Wellington Elevations: Interpolating the Bathymetry

It is important to note something from the very beginning. The interpolated bathymetry developed in this project does not reflect the actual bathymetry of the Wellington Harbour. It is my best guess based on the tools I had and the data I worked with. Furthermore, this interpolation is NOT the official product of any institution in New Zealand. It is an interpolation created by me only for the purposes of visualization.

welly_harbour-colour-and-aerial_FULLVIEW

Part of the goal when visualizing the Wellington landscape was to incorporate a better idea about what may be happening below the surface of the harbor. Various bathymetric scans in the past have gathered much of the information and institutions like NIWA have done the work visualizing that data. As for myself, I did not have access to those bathymetries; however, I did have a sounding point data set to work with, so I set about interpolating those points.

The data set, in CSV format, was over a million points; too dense for a single interpolation. I worked out a basic plan for the interpolation based on splitting the points into a grid, interpolate the smaller bits, then reassemble the grid tiles into a uniform bathymetry.

Conversion from CSV to shp
Using the open option (-oo) switch, OGR will convert CSV to shp seamlessly

ogr2ogr -s_srs EPSG:4167 -t_srs EPSG:4167 -oo X_POSSIBLE_NAMES=$xname* -oo Y_POSSIBLE_NAMES=$yname*  -f "ESRI Shapefile" $outputshapepath/$basenme.shp $i

View the full script here: https://github.com/IReese/wellyvation/blob/master/utils/convertcsv.sh

Gridding the Shapefile
With the shapefile in place, I next needed to break it into smaller pieces for interpolation. For now, I create the grid by hand in QGIS using the ‘Create Grid’ function. This is found under Vector>Reasearch Tools>Create Grid. Determining a grid size that works best for the interpolation is a bit of trial and error. You want the largest size your interpolation can manage without crashing. Using the grid tool from QGIS in very convenient, in that it creates an attribute table of the xmin, xmax, ymin, ymax corrodinates for each tile in the grid. These attributes become very helpful during the interpolation process.

Interpolating the Points
I switched things up in the interpolation methods this time and tried out SAGA GIS. I have been looking for a while now for a fast and efficient method of interpolation that I could easily build into a scripted process. SAGA seemed like a good tool for this. The only drawback, I had a very hard time finding examples online about how to use this tool. My work around to was to test the tool in QGIS first. I noticed when the command would run, QGIS saved the last command in a log file. I found that log, copied out the command line function, and began to build my SAGA command for my script from there.

Here is look at the command I used:


	saga_cmd grid_spline "Multilevel B-Spline Interpolation" -TARGET_DEFINITION 0 -SHAPES "$inputpoints" -FIELD "depth" -METHOD 0 -EPSILON 0.0001 -TARGET_USER_XMIN $xmin -TARGET_USER_XMAX $xmax -TARGET_USER_YMIN $ymin -TARGET_USER_YMAX $ymax -TARGET_USER_SIZE $reso -TARGET_USER_FITS 0 -TARGET_OUT_GRID "$rasteroutput/sdat/spline_${i}"

The full script can be found here: https://github.com/IReese/wellyvation/blob/master/utils/welly_interpolation_process.sh

I tested a number of methods and landed on ‘grid_spline’ as producing the best results for the project. It was useful because it did a smooth interpolation across the large ‘nodata’ spaces.

Once the initial interpolation was complete, I needed to convert the output to GeoTIFF since SAGA exports in an .sdat format. Easy enough since GDAL_TRANSLATE recognizes the .sdat format. I then did my standard prepping and formatting for visualization:


	gdal_translate "$iupput_sdat/IDW_${i}.sdat" "$output_tif/IDW_${i}.tif"
	gdaldem hillshade -multidirectional -compute_edges "$output_tif/IDW_${i}.tif" "$ouput_hs/IDW_${i}.tif"
	gdaladdo -ro "$output_tif/IDW_${i}.tif" 2 4 8 16 32 64 128
	gdaladdo -ro "$ouput_hs/IDW_${i}.tif"2 4 8 16 32 64 128

