Mapping Oil Palm Plantations Using WorldView-2 Satellite Imagery and Machine Learning Algorithms Article Swipe
Faradina Marzuki
,
Helmi Zulhaidi Mohd Shafri
,
Yuhao Ang
,
Nur Shafira Nisa Shaharum
,
Y P Lee
,
Shahrul Azman Bakar
,
Haryati Abidin
,
H. S. Lim
,
Rosni Abdullah
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1755-1315/1240/1/012013
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1755-1315/1240/1/012013
Currently, remote sensing has been used extensively in the agriculture industry for oil palm monitoring due to their large plantation area. Oil palm monitoring can be done by performing land cover classification using various classification methods and machine learning algorithms. This study was conducted to perform oil palm mapping using WorldView-2 satellite imagery and classify land cover features using machine learning algorithms such as Random Forest (RF) and Linear Support Vector Classifier (LSVC). A total of 58609 sampling points were classified into six classes which are water, built-up, bare soil, forest, mature oil palm (≥9 years) and young oil palm (3-8 years). The training and testing samples were split using 3-fold cross-validation. 67% of the total sampling points were used for training samples whereas the other 33% were used for testing samples. The methods used to validate the data in this study is by using spectral reflectance and Google Earth Pro. Accuracy assessment was conducted after obtaining the classification output such as Overall Accuracy (OA), Kappa Accuracy (KA), Precision, Recall and F1-score. As a result, the oil palm mapping using RF has a higher accuracy than LSVC which is 72.49% for OA and 62.98% for KA. The p -value obtained from the McNemar’s test conducted in this study is 0.683 (>0.05) which concludes that the predictive performance of the two models are equal.
Related Topics
Concepts
Random forest
Satellite imagery
Palm
Support vector machine
Land cover
McNemar's test
Artificial intelligence
Naive Bayes classifier
Machine learning
Palm oil
Computer science
Algorithm
Remote sensing
Mathematics
Environmental science
Statistics
Land use
Engineering
Agroforestry
Geography
Civil engineering
Physics
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1755-1315/1240/1/012013
- OA Status
- diamond
- Cited By
- 1
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386769480
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386769480Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1755-1315/1240/1/012013Digital Object Identifier
- Title
-
Mapping Oil Palm Plantations Using WorldView-2 Satellite Imagery and Machine Learning AlgorithmsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-01Full publication date if available
- Authors
-
Faradina Marzuki, Helmi Zulhaidi Mohd Shafri, Yuhao Ang, Nur Shafira Nisa Shaharum, Y P Lee, Shahrul Azman Bakar, Haryati Abidin, H. S. Lim, Rosni AbdullahList of authors in order
- Landing page
-
https://doi.org/10.1088/1755-1315/1240/1/012013Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1088/1755-1315/1240/1/012013Direct OA link when available
- Concepts
-
Random forest, Satellite imagery, Palm, Support vector machine, Land cover, McNemar's test, Artificial intelligence, Naive Bayes classifier, Machine learning, Palm oil, Computer science, Algorithm, Remote sensing, Mathematics, Environmental science, Statistics, Land use, Engineering, Agroforestry, Geography, Civil engineering, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- References (count)
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22Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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