landscape scale monitoring of forest treatments and disturbance
DESCRIPTION
2006. 2010. Tornado. Thin. Landscape Scale Monitoring of forest treatments and disturbance. S.E. Sesnie 1,2 , A.D. Olsson 1 , B.G. Dickson 1 , A. Leonard 3 , V. Stein Foster 3 and C. Ray 1. - PowerPoint PPT PresentationTRANSCRIPT
LANDSCAPE SCALE MONITORING OF FOREST TREATMENTS AND DISTURBANCES.E. Sesnie1,2, A.D. Olsson1, B.G. Dickson1, A. Leonard3, V. Stein Foster3 and C. Ray1
Tornado
Thin
2006 2010
Lab of Landscape Ecology and Conservation Biology, NAU1, US Fish & Wildlife Service2, USFS Kaibab NF3
2
Collaborative Partnerships
NAU
USFS-FIA
Kaibab NF
USFS R3
FWS
Stakeholders
USFS-4FRI
3
Previous assessmentsRegion Landscape
Patch
Disturbance 4
Fire Urbanization Insects & Disease
Silviculture
Wind Drought
5
Kaibab NF MonitoringNeed for landscape-scale, up-to-date and repeatable forest data complementary to: Monitoring planned and unplanned disturbance Assessing changes wildlife habitat Mitigating fire hazard and risk Biomass & forest carbon assessment Other planning objectives
6
Objectives
Model & map forest structure Inexpensive
Validated
Consistent/repeatable
BA SDI HGT CCTPA QMDOthers…
7
Study Area – Kaibab NF
1.6 million acres (647,497 ha) Ongoing management & 4-FRI Three major forest types
8
Methods – reference data
USFS Forest Inventory and Analysis (FIA) plots
Measure10% of state’s forest per year
n = 648 plots (2001 – 2009)
Monitor forest status and trends FVS compatible
~1 acre of forest land
9
Methods – remotely sensed dataLandsat TM (2006 & 2010)
6 spectral bands 2 dates (leaf on/off)
Spectral Derivatives NDVI NDVIc
Digital Elevation Model Elevation Terrain
39 Predictor VariablesLeaf-onTM1TM2TM3TM4TM5TM7BrightnessGreennessWetnessNDVI_onNDVIc_onNDVI4_onPCA1PCA2PCA3
Leaf-offTM1TM2TM3TM4TM5TM7BrightnessGreennessWetnessNDVI_offNDVIc_offNDVI4_offPCA1PCA2PCA3
DEMElevationSlopeAspect (trasp)RoughnessCTI
OthersNDVI_ratio
10
Methods – correction + modeling
Image correction: Uncorrected Landsat TM
Image normalization (MAD)
Atmospheric correction (FLAASH)
Terrain correction (C-correction)
Modeling: Random Forest (regression)
11
Literature & Tools
12
Results – outputs 2006 2010 Change (%)
13
Results – outputs
2006 2010
14
Results - ValidationBasal Area – Predicted vs. Actual
15
Results - Validation
Basal area Stand Density Index
All predictors Best subset Number of predictors
16
Results – Consistency
Compare 2006 and 2010 basal area
1km x 1km points (n = 4622)
Pearson correlation coefficients
Kruskal-Wallis One Way ANOVA on Ranks
17
Results – Consistency
BA 2006 (Uncorrected)
0 50 100 150 200
BA
201
0 (U
ncor
rect
ed)
0
50
100
150
200
BA 2006 (FLAASH)
0 50 100 150 200
BA
201
0 (F
LAA
SH
)
0
50
100
150
200
BA 2006 (MAD)
0 50 100 150 200
BA
201
0 (M
AD
)
0
50
100
150
200
BA 2006 (C)
0 50 100 150 200
BA
201
0 (C
)
0
50
100
150
200
BA 2006 (FLAASH/C)
0 50 100 150 200
BA
201
0 (F
LAA
SH
/C)
0
50
100
150
200
BA 2006 (MAD/C)
0 50 100 150 200
BA
201
0 (M
AD
/C)
0
50
100
150
200
r = 0.875 r = 0.89 r = 0.882
r = 0.862 r = 0.852r = 0.879
Between years for each correction technique (Basal area)
18
Results - Consistency
2006 basal area, H = 33.927 (P = <0.001)
2010 basal area, H = 26.090 (P = <0.001)
19
Monitoring applications
Treatment type
Fuel reduction Precommercial thin Group selection Broadcast burn
Cha
nge
(%)
0
10
20
30
40
50
Basal Area DensityCanopy Cover
Stand density index (SDI)
SDI change (%)
Treatment types 2006 - 2010
20
Conclusions
Terrain and atmospheric corrections important
We are likely to witness large and small disturbance events in the future
Forest structure data are consistent & sensitive to a variety of disturbance factors
The outputs support a range on of landscape scale analyses
21
Acknowledgments
Kaibab National Forest
USFS Forest Inventory and Analysis
program, Ogden UT
USFS Region 3 Office
US Fish & Wildlife Service