Multi-scale Forest Characterization from Airborne Lidar and Imagery

Mischa Hey WSI, Corvallis OR

~Finding the Forest in the Cloud~

Timber inventory Biomass/carbon estimation Forest growth models Species composition Fuels modeling Habitat suitability

Forestry Applications

Forestry Analysis Workflow Lidar classification and treatment

Surface modeling (bare earth, canopy)

Stem identification (canopy dominants)

Crown delineation (canopy dominants)

Stand delineation (relevant scale)

Crown attribution (Lidar and ancillary data) Field survey data (tree-scale)

Statistical modeling (tree-scale)

Field survey data (plot-scale)

Statistical modeling (plot-scale)

Stand attribution (Lidar and ancillary data)

Results and accuracy (plot)

Spatial scale of analysis appropriate for client’s

applications

Results and accuracy (tree)

Lidar treatment • Distinguish ground, buildings, and vegetation returns - Automated classification algorithms - Manual inspection and adjustment

Vegetation Ground class Ground cover Building

Stem Identification • Local maxima of canopy surface model

- Variable scale (depends on forest character) • Initial stem locations act as seed points for crown

discrimination

Crown and stand delineation • Customized segmentation and growth algorithms

- Based on canopy surface and Lidar intensity. - Employs contrast edge extraction, surface tension.

Tree approx Microstand

The Point of Objects… -Describe variability and distribution of returns within object.

Hardwoods: High canopy penetration Frequent multiple returns Lower intensity (leaf-off) Variable structure

Conifers: Low canopy penetration Few multiple returns Higher intensity (leaf-off) Distinct structure

Data Stack (Panther Creek, OR)

Slope Aspect

Lidar intensity Leaf-on satellite

Leaf-off satelite

Object structure

Echo density Roughness

Canopy height

Classified point cloud

Stream

Scales of information -Keeping meaning in the metrics

Mature Douglas Fir

Early seral Douglas Fir

Mixed stand

Red Alder

RGB image : for orientation Pixel-scale : Intensity, elevation Micro-stand : Percent cover, surface roughness Tree object : Lidar height percentiles, 3D shape intensity distribution

Attribution and context -Multi-dimensional and hierarchical

Object ID #: 0451673 Species type: Conifer Age class: Mature Height: 32m Crown diameter: 7.5m Live/dead: Live Stand type: Mixed Stand age class: Mature Stand area: .78 ha Percent cover: 76% Understory: Dense Hydrologic: Riparian Slope: 12° Soil: Sandy

Attribution and context -Multi-dimensional and hierarchical

Object ID #: 0451673 Proximity: - Water: 5m - Road: 147m - Patch edge:32m - Mature forest: 0m Neighborhoods: - # of snags: 9 - % conifer: 45 - % early seral: 57

Surface metrics Description Canopy slope* Mean slope of canopy surface model (nDSM) Canopy roughness* Local variability in elevation values of canopy classified points. Rumple* Measure of vertical complexity of the canopy surface. Bare earth Mean ground elevation of object Slope Mean ground slope of object

Point Metrics Description Height* Max elevation above ground of object Percent cover* Percent of Lidar returns not penetrating canopy Echo ratio Ratio of secondary and tertiary Lidar returns to first returns Point height distribution Height percentiles of vegetation classified Lidar returns

Spectral metrics Description Veg classified return intensity Intensity of vegetation classified laser pulses NDVI leaf-off* Normalized Difference Vegetation Index (leaf-off imagery) NDVI leaf-on* Normalized Difference Vegetation Index (leaf-on imagery) NDVI difference* Difference in NDVI between leaf-on and leaf-off

Contextual metrics Description Trees per hectare Density of tree objects within micro-stand Emergence Difference between object height and mean stand height.

*Metrics computed at tree and stand scale.

Inventory and Carbon

• Stem location and height • Canopy dominant individuals

• Species/Class predictions

• Accuracy assessment

• Tree variables • DBH, Basal Area, Volume • Field-based statistical models • Allometric equations

• Stand variables

• DBH (mean, quadratic mean) • Height (max, min, mean, Lorey’s) • BA (total, mean) • LAI, crown closure • Stratification

Not actually a forest… …statistical ordination technique

Random Forest Analysis

Iterative analysis of CART’s … randomly selects subset of predictor variables at each run.

Pro’s - Hierarchical and non-linear relationships - Categorical and continuous variables - High predictive accuracy - Suited to small sample #’s

Con’s - Black box (difficult to interpret) - Biased towards common classes

3000 tree locations as raw field data • Cadastral survey • Species

-80% training, 20% validation

Panther Creek, OR

Spatially joined plots and trees to delineated objects

• Stratified tree objects by height and class

• Classification to species

Results – Panther Creek, OR • Species predictions based on tree level data.

