Remote Sensing Based Riparian Forest Assessments...Precision Forestry 11/7/2016 Cooperative 6 •...

Precision Forestry Cooperative11/3/16 1

Remote Sensing Based Riparian Forest Assessments Prof. L. Monika MoskalAssociate Professor of Remote SensingDirector of Precision Forestry CooperativeAssociate Director, School of Environmental and Forest Sciences, College of the EnvironmentUniversity of Washington, Seattle, WA, USA

UW‐RSGALRemote Sensing and Geospatial Analysis Laboratory

Presented @ ForestStructure Research Project Meeting, Valencia, Spain


Talk Road Map

• Background• Who we are, what we do• Previous research that lead us to riparian assessments

• Why riparian assessment in WA State

• Research Examples• Ongoing/Completed

• Opportunities and Limitations


UW Precision Forestry Cooperative

Precision Forestry Cooperative ‐ PFC

Remote Sensing & Geospatial Analysis

Laboratory ‐RSGAL

(Dr.Moskal) +10

Natural Resource Spatial Informatics Group – NRSIG

(L. Rogers) 3

Operational Research

Laboratory ‐ ORL (Dr.Toth) +5

Precision Forestry Cooperative – PFCDirected by Dr. Moskal

PFC Affiliates 10+• H. Anderson, B. McGaughey, A. Hudakk – USDA FS PNW &

Idaho• C. Turgenson, A. Johnston – USGS Seattle, & Montana

PFC Affiliates 10+• H. Anderson, B. McGaughey, A. Hudakk – USDA FS PNW &

Idaho• C. Turgenson, A. Johnston – USGS Seattle, & Montana

Advisory Board – 30+• Public (BLM, FS, USGS, DNR…)• RS Industry (Quantum;

MicaSense…)• Private (Weyerhaeuser…)• Non‐profit (TNC, Forterra…)

Advisory Board – 30+• Public (BLM, FS, USGS, DNR…)• RS Industry (Quantum;

MicaSense…)• Private (Weyerhaeuser…)• Non‐profit (TNC, Forterra…)

http://www.cfr.washington.edu/research.pfc/

• 1999 WA state funded Advance Technology Initiative (ATI) $500K/bi‐biennium (includes 2 FTEs); L. M. Moskal @ PFC since 2006 (Director since 2012)

• Current biennium 1:10 return on investment (+$5 million); 75% out of state $• ForestRX– provide technology (geospatial and operation research) based management solution for healthy forest ecosystems which sustain resources that social, economic and public health are dependent on…

Assistant Research ProfessorDr.Kane +2


Ecosystem services

A variety of ecological applications require data from broad to fine spatial extents that cannot be collected using only field-based methods. Remote sensing data and techniques address these needs.


Riparian Function Assessments

1. Solar energy attenuation (thermal loading) –from forest inventory (and potentially LAI)

2. Large woody supply potential – from forest inventory

Thus riparian function can be derived from LAI and forest inventories

UW Precision Forestry Cooperative\Remote Sensing and Geospatial Analysis Laboratory

decision support tools can be driven by

geospatial data and applications

Understanding the riparian function supports watershed restoration and conservation


• Kim, S., R. J. McGaughey, H‐E., Andersen, G. Schreuder, 2009. Tree species differentiation using intensity data derived from leaf‐on and leaf‐off airborne laser scanner data, Remote Sensing of Environment, 113 (8), pg. 1575–1586.• Kim, S., T. Hinckley; D. Briggs. 2011. Classifying individual tree genera using stepwise cluster analysis based on height and intensity metrics derived from airborne laser scanner data. Remote Sensing of Environment, 115 (12), pg. 3329‐3342.• Vaughn N., L. M. Moskal and E. Turnblom, 2011. Fourier transformation of waveform LiDAR for species recognition, Remote Sensing Letters, 2(4); 347‐356.• Vaughn, N. and L. M. Moskal, 2012. Tree Species Detection Accuracy with Airborne Waveform Lidar, Special Issue on Laser Scanning in Forests, Remote Sensing, 4(2), 377‐403.• Zheng, Z., A. Kazakova, L. M. Moskal, D. Styers and N. Vaughn. 2016. Object‐Based Tree Species Classification in Urban Ecosystems using LiDAR and Hyperspectral Data. Forests, 7(6), 122‐138.

