assessing the limitations and capabilities of lidar and landsat 8 to estimate the aboveground...
TRANSCRIPT
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Assessing the Limitations and Capabilities of Lidar and Landsat to Estimate Aboveground Vegetation
Biomass and Cover in Semi-arid Rangeland Ecosystem Using a Machine Learning Algorithm
Shital DhakalMS in Hydrologic Sciences
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Semi-arid ecosystemArid & Semi arid cover 1/3rd of Earth’s land surface
These ecosystems are fragile due to water limitations
Important roles in ecology, hydrology and carbon cycling
(Pho
to b
y L.
Eng
ledo
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degr
ee)
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Semi-arid in NW United StatesHistorically dominated by sagebrush steppe
One of the most important plants on western rangelands
Ecological, hydrological and carbon cycle importance
An imperiled ecosystem!
Reasons are increased fire frequency and replacement by exotic plants
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Field based quantification Quantification is essential for modeling ecosystem, conservation of vegetation,
measuring fuel, understanding carbon flux etc.
Some in-situ measurement methods are available
E.g. Harvesting, Clip and Weigh, Visual estimation, Point intercept sampling
Destructive method (clipping- oven drying) are expensive and labor intensive
Point intercept sampling involves taking multiple field measurements
Big problem- Very small geographical extent!
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Can remote sensing provide the answer?
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Previous studies
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Hypothesis Satellite spectral remote sensing and Airborne Lidar can independently provide
large scale accurate estimation of vegetation characteristics in semi-arid rangeland.
Research questions What lidar based metrics are best to estimate biomass and cover of semi-arid vegetation ?
Is point cloud processing of lidar data better than lidar based raster data?
How does spectral remote sensing compare with lidar based estimation?
What methods can be best utilized to produce biomass and cover map across a large scale?
Can the available remote sensing technologies be used to estimate characteristics of both herbs and shrubs in rangeland?
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Outline of methodology - Study site
- Field Data Collection
- Lidar data & Lidar data processing
-Landsat & Landsat image processing
-Random Forest
- Results from Lidar
-Results from Landsat
-Lidar vs Landsat
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Study site
Morley Nelson Snake River Birds of Prey NCA, Boise
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Data collection
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Field data distribution (n = 141)
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
Percentage Cover
Freq
uenc
y of
fiel
d da
ta
100 200 300 400 500 600 100080
60
40
20
0
20
40
60
80
100 Shrub Herb
Biomass (g/m2)
Fequ
ency
of fi
eld
data
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Lidar data
Leica ALS 60 Lidar Sensor
65,000 hectares in 2012 and 9,000 hectares in 2013
Discrete small footprint lidar data
Point density of ~ 8 points per sq. meter
Vertical accuracy of ~ 3 cm
Flown at 1,500 m , acquiring ≥ 148,000 laser pulses per second
Leica ALS 60 Lidar sensor
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Lidar data processingLidar derived metrics
