ROAD IMPACT ASSESSMENT
ON HABITAT LOSS
IN LATIN AMERICA
Karolina Argote, Louis Reymondin, Carolina Navarrete, Denny
Grossman, Jerry Touval, Andy Jarvis
Decision and Policy Analysis Research Area (DAPA)
International Center for Tropical Agriculture (CIAT)
Conservation Biology Institute (CBI)
The Nature Conservancy (TNC)
Spot the road?
Project outcomes
This project presents a habitat change monitoring methodology
that can be used
to identify environmental
impacts of road
construction, and support improved design of future projects
that would minimize
negative environmental
impacts.
The project has also helped understand the nature of
environmental impacts of road infrastructure projects in distinct
contexts across Latin America, demonstrating the importance of
policy and ecosystem specific safeguards.
Objectives of the project
•
Evaluate the
environmental impacts
of road
infrastructure in the past through monitoring
of natural habitat loss pre
-
and post
-
construction for
5 road projects
across Latin
America
•
Demonstrate how this can be integrated into a
decision support tool
–
DATABASIN
•
Identify entry points by which
ex ante
assessment
can provide
improved safeguards
Content
1.
Methodologies
1.1. Terra
-
I
–
Habitat Change Monitoring
2. Road Impact Results
2.1. The BR
-
364 highway in Brazil.
2.2. The IIRSA projects in Peru.
2.3. The Pan
-
American Highway in Panama.
2.4. The Santa Cruz
–
Puerto Suarez corridor in Bolivia.
2.5. The Trans
-
Chaco Highway in Paraguay.
3.
Methodology of the Future Deforestation
Scenarios and Results
4.
Carbon
-
Conservation Interface
5.
Conclusions and Recommendations
Photo by Alvaro
Gaviria
in Cartagena del
Chaira
Parques
Nacionales
Naturales
de Colombia
1.1. Methodologies:
Terra
-
i
Methodology
Terra
-
i
is a system that monitoring the habitat change in Latin America
using Neural Network and satellite data
We
therefore
try
to
learn
how
each
pixel
(
site
)
responds
to
climate
,
and
any
anomoly
corresponds
to
human
impact
.
Neural
-
network,
is
a
bio
-
inspired
technology
which
emulates
the
basic
mechanism
of
a
brain
.
It
allows
…
To
find
a
pattern
in
noisy
dataset
To
apply
these
patterns
to
new
dataset
INPUTS
:
Past
NDVI
(MODIS
MOD13Q1
)
Previous
Rainfall
(
TRMM
)
OUTPUT
:
16
day
predicted
NDVI
1.1. Methodologies:
Terra
-
i
The
Bottom
-
Line
•
250m
resolution
•
Latin
American
coverage
(
currently
)
•
Satellite
data
to
monitorithe
habitat
every
16
days
•
Identification
of
habitat
change
events
•
Habitat
loss
data online
to
visualize
and
download
.
www.terra
-
i.org
1.1. Methodologies:
Interpreting the maps
1.1. Methodologies:
Interpreting the graphs
Area of
habitat
lost
Buffer distance
from the road (km)
Geographic
footprint of
the road
When did the habitat
loss happen within each
buffer?
BR
-
364 Highway, Brazil
Date
Acre Segment: Construction 2002 to 2010
Rondonia Segment: Construction 1985 to 1997
2.1. Road Impact Results:
BR
-
364 Highway, Brazil
Study Area
Section
2
:
a
corridor
of
515
km
which
connects
the
town
of
Rio
Branco
in
the
state
of
Acre
and
Porto
Velho
in
the
state
of
Rondônia
.
2.1. Road Impact Results:
BR
-
364 Highway, Brazil
Study Area
The
road
Cruzeiro
do
Sul
–
Porto
Velho
was
analyzed
into
two
different
sections
.
Section
1
:
a
corridor
of
623
km
Cruzeiro
do
Sul
-
Rio
Branco
in
the
state
of
Acre,
Brazil
.
This
section
passes
through
a
large
biological
corridor
of
the
state
of
Acre
which
has
been
regulated
by
39
protected
areas
connected
to
each
other
.
2.1. Road Impact Results:
BR
-
364 Highway, Brazil
2.1. Road Impact Results:
BR
-
364 Highway, Brazil
2.1. Road Impact Results:
BR
-
364 Highway, Brazil
Road impact
Comparing
the
two
segments
one
can
see
a
huge
difference
in
the
deforestation
rates
and
in
how
it
is
the
spatially
distributed
.
