Landscape Systems Modelling Context and Current Progress

Daniel Rutledge, Alexander Herzig, Robbie Price

EcoArm2ERA – LUMAN Joint Workshop Dublin, Ireland 11 June 2013

Context

• Global Twin Goals – Enhance overall quality of life for everyone and reduce (eliminate) poverty – Maintain and restore the life-supporting capacity of the planet

• Twin Goals must be met under added pressures of

– Continued population growth – Rising affluence – Increasing risk of environmental degradation or exceeding global planetary

“boundaries” or limits

• New Zealand Business Growth Agenda – Life ratio of exports to gross domestic product to 40% by 2025 – Substantial infrastructure investment (roads, broadband, etc.) – Encourage Research and Development – Encourage businesses to become more innovative to get the best returns for

the economy and environment

Twin Goals Visually

United Nations Human Development Index

Ecological Footprint

(Global

Hectares per Person)

An Example of “Safe

Operating Space”

Put another way:

New Zealand Sustainable Business Council

Enter Land-use Modelling

• Important tool for studying coupled human-natural systems and therefore helping achieve the Twin Goals

• Critical role in managing pathways on a finite planet – Multiple scales – Multiple domains – Key integrating mechanism – Setting limits - there is only so much land ( and water)

• Powerful platform for providing an evidence base for policy & planning, informing debate & decision-making, and visualising options

Global Scale: Integrated Assessment Modelling

Source: van Vuuren et al. 2011. The representative concentration pathways:

an overview. Climate Change 109: 5-31.

Land Use Harmonisation Project

luh.umd.edu

Source: Hurtt et al. 2011. Harmonization of land-use scenarios for the period

1500–2100: 600 years of global gridded annual land-use

transitions, wood harvest, and resulting secondary lands. Climate Change 109: 117-161.

National Scale: Belgium

Land use 2010

Strong Europe Global Economy

Regional Communities Transatlantic Markets

Source: de Kok et al. 2012. Spatial-dynamic visualization of long-term scenarios for

demographic, social-economic and environmental change in Flanders.

iEMSs 2012 Conference Proceedings

National Scale: New Zealand

Land Cover

Database

Land Use

Regional Scale

Land Use

Waikato Region

Land Use

Greater Dublin Region

What do these examples have in common?

Land use 2010

Strong Europe Global Economy

Regional Communities Transatlantic Markets

Land use 2010

Strong Europe Global Economy

Regional Communities Transatlantic Markets

They all depict landscapes as a set

of simple, non-overlapping categories

of land use or land cover or

often some (unappealing) hybrid of both.

Landscapes are treated conceptually as

“scanned paper maps”

What do these examples have in common?

Limitations

What are the effects of agricultural intensification?

How can we improve biodiversity outcomes in

peri-urban and urban areas?

Can we enhance ecosystem

services in existing land uses?

What are the opportunities for tourism

development in our rural areas?

Multiple land use?

Current Limitations Summarised

1. Confounding of cover and use

2. Difficult to depict more than one use or cover

3. Simplification of highly complex systems

4. Same model for many different types of processes

5. Substantial data pre- & re-processing hampers model development and analysis

6. Models focus on change and implicitly assume that any current state will endure (forever) unless it changes

What to do about it?

Build upon land use modelling by

moving towards

landscape systems modelling

Key Ideas

• Characterise landscapes (use, cover, tenure, etc.) as systems of interacting components operating at appropriate scales (as a start enforce strict separation of land use vs. land cover)

• Store all information elementally as data “atoms” thus moving away from categorical, continugous maps to facilitate more flexible and adaptable characterisation, modelling and analysis

– Scales more easily – Multiple uses possible – Multiple views of the same feature possible – Easier data re-use, analysis and comparison

• Never throw away any data!!!

