Lidar use for wetlands Annual MN wetlands conference January
18, 2012 Lian Rampi Joseph Knight
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Agenda What is Lidar? Wetland mapping methods Conclusions
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Lidar 101 What is Lidar? Light Detection and Ranging is an
active remote sensing technology that uses laser light (laser beams
up to 150,000 pulses per second) Measures properties of scattered
light to find range and other information of a distant target One
of the most accurate, suitable and cost-effective ways to capture
wide-area elevation information (vs. ground survey)
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Lidar 101 What is Lidar? Utilize a laser emitter-receiver
scanning unit, a GPS, an inertial measurement unit (IMU) attached
to the scanner, on board computer and a precise clock Data is
directly processed to produce detailed bare earth DEMs at vertical
accuracies of 0.15 meters to 1 meter Lidar cannot penetrate fully
closed canopies, water, rain, snow and clouds
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All available data is currently accessible via anonymous ftp
at: http://www.mngeo.state. mn.us/chouse/elevation/li dar.html
lidar.dnr.state.mn.us
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WETLAND MAPPING METHODS
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Wetland mapping methods Elevation data only 1)DEM resolution
for a Compound Topographic Index (CTI) Data fusion 2) Combination
of CTI, NDVI and soils data 3)Random Forest (RF) Classifier
4)Object based classification
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Wetland mapping methods Elevation data only 1)DEM resolution
for a Compound Topographic Index (CTI)
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Wetland mapping methods Elevation data only 1)DEM resolution
for a CTI What is the CTI: Indicator of potential saturated and
unsaturated areas within a catchment area (e.g. a watershed)
Function of the Natural log (ln) of the Specific Catchment Area
(As) in m and the Tangent (tan) of the slope ( ) in radians CTI =
ln [(As)/ (Tan ( )]
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Wetland mapping methods Elevation data only 1)DEM resolution
for a CTI Study area
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Wetland mapping methods Elevation data only 1)DEM resolution
for a CTI Goal: assess the CTI to examine how sensitive this index
is to the spatial resolution of several DEMs while predicting
wetlands 3 m Lidar 9 m Lidar 10m * 12m Lidar 24 m Lidar 30m * 33 m
Lidar * DEMs from the 10 m National Elevation Data and 30 m from
USGS
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Wetland mapping methods Elevation data only 1)DEM resolution
for a CTI Results
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Wetland mapping methods Elevation data only 1)DEM resolution
source Accuracy assessment results CTI (Threshold: CTI>= median
+ 1/2 sd) DEM%Overall Acc% User. Acc% Prod. Acc 3m lidar866885 9m
lidar887288 12m lidar897388 24m lidar907687 33m lidar907786 10 m
NED887677 30 m USGS847469 Accuracy Assessment using a local
reference data (wetland size: from 0.1 acres to 788 acres )
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Wetland mapping methods Elevation data only 1)DEM resolution
for a CTI Accuracy assessment results Omission Error Commission
Error
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Wetland mapping methods Data fusion 2)Combination of CTI,
Normalized Difference Vegetation Index (NDVI) and soils data
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Wetland mapping methods Data fusion 2)Combination of CTI, NDVI
and soils data Boolean and arithmetic steps using Spatial Analyst
tool from ArcGIS software Goal: Investigate the effectiveness of
combining CTI, NDVI, and hydric soils for mapping wetland
boundaries Data sets used: 24m CTI (Lidar) Hydric Soils NDVI = (NIR
band RED band ) / (NIR band + RED band)* * NDVI calculated from the
NAIP imagery, 2008
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Wetland mapping methods Data fusion 2)Combination of CTI, NDVI
and soils data Assumption behind NDVI
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Wetland mapping methods Data fusion 2)Combination of CTI, NDVI
and soils data Accuracy assessment results AcresCombination DEM
%Overall Acc % User. Acc % Prod. Acc 0.1 to 788CTI24m907687 0.1 to
788CTI + NDVI + Soils24m928286 >= to 1CTI + NDVI +
Soils24m928289
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Wetland mapping methods Data fusion 2)Combination of CTI, NDVI
and soils data Results
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Wetland mapping methods Data fusion 3) Random Forest (RF)
Classifier
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Wetland mapping methods Data fusion 3) Random Forest (RF)
Classifier Goal: investigate the use of the RF classifier for
mapping wetlands using different data types Study area: a small
area of the Big Stone lake park sub- watershed in Big Stone County,
MN
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Wetland mapping methods Data fusion 3)Random Forest (RF)
Classifier: Study area
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Wetland mapping methods Data fusion 3)Random Forest (RF)
Classifier Data sets used: Lidar DEM, Lidar intensity, Spring
2010(leaf off conditions) CTI derived from the 3m lidar DEM NAIP
imagery 2008, Leaf On aerial imagery Hydric Soils * Organic Matter
* Slope *NRCS SSURGO database
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Wetland mapping methods Data fusion 3)Random Forest (RF)
Classifier Data Used Lidar intensity
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Wetland mapping methods Data fusion 3)Random Forest (RF)
Classifier Data Used DEM and Slope (Lidar)
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Wetland mapping methods Data fusion 3)Random Forest (RF)
Classifier Data used CTI (Lidar)
