Estimation Of Net Primary Production (Npp) Using Remote Sensing Approach And Plant Physiological Modeling(pendugaan Net Primary Production (Npp) Menggunakan Pendekatan Penginderaan Jauh dan Modeling Fisiologis Tanaman)

Yon Sugiarto • Tania June • Bambang Sapto P.
Journal article Agromet • December 2008

Download full text
(English, 17 pages)

Abstract

Information Net Primary Production (NPP) of tropical forests is important for the development of realistic global carbon budgets and for projecting how these ecosystems will be affected by climate changes. This research utilized remotely sensed data and micrometeorological measurement to provide information on vegetation condition. The objective of this research is to estimate spatial NPP using remote sensing approach and plant physiological/micrometeorological modeling. The estimation of NPP is conducted using modeling approach, which is based on relationship between radiation use efficiency, photosyntetically active radiation and fraction of absorbed photosynthetically active radiation by the plants's canopy. Trend of NDVI derived using micrometeorological measurement showed an increase from 2001 to 2002, and then decrease from 2002 to 2004. Average different values (delta) between both methods used to derive NDVI is relatively constant around 0.33 with a high correlation of r2 = 0.98. Using remotely sensed data, the highest NPP values estimated is in year 2003 with value range between 2000 – 2500 (gC m-2 yr-1), less than 2% of the whole forest area. In 2003, 75% area has NPP between 1500 – 2000 (gC m-2 yr-1), meanwhile for 2002 and 2004 it is only 21% and 50 %, respectively. NPP values estimated using micrometeorological measurement show the increasing of NPP values from 2002 to 2003, and then decrease from 2003 to 2004. There is strong correlation between NPP values derived from the two methods with r2 = 0.98.

Metrics

  • 96 views
  • 26 downloads

Journal

Agromet

Agromet publishes original research articles and literature reviews in the fields of agricultural... see more