◦ Minimum Height ◦ Maximum Height◦ Height Range◦ Mean Height ◦ Median Absolute Deviation (MAD) from Median Height ◦ Mean Absolute Deviation (AAD) from Mean Height ◦ Height Variance◦ Height St. Deviation ◦ Height Skewness◦ Height Kurtosis ◦ Interquartile Range (IQR) of Height◦ Height Coefficient of Variation◦ Height Percentiles - 5th, 10th, 25th, 50th, 75th, 90th,& 95th ◦ Number of Lidar Returns ◦ Number of Lidar Vegetation Returns (nV) ◦ Number of Lidar Ground Returns (nG) ◦ Total Vegetation Density ◦ Vegetation Cover ◦ Percent of Vegetation in Height Range ◦ Canopy Relief Ratio ◦ Texture of Heights◦ Foliage Height Diverstiy (FHD)
Buffering
Height Filtering
Raster
Point Cloud
1 m
7 m
30 m
100 m
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Landsat 8 OLI Images the entire Earth every 16 days
Spatial Resolution of 30 m
Pushbroom Sensor
Generates 16-bit images
Eight fold increase in signal-to-noise ratio
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Landsat 8 OLI Images the entire Earth every 16 days
Spatial Resolution of 30 m
Pushbroom Sensor
Generates 16-bit images
Eight fold increase in signal-to-noise ratio
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Data Preparation (n=97+44)
11 Apr 2013
30 Jun 20134 Oct 2013
Topographic variables
Slope
Elevation
Aspect
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More on vegetation indicesSimple Ratio based VI-E.g. NDVI, SCI, VCI
Soil adjusted VI-E.g. SAVI, GSAVI, SATVI
Perpendicular VI-E.g. BI, GVI, WI
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Why random forest (RF)? One of the most accurate machine learning algorithms available
Not affected by multi-collinearity between variables
Well suited for analyzing complex non-linear dataset
Well suited for broad data i.e many predictors but low sample size
Gives estimate of what variables are important
Lidar model Landsat model
Sample Size 46 141
No. of Predictor variables 35 81
No. of Target variables 3 5
Data set from NCA:
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RF lidar raster analysis Area Resolution R2 RMSE Best Predictors
Total biomass
1 ha 1 m
0.74 141 Hstd, HAAD, H90, HSkew, Hvar, Htext
1 ha 7 m
0.70 152 Htext, FHDGT, H95, HAAD
1 ha 30 m
0.58 180 FHDGT, nV, HAAD, H5
1 ha 100 m
0.52 188 FHDGT, nV, H16, HAAD
Shrub biomass
1 ha 1 m
0.76 152 Hstd, HAAD , HCV, Hrange, FHDall
1 ha 7 m 0.67 143 Htext, FHDGT, HAAD
1 ha 30 m
0.50 176 FHDGT, HAAD, HCV
1 ha 100 m 0.4 184 Htext, H50, pG, nG
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RF lidar raster analysis Area Resolution R2 RMSE Best Predictors
Total biomass
1 ha 1 m
0.74 141 Hstd, HAAD, H90, HSkew, Hvar, Htext
1 ha 7 m
0.70 152 Htext, FHDGT, H95, HAAD
1 ha 30 m
0.58 180 FHDGT, nV, HAAD, H5
1 ha 100 m
0.52 188 FHDGT, nV, H16, HAAD
Shrub biomass
1 ha 1 m
0.76 152 Hstd, HAAD , HCV, Hrange, FHDall
1 ha 7 m 0.67 143 Htext, FHDGT, HAAD
1 ha 30 m
0.50 176 FHDGT, HAAD, HCV
1 ha 100 m 0.4 184 Htext, H50, pG, nG
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RF lidar point cloud analysis Area Resolution R2 RMSE Best Predictors
Total biomass
1 ha 1 m
0.71 147 HMAD, HSkew, HIQR, HAAD, Hstd, Hkurt, H90, HCV
1 ha 7 m
0.71 148 Htext, HIQR
1 ha 30 m
0.70 151 HAAD, H95, HIQR, pH1,pG
1 ha 100 m
0.67 160 H90, H95, Htext, veg_density
Shrub biomass
1 ha 1 m
0.73 129 HIQR, Hstd , HMAD, HCV
1 ha 7 m 0.72 132 Htext, H90, HIQR, HCV
1 ha 30 m
0.65 146 H90, HIQR, Htext, pH1
1 ha 100 m 0.64 151 H95, Htext, pH1, GIQR,FHDGT
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RF point cloud analysis Area Resolution R2 RMSE Best Predictors
Total biomass
1 ha 1 m
0.71 147 HMAD, HSkew, HIQR, HAAD, Hstd, Hkurt, H90, HCV
1 ha 7 m
0.71 148 Htext, HIQR
1 ha 30 m
0.70 151 HAAD, H95, HIQR, pH1,pG
1 ha 100 m
0.67 160 H90, H95, Htext, veg_density
Shrub biomass
1 ha 1 m
0.73 129 HIQR, Hstd , HMAD, HCV
1 ha 7 m 0.72 132 Htext, H90, HIQR, HCV
