Mapped drainage ditches in forested landscapes

Published: 15 July 2022| Version 1 | DOI: 10.17632/zxkg43jsx8.1
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Description

This data contains 1607 km of manually digitized ditch channels from Sweden and high-resolution digital elevation models for the same areas. The data was digitized in 10 regions that were dominated by forests. Each region was selected to achieve a good representation of different landscape properties concerning topography, soil conditions, runoff, land use, and tree species. A compact laser-based system (Leica ALS80-HP-8236) was used to collect LiDAR data from an aircraft flying at 2888 -3000 m. The ALS point clouds have a last and only return density of 1-2 points m-2 and were divided into 55 tiles with the size of 2.5 x 2.5 km each and covering a total of 344 km2. DEMs with 0.5 m resolution were created from the LiDAR point clouds using a tin gridding approach implemented in Whitebox tools 1.4.0 (https://github.com/jblindsay/whitebox-tools). The data were digitized in a collaboration between the Swedish university of agricultural sciences: https://www.slu.se/ and the Swedish Forest Agency: https://www.skogsstyrelsen.se/ ditchlines.zip contains vector lines of the digitized ditch channels. vectorfiles.zip contains polygons where the ditches were digitized and the extent of the raster tiles. rastertiles.zip contains raster tiles with a resolution of 0.5 m covering the same areas.

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Steps to reproduce

A compact laser-based system (Leica ALS80-HP-8236) was used to collect LiDAR data from an aircraft flying at 2888 -3000 m. The ALS point clouds have a last and only return density of 1-2 points m-2 and were divided into 55 tiles with the size of 2.5 x 2.5 km each and covering a total of 344 km2. DEMs with 0.5 m resolution were created from the LiDAR point clouds using a tin gridding approach implemented in Whitebox tools 1.4.0 (https://github.com/jblindsay/whitebox-tools). Ditches were manually digitized as vector lines based on a hillshaded DEM and an HPMF. This made it easy to separate local ridges from local depressions (e.g. ditch channels).

Categories

Hydrology, Machine Learning, Spatial Analysis, Forest Management, Drainage Ditch, Deep Learning

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