LiDAR Data Management Lessons for Geospatial Data Managers

(1)

LiDAR Data Management Lessons

for Geospatial Data Managers

By:

By: Morris County Department of Planning, Morris County Department of Planning,

Development & Technology

(2)

Background

–

2005 Morris County

LiDAR Acquisition

• • 5 year 5 year Orthophotography Orthophotography Plan Plan •

• Beaver Brook Beaver Brook Watershed 2

Watershed 2’’

Contours

(3)

LiDAR presents the same issues, now, that

Orthophotograpy data raised 15 Years ago.

•Data storage issues

•Software rendering issues •Plotting issues

•Extraction issues

•Data distribution issues •Staff experience

(4)

LiDAR

’

s

Challenge

•

(5)

DTM

(6)

Guiness

Record Cake

Analagy

Simple Cake

Guiness

Guiness Record CakeRecord Cake

Wedding Cake

(7)

Draw from your Ortho

’

Experience

Many similarities exist between LiDAR and

Many similarities exist between LiDAR and OrthophotographyOrthophotography

– Large, continuous datasets

– Tile based storage requirements – Feature extraction potential

– Historical, change detection potential

(8)

Leverage LiDAR data beyond a single

purpose to maximize your ROI

•

• The decision to obtain LiDAR often The decision to obtain LiDAR often

originates from a single need in a single

agency.

•

• We felt a responsibility to look for data We felt a responsibility to look for data uses beyond the Beaver Brook Project,

uses beyond the Beaver Brook Project,

because LiDAR data is too valuable to

“

(9)

Managing LiDAR Data

Elevation Elevation Dataset Dataset Choices Choices Data Data Users Users Supported Supported Applications Applications Data Storage Data Storage Decisions Decisions

(10)

Data Users

Internal user needs will

dictate which datasets you

may want to store develop

•

• Telecommunications Telecommunications –– use LiDAR to examine use LiDAR to examine vegetative growth beneath power lines

vegetative growth beneath power lines

•

• Hydrologists Hydrologists –– interested in interested in DEMsDEMs to model surface to model surface water flow characteristics

water flow characteristics

•

• Engineers Engineers –– use contours for site analysisuse contours for site analysis •

• Planners Planners –– use TINS, DEMS and use TINS, DEMS and DSMsDSMs to provide to provide clearer 3 dimensional views of places for public

clearer 3 dimensional views of places for public

presentations presentations Elevation Elevation Dataset Dataset Choices Choices Data Data Users Users Supported Supported Applications Applications Data Storage Data Storage Decisions Decisions

(11)

Data Users

External user needs will

influence your data

storage decisions

•

• Typical town in Morris County requires 25-Typical town in Morris County requires 25-50 tiles of data.50 tiles of data. •

• Extremely inefficient to extract data manually –Extremely inefficient to extract data manually – especially especially when you

when you’’re considering multiple datasetsre considering multiple datasets Elevation Elevation Dataset Dataset Choices Choices Data Data Users Users Supported Supported Applications Applications Data Storage Data Storage Decisions Decisions

(12)

Data Users

Staff Constraints

• • AvailabilityAvailability •

• Technical SkillsTechnical Skills •

• Other Project CommitmentsOther Project Commitments

•

• Time ConstraintsTime Constraints

(13)

Elevation Dataset Choices

•

• You should always keep raw LiDAR data You should always keep raw LiDAR data

Addition Elevation Datasets

•

•Digital Elevation Models Digital Elevation Models (Bare Earth)(Bare Earth) •

•Surface Models Surface Models (First return and/or last return LiDAR)(First return and/or last return LiDAR) • •TINsTINs • •ContoursContours Elevation Elevation Dataset Dataset Choices Choices Data Data Users Users Supported Supported Applications Applications Data Storage Data Storage Decisions Decisions

(14)

Supported Applications

• • AutoCADAutoCAD • • ESRIESRI – ArcGIS Desktop – Spatial Analyst – 3D Analyst •

• Applied Imagery QT ModelerApplied Imagery QT Modeler

(15)

Data Storage Decisions

•

• Data Types Data Types •

• Data Manager time Data Manager time constraints

constraints

•

• Storage constraintsStorage constraints

(16)

Data Storage Decisions

•

• Need to consider the Need to consider the software and

software and

workstation limitations

when creating larger

aggregated datasets. aggregated datasets. Elevation Elevation Dataset Dataset Choices Choices Data Data Users Users Supported Supported Applications Applications Data Storage Data Storage Decisions Decisions

(17)

Data Storage Decisions

•

• Tile BasedTile Based •

• Municipal basedMunicipal based •

• County basedCounty based

•

• Watershed basedWatershed based

(18)

Data Storage Decisions

Tiling LiDAR Tiling LiDAR Elevation Elevation Dataset Dataset Choices Choices Data Data Users Users Supported Supported Applications Applications Data Storage Data Storage Decisions Decisions •

•Should be consistent with your other spatial Should be consistent with your other spatial datasets

datasets

•

•Tiling size depends on density, point spacing, of Tiling size depends on density, point spacing, of your data.

your data.

