Integrating Drone Truthing and Functional Classification of Remote Sensing Time Series for Supervised Vegetation Mapping
Accurate vegetation mapping is essential for monitoring biodiversity and managing habitats, particularly in the context of increasing environmental pressures and conservation needs. Ground truthing plays a crucial role in ensuring the accuracy of supervised remote sensing maps, as it provides the hi...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| author | Giacomo Quattrini Simone Pesaresi Nicole Hofmann Adriano Mancini Simona Casavecchia |
| author_facet | Giacomo Quattrini Simone Pesaresi Nicole Hofmann Adriano Mancini Simona Casavecchia |
| author_sort | Giacomo Quattrini |
| collection | DOAJ |
| description | Accurate vegetation mapping is essential for monitoring biodiversity and managing habitats, particularly in the context of increasing environmental pressures and conservation needs. Ground truthing plays a crucial role in ensuring the accuracy of supervised remote sensing maps, as it provides the high-quality reference data needed for model training and validation. However, traditional ground truthing methods are labor-intensive, time-consuming and restricted in spatial coverage, posing challenges for large-scale or complex landscapes. The advent of drone technology offers an efficient and cost-effective solution to these limitations, enabling the rapid collection of high-resolution imagery even in remote or inaccessible areas. This study proposes an approach to enhance the efficiency of supervised vegetation mapping in complex landscapes, integrating Multivariate Functional Principal Component Analysis (MFPCA) applied to the Sentinel-2 time series with drone-based ground truthing. Unlike traditional ground truthing activities, drone truthing enabled the generation of large, spatially balanced reference datasets, which are critical for machine learning classification systems. These datasets improved classification accuracy by ensuring a comprehensive representation of vegetation spectral variability, enabling the classifier to identify the key phenological patterns that best characterize and distinguish different vegetation types across the landscape. The proposed methodology achieves a classification accuracy of 92.59%, significantly exceeding the commonly reported thresholds for habitat mapping. This approach, characterized by its efficiency, repeatability and adaptability, aligns seamlessly with key environmental monitoring and conservation policies, such as the Habitats Directive. By integrating advanced remote sensing with drone-based technologies, it offers a scalable and cost-effective solution to the challenges of biodiversity monitoring, enabling timely updates and supporting effective habitat management in diverse and complex environments. |
| format | Article |
| id | doaj-art-6281748e16df43ac954ebfe676bb8afb |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-6281748e16df43ac954ebfe676bb8afb2025-01-24T13:48:08ZengMDPI AGRemote Sensing2072-42922025-01-0117233010.3390/rs17020330Integrating Drone Truthing and Functional Classification of Remote Sensing Time Series for Supervised Vegetation MappingGiacomo Quattrini0Simone Pesaresi1Nicole Hofmann2Adriano Mancini3Simona Casavecchia4Department of Agricultural, Food and Environmental Sciences, D3A, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, ItalyDepartment of Agricultural, Food and Environmental Sciences, D3A, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, ItalyDepartment of Agricultural, Food and Environmental Sciences, D3A, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, ItalyDepartment of Information Engineering, DII, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, ItalyDepartment of Agricultural, Food and Environmental Sciences, D3A, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, ItalyAccurate vegetation mapping is essential for monitoring biodiversity and managing habitats, particularly in the context of increasing environmental pressures and conservation needs. Ground truthing plays a crucial role in ensuring the accuracy of supervised remote sensing maps, as it provides the high-quality reference data needed for model training and validation. However, traditional ground truthing methods are labor-intensive, time-consuming and restricted in spatial coverage, posing challenges for large-scale or complex landscapes. The advent of drone technology offers an efficient and cost-effective solution to these limitations, enabling the rapid collection of high-resolution imagery even in remote or inaccessible areas. This study proposes an approach to enhance the efficiency of supervised vegetation mapping in complex landscapes, integrating Multivariate Functional Principal Component Analysis (MFPCA) applied to the Sentinel-2 time series with drone-based ground truthing. Unlike traditional ground truthing activities, drone truthing enabled the generation of large, spatially balanced reference datasets, which are critical for machine learning classification systems. These datasets improved classification accuracy by ensuring a comprehensive representation of vegetation spectral variability, enabling the classifier to identify the key phenological patterns that best characterize and distinguish different vegetation types across the landscape. The proposed methodology achieves a classification accuracy of 92.59%, significantly exceeding the commonly reported thresholds for habitat mapping. This approach, characterized by its efficiency, repeatability and adaptability, aligns seamlessly with key environmental monitoring and conservation policies, such as the Habitats Directive. By integrating advanced remote sensing with drone-based technologies, it offers a scalable and cost-effective solution to the challenges of biodiversity monitoring, enabling timely updates and supporting effective habitat management in diverse and complex environments.https://www.mdpi.com/2072-4292/17/2/330phytosociologyvegetation mappingremote sensing time seriesMFPCAground truthingdrone |
| spellingShingle | Giacomo Quattrini Simone Pesaresi Nicole Hofmann Adriano Mancini Simona Casavecchia Integrating Drone Truthing and Functional Classification of Remote Sensing Time Series for Supervised Vegetation Mapping Remote Sensing phytosociology vegetation mapping remote sensing time series MFPCA ground truthing drone |
| title | Integrating Drone Truthing and Functional Classification of Remote Sensing Time Series for Supervised Vegetation Mapping |
| title_full | Integrating Drone Truthing and Functional Classification of Remote Sensing Time Series for Supervised Vegetation Mapping |
| title_fullStr | Integrating Drone Truthing and Functional Classification of Remote Sensing Time Series for Supervised Vegetation Mapping |
| title_full_unstemmed | Integrating Drone Truthing and Functional Classification of Remote Sensing Time Series for Supervised Vegetation Mapping |
| title_short | Integrating Drone Truthing and Functional Classification of Remote Sensing Time Series for Supervised Vegetation Mapping |
| title_sort | integrating drone truthing and functional classification of remote sensing time series for supervised vegetation mapping |
| topic | phytosociology vegetation mapping remote sensing time series MFPCA ground truthing drone |
| url | https://www.mdpi.com/2072-4292/17/2/330 |
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