1 ha 30 m
0.65 146 H90, HIQR, Htext, pH1
1 ha 100 m 0.64 151 H95, Htext, pH1, GIQR,FHDGT
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RF lidar analysis (Herb Biomass) Area Source Resolution R2 RMSE Best Predictors
Herb biomass
1 ha Raster 1m
0.2 6.86 HSkew, Htext
1 ha Point Cloud
1m
0.19 7.54 HCV, Htext, HSkew
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RF Landsat analysis Calibration (n=97) Validation (n=44)
R2 RMSE Variables R2 RMSEShrub Cover 0.63 7 June30 SATVI, June30 GVI,
Oct4 SAVI, Oct4 MSAVI,June30 NBR
0.44 8
Herbaceous Cover 0.69 13 Oct4 MIRI, June30 MSI,Oct4 Green,June30 SATVI
0.63 16
Total Biomass 0.54 147 June30 SATVI, June30 GVI,Oct4 NIR, Oct4 MSAVI,Oct4 Red, Oct4 SAVI
0.37 158
Shrub Biomass 0.6 126 June30 SATVI, June30 GVI, Oct4 MSAVI, June30 NDWI,Oct4 GSAVI
0.53 128
Herb Biomass 0.49 65 Apr11 NIR, June30 MSI,June30 NIR, Oct4 NIR
0.3 64
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RF Landsat analysis Calibration (n=97) Validation (n=44)
R2 RMSE Variables R2 RMSEShrub Cover 0.63 7 June30 SATVI, June30 GVI,
Oct4 SAVI, Oct4 MSAVI,June30 NBR
0.44 8
Herbaceous Cover 0.69 13 Oct4 MIRI, June30 MSI,Oct4 Green,June30 SATVI
0.63 16
Total Biomass 0.54 147 June30 SATVI, June30 GVI,Oct4 NIR, Oct4 MSAVI,Oct4 Red, Oct4 SAVI
0.37 158
Shrub Biomass 0.6 126 June30 SATVI, June30 GVI, Oct4 MSAVI, June30 NDWI,Oct4 GSAVI
0.53 128
Herb Biomass 0.49 65 Apr11 NIR, June30 MSI,June30 NIR, Oct4 NIR
0.3 64
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RF Landsat analysis Calibration (n=97) Validation (n=44)
R2 RMSE Variables R2 RMSEShrub Cover 0.63 7 June30 SATVI, June30 GVI,
Oct4 SAVI, Oct4 MSAVI,June30 NBR
0.44 8
Herbaceous Cover 0.69 13 Oct4 MIRI, June30 MSI,Oct4 Green,June30 SATVI
0.63 16
Total Biomass 0.54 147 June30 SATVI, June30 GVI,Oct4 NIR, Oct4 MSAVI,Oct4 Red, Oct4 SAVI
0.37 158
Shrub Biomass 0.6 126 June30 SATVI, June30 GVI, Oct4 MSAVI, June30 NDWI,Oct4 GSAVI
0.53 128
Herb Biomass 0.49 65 Apr11 NIR, June30 MSI,June30 NIR, Oct4 NIR
0.3 64
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Nearest neighbor imputation Replacing missing data with substituted values In comparison Interpolation predicts values at un-sampled locations Biomass is produced as weighted averages of selected variables The variables are selected by Random Forest The reference data should cover the entire phenomenon of interest We used yaimpute package in R for Imputation
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Lidar biomass maps
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Landsat biomass maps
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Landsat biomass maps
100
200
300
400
500
600
600+
60 40 20 0 20 40 60 80
Percent distribution
Biom
ass (
g/m
2)
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Landsat cover maps
10
20
30
40
50
60
70
80
90
100
20 10 0 10 20 30 40 50 60 70 80
Percent distribution
Perc
ent C
over
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Landsat vs Lidar Landsat 8 OLI Lidar
R2 RMSE Variables R2 RMSE VariablesShrub Cover 0.75 6.5 June30 SATVI,
Oct4 GSAVI,Apr11 DVI
0.74 6.7 Hrange, FHDAll
Herbaceous Cover 0.6 12.5 Apr11 DVI,June30 MSI, June30 VCI,June30 SATVI, Oct4 BI
0.21 17.5 Hrange, HIQR
Total Biomass 0.57 177 Oct4 BI, Oct4 NIR, Elevation, Oct4 SWIR,Oct4 MSAVI
0.68 156 FHDAll, Hstd,AAD, Hrange, HSkew
Shrub Biomass 0.61 151 June30 DVI, Oct4 BI,Elevation, Apr11 DVI
0.75 126 Hstd, Hrange,FHDAll, HCV
Herb Biomass 0.57 57 June30 GSAVI,June30 MSAVI,June30 SWIR, Oct4 GVI
0.12 83 H10, HSkew, CRR,AAD
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Summary I 1) The vegetation cover and biomass of shrubs in large scale was successfully modeled using
multispectral imagery (Landsat 8) and airborne lidar.
