Periodic Reporting for period 1 - QUAFORD (Towards a Worldwide Quantification of Forest Degradation)
Reporting period: 2017-09-01 to 2019-08-31
The above-mentioned problems imply that the potential of forests to sequester carbon via reduction in forest degradation is unknown, as is the potential contribution of forests to mitigating climate change. Moreover, because the currently used methods apply the absolute values of forest degradation indicators, the extent of forest degradation cannot be compared in biomes in which the values of the indicator used are very different. This is a very serious problem, as restoration priorities for different types of forest or biomes cannot be defined. These deficiencies reduce the credibility of the process of estimating forest degradation and represent a serious problem regarding the demand for transparent, verifiable and reproducible methods of quantifying forest degradation – a demand brought about by the increasing awareness of climate change in society.
The objective of the QUAFORD project was to develop a new methodology for estimating the occurrence and extent of forest degradation based on information obtained in a single inventory. The proposed methods consider the maximum biomass achievable in a given site at maturity as a reference value for comparison with the actual biomass stocks.
Within the QUAFORD project, we evaluated the performance of the models required to apply the proposed methodology, by estimating site quality and potential biomass stocks in forest stands where past management is known. We observed that the performance of the methodology was biologically consistent for predicting the variables of interest, and the model developed can be fitted robustly with data from a single inventory. We studied collections of plots in even aged monocultures known to be at maximum stocking capacity, in order to determine the feasibility of modelling the maximum biomass stock of a given species as a function of site productivity. We observed that this relationship can easily be modelled with this source of information. However, as these biometric relationships are more difficult to model in natural forests, we established a series of plots in natural, mature Fagus sylvatica forests, which have not recently been perturbed, across Spain. We observed the existence of a strong relationship between biomass at maturity and site conditions in the plots.
Finally, we evaluated the effect on the models of having only a probabilistic based sample in which the degree of perturbation of each plot is unknown. For this purpose, we compiled the most recent National Forest Inventory data for the Fagus sylvatica plots for the whole of Spain. We observed a trend in the outermost point cloud for site productivity and current biomass stock values that can potentially be modelled by edge-fitting techniques. We compared the results with those obtained for a series of plots characterized by maximum stocking conditions, and we concluded that both sources of information yielded similar findings. This led us to conclude that probabilistic based samples are valid for applying the proposed MSC approach.
Although when we wrote the proposal we expected to be able to define thresholds for the index that would enable us to determine whether or not a given stand was degraded, the reduction in biomass stocks relative to the value that can be reached at maturity may also be due to the application of sustainable management. Therefore, the only way that the proposed index can distinguish forest management from degradation is by characterizing the average value across a series of plots established in a managed area. For national scale assessments, the value of the index is informative for a single assessment about the carbon content of a forest, when all plots are analysed together. If the managed areas are mapped and removed from the analysis, the index provides information about the extent of degradation of the forests. In the final activity of the QUAFORD project, we applied the new methodology to National Forest Inventory data from 11 countries, provided by the FAO.