Transcript LELUP_2.2_Understanding_Historic_LUC_2015_05

Introduction to Section
10 minutes
Drivers of Change
30 minutes
Deforestation vs Degradation
10 minutes
Direct vs Indirect
10 minutes
Method for estimating change
10 minutes
Exercise
Example
30 minutes
20 minutes
Summary
10 minutes
Name
Affiliation
David Saah; Co-Lead
University of San Francisco, SIG
Name
Affiliation
Phan Xuan Thieu
Vinh University, Vietnam
Mohd Zaki Hamzah; Co-Lead University Putra Malaysia
Chalita Sriladda
USAID-LEAD
Khamla Phanvilay, Co-Lead
National University of Laos
Hoang Thi Thu Duyen
Vietnam Forestry University, Vietnam
Cao Thuy Anh
Dalat University, Vietnam
Ladawan Puangchit
Kasetsart University, Thailand
Chalermpol Samranpong
Chiang Mai University, Thailand
Do Anh Tuan
Vietnam Forestry University, Vietnam
Pham Thanh Nam
USAID LEAF Vietnam
Lyna Khan
Royal University of Phnom Penh, Cambodia
Peter Stephen
USAID LEAF Bangkok
Le Ba Thuong
Vietnam Forestry University, Vietnam
Hoang Vinh Phu
Vinh University, Vietnam
Napat Jakwattana
University of Phayao, Thailand
Vipak Jintana
Kasetsart University, Thailand
Nur Anishah Binti Aziz
University Kebangsaan Malaysia
Kulala Mulung
PNG University of Technology
Ratcha Chaichana
Kasetsart University, Thailand
Sureerat Lakanavichian
Chiang Mai University, Thailand
Somvilay Chanthalounnavong National University of Laos
Thavrak Huon
Royal University of Agriculture, Cambodia Vongphet Sihapanya
National University of Laos
Athsaphangthong Munelith
USAID LEAF Laos
David Ganz
USAID LEAF Bangkok
Attachai Jintrawet
Chiang Mai University, Thailand
Chi Pham, Project Coordinator USAID LEAF Bangkok
Chanin Chiumkanokchai
USAID LEAF Bangkok
Kent Elliott
US Forest Service
Lam Ngoc Tuan
Dalat University, Vietnam
Beth Lebow
US Forest Service
Mark Fenn
USAID Vietnam Forests & Deltas
Geoffrey Blate
US Forest Service
Low Emission Land Use Planning (LELUP)
Section 2. Assessment of Current and Historical Condition
2.2. Understanding Historical Land Use Change and Current Condition
Regional Climate Change Curriculum Development
1.1. Regulatory Assessments
1.2. Stakeholder Engagement
1.3. Planning & Development
Goals & Objectives
MONITORING &
EVALUATION
NEGOTIATING
&
PRIORITIZING
IMPLEMENTATION PLAN
ENABLING
ENVIRONMENT
Low
Emission
Land Use
Planning
ANALYSIS OF
FUTURE
OPTIONS
ASSESSMENT
OF CURRENT
CONDITION
2.1. Environment, Social, &
Economic Data Needs
2.2. Understanding Historic Land
Use Change
At the end of this session, learners will be able to:

Determine drivers (or causes) of historical land use
change and the ‘actors’ involved in these processes.

Evaluate process (spatial and non-spatial) to help
quantify historical land use change.

Quantify the current resource condition from which to
compare future change.

Session Introduction

Drivers of Change

Deforestation vs. Degradation

Direct vs. Indirect Drivers

Method for estimating change

Exercise

Example

Summary
NOW
Drivers of Change
BAU
Goal / Objective
Time/Space
Rules of the Game

Agricultural expansion

Timber production

Infrastructure development

Economic growth

Demographic changes

Governance

Technology

Environmental issues
Rate of agricultural expansion in Southeast Asia increased from
0.7% pa between 1997 and 2002 to 1.2% between 2002 and
2007 and 1.7% between 2007 and 2009
Resurgence after 2001
120
80
60
40
20
0
19
70
19
75
19
80
19
85
19
90
19
95
20
00
20
05
20
10
Millions CUM .
100
Indonesia
Malaysia
Thailand
Philippines
Vietnam
Myanmar
Lao PDR
Cambodia
Road network
expansion greatest in
Viet Nam and Thailand

