Identification of carbon stock changes at a
national level using a combination of remote
sensing and ground-based inventory for
REDD+ in Paraguay
Satellite images for
Paraguay in 2010
(ALOS/AVNIR-2 data)
Presenter: Vega Isuhuaylas, Luis Alberto
Researcher at Climate Change Laboratory
Forestry and Forest Products Research Institute (FFPRI) - Japan
Background
• REDD+ is identified as one of the most effective means to
reduce GHG emission in the post-Kyoto climate change
negotiation.
• A reliable and credible system of measurement, reporting and
verification (MRV) of forest carbon changes is a cornerstone
of any national REDD+.
• An MRV system should follow the international requirements
and also be adapted to the country’s specific conditions, e.g.
vegetation, economy, culture, institution and/or the
deforestation/degradation drivers.
Objective
• The objective of this study is to develop potential
methodologies for a forest carbon change MRV system that
could be implemented in REDD+ in Paraguay.
• The research aims the following outputs;
• Land-use and land-use change MRV by satellite remote sensing
• Forest carbon change MRV by combination of remote sensing and
ground measurements
• Monitoring for Forest carbon change on main forest types
• Allometric equations for estimation forest biomass in Paraguay.
Forest carbon stock estimation by
combination of remote sensing and ground
measurements
remote sensing
Total carbon
=
stock
ground measurements
Ȉ (Forest areai x Averaged carbon stocki)
Forest areai : forest area of forest type i
Averaged carbon stocki : averaged carbon stock in forest type i
• The method to calculate the carbon stock involves monitoring
forest land and summing up the carbon stock of the forest
area for important forest types.
Two types of data from field survey:
• Data for area estimation
– Training data for image classification
– Verification data for the result of classification
• Data for estimation of averaged carbon stock per
area unit as emission factor
– Tree census data
– Destructive sampling data
(Allometric equations)
Field survey for estimating carbon stock
Land-use and land-use change MRV
by satellite remote sensing
Discussion on the definition of forest and
forest types from satellite data
Field Survey: Collection of ground truth data/
validation data
Production of forest type map 2010 by objectoriented classification from satellite data
Validation of the result using field data
Change detection of land-use between 19901995, 1995-2000, 2000-2005, and 2005-2010
Monitoring Land Cover Change using remote sensing
Land Cover - Forest Area from satellite data (2010)
Ecoregions
of Paraguay
AF - Atlantic Forest of Upper Paraná
DC
HC - Humid Chaco
HC
AF
Ecoregion:
Atlantic Forest
Total Forest Area: 1 411 (1000Ha)
Forest – Non Forest Prediction accuracy:
95.6% , CI (95.4% , 95.7%)
DC- Dry Chaco
Ecoregion:
Humid Chaco
(16.4%)
Total Forest Area: 3 364 (1000Ha) (26.2%)
Forest – Non Forest Prediction accuracy:
90.0% , CI (88.9% , 91.0%)
9
Forest area 1990 – 2010 (Atlantic Forest)
Forest Area (1000 Ha)
4000
Average deforestation rate:
81 *103 ha/year
3500
3000
2500
2000
Forest Area (1000 Ha)
1500
1000
500
0
1985
1990
2000
1995
1990
Forest area:
3 150 * 103 ha
1995
>
Forest area:
2 755 * 103 ha
2005
2010
2015
2000
>
Forest area:
2 459 * 103 ha
2010
>
Forest area:
1 411* 103 ha
10
1
0
Forest carbon stock change MRV by combination
of remote sensing and ground measurements
Forest carbon stock monitoring in sample plots
Destructive sampling for allometric equation
development and forest biomass estimation
Forest carbon stock estimation by
combination of remote sensing and ground
measurements
Change detection of carbon stock between
1990-1995, 1995-2000, 2000-2005, and
2005-2010
Forest carbon stock monitoring
in sample plots
Design of Plot distribution
Ecoregion and Degradation level
Forest type
(Ecoregion)
Mature Forest
(1ha)
Slightly Degraded
Forest (0.2ha)
Degraded Forest
(0.2ha)
Atlantic forest
9+1
10
10
Humid Chaco
1+9
10
10
Dry Chaco
7+3
10
10
Red : already surveyed plots, Black : new plots
• It is necessary to discuss on classification of degradation types
• Tree census data was obtained.
Establishment of sample plots
䖂Parque defensores de䡈 Chaco㻌
䕿 PSP reported 2011
䖂 PSP reported 2012
䕧 Temporary plot
䕿Cerro Leon
䖂Est. la Patria
䖂Escola Agricola
䖂Victoria S.A.
䕿Lagna-Pora
IPTA410
䕿Salazar
IPTA312
䕿Agroganadera㻌 JO
Tree census data
䕧Lima
䖂Reserva Ecol. Itavo
䕿Golondrina y Morombi
䖂Est. Santa Maria Doce
Est. San Cayetano 䖂 䕧Emboscada
䕧Yhu
Asuncion㻌 䖩
䕧Yguazu
䕿CFAP
Golondrina
䖂Privada Tapyta
䖂San Rafael 1,2
䕧Pirapo
Destructive sampling
Total biomass
R2 = 0.9845
Preparation for weight measurement
Root of sample#5
Tree felling by heavy machine
Measuring trunk weight
Allometric equations
14
1
4
Forest Carbon Stock Estimation (2010): Atlantic Forest
Class1 Class2 Class3
Biomass 92.88 138.75 181.74
(ton/ha)
Area
558.6 622.4 230.3
(1000 ha)
Total㻌 B
51.9
86.4
41.9
(106 ton)
Total:
Forest Area
Total biomass
Total Forest
Carbon Stock
1411.5 (1000 ha)
180.1 ( 106 ton)
90.1 ( 106 ton)
Calculation of carbon stock at a national level
Remote sensing
Field survey
Classification decision
Plotless sampling
in each class
Eco-region
Image
classification
Plot survey in
each class
Degradation
No/light
Atlantic
Medium
Heavy
Allometry
Chaco Humid
Verification
Chaco Seco
Estimation of forest area by forest
type and disturbance degree
Calculation of
carbon stock
Estimation of mean carbon stock
by forest type and disturbance
degree
7RWDOFDUERQVWRFN Ȉ)RUHVWDUHDi x mean carbon stocki)
Conclusions
• Potential methodologies for a forest carbon stock change
MRV system were developed for REDD+ in Paraguay.
• In REDD monitoring, not forest area change but forest
carbon stock change is asked for.
• Combination with ground-based inventory is essential.
• Monitoring of forest degradation differs in the possibility
of detection by cause and degree.
• Monitoring methodology changes with the situation of the
forest of each country, and the data & information that
can be used.
Thank you for your attention!