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A  Risk to ‘Uncontacted’ Tribes: Vegetation Loss in the Amazon Rainforest

Benjamin  Ace  GIS:  Fall  2014  

Final  Project  Presenta<on  

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Indians, they sustain themselves using forest resources and live away from the mainstream or dominant society with whom they rarely have peaceful contact. There are about 100 ‘uncontacted’  tribes in the world.

‘Uncontacted’ Tribes

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•  Resource extraction (timber, oil, minerals)

•  Bacteria and viruses they lack immunity to

•  Colonization •  Forest fires •  Violence from illegal loggers and

drug smugglers •  Denials of their existence to

justify continued extraction

Risks to ‘Uncontacted’ Tribes

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Project Goal: Map risk posed by Amazon deforestation to ‘uncontacted’ tribes who live there.

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Measuring Vegetation Loss: Obtaining the data

A Normalized Difference Vegetation Index (NDVI) dataset was used. The NDVI data measures light differences recorded in satellite data and can be used to determines spatial distribution of vegetation.

A more ideal data set to use would have shown forest loss explicitly due to deforestation. While possible to obtain this data the preprocessing time required for data covering the extent of the Amazon Rainforest was greater than available.

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Primary Data Source United States Geological Survey Land Cover Institute, Maximum Green Vegetation Fraction (MGVF) Data Set: shows yearly world average vegetation coverage on a ten point index (1 = 100% veg., 0 = 0% veg.)

North Atlantic Ocean

North Atlantic Ocean

Amazon Rainforest

2001 Coverage

Amazon Rainforest

2012 Coverage

Slight vegetation loss can be seen at small scale between 2001 and 2012 but vegetation loss becomes apparent at large scale where areas of 100% coverage are diminished in 2012

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2012 Vegetation Loss Determining vegetation loss 2001 to 2012: 1.  Export vector containing 2001 spatial extent of

single MGVF value ex: select MGVF = 1 (complete veg. cover)

2.  Export vector containing 2012 spatial extent of MGVF values less than value from step one,

ex: select MGVF < 1 3.  Export spatial intersect of step 1 and 2 vectors

i.e. retains area of veg. cover loss 4.  Repeat steps one through 3 for each MGVF value 5.  Merge all step 3 export vectors

Resulting vector retains extent of veg. loss (2012 MGVF < 2001 MGVF) while excluding extent of no change (2012 MGVF = 2001 MGVF) and extent of veg. gain (2012 MGVF > 2001 MGVF)

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Verifying Results

I compared my results (right) to the University of Maryland’s (UM) Global Forest Loss 2000 to 2012 data (left)

While the two data sets have a similar extent the UM data is higher resolution and more accurate. This is evident by the herringbone pattern seen in the UM data which is typical of deforestation

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Locating ‘Uncontacted’ Tribes Survival International, an organization defending rights of ‘uncontacted’ tribes, released data containing approximate spatial information on all known Amazon tribes.

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To locate potential tribes at risk a percentage vegetation loss was calculated within a 50 mi radius sample buffer of each tribe point.  

Findings: Buffers including the Ayroeo Tribe in Paraguay and the Apiaká Tribe in Brazil showed the highest vegetation loss at just over 12%

Apiaká  

Ayroeo  

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Conclusions

Time constraints did not allow preprocessing necessary to obtain comprehensive deforestation data and therefore risk was unable to be measured at greatest possible accuracy.

Next Steps: •  Succeed at getting University of Maryland’s (UM) Global Forest change data into a usable format •  Improve risk measurement by calculating percent loss within tribe forest extent defined by Survivor rather

than merely a 50 mi buffer •  Determine the impact of forest gain within the tribe extent •  Map additional risks such as proximity to population densities, road networks, mineral resources, etc.