Shiva Khanal

Candidature

PhD Candidate

Thesis Title

Objective quantification of national forest cover and carbon stocks in support of the REDD+ Programme - a methodological study combining remote sensing, forest inventory data and statistical modelling

Research Project

Deforestation and forest degradation cause carbon emissions while sustainable management of forests can increase carbon sequestration. The Reducing Emissions from Deforestation and Forest Degradation (REDD+) scheme was proposed as a cost-effective approach for developing countries to reduce carbon emissions through a financial incentive mechanism. A reference level (expressed as tonnes of CO2 equivalent per year for a reference period) provides a benchmark to evaluate future emission pathways for countries. Transparent, rigorous, and scientifically sound methods are required to establish forest reference levels and measurement, and a reporting and verification (MRV) system for monitoring changes in carbon stocks. Accurate monitoring of forest carbon stocks is a fundamental requirement for REDD+ but has been a critical challenge for many REDD+ countries.

Using Nepal as a case study, I aim to improve the estimates of national forest carbon stocks by modelling spatiotemporal variation in forest carbon and its drivers. I will be using field datasets collected as part of the national forest resource assessment of Nepal. The general approach for the study involves integration of field observation with spatial datasets such as satellite images, terrain and gridded climate data using models.

Species composition, human intervention/disturbance, topography and climate are recognized as the major drivers of the magnitude, spatial distribution, and uncertainty in estimates of forest carbon stocks.  I aim to understand how these sources of uncertainty can be minimized to improve the quantification of national scale forest carbon stocks.  Integrated understanding of forest carbon dynamics requires consideration of key carbon pools.  As compared to forest above ground biomass (AGB), the soil organic carbon (SOC) pool is often ignored in reporting.  I aim to provide the most accurate estimate of national forest SOC stocks and examine the environmental controls through spatially-explicit prediction of SOC using field sample of SOC stocks and environmental co-variables.  The research outputs will help better understand the variability in forest carbon in general and, importantly, be directly relevant to supporting improvements in forest carbon monitoring approaches for REDD+ countries.

Supervisors

A/Professor Matthias Boer, Professor Belinda Medlyn, Dr Rachael Nolan