Mapping Wetland Inundation Dynamics using Google Earth Engine · 2019-08-13 · LiDAR for Wetland Mapping Google Earth Engine (GEE) Automated Wetland Inundation Mapping Using GEE

Mapping Wetland Inundation Dynamics using Google Earth Engine

Qiusheng Wu, PhDDepartment of Geography

University of Tennessee, Knoxvillehttps://wetlands.io

https://gishub.org/wmc Wetland Mapping Consortium Webinar

August 14, 2019

slides

Collaborators

● US-EPA: Charles Lane, Heather Golden

● US-FWS/NWI: Megan Lang

● Iowa State U.: Xuecao Li, Yuyu Zhou

● Ohio State U: Kaiguang Zhao

● U of Guelph: Ben DeVries

● Google: Nicholas Clinton

https://gishub.org/2019-rse

download article2

Outline▪ Wetlands in the Prairie Pothole Region (PPR)

▪ National Wetland Inventory (NWI)

▪ Global Surface Water Explorer (GSWE)

▪ LiDAR for Wetland Mapping

▪ Google Earth Engine (GEE)

▪ Automated Wetland Inundation Mapping Using GEE

▪ Earth Engine Apps Demo

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Wetlands in the Prairie Pothole Region (PPR)Prairie Pothole Region

Area = 715, 000 km2

Five states:● North Dakota● South Dakota● Minnesota● Montana● Iowa

Median size:1,600 m2

< 2 Landsat pixels

Depth:< 1 m

Water supplies:● Rainfall● Snowmelt● Upland inflow● Groundwater

seepage

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Fill-Merge-Spill Wetland Hydrology

1-m ResolutionUSDANAIP Imagery

NationalAgriculture Imagery Program (NAIP)

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National Wetlands Inventory (NWI)https://www.fws.gov/wetlands/data/Mapper.html

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National Wetlands Inventory (NWI)

▪ NWI is the most spatially and categorically detailed wetland inventory available for the contiguous U.S.

▪ Water regimes▪ Temporarily flooded (PEM1A)▪ Seasonally flooded (PEM1C)▪ Semi-permanently flooded (PEM1F)▪ Seasonally saturated (PEM1B)

▪ Limitations▪ Manual interpretation and digitization of

single-date aerial imagery▪ BW aerial imagery acquired in the 1980s▪ Inundation status may not be current

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Global Surface Water Explorerhttps://www.fws.gov/wetlands/data/Mapper.html

https://global-surface-water.appspot.com/

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Global Surface Water Explorer (GSWE)

▪ GSWE was jointly developed by the European Commission’s Joint Research Centre (JRC), UN Environment and Google

▪ Based on 30-m Landsat data (1984-2018)▪ GSWE provides the location and temporal

distribution (monthly) of global water surfaces over the past 35 years

▪ Limitations▪ Largely failed to capture small sub-hectare

wetland features▪ Omission of inundation pixels around the

edges of wetland features

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Estimating Water Storage of Wetland DepressionsLight Detection and Ranging (LiDAR)

• Wu, Q., & Lane, C. R. (2016). Delineation and quantification of wetland depressions in the Prairie Pothole Region of North Dakota. Wetlands, 36(2), 215-227. https://doi.org/10.1007/s13157-015-0731-6

Contour tree

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Modeling Wetland Hydrologic Connectivity

• Wu, Q., & Lane, C. R. (2017). Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery. Hydrology and Earth System Sciences, 21(7), 3579. https://doi.org/10.5194/hess-21-3579-2017

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Modeling Wetland Hydrologic Connectivity

• Wu, Q., & Lane, C. R. (2017). Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery. Hydrology and Earth System Sciences, 21(7), 3579. https://doi.org/10.5194/hess-21-3579-2017

Source code available at https://gishub.org/2017-HESS

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Efficient Algorithm for Delineating Nested Hierarchy

• Wu, Q., Lane, C. R., Wang, L., Vanderhoof, M. K., Christensen, J. R., & Liu, H. (2019). Efficient Delineation of Nested Depression Hierarchy in Digital Elevation Models for Hydrological Analysis Using Level‐Set Method. JAWRA Journal of the American Water Resources Association, 55(2), 354-368. https://doi.org/10.1111/1752-1688.12689

https://gishub.org/2019-JAWRA

Source code available at:

