HR: 0800h
AN: B11B-0152    [Abstracts]
TI: MONITORING THE PHILIPPINE FOREST COVER CHANGE USING NDVI PRODUCTS OF REMOTE SENSING DATA
AU: * Torres, R C
EM: torres@higp.hawaii.edu
AF: HIGP/SOEST University of Hawaii at Manoa, 1680 East-West Rd, Honolulu, HI 96822 United States
AU: Mouginis-Mark, P
EM: pmm@higp.hawaii.edu
AF: HIGP/SOEST University of Hawaii at Manoa, 1680 East-West Rd, Honolulu, HI 96822 United States
AU: Wright, R
EM: wright@higp.hawaii.edu
AF: HIGP/SOEST University of Hawaii at Manoa, 1680 East-West Rd, Honolulu, HI 96822 United States
AU: Garbeil, H
EM: harold@higp.hawaii.edu
AF: HIGP/SOEST University of Hawaii at Manoa, 1680 East-West Rd, Honolulu, HI 96822 United States
AU: Craig, B
EM: bcraig@higp.hawaii.edu
AF: HIGP/SOEST University of Hawaii at Manoa, 1680 East-West Rd, Honolulu, HI 96822 United States
AB: The Philippines has one of the world's fastest disappearing forest cover, which is being lost to natural processes and landscape-modifying human activities. Currently, forested landscape covers 24% (i.e., 7.2 million hectares) of the Philippines' total land area, of which only 800,000 hectares are considered as old-growth forests. Occasionally, volcanic activities and earthquakes cause large-scale impacts on the forest cover, but the systematic reduction of the country's forest has been sustained through unregulated logging operations and other human-induced landscape modification. Reforestation and watershed protection have become important public policy programs as forest denudation is linked to recent devastating landslides, debris flows and flashfloods. However, many watershed areas that are at risk to deforestation are hardly accessible to ground-based monitoring. A spaced-based monitoring system facilitates an efficient and timely response to changes in the quality and extent of the Philippine forest cover. This monitoring system relies in the generation of Normalized Difference Vegetation Index (NDVI) products from the red and infrared bands of remote sensing data, which correlates with the amount of chlorophyll in the vegetation. Given the existing forest classification maps, non-forested regions are masked in the data analysis, so that only forest-related changes in the vegetation are shown in the NDVI image difference products. A combination of two MODIS-bearing satellites, i.e., Terra and Aqua, acquire high temporal and moderate spatial resolution data, enabling the countrywide detection of vegetation changes within a certain observation period. MODIS data are calibrated for setting the pixel quality thresholds, which minimize the artifact of clouds and haze in the analysis. Areas showing dramatic changes are further investigated using higher resolution data, such as ASTER and Landsat 7 ETM. Sequential NDVI products of remote sensing data provide improved spatial information for the assessment of a natural disaster, warning of potential hazardous situations, detection of illegal forest-clearing activities and management of the reforestation effort.
DE: 1640 Remote sensing
DE: 0400 Biogeosciences
SC: Biogeosciences [B]
MN: 2004 AGU Fall Meeting