortunately, the classification does not consider the geomorphologic condition. Accordingly, a Landslide Expert System was developed to modify these problems. The system uses multi-date SPOT image data to develop the landslide database. The threshold slope which becomes vulnerable to landslides is obtained by the K-means method. Then, an innovative Data Mining technique – Discrete Rough Sets (DRS) – is applied to obtain the core variables and their relevant thresholds. Finally, the Expert Knowledge Translation Platform (EKTP) is used to create the rules for classification. This study used a new approach called ‘Rough Set Tree’ to demonstrate the performance of the approach. The classification of landslide vulnerable areas, bare land, rock, streams, and water-body is greatly improved.

The analysis, measurement, and computation of remote sensing images often require an enhanced supervised classification technique to develop an efficient spatial decision support system. Rice is a crop of global importance, which has drawn a great interest in using remote sensing techniques for evaluating its production. Ancillary information is widely used to improve the classification accuracy of satellite images. However, few of these studies questioned the importance and strategies of using this ancillary information. The enhanced decision support system in our study has two stages. In the first stage, the images are obtained from the remote sensing technique and the ancillary information is employed to increase the accuracy of classification. In the second stage, it is decided to construct an efficiently supervised classifier, which is used to evaluate the ancillary information. Back-propagation neural network (BPN) with extended delta bar delta (EDBD) algorithm is incorporated into our decision support classifier system. This classifier renders two crucial contributions: (1) the EDBD algorithm accelerates the convergence speed of the learning process and (2) the relative importance (RI) on each band of ancillary information is evaluated rationally.