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Fuzzy logic mapping of high and low-producing grassland
NZEUC, Auckland, 12-14 August, 2019
Deborah Burgess, Ministry for the Environment
Andrew Manderson, Manaaki Whenua, Landcare Research
• Context – Deb
• What was the problem? – Deb
• The real problem – Andrew
• Solution – Andrew
• Outcome – Deb
Outline
Context• New Zealand’s Greenhouse Gas Inventory
Context• New Zealand’s Greenhouse Gas Inventory
• 5 sectors – emissions and removals
EmissionsRemovals
Context• New Zealand’s Greenhouse Gas Inventory
• 5 sectors – emissions and removals
EmissionsRemovals
Context• New Zealand’s Greenhouse Gas Inventory
• 5 sectors – emissions and removals
EmissionsRemovals
Context• New Zealand’s Greenhouse Gas Inventory
• 5 sectors – emissions and removals
EmissionsRemovals
Context• New Zealand’s Greenhouse Gas Inventory
• 5 sectors – emissions and removals
EmissionsRemovals
Context• New Zealand’s Greenhouse Gas Inventory
• 5 sectors – emissions and removals
EmissionsRemovals
Context• New Zealand’s Greenhouse Gas Inventory
• 5 sectors – emissions and removals
• LULUCF – Land Use, Land-Use Change and Forestry
EmissionsRemovals
Context• New Zealand’s Greenhouse Gas Inventory
• 5 sectors – emissions and removals
• LULUCF – Land Use, Land-Use Change and Forestry
Context• New Zealand’s Greenhouse Gas Inventory
• 5 sectors – emissions and removals
• LULUCF – Land Use, Land-Use Change and Forestry
Context• New Zealand’s Greenhouse Gas Inventory
• 5 sectors – emissions and removals
• LULUCF – Land Use, Land-Use Change and Forestry
Context• New Zealand’s Greenhouse Gas Inventory
• 5 sectors – emissions and removals
• LULUCF – Land Use, Land-Use Change and Forestry
Context• New Zealand’s Greenhouse Gas Inventory
• 5 sectors – emissions and removals
• LULUCF – Land Use, Land-Use Change and Forestry
• Four national land use maps, 12 land use classes, 28 years of change
What was the problem?
What was the problem?• We’ve ignored the grass….
What was the problem?• We’ve ignored the grass….
• Actual change not reflected in maps
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016
Grassland area 1990 - 2016 (k ha)
Grassland - high producing Grassland - low producing
What was the problem?• We’ve ignored the grass….
• Actual change not reflected in maps
• Inconsistent with data reported elsewhere
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016
Grassland area 1990 - 2016 (k ha)
Grassland - high producing Grassland - low producing
What was the problem?• We’ve ignored the grass….
• Actual change not reflected in maps
• Inconsistent with data reported elsewhere
• High and low-producing grassland hard to map from imagery alone
NZTM 1,815,025 5,425,650
What was the problem?• We’ve ignored the grass….
• Actual change not reflected in maps
• Inconsistent with data reported elsewhere
• High and low-producing grassland hard to map from imagery alone
• There is no other single good data source to use
NZTM 1,815,025 5,425,650
What was required?• A transparent, accurate way to map high
and low-producing grassland that...
What was required?• A transparent, accurate way to map high
and low-producing grassland that...
• can be backcast to the 2008 and 2012 maps as well as mapping grassland at 2016 and…
What was required?• A transparent, accurate way to map high
and low-producing grassland that...
• can be backcast to the 2008 and 2012 maps as well as mapping grassland at 2016 and…
• could also be repeated for future versions of the map
… and could we have fries with that?
… and could we have fries with that?• Would it be possible to make a start on
mapping the type of grassland land use by splitting it into:
• Dairy grazing
… and could we have fries with that?• Would it be possible to make a start on
mapping the type of grassland land use by splitting it into:
• Dairy grazing
• Non-dairy grazing
… and could we have fries with that?• Would it be possible to make a start on
mapping the type of grassland land use by splitting it into:
• Dairy grazing
• Non-dairy grazing
• Un-grazed grassland
The real problem• It shouldn’t be a problem!
• We know the grassland extent
• Only two classes – high & low
• Simple. Model it.
• Lots of problems; the two big ones:
• Limited land management data (spatial). Key production driver.
Modelled annual pasture yield(aggregated daily modelling @100m res)(soil/climate driven model)
The real problem• It shouldn’t be a problem!
• We know the grassland extent
• Only two classes – high & low
• Simple. Model it.
