Tree Root Architecture: Patterns and Geotechnical Roles in Levees Shih Ming Chung and Alison M. Berry University of California, Davis Collaborators Gerald Bawden, U.S.G.S.; Keir Keightley, UC Davis; Sandra Bond, U.S.G.S.; John Lichter, Tree & Associates; Vic Claassen, UC Davis Levee Vegetation Research Symposium Sacramento, California August 28, 2012
California s vegetated levees provide critical flood protection River Partners Pocket Levee, Sacramento River Yet riparian forests provide critical habitat and ecosystem services. How should levee trees be managed? G Fricker
This symposium: What can science contribute? The central scientific question: What is the impact of trees and tree roots on levee safety? Slope stabilization Seepage Key knowledge gap identified: How do tree roots actually grow in levees? CLVRP project created: Tree Root Architecture in Levees
Project Objectives 1. To excavate and digitally map tree root systems in levees. 2. To create 3D models of tree root architecture that will help determine the potential risks and benefits of tree roots in levees. Prologue what we know Tree root system properties Limits of methodology
WHERE: Surface 1-3.5 ft provide best conditions for growth. Typically 80% of the root system is in this zone. 1 meter
WHAT Tree roots in many soil conditions grow horizontally more than vertically. Root architecture: combinations of 3 or 4 major woody systems Structural roots Horizontal roots Sinker roots Tap root spp./soils
HOW: The actual architectural pattern depends on key growth conditions: Soil moisture Aeration Mechanical impedance (soil compaction) 1 meter Soil surface Lyford 1980 Conditions are usually best in the upper part of the soil profile.
Trench profile mapping could show that tree roots avoid compacted soil layers in Mayhew Levee. But this method is limited, because Horizontal (ft) 20 21 22 23 24 25 26 27 28 29 30 31 32 0 b.d. Depth (ft) 1 2 3 4 zone with almost no roots 1.42 1.6 3 1.38 Roots <10mm Roots 10-19 mm Tree root systems are LARGE. Tree root systems in situ are SURROUNDED BY SOIL. Tree root systems are THREE DIMENSIONAL.
One way to overcome these hurdles 2. 3D laser imaging (ground based T LiDAR) 1. Air Spade excavation (pneumatic) 3. Digitized data enables precise measurement and 3D modeling of key spatial parameters.
Summary As you will see, architectural patterns of trees growing in sandy levees in California have distinctive, quantifiable properties. The modeling methods in Shih Ming s presentation can be widely applied. However, specific configurations of tree root systems will vary according to soil, climate and levee properties (see Caroline Zanetti s presentation). This symposium brings together many approaches and new sources of information to understand behavior of tree root systems in levees, as we will hear in presentations to follow. We all contribute to a much needed scientific knowledge base about biomechanical and geotechnical properties of trees on levees, with insights from different angles, much like the story of the blind men and the elephant.
Tree root growth patterns and geotechnical roles on levee Part II Methods and Applications Shih Ming Chung and Alison M. Berry
Fieldwork Lab processing 1. New field method for acquiring root system data 2. Data processing and model building 3. Analyzing root system patterns along levees 4. Data/models applications Quantitative model Applications
Systematic approach to acquire and categorize in situ root system data Consider root system complexity and spatial attributes Use critical variables of tree root architecture to determine the range (root system extent) and resolution (minimum unit) Include above ground, slope, below ground compartments Classify the data as spatial points (1D), linear connections (2D), surface patterns (3D), and resulting thematic spatial composition
1 Pneumatic excavation Pneumatic Excavation And LiDAR Scanning (PEALS) system 3D and in-situ 15 Valley Oaks 5 Cottonwoods 2 T LiDAR scanning 20 TREES IN TOTAL Most trees (18/20) on the water-side levee slope. Two control trees (flat)
1 2 Pneumatic excavation (compressed air) in situ root systems 3 4
Ground Based Tripod LiDAR scanning 1 2 3 4 5
Process field data and acquire the resulting models Stage I: T-LiDAR point cloud data (3D) Stage II: conversion of point-cloud data - Tomographic slicing, hierarchical nested dataset, biomass model - Vectorization of polylines, topological dataset, vector model
