Soil and Land Management

Validating technologies for assessing and monitoring the impacts of re-wetting of peatland Indonesia using eddy flux towers coupled with the Chameleon sensors

Image
Chameleon sensors
Project code
SLAM/2020/118
Budget
AUD 250,000
Research program manager
Dr James Quilty
Project leader
Dr Samantha Grover, RMIT University
Duration:
SEP 2020
2021
FEB 2022
Project status
Legally committed/Active
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Overview

This project aims to empower communities and government to remotely and accurately monitor and evaluate peatland restoration, by integrating environmental data on peatland water and carbon fluxes with local decision-making structures. The interdisciplinary project proposes a combination of soil and ecological science with social science to ensure maximum benefit for communities and long-term useability.

Peatlands are increasingly being recognized as critical carbon stores in global climate change mitigation efforts. Peatland restoration has been identified as an effective way to reduce carbon emissions and increase carbon sequestration. Indonesia, containing the majority of the world’s tropical peatlands, has the opportunity to lead technical innovations in peatland restoration. The current drained state of much of Indonesia’s peatland estate renders it susceptible to fire, and the South East Asian Haze is caused by peat soil burning. National and international efforts to restore Indonesia’s vast degraded peatland estate continue to accelerate. 

Project outcomes

  • Continuing to monitor water, carbon dioxide and methane fluxes as a function of climate and hydrological conditions of a degraded peat forest using the eddy covariance flux tower and Chameleon sensor network installed in SLaM/2018/122.
  • Collaborating with community and local government stakeholders to socialise this approach, share initial data and explore uses and formats, via a series of workshops.
  • Identifying the knowledge and communication needs of community and government stakeholders for monitoring peatland restoration, to inform future design of a decision support tool.