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Modeling Deforestation Risks for Modeling Deforestation Risks for
the Maya Biosphere Reserve, the Maya Biosphere Reserve,
GuatemalaGuatemala
Modeling Deforestation Risks for Modeling Deforestation Risks for
the Maya Biosphere Reserve, the Maya Biosphere Reserve,
GuatemalaGuatemalaWolfgang Grunberg
School of Renewable Natural Resource SciencesThe University of Arizona
Tucson, Arizona, 85721, USA
July 14, 2000
AcknowledgementAcknowledgementAcknowledgementAcknowledgementThe author would like to thank the following organizations and
individuals for their indispensable help: ART Group - The University of Arizona CARE Guatemala CONAP - CEMEC CI - ProPeten WCS - Gainesville Perfecto Carillo, Teresita Chinchilla, Gary
Christopherson, Reno Fiedler, Georg Grünberg, D. Phillip Guertin, Vinicio Montero, Howard R. Gimblett, Gustavo Rodriguez Ortiz, Marco Antonio Palacios, Victor Hugo Ramos, Steven Sader, Claudio Saito, Norman Schwartz, William W. Shaw, Carlos Soza, Laura Stewart, and Craig Wissler
OverviewOverviewOverviewOverview The Maya Biosphere Reserve (MBR)
– Landscape, People, & Deforestation Methods & Results
– Data - Types, Sources, and Accuracy– Spatial Analysis
Roads, Settlements, & Soil Results– Deforestation Probability Surface
1986-99 Deforestation Probability Results
– Forecasting Deforestation 1999 Deforestation Forecast Results 2001 Deforestation Scenario Results
Discussion– Deforestation Model– Future Improvements– Conclusions
Guatemala*, Central AmericaGuatemala*, Central AmericaGuatemala*, Central AmericaGuatemala*, Central America
Area: 108,890 km2 Climate:Tropical; hot, humid in lowlands; cooler in highlands Terrain: Mostly mountains with narrow coastal plains and
rolling limestone plateau (Peten) Population: 12,300,000 (2.68 % growth rate) Ethnic Groups:
– 56 % Ladino (Mestizo)– 44 % Mayas and other
indigenous Peoples Literacy: 55.6 % Labor Force:
– Agriculture 58 %– Services 14 %– Manufacturing 14 %– Commerce 7 %– Construction 4 %– Other 3 %
* According to the CIA World Factbook 1999
The MBR and its Buffer Zones (ZAM and ZUM)The MBR and its Buffer Zones (ZAM and ZUM)The MBR and its Buffer Zones (ZAM and ZUM)The MBR and its Buffer Zones (ZAM and ZUM)
Founded 1990 21,130 km2 Reserve and Buffer Zone Hilly Limestone Carst Landscape 100-300 m Elevation 25° C Mean Annual Temperature 1600 mm Yearly Precipitation Average Predominantly Tropical Lowland Forest
The Agricultural FrontierThe Agricultural FrontierThe Agricultural FrontierThe Agricultural Frontier
Slash and BurnSlash and BurnSlash and BurnSlash and Burn
Road ConstructionRoad ConstructionRoad ConstructionRoad Construction
Oil Pipeline and RanchingOil Pipeline and RanchingOil Pipeline and RanchingOil Pipeline and Ranching
The Peoples and their Primary OccupationThe Peoples and their Primary OccupationThe Peoples and their Primary OccupationThe Peoples and their Primary Occupation
Itza Maya - Majority in 1 Settlement– Native Mayan population– Swidden Agriculture (Corn), Agroforestry, Forest Products
Ladino Petenero - Majority in 6 Settlements– Non-Immigrant Population of Hispanic Descent– Wage Labor, Swidden Agriculture, Agroforestry
Highland Mayas - Majority in 25 Settlements– Recent Immigrants from Guatemala’s Central Highlands– Swidden Agriculture
Ladino Sureño - Majority in 134 Settlements– Recent Immigrants of Hispanic and Mayan Descent– Swidden Agriculture and Ranching
Maya House with Corn Maya House with Corn FieldFieldMaya House with Corn Maya House with Corn FieldField
Ladino House along a Ladino House along a RoadRoadLadino House along a Ladino House along a RoadRoad
Methods - Methods - Data Used and Their SourcesData Used and Their SourcesMethods - Methods - Data Used and Their SourcesData Used and Their Sources
