open access

https://hdl.handle.net/2115/75478

Predicting oil palm leaf nutrient contents in kalimantan, indonesia by measuring reflectance with a spectroradiometer

英語

spectroradiometer

2019-10-02

Taylor & Francis

International journal of remote sensing

40(19)

7581-7602

Leaf nutrients are needed for oil palm growth and production, and the nutrient contents of oil palm leaves can be determined by the chemical analyses of the number 9 and 17 leaves for young and adult palms, respectively. However, the accurate selection of the proper leaf for sampling is problematic. Remote sensing techniques based on the reflectance values of leaves may easily monitor leaf nutrients in oil palm plantations. We studied leaf nutrient contents using spectral reflectance data to determine suitable wavelengths for predicting the contents of the most important leaf nutrients: nitrogen, phosphorus, potassium, calcium, magnesium, boron, copper, and zinc. The samples were taken from one oil palm plantation in Pundu, Central Kalimantan, Indonesia. The proposed vegetative indices, several common vegetative indices, and a stepwise regression that continued with a principal component regression were used to build models for predicting leaf nutrient contents. The proposed vegetative indices performed better than the common vegetative indices. For each of the leaf nutrients, models that included all of the significant variables from the stepwise regression and continued with principal component regression from the ultraviolet A and green to far red wavelength groups had better performance levels than models that included individually selected variables selected from each wavelength group. For total leaf nutrient content predictions, variables from the green wavelength group were always selected and contributed more to the models than any other group. Thus, our proposed vegetative indices and multivariate model may be used to predict leaf nutrient contents in oil palm plantations.

学術雑誌論文

Accepted Manuscript

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal International Journal of Remote Sensing on 20 Sep 2018, available online: http://www.tandfonline.com/10.1080/01431161.2018.1516323.

0143-1161

1366-5901

AA00234335