Yield estimation comparison of oil palm based on plant density coefficient variation index using spot-6 imagery in part of Riau

Abstract

Oil palm plantations consist of diverse plant density level that influence the appearance of soil surface or commonly in remote sensing terms called as soil background.Choosing the right density coefficient of vegetation transformation can decrease the noise of soil background for estimating oil palm yield.This research aims 1) to examine the accuracy of SPOT-6 to identify the oil palm l plant growth level and to estimate their yield 2) to know the variation of oil palm yield based on SAVI index vegetation using different density coefficient; and 3) to determine the best density coefficient to estimate the yield of oil palm.This research was held in part of Air Molek, Indragiri Hulu Regency, Riau, one of the largest oil palm plantations in Indonesia.This research method utilises SAVI transformation with density coefficient L-0, L-0.5, and L-1, and regression statistics analysis.The land-cover primary data is derived from SPOT-6 imagery archived in 13 rd June 2013.The field survey was conducted in the same month of image's acquisition time and 120 sample areas were taken during that time.Two steps of regression analyses were applied to see the correlation between, first, vegetation index value and oil palm plant; and second, oil palm plant, vegetation index values, and oil palm yield from field observation.These steps produced a model to estimate the oil palm yield based on the index values of yield and vegetation, and the productivity estimation.The result shows that SPOT-6 imagery has 96% accuracy level which is considered high for identifying the oil palm variation.The R value for L-0 density coefficient is 0.8, for L-0.5 is 0.81 whereas for L-1 is 0.82.The best plant's density coefficient for estimating oil palm yield/yield is L-0 with yield estimation accuracy of 83.33%.

Plant phenotyping relevance

SPOT-6画像とSAVI係数を比較し、油ヤシの生育状態・収量推定精度を検証する手法研究であり、植物形質の取得・推定が中心です。

Code and data availability

The paper describes SPOT-6 imagery, field yield data, and regression analysis, but provides no public phenotype dataset, imagery deposit, code, or model release. The LAPAN URL is cited only as a reference for radiometric correction guidance, not as a public deposit of this paper's data or analysis.

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