Nitrogen Content and Carbon Stock Prediction in Oil Palm using Satellite Image Analysis

Nitrogen content and carbon stock prediction method using satellite image analysis is inexpensive,
time-saving, labor-saving and accurate. This research aimed to predict nitrogen content and carbon stock on oil palm using satellite imagery. The research was conducted at IPB-Cargill Teaching Farm of Oil Palm, Jonggol, Bogor
Indonesia, starting from August until October 2016. Landsat 8 satellite imagery was used in this research with the digital number classes 17,000 – 22,000. Leaf nitrogen content observed in the field, analysed using Kjeldahl digestion method. Carbon stock was obtained using allometric method (above ground biomass =0.0976*Height+0.0706).
Sampling of the leaves frond number 17 and plant height of oil palm plant respectively was 25 samples. Prediction
model used weighted least square regression between the actual nitrogen content and the digital number, band
reflectance, vegetation index. The same model was used to estimate carbon stocks. The result showed that the best
model to estimate nitrogen content using the band reflectance with R2 = 0.964, and vegetation index with R2
= 0.987.
The best model for estimate carbon stocks using digital number with R2 = 0.874, band reflectance, with R2
= 0.856,and vegetation index with R2 = 0998. There was similarity between actual measurement and model for predicting nitrogen content and carbon stocks.