Forest tree species characterization with ALS• Kim et al. 2009 – ALS Intensity

– leaf-on data (73.1%) – leaf-off data (83.4%)– combined (90.6%)

• Kim et al. 2011 – Simple ALS-based structure and intensity– leaf-on (74.9%) – leaf-off (50.2%)– combined (74%)

• Vaughn et al. 2012 – ALS full-waveform – full-waveform (75%)

• Vaughn et al. 2011 – ALS full-waveform and discrete point– discrete point metrics (79.2%) – discrete point metrics & full-waveform (84.5%)

• Zhang et al. 2016 – Hyperspectral (crown segmentation of ALS)– all HyMap bands (82.7%)– optimized HyMap bands (88.1%)


LAI in heterogeneous urban forests, an ALS approach

StudyLAI

range Best LAIE modelLim et al., 2003 0.5‐4 LAIE = a + b1Mean Return Elevation

Riaño et al., 2004 0‐≈3 LAIE = a + b1LCI •100

Morsdorf et al., 2006 0‐2

LAIE = a + b1FCover (∑ Canopy First Returns/(∑ Canopy Last Returns + ∑

Canopy Only Returns)Solberg et al., 2006 0‐1.6

LAIE = a + b1ln(Ground Returns/Canopy Returns)

Richardson, J., Moskal, L. M. and S. Kim, 2009. Modeling Approaches to Estimate Effective Leaf Area Index from Aerial Discrete‐Return LiDAR, Agricultural and Forest Meteorology 149, 1152‐1160.


6 manuscripts by Zheng, Moskal, et al.:• provided a theoretical foundation for retrieving LAI from point cloud data obtained with discreet point TLS• estimation method explained 89.1% (rmse = 0.01 ; p <; 0.001) of the variation in results from validation data• extinction coefficient, gap fraction, overlapping effect, leaf orientation, and sampling design and protocols

TLS-based LAI in heterogeneous forests

• Zheng, G. Ma, L.X., He, W., Eitel, J.U.H., Moskal, L.M. and Zhang, Z.Y, 2016. Assessing the contribution of woody materials to forest angular gap fraction and effective leaf area index using terrestrial laser scanning (TLS) data. IEEE Transactions on Geoscience and Remote Sensing, 54(3), 1474-1484.

• Zheng, G., Moskal, L.M. and S-H. Kim, 2012. Retrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning , IEEE Transactions on Geoscience and Remote Sensing, 51(2);777-786.

• Zheng, G. and L.M. Moskal, 2012. Computational Geometry-Based Retrieval of Effective Leaf Area Index Using Terrestrial Laser Scanning, IEEE Transactions on Geosciences and Remote Sensing, 50(10); 3958 - 3969.

• Zheng, G. and L.M. Moskal, 2012. Leaf Orientation Retrieval from Terrestrial Laser Scanning Data, IEEE Transactions on Geoscience and Remote Sensing, 50(10); 3970 – 3979.• Zheng, G. and L.M. Moskal, 2012. Spatial variability of terrestrial laser scanning based leaf area index, International Journal of Applied Earth Observation and Geoinformation, 19, 226–237.• Zheng G. and L.M. Moskal, 2009. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors. Sensors, 9(4):2719-2745.


Improving the TLS-based LAI in heterogeneous forests

Finally result Surface class

Scatter class Linear class

• Ma, L., Zheng, G., Eitel, J., Moskal, L.M., He, W. and H. Huang. 2016. Improved Salient Feature‐Based Approach for Automatically Separating Photosynthetic and Nonphotosynthetic Components Within Terrestrial Lidar Point Cloud Data of Forest Canopies.IEEE Transactions on Geoscience and Remote Sensing, 54(2), 679‐696.

• Ma, L., Zheng, G., Eitel, J., Magney, T. and L. M. Moskal. 2016. Determining woody‐to‐total area ratio using terrestrial laser scanning (TLS), Agricultural and Forest Meteorology, 228‐229, 217‐228


Objectives• A simple comparable cost assessment for all data types covered.• Summary of specifications of all sensors covered in the assessment.• Literature review documenting accuracies.• A break out of outcomes for direct and modeled methods for each.• Focused synthesis, per indicator, summarizing the costs of data, costs of analysis, methods, accuracies and feasibility for Washington State.

