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Interannual multitemporalapplications of Landsat to forest
ecosystem monitoring and management
Randolph H. Wynne
Department of ForestryVirginia Polytechnic Institute and State University
wynne@vt.edu
Key Co-Is & Collaborators
• Chris Potter & Rama Nemani, NASA Ames• Christine Blinn, Valquiria Quirino, Susmita Sen,
Jess Walker, Tom Fox, Carl Zipper, & John Seiler, Virginia Tech
• Feng Gao & Jeff Masek, NASA Goddard• Sam Goward, UMD• Jason Moan, NC Division of Forestry• Buck Kline, VA Department of Forestry• Jim Ellenwood, USDA Forest Service
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
Key Themes
• Explicitly multitemporal
• Ties with rest of LDCM team
• Collaborators across agencies & organizations (coops, NGOs)
• Applied orientation
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, 2008)– Parlaying this success into NASA AISR proposal
• REDD pilot on 20,000 ha in SA• DSM pilots in Mojave and WV underway
DSM Involvement(Blinn)
• Landsat & ASTER mosaics
• Band ratio development (TM, ASTER, other)
• Regression tree modeling
• Model design and development
• Spatial statistics
• Temperature modeling
Leaf-off Mosaic
Leaf-on Mosaic
Spring Mosaic
• Remote Sensing for Forest Carbon Management– Landsat downscaling prototyped using STARFM for
three scenes; pine age class evaluation of algorithmic performance
– Downscaled product being used for reducing variance in FS models
– Regionwide CO2 efflux and LAI acquired at network of monospecific sites with alternative forest management strategies
– Assessment of VI’s empirical relationship with LAI and stand growth within and across years
– Interface with Ames LAI/fPAR team– CASA modeling with Landsat
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
É
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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
É
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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
A Regionwide Evaluation of A Regionwide Evaluation of Vegetation Indices for the Vegetation Indices for the
Prediction of Leaf Area Index in Prediction of Leaf Area Index in the Southeastthe Southeast
Study AreaStudy Area
17 path/row combinations
LAI and VI Relationships
Fertilization Effect on LAI and VI Relationship
15 plots from 8 stands in three L5 images acquired either April 11 or May 2, 2006
SR
LAI
6.56.05.55.04.54.03.53.02.5
4
3
2
1
0
-9901
FERT
LAI vs SR by Fertilization
STARFM NDVI Downscaling(Quirino, Gao)
Predicted Landsat Composites (left images) and Landat Images Used for Their Validation (right images): (a) Sample 1, (b) Sample 2, (c) Sample 3.
(a) (b) (c)
STARFM for Pines
Part of the Image Included Sample 1
Sample 2
Sample 3
Mean
Entire Image 65.8 52.5 79.2 65.8 Pine Area 46.4 49.3 56.1 50.6 Pines between 0 and 5 years 60.5 48.8 85.4 64.9 Pines between 5 and 10 years 67.4 72.8 77.1 72.4 Pines between 10 and 15 years 113.0 132.4 119.0 121.5 Pines older than 15 years 150.4 184.2 172.0 168.9
Part of the Image Included Sample 1
Sample 2
Sample 3
Mean
Entire Image 10.8 2.7 4.4 6.0 Pine Area 3.4 2.1 0.1 1.9 Pines between 0 and 5 years 4.5 -0.4 1.1 1.7 Pines between 5 and 10 years 3.2 2.2 -0.2 1.7 Pines between 10 and 15 years 3.4 2.7 -0.1 2.0 Pines older than 15 years 2.4 2.6 0.1 1.7
Results Good Enough to Move Toward FS Lag Analysis
Average Correlation Between Predicted and Daily Landsat NDVI
0.82
0.86
0.80
0.76
0.58
0.41
Entire Study Area
Pine Area
Pine Age Class 1- 0 to 4.9 years
Pine Age Class 2- 5 to 9.9 years
Pine Age Class 3- 10 to 14.9 years
Pine Age Class 4- older than 15years
Av
era
ge
R-s
qu
are
Retrospective Analysis of Growth and Yield (Blinn,
Goward, Masek)
52 sites fall inside more than 1 Landsat scene
Retrospective Analysis cont...
