Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest Ecosystems
The study of vegetation phenology has great relevance in many fields since the importance of knowing timing and shifts in periodic plant life cycle events to face the consequences of global changes in issues such as crop production, forest management, ecosystem disturbances, and human health. The av...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/3/721 |
| _version_ | 1797484983787454464 |
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| author | Federico Filipponi Daniela Smiraglia Emiliano Agrillo |
| author_facet | Federico Filipponi Daniela Smiraglia Emiliano Agrillo |
| author_sort | Federico Filipponi |
| collection | DOAJ |
| description | The study of vegetation phenology has great relevance in many fields since the importance of knowing timing and shifts in periodic plant life cycle events to face the consequences of global changes in issues such as crop production, forest management, ecosystem disturbances, and human health. The availability of high spatial resolution and dense revisit time satellite observations, such as Sentinel-2 satellites, allows high resolution phenological metrics to be estimated, able to provide key information from time series and to discriminate vegetation typologies. This paper presents an automated and transferable procedure that combines validated methodologies based on local curve fitting and local derivatives to exploit full satellite Earth observation time series to produce information about plant phenology. Multivariate statistical analysis is performed for the purpose of demonstrating the capacity of the generated smoothed vegetation curve, temporal statistics, and phenological metrics to serve as temporal discriminants to detect forest ecosystems processes responses to environmental gradients. The results show smoothed vegetation curve and temporal statistics able to highlight seasonal gradient and leaf type characteristics to discriminate forest types, with additional information about forest and leaf productivity provided by temporal statistics analysis. Furthermore, temporal, altitudinal, and latitudinal gradients are obtained from phenological metrics analysis, which also allows to associate temporal gradient with specific phenophases that support forest types distinction. This study highlights the importance of integrated data and methodologies to support the processes of vegetation recognition and monitoring activities. |
| first_indexed | 2024-03-09T23:13:17Z |
| format | Article |
| id | doaj.art-fc2560a3c23046c7a1e56a5d88d42e2f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T23:13:17Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-fc2560a3c23046c7a1e56a5d88d42e2f2023-11-23T17:42:24ZengMDPI AGRemote Sensing2072-42922022-02-0114372110.3390/rs14030721Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest EcosystemsFederico Filipponi0Daniela Smiraglia1Emiliano Agrillo2Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Roma, ItalyItalian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Roma, ItalyItalian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Roma, ItalyThe study of vegetation phenology has great relevance in many fields since the importance of knowing timing and shifts in periodic plant life cycle events to face the consequences of global changes in issues such as crop production, forest management, ecosystem disturbances, and human health. The availability of high spatial resolution and dense revisit time satellite observations, such as Sentinel-2 satellites, allows high resolution phenological metrics to be estimated, able to provide key information from time series and to discriminate vegetation typologies. This paper presents an automated and transferable procedure that combines validated methodologies based on local curve fitting and local derivatives to exploit full satellite Earth observation time series to produce information about plant phenology. Multivariate statistical analysis is performed for the purpose of demonstrating the capacity of the generated smoothed vegetation curve, temporal statistics, and phenological metrics to serve as temporal discriminants to detect forest ecosystems processes responses to environmental gradients. The results show smoothed vegetation curve and temporal statistics able to highlight seasonal gradient and leaf type characteristics to discriminate forest types, with additional information about forest and leaf productivity provided by temporal statistics analysis. Furthermore, temporal, altitudinal, and latitudinal gradients are obtained from phenological metrics analysis, which also allows to associate temporal gradient with specific phenophases that support forest types distinction. This study highlights the importance of integrated data and methodologies to support the processes of vegetation recognition and monitoring activities.https://www.mdpi.com/2072-4292/14/3/721plant phenologyphenological metricsvegetationEO time series analysistemporal discriminantforest ecosystems |
| spellingShingle | Federico Filipponi Daniela Smiraglia Emiliano Agrillo Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest Ecosystems Remote Sensing plant phenology phenological metrics vegetation EO time series analysis temporal discriminant forest ecosystems |
| title | Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest Ecosystems |
| title_full | Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest Ecosystems |
| title_fullStr | Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest Ecosystems |
| title_full_unstemmed | Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest Ecosystems |
| title_short | Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest Ecosystems |
| title_sort | earth observation for phenological metrics eo4pm temporal discriminant to characterize forest ecosystems |
| topic | plant phenology phenological metrics vegetation EO time series analysis temporal discriminant forest ecosystems |
| url | https://www.mdpi.com/2072-4292/14/3/721 |
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