Here is look at the interpolated harbour bathymetry, hillshaded, with Wellington 1m DEM hillshade added over top
welly_harbour_bw_all

And here is a look at the same bathy hillshade with coloring
welly_harbour_bw-and-aerial

Visualizing the Bathymetry
With the bathymetry, complete it was simply a matter of building it into the existing visualization I built for the Wellington Region. Learn more about the project here. The visualization was four steps:

Hillshade
addedbathy_bathyonlypng
Color
addedbathy_bathyonly_withcolor
Aerial Imagery
addedbathy_bathyonly_withcoloraerial
Then merge the models together
addedbathy_final

Easy as, eh? Let me know what you think!

Building the Wellington Model with 1m DEM and DSM

As interest in LiDAR derived elevation increases, so grows the interest in the capabilities. LiDAR derived elevation data has been great for my visualization game and in helping me communicate the story out about what LiDAR can do. It all starts with a picture to get the imagination going.

wellyvation

The Wellington model derived for this project is part of an ongoing project to help increase the exposure of the Wellington 1m DEM/DSM elevation data derived from LiDAR. Step one for me is getting a working model built in QGIS, capturing still images, and increasing interest in the data.

I’ve talked about the processing of the elevation data for Wellington visualizations in the past, here, so for this post I’m only focusing on the blending of the data sets in building the model. This project is a good model since it encompasses a number of subtle techniques to get the model to stand out. This post is one of a two part series; the second post discusses the techniques used to derive and visualize the bathymetry for the surrounding harbor.

Let’s start with the base, Aerial Imagery.
wellyhabour_aerialonly

Blended with a hillshade
wellyhabour_aerial_withHS

DSM added for texture and context
wellyhabour_aerial_withDSMHS

Slope added to define some edges
wellyhabour_aerial_withDSMDEMSLOPEHS

Some darker shading added to the bathymetry to frame the elevation data
wellyhabour_aerial_withDSMDEMSLOPEHS_darkenframe

And finally some added bathymetry to lighten the edges at the shoreline enhancing the frame a bit more.
wellyhabour_aerial_withDSMDEMSLOPEHS_edgeframe

In the end there is some post-processing in Photoshop to lighten up the image. Honestly, this could have been done in QGIS, but I was being lazy. For the images produced, there was no need to retain the georeferencing, and when that is the case, I rely on Photoshop for color and light balancing.

The greatest difficultly in this project so far has been trying to create a universal model for the data set. I’m finding that as I visualize different regions using this model, I need to adjust the hillshading quite significantly to draw out different features. Take a look at the images here. It is the same model, but with the noticeably different gradients used in the hillshades. The techniques used for the images in this post worked well for the urban region shown, but fall apart as you move further out into the more mountainous regions. Much of the blending is too harsh and turns the mountains into a black muddled mess. I am almost there, but like any project, it takes a good bit of subtle tweaking of the blending to get a universal image to work.

The entire base mapping work is completed in QGIS. The elevation data was processed using GDAL and the bathymetric interpolations were produced SAGA GIS. There are no color palettes for this project. The aerial imagery does all the work in that department.

Base data can be found here:
DEM: https://data.linz.govt.nz/layer/53621-wellington-lidar-1m-dem-2013/
DSM: https://data.linz.govt.nz/layer/53592-wellington-lidar-1m-dsm-2013/
Aerial Imagery: https://data.linz.govt.nz/layer/51870-wellington-03m-rural-aerial-photos-2012-2013/

The next post covers the development of the bathymetry for the surrounding harbor. Thanks for having a look and let me know what you think.

The Rejects

Sometimes there is simply not enough room for all the ideas. Sometimes you want all the images to make it to the final round.

wairarapa

In a recent project to promote some of our elevation data, I was asked to present a number of ideas for a 2000mm x 900mm wall hanging. The piece was to act as a conversation starter and demonstrate some of the finer details elevation from LiDAR possesses.