1986, 90, 93, 95, 97, and 99 Forest Change Detection Images based on NDVI analysis of 30 m resolution TM Landsat Images:
– Maine Image Analysis Laboratory, University of Maine 1:200,000 Soil Map, reclassified for agricultural suitability:
– CONAP and FAO 194 Settlement locations and associated socio-economic data from
1820 to 1999:
– CARE Guatemala and CEMEC-CONAP Roads and associated attributes:
– CEMEC-CONAP, WCS-Gainesville, and SEGEPLAN Administrative boundaries:
– CEMEC-CONAP and WCS-Gainesville
The Vector and Raster Themes have a Root Mean Square Error of 400 Meter to Each Other
Methods - Methods - Spatial AnalysisSpatial AnalysisMethods - Methods - Spatial AnalysisSpatial Analysis
Settlement Points Analysis:
– 20 concentric 1 km buffers per settlement and analysis year
– Averaged deforestation distance decay curves according to socio-economic categories
Soil Polygons Analysis:
– Reclassification according to agricultural suitability
– % deforestation per soil category and analysis year Road Lines Analysis:
– Only perennial roads were included in the models
– The entire area is assumed to be easily penetrated on foot, with mules, or with 4-wheel-drive vehicles
– Perennial roads, however, are significant for market access and public transportation
Buffering the El Naranjo SettlementBuffering the El Naranjo SettlementBuffering the El Naranjo SettlementBuffering the El Naranjo SettlementFounded 1981; Ladino Sureño Majority; in Transition from Agriculture to Ranching; 3500 Inhabitants in 1996
Perennial RoadPerennial RoadPerennial RoadPerennial Road
Results - Results - Deforestation Distance Decay Deforestation Distance Decay CurvesCurves
According to the Settlements’ Primary According to the Settlements’ Primary Occupation Occupation
Results - Results - Deforestation Distance Decay Deforestation Distance Decay CurvesCurves
According to the Settlements’ Primary According to the Settlements’ Primary Occupation Occupation
Exclusion of Wage Labor Settlements from the Model due to Minimal Deforestation Impact
Average Deforestation of Settlements in 1997 - Primary Occupation
0
10
20
30
40
50
60
70
80
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Distance to Settlement (km)
% A
vera
ge D
efor
esta
tion
Agriculture (85 Samples)Transition from Agriculture to Ranching (52 Samples)Ranching (16 Samples)Forestry etc. (13 Samples)Wage Labor (9 Samples)
Average Deforestation of Settlements in 1997 - Primary Occupation
0
10
20
30
40
50
60
70
80
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Distance to Settlement (km)
% A
vera
ge D
efor
esta
tion
Agriculture (85 Samples)Transition from Agriculture to Ranching (52 Samples)Ranching (16 Samples)Forestry etc. (13 Samples)Wage Labor (9 Samples)
Results - Results - Deforestation and Agricultural Deforestation and Agricultural SoilSoil
SuitabilitySuitability
Results - Results - Deforestation and Agricultural Deforestation and Agricultural SoilSoil
SuitabilitySuitability More Deforestation on Well Draining Soils than on Poorly
Draining Soils
Accumulated Deforestation vs. Soil Quality
0
5
10
15
20
25
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
Year
% D
efor
esta
tion
Well Draining Deep Soils Poorly Draining Deep Soils
Well Draining Shallow Soils Poorly Draining Shallow Soils
Accumulated Deforestation vs. Soil Quality
0
5
10
15
20
25
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
Year
% D
efor
esta
tion
Well Draining Deep Soils Poorly Draining Deep Soils
Well Draining Shallow Soils Poorly Draining Shallow Soils
Methods - Methods - Deforestation Probability Deforestation Probability SurfaceSurfaceMethods - Methods - Deforestation Probability Deforestation Probability SurfaceSurface
Cell by Cell Logistic Regression for Each Analysis Year (1986 to 1999) using 5 % Stratified Random Samples (> 1,100,000 cells):– Dependent Variable: Deforested (1) / Forested (0)– Independent Variables: LN distance to Roads, LN
