Machine learning-based forest burned area detection with various input variables: A case study of South Korea
Recently, an increase in wildfire incidents has caused significant damage from economical, humanitarian, and environmental perspectives. Wildfires have increased in severity, frequency, and duration because of climate change and rising global temperatures, resulting in the release of massive volumes...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI
2022
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/100985/1/MohdNadzri2022_MachineLearningBasedForestBurnedArea.pdf |
| _version_ | 1796866978537799680 |
|---|---|
| author | Lee, Changhui Park, Seonyoung Kim, Taeheon Liu, Sicong Md. Reba, Mohd. Nadzri Oh, Jaehong Han, Youkyung |
| author_facet | Lee, Changhui Park, Seonyoung Kim, Taeheon Liu, Sicong Md. Reba, Mohd. Nadzri Oh, Jaehong Han, Youkyung |
| author_sort | Lee, Changhui |
| collection | ePrints |
| description | Recently, an increase in wildfire incidents has caused significant damage from economical, humanitarian, and environmental perspectives. Wildfires have increased in severity, frequency, and duration because of climate change and rising global temperatures, resulting in the release of massive volumes of greenhouse gases, the destruction of forests and associated habitats, and the damage to infrastructures. Therefore, identifying burned areas is crucial for monitoring wildfire damage. In this study, we aim at detecting forest burned areas occurring in South Korea using optical satellite images. To exploit the advantage of applying machine learning, the present study employs representative three machine learning methods, Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and U-Net, to detect forest burned areas with a combination of input variables, namely Surface Reflectance (SR), Normalized Difference Vegetation Index (NDVI), and Normalized Burn Ratio (NBR). Two study sites of recently occurred forest fire events in South Korea were selected, and Sentinel-2 satellite images were used by considering a small scale of the forest fires. The quantitative and qualitative evaluations according to the machine learning methods and input variables were carried out. In terms of the comparison focusing on machine learning models, the U-Net showed the highest accuracy in both sites amongst the designed variants. The pre and post fire images by SR, NDVI, NBR, and difference of indices as the main inputs showed the best result. We also demonstrated that diverse landcovers may result in a poor burned area detection performance by comparing the results of the two sites. |
| first_indexed | 2024-03-05T21:20:17Z |
| format | Article |
| id | utm.eprints-100985 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:20:17Z |
| publishDate | 2022 |
| publisher | MDPI |
| record_format | dspace |
| spelling | utm.eprints-1009852023-05-23T10:23:44Z http://eprints.utm.my/100985/ Machine learning-based forest burned area detection with various input variables: A case study of South Korea Lee, Changhui Park, Seonyoung Kim, Taeheon Liu, Sicong Md. Reba, Mohd. Nadzri Oh, Jaehong Han, Youkyung GE Environmental Sciences Recently, an increase in wildfire incidents has caused significant damage from economical, humanitarian, and environmental perspectives. Wildfires have increased in severity, frequency, and duration because of climate change and rising global temperatures, resulting in the release of massive volumes of greenhouse gases, the destruction of forests and associated habitats, and the damage to infrastructures. Therefore, identifying burned areas is crucial for monitoring wildfire damage. In this study, we aim at detecting forest burned areas occurring in South Korea using optical satellite images. To exploit the advantage of applying machine learning, the present study employs representative three machine learning methods, Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and U-Net, to detect forest burned areas with a combination of input variables, namely Surface Reflectance (SR), Normalized Difference Vegetation Index (NDVI), and Normalized Burn Ratio (NBR). Two study sites of recently occurred forest fire events in South Korea were selected, and Sentinel-2 satellite images were used by considering a small scale of the forest fires. The quantitative and qualitative evaluations according to the machine learning methods and input variables were carried out. In terms of the comparison focusing on machine learning models, the U-Net showed the highest accuracy in both sites amongst the designed variants. The pre and post fire images by SR, NDVI, NBR, and difference of indices as the main inputs showed the best result. We also demonstrated that diverse landcovers may result in a poor burned area detection performance by comparing the results of the two sites. MDPI 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/100985/1/MohdNadzri2022_MachineLearningBasedForestBurnedArea.pdf Lee, Changhui and Park, Seonyoung and Kim, Taeheon and Liu, Sicong and Md. Reba, Mohd. Nadzri and Oh, Jaehong and Han, Youkyung (2022) Machine learning-based forest burned area detection with various input variables: A case study of South Korea. Applied Sciences (Switzerland), 12 (19). pp. 1-20. ISSN 2076-3417 http://dx.doi.org/10.3390/app121910077 DOI : 10.3390/app121910077 |
| spellingShingle | GE Environmental Sciences Lee, Changhui Park, Seonyoung Kim, Taeheon Liu, Sicong Md. Reba, Mohd. Nadzri Oh, Jaehong Han, Youkyung Machine learning-based forest burned area detection with various input variables: A case study of South Korea |
| title | Machine learning-based forest burned area detection with various input variables: A case study of South Korea |
| title_full | Machine learning-based forest burned area detection with various input variables: A case study of South Korea |
| title_fullStr | Machine learning-based forest burned area detection with various input variables: A case study of South Korea |
| title_full_unstemmed | Machine learning-based forest burned area detection with various input variables: A case study of South Korea |
| title_short | Machine learning-based forest burned area detection with various input variables: A case study of South Korea |
| title_sort | machine learning based forest burned area detection with various input variables a case study of south korea |
| topic | GE Environmental Sciences |
| url | http://eprints.utm.my/100985/1/MohdNadzri2022_MachineLearningBasedForestBurnedArea.pdf |
| work_keys_str_mv | AT leechanghui machinelearningbasedforestburnedareadetectionwithvariousinputvariablesacasestudyofsouthkorea AT parkseonyoung machinelearningbasedforestburnedareadetectionwithvariousinputvariablesacasestudyofsouthkorea AT kimtaeheon machinelearningbasedforestburnedareadetectionwithvariousinputvariablesacasestudyofsouthkorea AT liusicong machinelearningbasedforestburnedareadetectionwithvariousinputvariablesacasestudyofsouthkorea AT mdrebamohdnadzri machinelearningbasedforestburnedareadetectionwithvariousinputvariablesacasestudyofsouthkorea AT ohjaehong machinelearningbasedforestburnedareadetectionwithvariousinputvariablesacasestudyofsouthkorea AT hanyoukyung machinelearningbasedforestburnedareadetectionwithvariousinputvariablesacasestudyofsouthkorea |