A review of remote sensing applications for oil palm studies

Figures & data

Figure 1. Worldwide consumption of vegetable oil.

Figure 2. Historical oil palm area. Source: MPOC (2014); BPS (2014).

Note: Area data is not available for the years 2001, 2002, and 2003 for Malaysia.

Figure 3. (a) Planting pattern for an oil palm plantation; (b) Worldview-2 satellite image of oil palm plantation owned by the Malaysian Palm Oil Board in Kluang, Johor (South of Peninsular Malaysia) taken on 23 March 2015 with spatial resolution of 0.5 m.

Figure 4. (a) Landsat 8 image taken in Selangor in 2010 (scale 1: 30,000) (USGS 2010); (b) Oil palm classified using multi-sensory approach: Landsat 8 and ALOS PALSAR (oil palm indicated in yellow) (JAXA/METI 2010).

Table 1. Relationship of biophysical parameters with age of oil palm using field measured data.

Biophysical parameter	Relationship with age and its coefficient of determination R^2	Saturation/stabilization	Reference
LAI	LAI = 0.000 × (age)^3 – 0.030 × (age)^2 + 0.714 × age – 0.892; R^2 = 0.96	10 years	Tan, Kanniah, and Cracknell (2013)
CPA	Age = 0.59 + 0.15 × CPA(m^2); R^2 = 0.88	13 years	Chemura, van Duren, and van Leeuwen (2015)
Height	Age = Height × 2.0325; R^2 = 0.90	Unknown	Tan, Kanniah, and Cracknell (2013)

Table 2. AGB allometric equation for oil palms.

Allometric equation	Reference
W = 725 + 197H	Khalid, Zin, and Anderson (1999)
where W = fresh weight (kg); H = palm height (m).
Wet weight (kg/tree) = 1.5729 × palm stem height (cm) – 8.2835 (R^2 = 0.9746)	Thenkabail et al. (2004)
Dry weight (kg/tree) = 0.3747 × palm stem height (cm) + 3.6334 (R^2 = 0.9804)
AGBtrunk = 100 π (rz)^2 hρ	Corley and Tinker (2008)
where ρ = (0.0076x + 0.083)/100; r = radius of the trunk (cm) without frond bases;
z = ratio of trunk diameter below frond bases and measured diameter above frond bases (sample trunks = 0.777); h = height of the trunk (m); ρ = trunk density (kg m^−3); x = age of the oil palm.
AGBfrond = 0.102ld + 0.21
where l = length of petiole (cm); d = depth of petiole (cm)
Standing biomass (tonne/ha)= –0.00020823 × x^4 + 0.000153744 × x^3 − 0.011636 × x^2 + 7.3219x − 6.3934	Henson and Chang (2003)
where x = age of plantation in years

Table 3. Relationship of remotely sensed data with AGB of oil palm.

Source of data and its parameters	Regression model	Source
IKONOS data	Dry biomass (kg m^−2) = 0.0046e^10.814 × NDVI43;	Thenkabail et al. (2004)
NDVI and optical band reflectance (Band 3 and 4)	Dry biomass (kg m^−2) = 1499.3e^−66.64 × band 3 reflectance;
	Dry biomass (kg m^−2) = 1595e^−0.0338 × band 3 digital number;
	(Note: NDVI43 = Normalized value of band 4 and 3);
	Accuracy = 64% – 72%
SPOT 5 data	Ba=106.37 × (Band 1) – 33.72 × (Band2) + 124.33 × (Band 3) + 40.73 × (Band 4) – 130.71;	Singh, Malhi, and Bhagwat (2014b)
Vegetation indices and optical band reflectance	Ba = –2175.4(Band 4) + 2628.3;
	Ba =  – 2776(Band 3) + 2817.4;
	(Note: Ba is the mean biomass in Mg/ha; Goodness of fit, R^2 = 0.851, 0.833, and 0.800, respectively
SPOT 5 data	AGB (Mg/ha) =  –4773.26 × (PC1) + 5171 × (PC2) – 1817.546 × (PC3) + 61,036.76	Singh, Malhi, and Bhagwat (2014a)
Fourier transform textural ordination (FOTO)	(Note: PC is the principle component ranking in order of 1, 2, and 3; Goodness of fit R^2 = 0.830)
Landsat ETM+ data	AGB (Mg/ha) = 0.45(44/12)e^((s1 − 0.5877) / 0.029436)	Morel, Fisher, and Malhi (2012)
Vegetation indices, best performing: NFDI	(Note: s1 is the shade, one of the endmembers to calculate NDFI; Goodness of fit R^2 = 0.800

Note:

NFDI − Normalized Difference Fraction Index.

Figure 5. Actual fresh fruit bunches yield across the age of oil palms (Ismail and Mamat 2002).

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