## 4 Geomorphological Classification  
The term geomorphology refers to landform features, which result from physical, biological, and chemical processes. There are several methods used to interpret geomorphological maps, e.g., manual interpretation using orthophoto visualization and field surveys by geomorphologists or other experts, and automated classification without human expertise utilizing remote sensing data. In this research, automatic classification was applied using a DEM based on geo-signatures, i.e., slope, convexity, and texture referenced from the past study of [PERSON] and [PERSON] (2007) [9]. Few geomorphological maps are available in Thailand. The current practice in seismic studies in Thailand bases the soil characteristics on geology. The concept of geomorphology in Thailand was first introduced by Japanese researchers. The purpose of early geomorphology studies in Thailand was for flood disaster planning and mitigation. The map production method involves manual interpretation by specialists based on aerial images, which is combined with available maps or other data [34; 35]. In past studies, geomorphological maps of Thailand were available only in Bangkok watershed areas. Since no geomorphological map is available in the study area, the geomorphological classification was also covered in this study. Later, studies of geomorphological mapping pivoted toward automatic classification based on DEMs, owing to less time being required and without the essential need for expertise in interpretations. The geo-signatures that appear in past studies comprise slopes, aspects, elevations, convexities (curvatures), textures (peaks, pits, ridges, and passes) and the normalized difference vegetation index (NDVI) [36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49]. Different studies proposed the use of the methodology with different techniques, which require different DEM resolutions, filter window sizes, and numbers of classifying units. The literature review in this research scaled down the factors of classification to the classification method, i.e., PBC, OBC, AOI (such as the coverage scene size of the study area), and DEM resolution. The studies of [PERSON] and [PERSON] (2007), and [PERSON] et al. (2018), were selected in this study [9; 50], as these researchers utilized the same geo-signature indices with the PBC and OBC methods, respectively. The indices include slope, convexity, and texture (i.e., the density of pits and peaks). In the PBC presented in the [PERSON] and [PERSON] (2007) study, the digital number values (DN) from the three indices were classified by a decision tree with the mean value threshold of each signature [9]. As the classification is based on the mean value, the number of pixels involved, and the resolution affect the result. The methodologies of two AOIs, i.e., provincial and continental coverage scales, were applied in the current study. AOI1 covers Chiang Mai, Chiang Rai, and part of Lumbhun, while AOI2 covers continental Asia-Oceania (see Figure 2). Both scenes were available in 1000 and 250 m resolutions. The classification of four samples resulted in 16 classes. The description of classified categories of geomorphologic units (see Figures 3 and 4) is applicable to the global landscape broad definition, which should be referred to with caution. To obtain 16 geomorphology units, four major groups of geomorphology units were first classified, i.e., mountain, high-land slope, weak rock plain, and low-land plain; changes in AOIs and resolutions affect the pixels in these four major classes. Thermal change detection was applied to detect the movement of the class changing for each pixel between each sample, according to the changes in the AOIs and resolutions. The results in this study that followed the methodology of automating PBC from the past research of [PERSON] and [PERSON] (2007) using four samples, suggested that the best factors were in PBC (see Section 6.1). The best PBC results were then compared with the OBC results from the study of [PERSON] et al.