Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models
<p>Most readily available land use/land cover (LULC) data are developed using growing season remote sensing images often at annual time steps, but seasonal changes in remote sensing data can lead to inconsistencies in LULC classification, which could impact geospatial models based on LULC. We...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2024-12-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/28/5295/2024/hess-28-5295-2024.pdf |
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| author | D. T. Myers D. Jones D. Oviedo-Vargas J. P. Schmit D. L. Ficklin X. Zhang |
| author_facet | D. T. Myers D. Jones D. Oviedo-Vargas J. P. Schmit D. L. Ficklin X. Zhang |
| author_sort | D. T. Myers |
| collection | DOAJ |
| description | <p>Most readily available land use/land cover (LULC) data are developed using growing season remote sensing images often at annual time steps, but seasonal changes in remote sensing data can lead to inconsistencies in LULC classification, which could impact geospatial models based on LULC. We used the Dynamic World near-real-time global LULC dataset to compare how geospatial environmental models of water quality and hydrology respond to LULC estimated from growing vs. non-growing season data for temperate watersheds of the eastern United States. Non-growing season data resulted in LULC classifications that had more built area and less tree cover than growing season data due to seasonal impacts on classifications rather than actual LULC changes (e.g., quick construction or succession). In mixed-LULC watersheds, seasonal LULC classification inconsistencies could lead to differences in model outputs depending on the LULC season used, such as differences in watershed nitrogen yields simulated by the Soil and Water Assessment Tool. Within reason, using separate calibration for each season may compensate for these inconsistencies but lead to different model parameter optimizations. Our findings provide guidelines on the use of near-real-time and high-temporal-resolution LULC in geospatial models.</p> |
| format | Article |
| id | doaj-art-2e1dc4df378940df8551532eaa4a8feb |
| institution | Kabale University |
| issn | 1027-5606 1607-7938 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj-art-2e1dc4df378940df8551532eaa4a8feb2024-12-06T12:57:19ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382024-12-01285295531010.5194/hess-28-5295-2024Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental modelsD. T. Myers0D. Jones1D. Oviedo-Vargas2J. P. Schmit3D. L. Ficklin4X. Zhang5Stroud Water Research Center, 970 Spencer Road, Avondale, PA 19311, USANational Park Service National Capital Region Network, 4598 MacArthur Blvd. NW, Washington, DC 20007, USAStroud Water Research Center, 970 Spencer Road, Avondale, PA 19311, USANational Park Service National Capital Region Network, 4598 MacArthur Blvd. NW, Washington, DC 20007, USADepartment of Geography, Indiana University Bloomington, 701 E. Kirkwood Avenue, Bloomington, IN 47405, USAHydrology and Remote Sensing Laboratory, United States Department of Agriculture Agricultural Research Service, Bldg. 007, Rm. 104, BARC-West, Beltsville, MD 20705-2350, USA<p>Most readily available land use/land cover (LULC) data are developed using growing season remote sensing images often at annual time steps, but seasonal changes in remote sensing data can lead to inconsistencies in LULC classification, which could impact geospatial models based on LULC. We used the Dynamic World near-real-time global LULC dataset to compare how geospatial environmental models of water quality and hydrology respond to LULC estimated from growing vs. non-growing season data for temperate watersheds of the eastern United States. Non-growing season data resulted in LULC classifications that had more built area and less tree cover than growing season data due to seasonal impacts on classifications rather than actual LULC changes (e.g., quick construction or succession). In mixed-LULC watersheds, seasonal LULC classification inconsistencies could lead to differences in model outputs depending on the LULC season used, such as differences in watershed nitrogen yields simulated by the Soil and Water Assessment Tool. Within reason, using separate calibration for each season may compensate for these inconsistencies but lead to different model parameter optimizations. Our findings provide guidelines on the use of near-real-time and high-temporal-resolution LULC in geospatial models.</p>https://hess.copernicus.org/articles/28/5295/2024/hess-28-5295-2024.pdf |
| spellingShingle | D. T. Myers D. Jones D. Oviedo-Vargas J. P. Schmit D. L. Ficklin X. Zhang Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models Hydrology and Earth System Sciences |
| title | Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models |
| title_full | Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models |
| title_fullStr | Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models |
| title_full_unstemmed | Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models |
| title_short | Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models |
| title_sort | seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models |
| url | https://hess.copernicus.org/articles/28/5295/2024/hess-28-5295-2024.pdf |
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