Remote Sensing of Meadow

Hydrogeomorphic Types Using

LiDAR and Multispectral

Imagery

Austen A. Lorenz

Dr. Leo Blesius and Dr. Jerry Davis

• Introduction • Background Information • Research Questions

• Methods • Hydrogeomorphic Classification • Study Area • Software and Data • Data Processing • Detection and Classification

• Results

• Discussion and Conclusion

Contents

• Can be found in the

Northern Sierra 300 to

2,750 m elevation

• Meadows range in size from

few m2 to several km2

Geography of Sierra Nevada Meadows

Introduction

Can Sierra Nevada meadows be detected with OBIA?

Can meadows be classified by hydrogeomorphic types with OBIA?

Research Questions

Introduction

• Most use multispectral imagery

• Many use DEM or LiDAR

• Ancillary data such as hydrography, soil data, USGS topographic maps

Remote Sensing of Wetlands Data

Introduction

Data Type Article(s)

Multispectral Air Photos Rampi et al. 2014, Szantoi et al. 2015

DEM Dingle Robertson et al. 2015, Li and Chen 2005, Mui et al. 2015, Sader et al. 1995

USGS Topo Maps Torbik et al. 2008

Geo Eye1 Mui et al. 2015

Indian IRS LISS-II Kindscher et al. 1998

Land Use Data Torbik et al. 2008

Landsat 7 Frohn et al. 2009, Li and Chen 2005, Mahoney et al. 2007, Torbik et al. 2008

Landsat TM Basham May et al. 1997, Sader et al. 1995

LiDAR Rampi et al. 2014

NAIP Halabisky et al. 2011

Hydrography Data Frohn et al. 2009, Sader et al. 1995

Soils Data Torbik et al. 2008, Sader et al. 1995

SPOT Basham May et al. 1997

Radarsat-1 Li and Chen 2005, Mahoney et al. 2007

Radarsat-2 Dingle Robertson et al. 2015

WorldView-2 Dingle Robertson et al. 2015, Mui et al. 2015

• Pixel based classification classifies pixels based on spectral signatures

• Object-based image analysis (OBIA)

Remote Sensing of Wetlands Methods

Introduction

Methods

• 40 x 25 km

• Includes many known meadows of varying sizes

• 25 km north-west of Lake Tahoe

• Average elevation 2000m

Study Area

Methods

Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA,USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community

¯ 0 10 20 km

Study Area

ASTER

LiDAR

Lake

Tahoe

Software

• ArcGIS 10.4: LiDAR processing and maps

• ERDAS Imagine 2015: Image processing

• eCognition 9.1: OBIA

Data

• LiDAR

• ASTER

Software and Data

Methods

• From Tahoe National Forest, USDA Forest Service

• Collected in 2013

• Point density 7-8 points m-2

• Point spacing 0.29-0.48 m

• Horizontal accuracy of 0.02-0.72 m (1 sigma)

• Vertical accuracy 5-35 cm (1 sigma)

• Specific flight date was not available in metadata

• The LiDAR was processed using the NSF NCALM (The National Center for Airborne Laser Mapping)

LiDAR

Methods

• From EarthExplorer

• Collected in 7/24/08

• Level- 1B geometric and radiometric corrections applied

• 0% cloud cover

• 15 m resolution at nadir

ASTER

Methods

Data Processing

Methods

DSM

First returns Classified bare

ground

LiDAR

data

DTM

Canopy

Percent slope

Orthorectification

Atmospheric

correction

Corrected

image

Red/NIR NDVI

ASTER

Curvature

NDWI Brightness

Flow

Accumulation

• Expert system

• OBIA creates groups of similar pixels into segments

• Segments act as objects

• Objects classified based shape, relationship to other objects, spectral value inside object, etc.

Object-based image analysis (OBIA)

Methods

Meadow Detection

Methods

Find Lakes

Find None

Meadows

Candidate

Meadows

Meadow

Segment

Images Brightness,

Slope, NDWI,

Canopy,

Elliptic Fit

Curvature,

Red/NIR,

Slope, NDVI

Flow

Accum.,Slope,

Canopy, NDVI,

Red/NIR, Area

Hydrogeomorphic Classification

Methods

HGM Class Definition Landform Major Water Source

Lacustrine Fringe

(LAC)

Meadow adjacent to large waterbody Lakes Groundwater

Depressional (DEP) Meadow in depression where water

does not flow through most of the year.

