modeling approach based on spatial factors … geomod-calakmul.pdf · myrna hall suny college of...
TRANSCRIPT
Modeling approach based on spatial factors (GEOMOD): Calakmul
Myrna Hall SUNY College of Environmental Science and [email protected]
US AID & Government of MexicoModeling Deforestation in Mexico and
Implications for Carbon Sequestration ProjectsMarch 19, 2003
2
Vegetation map for 2000 for Calakmul
3
GEOMOD: Sequence of events§ Analyze the rate of change in land use§ Analyze the pattern of spatial drivers (bio-
physical and socioeconomic)—an iterative process in the model simulation/validation that looks for the best agreement between the “real” and the simulated maps § Simulate the change in land use into the
future§ Calculate the potential carbon emissions
41995 2000
Land-use classification: from satellite imagery (TM)
5
Determine the candidate cells for deforestationCategorias de tipos de vegetación del mapa de la Selva Maya (Juhn 2000)
Cat_ID Uso/Cobertura Escenario 1 2
0 – Sin datos 0 0
1 – Selva Alta (High Evergreen Forest): 1 1
2 – Selva Baja (Low Evergreen Forest): 1 1
4 – Areas inundables (lowland flood areas, herb. veg.): 1 2
8 – Sabana (savannah): 2 2
9 – Vegetación Secundaria (secondary vgetation): 1 2
10 – Urbano/Agricultura/Potrero (urban/agriculture/pasture): 2 2
12 – Cuerpos de agua (water): 0 0
6
Secondary vegetation from
deforestation
Secondary as degraded
forest
7
1,587,580 ha
34,462 ha
4,514 ha (2.87%)
74,690 ha
1,540,736 ha
1,540,736 ha
1,540,736 ha
72,148 ha
121,535 ha48,821 ha
20,834 ha
27, 986 ha
39,102 ha
74,691 ha
6,512 ha
74,691 ha13,627 ha46,845 ha
33,046 ha
46,845 ha
Changes in vegetation cover in Campeche 1995 – 2000
Selva Alta y Baja
Vegetación Secundaria
Urbano/Agric/Potrero
8
TIPO USO SUEL 1 RateRGNVAL MUNICIPIO # CELLS 1995 2000 CELLS/YR HA/YR
1 HOPELCHEN 1072890 1052971 978906 14813 13332 CHAMPOTON 4075411 3838169 3550112 57611 51853 CALAKMUL 3821643 12675743 12531548 28839 25964 ESCARCEGA 73164 72898 72391 101 9
TOTAL 101365 9123
Determine the empirical rate of deforestation for 4 municipalities
of Campeche 1995 2000
(excluding the reforested cells)
9
Projections of the deforested area
Yr 2030Hopelchen = 39,995 ha
Champoton = 155,551 ha
Calakmul = 77,865 ha
Escarcega = 274 ha
Total = 273,685 ha
10
Search for the best agreement..
Mapa De Riesgo
celdas deforestedas = y
T2 Mapa Simulado
Proceder de Validación
Kappa deUbicación
De CalibraciónProceder
T1 Mapa de Uso de Suelos
Driver 4
Driver 3
Driver 2
Driver 1
T2 Mapa de Uso de Suelos
celdas deforestedas = y
11
The data used and their original sources• Caminos (con y sin pavimento) (fuente INEGI hojas 1:50,000)
• Hidrografía – perenne y intermitente (INEGI hojas 1:50,000 and 1:250,000 )
• Todas fuentes de agua (hojas INEGI 1:50,000)
• Cuerpos de agua perennes (INEGI 1:50,000)
• Areas inundables – perennes y intermitentes (INEGI 1:50,000)
• Elevación creada en formato TIN con curvas de nivel (INEGI 1:50,000) y valores del
MDE INEGI 1:250,000 acesado por convenencia entre los. Gobs. EU y EU Mexico • Ubicacion de Localidades (INEGI 1:50,000)
• Limites de los Municipios (varios – ECOSUR, UNAM Morelia)
• Limites de los Ejidos (B. Turner, Clark University)
• Ubicación de sitios arqueologicos (INEGI 1:50,000)
• Terrenos Agrícolas y Pastorales 1970 (Gob. Mexico).