More
localised
footprint, and
and
lower overall deforestation
levels. Nevertheless, increase
in last 2 years.
Much higher deforestation
rates, and much BIGGER
footprint >50km due to
secondary roads etc.
2.1. Road Impact Results:
BR
-
364 Highway, Brazil
2.1. Road Impact Results:
BR
-
364 Highway, Brazil
Protected Areas
Bom
Futuro
and
Jaciparaná
are
the
two
protected
areas
most
affected
by
deforestation
in
Rondônia
and
are
located
next
to
the
analyzed
road,
within
a
buffer
area
of
20
km
.
Actually,
the
deforestation
rate
in
Bom
Futuro
has
been
of
10
,
188
hectares
per
year
(adding
up
to
76
,
406
hectares
converted
in
7
.
5
years)
whereas
it
has
been
of
6
,
337
hectares
per
year
in
Rio
Jaciparaná
(adding
up
to
47525
hectares
converted
in
7
.
5
years)
.
2.1. Road Impact Results:
BR
-
364 Highway, Brazil
Conclusions
•
Section
1
:
Acre
.
Habitat
loss
of
19
,
542
hectares
was
recorded
per
year
in
average
in
a
buffer
area
of
50
km
of
the
Cruzeiro
do
Sul
-
Rio
Branco
Segment
•
Section
2
:
Rondonia
.
Habitat
loss
of
79
,
783
hectares
per
year
within
a
same
buffer
size
around
the
Rio
Branco
-
Porto
Velho
Segment
.
•
Much
higher
in
the
segment
Rio
Branco
-
Porto
Velho
(in
Rondonia)
than
in
Cruzeiro
do
Sul
-
Rio
Branco
(In
Acre)
likely
due
to
the
conservation
policies
implemented
in
Acre
state
.
Note
fewer
secondary
roads,
and
greater
protection
from
National
Parks
in
segment
1
.
IIRSA Project, Peru
Date
Construction: 1998 to 2007
2.2. Road Impact Results:
IIRSA Projects, Peru
Study Area
The
analyzed
roads
have
a
total
length
of
1584
km
and
go
through
all
Peru
from
the
Pacific
coast
to
the
Acre
state
in
Brazil
.
The
road
was
split
into
three
different
sections
for
the
analysis
:
Section 1
Andean
Section 2
Piedemonte
Section 3
Amazon
Section
1
:
752
km
.
Paita
on
the
Pacific
coast
(Piura)
to
Tarapoto
.
Section
2
:
381
km
.
Tarapoto
-
Huanuco
(where
it
passes
2
km
away
from
Tingo
Maria
National
Park)
.
Section
3
:
451
km
.
Tingo
Maria
(
Huanuco
)
-
Cruzeiro
do
Sul
(Acre,
Brazil)
.
2.2. Road Impact Results:
IIRSA Projects, Peru
2.2. Road Impact Results:
IIRSA Projects, Peru
Road Impact
Section
1
(
Paita
-
Tarapoto
)
:
accumulated
loss
of
40
,
794
hectares
(
5
,
439
Ha/
yr
)
.
Most habitat
loss in first
10km (45%)
Significant
increase in
deforestation in
past 3
-
4 years
2.2. Road Impact Results:
IIRSA Projects, Peru
2.2. Road Impact Results:
IIRSA Projects, Peru
Road Impact
Section
2
(
Tarapoto
-
Tingo
Maria
)
:
accumulated
loss
of
30
,
763
Ha
(
4
,
102
Ha/
yr
)
.
Most
impacted
areas
are
located
in
a
buffer
of
30
km
from
the
road
.
Most habitat
loss in first
30km (88%)
Significant
increase in
deforestation in
past 3
-
4 years
2.2. Road Impact Results:
IIRSA Projects, Peru
2.2. Road Impact Results:
IIRSA Projects, Peru
Road Impact
Section
3
(
Tingo
Maria
-
Cruzeiro
)
:
accumulated
loss
of
58
,
900
hectares
(
7
,
853
Ha/year)
.
Most
impacted
areas
are
located
in
a
buffer
of
30
km
from
the
road
.