Distance to Settlements, Well (1) / Poorly (0) Draining Soils
Soil drainswell/poorly
LN roaddistance 1997
LN sitedistance 1997
Deforestation1997
Variables:
-> 10.006 ->
x -1.087 =
x -0.430 =
x 0.955 =
Logisticregression
coefficients:
Independent
Dependent
Weighted Grids:
Deforestationprobability
surface 1997
Corrected y-intercept
logisticallytransformed
Sum ofweighted
grids
Soil drainswell/poorly
LN roaddistance 1997
LN sitedistance 1997
Deforestation1997
Variables:
-> 10.006 ->
x -1.087 =
x -0.430 =
x 0.955 =
Logisticregression
coefficients:
Independent
Dependent
Weighted Grids:
Deforestationprobability
surface 1997
Corrected y-intercept
logisticallytransformed
Sum ofweighted
grids
1986 - 1986 - Deforestation Deforestation
Probability Probability SurfaceSurface
1986 - 1986 - Deforestation Deforestation
Probability Probability SurfaceSurface
Observed Observed DeforestationDeforestationObserved Observed DeforestationDeforestation
Results Results 19861986
1990 - 1990 - Deforestation Deforestation
Probability Probability SurfaceSurface
1990 - 1990 - Deforestation Deforestation
Probability Probability SurfaceSurface
Observed Observed DeforestationDeforestationObserved Observed DeforestationDeforestation
Results Results 19901990
1993 - 1993 - Deforestation Deforestation
Probability Probability SurfaceSurface
1993 - 1993 - Deforestation Deforestation
Probability Probability SurfaceSurface
Observed Observed DeforestationDeforestationObserved Observed DeforestationDeforestation
Results Results 19931993
1995 - 1995 - Deforestation Deforestation
Probability Probability SurfaceSurface
1995 - 1995 - Deforestation Deforestation
Probability Probability SurfaceSurface
Observed Observed DeforestationDeforestationObserved Observed DeforestationDeforestation
Results Results 19951995
1997 - 1997 - Deforestation Deforestation
Probability Probability SurfaceSurface
1997 - 1997 - Deforestation Deforestation
Probability Probability SurfaceSurface
Observed Observed DeforestationDeforestationObserved Observed DeforestationDeforestation
Results Results 19971997
1999 - 1999 - Deforestation Deforestation
Probability Probability SurfaceSurface
1999 - 1999 - Deforestation Deforestation
Probability Probability SurfaceSurface
Observed Observed Deforestation &Deforestation &Man Caused Man Caused Wild Fires Wild Fires (Summer 1998)(Summer 1998)
Observed Observed Deforestation &Deforestation &Man Caused Man Caused Wild Fires Wild Fires (Summer 1998)(Summer 1998)
Results Results 19991999
Methods - Methods - Forecasting DeforestationForecasting DeforestationMethods - Methods - Forecasting DeforestationForecasting Deforestation
Forecasting Deforestation for 1999:– Forecasted Deforestation Probability Surface based on:
1997’s probability surface regression coefficients Roads and settlements observed in 1999
– Deforestation Forecast based on: Percent deforestation in 1997’s deforestation probability
zones– Comparing 1999 Observed and Forecasted Deforestation
The 2001 Deforestation Scenario:– Forecasted Deforestation Probability Surface based on:
1999’s probability surface regression coefficients 2001 roads scenario
– Deforestation Forecast based on: Percent deforestation in 1999’s deforestation probability
zones
Results - Results - The Forecasted 1999 The Forecasted 1999 DeforestationDeforestation
Probability SurfaceProbability Surface
Results - Results - The Forecasted 1999 The Forecasted 1999 DeforestationDeforestation
Probability SurfaceProbability Surface The 1999 Forecast is based on the 1997 Regression
Coefficients and in 1999 Observed Roads and Settlements
Results - Results - Forecasting Percent Area Forecasting Percent Area Deforested Deforested Results - Results - Forecasting Percent Area Forecasting Percent Area Deforested Deforested
0
10
20
30
40
50
60
70
0 -
0.5
0.05
- 0
.1
0.1
- 0.
