Abstract: In order to understand the local application of RS and GIS in classifying landscape and cartography, data of Landsat-8 OLI images and ASTER GDEM were used to landscape classification and cartography in Longquanyi District, Chengdu. The results showed that, ISODATA remote sensing image unsupervised classification method could automatically classify the types of landscape in the study area. The method could not only reduce the effects of manmade subjective judgments in the traditional classification, but also distinguish small scale landform types such as the valley and shallow hill to ensure the continuity and gradual changes of surface morphology. Compared with C5.0 and MLC, QUEST showed the higher overall classification accuracy, Kappa coefficient, average user accuracy, and average mapping accuracy of classification. Moreover, the average misclassification error and average omission error showed the order as QUEST < C5.0 < MLC, indicating that the QUEST decision tree classification method is the best in classifying land use/cover type in the study area. In addition, the combination with ArcGIS spatial analysis, map compilation technology and Python programming displayed much strong practicability, since the combined method could overcome the limitation of GIS platform general function and then improve the mapping efficiency. The Longquanyi District was divided into 18 kinds of landscape types. The landscape distribution characters were consistent with the actual regional landscape pattern. The results here suggest that the integrated application of QUEST remote sensing image decision tree classification, GIS spatial analysis and map compilation, Python programming technique take multiple landscape ecological classification indexes into consideration, and efficiently realize the landscape type classification and cartography in the study area. RS and GIS technology showed strong popularization and application values in mesoscale landscape classification and cartography.

Key words: non-ratio vegetation index, topographic effect, incomplete ratio vegetation index, complete ratio vegetation index, geometric optical model.