Most habitat
loss in first
30km (81%)
No apparent
increase in
deforestation
during or after
road
construction
2.2. Road Impact Results:
IIRSA Projects, Peru
2.2. Road Impact Results:
IIRSA Projects, Peru
Conclusions
Section
1
:
Andes
.
Footprint
more
localised
(<
10
km),
25
%
increase
in
habitat
loss
post
-
project
versus
pre
-
project
.
Section
2
:
Piedemonte
.
Larger
footprint
(
10
-
20
km),
and
>
doubling
of
deforestation
after
road
finalization
.
Section
3
:
Tingo
Mario
-
Cruzeiro
.
High
baseline
levels
of
deforestation
in
the
region,
but
no
increase
since
road
project
(major
sections
of
road
still
not
complete)
.
Pan
-
American Highway, Panama
Date
Construction: 1985 to 1990
2.3. Road Impact Results:
Pan
-
American Highway, Panamá
Study Area
The
Pan
-
American
Highway
is
located
in
the
Darien
province
in
Panama
at
the
eastern
end
of
the
country
and
its
length
is
approximately
262
km,
i
n
a
30
km
of
buffer
around
the
road
are
located
more
than
10
protected
areas
with
important
ecological
functions
.
2.3. Road Impact Results:
Pan
-
American Highway, Panamá
Habitat Change Monitoring
Methodology
i.
Reclassify
the
Land
Cover
Maps
of
1992
and
2000
using
ArcGIS
software
in
Vegetation
and
non
vegetation
maps
.
ii.
Generated
the
deforestation
map
of
1992
-
2000
.
iii.
Applied
the
Terra
-
I
Methodology
to
monitoring
the
habitat
change
between
2004
to
2010
.
iv.
Analyze
the
road
impact
in
buffers
areas
in
10
,
20
,
30
,
40
and
50
km
of
the
road
.
MAIN
INPUTS
For
generated
deforestation
maps
before
2000
:
A
dataset
of
land
cover
produced
by
the
Forest
Information
System
Project,
the
National
Environmental
Authority
(ANAM)
for
1992
and
2000
.
For
generated
deforestation
maps
between
2004
and
2010
:
Terra
-
I
dataset
.
2.3. Road Impact Results:
Pan
-
American Highway, Panamá
2.3. Road Impact Results:
Pan
-
American Highway, Panamá
The habitat loss is greater the
closest it’s to the road.
Vast majority of habitat change
occurred in the 1990’s directly
after road construction.
Deforestation 2004
-
2011 < 10%
of 1990’s levels.
Road impact
2.3. Road Impact Results:
Pan
-
American Highway, Panamá
Conclusions
•
Between
1992
and
2000
there
was
an
alarming
loss
of
7
%
of
the
total
national
forest
cover
in
Panama
which
is
equivalent
to
497
,
306
hectares
.
This
deforestation
is
localized
mostly
in
the
provinces
of
Panama
and
Darien
and
close
to
the
road
.
•
The
impact
occurs
mainly
in
the
direct
influence
area
of
the
road
(
0
to
10
km)
.
•
The
Darien
province
lost
24
%
of
its
forests,
and
Panama
23
%
.
Most
of
this
deforestation
occurred
in
Mixed
Cative
forest
in
order
to
create
new
cropland
areas
.
Santa Cruz
-
Puerto Suarez, Bolivia
Date
Construction: 2000 to 2011
2.4. Road Impact Results:
Santa Cruz
-
Puerto Suarez Corridor, Bolivia
Study Area
The
corridor
Santa
Cruz
-
Puerto
Suarez
is
located
in
the
South
East
of
Bolivia
.
Its
length
is
approximately
636
km
and
connects
the
towns
of
Santa
Cruz
de
la
Sierra
and
Puerto
Suarez
located
on
the
border
with
the
state
of
Mato
Grosso
do
Sul
in
Brazil
.
In
the
area
one
can
see
four
easily
distinguishable
types
of
ecoregions
:
Pantanal
,
Dry
Chaco,
Chiquitano
Dry
Forest
and
Cerrado
2.4. Road Impact Results:
Santa Cruz
-
Puerto Suarez Corridor, Bolivia
2.4. Road Impact Results:
Santa Cruz
-
Puerto Suarez Corridor, Bolivia
Road Impact
•
Road
still
under
construction
.