15
0.15
- 0
.2
0.2
- 0.
25
0.25
- 0
.3
0.3
- 0.
35
0.35
- 0
.4
0.4
- 0.
45
0.45
- 0
.5
0.5
- 0.
55
0.55
- 0
.6
0.6
- 0.
65
0.65
- 0
.7
0.7
- 0.
75
0.75
- 0
.8
0.8
- 0.
85
0.85
- 0
.9
0.9
- 0.
95
0.95
- 1
Deforestation Probability Zone
% o
f Pro
ba
bili
ty Z
on
e D
efo
rest
ed
1986
1990
1993
1995
1997
1999
0
10
20
30
40
50
60
70
0 -
0.5
0.05
- 0
.1
0.1
- 0.
15
0.15
- 0
.2
0.2
- 0.
25
0.25
- 0
.3
0.3
- 0.
35
0.35
- 0
.4
0.4
- 0.
45
0.45
- 0
.5
0.5
- 0.
55
0.55
- 0
.6
0.6
- 0.
65
0.65
- 0
.7
0.7
- 0.
75
0.75
- 0
.8
0.8
- 0.
85
0.85
- 0
.9
0.9
- 0.
95
0.95
- 1
Deforestation Probability Zone
% o
f Pro
ba
bili
ty Z
on
e D
efo
rest
ed
1986
1990
1993
1995
1997
1999
The 1997 and 1999 Observed Probability Zone Deforestation Percentages were used respectively for the 1999 Deforestation Projection and 2001 Scenario
Results - Results - 1999 Deforestation Forecast1999 Deforestation ForecastResults - Results - 1999 Deforestation Forecast1999 Deforestation Forecast
0
50
100
150
200
250
300
0 -
0.5
0.05
- 0
.1
0.1
- 0.
15
0.15
- 0
.2
0.2
- 0.
25
0.25
- 0
.3
0.3
- 0.
35
0.35
- 0
.4
0.4
- 0.
45
0.45
- 0
.5
0.5
- 0.
55
0.55
- 0
.6
0.6
- 0.
65
0.65
- 0
.7
0.7
- 0.
75
0.75
- 0
.8
0.8
- 0.
85
0.85
- 0
.9
0.9
- 0.
95
0.95
- 1
Deforestation Probability Zones
De
fore
ste
d A
rea
(km
^2)
1999 PredictedDeforestation
1999 ObservedDeforestation
0
50
100
150
200
250
300
0 -
0.5
0.05
- 0
.1
0.1
- 0.
15
0.15
- 0
.2
0.2
- 0.
25
0.25
- 0
.3
0.3
- 0.
35
0.35
- 0
.4
0.4
- 0.
45
0.45
- 0
.5
0.5
- 0.
55
0.55
- 0
.6
0.6
- 0.
65
0.65
- 0
.7
0.7
- 0.
75
0.75
- 0
.8
0.8
- 0.
85
0.85
- 0
.9
0.9
- 0.
