Logging roads in the Amazon Basin: building process and modeling challenges Eugenio Arima Robert Walker Stephen Perz Marcellus Caldas Department of Geography Michigan State University III LBA SCIENTIFIC MEETING BRASILIA, 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 areaMaster 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 Trails HLT Photo: Imazon

Harvesting Logging Trails Roads we usually associated with a logging operation Harvest purposes Roads are abandoned after harvest If not disturbed again (2 nd 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 Roads Summary 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