Site Productivity and Land Classification Lecture 13: Forest Ecology 550.

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


2) Overstory tree species

– Each species occupies its own niche


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


3) understory species– Other examples:

Ephemerals often have a narrow ecological niche


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

100

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


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?


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)


7) Remote Sensing– Normalized Difference Vegetation Index

(NDVI)= (NIR - R)/(NIR + R)

Remote Sensing: Global Classification of Vegetation

Predicted versus Measured LAI

July LAI

0

1

2

3

4

5

6

7

0 1 2 3 4 5 6 7

Predicted (n-1 jackknife)

Ob

serv

ed

Corn

Soybeans

August LAI

0

1

2

3

4

5

6

7

0 1 2 3 4 5 6 7


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

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7 8 9 10


Obs

erve

d

Conifer Cover (%)

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70


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

400

600

800

1000

1200

0 200 400 600 800 1000 1200

Observed NPP

Pred

icte

d N

PP

RMSE = 87.143

Remote Sensing Disturbance

0

10

20

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

1000

2000

3000

4000

5000

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

Site Productivity and Land Classification Lecture 13: Forest Ecology 550.

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Transcript of Site Productivity and Land Classification Lecture 13: Forest Ecology 550.