Shrinking - Splitting Expanding - Merging

Tracking the evolution of the boundary shape

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Efficient Algorithm for Delineating Nested Hierarchy

Contour-tree method• Bottom-up approach• Vector-based• Built on ArcPy• Windows only• Run time: 117 s

Level-set method• Top-down approach• Raster-based• Built on Python• Windows, Mac, Linux• Run time: 0.75 s

Method comparison

~150 times faster• Wu, Q., Lane, C. R., Wang, L., Vanderhoof, M. K., Christensen, J. R., & Liu, H. (2019). Efficient Delineation of Nested Depression Hierarchy in Digital

Elevation Models for Hydrological Analysis Using Level‐Set Method. JAWRA Journal of the American Water Resources Association, 55(2), 354-368. https://doi.org/10.1111/1752-1688.12689

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Research Challenge

▪ To effectively manage wetlands, we need contemporary information about their location, extent, inundation dynamics, and drivers of change

▪ The NWI dataset provides the most spatially and categorically detailed wetland inventory for the contiguous U.S., but it has traditionally relied on costly manual interpretation of aerial imagery to generate data.

▪ Many regions (e.g., PPR) have outdated NWI. Automated workflows to enable more rapid, cost effective updates to the NWI dataset are highly desirable.

▪ Previous studies on mapping wetland inundation dynamics largely used moderate spatial resolution satellite images (e.g., Landsat, Sentinel)

▪ Massive computing power is needed to process large-volume datasets (e.g., NAIP)▪ How can we better integrate fine-resolution LiDAR data and NAIP imagery for

mapping (small) wetlands at large geographic scales?

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Google Earth vs. Earth Engine▪ Google Earth

▪ Google Earth enables you to travel and learn about the world through a virtual globe. You can view satellite imagery, maps, terrain, 3D buildings and much more.

▪ Earth Engine

▪ Earth Engine, on the other hand, is a tool for analyzing geospatial information.

▪ Although Earth Engine has a data catalog, not everything available in Google Earth is available for analysis in the Earth Engine catalog. Similarly, much of the data in Earth Engine is not currently available for visualization in Google Earth.

https://earthengine.google.com https://google.com/earth 21

Before Earth EngineAfter Earth Engine

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Earth Engine Code Editor https://code.earthengine.google.com/

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Study Area and Geospatial Datasets▪ Study Area

▪ 3 HUC-8 subbasins (16,576 km2 in total)

▪ 26 HUC-10 watersheds (318 ~ 998 km2)

▪ Datasets▪ LiDAR data (2011-2016) - 107.65 GB

▪ NAIP imagery (2009, 2010, 2012, 2014, 2015, 2017)

▪ National Wetlands Inventory [NWI] (1980s)

▪ JRC Global Surface Water (1984-2018)

• Wu, Q., Lane, C. R., Li, X., Zhao, K., Zhou, Y., Clinton, N., DeVries, B., Golden, H. E., & Lang, M. W. (2019). Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine. Remote Sensing of Environment, 228, 1-13. https://doi.org/10.1016/j.rse.2019.04.015

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Examples of Depressional Wetlands in the PPR


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Automated GEE Algorithm for Inundation Mapping


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JRC Surface Water Occurrence Product

Occurrence > 90%

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LiDAR-derived Surface Depressions

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NAIP-derived Wetland Inundation Maps

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Landsat-derived JRC Surface Water Extent vs. NWI

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Landsat-derived JRC Surface Water Extent vs. NWI

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NAIP-derived Surface Water Extent vs. NWI

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Earth Engine Apps DemoDemo: https://gishub.org/2019-RSE-GEECode: https://gishub.org/2019-RSE-Code

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Earth Engine Apps Demo https://gishub.org/gee

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Any questions

Thank you!

Qiusheng WuDepartment of Geography

University of TennesseeEmail: [email protected]

Website: https://wetlands.io

wetlands.io37

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Mapping Wetland Inundation Dynamics using Google Earth Engine · 2019-08-13 · LiDAR for Wetland Mapping Google Earth Engine (GEE) Automated Wetland Inundation Mapping Using GEE