site productivity and land classification lecture 13: forest ecology 550
TRANSCRIPT
Site Productivity and Land Classification
Lecture 13:
Forest Ecology 550
Objectives
Discuss indirect ways to measure site productivity– Briefly discuss land classification– Introduce ecosystem process models
What role can remote sensing play in estimating forest species composition, structure, and function.
Site Productivity
Definition– Sites potential to produce one or more natural
resources– Sustainable– Manage for multiple resources
Site Productivity: indirect measurement approaches
1) Site index: Forest measurement to measure site quality– Based on height of the dominant and co-dominant
trees based on some standard age Age depends on location and stand type
– Typically 50 years but..
Forest Productivity: Site Index Curve
0
20
40
60
80
100
120
0 10 20 30 40 50 60 70 80 90
tree age (yrs)
Tre
e h
eig
ht
(ft)
Site index curves: Pros/cons
Pros– Easy and inexpensive– Height growth is less
sensitive than basal area growth to stocking density.
Cons- very site dependent
(soils, topography, aspect)
- Differs among species- Requires trees growing
on the site- Cannot capture dynamic
nature of tree growth and global change
Site Productivity: indirect measurement approaches
2) Overstory tree species
– Each species occupies its own niche
Site Productivity: indirect measurement approaches
2) Overstory tree species– Each species occupies its own niche– Advantages
Allows you to make quick assumptions about a given area
– Disadvantages Challenging for species that are able to exist in a wide
range of climates
Site Productivity: Indirect measurement approaches
3) understory species– Definition: use of
understory species to make classifications of site
– Advantages: More sensitive to micro-
climate differences Indicator species
– Disadvantages What about disturbance
Site Productivity: Indirect measurement approaches
3) understory species– Other examples:
Ephemerals often have a narrow ecological niche
Site Productivity: Indirect measurement approaches
4) Ecological Site Classification
– Primary means is through Habitat Typing
Identified by distinct understory plant assemblages
natural vegetation to identify ecologically equivalent landscape units
– growth – natural resource use
potential
Soil usually sand to loamy sand. At least two species present:low sweet blueberry, wintergreen,sweet fern, pipsissewa, cow wheat, witch hazel, maple-leaf viburnum, pointed leaf tick treefoil
witch hazel, maple-leaf viburnum, pointed leaf tick treefoil
Species on right rareor absent blueberry, wintergreen
Species on right rareor absent
At least 2 presenthoneysuckle, twistedstalk, partridgeberry,yellow beadlilly, shieldfern, ironwood
Sum of the coverage > 2x’s the sum of speciesIn right boxtrailing arbutus, bearberry, reindeer moss
hazelnut, falseSoloman’s seal, barren strawberry
AQVibPMVQAE AQV
Examples of WI Habitat Types
Examples of WI Habitat Types
Maianthemum
Sweet anise/osmorhizaCoptis
Habitat Types: Comparisons in WI
Litterfall C and N generally increase from low quality to high quality habitat type
0
500
1000
1500
2000
2500
QAE AQV PMV AVVib ATD AViO
Habitat type
litte
rfal
l C (
kg/h
a)
non-leafleaf
0
5
10
15
20
25
30
35
QAE AQV PMV AVVib ATD AViO
Habitat type
N (
kg/h
a/y
r)
Habitat Types
Advantages– Fairly detailed– Qualitative formulae
Disadvantages– May take some time to
identify the factors in the stand
Site Productivity: Indirect Measurement Approaches
5) Environmental Relationships/factors– Simple relationships between one of more variable and tree
growth.
0
20
40
60
80
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120
140
160
0 1000 2000 3000 4000 5000
Elevation (ft)
Site
ind
ex
(fe
et a
t 50
ye
ars
)
–from E.C. Steinbrenner. 1981. Forest soil productivity relationships. In Forest Soils of the Douglas-fir Region).
Environmental relationships/factors cont..