Stage I 1 2 3 Prescan: aboveground architecture and soil surface LiDAR scan after root excavation Assembly of aboveground, soil surface, and root system 4 3D LiDAR image assembly of Pocket Levee Oaks (The Pocket site, Sacramento, CA)
Stage II Tomographic slicing (hierarchical nested dataset) Root Biomass Model Vectorization of polylines (topological dataset) Root Vector Model
3D Point cloud data, XYZ format (x3, y3, z3) (xn, yn, zn) (x1, y1, z1) (x2, y2, z2) Set up the coordinate system
Origin of the coordinate system tree trunk center at the slope surface z axis: vertical axis y axis: perpendicular to the levee Upslope Downslope x axis: parallel to the levee
Root biomass model Design the model based on spatial patterns: size, linkage, distribution Use root cross sectional area or diameter as the critical variable Sample the variables in virtual trench profile (2D sampling) Tomographically reconstruct the 3D hierarchical nested dataset
Virtual trench profile as the tomographic slices Virtual trench profile Root CSA
Tomographic reconstruction can use linear or radial slices Longitudinal Radial (Upslope, Downslope, Vertical)
2D plot R statistical program Spatial analysis format ArcGIS Spatial metrics Fragstat Data sheet for root biomass model Slice ID Root # Root CSA /Slice Root Cumulative CSA DTC n n (m 2 ) % (m)
Root vector model branching pattern and directionality Vectorize the point cloud data as polylines Using azimuth angle (to the north pole), zenith angle (to sky), root length, root order as the critical variables Reconstructing Topological relationship
Vectorizing the point cloud data Making Polylines Point-cloud data
Data sheet for root vector model Root ID Root order Azimuth angle Vertical angle Branching angle Root length Branching point Root Type n n Degree ( ) Degree ( ) Degree ( ) m m H, V, O
Data analysis Root system info Root system morphological plasticity in relation to levee geometry
Root system morphological plasticity in relation to levee geometry Horizontal growth pattern asymmetry Slope profile symmetry Parallel to the levee 2 nd order 3 rd 4 th 3 rd Shallow surface growth pattern Tap root 5 th Alternating growth directions 0 o 360 o
Root system Upslope VS. Downslope asymmetry Upslope Downslope Upslope Levee side Downslope river side Further Root Extent 5 11 Greater Biomass 5 11 Higher Root Number 4 12 0 m Downslope side: more trees have further root extent, greater biomass, and higher root number Why are more roots on downslope side of the levee than upslope side? Bulk density 150 m
Trees growing in flat ground General patterns of root systems on a flat plain may be more symmetrical (Stokes and Mettheck, 1996) Our results (One cottonwood and one valley oak in Vernalis) correspond with the previous study Symmetrical and absence of taproot Root biomass radially distributes with an exponential decreasing trend Cottonwood Valley Oak
Data/Model applications 1.Below ground biomass estimation 2.Levee slope stability modeling 3.Levee slope surface erosion mapping 4.Levee vulnerability assessment
2. Levee slope stability modeling Virtual trench profiles can be used to locate the root system within the potential failure plane of levee slope for modeling RAR and CSA can then be used to determine Factor Of Safety (FOS) in levee slope stability model Virtual trench profile of potential failure plane RAR and CSA
3. Levee slope surface erosion mapping Land use and land cover mapping General geomorphology mapping Hydrological process mapping (soil plant/root system water dynamics) Resulting thematic map: Slope surface erosion map
4. Levee vulnerability assessment Surface process evaluation Subsurface connectivity and slope stability evaluation Potential root system occurrence evaluation Resulting vulnerability assessment map
Acknowledgments Collaborators Gerald Bawden, U.S. Geological Survey Sandra Bond, U.S. Geological Survey Keir Keightley, Geography, UC Davis John Lichter, Tree & Associates Vic Claassen, Soils & Revegetation, UC Davis Oliver Kreylos Jeffrey Wu Molly Ferrell Jaylee Tuil Ron Lane Student field crews: 5 seasons/4 locations RD 1000 Paul Devereux DWR Roy Kroll Dave Williams San Joaquin River National Wildlife Refuge Eric Hopson Student lab interns: Stephanie Lau Gary Bladen Hannah Stone Ryan Sresovich Joe Schlottman River Partners Stephen Sheppard Julie Rentner