Difficulties in detecting and measuring forest degradation could finally be overcome through integrating multiple readily available sources of data. While remote sensing (RS) data remains to be the primary material for forest degradation assessment, other sources of data must be explored to enhance detection and estimates. The usual RS approaches may not work successfully in forest degradation measurement because of poor discrimination of subtle changes that occur in forest such as change in carbon stock or aboveground biomass.
By using tools such as Google Earth and complementing these with local knowledge, Prof. Arvin P. Vallesteros, a faculty member of the Nueva Vizcaya State University, has developed a simple yet reliable method that would not solely depend on RS data. He reported his initial findings toward his dissertation during SEARCA’s Agriculture and Development Seminar on 10 May 2011.
In his study, Prof. Vallesteros compared two approaches to determine aboveground biomass (AGB) estimates, one of the indicators of forest degradation. Comparisons made were based on the accuracy levels of the approaches. Brown’s formula was used in estimating the AGB for both approaches.
The first approach classified land cover into different classes (Fc1, closed canopy forest; Fc2, relatively open to relatively closed canopy forests; Fc3, low-density forest; Grass-Brush-Cultivated, or GBC; and rice paddies and water, or RW) by specified thresholds [of certain spectral responses] through the vecter technique, which was introduced in the research. Analysis of the data is done using GIS software, which people are more familiar with than RS software.
Local knowledge was also needed for the first approach, as some datasets had no available field or inventory data. Focus group discussions were especially important in drawing out local knowledge.
In this approach, class movement could also be noted and patterns could be seen. From 1989 to 2010, it was observed that closed canopy forests declined. On the other hand, Fc2 net loss was relatively small as forest loss was compensated by forest regrowth mainly via abandonment of kaingin or long fallow. Both Fc3 and GBC increased. An increase in Gmelina plantations caused this rise in Fc3, while forest clearing for cultivation increased the GBC.
For the second approach, linear regression was used in estimating AGB values. Spectral data served as the independent variables. Different models, which gave different AGB estimates, were developed throughout the course of the study, and best models were identified using stepwise regression.
The findings indicated that if measures of model performance other than stepwise regression were used, the resulting best models may be different from those of the study. Also, the estimates of total AGB produced using the two approaches are comparable.
Prof. Vallesteros conducted his research in Brgy. Maasin, Quezon, Nueva Vizcaya. Ninety percent of the land is considered public. There are programs being held to improve forest cover, as much of it is being cleared for agriculture. (Amy Christine S. Cruz)