Some
direct
impacts
especially
close
to
Santa
Cruz
.
•
Major
indirect
impacts
of
fires
originating
from
“
slash
and
burn
”
practices,
especially
in
2010
.
2.4. Road Impact Results:
Santa Cruz
-
Puerto Suarez Corridor, Bolivia
Conclusions
•
Too
early
to
say
what
direct
impacts
are
until
road
fully
connects
Bolivia
with
Brazil
•
Nevertheless,
clear
indirect
impact
through
fires
originating
from
slash
and
burn
activity,
especially
in
the
region
of
Santa
Cruz
Trans
-
Chaco Highway, Paraguay
Date
Construction: 2002 to 2006
2.5. Road Impact Results:
The Trans
-
Chaco Highway, Paraguay
Study Area
The
trans
-
Chaco
highway
length
approximately
736
km,
extending
from
the
boundaries
between
Bolivia
and
Paraguay
near
the
military
post
Mayor
Infante
Rivarola
in
the
department
of
Boqueron
until
it
intersects
with
the
9
th
Road
which
runs
through
the
Dry
Chaco
in
Boqueron
continuing
through
the
department
of
Presidente
Hayes
across
the
humid
Chaco
region
up
to
the
Asuncion
metropolitan
area
in
the
Central
Department
.
2.5. Road Impact Results:
The Trans
-
Chaco Highway, Paraguay
Habitat Change Monitoring
Methodology
i.
Classify
the
Landsat
-
4
satellite
images
using
the
k
-
Means
Algorithm
.
ii.
Generated
the
deforestation
map
of
2000
-
2004
.
iii.
Applied
the
Terra
-
I
Methodology
to
monitoring
the
habitat
change
between
2004
to
2011
.
iv.
Analyze
the
road
impact
in
buffers
areas
in
10
,
20
,
30
,
40
and
50
km
of
the
road
.
MAIN
INPUTS
For
generated
deforestation
maps
between
2000
and
2004
:
D
ataset
from
the
high
spatial
resolution
satellite
Landsat
4
Thematic
Mapper
in
the
Dry
Chaco
ecoregion
.
For
generated
deforestation
maps
between
2004
and
2010
:
Terra
-
I
dataset
.
2.5. Road Impact Results:
The Trans
-
Chaco Highway, Paraguay
2.5. Road Impact Results:
The Trans
-
Chaco Highway, Paraguay
2.5. Road Impact Results:
The Trans
-
Chaco Highway, Paraguay
Road Impact
•
Total
of
650
,
000
Hectares
lost
in
50
km
buffer
since
2004
•
Massive
increase
since
2007
(project
completion)
•
Large
footprint,
covering
>
50
km
from
road
2.5. Road Impact Results:
The Trans
-
Chaco Highway, Paraguay
Conclusions
•
Very
high
levels
of
deforestation
pre
-
and
post
-
road
construction
•
But
>
300
%
increase
in
deforestation
rates
since
road
finished,
with
a
footprint
that
likely
goes
beyond
50
km
buffer
Methodologies:
Future Deforestation Scenarios
Steps…
1)
Train neural network to recognize pixels that are likely to be deforested
2)
Create maps of potential deforestation
3)
Select random pixels from the potential maps
4)
Repeat 2 and 3
INPUTS
The distance to the nearest road,
to the nearest river,
to the nearest city (> 1000 people)
The elevation
The base map (what was already deforested before the
analysis with terra
-
I (from human classification of the
terra
-
i
clusters))
The detection from the terra
-
I model.
This methodology
is still
under
development, nevertheless its
implementation as proof of concept
in Brazil generated good results in
areas with clear patterns of
deforestation.
Currently, the tool only takes into account topographic variables, but the idea is in the future is
to include others inputs such as administrative information (protected areas, country…), social
information (small farmers, industrial exploitation, community managed forest…) and
ecosystems in order to improve the estimation.
3. Future Deforestation Scenarios:
Applied in BR
-
364 Road,
Brasil
Applied
The
study
area
was
the
area
around
the
road
Rio
Branco
-
Porto
Velho,
in
Brazil
from
the
1
st
of
January
2004
to
the
10
th
of
June
2011
.
Constant
rate
to
10
’
000
hectares
per
16
days
period
(the
average
rate
recorded
by
Terra
-
i
in
this
area)
Sampled
10
’
000
pixels
to
train
the
neural
network
.