Often with small seasonal waterbody

Topographic

depressions

Runoff, precipitation

Riparian (RIP) Meadow with developed channel

throughout

Topographic

flowlines

Over bank flow from channel,

throughflow

Subsurface (SUB) Meadows that does not have develop

stream channel throughout. Can have

channel for portions

Topographic

flowlines

Groundwater, throughflow, and

overland flow

Discharge Slope

(DS)

Meadow found at toe slopes. Channels

may be present to move water from

discharge area

Toeslopes Groundwater, overland flow

from hillslope

Hydrogeomorphic Classification

Methods

Lacustrine Fringe

Hydrogeomorphic Classification

Methods

Depressional

Hydrogeomorphic Classification

Methods

Riparian

Hydrogeomorphic Classification

Methods

Subsurface

Hydrogeomorphic Classification

Methods

Discharge Slope

Hydrogeomorphic Classification

Methods

Meadow

Next to

waterbody?

Yes

No

Depressional

> 450 m2? Yes

Lacustrine

Fringe

In a

depression?

Yes

Yes

Has developed

channel

throughout?

Yes

Riparian Subsurface

No

No

No

Discharge

slope

In topographic

flowline?

Meadow detection accuracy

• Compared detected meadows to Google Earth

• Compared detected meadows to TNF

Hydrogeomorphic accuracy

• Compared classified meadows to Google Earth

• Compared classified meadows to TNF

Accuracy Assessment

Methods

Results

Results

Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AEX,Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community

HGM Class

DEP

DS

LAC

RIP

SUB

0 2 4 km

Reference (TNF) User's Accuracy

Meadow Other Total

Classified Meadow 174 42 216 81%

Other 138 246 384 64%

Total 312 288 600

Producer's

Accuracy

56% 85% Overall Accuracy 70%

Meadow Detection

Results

Meadow Detection

Results

Reference (Google Earth) User's Accuracy

Meadow Other Total

Classified Meadow 188 12 200 94%

Other 3 297 300 99%

Total 191 309 500

Producer's

Accuracy

98% 96% Overall Accuracy 97%

Meadow HGM Classification

Results

Reference (Google Earth) User's Accuracy

DEP DS LAC RIP SUB Total %

Classified DEP 8 0 0 0 1 9 89%

DS 3 41 0 5 19 68 60%

LAC 2 0 21 0 0 23 91%

RIP 0 0 0 47 10 57 82%

SUB 3 8 1 10 102 124 82%

Total 16 49 22 62 132 281

Producer's

Accuracy

% 50% 84% 95% 76% 77%

Overall Accuracy 78%

Reference (TNF) User's Accuracy

DEP DS LAC RIP SUB Total %

Classified DEP 1 0 0 1 0 2 50%

DS 0 3 0 20 8 31 10%

LAC 2 1 6 4 0 13 46%

RIP 0 0 0 48 0 48 100%

SUB 3 3 2 68 4 80 5%

Total 6 7 8 141 12 174

Producer's

Accuracy

% 17% 43% 75% 34% 33%

Overall Accuracy 36%

Meadow HGM Classification

Results

Discussion & Conclusion

• Misdetection and Misclassification

• Algae

• Clear cuts

• Other human influences

• Precipitation

• Elevation

Sources of Error

Discussion

Sources of Error

Discussion

• Class Definitions cause difference from classified HGM to TNF HGM

Sources of Error

Discussion

• Class Definitions cause difference from classified HGM to TNF HGM

• LiDAR detection errors

• LiDAR preprocessing errors

• Misclassified LiDAR ground points

• Interpolation of LiDAR points to raster

• Multispectral imagery blunders

• Scale

• Accuracy assessment errors

Sources of Error

Discussion

• Meadows can be detected with OBIA

• Meadow can be classified by HGM with OBIA but more work is needed to refine

• LiDAR is a power tool for analyzing landforms

• Future models should include precipitation and elevation

Conclusion

Holland, Robert F. 1986. “Preliminary Description of the Terrestrial Natural Communities of California.” Sacramento.

