Using three decades of Landsat data

to characterize trends and

interannual variation in

boreal and temperate forest

phenology

Eli Melaas, Damien Sulla-Menashe,

& Mark Friedl

1

Background

• Spring phenology is: – Coherent fingerprint of climate change

– Tightly coupled with land-atmosphere exchange of carbon and water

• Most retrospective analyses of continental trends in phenology use AVHRR – Potential AVHRR issues:

• Snow cover

• Land cover heterogeneity

• Low sensor quality

– Resolved using Landsat TM/ETM+ data: • Fmask screening of snow/clouds

• Medium resolution data

• High sensor quality

• Disturbance detection

2

Wang et al. 2011 PNAS

Sp

rin

g N

et E

cosy

stem

Ex

chan

ge

(gC

m-2

)

Spring phenology date (DOY)

Keenan et al. 2014 NCC

• Annual timing of spring is driven by combination of: – cold Twinter (chilling)

– warm Tspring (forcing)

– photoperiod

• Zohner et al. (2016) Nature Climate Change: – Species from shorter winters (≤ 6 months

with Tavg < 5°C) rely on photoperiodism

– Photoperiod may only constrain climate shifts in spring phenology at lower latitudes

Background

3

Study Questions / Goals

• Perform Retrospective Trend Analysis

– How has timing of SOS changed across N. America during 1982-2013?

– What is the statistical significance of this trend?

• Test Zohner et al.’s working hypothesis:

– Warm temperate species spring phenology driven by photoperiod

– Cold temperate/boreal species spring phenology driven by spring warming

4

Study Region / Methods

5

• Exclude Landsat pixels with:

- Inconsistent seasonality

- Insufficient EVI amplitude

- Agriculture (NLCD / EOSD)

One Landsat pixel

1. Spring Warming

(Sarvas et al. 1972)

Phenology Models

6

R f =

28.4

1+ exp(3.4 - 0.185*T )T > 0

0 T £ 0

ì

íï

îï

SOS = R fp0=12.5

200

å

R f =T - 5 T > 0

0 T £ 0

ìíî

SOS = R fFT

230

å

R f =

28.4

1+ exp(3.4 - 0.185*T )

daylength

10

æ

èç

ö

ø÷

3.9

T > 0

0 T £ 0

ì

íï

îï

SOS = R fp0=11.7

761

å

2. Freeze-Thaw

(Barr et al. 2004; Kim et al. 2012)

3. Photoperiod (Blumel & Chmielewski 2012)

Methods

Assessment

• Compare Landsat phenology with ground observations (Flux tower photosynthesis)

Trend Analysis

• Estimate magnitude (Theil-Sen) and significance (Mann-Kendall) of SOS trend across Landsat pixels within 500 m grid cells for each sidelap region

Modeling

• Train phenology models using PhenoCam and surface meteorological data

• Run models using NARR 2 m Tair data (32 km) and compare predictions with Landsat SOS

7

Location of PhenoCam sites

Sample NARR data

Landsat Phenology Assessment

8

Melaas et al. 2016 RSE

EOS

Results: SOS Trends

9

−6 −4 −2 0 2 4 6

1982−2013 Trend in AGDD for DOY 91−150 (GDD/yr)

Results

10

Results

11

Take Home Messages:

• Divergence in long-term SOS trend across North American forests

• Our evidence supports Zohner et al.’s hypothesis that warm temperate species phenology will be photoperiod-constrained