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An ArcGIS toolbox for identifying nested depressions in digital elevation models (DEMs)

Home Page: https://doi.org/10.6084/m9.figshare.8866178

Python 100.00%
python lidar digital-elevation-model gis remote-sensing image-processing geospatial

depression-analysis-toolbox's Introduction

Depression-Analysis-Toolbox

This repository contains an ArcGIS toolbox and associated Python scripts for delineating nested depressions in digital elevation models (DEMs).

References

  • Wu, Q., Liu, H., Wang, S., Yu, B., Beck, R., & Hinkel, K. (2015). A localized contour tree method for deriving geometric and topological properties of complex surface depressions based on high-resolution topographic data. International Journal of Geographical Information Science, 29(12), 2041-2060. https://doi.org/10.1080/13658816.2015.1038719

  • Wu, Q., & Lane, C. R. (2016). Delineation and quantification of wetland depressions in the Prairie Pothole Region of North Dakota. Wetlands, 36(2), 215-227. https://doi.org/10.1007/s13157-015-0731-6

  • Wu, Q., Deng, C., & Chen, Z. (2016). Automated delineation of karst sinkholes from LiDAR-derived digital elevation models. Geomorphology, 266, 1-10. https://doi.org/10.1016/j.geomorph.2016.05.006

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