MAPPING LAND COVER TIME SERIES USING LANDSAT-8 AND SENTINEL-1 IN SOUTH KALIMANTAN
Remote sensing has been widely used for forest monitoring. However, most forest monitoring systems largely rely on optical images that limit temporal analyses due to cloud cover, particularly in the tropics. The current study used integration of optical Landsat-8 and Sentinel-1 SAR to produce indivi...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-02-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVIII-4-W6-2022/319/2023/isprs-archives-XLVIII-4-W6-2022-319-2023.pdf |
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| author | I. L. Sari I. L. Sari C. J. Weston G. J. Newnham L. Volkova |
| author_facet | I. L. Sari I. L. Sari C. J. Weston G. J. Newnham L. Volkova |
| author_sort | I. L. Sari |
| collection | DOAJ |
| description | Remote sensing has been widely used for forest monitoring. However, most forest monitoring systems largely rely on optical images that limit temporal analyses due to cloud cover, particularly in the tropics. The current study used integration of optical Landsat-8 and Sentinel-1 SAR to produce individual year land cover classification maps for Kalimantan, Indonesia that differentiate between native forest and tree plantations, such as oil palm and rubber. We applied a Bayesian network to produce a time series of land cover classification that improved accuracy of individual year land cover maps. Accuracy assessment using a confusion matrix showed that final map had overall accuracy of 90%, while user's and producer's accuracy for each land cover class was above 85%, except non–forest, which had 76% producer's accuracy due to errors in the classification between young rubber plantations and non–forest. Improved maps will support Indonesia's national forest monitoring system and sustainable forest management. |
| first_indexed | 2024-04-10T16:39:24Z |
| format | Article |
| id | doaj.art-cfe80e0035664b4688bf153169b8ea9b |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-10T16:39:24Z |
| publishDate | 2023-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-cfe80e0035664b4688bf153169b8ea9b2023-02-08T09:38:08ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342023-02-01XLVIII-4-W6-202231932510.5194/isprs-archives-XLVIII-4-W6-2022-319-2023MAPPING LAND COVER TIME SERIES USING LANDSAT-8 AND SENTINEL-1 IN SOUTH KALIMANTANI. L. Sari0I. L. Sari1C. J. Weston2G. J. Newnham3L. Volkova4School of Ecosystem and Forest Sciences, Faculty of Science, The University of Melbourne, Creswick, AustraliaResearch Center for Remote Sensing, National Research and Innovation Agency (BRIN), Jakarta, IndonesiaSchool of Ecosystem and Forest Sciences, Faculty of Science, The University of Melbourne, Creswick, AustraliaCSIRO Land and Water, Clayton South, AustraliaSchool of Ecosystem and Forest Sciences, Faculty of Science, The University of Melbourne, Creswick, AustraliaRemote sensing has been widely used for forest monitoring. However, most forest monitoring systems largely rely on optical images that limit temporal analyses due to cloud cover, particularly in the tropics. The current study used integration of optical Landsat-8 and Sentinel-1 SAR to produce individual year land cover classification maps for Kalimantan, Indonesia that differentiate between native forest and tree plantations, such as oil palm and rubber. We applied a Bayesian network to produce a time series of land cover classification that improved accuracy of individual year land cover maps. Accuracy assessment using a confusion matrix showed that final map had overall accuracy of 90%, while user's and producer's accuracy for each land cover class was above 85%, except non–forest, which had 76% producer's accuracy due to errors in the classification between young rubber plantations and non–forest. Improved maps will support Indonesia's national forest monitoring system and sustainable forest management.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVIII-4-W6-2022/319/2023/isprs-archives-XLVIII-4-W6-2022-319-2023.pdf |
| spellingShingle | I. L. Sari I. L. Sari C. J. Weston G. J. Newnham L. Volkova MAPPING LAND COVER TIME SERIES USING LANDSAT-8 AND SENTINEL-1 IN SOUTH KALIMANTAN The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | MAPPING LAND COVER TIME SERIES USING LANDSAT-8 AND SENTINEL-1 IN SOUTH KALIMANTAN |
| title_full | MAPPING LAND COVER TIME SERIES USING LANDSAT-8 AND SENTINEL-1 IN SOUTH KALIMANTAN |
| title_fullStr | MAPPING LAND COVER TIME SERIES USING LANDSAT-8 AND SENTINEL-1 IN SOUTH KALIMANTAN |
| title_full_unstemmed | MAPPING LAND COVER TIME SERIES USING LANDSAT-8 AND SENTINEL-1 IN SOUTH KALIMANTAN |
| title_short | MAPPING LAND COVER TIME SERIES USING LANDSAT-8 AND SENTINEL-1 IN SOUTH KALIMANTAN |
| title_sort | mapping land cover time series using landsat 8 and sentinel 1 in south kalimantan |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVIII-4-W6-2022/319/2023/isprs-archives-XLVIII-4-W6-2022-319-2023.pdf |
| work_keys_str_mv | AT ilsari mappinglandcovertimeseriesusinglandsat8andsentinel1insouthkalimantan AT ilsari mappinglandcovertimeseriesusinglandsat8andsentinel1insouthkalimantan AT cjweston mappinglandcovertimeseriesusinglandsat8andsentinel1insouthkalimantan AT gjnewnham mappinglandcovertimeseriesusinglandsat8andsentinel1insouthkalimantan AT lvolkova mappinglandcovertimeseriesusinglandsat8andsentinel1insouthkalimantan |