In the end, the image above was the chosen candidate. Below are the drafts I initially presented for review. You can see the difference in treatment from the original ideas to the final product. Personally, I really enjoyed the images developed for the draft series, I liked the silvery undertones, and I thought it was a shame to merely let these images sit on my hard drive.
Below, you’ll find a brief description about a few challenges faced in the image development.

near_lake_ferry
nice_farm
masterton_region
random
draft_wairarapa

Artifacts and Finer Details
The hardest part of this job was drawing out the finer details of the chosen location. There was a strong interest in showing the ancient river bed; however, without a good bit of tweaking in the hillshades, the image is quite flat. After some trial and error, I found I could get a good contrast by limiting the hillshade values range to 170-190. That’s it, but the readability of the project really hinged on the simple tweak. It really made the details stand out.
That said, the gain in detail also revealed a significant artifact in the data. If you go back up and have a closer look, you will find diagonal depressions running across the images in equal intervals. These are lines from where the LiDAR scans overlap. I haven’t quite had the time to figure out how to remove these from the original data source, so for now I leave them in as conversational piece around improving LiDAR capture practices.
As usual, all map layout work was completed on QGIS, with the bulk of the data processing done using GDAL. The ‘Reject’ images for this post are direct exports from QGIS, with no manipulation apart from some down-sampling and cropping in Photoshop.

Base data can be found here:
DEM: https://data.linz.govt.nz/layer/53621-wellington-lidar-1m-dem-2013/
DSM: https://data.linz.govt.nz/layer/53592-wellington-lidar-1m-dsm-2013/
Aerial Imagery: https://data.linz.govt.nz/layer/51870-wellington-03m-rural-aerial-photos-2012-2013/

I produced a public repository for some of the scripting work. This repository is not specific to the above project but does contain some of the base processing I did on the Wellington elevation data: https://github.com/IReese/wellyvation/tree/master/utils
Hope you like and thanks for checking in!

Processing and Visualizing Auckland 1m DEM/DSM Elevation Data

About two years ago, I took on a cartographic project visualizing the Auckland 1m DEM and DSM found publicly via the LINZ Data Service (LDS) here: DEM, DSM. The goal at the time was to develop a base map for the extraction of high resolution images for use in various static media. It was a good piece of work with some fun challenges in scripting and gradient development. Included herein are notes about processing the data with QGIS and BASH, building the gradients, and blending the base maps using QGIS.
two_volcanoes_forWebImg 1: Conference media developed for International Cartography Conference (ICC) 2018, Washington DC.
Processing the Data
The original data download was 7GB per data set (DEM and DSM). Each data set contained 6423 individual files at 1.4MB.  This set up was pretty hard to work with in QGIS; so, initially I processed each data set for ease of viewing.  This processing included grouping the data into larger files matching the LINZ Topo 50 Map Grid Sheets and running a few processes like GDALADDO (overviews), GDALDEM (hillshades), and GDALBUILDVRT (virtual mosaic).

The basic idea of formatting the data is as follows:

gdaldem hillshade -multidirectional -compute_edges input.tif output.tif
gdaladdo -ro input.tif 2 4 8 16 32 64 128
gdalbuildvrt outputvrt.vrt *.tif
Click the arrow to the left to view the full BASH script below:

#!bin/bash

# The purpose of this script is to process the Auckland 1m DEM and DSM elevation
# data into more manageable pieces for easier viewing in QGIS...  

#... The original
# elevation tile downloads from LDS contain 6423 individual tiles. The
# downloaded elevation tiles are reworked into tiffs the same size as the NZ
# LINZ Topo50 Map Sheets 
#(https://data.linz.govt.nz/layer/50295-nz-linz-map-sheets-topo-150k/).  In
# this case, the original data contains an identifier, like 'AZ31', within 
# the tile name that associates it with Topo50 Map Sheets.  This script 
# extracts that identifier, makes a list of the files containing the identifier
# name, makes a vrt of the items in the list, creates hillshades from that vrt,
# then formats for quicker viewing in QGIS.

