Abstract
Developing countries that intend to implement the United Nations REDD-plus (Reducing Emissions from Deforestation and forest Degradation, and the role of forest conservation, sustainable management of forests, and enhancement of forest carbon stocks) framework and obtain economic incentives are required to estimate changes in forest carbon stocks based on the IPCC guidelines. In this study, we developed a method to support REDD-plus implementation by estimating tropical forest aboveground biomass (AGB) by combining airborne LiDAR with very-high-spatial-resolution satellite data. We acquired QuickBird satellite images of Kampong Thom, Cambodia in 2011 and airborne LiDAR measurements in some parts of the same area. After haze reduction and atmospheric correction of the satellite data, we calibrated reflectance values from the mean reflectance of the objects (obtained by segmentation from areas of overlap between dates) to reduce the effects of the observation angle and solar elevation. Then, we performed object-based classification using the satellite data (overall accuracy = 77.0%, versus 92.9% for distinguishing forest from non-forest land). We used a two-step method to estimate AGB and map it in a tropical environment in Cambodia. First, we created a multiple-regression model to estimate AGB from the LiDAR data and plotted field-surveyed AGB values against AGB values predicted by the LiDAR-based model (R2 = 0.90, RMSE = 38.7 Mg/ha), and calculated reflectance values in each band of the satellite data for the analyzed objects. Then, we created a multiple-regression model using AGB predicted by the LiDAR-based model as the dependent variable and the mean and standard deviation of the reflectance values in each band of the satellite data as the explanatory variables (R2 = 0.73, RMSE = 42.8 Mg/ha). We calculated AGB of all objects, divided the results into density classes, and mapped the resulting AGB distribution. Our results suggest that this approach can provide the forest carbon stock per unit area values required to support REDD-plus.
Highlights
Current CO2 emissions from the forestry sector are mainly due to deforestation in developing countries, exceeding 10% of total anthropogenic emissions, which is the major cause of climate change [1]
The REDD-plus (Reducing Emissions from Deforestation and forest Degradation, and the role of forest conservation, sustainable management of forests, and enhancement of forest carbon stocks) framework has been developed under the UN Framework Convention on Climate Change (UNFCCC) to reduce emissions in developing countries
The UNFCCC requests that forest monitoring at a national level should follow the IPCC guidelines [2] to quantify the changes in the amount of carbon stored in forests [3]
Summary
Current CO2 emissions from the forestry sector are mainly due to deforestation in developing countries, exceeding 10% of total anthropogenic emissions, which is the major cause of climate change [1]. According to the IPCC guidelines, emissions can be estimated by multiplying the area of land use change (activity data) by the corresponding change in carbon stock per unit area (emission factor). It is expected that the area of land use change is identified from remote sensing data and carbon stock per unit area is estimated from ground-based inventory data. A systematic field inventory program to obtain the emission factors is difficult to implement for developing countries because of limited resources and inaccessibility to forests. It is difficult to establish the expertise required to design field surveys needed for reliable data These countries prefer to use an alternative method for obtaining the data required to estimate the forest carbon stock per unit area so that they can use this data to calculate emission factors under the REDD-plus framework
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