Reforestation Management to Prevent Ecosystem Collapse in Stochastic Deforestation

The increasing rate of deforestation, which began decades ago, has significantly impacted on ecosystem services. In this context, secondary forests have emerged as crucial elements in mitigating environmental degradation and restoration. This study is motivated by the need to understand the reforestation management in secondary forests to prevent irreversible ecosystem damage. We begin by setting the drift and volatility in stochastic primary forests. However, it is more manageable to take control of replantation. We employ a dynamic programing approach, integrating ecological and economic perspectives to assess ecosystem services. To simulate a real-world case, we investigate the model in the Brazil Amazon Basin. Special attention is given to the outcome at the turning point, tipping point, and transition point, considering a critical threshold beyond which recovery becomes implausible. Our findings suggest that reducing tenure costs has advantages, while substitution between primary and secondary forests is not necessarily effective in postponing ecosystem collapse. This research contributes to a broader goal of sustainable forest management and offers strategic guidance for future reforestation initiatives in the Amazon Basin and similar ecosystems worldwide.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Forestry

Department dc:contributor.department

Forestry

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Chong, Fayu

Chairs dc:contributor.committeechair

• Amacher, Gregory S.
• Cobourn, Kelly M.

Committee member dc:contributor.committeemember

• Sullivan, Jay

• deforestation
• reforestation
• ecosystem collapse
• stochastic dynamic programming
• tenure cost

Statement dc:rights

• Attribution-NonCommercial 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nc/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/10919/119119

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/119119