Time Period: April 1, 2015 ‐ June 30, 2015

Feasibility of applying remote sensing to a riparian stand conditions assessmentsDr. L. M. Moskal & A. Cooke, PFC-NRSIG

~9 million hectares timberlandState area: 184,827 km2

~14 million hectares timberlandCountry area: 505,990 km2

SPAIN WASHINGTON STATE


Summary Diagrams

Aerial Photography (NAIP) 1940 onwards

Aerial LiDAR 2000 onwards Satellite Imagery 1970 onwards

Pre 1970 (earliest imagery from 1940’s)Post 2000Post 2010


Characterizing Large Woody Debris at Cedar Creek (King County)Dr. J. Richardson, PFC-RSGAL

• Hybrid Object Based (OBIA) Manual Approach on LiDAR & Imagery

EXAMPLE OF COMPLETED RSGAL RESEARCH

Richardson J. and L. M. Moskal. Accepted 2016. An Integrated Approach for Monitoring Contemporary and Recruitable Large Woody Debris. Remote Sensing


• afdsa

Hydraulics Modelling, Olympic Peninsula, WashingtonC. Vondrasek, PFC-RSGAL

LiDAR driven HEC‐RAS model where blue is the 50‐year peak flow, yellow is the 2 year peak flow, 2009 and 2013 NAIP imagery, green line is a GIS stream channel, blue lines are off‐channel corridors validated in the field

EXAMPLE OF COMPLETED RSGAL RESEARCH


Stream Depth Measurement from Green vs. NIR LiDAR

NIR LiDAR Leica ALS 60Green LiDAR Riegl VQ820‐6LAI

• More refined green lidarwaveform processing (currently 7 returns per pulse

• Scanning angles to intercept fluvial features (not valley walls and vegetation)

Richardson, J. and L. M. Moskal, 2014. Assessing the utility of green LiDAR for characterizing bathymetry of heavily forested canopies. (2014) Remote Sensing Letters, 5(4), 352-357.

• LAI based on: Richardson, J., Moskal, L. M. and S. Kim, 2009. Modeling Approaches to Estimate Effective Leaf Area Index from Aerial Discrete-Return LIDAR, Agricultural and Forest Meteorology, 149; 1152-1160.


How about UAS data? • CIR Imagery Flown Oct. 25th 2015

• MicaSense mission 2017

Data: Curtesy Quantum International, Portland, OR

MicaSense RedEdge™


For Comparison of Methods

PhoDAR (SfM) 5 cm accuracy on crown heights (compared to field data)

Precision Forestry Cooperative

PFC current work using MicaSense

PFC Red‐Edge MicaSense Camera mounted on a Leica Airbot droneFor flood plain vegetation assessment in southwestern PolandCollaboration with Dr. Przemyslaw Tymkow (Wroclaw) & Dr. Piotr Wezyk (Krakow)


Riparian Inventory of Sealaska Tribal corporation lands in coastal AlaskaA. Cooke, PFC- NRSIG

For Martin Environmental/Sealaska Corp. ‐ characterizing riparian stand conditions and understanding the impacts of various forest management practices on streams. NAIP imagery based approach.

EXAMPLE COMPLETED NRSIG RESEARCH

LiDAR Availability!?


Goodman Creek Hoh Tribe AcquisitionJune 2012

Snahapish CreekQuinault Tribe Acquisition

April 2011LiDAR coverage, Washington Olympic Peninsula


ECOSELA forest ecosystem service marketing tool

ECOSEL is an analytical modeling tool that aids forest landowners and managers in selling ecosystem services from their working forestland.

What ecosystem services should the landowner be thinking about producing?

Viewshed aesthetics, riparian function, carbon sequestration, water quality, habitat …?

http://ecosel.cfr.washington.edu/

EXAMPLE OF ONGOING NRI (Toth Lab) RESEARCH


http://depts.washington.edu/ssafr17


Thank you!

PFC Team:• Dr. Jeffery Richardson• Dr. Van Kane• Dr. Sandor Toth• Chris Vondrasek, MS• Luke Rogers, MS• Andrew Cooke, MS• Meghan Halabisky, PhD (ABD)• Caileigh Shoot

• Megan O’Shea, PFC Administrative Specialist

PFC Collaborators:• Dr. Christian Torgersen. U.S. Geological Survey

• Dr. George McFadden, U.S. Bureau of Land Management

• Dr. Douglas Martin, Martin Environmental

• Dr. Akira Kato, Chiba University• Dr. Guang Zhang, Nanjing University

• Kate Akyuz and Jesse Alton, King County

• Quantum Spatial

Remote Sensing Based Riparian Forest Assessments...Precision Forestry 11/7/2016 Cooperative 6 •...

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