# All data is downloaded in EPSG:2193 and in GeoTiff format
# Auckland DEM here: https://data.linz.govt.nz/layer/53405-auckland-lidar-1m-dem-2013/
# Auckland DSM here: https://data.linz.govt.nz/layer/53406-auckland-lidar-1m-dsm-2013/

# Place ZIPPED files in directory of choice

# Set root directory for project. PLACE YOUR OWN DIRECTORY HERE.
BASEDIR=[PLACE/YOUR/OWN/BASE/DIRECTORY/FILEPATH/HERE]


# Create supporting variables
dSm_dir=$BASEDIR/dSm_elevation
dEm_dir=$BASEDIR/dEm_elevation
dSm_list_dir=$BASEDIR/lists/dSmlist
dEm_list_dir=$BASEDIR/lists/dEmlist


# Create file structure
mkdir $BASEDIR/lists
mkdir $dSm_dir
mkdir $dEm_dir
mkdir $dSm_list_dir
mkdir $dEm_list_dir

# Extract data
unzip $BASEDIR/lds-auckland-lidar-1m-dsm-2013-GTiff.zip -d $dSm_dir
unzip $BASEDIR/lds-auckland-lidar-1m-dem-2013-GTiff.zip -d $dEm_dir

# Delete zipped files
# rm -rf $BASEDIR/lds-auckland-lidar-1m-dsm-2013-GTiff.zip
# rm -rf $BASEDIR/lds-auckland-lidar-1m-dem-2013-GTiff.zip

# Loop to process both DEM and DSM data
demdsm="dEm dSm"
for opt in $demdsm
do
# Variables, dEm and dSm, are created for naming purposes and moving data to
# the correct directories 
tempvar=""$opt"_dir"
tempvar_list=""$opt"_list_dir"
capvar="${opt^^}_"

	# Identify associated Topo50 map sheet name.  Make it as a list held as a variable
	unique=$( find ${!tempvar} -name "*.tif" | sed "s#.*$capvar##" | sed 's#_.*##' | sort | uniq )

	# from the 'unique' variable, create a list of files with similar Topo50 idenifier
	for i in $unique
	do
		# List all available tiffs in directory 
		namelist=$( find ${!tempvar} -name "*.tif" -maxdepth 1 )
		# Compare unique name to identifier in available tiffs name.  If 
		# there is a match between the unique name and identifier in the 
		# tiff name, the name is recorded in a list.
		for j in $namelist
		do
			namecompare=$( echo $j  | sed "s#.*$capvar##" | sed 's#_.*##' )
			echo $namecompare
			if [ $i = $namecompare ]
			then
				echo $j	>> ${!tempvar_list}/$i.txt	
			fi
		done
	done

	# Create list of available .txt file 
	listsnames=$( find ${!tempvar_list} -name "*.txt" )

	for k in $listsnames
	do
		# list contents of .txt file into variable
		formerge=$( cat $k )
		# prepare file name to use as vrt name
		filename=$( basename $k | sed 's#.txt##' )
		#echo $filename
		#echo $formerge
		# Build VRT of elevation files in same size as Topo50 grid 
		gdalbuildvrt ${!tempvar}/$filename.vrt $formerge 
	done

	# Change directory to 'Merged DEMs'
	cd ${!tempvar}

	# Make directory to store hillshade files
	mkdir hs

	# Clean out overviews
	find -name "*.vrt" | xargs -P 4 -n4 -t -I % gdaladdo % -clean

	# Create hillshade from VRTs
	find -name "*.vrt"  | xargs -P 4 -n4 -t -I % gdaldem hillshade -multidirectional -compute_edges % hs/%.tif