• Tightly couple the database, model base, analysis base (i.e. GIS) and

visualisation (e.g., LUMASS) to avoid costly re-processing/handling as much as possible

Critical Tensions

Simplicity Complexity

Causation

Reductionism Holism

Correlation

Realism Abstraction

Parsimony

Understanding

Completeness

Confusion

Progress to Date

Operational

Version 1 almost complete

In Development

• Integrates data and information on – environment (cover, soils, topography, landform) – economics (use, management) – society and culture (values, perceptions)

• Stores landscape elements with unique realisations in space and time represented as individual data “atoms”

• Advantages – Better handling of spatio-temporal analyses, use only what is needed – Reduction of data re-processing (e.g., reduced “wheel reinvention”) – Increased capacity to incorporate data as it becomes available – Helps bridge the gap between objective and subjective representations of

landscapes by facilitating multiple representations of similar elements – Assists quantification of uncertainty by building a weight of evidence

regarding landscape composition, configuration, function, service, value, and significance

Data Atomisation Example

Input Data

Land Resource

Inventory

Data Atoms

1w 1 Field = “LUC”

Value = “LUC”

16.0 Field = “CCAV”

Value = “CCAV”

Value = 2 if “PRSIAV” > 21

Field = “PRSIAV”

Value = 1 if “PRSIAV” < 20 2

Atomiser +

Rules

NZLD Data Storage

Elements

Time

Atoms

V2000 (54769)

Lakes

& Ponds V2001 (9189)

V2008 (9295)

V2010 (57100)

Topographic Maps

Land Cover Database

Lakes

V1996 (9141)

Ponds V2000 (740)

V2010 (1896)

Example: Workflows to

generate different land

use classifications

in New Zealand

LANDCLASS Landscape Classification & Analysis Support System

Classification Rules

Atomiser Rules

Input Data

Data Atoms

Land Use

ATOMISER

CLASSIFIER

Land Use Classification

Rules

PARAMETER FILE

DATABASE

Input Data Location(s)

LANDCLASS

• Support achievement of the Twin Goals with an emphasis on the New Zealand situation

• Facilitate exploration of scenarios of future landscape development especially over the long-term, e.g., 100 years

• Consideration explicitly interplay among – Resource Availability (e.g., energy, water, minerals) – Thresholds & Limits (ecosystem services, biodivesity) – Opportunities (e.g., technology, innovation, behaviour change) – Adaptive Strategies (e.g., localisation, multifunctional landscapes, resource

efficiency)

• Scale systems & complexity as needed to address the issues(s) at hand

• Facilitate multi-model comparisons (e.g., ensembles)

Atomisation of use & cover

Cover-Use Relationship USE

Not Used Conservation Production Urban

CO

VE

R

Native Vegetation

Non-Native

Vegetation

(Weeds)

Plantation Forest

Grassland & Arable

Built Environment

& Infrastructure

Human Activity H

um

an A

ctivity

TIME

C x U

Possible

States

(C x U)2

Possible

Transitions

33 x 34 =

1,122

1,258,000

Dairy Farms: Top 10 Land Covers (Agribase 2008 x LCDB2)

High Producing Exotic Grassland 1,629,310

Indigenous Forest 71,392

Manuka and or Kanuka 21,045

Pine Forest - Closed Canopy 17,390

Short-rotation Cropland 15,203

Broadleaved Indigenous Hardwoods 13,292

Low Producing Grassland 12,463

Pine Forest - Open Canopy 11,392

Gorse and Broom 8,751

Deciduous Hardwoods 5,567

Broadleaf Indigenous Hardwoods on Production Land

(Agribase 2008 x LCDB2)

Beef 31,903

Dairy 13,292

Deer 5,246

Forestry 44,209

Sheep 30,462

Sheep &

Beef

127,801

TOTAL 252,913

Total Indigenous Hardwoods

in LCDB 2: ~567,000 ha

At least 45% of Remaining Total

Indigenous Hardwoods occurs

on Production Land Uses

Next Steps

• Complete Version 1 of NLZD

• Analyse combined land-use/land-cover maintenance and change

• Develop feasible suite of models for exploring future landscape evolution (e.g., ecosystem services)

Example: Urban Food Production in Hamilton

Advantages

1. Scalability

2. Extensibility

3. Multiple Covers, Uses,

Functions, and

Processes

4. Match Scale of

Process & Model

Disadvantages

1. More substantial data

requirements

2. Increased complexity

of data storage and

handling

3. Lack of knowledge

about many complex

processes

Kia ora

Thank you for listening.