Quantification of Forest Disturbance Intensity Using Time ... · 30/05/2018 · 18 1984 1989 1994...
Transcript of Quantification of Forest Disturbance Intensity Using Time ... · 30/05/2018 · 18 1984 1989 1994...
Quantification of Forest Disturbance Intensity Using Time Series Landsat Observations
and Field Inventory DataChengquan Huang1, Xin Tao1, Feng Aron Zhao1, Karen Schleeweis2, Jeff
Masek3, Samuel Goward1, and Jennifer Dungan4
1 Department of Geographical Sciences, University of Maryland, 2 US Forest Service, Rocky Mountain Research Station, 3 NASA Goddard Space Flight
Center, 4 NASA Ames Research Center
May 30, 2018
Land Cover/Land Use Changes (LC/LUC) and Impacts on Environment in South/Southeast Asia -International Regional Science Meeting, 28-30th May, 2018, Philippines
Impacts of Forest Disturbances Are Broad• Climate change and Earth system processes
• Energy balance • Greenhouse gas emission• Surface roughness and albedo
• Biogeochemical and hydrological processes
• Environment, biodiversity and human well being
• Soil erosion• Habitat loss• Timber and other forestry resources
• Impacts depend on disturbance intensity• Disturbance detection and characterization
important
Fire Harvest Storm Insect
Multi-Decade Disturbance Records By Satellites
SE Asia Good time series since 1980s• 200+ Landsat 4-5 images
from 1987 to 1999 for Manila area
1987-12-30 1991-07-02
Landsat record of Mt. Pinatubo eruption
Free Data Policy
Sentinel 2Sentinel 1
And more ……
Forest Monitoring Using Annual Time Series
Landsat Time Series Stack (LTSS)
Tracking Forest Disturbance Using LTSS –Vegetation Change Tracker (VCT)
1989 19981996199419921990
+ + ++++
0
2
4
6
8
10
12
14
16
18
1984 1989 1994 1999 2004
Year
FI
Year of disturbance
𝐹𝐹𝐹𝐹 =𝑏𝑏 − �𝑏𝑏𝑓𝑓𝑆𝑆𝐷𝐷𝑓𝑓
Persisting nonforestPersisting forest Water
2002200320042005
Pre-198519851986198719881989
199019911992199319941995
199619971998199920002001
No-change Classes
Disturbance Year Classes
Disturbance Year Map
Available from: https://daac.ornl.gov/NACP/guides/NAFD-NEX_Forest_Disturbance.html(Goward, Huang, et al. 2015; Zhao, Huang, et al. 2017)
Forthcoming
US and Canada, 30 years (1986-2015)
Annual Forest Disturbance Record for US and Canada
Spectral Change Intensity from VCT
0
2
4
6
8
10
12
14
16
18
1984 1989 1994 1999 2004
Year
FI
1989 19981996199419921990
+ + ++++
𝐹𝐹𝐹𝐹 =𝑏𝑏 − �𝑏𝑏𝑓𝑓𝑆𝑆𝐷𝐷𝑓𝑓
Spec
tral
cha
nge
October 2008July 2006
Deriving Disturbance Intensity Estimates from Inventory Data
Forest Inventory and Analysis (FIA) Plot Data
• One plot every 5 km• Measured by field crew at 5
year intervals in eastern US• If disturbed, can calculate
percent basal area removal (PBAR) from repeat measurements
• > 2000 plots in North and South Carolina, > 400 disturbed and used as reference
PBAR-Spectral Change Correlation Low
y = 1.2311x + 773.8R² = 0.1236
400
500
600
700
800
900
1000
0 20 40 60 80 100Nor
mal
ized
Ratio
_FI
PBAR (%)
y = 4.0224x + 235.17R² = 0.1991
0200400600800
100012001400
0 20 40 60 80 100Nor
mal
ized
Ratio
_NBR
PBAR (%)
y = 1.3567x + 127.95R² = 0.065
-1000
100200300400500600700
0 20 40 60 80 100Nor
mal
ized
Ratio
_NDV
II
PBAR (%)
y = -2.9386x - 152.75R² = 0.2128
-1000
-800
-600
-400
-200
0
0 20 40 60 80 100
Delta
_NBR
PBAR (%)
y = -1.0764x - 98.398R² = 0.0684
-600-500-400-300-200-100
0100200
0 20 40 60 80 100
Delta
_NDV
I
PBAR (%)
y = 6.8327x + 352.89R² = 0.1508
0500
100015002000250030003500
0 20 40 60 80 100
Delta
_FI
PBAR (%)
Great Improvements Using Random Forest Modeling and Multiple Variables
y = 1.1503x - 6.1382R² = 0.6652
0
10
20
30
40
50
60
70
80
90
100
0 50 100
FIA
PBAR
(%)
Modeled PBAR (case 1, %)
y = 1.3111x - 16.731R² = 0.6185
0
10
20
30
40
50
60
70
80
90
100
0 50 100
FIA
PBAR
(%)
Modeled PBAR (case 2, %)
y = 1.2308x - 11.276R² = 0.696
0
10
20
30
40
50
60
70
80
90
100
0 50 100
FIA
PBAR
(%)
Modeled PBAR (case 3, %)
Scenario 1 Scenario 2 Scenario 3Delta variables: FI2 – FI1, NDVI2 – NDVI1,NBR2 – NBR1, udB42 – udB41, udB52 – udB51,
Normalized before/after ratio: 1 – FI1 / FI2,1 – NDVI2 / NDVI1, 1 – NBR2 / NBR1,1 – udB51 / udB52,1 – NDMI2 / NDMI1
Delta variables + Normalized before/after ratio
Time Integrated PBAR Map (1985-2015)
Blue Ridge
Piedmont
Southeastern Plains
Southern Coastal Plain
Middle Atlantic Coastal Plain
EPA Level III Ecoregions
Temporal Variability of Disturbance Area Dominated by Partial Events
According to USFS/FIA (Smith et al. 2009), clear cut accounted for 41% of total harvest area.
Salamat!
NASA LCLUC ProgramUSGS LandCarbonFree Data Policy
NASA NEX