Logging visualization: data pipeline documentation

leaflet howl oregonwild

November 16, 2018

The logging visualization site integrates data from multiple sources

The purpose of this post is to document the data processing steps applied to the original sources of the data, to create the layers used by the logging visualization site.

Data pipeline in a nutshell

The diagram below depicts the flow of data from the original sources (left) mapped to the site navigation structure (right).

Primary data sources

Name	Description	Format
Global Forest Change	Results from time-series analysis of Landsat images in characterizing global forest extent and change.	GeoTIFF
Clearcuts in Federal Lands	Clearcuts in federal public lands documented by Oregon Wild using aerial photography and logging records. Original dataset enhanced with the latest logging records from the US Forest Service and the BLM (below).	Shapefile
USFS Timber Harvests	United States Forest Service (USFS) dataset which depicts areas treated as a part of the Timber Harvest program of work. Activities are self-reported by Forest Service Units.	Shapefile
BLM Harvest Treatments	Bureau of Land Management (BLM) dataset which represents completed harvest land treatments on BLM managed lands in the states of Oregon and Washington. Harvest treatments are the cutting and removal or trees or biomass.	GDB

Processing

Ref	Process	Input	Output	Tools	Commands
①	Clip	Global Forest Change GeoTIFF (merged granules 50N-130W and 50N-120W) Mask Vector Layer: Oregon boundary excluding Forest Service, BLM, NPS, and BIA Mask Vector Layer: Warm Springs boundary	Clipped GeoTIFF (minus Warm Springs) Clipped GeoTIFF (Warm Springs)	QGIS	QGIS -> Raster -> Extraction -> Clip Raster By Mask Layer…
②	Merge	Clipped GeoTIFF (minus Warm Springs) Vector Layer: Clearcuts in federal lands Vector Layer: Clearcuts in Warm Springs	State-level clearcuts tiles: {z}/{x}/{y}.png	Tilemill mb-util	Tilemill -> export mb-util -> export
③	Gen	Clearcuts in Federal Lands	Federal lands clearcuts vector tiles: {z}/{x}/{y}.pbf	mapshaper tippecanoe	mapshaper -i fedcuts/fedcuts.shp -proj wgs84 -verbose -o format=geojson precision=0.0001 fedcuts.json tippecanoe –layer=fedcuts –name=fedcuts –no-tile-compression –minimum-zoom=9 –maximum-zoom=14 –simplification=20 –simplify-only-low-zooms –output-to-directory “fedcuts-vtiles/all” fedcuts.json Note: Clipped versions of underreported areas (Fremont-Winema, Mount Hood, and Siuslaw) are also generated using the above recipe
④	Split	Vector Layer: USFS Timber Harvests Vector Layer: BLM Harvest Treatments	Area-level (National Forests and BLM District Offices) detailed vector tiles: {z}/{x}/{y}.pbf	mapshaper tippecanoe	genvtiles.sh
⑤	Poly	Clipped GeoTIFF (minus Warm Springs) Clipped GeoTIFF (Warm Springs)	Private/state/tribal vector tiles: {z}/{x}/{y}.pbf	QGIS mapshaper tippecanoe	QGIS -> Raster -> Conversion -> Polygonize mapshaper -> simplify (Visvalingam, 30%), clean, filter remove-empty, filter ‘this.area>12000’, export precision 0.0001 mapshaper combine-files, export mapshaper hansen-private-state-tribal.json -each ‘assignedId=this.id’ -o format=geojson hansen-private-state-tribal-i.json tippecanoe –layer=timberharvest –name=timberharvest –no-tile-compression –minimum-zoom=6 –maximum-zoom=14 –include=assignedId –simplification=20 –simplify-only-low-zooms –coalesce –maximum-tile-bytes=100000 –output-to-directory “hansen” hansen-private-state-tribal-i.json mapshaper hansen-private-state-tribal-i.json -each ‘GIS_ACRES=Math.round(this.area*0.000247105)’ -filter-fields assignedId,YEAR,GIS_ACRES -o format=json hansen/timber-or-s-info.json