https://www.tandfonline.com/doi/abs/10.1080/13658810802467969 Abstract We describe a method of morphometric characterisation of landform from digital elevation models (DEMs). The method is implemented first by classifying every location into morphometric classes based on the mathematical shape of a locally fitted quadratic surface and its positional relationship with the analysis window. Single‐scale fuzzy terrain indices of peakness, pitness, passness, ridgeness, and valleyness are then calculated based on the distance of the analysis location from the ideal cases. These can then be combined into multi‐scale terrain indices to summarise terrain information across different operational scales. The algorithm has four characteristics: (1) the ideal cases of different geomorphometric features are simply and clearly defined; (2) the output is spatially continuous to reflect the inherent fuzziness of geomorphometric features; (3) the output is easily combined into a multi‐scale index across a range of operational scales; and (4) the standard general morphometric parameters are quantified as the first and second order derivatives of the quadratic surface. An additional benefit of the quadratic surface is the derivation of the R 2 goodness of fit statistic, which allows an assessment of both the reliability of the results and the complexity of the terrain. An application of the method using a test DEM indicates that the single&; and multi;scale terrain indices perform well when characterising the different geomorphometric features. Barn Owl
OOOOOOH. MY EYES ARE BLEEDING. This stuff is hard enough to read, but without paragraphs, it’s killing my eyes.
EXPERIMENT TO SEE IF PARAGRAPHS CAN BE MADE https://www.tandfonline.com/doi/abs/10.1080/13658810802467969 Abstract We describe a method of morphometric characterisation of landform from digital elevation models (DEMs).
The method is implemented first by classifying every location into morphometric classes based on the mathematical shape of a locally fitted quadratic surface and its positional relationship with the analysis window.
Single‐scale fuzzy terrain indices of peakness, pitness, passness, ridgeness, and valleyness are then calculated based on the distance of the analysis location from the ideal cases. These can then be combined into multi‐scale terrain indices to summarise terrain information across different operational scales.
The algorithm has four characteristics: (1) the ideal cases of different geomorphometric features are simply and clearly defined;
(2) the output is spatially continuous to reflect the inherent fuzziness of geomorphometric features;
(3) the output is easily combined into a multi‐scale index across a range of operational scales; and (4) the standard general morphometric parameters are quantified as the first and second order derivatives of the quadratic surface.
An additional benefit of the quadratic surface is the derivation of the R 2 goodness of fit statistic, which allows an assessment of both the reliability of the results and the complexity of the terrain.
An application of the method using a test DEM indicates that the single&; and multi;scale terrain indices perform well when characterising the different geomorphometric features. Barn Owl