• Densidad de población empleada en el Sector 1 (CENSO 1990 and 2000)
12
NombreMapa # Clases Rango/Clase Descripción
Hstst 22 3000 m Dist. de Sitios Arqueológicos
Dem 19 20 m Elevación
Towns 19 2000 m Distancia de Localidades
Ag70 25 3000 m Distancia de Agric. 1970
Strmp 28 1000 m Dist. de Corrientes Perennes
Sect1 10 10 pers/km2 1990 Dens. de Población. Agric/Forestal
Rds 25 1000 m Distancia De Caminos
Ejido 115 Ejidos
Allwtn 21 1000 m Dist. de todos fuentes de agua
Watp 25 1000 m Dist. de fuentes perennes de agua
Wetlt 2 Wetl/Non_Wetl Todos areas inundables
Wetls 2 Wetl/Non_Wetl Areas inundables intermitentes
Wetlp 2 Wetl/Non_Wetl Areas inundables perennes
13
The spatial pattern of drivers
14
R = (C2 + (N – C) 2) / N
donde:
R = the expected success rate for random selection
C = the number of cells that change
N = the total number of cells
The important issue is how much would the success rte improve using GEOMOD. This is referred to as the ‘Kappa’ statistic. It is an index of agreement and is calculated as follows:
K = (the success of GEOMOD – random chance)
100% - the random chance
15
Total Region Hopelchen Champoton Calakmul%Correct Kappa %Correct Kappa %Correct Kappa %Correct Kappa
DRIVERS
Sitios 93.7404 0.2307 88.3283 0.0907 90.9781 0.2993 98.1115 0.0029
Arqueol.
MDT 93.2479 0.1702 87.7421 0.0450 90.0365 0.2261 98.1153 0.0049
Localid. 93.2351 0.1686 89.2283 0.1608 89.6001 0.1922 98.1325 0.0140
Agric. 92.9321 0.1314 88.3934 0.0957 89.1697 0.1588 98.1093 0.0017
1970
Corr. 92.8640 0.1230 89.6282 0.1919 88.6867 0.1213 98.1170 0.0058
Per.
16
Low vulnerability
High vulnerability
Risk map for deforestation in Campeche
17
Forest area 2000 - 2030
0
0.5
1
1.5
2
2000 2005 2010 2015 2020 2025 2030
Mill
on
es
Ano
Ha
Selva Alta
Selva Baja
Veg. Sec.
Total
Projection à 285,000 ha deforestation
183.09634.43VegetaciónSecundaria
3.83442.62Selva Baja
5.98566.51Selva Alta
Ton C /30x30 m cell
Ton C / ha
CATID
Vegetation type
Projection à 14,200,000 tons of carbon lost 13.1 +/- adjusted for
reforestation
Emisiones incrementales por tipo de vegetacion
0.00.51.01.52.02.53.0
2005 2010 2015 2020 2025 2030
Mil
lon
es
Ano
To
nel
adas
C Selva Alta
Selva Baja
Veg. Sec.
Total
19
Emisiones de Carbono (+) y reduciones (-)
-2.0
-1.0
0.0
1.0
2.0
3.0
Mil
lon
es
Ano
To
nel
ada
C
Selva Alta
Selva Baja
Veg. Sec.
Emisions Totales
Reforestacion
Emisiones totales ajustadas
Carbon emissions (+) and sequestration (-)
20
Dynamic simulation
21
ConclusionsAdvantages of spatial modeling with GEOMOD:
• Based on empirical analyses;
• Validation is scientifically rigorous;
• Specific locations of areas of land use change;
therefore better estimation of potential changes in carbon stocks
Disadvantages
• The cost to classify the satellite imagery
• Availability of data
• The process is complicated with a “steep learning curve”
22
AcknowledgementsDr. Sandra Brown, Winrock
Dr. Charles Hall, SUNY College of Environmental Science and Forestry
David Antonioli, US AID, Mexico
Alejandro Flamenco Sandoval, ECOSUR
Miguel Angel Castillo, ECOSUR
Michael Cairns, Patti Haggerty, US EPA
Kim Batchelder, The Nature Conservancy
Jenny Ericson, The Nature Conservancy
Larry Gorenflo, Argonne National Laboratories, US
Dr. Betty Faust, CINVESTAV, Mérida, Yucatán
Stephen Ambagis, Clark University (ahora en Niger)
Dr. Gil Pontius, Dr. Billie Turner, Clark University
Russell Aicher, SUNY College of Environmental Science and Forestry