95
0.95
- 1
Deforestation Probability Zones
De
fore
ste
d A
rea
(km
^2)
1999 PredictedDeforestation
1999 ObservedDeforestation
1999 Forecasted Deforestation for Each Probability Zone = Area of Forecasted 1999 Deforestation Probability Zone
x % of Zone Deforested in 1997 1999 Observed vs Predicted Deforestation
Results - Results - Testing the 1999 Deforestation Testing the 1999 Deforestation ForecastForecastResults - Results - Testing the 1999 Deforestation Testing the 1999 Deforestation ForecastForecast
Deforestation (km^2)DeforestationProbability
Zone
% deforestedin 1997
Forecasted 1999Probability Surface Area
(km^2)Predicted Observed
Difference(km^2)
%Difference
0 - 0.5 0.39 x 2562.9 = 9.9 7.2 2.7 38.010.05 - 0.1 0.14 x 3463.9 = 4.9 1.7 3.2 186.590.1 - 0.15 0.54 x 1839.8 = 10.0 11.2 -1.2 -10.400.15 - 0.2 1.33 x 1474.7 = 19.7 21.1 -1.4 -6.720.2 - 0.25 2.12 x 1240.7 = 26.3 28.7 -2.3 -8.160.25 - 0.3 2.15 x 1025.7 = 22.1 26.3 -4.2 -16.100.3 - 0.35 2.76 x 882.4 = 24.3 31.9 -7.6 -23.810.35 - 0.4 5.38 x 788.2 = 42.4 45.2 -2.7 -6.030.4 - 0.45 9.05 x 749.8 = 67.8 71.9 -4.1 -5.710.45 - 0.5 11.06 x 693.1 = 76.7 81.6 -4.9 -6.050.5 - 0.55 12.94 x 680.3 = 88.1 96.9 -8.9 -9.160.55 - 0.6 16.01 x 697.7 = 111.7 121.5 -9.8 -8.080.6 - 0.65 18.22 x 698.1 = 127.2 137.8 -10.6 -7.710.65 - 0.7 20.29 x 682.8 = 138.5 146.9 -8.4 -5.710.7 - 0.75 25.40 x 655.6 = 166.5 168.5 -2.0 -1.200.75 - 0.8 30.30 x 620.3 = 187.9 181.1 6.8 3.770.8 - 0.85 35.09 x 596.1 = 209.2 201.0 8.2 4.060.85 - 0.9 40.52 x 559.3 = 226.6 214.3 12.3 5.760.9 - 0.95 50.10 x 497.0 = 249.0 235.2 13.8 5.880.95 - 1 66.37 x 333.6 = 221.4 206.0 15.5 7.51
2030.4 2036.1 9.9 0.49
Deforestation (km^2)DeforestationProbability
Zone
% deforestedin 1997
Forecasted 1999Probability Surface Area
(km^2)Predicted Observed
Difference(km^2)
%Difference
0 - 0.5 0.39 x 2562.9 = 9.9 7.2 2.7 38.010.05 - 0.1 0.14 x 3463.9 = 4.9 1.7 3.2 186.590.1 - 0.15 0.54 x 1839.8 = 10.0 11.2 -1.2 -10.400.15 - 0.2 1.33 x 1474.7 = 19.7 21.1 -1.4 -6.720.2 - 0.25 2.12 x 1240.7 = 26.3 28.7 -2.3 -8.160.25 - 0.3 2.15 x 1025.7 = 22.1 26.3 -4.2 -16.100.3 - 0.35 2.76 x 882.4 = 24.3 31.9 -7.6 -23.810.35 - 0.4 5.38 x 788.2 = 42.4 45.2 -2.7 -6.030.4 - 0.45 9.05 x 749.8 = 67.8 71.9 -4.1 -5.710.45 - 0.5 11.06 x 693.1 = 76.7 81.6 -4.9 -6.050.5 - 0.55 12.94 x 680.3 = 88.1 96.9 -8.9 -9.160.55 - 0.6 16.01 x 697.7 = 111.7 121.5 -9.8 -8.080.6 - 0.65 18.22 x 698.1 = 127.2 137.8 -10.6 -7.710.65 - 0.7 20.29 x 682.8 = 138.5 146.9 -8.4 -5.710.7 - 0.75 25.40 x 655.6 = 166.5 168.5 -2.0 -1.200.75 - 0.8 30.30 x 620.3 = 187.9 181.1 6.8 3.770.8 - 0.85 35.09 x 596.1 = 209.2 201.0 8.2 4.060.85 - 0.9 40.52 x 559.3 = 226.6 214.3 12.3 5.760.9 - 0.95 50.10 x 497.0 = 249.0 235.2 13.8 5.880.95 - 1 66.37 x 333.6 = 221.4 206.0 15.5 7.51
