QUANTIFYING FOREST COVER CHANGES IN THE PHILIPPINES FROM 2000-2012 FROM LANDSAT-DERIVED GLOBAL FOREST COVER DATASET

摘要

Updated information of forest resources is vital in proper management and policy formulation from local to national scale. However, data generation regarding forest maps and statistics in the Philippines is hampered by multiple imagery sources, varying scale and resolutions, undisclosed methods of mapping and different forest classes and definitions used. In this study, processed Landsat forest cover data was analyzed to quantitatively measure the rates of forest loss on the regional level, to analyze its level of applicability and to validate forest changes through various geospatial and other forestry data. Raw data consists of forest tree cover, loss, gain and loss year layers. Five forest vectors were used to extract forest areas from the main data rasters before subsetting them regionally. A forest tree cover threshold of 85 percent and above was used to isolate densely forested rasters. These rasters were further analyzed to produce gain, loss and aggregate net change ranking; annual forest loss and annual forest cover trends, with their corresponding maps and statistics. Among the regions, Zamboanga Peninsula had the highest aggregate net change (-7.18%) followed by CALABARZON (-3.41%) and CARAGA (-3.16%) from 2000-2012. CARAGA had the highest average annual forest loss of -147.53 ha and a correlation of 0.7. On the other hand, Zamboanga and CALABARZON had average annual forest loss which were less than hundred hectares (-73.10 ha and -31.4 ha; r = 0.34 and r = 0.13, respectively). However, only CARAGA had a significant correlation, with p value ＜ 0.05 (0.0098). In terms of annual forest tree cover, CARAGA had the highest average annual forest cover increment of -1,238.16 ha, followed by Zamboanga Peninsula (-830.12 ha) and CALABARZON (-527.61 ha). Forest cover increments exhibited negative values since forest recovery is always outweighed by forest extraction. Error matrix of loss, gain and no change registered an overall accuracy of 86.48% and 92.26%, for loss and gain respectively. Yet, the assessment had lower user accuracy values compared to that of producers'. This might be affected by inadequate high resolution images (mainly Google Earth and Landsat) used in the satellite based validation. The research showed that freely available forest global datasets could be used to pinpoint significant areas where forest loss is occurring. Correlation analysis with other forestry data and field validation through group discussions and interviews may also illustrate insights regarding the drivers of forest loss on a particular area.