The use of Landsat-8 and Sentinel-2 imageries in detecting and mapping rubber trees

Yusof, Norbazlan Mohd; Shafri, Helmi Zulhaidi Mohd; Shaharum, Nur Shafira Nisa

doi:10.1007/s42464-020-00078-0

Cited by 5 publications

(5 citation statements)

References 36 publications

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“…Built-up and bare soil also have a higher score for all evaluation metrics compared the other classes such as forest, mature oil palm and young oil palm. This is because these vegetations have almost similar spectral reflectance which leads to misclassification [5]. In future research, improvements can be made to increase the precision of land cover features having almost similar spectral signature such as forest, mature oil palm and young oil palm.…”

Section: Classification Resultsmentioning

confidence: 99%

“…Several studies have utilized remote sensing to perform land cover classification. A researcher found out that Support Vector Machine (SVM) classifier produced a higher Overall Accuracy (OA) than Artificial Neural Network (ANN) and Spectral Angle Mapper (SAM) for both Landsat-8 as well as Sentinel-2 satellite data [5]. It is supported by another study [6] at which it is discovered that SVM has a higher OA (84.7%) than LR (82.97%), RF (83.91%), ML (83.01).…”

Section: Introductionmentioning

confidence: 99%

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Mapping Oil Palm Plantations Using WorldView-2 Satellite Imagery and Machine Learning Algorithms

Marzuki,

Shafri,

Ang

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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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.

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Section: Classification Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping Oil Palm Plantations Using WorldView-2 Satellite Imagery and Machine Learning Algorithms

Marzuki,

Shafri,

Ang

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…Mishra et al (2017) compared MLC, RF, SVM, and ANN in classifying Dual-polarimetric C-band SAR data and found that RF and SVM produced the best results. Yusof et al (2021) found that ANN yielded the worst results in their research for evaluating SVM, SAM, and ANN for classifying Landsat-8 and Sentinel-2 imageries (Dixit & Agarwal, 2020). Ambinakudige & Intsiful (2022) implemented SVM and ANN using hyperspectral data , they found that SVM producing superior outcomes to ANN (Alshari et al,2022).…”

Section: Random Forestmentioning

confidence: 99%

Assessment of Random Forest and Neural Network for Improving Land Use/ Land Cover Mapping from LIDAR Data and RGB Image: A Case Study of Magaga-El-Menia Governorate, Egypt

Taha,

Mandouh

2024

Geoplanning J. Geomatics Plann.

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The goals of this article are to improve classification of land use/land cover information using LIDAR data and RGB images, as well as to compare the performance of various supervised machine learning classifiers (random forest and neural network) for extracting land use/land cover information. The 3D coordinates are first transferred to a high-resolution raster via interpolation. Height and intensity raster grids are formed. Second, various raster maps - a normalized digital surface model (nDSM), the difference of returns, and the LiDAR intensity image -are combined to create a multi-channel image. Five scenarios with different combinations were created. Finally, on the five separate datasets, several classifications based on random forest and neural network classifiers were performed. The classification findings were subjected to a quantitative accuracy check. A comparison of these five methodologies has been conducted. Following that, morphological operations were used to eliminate noise. The results revealed also that the fourth approach is the best followed by the third approach then the last approach then the second approach followed by the first approach. It was discovered that random forest classification outperforms neural network classification in terms of classification accuracy.

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“…This study covered the literature review about ANN classifier from previous studies as follow: (Mishra et al, 2017;Kadavi and Lee, 2018;Dibs et al, 2020;Dixit and Agarwal, 2020;Ekumah et al, 2020;Hamad, 2020;Kaya and Görgün, 2020;MohanRajan et al, 2020;Navin and Agilandeeswari, 2020;Rojas et al, 2020;Saddique et al, 2020;Xu et al, 2020;Angessa et al, 2021;Bhattacharya et al, 2021;Dede et al, 2021;Ghayour et al, 2021;Sang et al, 2021;Xie et al, 2021;Yusof et al, 2021;Ambinakudige and Intsiful, 2022;Fantinel et al, 2022;Gogumalla et al, 2022;Rizvon and Jayakumar, 2022;Theres and Selvakumar, 2022).…”

Section: Ann Classifiermentioning

confidence: 99%

“…In their research using SVM, SAM, and ANN in Yusof et al (2021), indicated that ANN produced the worst results (Dixit and Agarwal, 2020).…”

Section: Ann Classifiermentioning

confidence: 99%

Classification of land use/land cover using artificial intelligence (ANN-RF)

Alshari

Abdulkareem

Gawali

2023

Front. Artif. Intell.

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Because deep learning has various downsides, such as complexity, expense, and the need to wait longer for results, this creates a significant incentive and impetus to invent and adopt the notion of developing machine learning because it is simple. This study intended to increase the accuracy of machine-learning approaches for land use/land cover classification using Sentinel-2A, and Landsat-8 satellites. This study aimed to implement a proposed method, neural-based with object-based, to produce a model addressed by artificial neural networks (limited parameters) with random forest (hyperparameter) called ANN_RF. This study used multispectral satellite images (Sentinel-2A and Landsat-8) and a normalized digital elevation model as input datasets for the Sana'a city map of 2016. The results showed that the accuracy of the proposed model (ANN_RF) is better than the ANN classifier with the Sentinel-2A and Landsat-8 satellites individually, which may contribute to the development of machine learning through newer researchers and specialists; it also conventionally developed traditional artificial neural networks with seven to ten layers but with access to 1,000's and millions of simulated neurons without resorting to deep learning techniques (ANN_RF).

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The use of Landsat-8 and Sentinel-2 imageries in detecting and mapping rubber trees

Cited by 5 publications

References 36 publications

Mapping Oil Palm Plantations Using WorldView-2 Satellite Imagery and Machine Learning Algorithms

Mapping Oil Palm Plantations Using WorldView-2 Satellite Imagery and Machine Learning Algorithms

Assessment of Random Forest and Neural Network for Improving Land Use/ Land Cover Mapping from LIDAR Data and RGB Image: A Case Study of Magaga-El-Menia Governorate, Egypt

Classification of land use/land cover using artificial intelligence (ANN-RF)

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