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DETERMINING CRITICAL MANAGEMENT AREAS IN
NATURAL PARKS METHODOLOGY
[email protected] [email protected] Science, Universidad de los Andes, Bogotá, Colombia
Determining Critical Management Area s in Natural [email protected], [email protected] 2
¿HOW TO HANDLE WITH PROBLEMS INTO A NATURAL PARK?
This methodology helps you to gather data, alphanumeric and geographic, in order to identify critical management areas in your park.
It helps you to know what and where problems are and to handle with them, giving you information to make better decisions
Determining Critical Management Area s in Natural [email protected], [email protected] 3
Methodology FlowENVIRONMENT KNOWLEDGE
Problem Variables & Kind
VARIABLES RANKING
CatalystVariables
DeterminantVariables
InformativeVariables
VARIABLES CRITICAL LEVEL - CUALITATIVE MODEL
Variable Critical Level High Medium Low
Level conditions
VARIABLES OVERLAPPING
Biomes / FiresHigh Lands
(High)Woods(High)
50% or more(High)
fh High fh High
Grass(Low)
50% or less(Medium)
Just a few onone (Low)
f Medium
h High h High f Medium
h Medium h Medium fh Low
Arrows point tovariable with ahigher precedence
No data
Low Critical Level
High Critical Level
Medium Critical Level
OVERVIEW
PROBLEMS RANKING - VARIABLES SELECTIONProblem RankingVariables & Kind
Variables selected(4 or 5)
2 VARIABLES OVERLAY MODEL ANDGENERALIZATION
Variable 1 + 2Coverage
Determining Critical Management Area s in Natural [email protected], [email protected] 4
Environment Knowledge
Identify management and preservation problems Identify and associate variables related with
each problem Tips to identify variables: Think about kind of variables (General –roads,
topography, etc.-, vegetation, natural disasters, social and economic situation and public order)
Problem Variables & Kind
Determining Critical Management Area s in Natural [email protected], [email protected] 5
Problems Ranking and Variables Selection Recognize more relevant problems Select variables related with those problems
Check selected variables are suitable for analysis Is there enough, current, historical and accurate information? Is there geographic information about problems and
variables? Can it be represented as polygons?
Problem RankingVariables & Kind Variables selected(4 or 5)
Determining Critical Management Area s in Natural [email protected], [email protected] 6
Variables Ranking
Detect variables determining problems Find variables acting as catalysts Distinguish variables giving general information
DeterminantVariables
InformativeVariables
CatalystVariables
Determining Critical Management Area s in Natural [email protected], [email protected] 7
Variables Critical Level – Qualitative Model Define when level is high, medium or low for
each determinant variable
Variable Critical Level High Medium Low
Level conditions
FIRES LEVEL50% or more in the last 10years High
Less than 50% in the last 10years Medium
LowNo fires in the last 10 years
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Variables Overlapping
Determine what happens when 2 or more variables are at same time and at same place
Which variable gets the situation worse? Which one attenuate problems? What is the effect of catalyst variables?
Biomes / FiresHigh Lands
(High)Woods(High)
50% or more(High)
High High
Grass(Low)
50% or less(Medium)
Just a few onone (Low)
Medium
High High Medium
Medium Medium Low
Arrows point tovariable with ahigher precedence
Determining Critical Management Area s in Natural [email protected], [email protected] 9
2 Variables Overlay Model and Generalization
Generalization: Overlaying 3 variables is the result between 1 variable and 2 variables overlaid; overlaying 4 variables is the result between 2 variables overlaid and other 2 variables overlaid and so.
Variable 1(POLY)
Catalyst(POLY)
OverlappingTable CatalystVs Variable 1
Variable 1 +CatalystOverlay
UNION
Reclassification
Variable 1 +CatalystCoverage
Variable 2(POLY)
Catalyst(POLY)
OverlappingTable CatalystVs Variable 2
Variable 2 +CatalystOverlay
UNION
Reclassification
Variable 2 +CatalystCoverage
UNION
Variable 1 + 2Overlay
Reclassification
Overlapping TableVariable 1 VsVariable 2
Variable 1 + 2Coverage
Determining Critical Management Area s in Natural [email protected], [email protected] 10
Overview (1)
Step 1: 2 Determinant variables
No data
Park’s
Private Company’s
Goverment Company’s
Land Owners
No data
Grass
Woods & High Lands
Small woods
Biomes
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Overview (2)
Step 2: Catalyst Step 3: OverlayForest Control Points
Overlay
No data
Low Critical Level
High Critical Level
Medium Critical Level
Determining Critical Management Area s in Natural [email protected], [email protected] 12
Conclusions
This methodology is very helpful to handle problems related with management and preservation in natural parks.
Problem, variable and overlaying tables represent park and environment knowledge and are very meaningful to make better decisions. It is also useful to share and delegate responsibilities.
Methodology can be used as a simulation tool, changing variables roles. In that way, it is possible to find out what happens when catalyst, determinant and informative variables change.
It is still necessary to develop a tool which implements this methodology and to handle issues such as compatibilities among coverages -point, line and polygons- and to support overlaying variable model.
Determining Critical Management Area s in Natural [email protected], [email protected] 13
Bibliography
[Dia96] Díaz, Liliana; Lastra, Carolina. Siezcha, Sistema de Información Espacial para la Determinación de Zonas Críticas de Manejo en el Parque Nacional Natural Chingaza. Tesis, Universidad de los Andes, Bogotá, Colombia. 1996
[Con97] Conesa Fernández, Vicente. Guía Metodológica para la evaluación del impacto ambiental. 3era edición, impresión del 2000. Ediciones Mundi – Prensa, Madrid, España. 1997
Determining Critical Management Area s in Natural [email protected], [email protected] 14
Acknowledgments
Authors of this job want to acknowledge to Carolina Lastra and Liliana Díaz for their previous job in this area, Siezcha, and to Carlos Lora who helped with his knowledge as a Chingaza Natural National Park's Chief, he gave us valuable information to develop this methodology. Finally we want to thank to Adriana Forero and Ezana Habte, teachers of the Foreign Language Department at Universidad de la Sabana, who help in the translation of this paper.