	# Create external overviews of VRTs
	find -name "*.vrt" | xargs -P 4 -n4 -t -I % gdaladdo -ro % 2 4 8 16 32 64 128

	# Create vrt of elevation VRTs
	gdalbuildvrt $opt.vrt *.vrt

	# change directory to hillshade directory
	cd ${!tempvar}/hs

	rename s#.vrt## *.tif

	# Clean out old overviews
	find -name "*.tif" | xargs -P 4 -n4 -t -I % gdaladdo % -clean

	# Create external overviews of HS tiffs
	find -name "*.tif" | xargs -P 4 -n4 -t -I % gdaladdo -ro % 2 4 8 16 32 64 128

	# Create vrt of Hillshade tiffs
	gdalbuildvrt "$opt"_hs.vrt *.tif

done

View the FULL BASH SCRIPT HERE via Git Gist.

Building the Gradients
Getting a natural transition through the land and sea was difficult. I was presented with two challenges; 1. building a colour gradient for elevations spanning mountain tops to undersea and 2. determining which intertidal feature to model. 

Studying Aerial Imagery from the region, I determined three zones I’d develop gradients for:

  1. Bathymetric
  2. Intertidal
  3. Terrestrial

With the gradient zones in place, I developed a few additional rules to keep the project linked visually.

  1. The colours for each gradient would be linked in tone, but distinct from each other.
  2. The bathymetry colour would frame the intertidal and terrestrial data.
  3. The deep sea blue would anchor the colour pallet.

Finally, I needed an intertidal model. Since, there are a number of different intertidal zones represented around Auckland: estuaries, sandy beaches and rocky shores and I am using only one DEM, I needed to choose which intertidal zone to represent in the image.  One colour gradient does not fit all. I had to make a choice.  I decided to use the mud flats and estuaries as the primary intertidal model.  The DEM included the channels in the mudflats and I really liked the shapes they made.  They also covered wide areas and were a prominent feature.elevation_cross_sectionImg 2: Elevation model focusing on marshy tidal zones

With the intertidal model determined and the zones set, I developed the colour gradients.

Elevation Value Colour Zone
-1.0 Bathymetric
-0.75 Bathymetric
0.5 Intertidal
1.0 Intertidal
1.7 Intertidal
1.8 Terrestrial
25 Terrestrial
100 Terrestrial
500 Terrestrial

Note: The elevation values used do not necessarily correlate with the actual elevations where these zones transition. They are a best estimation based on samplings from aerial imagery.

Blending the Layers
For the final step, I blended the layers in QGIS.  I needed the hillshades I developed from the DEM and DSM, plus the original DEM elevation. That’s it.  Three layers for the whole thing. The DEM elevation carried all the colour work, the DSM hillshades gave the detail, and the DEM hillshade added some weight to the shaded areas. Here is the order and blending for the project in QGIS:

  1. DSM Hillshade: multiply, brightness 50%, black in hillshade set to #333333
  2. DEM Hillshade: multiply, brightness 50%
  3. DEM: contrast 10%

Overall, the base image proved to be a success and the script has been useful across a number of projects. The images have ended up in a good bit of internal and conference media and I have seen steady use for almost two years now. For me, the image is getting tired and I’d eventually love to redevelop the gradients; but, I am happy to have a chance to write about it and get some more external exposure. In the future, I am looking to develop this map a bit further and present it as a web map as well. Time will tell whether this happens or not. I think it would take a directive from an outside source.

I’d be keen to hear your comments below or get in touch if you are interested in learning more.

rando_forWebImg 3: Promotional media

Open Source GIS Data Processing and Cartography Tools

header_auckland_lidar


This list is designed as a basic overview of tools I use for open source geospatial data processing and visualization. This list is not definitive. Instead, it is a collection of tools I use at various times to complete static or scalable cartographic projects as of mid-2018. This particular post is not a ‘how to’. It is just the tools with a brief description. Other posts on this site describe in detail how they are implemented.