•

•If your going to reproduce your data on CDIf your going to reproduce your data on CD’’s s ––

you need to make sure your tile size complements

disk storage characteristics (650 MB CD 4.5 GB

DVD)

(19)

Data Storage Decisions

5,000’ 5,000’ Elevation Elevation Dataset Dataset Choices Choices Data Data Users Users Supported Supported Applications Applications Data Storage Data Storage Decisions Decisions Morris County Morris County LiDAR Tiles LiDAR Tiles

(20)

Indexing your LiDAR Data

OBJECTID

OBJECTID SHAPE *SHAPE * XMINXMIN YMINYMIN XMAXXMAX YMAXYMAX Image LabelImage Label

1 1 PolygonPolygon 490,000 490,000 820,000 820,000 495,000 495,000 825,000 825,000 MC490825MC490825 2 2 PolygonPolygon 480,000 480,000 800,000 800,000 485,000 485,000 805,000 805,000 MC480805MC480805 3 3 PolygonPolygon 485,000 485,000 800,000 800,000 490,000 490,000 805,000 805,000 MC485805MC485805 4 4 PolygonPolygon 490,000 490,000 800,000 800,000 495,000 495,000 805,000 805,000 MC490805MC490805 5 5 PolygonPolygon 495,000 495,000 800,000 800,000 500,000 500,000 805,000 805,000 MC495805MC495805 6 6 PolygonPolygon 500,000 500,000 800,000 800,000 505,000 505,000 805,000 805,000 MC500805MC500805 7 7 PolygonPolygon 505,000 505,000 800,000 800,000 510,000 510,000 805,000 805,000 MC505805MC505805 8 8 PolygonPolygon 510,000 510,000 800,000 800,000 515,000 515,000 805,000 805,000 MC510805MC510805 9 9 PolygonPolygon 465,000 465,000 795,000 795,000 470,000 470,000 800,000 800,000 MC465800MC465800 10 10 PolygonPolygon 470,000 470,000 795,000 795,000 475,000 475,000 800,000 800,000 MC470800MC470800 11 11 PolygonPolygon 475,000 475,000 795,000 795,000 480,000 480,000 800,000 800,000 MC475800MC475800 12 12 PolygonPolygon 480,000 480,000 795,000 795,000 485,000 485,000 800,000 800,000 MC480800MC480800 13 13 PolygonPolygon 485,000 485,000 795,000 795,000 490,000 490,000 800,000 800,000 MC485800MC485800 14 14 PolygonPolygon 490,000 490,000 795,000 795,000 495,000 495,000 800,000 800,000 MC490800MC490800 15 15 PolygonPolygon 495,000 495,000 795,000 795,000 500,000 500,000 800,000 800,000 MC495800MC495800 16 16 PolygonPolygon 500,000 500,000 795,000 795,000 505,000 505,000 800,000 800,000 MC500800MC500800

(21)

Data Extraction

•

• Typical town in Morris County requires 25Typical town in Morris County requires 25- -50 tiles of data.

50 tiles of data.

•

• Extremely inefficient to extract data Extremely inefficient to extract data manually

manually –– especially when youespecially when you’’re re considering multiple datasets

considering multiple datasets

•

• Need good file structureNeed good file structure •

• 1 need to develop scripts to perform basic 1 need to develop scripts to perform basic file i/o

(22)

Programmatic Data Extraction

(23)

Folder Based Data Extraction

(24)

Delivery Media

• • CDsCDs • • DVDsDVDs •

• Portable Hard DrivesPortable Hard Drives •

• FTP SitesFTP Sites •

(25)

? ? ? Questions ? ? ?

County of Morris

(26)

(27)

Provided

Provided LiDARLiDAR to FEMA for map to FEMA for map

modernization program

Provided Elevation datasets to all

municipalities in Morris County

(28)

' Create the RasterSurfaceOp object

' Create the RasterSurfaceOp object Dim

Dim pSurfaceOp pSurfaceOp AsAs esriGeoAnalyst.ISurfaceOpesriGeoAnalyst.ISurfaceOp Set

Set pSurfaceOp = New RasterSurfaceOppSurfaceOp = New RasterSurfaceOp

' Declare the input raster object

' Declare the input raster object Dim

Dim pInputDataset pInputDataset AsAs IGeoDatasetIGeoDataset

' Calls function to open a raster dataset from disk

' Calls function to open a raster dataset from disk Set

Set pInputDataset = OpenRasterDataset("J:pInputDataset = OpenRasterDataset("J:\\ElevationElevation\\Morris4FtDEM", "r380715")Morris4FtDEM", "r380715")

' Declare the output raster object

' Declare the output raster object Dim

Dim pOutputRaster pOutputRaster AAs IGeoDatasets IGeoDataset

' Calls the method

' Calls the method Set