1. Lidar use for wetlands Annual MN wetlands conference January 18, 2012 Lian Rampi Joseph Knight

2. Agenda What is Lidar? Wetland mapping methods Conclusions

3. 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)

4. 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

5. 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

6. WETLAND MAPPING METHODS

7. 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

8. Wetland mapping methods Elevation data only 1)DEM resolution for a Compound Topographic Index (CTI)

9. 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 )]

10. Wetland mapping methods Elevation data only 1)DEM resolution for a CTI  Study area

11. 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

12. Wetland mapping methods Elevation data only 1)DEM resolution for a CTI Results

13. 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 )

14. Wetland mapping methods Elevation data only 1)DEM resolution for a CTI Accuracy assessment results Omission Error Commission Error

15. Wetland mapping methods Data fusion 2)Combination of CTI, Normalized Difference Vegetation Index (NDVI) and soils data

16. 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

17. Wetland mapping methods Data fusion 2)Combination of CTI, NDVI and soils data  Assumption behind NDVI

18. 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

19. Wetland mapping methods Data fusion 2)Combination of CTI, NDVI and soils data Results

20. Wetland mapping methods Data fusion 3) Random Forest (RF) Classifier

21. 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

22. Wetland mapping methods Data fusion 3)Random Forest (RF) Classifier: Study area

23. 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

24. Wetland mapping methods Data fusion 3)Random Forest (RF) Classifier  Data Used – Lidar intensity

25. Wetland mapping methods Data fusion 3)Random Forest (RF) Classifier  Data Used – DEM and Slope (Lidar)

26. Wetland mapping methods Data fusion 3)Random Forest (RF) Classifier  Data used – CTI (Lidar)

27. 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

28. 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

29. Wetland mapping methods Data fusion 3) Random Forest (RF) Classifier Results UB (Unconsolidated bottom) EM (Emergent wetland) CW (Cultivated wetland)

30. Wetland mapping methods Data fusion 3)Random Forest (RF) Classifier Accuracy assessment results Classification% Overall Acc% User. Acc% Prod. Acc Random Forest Classification 919489 NWI637839

31. Wetland mapping methods Data fusion 4) Object based classification

32. 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

33. 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

34. 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

35. 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

36. 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

37. Wetland mapping methods Data fusion 4) Object based classification Results OBIA wetland polygons

38. Wetland mapping methods Data fusion 4) Object based classification - Results OBIA wetland polygons North East area, Chanhassen CityCentral East area, Chanhassen City

39. 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

40. Wetland mapping methods Data fusion 4) Object based classification Accuracy assessment results Combinations %Overall Acc% User. Acc% Prod. Acc CTI897584 Object-based Classification 958791

41. Wetland mapping methods brief review

42. 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

43. 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

44. CONCLUSIONS

45. 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

46. 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

47.  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

48.  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

49. Thank you for your attention!