Cargando…
Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia
This study aims to develop a software framework for predicting aridity using vegetation indices (VI) from LANDSAT 8 OLI images. VI data are predicted using machine learning (ml): Random Forest (RF) and Correlation and Regression Trees (CART). Comparison of prediction using Artificial Neural Network...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8157165/ https://www.ncbi.nlm.nih.gov/pubmed/34084916 http://dx.doi.org/10.7717/peerj-cs.415 |
_version_ | 1783699620122066944 |
---|---|
author | Prasetyo, Sri Yulianto Joko Hartomo, Kristoko Dwi Paseleng, Mila Chrismawati |
author_facet | Prasetyo, Sri Yulianto Joko Hartomo, Kristoko Dwi Paseleng, Mila Chrismawati |
author_sort | Prasetyo, Sri Yulianto Joko |
collection | PubMed |
description | This study aims to develop a software framework for predicting aridity using vegetation indices (VI) from LANDSAT 8 OLI images. VI data are predicted using machine learning (ml): Random Forest (RF) and Correlation and Regression Trees (CART). Comparison of prediction using Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest neighbors (k-nn) and Multivariate Adaptive Regression Spline (MARS). Prediction results are interpolated using Inverse Distance Weight (IDW). This study was conducted in stages: (1) Image preprocessing; (2) calculating numerical data extracted from the LANDSAT band imagery using vegetation indices; (3) analyzing correlation coefficients between VI; (4) prediction using RF and CART; (5) comparing performances between RF and CART using ANN, SVM, k-nn, and MARS; (6) testing the accuracy of prediction using Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE); (7) interpolating with IDW. Correlation coefficient of VI data shows a positive correlation, the lowest r (0.07) and the highest r (0.98). The experiments show that the RF and CART algorithms have efficiency and effectivity in determining the aridity areas better than the ANN, SVM, k-nn, and MARS algorithm. RF has a difference between the predicted results and 1.04% survey data MAPE and the smallest value close to zero is 0.05 MSE. CART has a difference between the predicted results and 1.05% survey data MAPE and the smallest value approaching to zero which is 0.05 MSE. The prediction results of VI show that in 2020 most of the study areas were low vegetation areas with the Normalized Difference Vegetation Index (NDVI) < 0.21, had an indication of drought with the Vegetation Health Index (VHI) < 31.10, had a Vegetation Condition Index (VCI) in some areas between 35%–50% (moderate drought) and < 35% (high drought). The Burn Area Index (dBAI) values are between −3, 971 and −2,376 that show the areas have a low fire risk, and index values are between −0, 208 and −0,412 that show the areas are starting vegetation growth. The result of this study shows that the machine learning algorithms is an accurate and stable algorithm in predicting the risks of drought and land fire based on the VI data extracted from the LANDSAT 8 OLL imagery. The VI data contain the record of vegetation condition and its environment, including humidity, temperatures, and the environmental vegetation health. |
format | Online Article Text |
id | pubmed-8157165 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81571652021-06-02 Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia Prasetyo, Sri Yulianto Joko Hartomo, Kristoko Dwi Paseleng, Mila Chrismawati PeerJ Comput Sci Artificial Intelligence This study aims to develop a software framework for predicting aridity using vegetation indices (VI) from LANDSAT 8 OLI images. VI data are predicted using machine learning (ml): Random Forest (RF) and Correlation and Regression Trees (CART). Comparison of prediction using Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest neighbors (k-nn) and Multivariate Adaptive Regression Spline (MARS). Prediction results are interpolated using Inverse Distance Weight (IDW). This study was conducted in stages: (1) Image preprocessing; (2) calculating numerical data extracted from the LANDSAT band imagery using vegetation indices; (3) analyzing correlation coefficients between VI; (4) prediction using RF and CART; (5) comparing performances between RF and CART using ANN, SVM, k-nn, and MARS; (6) testing the accuracy of prediction using Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE); (7) interpolating with IDW. Correlation coefficient of VI data shows a positive correlation, the lowest r (0.07) and the highest r (0.98). The experiments show that the RF and CART algorithms have efficiency and effectivity in determining the aridity areas better than the ANN, SVM, k-nn, and MARS algorithm. RF has a difference between the predicted results and 1.04% survey data MAPE and the smallest value close to zero is 0.05 MSE. CART has a difference between the predicted results and 1.05% survey data MAPE and the smallest value approaching to zero which is 0.05 MSE. The prediction results of VI show that in 2020 most of the study areas were low vegetation areas with the Normalized Difference Vegetation Index (NDVI) < 0.21, had an indication of drought with the Vegetation Health Index (VHI) < 31.10, had a Vegetation Condition Index (VCI) in some areas between 35%–50% (moderate drought) and < 35% (high drought). The Burn Area Index (dBAI) values are between −3, 971 and −2,376 that show the areas have a low fire risk, and index values are between −0, 208 and −0,412 that show the areas are starting vegetation growth. The result of this study shows that the machine learning algorithms is an accurate and stable algorithm in predicting the risks of drought and land fire based on the VI data extracted from the LANDSAT 8 OLL imagery. The VI data contain the record of vegetation condition and its environment, including humidity, temperatures, and the environmental vegetation health. PeerJ Inc. 2021-05-18 /pmc/articles/PMC8157165/ /pubmed/34084916 http://dx.doi.org/10.7717/peerj-cs.415 Text en ©2021 Prasetyo et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Prasetyo, Sri Yulianto Joko Hartomo, Kristoko Dwi Paseleng, Mila Chrismawati Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia |
title | Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia |
title_full | Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia |
title_fullStr | Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia |
title_full_unstemmed | Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia |
title_short | Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia |
title_sort | satellite imagery and machine learning for identification of aridity risk in central java indonesia |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8157165/ https://www.ncbi.nlm.nih.gov/pubmed/34084916 http://dx.doi.org/10.7717/peerj-cs.415 |
work_keys_str_mv | AT prasetyosriyuliantojoko satelliteimageryandmachinelearningforidentificationofaridityriskincentraljavaindonesia AT hartomokristokodwi satelliteimageryandmachinelearningforidentificationofaridityriskincentraljavaindonesia AT paselengmilachrismawati satelliteimageryandmachinelearningforidentificationofaridityriskincentraljavaindonesia |