Estimating Long-Term Fractional Vegetation Cover Using an Improved Dimidiate Pixel Method With UAV-Assisted Satellite Data: A Case Study in a Mining Region
Accurate long-term estimation of fractional vegetation cover (FVC) is crucial for monitoring vegetation dynamics. Satellite-based methods, such as the dimidiate pixel method (DPM), struggle with spatial heterogeneity due to coarse resolution. Existing methods using unmanned aerial vehicles (UAVs) co...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10845181/ |
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| author | Shuang Wu Lei Deng Qinghua Qiao |
| author_facet | Shuang Wu Lei Deng Qinghua Qiao |
| author_sort | Shuang Wu |
| collection | DOAJ |
| description | Accurate long-term estimation of fractional vegetation cover (FVC) is crucial for monitoring vegetation dynamics. Satellite-based methods, such as the dimidiate pixel method (DPM), struggle with spatial heterogeneity due to coarse resolution. Existing methods using unmanned aerial vehicles (UAVs) combined with satellite data (UCS) inadequately leverage the high spatial resolution of UAV imagery to address spatial heterogeneity and are seldom applied to long-term FVC monitoring. To overcome spatial challenges, an improved dimidiate pixel method (IDPM) is proposed here, utilizing 2021 Landsat imagery to generate FVC<sub>DPM</sub> via DPM and upscaled UAV imagery for FVC<sub>UAV</sub> as ground references. The IDPM uses the pruned exact linear time method to segment the normalized difference vegetation index (NDVI) into intervals, within which DPM performance is evaluated for potential improvements. Specifically, if the difference (D) between FVC<sub>DPM</sub> and FVC<sub>UAV</sub> is nonzero, NDVI-derived texture features are incorporated into FVC<sub>DPM</sub> through multiple linear regression to enhance accuracy. To address temporal challenges and ensure consistency across years, the 2021 NDVI serves as a reference for inter-year NDVI calibration, employing least squares regression (LSR) and histogram matching (HM) to identify the most effective method for extending the IDPM to other years. Results demonstrate that 1) the IDPM, by developing distinct DPM improvement models for different NDVI intervals, considerably improves UAV and satellite data integration, with a 48.51% increase in <italic>R</italic><sup>2</sup> and a 56.47% reduction in root mean square error (RMSE) compared to the DPM and UCS and 2) HM is found to be more suitable for mining areas, increasing <italic>R</italic><sup>2</sup> by 25.00% and reducing RMSE by 54.05% compared to LSR. This method provides an efficient, rapid solution for mitigating spatial heterogeneity and advancing long-term FVC estimation. |
| format | Article |
| id | doaj-art-32bde317302f41f192614eae2fccb013 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-32bde317302f41f192614eae2fccb0132025-02-04T00:00:16ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01184162417310.1109/JSTARS.2025.353143910845181Estimating Long-Term Fractional Vegetation Cover Using an Improved Dimidiate Pixel Method With UAV-Assisted Satellite Data: A Case Study in a Mining RegionShuang Wu0https://orcid.org/0000-0002-5996-4600Lei Deng1https://orcid.org/0000-0002-4574-7381Qinghua Qiao2https://orcid.org/0000-0002-1860-289XCollege of Resource Environment and Tourism, Capital Normal University, Beijing, ChinaCollege of Resource Environment and Tourism, Capital Normal University, Beijing, ChinaNatural Resources Survey and Monitoring Research Centre, The Chinese Academy of Surveying and Mapping, Beijing, ChinaAccurate long-term estimation of fractional vegetation cover (FVC) is crucial for monitoring vegetation dynamics. Satellite-based methods, such as the dimidiate pixel method (DPM), struggle with spatial heterogeneity due to coarse resolution. Existing methods using unmanned aerial vehicles (UAVs) combined with satellite data (UCS) inadequately leverage the high spatial resolution of UAV imagery to address spatial heterogeneity and are seldom applied to long-term FVC monitoring. To overcome spatial challenges, an improved dimidiate pixel method (IDPM) is proposed here, utilizing 2021 Landsat imagery to generate FVC<sub>DPM</sub> via DPM and upscaled UAV imagery for FVC<sub>UAV</sub> as ground references. The IDPM uses the pruned exact linear time method to segment the normalized difference vegetation index (NDVI) into intervals, within which DPM performance is evaluated for potential improvements. Specifically, if the difference (D) between FVC<sub>DPM</sub> and FVC<sub>UAV</sub> is nonzero, NDVI-derived texture features are incorporated into FVC<sub>DPM</sub> through multiple linear regression to enhance accuracy. To address temporal challenges and ensure consistency across years, the 2021 NDVI serves as a reference for inter-year NDVI calibration, employing least squares regression (LSR) and histogram matching (HM) to identify the most effective method for extending the IDPM to other years. Results demonstrate that 1) the IDPM, by developing distinct DPM improvement models for different NDVI intervals, considerably improves UAV and satellite data integration, with a 48.51% increase in <italic>R</italic><sup>2</sup> and a 56.47% reduction in root mean square error (RMSE) compared to the DPM and UCS and 2) HM is found to be more suitable for mining areas, increasing <italic>R</italic><sup>2</sup> by 25.00% and reducing RMSE by 54.05% compared to LSR. This method provides an efficient, rapid solution for mitigating spatial heterogeneity and advancing long-term FVC estimation.https://ieeexplore.ieee.org/document/10845181/Dimidiate pixel method (DPM)fractional vegetation cover (FVC)landsat imageryunmanned aerial vehicle (UAV) |
| spellingShingle | Shuang Wu Lei Deng Qinghua Qiao Estimating Long-Term Fractional Vegetation Cover Using an Improved Dimidiate Pixel Method With UAV-Assisted Satellite Data: A Case Study in a Mining Region IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Dimidiate pixel method (DPM) fractional vegetation cover (FVC) landsat imagery unmanned aerial vehicle (UAV) |
| title | Estimating Long-Term Fractional Vegetation Cover Using an Improved Dimidiate Pixel Method With UAV-Assisted Satellite Data: A Case Study in a Mining Region |
| title_full | Estimating Long-Term Fractional Vegetation Cover Using an Improved Dimidiate Pixel Method With UAV-Assisted Satellite Data: A Case Study in a Mining Region |
| title_fullStr | Estimating Long-Term Fractional Vegetation Cover Using an Improved Dimidiate Pixel Method With UAV-Assisted Satellite Data: A Case Study in a Mining Region |
| title_full_unstemmed | Estimating Long-Term Fractional Vegetation Cover Using an Improved Dimidiate Pixel Method With UAV-Assisted Satellite Data: A Case Study in a Mining Region |
| title_short | Estimating Long-Term Fractional Vegetation Cover Using an Improved Dimidiate Pixel Method With UAV-Assisted Satellite Data: A Case Study in a Mining Region |
| title_sort | estimating long term fractional vegetation cover using an improved dimidiate pixel method with uav assisted satellite data a case study in a mining region |
| topic | Dimidiate pixel method (DPM) fractional vegetation cover (FVC) landsat imagery unmanned aerial vehicle (UAV) |
| url | https://ieeexplore.ieee.org/document/10845181/ |
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