LiDAR imagery improves classification of forested landforms in the Susquehanna/Shale Hills

Critical Zone Observatory region of Pennsylvania. Kristen Brubaker1, Elizabeth Boyer1, Katie Gaines2

1Penn State University , School of Forest Resources 2Penn State University Intercollege Graduate Degree Program in Ecology and Horticulture Department

.

Landform Descriptions

• We successfully classified dominant landforms of

the landscape using readily-available LiDAR data.

• With these compact landform classes, we are able

to capture major differences in both forest structure

and vegetation types.

Leading Ridge Watersheds and the Shale

Hill/Susquehanna CZO

Vegetation Patterns

Goals

• Use lidar-derived 10m DEM to create a landform

classification system for Leading Ridge Watershed

based on patterns of curvature.

• Explore the relationship between landform classes

and vegetation structure and composition on

Leading Ridge Watershed

Interesting Conclusions

Objectives

Representative of the Ridge & Valley province of central

Pennsylvania, the Leading Ridge (ridge) and Shale Hills (valley)

watersheds were established in the late 1950s for long-term

study of hydrological and geochemical processes controlling

stream flow and water quality. The Leading Ridge control basin

shown here is 294 acres. It is currently home to a 100 year old

oak-hickory forest like many ridges in this Province.

Problem: Scientists are looking for concise,

effective methods to predict and understand

vegetation distributions based on synoptic data

sources.

Plan and profile

curvature were

calculated and

combined into nine

curvature classes

Four types of

landforms were

delineated

Group Name Pattern of Curvature Geology and Soils

1 rock ridgelets Profile curvature and plan

curvature >95% convex

Underlain by shale, soils

primarily Berks/Berks-

Weikert.

2 hidden hollows Profile curvature and plan

curvature both >95%

concave

Underlain by shale, soils

primarily Andover and

Buchanan

3 Scalloped

Slopes

Profile curvature and plan

curvature both undulating,

similar proportions of all 9

categories

Underlain by shale and

sandstone of various

groups, soils of Laidig,

Dekalb, or Hazelton

4 Rounded

Ridges

Profile curvature >95%

convex or flat, plan

curvature more evenly

undulating

Underlain primarily by

Tuscarora quartzite and

sandstone, soils primarily

Hazelton, Dekalb, or rubble

Landform Modeling

Lidar-derived

tree heights

(background)

with measured

tree heights

(circles)

Overstory & understory vegetation were sampled at

32 plots throughout the watershed spanning landform

gradients. Vegetation data were analyzed using

multivariate statistics to evaluate how forest structure

and community distribution vary by landform class.

Multi-Response Permutation Procedure (MRPP) was

then used to determine statistical significance of the

differences across landforms.

ba

Trees

qupr

heightLAI

total

Forest Structure

Axis 1

Axis

2 Group

1234

summary

Axis 1

Axis

2 Group

1234

Principal Components

Analysis was used for forest

structure analysis, using

metrics including basal area,

height, tree density, and total

diversity. The forest structure

distribution is significantly

different in each landform

class.

A Nonmetric Multidimensional

Scaling technique was used

for vegetation community

analysis, on a presence-

absence dataset including 60

vegetation species. The

vegetation community

distribution is significantly

different in each landform

class.

The following landform classifications are useful to predict

the vegetation communities that are found within: