| タイトル |
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en
Predicting oil palm leaf nutrient contents in kalimantan, indonesia by measuring reflectance with a spectroradiometer
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| 作成者 |
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| アクセス権 |
open access |
| 権利情報 |
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en
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.
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| 主題 |
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Other
en
macronutrient
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Other
en
micronutrient
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Other
en
oil palm
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Other
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spectral
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Other
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reflectance
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Other
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spectroradiometer
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NDC
614
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| 内容注記 |
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Abstract
en
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.
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| 出版者 |
en
Taylor & Francis
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| 日付 |
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| 言語 |
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| 資源タイプ |
journal article |
| 出版タイプ |
AM |
| 資源識別子 |
HDL
http://hdl.handle.net/2115/75478
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| 関連 |
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isVersionOf
DOI
https://doi.org/10.1080/01431161.2018.1516323
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| 収録誌情報 |
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PISSN
0143-1161
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EISSN
1366-5901
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NCID
AA00234335
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en
International journal of remote sensing
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巻40
号19
開始ページ7581
終了ページ7602
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| ファイル |
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| コンテンツ更新日時 |
2023-07-26 |
