U.S. Department of the Interior

U.S. Geological Survey

Collin Homer, USGS

Matt Bobo, BLM

Remote Sensing Characterization and

Monitoring of Shrubland Components across

the Western United States

1989 Landsat

2006 Landsat

What our brain can see and process, is much more difficult to code in an algorithm and then repeat….again and again under changing circumstances…

The ability of remote sensing to offer measurement and

monitoring of the landscape at first glance seems obvious…

Guiding principles for USGS shrubland remote

sensing component characterization

Recognize the difficult remote sensing issues

of semi-arid shrub lands require robust approch

Create consistent data over large areas

Use a method that quantifies shrub components in a repeatable, objective way

Support work at multiple spatial scales and time periods

Provide a good foundation for monitoring

Sustainable and affordable

Fractional vegetation

How to distill remote sensing data into meaningful information?

Labeled categories

% Bare Ground fraction Land Cover classes

-More quantifiable and repeatable

-Can support many applications

-Easily generated across scales

-Easier to monitor subtle change

-Usually needs more effort to apply

-Easier to automate

-Good for very targeted applications

-Can be easier to understand

-Difficult to repeat and monitor

-Not application generic

-Only works for targeted scale

-Hard to automate

Shrub/Bare ground products started to be developed in 2004

1 Meter Frame Components include: •Bare ground percent

•Shrub percent

•Herbaceous percent

•Litter percent

•Sagebrush percent

•Big Sagebrush percent

•Wyomingensis Sagebrush percent

•Shrub height

Component proportions are field

measured as 1m frames and

extrapolated to raster cells at various

resolutions (2.4m, 30m and 56m) using

regression tree models using 1%

increments

30m Landsat Shrub prediction

Percent Bare Ground modeled @ 3 scales

2.4m 30m

56m

Products can be produced at multiple scales,

with the same approach

1$ per acre 1 cent per acre 1/5 cent per acre

Elevation Slope

Spring Summer

Fall

Regression tree models are used to “mine” large amounts

of data to create target predictions

Input Database

Shrub estimate

1 % intervals

Regression Tree

Ancillary Data

Aspect

Position Index

Training

Data

Shrub

Approach first regionally developed in Wyoming

Approach now has been further scaled up and

implemented in the western U.S. with BLM AIM and

USGS funding

New advancements include special Landsat 8

mosaics and climate zones modeling

Mask

Mask

Bare Ground Prediction

Sagebrush Prediction

WorldView2 Imagery

High resolution

training first developed

at 2 meter resolution

Clover valley ranch, Ca

Mask

Sagebrush

Prediction Bare ground

Prediction

Then applied wall to wall at 30m resolution

All Sage Cover (%)

ValueHigh : 102

Low : 0

Annual Herbaceous Cover (%)

ValueHigh : 102

Low : 0

Bare Ground (%)

ValueHigh : 102

Low : 0

0

All Sage Height (cm)

ValueHigh : 178

Low : 0

All Shrub Height (cm)

ValueHigh : 428

Low : 0

All Sage Cover (%)

ValueHigh : 102

Low : 0

Annual Herbaceous Cover (%)

ValueHigh : 102

Low : 0

Bare Ground (%)

ValueHigh : 102

Low : 0

0

All Sage Height (cm)

ValueHigh : 178

Low : 0

All Shrub Height (cm)

ValueHigh : 428

Low : 0

All Sage Cover (%)

ValueHigh : 102

Low : 0

Annual Herbaceous Cover (%)

ValueHigh : 102

Low : 0

Bare Ground (%)

ValueHigh : 102

Low : 0

0

All Sage Height (cm)

ValueHigh : 178

Low : 0

All Shrub Height (cm)

ValueHigh : 428

Low : 0

All Sage Cover (%)

ValueHigh : 102

Low : 0

Annual Herbaceous Cover (%)

ValueHigh : 102

Low : 0

Bare Ground (%)

ValueHigh : 102

Low : 0

0

All Sage Height (cm)

ValueHigh : 178

Low : 0

All Shrub Height (cm)

ValueHigh : 428

Low : 0

All Sage Cover (%)

