Google Earth Engine for Landsat Image Processing and Monitoring Land Use/Land Cover Changes in the Johor River Basin, Malaysia

Kang, Chuen Siang; Kanniah, Kasturi Devi; Najib, Nazarin Ezzaty Mohd

doi:10.1109/igarss47720.2021.9554768

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…The RF and SVM show a strong ability to map small water bodies. Similar results have been reported by [62]. highlighting the efficiency of SVM in delineating small waterbodies.…”

Section: Discussionsupporting

confidence: 90%

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Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

Bhungeni,

Ramjatan,

Gebreslasie

2024

Preprint

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Analysis of land use/land cover (LULC) in the catchment areas is the first action toward safeguarding the freshwater resources. The LULC information in the watershed has gained popularity in the natural science field as it helps water resource managers and environmental health specialists develop natural resource conservation strategies based on available quantitative information. Thus, remote sensing is the cornerstone in addressing environmental-related issues at the catchment level. In this study, the performance of four machine learning algorithms (MLAs), such as Random Forests (RF), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Naïve Bayes (NB) was investigated to classify the catchment into nine relevant classes of the undulating watershed landscape using Landsat 8 Operational Land Imager (L8-OLI) imagery. The assessment of the MLAs were based on the visual inspection of the analyst and the commonly used assessment metrics, such as user’s accuracy (UA), producers’ accuracy (PA), overall accuracy (OA), and kappa coefficient. The MLAs produced good results, where RF (OA= 97.02%, Kappa= 0.96), SVM (OA= 89.74 %, Kappa= 0.88), ANN (OA= 87%, Kappa= 0.86), and NB (OA= 68.64 Kappa= 0.58). The results show the outstanding performance of the RF model over SVM and ANN with a small margin. While NB yielded satisfactory results, which could be primarily influenced by its sensitivity to limited training samples. In contrast, the robust performance of RF could be due to an ability to classify high-dimensional data with limited training data.

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“…The RF and SVM show a strong ability to map small water bodies. Similar results have been reported by [62]. highlighting the efficiency of SVM in delineating small waterbodies.…”

Section: Discussionsupporting

confidence: 90%

“…The overall accuracy represents the sum of correctly labelled pixels for all the classes in the input image as per provided test sample sites and is mathematically expressed as a fraction of correctly labelled pixels and the total number of the test sample sites [62].…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

Bhungeni,

Ramjatan,

Gebreslasie

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…A strong ability to map small waterbodies was demonstrated by the RF and SVM models. Similar results have been reported by [65], highlighting SVM's efficiency in mapping small waterbodies. However, it performed poorly in defining mining pits.…”

Section: Discussionsupporting

confidence: 89%

“…The overall accuracy represents the sum of correctly labelled pixels for all the classes in the input image as per the provided test sample sites and is mathematically expressed as a fraction of correctly labelled pixels and the total number of test sample sites [65].…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Evaluating Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

Bhungeni,

Ramjatan,

Gebreslasie

2024

Remote Sensing

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Analysis of land use/land cover (LULC) in catchment areas is the first action toward safeguarding freshwater resources. LULC information in the watershed has gained popularity in the natural science field as it helps water resource managers and environmental health specialists develop natural resource conservation strategies based on available quantitative information. Thus, remote sensing is the cornerstone in addressing environmental-related issues at the catchment level. In this study, the performance of four machine learning algorithms (MLAs), namely Random Forests (RFs), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Naïve Bayes (NB), were investigated to classify the catchment into nine relevant classes of the undulating watershed landscape using Landsat 8 Operational Land Imager (L8-OLI) imagery. The assessment of the MLAs was based on a visual inspection of the analyst and commonly used assessment metrics, such as user’s accuracy (UA), producers’ accuracy (PA), overall accuracy (OA), and the kappa coefficient. The MLAs produced good results, where RF (OA = 97.02%, Kappa = 0.96), SVM (OA = 89.74%, Kappa = 0.88), ANN (OA = 87%, Kappa = 0.86), and NB (OA = 68.64%, Kappa = 0.58). The results show the outstanding performance of the RF model over SVM and ANN with a significant margin. While NB yielded satisfactory results, its sensitivity to limited training samples could primarily influence these results. In contrast, the robust performance of RF could be due to an ability to classify high-dimensional data with limited training data.

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Land Use Land Cover Classification using Machine Learning

Waghela

Patel

Sudesan

et al. 2022

2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)

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Google Earth Engine for Landsat Image Processing and Monitoring Land Use/Land Cover Changes in the Johor River Basin, Malaysia

Cited by 5 publications

References 5 publications

Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

Evaluating Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

Land Use Land Cover Classification using Machine Learning

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