Forestry Applications of Remote Sensing for LDCM: Focus on US Southeast

Randolph H. Wynne

Department of ForestryVirginia Polytechnic Institute and State University

wynne@vt.edu

Key Co-Is

• Chris Potter, NASA Ames

• Xue Liu, GMU

• Jim Ellenwood, USDA Forest Service FHTET

• Christine Blinn, Carl Zipper, & John Seiler, VPI&SU

Overall Objective

• R&D in support of increased utilization of Landsat data for forest science and management, with particular focus on US Southeast, including forest industry

Update Summary

• IGSCR in support of USDA Forest Service FIA Phase I in areas of rapid change (mid-NLCD cycle, indiv. scenes or mid-decadal)– Imagine automation complete (Musy et al.

2006; ASPRS Leica paper award)– Shared memory parallelization complete and

publicly available (Phillips et al. 2007)– Parlayed this success into NASA proposal to

parallelize LEDAPS surface reflectance protocol (no news yet)

Update Summary II

• Remote Sensing for Forest Carbon Management– Landsat EVI downscaling prototype complete

for first study area, though awaiting comparison with Goddard MODIS ACCESS product stream

– Region wide CO2 efflux and LAI acquired at network of monospecific sites with alternative forest management strategies

– Assessment of VI’s empirical relationship with LAI within and across years

Decision Support for Forest Carbon Management: From Research to OperationsMODELS

ESE NASA-CASAGYC PTAEDA 3.1 FASTLOBUSDA Forest Service FORCARB

ESE MISSIONS Aqua Terra Landsat 7 ASTER

Analysis Projects IGBP-GCTE IGBP-LUCC USDA-FS FIA USDA-FS FHM

Ancillary Data SPOT AVHRR NDVI Forest inventory data VEMAP climate data SRTM topographic data

DECISION SUPPORT:

Current DSTs•COLE (county-scale)•LobDST (stand-scale)

• Growth and yield• Product output• Financial evaluations

•CQUEST (1 km pixels)• Ecosystem carbon

pools (g C/m2)• Partitioned NPP (g C/m2/yr)• NEP (g C/m2/yr)

Linked DSTs and Common Prediction

Framework (multiscale)

• Growth• Yield• Product output• Ecosystem carbon pools• Partitioned NPP • NEP• Total C sequestration• Forecasts and scenarios

Information Products, Predictions, and Data

from NASA ESEMissions:

- MODAGAGG- MOD 12Q1- MOD 13- MOD 15A2- ETM+ Level 1 WRS- AST L1B and 07

VALUE & BENEFITS

Improve the rate of C sequestration in managed forests

Decrease the cost of forest carbon monitoring and management

Potentially slow the rate of atmospheric CO2 increase

Enhance forest soil quality

Inputs Outputs Outcomes Impacts

MODIS EVI MODIS EVI

Pine coverage Pine coverage thresholding (10448 thresholding (10448

pixels found) pixels found)

Statistical testing (4541 Statistical testing (4541 pure pine pixels found) pure pine pixels found)

30m Landsat EVI30m Landsat EVI 250m MODIS EVI250m MODIS EVI NLCD 2001 Land Cover NLCD 2001 Land Cover

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É

É

É

Ý

b

b

b

b

19BBE1Kp

58DBP6L

34SCW3Kp35RBW3Kp

25RBE2Kp

26RBE3aKp

34SCW3Kp

34SCW3Kp

26BBE2Kp

34SCW4Kp+

19LBW3Kp

35SEW3Kp25SCW3Kp+

35SEW3Kp

58DBP6L

26SCE2Kp

35SEW3Kp

23SBW3Kp

25SEW3Kp35SEW3Kp

23SCW3Kp

19SBW2Kp23LBW3Kp

23SCW3Kp58DBP6L 35LBW3Kp+

58DBP6L

23SBW3Kp

23SCW3Kp

23SCW3Kp

1:12118

Map: 24004ALat: 32° 10.5Long: 84° 37.9

Sand Pine Guidelines

Sand Pine - Well Suited

Sand Pine - Mod. Well Suited

Not Suited for Sand Pine

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É

É

É

Ý

b

b

b

b

Sand PineLAI 2.4

No Fert LobLAI 1.3

RW18 Study AreaLAI 1.7

96PL 1

34BH16

96PL 182BH16

86PL 1

86PL 1

79NP 4

82BH16

82BH16

96PL14

82BH16 34BH1686PL 182BH16

34BH16

96PL 1

34BH1634BH16 34BH16

37BH16

99PL 137BH16

99PL 137BH16

1:12118

Map: 24004ALat: 32° 10.5Long: 84° 37.9

Dec_2004

0.333 - 0.667

0.668 - 1

1.001 - 1.333

1.334 - 1.667

1.668 - 2

2.001 - 2.333

2.336 - 2.667

2.668 - 3

3.001 - 4

No Data

LandSat Image

LAI (Leaf Area Index):•stand stratification for inventory•identification of poor-performing stands for early harvest•identification of stands with high levels of competition

LAI plus GE (Growth Efficiency)Provides ability to estimate stand-level response to silviculture: (fertilization, release, tillage)

Growth = 7.2 tonsGrowth = 3.9 tons

Growth = 5.1 tons

Remote Sensing Estimation of LAI

0.0

1.0

2.0

3.0

4.0

0.0 1.0 2.0 3.0 4.0

LAI Observed

LA

I E

stim

ated

LAI and VI Relationships

Update Summary III

• Forest Health– Southern Pine Beetle hazard rating pilot (Landsat +

NC Statewide Lidar)– Working closely with FHTET to use current scenes

(rather than prior MRLC data from NLCD mapping regions) and FIA data to estimate key forest biophysical parameters using non-parametric approach

– Also integrating forest age (up to 35 years…) into hazard rating (normalized approach, e.g., Browder et al. 2005, Healey et al. 2005)

Update Summary IV

• OSM Abandoned Minelands Reforestation Pilot– Focus on WV and western VA– Detection of previously mined areas (first

phase)– Assessment of reclamation quality

WV site 3

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000

0 2 4 6 8 10 12

Landsat scene

ND

VI

Sample 1

Sample 2

Sample 7

Sample 8

Sample 9

Sample 10

Sample 3

June 1987

Aug. 1988

Sept. 1994

Sept. 1998

Mar. 2000

Apr. 2000

June 2000

Oct. 2000

May 2002

June 2003

June 1988

Landsat scene dates

Thanks!

Randolph H. Wynne