Transcript Climate change mitigation related to Tanzanian forests. Key factors

2111
2005
Climate change mitigation related to Tanzanian forests
Key factors for analysis and research prioritizing
Ole Hofstad
2111
2005
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
Organisation of the presentation
 Mitigating climate change through REDD
 Monitoring
 Carbon accounting
 PES mechanisms
 Land-use change modelling
 Policy measures within the forest sector
 Other policies
3
www.umb.no
Department of Ecology and Natural Resource Management
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Climate change mitigation and Tanzanian forests
Carbon stocks
4
www.umb.no
Department of Ecology and Natural Resource Management
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Climate change mitigation and Tanzanian forests
GHG emissions
5
www.umb.no
Department of Ecology and Natural Resource Management
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Climate change mitigation and Tanzanian forests
The importance of degradation
6
www.umb.no
 area and density
 technologies
 sampling
 accuracy
Department of Ecology and Natural Resource Management
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Climate change mitigation and Tanzanian forests
Monitoring forest ecosystems
 frequency
 costs
7
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
The monitoring problem may be considered as two
separate components:
1. estimating areas of different vegetation types (e.g.:
forest, woodland, savannah, cropland, etc.), and
2. estimating the average biomass density (tons/ha) in
each vegetation type.
8
Cropland and burned bush in
Northern Mozambique
(Photo: E. H. Hansen)
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
Area estimates
 Areas may be measured on the ground, either by
triangulation using surveying equipment, or GPS. These
methods are both time consuming and expensive and
best suited for small areas with very high precision
requirements.
 Areas may be measured on aerial photographs. This is
expensive if aerial photography is ordered for this
particular use alone.
 Areas may be measured on satellite images based on
9
reflected sunlight. Classification of vegetation types may
be assisted by competent personnel, or be made
unassisted by computer. Using satellite images is the
preferred method in most modern applications for large
areas of low unit value.
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
10
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
Biomass measurements
 Biomass density may be measured on temporary or permanent
sample plots in the field. Trees (and bushes) are measured in
various ways, e.g. stem diameter, height, crown diameter, etc.
These measurements are transformed by allometric functions
into estimates of volume or weight of individual trees or
bushes.
 Biomass density may be estimated on the basis of crown cover
measured on aerial photos.
 Biomass estimates may be based on data collected by the use
of light emitted from an airborne or satellite laser, or
 from an airborne or satellite radar.
11
The three latter methods (photo, laser, radar) require some sample
plots on the ground where trees are measured manually. Such data
is necessary in order to calibrate the remote sensing data.
www.umb.no
Department of Ecology and Natural Resource Management
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Climate change mitigation and Tanzanian forests
Combining area estimates with estiamated biomass density
12
www.umb.no
Department of Ecology and Natural Resource Management
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Climate change mitigation and Tanzanian forests
Air-borne laser
13
www.umb.no
Points of reflection distributed in space
Department of Ecology and Natural Resource Management
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Climate change mitigation and Tanzanian forests
Remote sensing of biomass density in forests
14
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
Sampling
 Stratified sampling
 Sampling percentage
 Permanent plots
 Temporary plots
Stratification:
15

Forest types [rain forest (flooded or not), montane forest, seasonal green forest, open forest,
shrub, savanna, etc.], cropland, grazing land

