Modeling approach based on spatial factors (GEOMOD): Calakmul

Myrna Hall SUNY College of Environmental Science and Forestrymhhall@esf.edu

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

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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)

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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

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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

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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)

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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

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The spatial pattern of drivers

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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

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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.

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Low vulnerability

High vulnerability

Risk map for deforestation in Campeche

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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

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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 (-)

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Dynamic simulation

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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”

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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