Logging roads in the Amazon Basin: building process and

modeling challenges

Eugenio ArimaRobert WalkerStephen Perz

Marcellus Caldas

Department of Geography Michigan State University

III LBA SCIENTIFIC MEETINGBRASILIA, JULY 2004

Motivation

• Land cover change in tropical forests• The “Do roads cause deforestation” issue• Government-built roads• Private sector: loggers are very active in the

Amazon• Literature focus on impact of roads on landscape

and signature detection in satellite images• Little is known about how and why logging roads’

routes are chosen in the first place• Critical information to understand forest

fragmentation

Objectives of Presentation

• Present the process of logging road building

• Information needed to model logging roads

• Advances in modeling applied to a portion of the

Transamazonia region, Brazil

Fragmentation Pattern is a function of road network

Hierarchical Tier of Roads

1. Federal Road System

2. State/County roads

3. Settlement roads

4. Harvesting logging trails

3 & 4 built by private sector, usually loggers

The process of logging road building

Step 1. Define a destination• High-valued timber region• Or, very specific points such as

• Potential port• Farm

Step 2. “Estrada mestre” or master road linking the current infrastructure to destination

Logging area Master road

Stylized process of logging road building

Master logging road in Transamazonia

Photo: E. Arima

Master Logging Roads

• Permanent roads• Can provide access to land => frontier expansion• Become SETTLEMENT roads• Use: transportation at large (not only of logs),

access ports, terras devolutas• Length: hundreds of kilometers• Easily detectable in satellite images by visual

interpretation

The process of logging road building

Step 3: Once the master road is in place,Build logging trails to reach trees

A logging trail may become another master road if more timber is found

Harvesting Logging TrailsHLT

Photo: Imazon

Harvesting Logging Trails

• Roads we usually associated with a logging

operation

• Harvest purposes

• Roads are abandoned after harvest

• If not disturbed again (2nd harvest, fire) forest

can recover fast

• Fine resolution

Modeling Challenges

• Harvest logging trails• Settlement logging roads (master roads)

Fine scale harvesting site - Acre, Brazil

Scale in Meters!Cell resolution: 1m

Data kindly provided by J. Grogan & D. Valle (IMAZON, Brazil)

GIS least cost path solution

Problem: Parallel network

“Blend Model” Tomlin’s & Spanning Tree

Harvesting Logging Trails

Information Needed:• Tree distribution• High resolution DEM

Assumption:• Cost minimization

Challenge:• Algorithm• A true minimum Steiner Tree solution yet to be implemented (NP-hard problem though)

Examples of settlement logging roads in Transamazon

Destination indeterminate - a simulation

Usually, need origin and destinations to model paths• Assume uniform distribution of trees and capital constraint• Then, can find paths that maximize profits

Destination determinate Roads

Objective: access any portion of the Tutui River

Even when we include destination, real path not replicated

“Easier” to model GISwise

Very specific destination

Access to chapadao

Upper part of river is easier to cross

Original route

Least path problem complicates fast…

3D rendition of chapadao (SRTM)

Chapadao

Correct functional form for cost=f(slope) is crucial in determining the route

• Loggers prefer chapadoes: large trees are sparse, potential agricultural area• Baixadas are avoided: landfill needed• Grotas (along rivers): leveled area but tree density is higher

Regional Scale Logging RoadsSummary

Information needed: Spatial Objectives of Loggers• How choice of destination is made?• What are the social interactions and institutional

constraints that determine detours in routes? (NPP)• How slope is translated into construction and

transportation costs?• Algorithm is not a major constraint, GIS LCP works• Challenge is to model the constraints to route or

necessary points of passage (endogenous variables)• Discrepancy between scales: landscape data and

information used by loggers to make route decisions.

Conclusions

• Local scale logging roads: computational challenge• Regional scale logging roads: data challenge

• Social• Economic• Micro political-economy• Implicit spatial objectives• Empirical, field work is necessary