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In this study, we investigate the use of morphometric parameters and fuzzy membership functions to perform landform classification for different case areas of Zagros Mountains from digital elevation models (DEMs). First, multiscale DEMs with scales of 5 to 45 cells are generated using the lifting scheme. The maximum curvature for the scale of five cells has the lowest standard deviation, and hence, is determined to be the characteristic scale. Data layers are produced from the DEM of this scale for slope, minimum and maximum curvatures, and plan and profile curvatures. The fuzzy membership rules for these data layers are used to determine the landform classes. Comparison of the results of landform classification using the fuzzy classification method and topographic position index (TPI) with the geology map of the study area show that the fuzzy classification method provides higher accuracy (81 %) as compared to TPI (42 %). This is because for the fuzzy classification method, sloping areas are separated into sloping and non-sloping areas, and the membership functions are defined to prevent landforms belonging to the sloping areas from being classified in the non-sloping areas and vice versa.

In this paper, it is proposed that the mapping of uncertainties of the three predominant physiographic features of terrains, which are mountain, basins and piedmont slopes, using variation in the spatial resolution over which these landforms are defined, can be performed with fuzzy classification. The proposed methodology allows for the generation of fuzzy certainty maps which assign high levels of uncertainty to regions with high levels of change across scales. This paper demonstrates that fuzzy certainty maps provide a better quantification of landform character than Boolean landform maps alone. In terms of sensitivity to noise, the methodology is able to identify narrow bridges, and spurious landforms, and assign these errors with low certainty values. However, it is unable to identify spurious modifications to landform shape, with these errors being assigned high certainty values. Ground truth maps are required to identify these errors.

Analysis of surface textures of physiographic features extracted from multiscale digital elevation models via grey level co-occurrence matrix This paper is aimed at employing grey level co-occurrence matrix (GLCM) to analyse the surface textures of physiographic features extracted from multiscale digital elevation models (DEMs). Four GLCM parameters, energy, contrast, autocorrelation and entropy, are computed for horizontal (0°), vertical (90°) and diagonal (45 and 135°) cell pair orientations. For the respective DEMs and physiographic features, varying patterns are observed in the plots of the GLCM parameters due to varying surface profiles and the changes that occur over the scales. Due to the smoothing of the terrain during multiscaling, the features have increasing values of energy and entropy, and decreasing values of contrast and entropy, indicating decreasing roughness. Mountains have the highest roughness as compared to the other features over the scales, while basins have the lowest roughness. For each parameter, similar trends are observed in the plots for the four different cell pair orientations, indicating similar trends of change of surface texture in the different orientations over the scales. However, varying values are observed for the different orientations, depending on textural uniformity in the corresponding orientations. The results obtained demonstrate that GLCM can be an appropriate tool for classifying landforms from multiscale DEMs based on the different texture characteristics of the landforms.

In this paper, a modification of the algorithm proposed by Ahmad Fadzil et al. (2011) for surface roughness computation from digital elevation models (DEMs) via multiscale analysis is presented. The new algorithm takes into account that the three predominant physiographic features of terrains (mountains, basins and piedmont slopes) have distinct curvature region distributions and hence, distinct roughness characteristics. To this end, the surface roughness of individual cells of DEMs is computed by identifying the curvature region distribution and roughness characteristics of each individual mountain, basin and piedmont slope region. The modified algorithm allows for the localisation and quantification curvature regions over varying scales for specific regions, providing a more appropriate surface roughness parameter.

In this paper, an algorithm to compute surface roughness of digital elevation model (DEM) terrains via multiscale analysis is proposed. The algorithm employs the lifting scheme to generate multiscale DEMs. At each scale, the areas of pixels that are modified are computed. Granulometric analysis is employed to compute the average area of curvature regions in the terrain, and the average roughness of the terrain due the distribution of curvature regions. The selected case studies of the algorithm implementation demonstrated that the proposed algorithm provides a surface roughness parameter that is realistic with respect to the amplitudes and frequencies of the terrain, invariant with respect to rotation and translation, and has intuitive meaning. The algorithm allows for a good quantification of a region's convexity/concavity over varying scales, distinguishing between shallow and deep incisions of valleys and ridges of the terrain, and hence, provides an accurate surface roughness parameter.

In this paper, the effect of morphological smoothening by reconstruction on the extraction of peaks and pits from digital elevation models (DEMs) is studied. First, a mathematical morphological based algorithm to extract peaks and pits from DEMs is developed. Morphological smoothening by reconstruction is then implemented on the DEM of Great Basin using kernels of different sizes. The number of peaks and pits extracted from the resultant DEMs is computed using connected component labeling and the results are compared. This work is aimed at studying the capabilities of morphological smoothening by reconstruction in the modeling of objects and processes operating within an environment.

The derivation of spatial significance is an important aspect of geospatial analysis and hence, various methods have been proposed to compute the spatial significance of entities based on spatial distances with other entities within the cluster. This paper is aimed at studying the spatial significance of mountain objects extracted from multiscale digital elevation models (DEMs). At each scale, the value of spatial significance index SSI of a mountain object is the minimum number of morphological dilation iterations required to occupy all the other mountain objects in the terrain. The mountain object with the lowest value of SSI is the spatially most significant mountain object, indicating that it has the shortest distance to the other mountain objects. It is observed that as the area of the mountain objects reduce with increasing scale, the distances between the mountain objects increase, resulting in increasing values of SSI. The results obtained indicate that the strategic location of a mountain object at the centre of the terrain is more important than its size in determining its reach to other mountain objects and thus, its spatial significance.