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Wetland mapping methods Data fusion 3)Random Forest (RF)
Classifier Results Intensity Green band CTI Blue band Red band IR
band DEM Hydric Soils Slope OM Random Forest results: Top 10
important variables Mean Decrease Gini
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Wetland mapping methods Data fusion 3) Random Forest (RF)
Classifier - Results Partial dependence on Intensity Partial
dependence on Green bandPartial dependence on CTI Partial
dependence on DEM Partial dependence on IR band Intensity Green
bandCTI IR band DEM
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Wetland mapping methods Data fusion 3) Random Forest (RF)
Classifier Results UB (Unconsolidated bottom) EM (Emergent wetland)
CW (Cultivated wetland)
Wetland mapping methods Data fusion 4) Object based
classification
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Wetland mapping methods Data fusion 4)Object based
classification Goal: Evaluate the performance of an object based
classification for identifying wetlands Data sets used 2003, 2008
NAIP leaf on imagery 2005 NAIP leaf off imagery NDVI leaf off 2005
and leaf on 2008 3 m DEM Slope CTI 3m Thematic lake layer
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Wetland mapping methods Data fusion 4)Object based
classification Pilot study area The Northeast and Central East area
of the city of Chanhassen Good representation of the variety of
wetland types in the entire city
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Wetland mapping methods Data fusion 4) Object based
classification Methodology 1.Image segmentation 2.Hierarchical
object-based classification These objects were classified either as
wetlands or uplands/others : Urban areas: residential areas,
buildings and roads Lakes Tree canopy Agricultural fields Grasses
and bare soils
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Wetland mapping methods Data fusion 4) Object based
classification Methodology 2) Hierarchical object-based
classification based on the following attributes: Shape Color
Texture Object features : NDVI values Imagery brightness values
Infrared band & red band mean values reflectance from optical
imagery
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Wetland mapping methods Data fusion 4) Object based
classification Methodology Main algorithms used: Image
classification Image object fusion Morphology operations Geographic
Information System (GIS)-post processing to generalize objects
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Wetland mapping methods Data fusion 4) Object based
classification Results OBIA wetland polygons
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Wetland mapping methods Data fusion 4) Object based
classification - Results OBIA wetland polygons North East area,
Chanhassen CityCentral East area, Chanhassen City
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Wetland mapping methods Data fusion 4) Object based
classification - Results OBIA wetland polygons Reference data
wetlands polygons North East area, Chanhassen CityCentral East
area, Chanhassen City
Accuracy assessment Combination %Overall Acc% User. Acc% Prod.
Acc CTI 24 m 907687 CTI + NDVI + Soils Boolean and arithmetic
classification 928289 Random Forest Classification 919489
Object-based Classification 958791
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CTICombination CTI + Soils + NDVI Random Forest OBIA with
eCognition Developer ProsRequires Elevation data only Lidar is
available for most part of MN Open Source program available for CTI
calculation: Whitebox GAT Free extensions and toolbox (TauDEM,
ArcHydro) for ArcGIS 9.3 Help to solve the problem of wetlands
topographically suitable for wetlands because of the low elevation
Soil data and NAIP aerial imagery (1 m ) available to the public
(no charge) Combination bring all layers together and increase
accuracy of wetland identification Free Software package Output
graphs of key variables, Gini index, confidence maps, and land
classification GUI interface of Random Forest required same size
resolution and grid alignment for land cover classification map
output Allow data fusion of different type of data and spatial
resolution Classification of objects shapes (groups of homogeneous
pixels) Allows to add more elements of image interpretation beside
spectral characteristics for classification of objects ConsDoes not
work well for every area in the landscape with low elevation
Technical knowledge to process Lidar data Require ESRI extension
(Spatial analyst: raster calculator, reclassify) Require manual
reclassification steps Necessary statistical knowledge and ability
to interpret results Software requirement expensive CPU storage
requirements for faster processing Pros and cons of each
method
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CONCLUSIONS
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1) DEM quality is important for the development of terrain
indices used for mapping wetlands. 2) LIDAR DEM outperforms 10 m
NED & 30 m USGS in accuracy assessment. 3) Random forest helped
to determine key input variables for wetland mapping classification
and resulted in higher accuracy for wetland mapping.
Conclusion
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4) Combination of lidar DEM, CTI, aerial imagery and NDVI for
an object based classification performs better with higher overall
accuracy compared to the CTI method. 5)Several factors to keep in
mind to decide which method is the best for wetland mapping.
Conclusion
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David Mulla and his research group (UMN) Paul Bolstad (UMN)
Remote Sensing and Geospatial Analysis Laboratory (UMN): Jennifer
Corcoran Bryan Tolcser Steve Kloiber (MN, DNR) Tim Loesch (MN, DNR)
Carver County Acknowledgments
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Funding for this project was provided by the Minnesota the
Environment and Natural Resources Trust Fund through the Department
of Natural Resources (MN DNR) Acknowledgments