0
20
40
60
80
100
120
140
0 10 20 30 40 50 60
total soil depth (inches)
site
ind
ex
(ft @
50
ye
ars
)
–from E.C. Steinbrenner. 1981. Forest soil productivity relationships. –In Forest Soils of the Douglas-fir Region).
Environmental relationships/factors cont..
Leaf biomass
AN
PP
Site Productivity: Indirect Measurement Approaches
6) Ecosystem Process Models– based on biophysical and ecological principles– every physiological process model has some level
of empiricism
PPT
LAIevaporation
Soil water
Soil wateroutflow
transpiration
photosynthesis
respiration
LAI
General Outline for the conceptual framework of biome-BGC
Remember our radiation lecture?
Site Productivity: Indirect Measurement Approaches
7) Remote Sensing– Common Vegetation indices derived from
radiation reflectance measured using satellites– Simple ratio = (near infra-red(NIR)/red (R)
wavelength)– Normalized Difference Vegetation Index
(NDVI)= (NIR - R)/(NIR + R)
Site Productivity: Indirect Measurement Approaches
7) Remote Sensing– Normalized Difference Vegetation Index
(NDVI)= (NIR - R)/(NIR + R)
Remote Sensing: Global Classification of Vegetation
Predicted versus Measured LAI
July LAI
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0 1 2 3 4 5 6 7
Predicted (n-1 jackknife)
Ob
serv
ed
Corn
Soybeans
August LAI
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1
2
3
4
5
6
7
0 1 2 3 4 5 6 7
Predicted (n-1 jackknife)
Ob
se
rve
d
Corn
Soybeans
Corn LAI=4.41+0.63*CCIcj R2=0.61
Soy LAI=1.54+0.49*CCIsj R2=0.58
ETM+ predictions of July LAICorn LAI=4.00+0.45*CCIca R2=0.63
Soy LAI=3.44+0.49*CCIsa R2=0.27
ETM+ predictions of Aug. LAI
LAI
0
1
2
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9
10
0 1 2 3 4 5 6 7 8 9 10
Predicted (n-1 jackknife)
Obs
erve
d
Conifer Cover (%)
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30
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50
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70
0 10 20 30 40 50 60 70
Predicted (n-1 jackknife)
Ob
serv
ed
RMSE=9.09Slope=0.98Intercept=1.46R=0.84
1:1 1:1
Canonical indicesETM+ March, June
RMSE=1.19Slope=1.00Intercept=0.10R=0.74
Canonical indicesETM+ March, June
Generally how do you get the remote sensed values?
Modis GPP project
GPP (gC m-2 d-1) = PAR * fAPAR * g
Where:
– PAR = from climate model
– fAPAR = from MODIS reflectances
g ( gC MJ-1) = GPP / APAR
MODIS g from lookup table Spatial Resolution is 1 km Temporal Res. is 8-day mean
AGRO 2000 NPP (using observed July LAIs)
y = 0.8803x + 53.24
R2 = 0.8566
0
200
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1200
0 200 400 600 800 1000 1200
Observed NPP
Pred
icte
d N
PP
RMSE = 87.143
Remote Sensing Disturbance
0
10
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30
40
50
60
30-39 40-49 50-59 60-69 70-79 80-89 90-99Decade
Est
imat
ed B
urn
ed A
rea
(km
2 x 1
000)
Manitoba
Saskatchewan
981995
198981
50 km
- Disturbances are an important component of any forest ecosystem- Disturbances have no effect on the C budget if the system is in steady state
Fire frequency and extent has increased 270% in recent decadesIn Saskatchewan and Manitoba
2003 NDVI Fire Scar Profiles, Northern Manitoba
-1000
0
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6000
7000
8000
9000
65 81 97 113 129 145 161 177 193 209 225 241 257 273 289 305
Day of Year
ND
VI
(x10
000) 1981
1989
1995
1998
2003
2002 MODIS Image Manitoba-Saskatchewan
2003 NDVI 3-date Composite
Fire scar profiles taken from 2003 NDVI seasonal data.Selected burn areas shown in image on the right.
snowmelt
leaf expansion
2003 fire
max leaf area
Fire date
Hudson Bay