• Objects assigned to most probable class based on RFA models • “Moderate” (Kappa = 42.7) • Rare and sub-dominant species had low-no predictive power

Douglas fir Red alder Bigleaf maple Western hemlock Red cedar Producers Douglas fir 464 11 0 4 2 96.5% Red alder 44 50 2 9 4 45.9% Bigleaf maple 15 8 6 1 1 19.4% Western hemlock 12 2 0 0 0 0 Red cedar 12 0 0 0 8 40.0% Grand fir 8 0 0 0 0 0 Dogwood 2 0 0 0 0 0 Cascara 3 0 0 0 0 0 Cherry 8 0 0 0 0 0 Pacific yew 1 0 0 0 0 0 Madrone 0 0 0 0 0 0 Willow 0 0 0 0 0 0

Overall acc Users 81.5% 70.4% 75.0% 0% 53.3% 77.99%

Results – Panther Creek, OR • Species predictions based on tree level data. • Presence/ absence accuracy for individual species • Rare and sub-dominant species had low-no predictive power Douglas Fir

absent present producer absent 29 16 64.44% present 21 287 93.18% user 58.00% 94.72% 89.52% k=.55 Western hemlock

absent present producer absent 317 3 99.06% present 24 9 27.27% user 92.96% 75.00% 92.35% k=.37 Red Cedar

absent present producer absent 333 2 99.40% present 12 6 33.33% user 96.52% 75.00% 96.03% k=.44 Red Alder

absent present producer absent 281 4 98.60% present 36 32 47.06% user 88.64% 88.89% 88.67% k=.55

Conifer

absent present producer

absent 13 11 54.17%

present 10 319 96.96%

user 56.52% 96.67% 94.05% k=.52

Deciduous

absent present producer

absent 247 5 98.02%

present 58 43 42.57%

user 80.98% 89.58% 82.15% k=.48

100 plots provided as raw field data • Variable radius • BAF = 20 • 742 trees

-80% training, 20% validation

Middle Fork East River, ID

Spatially joined plots and trees to delineated objects

• Computed tree volumes • National Volume Estimator (USFS)

• Calculated tree locations • Summarized metrics for plots

Diameter at breast height RMSE 5.57 MAE 4.12

Variation Explained 44.92%

Tree Height RMSE 18.48 MAE 12.86

Variation Explained 60.49%

Volume RMSE 38.93 MAE 22.32

Variation Explained 27.35%

Base of crown RMSE 11.93 MAE 8.76

Variation Explained 45.07%

Results – Middle Fork East River, ID • Metric predictions based on tree level data.

Predicted user accuracy AF CE DF ES GF HA LP WB WP WH WL

Fiel

d m

easu

red

AF 21 1 1 0 0 0 0 1 0 1 0 84.00% CE 1 16 2 1 6 1 0 0 0 2 0 55.17% DF 3 2 10 0 2 0 0 0 0 0 0 58.82% ES 0 1 0 3 0 0 0 0 0 0 0 75.00% GF 1 3 0 0 9 0 0 0 0 0 0 69.23% HA 0 0 0 0 0 0 0 0 0 1 0 0.00% LP 1 0 0 0 0 0 1 0 0 0 0 50.00% WB 2 0 0 0 0 0 0 2 0 0 1 40.00% WP 0 1 0 0 0 0 0 0 0 0 1 0.00% WH 3 2 0 0 1 0 0 0 0 8 1 53.33% WL 0 2 0 0 1 0 0 0 0 0 4 57.14%

producer accuracy

65.63% 57.14% 76.92% 75.00% 47.37% 0.00% 100.00% 66.67% 0.00% 66.67% 57.14% 61.67% overall

accuracy

Predicted Habitat

Fiel

d m

easu

red

DF GF RC SF WH DF 0 0 0 1 1 0.00% GF 0 0 0 0 1 0.00% RC 0 0 0 0 4 0.00% SF 0 0 0 5 1 83.33% WH 0 2 3 0 2 28.57%

0.00% 0.00% 0.00% 83.33% 22.22% 35.00%

Predicted grouped habitat DF GF RC/WH SF

DF 0 0 1 1 0.00% GF 0 0 1 0 0.00% RC /WH 0 2 9 0 81.82% SF 0 0 1 5 83.33%

0.00% 0.00% 75.00% 83.33% 70.00%

Results – Middle Fork East River, ID • Species and habitat predictions based on tree and plot level data.

Results – Middle Fork East River, ID • Metric predictions based on plot level data.

Mean DBH RMSE 6.79 MAE 5.01

Variation Explained 31.36%

Total Basal Area / acre RMSE 2579.99 MAE 2091.0

Variation Explained 33.30%

Total wood volume / acre RMSE 2239.91 MAE 1857.97

Variation Explained 53.14%

Quadratic mean DBH RMSE 7.21 MAE 5.23

Variation Explained 32.98%

100 plots selected for pilot study • Fixed radius • 1/5th acre

-80% training, 20% validation

East Cascades, OR

Spatially joined plots to stand object polygons

• Computed stand metrics • Volume • Diameter Breast Height • Basal Area • Crown Competition Factor • Trees per acre

Results – Warm Springs, OR • Metric predictions based on plot level data.

Basal area per acre (DBH>5”) RMSE 74.27 MAE 56.78

Variation Explained 68.44% P-Value <0.001

Mean DBH (DBH>5”) RMSE 2.62 MAE 2.21

Variation Explained 36.21% P-Value <0.001

Mean height (largest 100 trees) RMSE 18.67 MAE 14.29

Variation Explained 74.27% P-Value <0.001

Cubic stand volume (stump/top) RMSE 3409.76 MAE 2464.24

Variation Explained 76.56% P-Value <0.001

Discussion… • Overall decent predictive models

• Height and volume models perform better than DBH and BA • Species-level predictions show promise...

• Potential sources of error…

• Forests are complex systems (always limiting) • Positional accuracy of field data • Variable radius plots (inexplicit boundaries) • Top vs. bottom perspective (different forest characteristics)

• Continued research…

• Further field efforts (ID delineated objects in field) • Additional scales of objects • Point-based segmentation • Iterative classification (requires potentially hundreds of samples)