2030.4 2036.1 9.9 0.49
Difference between 1999 Predicted and Observed Deforestation
Results - Results - The Forecasted 2001 The Forecasted 2001 Deforestation Deforestation
Probability SurfaceProbability Surface
Results - Results - The Forecasted 2001 The Forecasted 2001 Deforestation Deforestation
Probability SurfaceProbability Surface The 2001 Forecast is based on the 1999 Regression
Coefficients and a 2001 Roads Scenario
Results - Results - The 2001 ScenarioThe 2001 ScenarioResults - Results - The 2001 ScenarioThe 2001 Scenario
0
50
100
150
200
250
300
0 -
0.5
0.05
- 0
.1
0.1
- 0.
15
0.15
- 0
.2
0.2
- 0.
25
0.25
- 0
.3
0.3
- 0.
35
0.35
- 0
.4
0.4
- 0.
45
0.45
- 0
.5
0.5
- 0.
55
0.55
- 0
.6
0.6
- 0.
65
0.65
- 0
.7
0.7
- 0.
75
0.75
- 0
.8
0.8
- 0.
85
0.85
- 0
.9
0.9
- 0.
95
0.95
- 1
Deforestation Probability Zone
De
fore
ste
d A
rea
(km
^2)
1986
1990
1993
1995
1997
1999
2001
0
50
100
150
200
250
300
0 -
0.5
0.05
- 0
.1
0.1
- 0.
15
0.15
- 0
.2
0.2
- 0.
25
0.25
- 0
.3
0.3
- 0.
35
0.35
- 0
.4
0.4
- 0.
45
0.45
- 0
.5
0.5
- 0.
55
0.55
- 0
.6
0.6
- 0.
65
0.65
- 0
.7
0.7
- 0.
75
0.75
- 0
.8
0.8
- 0.
85
0.85
- 0
.9
0.9
- 0.
95
0.95
- 1
Deforestation Probability Zone
De
fore
ste
d A
rea
(km
^2)
1986
1990
1993
1995
1997
1999
2001
2001 Predicted Deforestation vs Observed Deforestation
Results - Results - The 2001 Scenario ContinuedThe 2001 Scenario ContinuedResults - Results - The 2001 Scenario ContinuedThe 2001 Scenario Continued
The 2001 Scenario forecasts an increase in deforestation since of 14.5 % (295 km2) since 1999
Deforestation (km^2)DeforestationProbability Zone 1999 2001
Difference(km^2)
% Difference
0 - 0.5 7.2 4.0 -3.2 -44.100.05 - 0.1 1.7 1.8 0.1 3.920.1 - 0.15 11.2 13.2 2.1 18.430.15 - 0.2 21.1 23.4 2.4 11.160.2 - 0.25 28.7 29.9 1.3 4.400.25 - 0.3 26.3 28.3 1.9 7.340.3 - 0.35 31.9 34.8 2.8 8.890.35 - 0.4 45.2 49.5 4.3 9.520.4 - 0.45 71.9 77.3 5.4 7.450.45 - 0.5 81.6 87.1 5.5 6.770.5 - 0.55 96.9 101.3 4.4 4.520.55 - 0.6 121.5 126.1 4.6 3.810.6 - 0.65 137.8 145.4 7.6 5.530.65 - 0.7 146.9 159.8 12.9 8.780.7 - 0.75 168.5 191.1 22.5 13.370.75 - 0.8 181.1 218.9 37.8 20.900.8 - 0.85 201.0 244.0 43.0 21.390.85 - 0.9 214.3 259.0 44.7 20.870.9 - 0.95 235.2 285.1 49.9 21.230.95 - 1 206.0 251.1 45.2 21.93
2036.1 2331.4 295.3 14.50
Deforestation (km^2)DeforestationProbability Zone 1999 2001
Difference(km^2)
% Difference