Available Imagery: 5 Landsat scene stacks were provided by the University of Maryland (UMD) courtesy of Sam Goward & Chengquan Huang
Each scene stack contained a time series of at least 12 images from 1984 through 2005 (at least 1 image every other year)
Scene stacks were already: coregistered (aligned with each other) converted to surface reflectance
Image Date
Sim
ple
Rat
io (
SR)
200019981996199419921990198819861984
20
15
10
5
TOA RefSurface Ref
Variable
SeptMay
JulOctSept
Aug
OctOctSept
OctSept
SeptOct
OctSept
Aug
Sept
May
Jul
Oct
Sept
Aug
OctOct
Sept
Oct
Sept
Sept
Oct
Oct
Sept
Aug
Plot 2109 SR Comparison
13 years old in 1984
Summary Conversion of Landsat data to surface reflectance resulted in more scene to scene variation then TOA reflectance.
Thus far, it appears that the stands used to develop the LPG&Y Coop’s growth and yield models had relatively low leaf areas.
Caveat: The Flores LAI equation was not validated for use with earlier Landsat TM scenes.
Updated growth and yield models may be needed to accurately predict the outcomes of current silvicultural regimes.
Update Summary III(Moan, Ellenwood)
• Forest Health– Southern Pine Beetle hazard rating pilot (Landsat +
NC Statewide Lidar)– Slight improvement in Landsat-based host BA models
(Cubist) when statewide lidar-derived DEM used – Also integrating forest age into hazard rating using DI
for age classes deemed susceptible to SPB from CART/Cubist models
Update Summary IV• OSM Abandoned Minelands Reforestation
Pilot (Sen, Zipper, Masek)– Focus on WV and western VA– Detection of previously mined areas (first
phase)– Assessment of reclamation quality– Using explicit trajectory-based approach
Inde
x V
alue
Time
Clear-cutting
Vegetated
Rev
eget
atio
n
Re-vegetated
0
Y
X
Mined
Fig.1 Ideal Spectral Trajectory of a reclaimed mined vegetation
•Disturbance like mining has distinctive temporal progressions of spectral values
• Temporal trend of vegetation recovery termed as “spectral trajectory”
• Spectral trajectories function as a diagnostic of the change
•Multitemporal trends are more stable than individual image pixel change reading (Kennedy et al, 2007).
Multitemporal approach-Rationale
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
1988, points not mined 1991, points already mined
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
NDV
I
Years
NDVI
64
67
-20
-15
-10
-5
0
5
Dis
turb
ance
Inde
x va
lues
Years
Disturbance Index
64
67
Search for the ideal index value:
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
ND
VI v
alue
s
Date
ndvi_mined_64
ndvi_mined_67
ndvi_notmined_25
ndvi_notmined_26
ndvi_notmined_27
ndvi_notmined_28
Comparison of NDVI of mined and surrounding un-mined points
Location of the mined and un-mined points on the False color image of 1998
•Points 64 and 67 are mined points
•Points 25, 26, 27 and 28 are un-mined points.
Comparison of NDVI values of mined and un-mined points
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
ND
VI a
nd N
orm
aliz
ed N
DV
I val
ues
Years
64_Norm_NDVI
67_Norm_NDVI
64_NDVI
67_NDVI
NDVI and normalized NDVI values for pts.64 and 67
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
ND
VI v
alu
es
RSR
val
ue
Date
rsr ndviPt. 64
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0
50
100
150
200
250
9/17/84 6/6/87 6/8/88 9/21/91 9/29/94 9/5/97 9/24/98 6/9/00 5/22/02 6/2/03 9/11/05
ND
VI
ND
MI
Years
ndmi ndviPt.64
Reduced Simple Ratio (RSR) and NDVI values for pt.64
NDMI and NDVI values for pt.64
Some other promising indices:
-20
0
20
40
60
80
100
120TC
2
Years
Tasseled Cap 2
6467
TC2 (Greenness) values for pts. 64 and 67
Update Summary V• MEASURES (Management-scale Ecological
Assessment Using Remote Sensing; Potter, Kline, Blinn, Saleh, Walker)
• Key features: web-delivery, RS (lidar, digital orthoimagery, Landsat), scale, validated numbers, real-time modeling
• Foundation, state, and Forest Service support• Initially just C and water; biodiversity in development• Linkage with BSI and NGO Ecosystem Services
model clearinghouse (TNC)
Conclusions
Landsat-scale = management scale (almost perfect spatial resolution)
Seasonal and interannual time series now pro forma (function, process)
Importance of data set continuity(Why not 8-day revisit akin to Resource21)
Synergism from working more as team
Multitemporal in situ data sets greatly increase value of Landsat data
Thanks!
Randolph H. Wynne
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