Linking FIA Data and Satellite Imagery to Build a Habitat model for the Marbled

MurreletMartin G. Raphael

Pacific Northwest Research StationFunding contributed by:

Region 6, USFS, PNW Research StationUSDI Fish and Wildlife Service

Northwest Forest Plan of 1994• Conservation plan for older forests and

species on 57 mill. ac. of federal land• Effectiveness Monitoring modules for

older forest, n. spotted owl, marbled murrelet, watershed condition

• Key questions for monitoring older forest: – How much, how is it changing, how

might it change in the future?– Is the Plan providing for its

conservation and management?

Physiographic

provinces(57 mill. ac.,46 mill. ac

forest)

USA

A primary objective of the Northwest Forest Plan was to achieve:

“maintenance and/or restoration of habitat conditions for the Northern Spotted Owl and the Marbled Murrelet that will provide for viability of each species -- for the owl, well distributed along its current range on federal lands, and for the murrelet so far as nesting habitat is concerned”

--FEMAT 1993:iv

Objectives• Estimate amount and distribution of

murrelet nesting habitat in WA, OR, CA• Estimate change over time – from start

of plan to now• Make estimates over all lands within

the murrelet range in WA, OR, CA• Use existing sources for environmental

variables (e.g., IMAP, PRISM)

Needs for regional vegetation information• Methods that integrate plot and remotely sensed data to provide

info.:– Consistent over large, multi-ownership regions (“all lands”)– Spatially explicit (mapped)– Detailed attributes of forest composition and structure– Support integrated landscape analyses of multiple forest

values• Latest challenge: provide trend information that is spatial

– Monitoring older forest for Northwest Forest Plan

Effectiveness Monitoring for Late-Successional and Old-Growth Forest (LSOG)• Objective: develop tools and data to assess change in older

forest– Gradient nearest neighbor (GNN) imputation (maps of

detailed forest attributes)– Change detection from Landsat time series (LandTrendr)

(trends)• Approach: minimize sources of error in models, map real change

– Corroborate with sample-based estimates• Monitoring report every 5 years

– 10-year report (Moeur et al. 2005)– In progress: 15-year report– 1996 to 2006 (Wash. and Oreg.), 1994 to 2007 (Calif.)

* Moeur, M., et al. 2005. Northwest Forest Plan–The first 10 years (1994-2003): status and trend of late-successional and old-growth forest. Gen. Tech. Rep. PNW-GTR-646.

Gradient Nearest Neighbor Imputation (GNN)

k=1

Accuracy assessment (‘obsessive transparency’)• Local- (plot-) scale accuracy via cross-

validation:– Confusion matrices, kappa statistics,

root mean square errors, scatterplots, etc.

• Landscape- to regional-scale accuracy: – Area distributions in map vs. plot sample– Range of variation in map vs. plot

sample– Riemann et al. (2010) diagnostics– Bootstrap variance estimators for kNN

(Magnussen et al. 2010)• Spatial depictions of uncertainty:

– Variation among k nearest neighbors– Distance to nearest neighbor(s)

(sampling sufficiency)• ‘Look-and-feel’ issues

1

2 3 4

5 6 7* 8 9

10 11 12

13

local(1-ha

plot) scale

regionalscale

landscape- or watershed-

scale

Oregon

LSOG change from GNN ‘bookend’ maps, 1994/6 to 2006/7

• GNN models and change at 30-m pixel scale

• Recommend summarizing to coarser scales

• Example: 10-km hexagonsLSOGchange(% of forest)

Change in habitat suitabilityNWFP Effectiveness

Monitoring

• Maxent (machine learning) models based on forest structure and composition attributes from GNN, trained with nest location data

• Subtract models to get change

Marbled murrelet

Northern spotted owl

Modeling Marbled Murrelet Nesting Habitat: Estimating Nesting Habitat Suitability

Natural History Fish-eating

seabirdDistributed

along West coast S to Monterey

Bay Nests on limbs

of big conifers Nests within 20

to 50 km from shore

Model Form

• Presence/available• Presence = set of murrelet nests plus

equal number of “occupied” sites• Available = entire landscape within

study region that is “capable” of being habitat– We masked out barren lands, non-

forested areas

Variable selection• Team developed initial list from

available data based on experience, literature

• Variables must cover range– Used GNN for forest attributes– PRISM for climate variables– DEM for slope, aspect

• Ran correlations, dropped one if r > 0.9– Kept variable with better support in

literature

Model area

Nests Occupied

WA 54 54OR 65 65CA 52 52Total 171 171

Murrelet Sites

Examples of GNN data

Platforms per Tree (all species)We used these data to derive a new variable from the GNN data

Washington Oregon California0

200

400

600

800

1000

1200

1400

1600

Fed. Reserved

Fed. Nonreserved

Nonfederal

High

er-s

uita

bilit

y ha

bita

t (th

ousa

nds o

f acr

es) Baseline

estimate

Washington Oregon California0.0

0.5

1.0

1.5

2.0

2.5 Baseline (1994/96)

Maxent bookend

LandTrendr

Hig

her

suit

abili

ty h

abit

at (m

il-lio

ns o

f acr

es)

Change in habitat from 1994/96 to 2006/07

Baseline Fire Harvest Other Total Percent

- Thousands of acres -

Fed.reserved

2,163.1 51.6 8.8 3.7 64.2 3.0

Fed.Non-reserved

262.7 5.3 6.5 0.8 12.6 4.8

Non-fed

1,386.6 0.9 394.3 18.7 413.9 29.8

Loss of suitable habitat from baseline to 2006/07

Murrelet population sizein relation to amount of

nesting habitat

Murrelet numbers are declining

Is amount and trend of nesting habitat a primary driver of population trend?

• Spatial distribution of murrelets is well-predicted by spatial distribution of habitat• Habitat trend is not as clear, but

suggest a possible correlation• If marine conditions are the driver,

we’d expect similar trends among other related birds and we don’t see such trends

Sources of uncertainty in overall monitoring results

• Multiple estimates, lots of moving parts with different limitations – Map- and plot-based estimates can’t be compared

statistically– Look for corroboration– Complexity and uncertainty pose challenges for users

• Error in model-based estimates– Error in plots, spatial predictors; model specification; etc.– Limitation of Landsat for mapping LSOG recruitment– Time period is short (10-13 years), and data will improve

• Uncertainty associated with murrelet habitat definition: – Habitat attributes can be affected by one or a few trees– Disturbance can create habitat gain, habitat loss, or no

change

Products from NWFP monitoring study • GNN models and diagnostics available for download

– 2006/7 and 1994/96 vegetation maps and accuracy assessments

• 15-year reports (PNW GTRs) published or in press:– LSOG, northern spotted owl, marbled murrelet, watershed

condition