Automated accuracy assessment for ridge and valley polylines using high-resolution digital elevation models

Abstract

For better understanding of geologic and geomorphic processes responsible for the formation of ridges and valleys widely distributed over the Earth's surface, accurate delineation of the features is very important. This paper intro duces an automated method for accuracy assessment of ridge and valley polylines using high-resolution digital elevation models (DEMs) derived from light detection and ranging (LiDAR) data and software implementation of a Python add-in toolbar for Esri's ArcGIS 1. Compared with existing methods that use two input polyline layers for comparison, the new method automatically extracts reference points from the DEM on-the-fly. In addition to producing statistical results of positional accuracy following the National Standard for Spatial Data Accuracy, the method also identifies locations where inaccuracies occur, so that the polylines can be edited and reevaluated. Two data sets from New Mexico and Colorado are also used as samples to demonstrate the application of the geographic information system (GIS) add-in. The results suggest that the free GIS add-in can be widely used by geoscientists.

Projection-based terrain feature line extraction from point cloud data

“…As per the results of an experiment, the correctness of this method was proved to be higher than rasterbased maximum gradient deterministic eight (D8) algorithm. Another automated method along with a Python add-in for ArcGIS software [17] was presented for assessing the accuracy of ridge and valley features using high-resolution DEMs from airborne LiDAR, eliminating the need for pre-existing reference layers and identifying positional inaccuracies. The limitations of this method include challenges with scale, resolution and U-shaped valleys.…”

“…A minor problem with this method is that it lls empty spaces in every section, thereby increasing computation time. While Maurya et al's approach to ridge and valley analysis serves as the primary inspiration for the feature line extraction method presented in this paper, the techniques developed by Zhou et al [16], Dongs [17], and Mao et al [20] provided the basis for creating a method to form a network between maxima and minima points.…”

A semi-automated method for extracting channels and channel profiles from lidar-derived digital elevation models

“…The vectorized channel feature (long.shp) is used to extract a longitudinal profile from the DEM and produce an Excel TM spreadsheet (long.xls) in the output folder. Optionally, if cross profiles are needed, the cross profiles will be generated automatically using a method developed by Dong (2017). The locations of the cross profiles are saved in a shapefile (cross.shp) in the output folder, along with individual Excel TM spreadsheets (cross1.xls, cross2.xls, etc.).…”

“…Factors affecting the positional accuracy of channels include the channel detection algorithm, mathematical morphological operation and thinning of channel cells, and raster to vector conversion. Using the channel feature as the "tested source" and the high-resolution DEM as the "reference source, " a method developed by Dong (2017) is employed to calculate the mean absolute error (MAE), the rootmean-square error (RMSE), and the National Standard for Spatial Data Accuracy (NSSDA) developed by the Federal Geographic Data Committee (FGDC, 1998). These three measures are calculated from 100 random sampling points along the channel feature.…”