Transcript Lidar use for wetlands
Lidar use for wetlands
Annual MN wetlands conference January 18, 2012 Lian Rampi Joseph Knight
Agenda
What is Lidar?
Wetland mapping methods Conclusions
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)
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
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
WETLAND MAPPING METHODS
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
Wetland mapping methods
Elevation data only 1) DEM resolution for a Compound Topographic Index (CTI)
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 m² and the Tangent (tan) of the slope ( β ) in radians
CTI = ln [(As)/ (Tan ( β )]
Wetland mapping methods
Elevation data only
1) DEM resolution for a CTI Study area
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
Wetland mapping methods
Elevation data only
1) DEM resolution for a CTI
Results
Wetland mapping methods
Elevation data only
1) DEM resolution source
Accuracy assessment results DEM
3m lidar 9m lidar 12m lidar 24m lidar 33m lidar 10 m NED 30 m USGS
CTI (Threshold: CTI>= median + 1/2 sd) %Overall Acc % User. Acc
86 88 89 90 90 88 84 68 72 73 76 77 76 74
% Prod. Acc
85 88 88 87 86 77 69
Accuracy Assessment using a local reference data (wetland size: from 0.1 acres to 788 acres )
Wetland mapping methods
Elevation data only
1) DEM resolution for a CTI
Accuracy assessment results Omission Error Commission Error
Wetland mapping methods
Data fusion 2) Combination of CTI, Normalized Difference Vegetation Index (NDVI) and soils data
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
Wetland mapping methods
Data fusion
2) Combination of CTI, NDVI and soils data
Assumption behind NDVI
Wetland mapping methods
Data fusion
2) Combination of CTI, NDVI and soils data
Accuracy assessment results
Acres Combination 0.1 to 788 CTI 0.1 to 788 CTI + NDVI + Soils >= to 1 CTI + NDVI + Soils DEM 24m 24m 24m %Overall Acc 90 92 92 % User. Acc 76 82 82 % Prod. Acc 87 86 89
Wetland mapping methods
Data fusion
2) Combination of CTI, NDVI and soils data
Results
Wetland mapping methods
Data fusion 3) Random Forest (RF) Classifier
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
Wetland mapping methods
Data fusion
3) Random Forest (RF) Classifier: Study area
Elevation DEM
365 294
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
Wetland mapping methods
Data fusion
3) Random Forest (RF) Classifier
Data Used – intensity Lidar
Wetland mapping methods
Data fusion
3) Random Forest (RF) Classifier
Data Used – DEM and Slope (Lidar)
Wetland mapping methods
Data fusion
3) Random Forest (RF) Classifier
Data used – CTI (Lidar)
Wetland mapping methods
Data fusion
3) Random Forest (RF) Classifier
Results Random Forest results: Top 10 important variables CTI Intensity Green band IR band DEM Red band Slope Blue band Hydric Soils OM Mean Decrease Gini
Wetland mapping methods
Data fusion
3) Random Forest (RF) Classifier - Results
Partial dependence on Intensity Partial dependence on Green band Partial dependence on CTI Intensity Partial dependence on IR band Green band Partial dependence on DEM CTI IR band DEM
Wetland mapping methods
Data fusion
3) Random Forest (RF) Classifier
Results CW (Cultivated wetland) UB (Unconsolidated bottom) EM (Emergent wetland)
Wetland mapping methods
Data fusion
3) Random Forest (RF) Classifier
Accuracy assessment results Classification
Random Forest Classification NWI
% Overall Acc 91 63 % User. Acc 94 78 % Prod. Acc 89 39
Wetland mapping methods
Data fusion 4) Object based classification
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
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
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
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
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
Wetland mapping methods
Data fusion
4) Object based classification
Results
OBIA wetland polygons
Wetland mapping methods
Data fusion
4) Object based classification - Results
North East area, Chanhassen City Central East area, Chanhassen City
OBIA wetland polygons
Wetland mapping methods
Data fusion
4) Object based classification - Results
North East area, Chanhassen City Central East area, Chanhassen City
OBIA wetland polygons Reference data wetlands polygons
Wetland mapping methods
Data fusion
4) Object based classification
Accuracy assessment results Combinations
CTI Object-based Classification
%Overall Acc
89
95 % User. Acc
75
87 % Prod. Acc
84
91
Wetland mapping methods brief review
Accuracy assessment
Combination CTI 24 m CTI + NDVI + Soils Boolean and arithmetic classification Random Forest Classification Object-based Classification %Overall Acc 90 92 91 95 % User. Acc % Prod. Acc 76 87 82 94 87 89 89 91
Pros and cons of each method
Pros Cons
CTI Requires Elevation data only
Lidar is available for most part of MN
Combination CTI + Soils + NDVI
Help to solve the problem of wetlands topographically suitable for wetlands because of the low elevation Open Source program available for CTI calculation: Whitebox GAT Soil data and NAIP aerial imagery (1 m ) available to the public (no charge) Free extensions and toolbox (TauDEM, ArcHydro) for ArcGIS 9.3
Does not work well for every area in the landscape with low elevation Combination bring all layers together and increase accuracy of wetland identification
Require ESRI extension (Spatial analyst: raster calculator, reclassify) Technical knowledge to process Lidar data
Require manual reclassification steps Random Forest OBIA with eCognition Developer
Free Software package
Output graphs of key variables, Gini index, confidence maps, and land classification
Allow data fusion of different type of data and spatial resolution
Classification of objects shapes (groups of homogeneous pixels)
GUI interface of Random Forest required same size resolution and grid alignment for land cover classification map output Allows to add more elements of image interpretation beside spectral characteristics for classification of objects
Necessary statistical knowledge and ability to interpret results Software requirement expensive
CPU storage requirements for faster processing
CONCLUSIONS
Conclusion
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
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.
Acknowledgments
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
Funding for this project was provided by the Minnesota the Environment and Natural Resources Trust Fund through the Department of Natural Resources (MN DNR)