Eight percent of my work is processing data leading to a visual product. Although unseen, it is this data work that remains the bulk of my cartographic projects and the portion I can truly say remains open source.

Some of my visual cartographic work does not involve open source tools. One disclaimer I feel is necessary to confess to the open source world is this: I use open source tools for geospatial processing; however, I rely heavily on Adobe CS for post processing static visual work. There are open source tools replacing InDesign, Illustrator, and Photoshop; however, in my professional experience, very few programs compare to the image processing capabilities of Adobe CS. Also, I must confess occasionally look to ESRI for interpolation and visualizing LiDAR point clouds.

Open Source Tools in Geospatial Data Processing

Ubuntu
I work with Linux (Ubuntu) and Windows. I use Ubuntu for all my GIS data processing and base cartographic work. I run Adobe CS off a Windows machine for post processing static published work.  Linux has a number of flavors and all provide the ease and control of terminal processing. Ubuntu, one flavor of Linux, has several GIS packages, like GDAL as part of the standard install.

QGIS
QGIS is a GIS application/editor. It is my go to for testing data and static visualization work.  QGIS is always evolving. Every year it gets better.  I rely on QGIS for the construction of base images. Basically, the base map visuals that go into more complex cartographic layouts and posters. Layouts, legends, supporting visuals, and labeling are most often handled using Adobe.

GDAL
GDAL is a geospatial command line raster processing tool. It is fast, reliable, and covers almost all the basic needs of geospatial raster processing. Reprojection, transformation, interpolation, masking, calculations, and a number of things I am completely unaware of. GDAL commands are easy to add to scripts using BASH or Python and are a breeze for batch processing. Many QGIS raster tools are built using GDAL in the backend.

Along with GDAL, users get OGR2OGR for their vector processing and conversions.

PostgreSQL/PostGIS
PostgreSQL is an open source database manager. PostGIS is the geospatial extension added to this database. Processing geospatial data inside a database is clean and efficient. PostgreSQL offers all the power of scripting and allows for complex operations and analysis on data. PostgreSQL integrates well with QGIS and tables are easily visualized using the QGIS DBManager. I use PostgreSQL/PostGIS for as much vector processing as I can and integrate it into my BASH scripts.

See also psql2shp and shp2psql for moving shapefiles between data bases. (http://www.bostongis.com/pgsql2shp_shp2pgsql_quickguide.bqg)

BASH
Ubuntus’ command processor tool, BASH offers a selection of programming tools like: for loops, while loops, variables, arrays , and lists to wrap around command line operations. BASH does not get too complex, but will go a long way. Anything that can be executed in the terminal can be built into a BASH script. Need to locate five hundred rasters of the same resolution, move and rename them? BASH can do that. Need to set up a series of GDAL processes in succession that might take three days to complete? Run them through BASH. Need to create a geospatial plugin for QGIS? BASH will NOT do that. See Python for all your plug in construction needs.

MapProxy
MapProxy is an open source tool for creating raster tile caches and serving scalable maps. For me, its most useful advantage is in its ability to create tile caches in any projection. MapProxy also acts as a map server. It will render tiles on the fly or serve tile caches as needed. This unassuming tool is easy to use and allows for a lot of customization in outputs. It also creates WMTS GetCapabilites documents and serves WMTS for testing.

CartoCSS
CartoCSS is a syntax language used to style geospatial data into dynamic, scalable maps. CartoCSS allows for ease of styling across multiple zoom levels. CartoCSS is a good language to incorporate raster blending and gradients.

Yes, I still prefer raster tiling and use Tilemill to design scalable maps. I like raster tiling because it still allows me to use raster data. Half the data used in GIS. Plus very little compares to the visual quality. Please do not leave me comments extolling the benefits of vector tiling. I do know these benefits, especially in labeling, and will discuss them later.

As part of CartoCSS, I should also note the use of Mapnik XMLs to format CartoCSS for MapProxy. Although a small step, knowing how to manipulate these documents goes a long way in developing custom projections.