Economies growing rapidly and
demands on forest resources
will increase

Poverty and migration remain
key issues
Southeast Asia population:

593 million in 2010 → 657 million in 2020
Rapid urbanization:

47% urban in 2010 → 54% in 2020
2.50
Singapore
Corruption worsening
except in Indonesia and
Brunei
2.00
Control of corruption score
1.50
1.00
Brunei
0.50
Malaysia
0.00
1998 2000 2002 2004 2006 2008 2010
Thailand
-0.50
Viet Nam
Indonesia
Philippines
-1.00
Lao PDR
Cambodia
-1.50
Source: World Bank
Myanmar
-2.00

Productivity enhancement

Processing technologies
Deforestation

“The long-term or permanent conversion of land from
forested to non-forested land”

Example where definition is for 20% forest cover
Forest
Non-Forest
≥ 20% Canopy
< 20% Canopy
Δ
90% Canopy
10% Canopy
Degradation

“Changes within the forest which negatively affect the
structure or function of the stand or site, and thereby lower
the capacity to supply products and/or services”.

Example where definition is for 20% forest cover
Forest
Forest
≥ 20% Canopy
Still ≥ 20% Canopy
Δ
80% Canopy
50% Canopy
10% tree
cover
25% tree
cover
30% tree
cover
43
37
36
Aboveground forest carbon (Mt C)
4,971
4,498
4,410
Belowground forest carbon (Mt C)
1,335
1,203
1,179
Total Forest carbon (Mt C)
6,306
5,701
5,589
147
152
153
Forest definition (canopy cover %)
Forest Area (M ha)
Average Carbon Density (t C/ha)
Source: http://rainforests.mongabay.com/deforestation/2000/Papua_New_Guinea.htm
Country
Bangladesh
Bhutan
Cambodia
India
Indonesia
Laos
Malaysia
Nepal
PNG
Philippines
Thailand
Vietnam
Total
Average Loss in Average Loss as a %
Forest (ha yr-1) of Cover in 2000 (%)
8,163
0.4%
3,858
0.2%
56,532
0.6%
205,246
0.5%
690,208
0.7%
85,965
0.5%
230,988
1.1%
16,085
0.3%
48,590
0.1%
38,220
0.4%
133,608
0.8%
54,364
0.4%
1,571,826
0.5%
Deforestation Data

Agriculture is the key
driver of
deforestation (about
80%)

Both commercial
and subsistence

Food demand is
projected to
increase by 70%
globally over the
next 40 years
Deforestation Data

Drivers different for forest
degradation

Primarily logging and fuel-wood
charcoal

In subtropical Asia timber logging
is key driver

In Africa it is wood for fuel
Sources (+) and sinks
(-) of carbon (TgC yr-1)
from activities
contributing to
deforestation and
forest degradation in
tropical regions.
Direct Drivers
Infrastructure
Extension
Demographic
Agricultural
Expansion
Economic
Wood
Extraction
Technological
Underlying Causes
Policy
Cultural
Assessment of historical drivers of landscape change is
essential:

Are drivers directly or indirectly causing the problem?

What is the scale at which the drivers operate?

What is the drivers trend or trajectory?

Do the drivers interact?

