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

Tri Mulyadi and Hariyadi and Sudrajat and Kustiyo (2017) Nitrogen Content and Carbon Stock Prediction in Oil Palm using Satellite Image Analysis. Asian Journal of Applied Sciences, 5 (4). pp. 677-683. ISSN 2321 – 0893

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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.

Item Type: Article
Uncontrolled Keywords: satellite imagery, vegetation index, nitrogen, carbon stock
Subjects: Teknologi Penginderaan Jauh > Pengelolaan dan Pengembangan > Citra Satelit
Divisions: Deputi Penginderaan Jauh > Pusat Teknologi dan Data Penginderaan Jauh
Depositing User: Dinar Indrasasi
Date Deposited: 09 Feb 2021 08:12
Last Modified: 09 Feb 2021 08:12

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