Kindscher, K., A. Fraser, M. E. Jakubauskas, and D. M. Debinski. 1998. “Identifying Wetland Meadows in Grand Teton National Park Using Remote Sensing and Average Wetland Values.”

Wetlands Ecology and Management 5: 265–73.

Li, Junhua, and Wenjun Chen. 2005. “A Rule-Based Method for Mapping Canada ’s Wetlands Using Optical, Radar and DEM Data.” International Journal of Remote Sensing 26 (22): 5051–69.

doi:10.1080/01431160500166516.

Mahoney, Matthew L., Alan R. Hanson, and Scott Gilliland. 2007. “An Evaluation of a Methodology for Wetland Classification and Inventory for Labrador.” Technical Report Series No. 480.

Sackville. papers2://publication/uuid/0EEEBCC3-9139-423B-934F-31EA3A89A098.

Moffett, Kevan B, and Steven M Gorelick. 2013. “Distinguishing Wetland Vegetation and Channel Features with Object-Based Image Segmentation.” International Journal of Remote Sensing

34 (4): 1332–54.

Mui, Amy, Yuhong He, and Qihao Weng. 2015. “An Object-Based Approach to Delineate Wetlands across Landscapes of Varied Disturbance with High Spatial Resolution Satellite Imagery.”

ISPRS Journal of Photogrammetry and Remote Sensing 109. International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS): 30–46. doi:10.1016/j.isprsjprs.2015.08.005.

Ouyang, Zu-Tao, Mo-Qain Zhang, Xiao Xie, Qi Shen, Hai-Qiang Gou, and Bin Zhao. 2011. “A Comparison of Pixel-Based and Object-Oriented Approaches to VHR Imagery for Mapping

Saltmarsh Plants.” Ecological Informatics 6 (2): 136–46.

Ozesmi, Stacy L., and Marvin E. Bauer. 2002. “Satellite Remote Sensing of Wetlands.” Wetlands Ecology and Management 10: 381–402.

Rampi, Lian P., Joseph F. Knight, and Keith C. Pelletier. 2014. “Wetland Mapping in the Upper Midwest United States : An Object-Based Approach Integrating Lidar and Imagery Data.”

Photogrammetric Engineering & Remote Sensing 80 (5): 439–49. doi:10.14358/PERS.80.5.439.

Ratliff, Raymond D. 1982. “A Meadow Site Classification for the Sierra Nevada , California.” General Technical Report PSW. Berkeley, CA.

———. 1985. “Meadows in the Sierra Nevada of California: State of Knowledge.” General Technical Report PSW-84. Berkeley, CA.

http://www.fs.fed.us/psw/publications/documents/psw_gtr084/.

Sader, Steven A., Douglas Ahl, and Wen Shu Liou. 1995. “Accuracy of Landsat-TM and GIS Rule-Based Methods for Forest Wetland Classification in Maine.” Remote Sensing of Environment

53 (3): 133–44. doi:10.1016/0034-4257(95)00085-F.

Szantoi, Zoltan, Francisco J Escobedo, Amr Abd-Elrahman, Leonard Pearlstine, Bon Dewitt, and Scot Smith. 2015. “Classifying Spatially Heterogeneous Wetland Communities Using Machine

Learning Algorithms and Spectral and Textural Features.” Environmental Monitoring and Assessment 187 (5): 262. doi:10.1007/s10661-015-4426-5.

Torbik, Nathan, Patrick Lawrence, and Kevin Czajkowski. 2008. “Application and Assessment of a GIScience Model for Wetlands Identification.” In Wetlands and Water Resource Modeling

and Assessment: A Watershed Perspective, edited by Wei Ji, 3–13. Boca Raton: CRC Press.

Usmerjena, Objektno, Analiza Podatkov, and Daljinskega Zaznavanja. 2011. “Object-Based Image Analysis of Remote Sensing.” Geodetski Vestnik 55 (4): 665–88.

Wiexelman, Dave, Barry Hill, David Cooper, Eric Berlow, Joshua H. Viers, Sabra E. Purdy, Amy Merril, and Shana Gross. 2011. “A Field Key to Meadow Hydrogeomorphic Types for the

Sierra Nevada and Southern Cascade Ranges in California.” Vallejo, CA.

