Project Overview

Starting from September 2021, The Conservancy Association (CA) and School of Biological Sciences, The University of Hong Kong (HKU) launched the "Forest Carbon Stock Estimation in Hong Kong" project (the Project). The Project has received a 3-year (2021-24) funding support from The Hongkong Bank Foundation.

 

Carbon dioxide is the main greenhouse gas leading to climate change and scientists around the globe have been looking for methods to reduce carbon dioxide emissions and increase carbon dioxide removal (CDR) or negative emissions. It is believed that forests are effective in carbon sequestration.
 

Anthropogenic-induced global warming was around 1 °C above pre-industrial levels in 2017 and is increasing by around 0.2 °C per decade. The majority of countries in the world (195 nations) signed the Paris Agreement in December 2015 agreeing to hold the global average temperature increase to substantially below 2 °C and investing in climate change mitigations in limiting the temperature increase to 1.5 °C of pre-industrial levels.
 

The recent IPCC special report Global Warming of 1.5 °C has identified forest conservation, forest management, reforestation and afforestation as important strategies in achieving CDR which is also bringing positive impacts on nutrients, biodiversity, and ecosystem services and is low in energy requirement in comparison with other strategies such as bioenergy with carbon capture and storage.
 

Accurate estimation of forest carbon stocks and stock changes are important in formulating long-term strategies in climate mitigation mitigations. They are important to local and global climate change mitigation policies. Allometric equations or models are widely used to estimate the above-ground biomass (AGB) of forest trees. However, the accuracy of these models depends on the availability of accurate field data. Accurate local data and models contribute to accurate regional and global carbon stock assessments but it is not available now.
 

Meanwhile, the project would also involve remote sensing methods in forest mapping using laser technology at the same time. Using airborne Light Detection and Ranging (LiDAR), data could be collected quickly in large areas to enable large-scale estimation of AGB at the landscape level.
 

Key Objectives

 

With the foundation laid by the ForestGEO plots and other forest and reforestation studies in Hong Kong, this study is set out to:

 

1. Evaluate the existing allometric models/ equations for aboveground biomass estimation and identify the most appropriate ones for various forest types in Hong Kong using field data collected in this project;
    

2. Estimate the forest carbon stock of different forest types in Hong Kong using the most appropriate allometric models and field data collected in this project; Develop a carbon calculator for existing forests and reforestation projects for public education on climate change and carbon sequestration;
    

3. Test and refine the effectiveness of remote sensing methods using Drone LiDAR and/ or terrestrial laser scanning in estimating AGB in Hong Kong. This innovative approach is a pilot test in Hong Kong for extended application to the whole of South China (see next objective);
    

4. A potential Phase II objective of this study is to integrate the other ForestGEO plots data in South China including Jianfengling in Hainan, and Dinghushan and Heishiding in Guangdong to calibrate a regional allometric model for forest carbon estimation across vegetation types in South China. With a much larger geographical area, it would help more accurately to assess the potential of reducing deforestation, forest management, reforestation, and afforestation in South China for climate change mitigation, which would contribute to meeting China’s carbon-neutral target by 2060.