Agro-ecological zones, regions, districts

Biomass density

The smaller the reporting unit, the larger sampling percentage is required to give precise
estimates
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
Proposed laser project
 1. If FRA2010/NFI decides to measure ground plots either from FRA2010 tiles or along
the lines formed by FRA2010 tiles (see map), we should consider offering to fly LiDAR
along these lines of FRA2010 tiles in all, or parts of, Tanzania. If we fly all over
Tanzania, it will imply flying a total distance of ca 9000 stripe-km, which will give a
systematic sample of laser data for all of Tanzania. Calibrated with field data from
below the flight corridors, one would be able to give a national biomass estimate for
the whole of Tanzania in less than one year (given that field data are measured
during the same period). We may even be able to break the estimate down into
regional partial estimates.
 2. In addition we should select one of the three "ecosystems" as an object for detailed
studies, where we either fly wall-to-wall with LiDAR or fly stripes very close (as
proposed in Brazil) in an area of 5-10,000 km2. In this area we must establish a set of
separate sample plots on the ground. Observations from these plots will be used to
calibrate LiDAR measurements of biomass. This set of data will serve two purposes:
– 2a: GEO/FCT sites
– 2b: detailed studies of design of laser-mapping of biomass through sampling
16
– 2c: “ground” validation of SAR-study. If we choose tropical rain forest as a case,
this will be complementary to Brazil since we may find higher biomass density
than in Amazonia.
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
Precision
Sm (%)
40 30 20 -
CV1
10 -
CV2
00
10
20
30
40
Sample plots (n)
50
Relationship between accuracy (Sm)
and number of plots (n) according to
different patterns of spatial variation
Sm = Standard error
CV = Coefficient of variation
For the REDD-activities in Tanzania, where a lot of different inventories will be
performed, it will be of crucial importance to gain basic knowledge on patterns of
spatial variation for biomass ha-1 (or volume or basal area ha-1) under different forest
conditions and plot designs. A research project to approach these challenges could be
performed along the following lines;
1.
17
2.
3.
Systematic review of previously performed inventories with respect to spatial
variation
Undertake inventories in selected study areas covering important vegetation types
and inventory designs
Perform theoretical inventory simulations in order to select optimal inventory
strategies under different conditions and requirements
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
Frequency
 How often will new area estimates be presented?
 How often shall biomass estimates be updated?
 Rotation on permanent sample plots
 Repeated flights [airplane or satellite] (with camera,
laser, or radar)
 Higher frequency, higher costs
18
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
CARBON IN FOREST
 Living biomass
– Trees, bushes, herbs
and grass
default data
• Above ground
2.
simple equations
• Roots
3.
to the use of country-specific data and
models to accommodate national
circumstances.
– Logging residues
– Ded branches, roots
and more
19
Three hierarchical tiers of methods that
range from:
1.
 Ded wood
 Soil
IPCC Guidelines:
It is good practice to use methods that
provide the highest levels of certainty,
while using available resources as
efficiently as possible.
Combination of tiers can be used.
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
LIVING BIOMASS
 Biomass expansion factor (BEF/BF)
– E.g. IPCC default value = 0.44 tons Dry
Matter / m3 fresh volume
 Biomass equation
– Allometric functions for whole trees or
fractions like stem, branches and roots.
– E.g.: Biomass above ground
• B = 0.3623 dbh1.382 h0.64
• B = - 4.22412 + 0.56 dbh2
20
 Field measurements and laboratory
measurement of wood density are
required.
www.umb.no
Department of Ecology and Natural Resource Management
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Climate change mitigation and Tanzanian forests
Land-use changes to achieve REDD
21
www.umb.no
Department of Ecology and Natural Resource Management
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Climate change mitigation and Tanzanian forests
Leakage
22
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
Global trade in forest products
23
Main trade flows of tropical roundwood 2007. (million m3)
Buongiorno, J., D. Tomberlin, J. Turner, D. Zhang, S. Zhu 2003. The Global Forest Products Model:
Structure, Estimation, and Applications.
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
24
Source: Jayant Sathaye, Lawrence Berkeley National Laboratory, California
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
25
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
26
www.umb.no
Department of Ecology and Natural Resource Management
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Climate change mitigation and Tanzanian forests
Land-use model
27
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
Land-use models at village or watershed level
 Namaalwa, J., P. L. Sankhayan & O. Hofstad 2007. A dynamic bio-
economic model for analyzing deforestation and degradation: An
application to woodlands in Uganda. Forest Policy and Economics, 9
(5):479-95.
 Sankhayan, P. L., M. Gera & O. Hofstad. 2007. Analysis of vegetative
degradation at a village level in the Indian Himalayan state of Uttarkhand –
a systems approach by using dynamic linear programming bio-economic
model. Int. J. Ecology and Environmental Sciences 33(2-3): 183-95.
 Hofstad, O. 2005. Review of biomass and volume functions for individual
trees and shrubs in southeast Africa. J. Tropical Forest Science, 17(1):4138.
 Namaalwa, J., W. Gombya-Ssembajjwe & O. Hofstad 2001. The profitability
of deforestation of private forests in Uganda. International Forestry Review
3: 299-306.
 Sankhayan, P. L. & O. Hofstad 2001. A village-level economic model of land
clearing, grazing, and wood harvesting for sub-Saharan Africa: with a case
study in southern Senegal. Ecological Economics 38: 423-40.
 Hofstad, O. & P. L. Sankhayan 1999. Prices of charcoal at various distances
28
from Kampala and Dar es Salaam 1994 - 1999. Southern African Forestry
Journal, 186:15-18.
 Hofstad, O. 1997. Woodland deforestation by charcoal supply to Dar es
Salaam. J.of Environmental Economics and Management, 33:17-32.
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
Tanzanian land-use and forest sector trade models
 Kaoneka, A.R.S. 1993. Land use Planning and quantitative modelling in Tanzania with
particular reference to agriculture and deforestation: some theoretical aspects and a
case study from the West Usambara mountains. Dr.Scient. Thesis, Agriculture
University of Norway, Aas.
 Monela, G. S. 1995. Tropical rainforest deforestation, biodiversity benefits and
sustainable land use: Analytical of economic and ecological aspects related to the
Nguru Mountains, Tanzania. Dr. Scient. Thesis, Department of Forestry, Agricultural
University of Norway.
 Ngaga, Y.M. 1998 Analysis of production and trade in forestry products of Tanzania.
Dr.Scient. Thesis, Agriculture University of Norway, Aas.
 Makundi, W. R. 2001. Potential and Cost of Carbon Sequestration in the Tanzanian
Forest Sector. Mitigation and Adaptation Strategies for Global Change, 6(3-4):335-53.
 Ngaga, Y. M. & B. Solberg 2007. Assessing the Suitability of Partial Equilibrium
Modelling in Analyzing the Forest Sector of Developing Countries: Methodological
Aspects with Reference to Tanzania. Tanzania Journal of Forestry and Nature
Conservation, 76:11-27.
 Monela, G. C. & J. M. Abdallah 2007. External policy impacts on Miombo forest
development in Tanzania. In: Dubé, Y. C. & F. Schmithüsen (eds.): Cross-sectoral
policy developments in forestry.
29
 Monela, G. C. & B. Solberg 2008. Deforestation and agricultural expansion in Mhonda
area, Tanzania. In: Palo, M. & H. Vanhanen (eds.): World forests from deforestation
to transition?
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
Policy measures
 General policies
– Good governance (legal system, transparency, corruption)
– Energy
– Agriculture
– Transport
 Sector specific measures
– PES (monitoring, verification)
– Projects (administrative costs, foreign assistance)
– Land; ownership and user rights
30
 Cost effectiveness and efficiency (Cost-Benefit)
www.umb.no
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Internat.
level
Fund
National
level
Local
level
31
International funding: carbon
market, global funds, bilateral
donors, NGOs, …
Information
processing
(IRA, FBD)
Flow of information
Department of Ecology and Natural Resource Management
Schematic view of a REDD PES system
Incentives
(flow of
money)
Participatory
monitoring
Satellite based
inf., plots
CBO,
villager
s
VC,
NRC
Village
forests
Public
www.umb.no
DC
fo rests
Mgt. of forest
reserves (Govt.,
FBD )
Forest
reserves
Climate change mitigation and Tanzanian forests
NORWEGIAN UNIVERSITY OF LIFE SCIENCES
Department of Ecology and Natural Resource Management
32
www.umb.no