A more effective approach for species-level classifications using multi-source remote sensing data: Validation and application to an arid and semi-arid grassland
The distribution of grassland communities significantly influences biodiversity conservation and resource management. Remote sensing datasets, such as Sentinel land cover images with high spatial and temporal resolution, have been commonly employed to simulate the distribution and community structur...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24003108 |
| _version_ | 1797213434387890176 |
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| author | Yuankang Li Tingxi Liu Yixuan Wang Limin Duan Mingyang Li Junyi Zhang Guixin Zhang |
| author_facet | Yuankang Li Tingxi Liu Yixuan Wang Limin Duan Mingyang Li Junyi Zhang Guixin Zhang |
| author_sort | Yuankang Li |
| collection | DOAJ |
| description | The distribution of grassland communities significantly influences biodiversity conservation and resource management. Remote sensing datasets, such as Sentinel land cover images with high spatial and temporal resolution, have been commonly employed to simulate the distribution and community structure of plant species in grasslands. Nevertheless, the precision of grassland and community classification requires further enhancement for more effective grassland management. In response, our study presents a dynamic workflow for simulating the distribution of grassland plant species under different hydroclimatic conditions. Leveraging a random forest classifier with multi-source remote sensing features, we mapped the distribution of nine plant communities and six types of land cover in the upstream grasslands of the Xilingol River Basin, Inner Mongolia from 2017 to 2022. Utilizing the Google Earth Engine platform, our findings reveal a remarkable consistency in the overall spatiotemporal accuracy and classification precision of grassland communities across different years, even under varying hydroclimatic conditions. When critical features were carefully selected, the integration of Sentinel-1 (C-band synthetic aperture radar), Sentinel-2 (surface reflectance), SRTM DEM 30 m (Digital Elevation Model data), and MOD16A2.006 (evapotranspiration/latent heat flux product) data yielded a high overall accuracy ranging from 87.0 % to 89.5 % over multiple years. Our study not only determined the spatial patterns of various grassland plant communities but also explored the mechanism of their responses to different hydroclimatic conditions. The proposed approach holds practical implications for fine-scale grassland classification, providing valuable support for landscape classification and monitoring efforts. Furthermore, the insights derived from our findings offer valuable guidance for land managers and policymakers, aiding them in making informed decisions related to grassland conservation and sustainable land use planning. |
| first_indexed | 2024-04-24T10:58:13Z |
| format | Article |
| id | doaj.art-222698ed0b264d62bbd0257d6aaaea44 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-04-24T10:58:13Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-222698ed0b264d62bbd0257d6aaaea442024-04-12T04:44:46ZengElsevierEcological Indicators1470-160X2024-03-01160111853A more effective approach for species-level classifications using multi-source remote sensing data: Validation and application to an arid and semi-arid grasslandYuankang Li0Tingxi Liu1Yixuan Wang2Limin Duan3Mingyang Li4Junyi Zhang5Guixin Zhang6The College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaThe College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China; Inner Mongolia Key Laboratory of Water Resource Protection and Utilization, College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China; Autonomous Region Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, China; Corresponding author at: The College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.The College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China; Inner Mongolia Key Laboratory of Water Resource Protection and Utilization, College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China; Autonomous Region Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, ChinaThe College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China; Inner Mongolia Key Laboratory of Water Resource Protection and Utilization, College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China; Autonomous Region Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, ChinaWater Resources Research Institute of Shandong Province, Shandong Provincial Key Laboratory of Water Resources and Environment, Jinan 250014, ChinaWater Resources Research Institute of Inner Mongolia Autonomous Region, Hohhot 010010, ChinaThe College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, ChinaThe distribution of grassland communities significantly influences biodiversity conservation and resource management. Remote sensing datasets, such as Sentinel land cover images with high spatial and temporal resolution, have been commonly employed to simulate the distribution and community structure of plant species in grasslands. Nevertheless, the precision of grassland and community classification requires further enhancement for more effective grassland management. In response, our study presents a dynamic workflow for simulating the distribution of grassland plant species under different hydroclimatic conditions. Leveraging a random forest classifier with multi-source remote sensing features, we mapped the distribution of nine plant communities and six types of land cover in the upstream grasslands of the Xilingol River Basin, Inner Mongolia from 2017 to 2022. Utilizing the Google Earth Engine platform, our findings reveal a remarkable consistency in the overall spatiotemporal accuracy and classification precision of grassland communities across different years, even under varying hydroclimatic conditions. When critical features were carefully selected, the integration of Sentinel-1 (C-band synthetic aperture radar), Sentinel-2 (surface reflectance), SRTM DEM 30 m (Digital Elevation Model data), and MOD16A2.006 (evapotranspiration/latent heat flux product) data yielded a high overall accuracy ranging from 87.0 % to 89.5 % over multiple years. Our study not only determined the spatial patterns of various grassland plant communities but also explored the mechanism of their responses to different hydroclimatic conditions. The proposed approach holds practical implications for fine-scale grassland classification, providing valuable support for landscape classification and monitoring efforts. Furthermore, the insights derived from our findings offer valuable guidance for land managers and policymakers, aiding them in making informed decisions related to grassland conservation and sustainable land use planning.http://www.sciencedirect.com/science/article/pii/S1470160X24003108Grassland communityFine-scale classificationGoogle Earth EngineRandom forest classifierTime series characteristics |
| spellingShingle | Yuankang Li Tingxi Liu Yixuan Wang Limin Duan Mingyang Li Junyi Zhang Guixin Zhang A more effective approach for species-level classifications using multi-source remote sensing data: Validation and application to an arid and semi-arid grassland Ecological Indicators Grassland community Fine-scale classification Google Earth Engine Random forest classifier Time series characteristics |
| title | A more effective approach for species-level classifications using multi-source remote sensing data: Validation and application to an arid and semi-arid grassland |
| title_full | A more effective approach for species-level classifications using multi-source remote sensing data: Validation and application to an arid and semi-arid grassland |
| title_fullStr | A more effective approach for species-level classifications using multi-source remote sensing data: Validation and application to an arid and semi-arid grassland |
| title_full_unstemmed | A more effective approach for species-level classifications using multi-source remote sensing data: Validation and application to an arid and semi-arid grassland |
| title_short | A more effective approach for species-level classifications using multi-source remote sensing data: Validation and application to an arid and semi-arid grassland |
| title_sort | more effective approach for species level classifications using multi source remote sensing data validation and application to an arid and semi arid grassland |
| topic | Grassland community Fine-scale classification Google Earth Engine Random forest classifier Time series characteristics |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X24003108 |
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