Sampling Vegetation Flashcards
Landscape-Scale Mapping
Why Do it?
Assess land-use and vegetation types
Allows us to monitor:
Land use/vegetation quality
Land use/vegetation change
Informs land-use and conservation policy
Phase 1 surveys
Rationale
How to do it?
Rapid mapping of habitats over a wide area
Relies on vegetation augmented by topographic features
Vegetation is easy to observe, identify & record -animals are small, mobile and seasonal
Every parcel of land in survey area mapped onto an OS map
Parcels are assigned to a (colour-coded)
habitat type
Habitat types can be verified/assigned using dominant species codes
Descriptive ‘target notes’ are used to identify key features (→→ Phase 2 survey)
Phase 1 survey – Habitat codes
Why is this useful?
In order to assign vegetation to habitat types we can use:
The ‘Broad Habitat System’ developed by the joint
Nature Conservation Commission (jNCC)
Biodiversity Action Plan (BAP) Broad Habitat Classification
10 High-level categories + 155 specific habitat types
37 habitat types
Can readily ID important habitats (locating SSSIs etc. via Phase 2 surveys)
Areas of limited interest can be ID’d as effective wildlife corridors
Local authorities can assess planning applications quickly
Aids further land use/conservation planning
Remote Sensing
Aerial photography and Satellite imagery for a basic overview
Its good but not a reliable substitute for Phase 1 survey
mapping remote or restricted access areas
mapping boundaries
finding green sites in urban areas
Limited by:
Image Quality
Seasonal variation (not much leaf in winter)
Time of day (shadow effect)
Resolution and ability to ID habitats
Satellite Imagery
Different land surfaces reflect different spectra - the amount of red declines with increased chlorophyll content but near-IR reflectance increases
Works by imaging reflectance of different wavelengths (especially red and infra-red)
Bare soils appear more ‘red’; vegetated surfaces are ‘greener’ with higher near-IR
Also possible to differentiate between broad habitat
types (forests vs. grasslands)
Satellite Pros and Cons
Limited by:
Cloud Cover
Costly and need for specialist interpretation
Poor resolution of habitat types
Useful for:
Quick assessment of land cover
Information on canopy only
Better image contrast than aerial techniques
Minimal conservation value, not so much now
National Vegetation Classification
Straddles Landscape- & Habitat-Scale surveys
NVC contrasts with broad-scale habitat mapping
(Phase 1) as it requires more information about
component plant species
The NVC offers less comprehensive coverage than
Phase 1, because it does not include habitats
lacking vascular plant growth (such as many
aquatic, rocky or urban sites).
NVC - Phytosociology
Each NVC community is uniquely defined by a combination
of frequency and abundance values for the species found
within it. This information is summarised in a standardised
tabular format, i.e. a ‘floristic table’.
Each floristic table includes all of the vascular plants,
bryophytes and macro-lichens that occur with a frequency
of 5% or more
Full descriptions of each of the 286 NVC communities are
contained within the NVC volumes.
What is The MG5a
(Cyanosurus cristatus – Centaurea nigra) grassland
The NVC Floristic tables
species,Frequency classes (occurrence in samples), Abundance Scores (Domin Scale)
NVC is about sampling plant communities
What does it rely on?
NVC relies on Percentage Cover estimates
from many standardised sample areas
You need to know how to:
ID plants
Quantify species abundance
This could be done by using quadrats?
How to quantify the Quadrat?
What are the issues?
Above-ground Plant Cover
Above-ground Biomass
Frequency of Contact
Count Individuals
Quadrats have issues
Do you count leaves/stems outside the
quadrat that emerge from within?
What if my total exceeds 100%?
Do I count bare ground/dead material?
Problems of Scale?
Allocation to an abundance class still relies on
a cover estimate (observer bias)
Where do you allocate cover when its near a
class boundary?
Domin has more boundaries than B-B or DAFOR, thus more
chance of erroneous allocations, but effects of error in
DAFOR & B-B are greater as bands are wider
Classes are discrete making statistical
comparison more tricky
What are the other types of ways to find a percentage yields?
There is a pin method and a laser method to identify the proportion of species to find a yield
Haircuts for Quadrats?
Biomass can be used to estimate:
Total above-ground Plant abundance
Park Grass Experiment
Relative abundance of component
species
Time consuming, but accurate!
