An Unsupervised Classification Algorithm for Multi-Temporal Irrigated Area Mapping in Central Asia
Sound water resources planning and management requires adequate data with sufficient spatial and temporal resolution. This is especially true in the context of irrigated agriculture, which is one of the main consumptive users of the world’s freshwater resources. Existing remote sensing met...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/10/11/1823 |
| _version_ | 1798018431567527936 |
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| author | Silvan Ragettli Timo Herberz Tobias Siegfried |
| author_facet | Silvan Ragettli Timo Herberz Tobias Siegfried |
| author_sort | Silvan Ragettli |
| collection | DOAJ |
| description | Sound water resources planning and management requires adequate data with sufficient spatial and temporal resolution. This is especially true in the context of irrigated agriculture, which is one of the main consumptive users of the world’s freshwater resources. Existing remote sensing methods for the management of irrigated agricultural systems are often based on empirical cropland data that are difficult to obtain, and that put into question the transferability of mapping algorithms in space and time. Here we implement an automatic irrigation mapping procedure in Google Earth Engine that uses surface reflectance satellite imagery from different sensors. The method is based on unsupervised training of a pixel-by-pixel classification algorithm within image regions identified through unsupervised object-based segmentation, followed by multi-temporal image analysis to distinguish productive irrigated fields from non-productive and non-irrigated areas. Ground-based data are not required. The final output of the mapping algorithm are monthly and annual irrigation maps (30 m resolution). The novel method is applied to the Central Asian Chu and Talas River Basins that are shared between upstream Kyrgyzstan and downstream Kazakhstan. We calculate the development of irrigated areas from 2000 to 2017 and assess the classification results in terms of robustness and accuracy. Based on seven available validation scenes (in total more than 2.5 million pixels) the classification accuracy is 77⁻96%. We show that on the Kyrgyz side of the Talas basin, the identified increasing trends over the years are highly significant (23% area increase between 2000 and 2017). In the Kazakh parts of the basins the irrigated acreages are relatively stable over time, but the average irrigation frequency within Soviet-era irrigation perimeters is very low, which points to a poor physical condition of the irrigation infrastructure and inadequate water supply. |
| first_indexed | 2024-04-11T16:24:01Z |
| format | Article |
| id | doaj.art-002d357989274d14b09d3bf207d2298c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T16:24:01Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-002d357989274d14b09d3bf207d2298c2022-12-22T04:14:14ZengMDPI AGRemote Sensing2072-42922018-11-011011182310.3390/rs10111823rs10111823An Unsupervised Classification Algorithm for Multi-Temporal Irrigated Area Mapping in Central AsiaSilvan Ragettli0Timo Herberz1Tobias Siegfried2Hydrosolutions Ltd., 8006 Zurich, SwitzerlandHydrosolutions Ltd., 8006 Zurich, SwitzerlandHydrosolutions Ltd., 8006 Zurich, SwitzerlandSound water resources planning and management requires adequate data with sufficient spatial and temporal resolution. This is especially true in the context of irrigated agriculture, which is one of the main consumptive users of the world’s freshwater resources. Existing remote sensing methods for the management of irrigated agricultural systems are often based on empirical cropland data that are difficult to obtain, and that put into question the transferability of mapping algorithms in space and time. Here we implement an automatic irrigation mapping procedure in Google Earth Engine that uses surface reflectance satellite imagery from different sensors. The method is based on unsupervised training of a pixel-by-pixel classification algorithm within image regions identified through unsupervised object-based segmentation, followed by multi-temporal image analysis to distinguish productive irrigated fields from non-productive and non-irrigated areas. Ground-based data are not required. The final output of the mapping algorithm are monthly and annual irrigation maps (30 m resolution). The novel method is applied to the Central Asian Chu and Talas River Basins that are shared between upstream Kyrgyzstan and downstream Kazakhstan. We calculate the development of irrigated areas from 2000 to 2017 and assess the classification results in terms of robustness and accuracy. Based on seven available validation scenes (in total more than 2.5 million pixels) the classification accuracy is 77⁻96%. We show that on the Kyrgyz side of the Talas basin, the identified increasing trends over the years are highly significant (23% area increase between 2000 and 2017). In the Kazakh parts of the basins the irrigated acreages are relatively stable over time, but the average irrigation frequency within Soviet-era irrigation perimeters is very low, which points to a poor physical condition of the irrigation infrastructure and inadequate water supply.https://www.mdpi.com/2072-4292/10/11/1823mapping irrigated areamulti-spectral satellite imageryunsupervised classificationmulti-temporal classificationcentral asiagoogle earth engine |
| spellingShingle | Silvan Ragettli Timo Herberz Tobias Siegfried An Unsupervised Classification Algorithm for Multi-Temporal Irrigated Area Mapping in Central Asia Remote Sensing mapping irrigated area multi-spectral satellite imagery unsupervised classification multi-temporal classification central asia google earth engine |
| title | An Unsupervised Classification Algorithm for Multi-Temporal Irrigated Area Mapping in Central Asia |
| title_full | An Unsupervised Classification Algorithm for Multi-Temporal Irrigated Area Mapping in Central Asia |
| title_fullStr | An Unsupervised Classification Algorithm for Multi-Temporal Irrigated Area Mapping in Central Asia |
| title_full_unstemmed | An Unsupervised Classification Algorithm for Multi-Temporal Irrigated Area Mapping in Central Asia |
| title_short | An Unsupervised Classification Algorithm for Multi-Temporal Irrigated Area Mapping in Central Asia |
| title_sort | unsupervised classification algorithm for multi temporal irrigated area mapping in central asia |
| topic | mapping irrigated area multi-spectral satellite imagery unsupervised classification multi-temporal classification central asia google earth engine |
| url | https://www.mdpi.com/2072-4292/10/11/1823 |
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