0 - 0.5 7.2 4.0 -3.2 -44.100.05 - 0.1 1.7 1.8 0.1 3.920.1 - 0.15 11.2 13.2 2.1 18.430.15 - 0.2 21.1 23.4 2.4 11.160.2 - 0.25 28.7 29.9 1.3 4.400.25 - 0.3 26.3 28.3 1.9 7.340.3 - 0.35 31.9 34.8 2.8 8.890.35 - 0.4 45.2 49.5 4.3 9.520.4 - 0.45 71.9 77.3 5.4 7.450.45 - 0.5 81.6 87.1 5.5 6.770.5 - 0.55 96.9 101.3 4.4 4.520.55 - 0.6 121.5 126.1 4.6 3.810.6 - 0.65 137.8 145.4 7.6 5.530.65 - 0.7 146.9 159.8 12.9 8.780.7 - 0.75 168.5 191.1 22.5 13.370.75 - 0.8 181.1 218.9 37.8 20.900.8 - 0.85 201.0 244.0 43.0 21.390.85 - 0.9 214.3 259.0 44.7 20.870.9 - 0.95 235.2 285.1 49.9 21.230.95 - 1 206.0 251.1 45.2 21.93
2036.1 2331.4 295.3 14.50
Discussion - Discussion - The ModelsThe ModelsDiscussion - Discussion - The ModelsThe Models
Pros:
– Can be used to estimate impacts of new roads and settlements in scenarios
– Simple model with relatively good results– Uses common spatial features such as roads, settlement
points, and simple soil maps Cons:
– Does not account for spatial and temporal autocorrelation– Does not account for road and settlement age– Does not predict deforestation location– Forecasting beyond 2 years is questionable due to changing
deforestation trends
Discussion - Discussion - Room for ImprovementsRoom for ImprovementsDiscussion - Discussion - Room for ImprovementsRoom for Improvements
Age of Road and Settlement Factor needs to be included
Spatial and temporal autocorrelation need to be addressed Differentiate settlement deforestation impacts according to their
socio-economic qualities River traffic and oil-pipelines need to be considered as access
ways
Water availability for ranching and agriculture could be included Slope and aspect data of adequate resolution in combination
with better soil maps may turn this regional model into a more localized version
Discussion - Discussion - Mostly Obvious Mostly Obvious ConclusionsConclusions
and Suggestionsand Suggestions
Discussion - Discussion - Mostly Obvious Mostly Obvious ConclusionsConclusions
and Suggestionsand Suggestions Clear relationship between the presence of roads and
settlements & deforestation Simplicity of model is advantageous for forecasting
deforestation in agricultural frontiers on a regional scale Suggestions for reducing deforestation risks:
– Control access to roads– Avoid building perennial roads or upgrading existing
intermittent roads to a perennial status– Pipelines and rivers need to be considered as possible
access routes– Avoid any new settlements in low deforestation risk areas– Consider supporting a forestry or wage-labor based
economy– In an agricultural frontier, regional deforestation trends are
not only controlled by access but also by soil quality
Thank You for Thank You for Your ParticipationYour Participation
Contact Information:Wolfgang Grunberg
School of Renewable Natural Resources, The University of Arizona, Tucson, AZ 85721, USA
Phone: 1 (520) 621 3045e-mail: [email protected]