(
7000
unchanged
and
3000
deforested)
This first implementation of this methodology gave encouraging results as by
comparing this result with the actual detected deforestation one can see that the
general patterns resulting from the simulation are convincing and quite similar to the
real events. However, various improvements could be instigated.
INPUTS
The distance to the nearest road,
to the nearest river,
to the nearest city (> 1000 people)
The elevation
The base map (what was already deforested before the
analysis with terra
-
I (from human classification of the
terra
-
i
clusters))
The detection from the terra
-
I model.
Potential deforestation at T=0
Potential deforestation at
T=150
Predicted deforestation
Actual
deforestation (Terra
-
i
)
Base map
PROOF OF CONCEPT
3. Future Deforestation Scenarios:
Applied in BR
-
364 Road,
Brasil
4. Carbon
-
Conservation Interface
Methodology
4. Carbon
-
Conservation Interface
Inputs
Critical
Natural
Habitat
(species,
Protected
Areas,
Terrestrial
Ecosystems)
Natural
Habitat
(terrestrial
Ecosystems)
Other
Conservation
Value
(CI
Hotspots,
TNC
Portfolio
Sites)
Carbon
4. Carbon
-
Conservation Interface
Output
Final
Index
Value
Combined
Carbon
and
Biodiversity
.
A synthesis of findings
•
Roads clearly a significant driver of deforestation
and land
-
use change, demonstrated in all 5
projects studies. Impacts are both direct and
indirect.
•
A road makes a different “footprint” depending
on the ecosystem (Andes <10km, Amazon ~50km,
Chaco >50km).
Lessons learned
•
Other factors such as secondary roads and rivers are
important drivers of habitat change and roads open access to
them.
•
As a Monitoring Tool Terra
-
I is only useful to analyze projects
after 2004. In the cases of Acre
-
Rondonia
, Peru and Paraguay
we had three cases where the full power of Terra
-
I could be
shown.
•
Local, national and international policies are clearly important
contexts that should be taken into account during and after
road construction as they have a clear link to land
-
use change
and can contribute to mitigation or exacerbation of the road
project
encironmental
impact.
Policy Recommendations
•
Regional
and
national
environmental
policies
in
place
can
significantly
reduce
the
number
of
hectares
deforested
during
and
after
the
road
construction
project
.
The
most
outstanding
case
can
be
found
in
Brazil
where
Rondonia
has
higher
deforestation
rates
compared
to
Acre
.
•
Most
of
the
protected
areas
affected
directly
or
indirectly
by
the
road
construction,
were
established
after
the
roads
where
built
.
In
cases
were
critical
ecosystems
are
endangered,
policy
makers
and
development
planners
should
consider
for
the
future,
well
in
advance,
critical
natural
habitats
for
conservation
and
biodiversity
hotspots
.
•
It’s
expected
that
infrastructure
allows
the
expansion
of
economic
activities
.
In
this
sense,
national
and
regional
policies
and
incentives
to
promote
sustainable
and
environmentally
friendly
agricultural
practices
are
also
important
.
In
the
case
of
the
slash
and
burn
method
in
Bolivia
and
Peru
causing
multiple
forest
fires,
more
national
policies
and
programs
to
promote
more
sustainable
practices
should
be
in
place,
such
as
the
Slash
and
Mulch
Agroforestry
Systems
.
It’s
also
key
to
have
increased
productivity
.
Land
policy
and
law
enforcement
are
also
relevant
in
terms
of
reducing
the
negative
environmental
impact
of
road
infrastructure
.
Thank you!
Acknowledgements
This
Consultancy
Project
was
conducted
by
the
International
Center
for
Tropical
Agriculture
(CIAT),
the
Nature
Conservancy
(TNC)
,
and
the
Conservation
Biology
Institute
(CBI)
for
the
Environmental
and
Social
Safeguards
Unit
of
the
Inter
-
American
Development
Bank
.
This
project
was
supported
with
funds
from
the
German
Federal
Bundesministerium
fuer
wirtschaftliche
Zusammenarbeit
und
Entwicklung
(BMZ)
in
the
framework
of
a
cooperation
program
between
the
Inter
-
American
Development
Bank
(IDB)
and
the
Deutsche
Gesellschaft
fuer
Internationale
Zusammenarbeit
(GIZ)
.
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