ValueHigh : 102

Low : 0

Annual Herbaceous Cover (%)

ValueHigh : 102

Low : 0

Bare Ground (%)

ValueHigh : 102

Low : 0

0

All Sage Height (cm)

ValueHigh : 178

Low : 0

All Shrub Height (cm)

ValueHigh : 428

Low : 0

Herbaceous Cover (%)

ValueHigh : 102

Low : 0

All Shrub Height (cm)

ValueHigh : 428

Low : 0

Litter Cover (%)

ValueHigh : 102

Low : 0

All Shrub Height (cm)

ValueHigh : 428

Low : 0

Big Sage Cover (%)

ValueHigh : 102

Low : 0

All Shrub Cover (%)

ValueHigh : 102

Low : 0

0

High : 100

Low : 0

High : 100

Low : 0

High : 100

Low : 0

High : 100

Low : 0

High : 100

Low : 0

High : 100

Low : 0

High : 100

Low : 0

Shrub component

list (with

estimates in 1%

increments)

now being

produced

Mask

Mask

Mask

Shrub Prediction

Bare Ground Prediction

Shrub Absolute Error

Bare Ground Absolute Error

Mask

Validation includes independent validation, cross validation and a

spatial absolute error model prediction with products

R² = 0.4117 0

5

10

15

20

25

30

35

0 10 20 30 40)

Shrub Cover: Independent Validation

R² = 0.7202 0

20

40

60

80

100

0 20 40 60 80 100

Bare Ground: Independent Validation

R² = 0.5182 0

5

10

15

20

25

30

35

40

-15 5 25 45 65

Herbaceous: Independent Validation

R² = 0.6101 0

10

20

30

40

50

60

70

0 20 40 60 80

Litter Cover: Independent Validation

SW

Landscape Independent Validation

Shrub, grass and

bare ground

product areas

planned

through 2015 (only nominal

base year funded)

Component Tested Applications include:

Wyoming sage grouse (core area refinement and

state-wide seasonal models)

Gunnison sage grouse habitat models

Wyoming grazing assessment (clear component

differences in allotments that failed LHS)

Capturing climate-based vegetation change over time

Future forecasting of vegetation change using

precipitation scenarios

In progress: fire fuel analysis, invasive monitoring,

restoration monitoring, climate temperature analysis

WLCI Bare Ground Prediction

2006 and 2010

2006

2010

Fire event captured

with an increase in

the 2010 Bare

Ground estimate

Percent

Wyoming bare ground change analysis across time

using Landsat (30m)

Annual precipitation

and remote sensing

component change is

significantly correlated

across 4 years in

Wyoming

Research results

from field

monitoring

Mean annual

precipitation and

sagebrush component

predictions from 1984

to 2011 over the study

area , with the linear

trend line.

Research results: Can quantify climate

change over time with

models looking at

continuous field

components and

precipitation historically,

and then quantify future

component change

predictions.

Precipitation

Bare Ground Change

Difference

-4 to -31

-1 to -3

0

1 - 3

4 - 33

Mask

Shrub

Bare Ground

Research Results: Bare Ground and Shrub

2006-2050 Change Examples using

future forecasting models

A1B Climate Scenario Result Bare ground - 1.1% increase

Shrub – 4% decrease

Sage grouse

nesting habitat

prediction,

year 2050

1993 1997

2004 2009

2011

Nevada example of increasing cheatgrass abundance, 1993-2011

White – masked out areas SW of Hot Springs Range

Cheatgrass quantity

Products are under the National Land Cover

Database Umbrella……

What will the future look like…..

Once base characterization is done, then plan to

produce products back in time to 1984

Moving forward products will be repeated every 5

years under NLCD umbrella

Potential for quick annual update under high

disturbance scenarios (fires)

Likely to continue to evolve to finer time and space

scales

Become a fundamental monitoring product that

underpins a broad variety of sagebrush steppe

habitat, management and scientific analysis

NLCD is a Landsat derived 30m

cell land cover product

database covering the United

States created by 10 Federal

partners (MRLC)

www.mrlc.gov