Wood, Spencer H. 1975. “Holocene Stratigraphy and Chronology of Mountain Meadows, Sierra Nevada, California.” California Institute of Technology. http://thesis.library.caltech.edu/5570/

Adam, Elhadi, Onisimo Mutanga, and Denis Rugege. 2010. “Multispectral and Hyperspectral Remote Sensing for Identification and Mapping of Wetland Vegetation: A Review.” Wetlands

Ecology and Management 18 (3): 281–96. doi:10.1007/s11273-009-9169-z.

Allen, Barbara H. 1987. “Forest and Meadow Ecosystems in California.” Rangelands 9 (3): 125–28.

“Aster Level 1 Data Products Specification (GDS Version) Version 1.3.” 2001. AG-E-E-2209-R03.

Basham May, A. Michele, J. E. Pinder, and G. C. Kroh. 1997. “A Comparison of Landsat Thematic Mapper and SPOT Multi-Spectral Imagery for the Classification of Shrub and Meadow

Vegetation in Northern California, USA.” International Journal of Remote Sensing 18 (18): 3719–28. doi:Doi 10.1080/014311697216577.

Benedict, Nathan B. 1981. “The Vegetation and Ecology of Subapline Meadows of the

Southern Sierra Nevada, California.” University of California, Davis.

Benedict, NB B, and Jack Major. 1982. “A Physiographic Classification of Subalpine Meadows of the Sierra Nevada, California.” Madrono 29 (1): 1–12. http://agris.fao.org/agris-

search/search/display.do?f=2012/OV/OV201201669001669.xml;US19830843003.

Benz, Ursula C, Peter Hofmann, Gregor Willhauck, Iris Lingenfelder, and Markus Heynen. 2004. “Multi-Resolution, Object-Oriented Fuzzy Analysis of Remote Sensing Data for GIS-Ready

Information.” Journal of Photogrammetry & Remote Sensing 58: 239–58. doi:10.1016/j.isprsjprs.2003.10.002.

Chapot, Brian F., and W. D. Billings. 1972. “Origins and Ecology of the Sierran Alpine Flora and Vegetation.” Ecological Monographs 42 (2): 163–99.

Dingle Robertson, Laura, Douglas J. King, and Chris Davies. 2015. “Object-Based Image Analysis of Optical and Radar Variables for Wetland Evaluation.” International Journal of Remote

Sensing 36 (23): 5811–41. doi:10.1080/01431161.2015.1109727.

Dronova, Iryna. 2015. “Object-Based Image Analysis in Wetland Research: A Review.” Remote Sensing 7: 6380–6413. doi:10.3390/rs70506380.

Dwire, Kathleen a., J. Boone Kauffman, and John E. Baham. 2006. “Plant Species Distribution in Relation to Water-Table Depth and Soil Redox Potential in Montane Riparian Meadows.”

Wetlands 26 (1): 131–46. doi:10.1672/0277-5212(2006)26[131:PSDIRT]2.0.CO;2.

Fites-Kaufman, J., P. Rundel, N. Stephenson, and D. A. Weixelman. 2007. “Montane and Subalpine Vegetation of the Sierra Nevada and Cascade Ranges.” In Terrestrial Vegetation of

California, edited by Michael Barbour, 3rd ed., 456–501. Berkeley: University of California Press.

Frohn, Robert C., Molly Reif, Charles Lane, and Brad Autrey. 2009. “Satellite Remote Sensing of Isolated Wetlands Using Object-Oriented Classification of Landsat-7 Data.” Wetlands 29 (3):

931–41. doi:10.1672/08-194.1.

Fryjoff-Hung, A, and Joshua H. Viers. 2012. “Sierra Nevada Multi-Source Meadow Polygons Compilation (v 1.0).”

Halabisky, Meghan, L. Monika Moskal, and Sonia A. Hall. 2011. “Object-Based Classification of Semi-Arid Wetlands.” Journal of Applied Remote Sensing 5 (1): 053511–053511.

References

• Sierra Fund

• Tahoe National Forest

• Dave Weixelman

• American Rivers

• San Francisco State

• And many more

Thank You

Questions?