2) We found that the best model to describe vegetation cover fractions included vegetation indices
calculated from multiple acquisitions dates.
3) Lidar was found to estimate shrub biomass slightly better than Landsat.
4) Validation is necessary for reducing the bias
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Summary II 4) Point cloud processing of lidar data significantly improves the estimation of biomass in
coarser scale compared to raster processing.
5) Lidar could not satisfactorily model the herbaceous biomass in the field site (R2 < 2).
6) As per our imputed map, the NCA contains ~ 345 metric ton of herbaceous biomass
and ~ 313 metric ton of shrub biomass.
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An application - fire events
0 1 2 3 4 5 6 7 8 90
40
80
120
160
200 Herb
Shrub
Fire frequency
Aver
age
biom
ass (
g/m
2)
0 1 2 3 4 5 6 7 8 90
20
40
60
80 Herb
Shrub
Fire frequency
Aver
age
cove
r (%
)
0 1 2 3 4 5 6 7 8 9 100
50
100
150
200
250
300
Fire frequency
Aver
age
biom
ass (
g/m
2)
0 1 2 3 4 5 6 7 8 9 100
102030405060708090
100
Herb
Fire frequency
Aver
age
cove
r (%
)
With imputed pixels
With in-situ field plots
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Conclusion We suggest to use lidar for biomass estimation and Landsat for herbaceous cover estimation Remote sensing can extended field based methods across larger scaleThe application of the methodology is wide. From land managers to ecologistSynergetic use of remote sensing data in future can produce better results.Can be repeated in other part of world with some minor tweak
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Acknowledgement Committee chair- Dr. Nancy F. Glenn
Subject Committee – Dr. Alejandro N. Flores, Dr. Douglas J. Shinneman, Dr. Aihua Li
Dr. Rupesh Shrestha, Lucas Spaete
Peter Olsoy, Kyle Anderson, Hamid Dashti, Reggie, Ann Marie, Andrew, Katie
and Ginikanda Yapa Mudiyanselage Nayani Thanuja Ilangakoon
Nepalese friends and everyone in Geosciences!
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Questions?
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Regression Analysis
0 200 400 600 800 1000 12000
100
200
300
400
500
600
700
800
900f(x) = 0.784685456219575 x + 75.5139754762692R² = 0.785026053341471
Predicted total AGB (g/m2)
Obs
erve
d AG
B (g
/m2)
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Field data distribution (n = 46) Herb Cover (%) Shrub Cover (%)
Herb Biomass (g/m2) Shrub Biomass (g/m2)
Minimum 0 0 2 0
Maximum 100 87 1207 3301
Mean ± SE 39 ± 1.47 12 ± 0.85 144 ± 7 414 ± 20
100 200 300 400 500 600 600+25
15
5
5
15
25 Herb Shrub
biomass (g/m2)
Freq
uenc
y of
fiel
d da
ta
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RF Analysis (70 x 70 Plot) Area Resolution R2 RMSE Best Predictors
Total biomass
70m x 70m 1 m
00.68 156 FHDall, Hstd, HAAD, Hrange, HSkew
Shrub biomass
70m x 70m 1 m
00.75 126 Hstd, Hrange, FHDall, HCV
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extras