Who are the key actors or stakeholders involved with
the identified driver; and
A very similar process can be used to identify historical and
future threats (drivers) to key biodiversity assets.
Direct Threats:
• Human activities:
Unsustainable Timber Harvest
• Natural phenomena:
Water availability limited in forest by
city
• Natural phenomena whose impact is
increased by other human activities
Type conversion from Forest to
scrubland and limited water
In small groups, each student is to carefully read the case study and then
discuss within their group the following questions:
1.
What are the direct and indirect drivers of deforestation and forest
degradation and the relationship between the two?
2.
What is the scale and historical trend for each of the drivers?
3.
Who are the actors involved for each of the identified drivers?
4.
By assessing historical drivers of landscape change, what information
can this provide the Florestania REDD+ Task Force in predicting
future landscape change?
Each student group will be required to provide a brief report on their findings.
1.
Prepare: Set time period for analysis. Ensure maps for
individual date are consistent (definitions, classifications,
sensors, etc).
2.
Overlay: Use GIS or image processing software to overlay
two land use maps from two different dates. Creates an
attribute table where each polygon or pixel contains the
recorded land use on both the 1st and 2nd dates.
3.
Simplify the attributes to a set of unique land use change
transitions.
4.
Create the land use change matrix:
The current land use planning goals in Lam Dong Province,
Vietnam are:
Category
Objective
Environmental
Maintain at least 61% forest cover by Percent forest cover
2015
Maintaining or improving ecological 1) Ratio of natural forest to plantations
integrity
2) Species type diversity
3) Richness
Reduce GHG emissions in the AFOLU Tons CO2 equivalents (tCO2e)/year
sector by 20% by 2020.
Increase annual GDP growth rate from GDP growth rate
12-15%
GDP per capita will reach USD 2,300 by GDP per capita
2015
Population growth reduced to 1.3% Population growth rate by urban and
(2015) and 1.2% (2020)
rural sectors
No poor households by 2020
General poverty rate by urban and rural
sectors
Economic
Social
Indicator
Land cover change analysis
2000
2005
2010
Land Use Change Matrix, 2005-2010 (hectares)
2010
2005
Broadleaf
evergreen forest
Deciduous forest
Bamboo forest
Mixed
wood&bamboo
Coniferuos
forest
Mixed broadleaf
& coniferuos
forest
Plantation
Bared land
Non forested
land
Total
B/leaf
E/green
forest
Mixed
broadleaf
Coniferuo
&
Plantation
s forest
coniferuos
forest
Deciduou
s forest
Bamboo
forest
Mixed
wood &
bamboo
194,669
24
938
36
15,417
0.0
2,483
0.0
37,739
13,888
32
7,431
110
0.5
28
154
0.2
6
2,714
640
3,202
1,827
852
706
14,111
1,248
10,606
229,995
18,217
60,660
10,467
44
6,305
69,327
40
13
2,167
901
7,787
97,054
167
6
26
23
113,227
983
1,047
1,975
7,945
125,402
193
178
1
0.0
7
393
15
75
412
53
19,936
35
353
37,109
202
601
1,000
8,200
22,121
46,647
1,884
107
1,963
2,604
344
92
4,491
9,407
13,827
34,721
264
208,787
8
15,622
1,110
50,030
563
93,960
201
114,418
33
21,255
Bared
land
Non
forested
land
Total
9,515
501 330,335 342,534
61,242 16,975 395.062 977.354
Landcover change analysis from 2005-2010
Remote Sensing- Derived Data
REDD+ Activity
Total Emissions and Removals MMT (CO2e)
1990-1995
Historical Period
1995-2000
2000-2005
2005-2010
12.4
8.8
8.8
9.9
Forest Degradation
7.8
8.0
5.9
5.7
Forest Enhancement
(2.4)
(3.3)
(3.4)
(2.8)
Afforestation and reforestation
(1.0)
(1.8)
(1.8)
(1.7)
Total
16.8
11.7
9.5
11.1
3.4
2.3
1.9
2.2
Deforestation
Annual
Species Diversity Analysis
2000
2005
2010
Species Diversity Analysis
Population Growth Rates
General Poverty Rate
Annual GDP Growth Rate
GDP per Capita
Convergence of Indicators
Convergence of Indicators

Increased level of complexity will challenge land use
planners and place additional pressures on land
management agencies.

Accurate and consistent historical data is notoriously
difficult to gather.

Connecting landscape data analysis and field level
interpretation is a challenge.
There is now increased investments (REDD+) that are
investing in quantifying drivers of change that can be
used for LE-LUP
Key Reference material to support this session:
Guidance on Low Emission Planning for the Forest and Land Use
Sector, Section 2.2
Drivers of Landscape Change

LEAF/ARKN-FCC (2014), Decision Support Tool: Identifying and
Addressing Drivers of Deforestation and Forest Degradation
(unpublished).