No 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 (R 2 = 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 (R 2 = 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.

This article discusses the importance of quality deforestation area estimates for reliable and credible REDD+ monitoring and reporting. It discusses how countries can make use of global spatial tree cover change assessments, but how considerable additional efforts are required to translate these into national deforestation estimates. The article illustrates the relevance of countries’ continued efforts on improving data quality for REDD+ monitoring by looking at Mexico, Cambodia, and Ghana. The experience in these countries show differences between deforestation areas assessed directly from maps and improved sample-based deforestation area estimates, highlighting significant changes in both magnitude and trend of assessed deforestation from both methods. Forests play an important role in achieving the goals of the Paris Agreement, and therefore the ability of countries to accurately measure greenhouse gases from forests is critical. Continued efforts by countries are needed to produce credible and reliable data. Supporting countries to continually increase the quality of deforestation area estimates will also support more efficient allocation of finance that rewards REDD+ results-based payments.

No abstract

No abstract