• Lots of problems; the two big ones:
• Limited land management data (spatial). Key production driver.
• Pasture yield varies from year to year
(long-term means or annual means?)
Annual pasture production (kg DM ha-1)
(source:Newton et al. all sites MAF-AgRes data)
1-M
onaB
ush
2-W
into
n3-A
rrow
tow
n4-C
rom
well
5-P
oolb
urn
Dry
6-P
oolb
urn
Irri
7-W
estp
ort
8-M
otu
eka
9-M
anutu
jke
10-W
airakeiF
lats
11-W
airakeiH
ill12-M
aste
rton
13-M
ara
ekakaho
14-D
arg
avi
lle15-H
am
ilton
16-R
angitik
eiF
lockH
s17-M
art
on1
18-M
art
on2
19-T
aie
riP
lain
20-T
aie
riH
ill21-W
inchm
ore
Dry
22-W
inchm
ore
Irri
23-S
outh
Kairapa
24-H
indon
25-R
anui
26-K
ow
whitirangi2
8cut
27-A
haura
28cut
28-W
aim
ate
14cut
29-W
aim
ate
28cut
30-S
tratford
14cut
31-S
tratford
28cut
32-W
aere
ngaokuri14cut
33-W
aere
ngaokuri28cut
34-W
indsorD
ry35-R
uakura
NH
A (
kg
DM
ha
-1)
0
5000
10000
15000
20000
25000
Our solution – fuzzy logic• Fuzzy logic grassland classification
• Funny name but serious method
• Abstract concept
• MBIE funded IDA project
• The key idea…
• Most GIS overlay for classification are Boolean (e.g. 0 or 1)
• Fuzzy logic overlay is based on degrees of truth (DoT)• A condition or statement can be true (value =
1.0), false (value = 0.0), or any of a continuum of values in-between.
Method – full overview
1. Literature review to identify definitions2. Definition deconstruction & reconstruction3. Identify & source relevant GIS data layers
Statement: Grassland is considered ‘high producing’ when… e.g. soil fertility is high… water is non-limiting… stocking rates are high… etc.
Method – full overview
1. Literature review2. Definition deconstruction & reconstruction3. Identify & source relevant GIS data layers
4. Data digging & analysis & modelling5. Transform inputs by relationships (fuz mbrs)
Method – (fuzzy relationships – slope)
1. Pasture production f{slope}2. Analysed NZLRI slope class by average CC3. Modelled feed-demand by CC4. Result roughly sigmoidal5. Refined with published expert knowledge
about slope limitations to pasture production6. A slope of 25 has highest uncertainty = 0.5 DoT6. Extract the function & transform 15m NZ slope to
DoT(12 functions for 12 data layers)
(note: ‘fuzzy value’ = degree of truth DoT)
Method – full overview
Method – fuzzy membership layers
Method – full overview
1. ESRI ArcGIS Fuzzy Overlay tools2. (fuzzy gamma overlay reproduced in Excel for testing)3. Intermediary layers
Method – x3 intermediary DoTs
Method – full overview
1. Greater confidence in 0.6 cf. 0.52. Aggregate to minimise complexity3. DoT result
Result (DoT map)• Intermediary output 1 (DoT map)
• (Darker = higher likelihood of being high producing)
Result (uncertainty)• Uncertainty
• (nearness to the threshold value)
Those fries…• Simple land use classification
• But limited spatial land use datasets
• Commercial datasets –publication restrictions
• Ratings data plus modelled ratings data validated with commercial dataset (RD combo)
• Considerable disagreement between land use datasets (spatial & aspatial – see graph)
Outcome• Credible grassland trend through time-
series
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016
Grassland area 1990 - 2016 (kha)
Grassland - high producing Grassland - low producing
Outcome• Credible grassland trend through time-
series
• Credible change from low to high-producing grassland
1990 \ 2008Grassland –high producing
Grassland – low producing
Grassland –high producing 5,707 0
Grassland –low producing 2 7,452
1990 \ 2008 Grassland - high producing
Grassland - low producing
Grassland -high producing 5,641 -Grassland -low producing 983 6,476
Before: Grassland change (kha)
After: Grassland change (kha)
Outcome• Credible grassland trend through time-
series
• Credible change from low to high-producing grassland
• Reasonable dairy area – 1.9 million ha
Further information
MfE Data Service :https://data.mfe.govt.nz/
16 national satellite imagery mosaics:
https://www.mfe.govt.nz/more/data/available-datasets/satellite-data-search
Google “satellite data MfE”