Leveraging Phenology to Assess Seasonal Variations of Plant Communities for Mapping Dynamic Ecosystems
Seasonally dynamic plant communities present challenges for remote mapping, but estimating phenology can help identify periods of peak spectral distinction. While phenology is widely used in environmental and agricultural mapping, its broader ecological applications remain underexplored. Using a tem...
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/10/1778 |
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| author | Thilina D. Surasinghe Kunwar K. Singh Lindsey S. Smart |
| author_facet | Thilina D. Surasinghe Kunwar K. Singh Lindsey S. Smart |
| author_sort | Thilina D. Surasinghe |
| collection | DOAJ |
| description | Seasonally dynamic plant communities present challenges for remote mapping, but estimating phenology can help identify periods of peak spectral distinction. While phenology is widely used in environmental and agricultural mapping, its broader ecological applications remain underexplored. Using a temperate wetland complex as a case study, we leveraged NDVI time series from Sentinel imagery to refine a wetland classification scheme by identifying periods of maximum plant community distinction. We estimated plant phenology with ground-reference points and mapped the study area using Random Forest (RF) with both Sentinel and PlanetScope imagery. Most plant communities showed distinct phenological variations between April–June (growing season) and September–October (transitional season). Merging phenologically similar communities improved classification accuracy, with April and September imagery yielding better results than the peak summer months. Combining both seasons achieved the highest classification accuracy (~77%), with key RF predictors including digital elevation, and near-infrared and tasseled cap indices. Despite its higher spatial resolution, PlanetScope underperformed compared to Sentinel, as spectral similarities between plant communities limited classification accuracy. While Sentinel provides valuable data, higher spectral resolution is needed for distinguishing similar plant communities. Integrating phenology into mapping frameworks can improve the detection of rare and ephemeral vegetation, aiding conservation efforts. |
| format | Article |
| id | doaj-art-d330ca9fa37646c4ad086bd2ed5e6eb4 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-d330ca9fa37646c4ad086bd2ed5e6eb42025-08-20T02:33:48ZengMDPI AGRemote Sensing2072-42922025-05-011710177810.3390/rs17101778Leveraging Phenology to Assess Seasonal Variations of Plant Communities for Mapping Dynamic EcosystemsThilina D. Surasinghe0Kunwar K. Singh1Lindsey S. Smart2Department of Biological Sciences, Bridgewater State University, Bridgewater, MA 02325, USAAidData, Global Research Institute, William & Mary, 400 Landrum Drive, Williamsburg, VA 23185, USAThe Nature Conservancy, World Office, Arlington, VA 22203, USASeasonally dynamic plant communities present challenges for remote mapping, but estimating phenology can help identify periods of peak spectral distinction. While phenology is widely used in environmental and agricultural mapping, its broader ecological applications remain underexplored. Using a temperate wetland complex as a case study, we leveraged NDVI time series from Sentinel imagery to refine a wetland classification scheme by identifying periods of maximum plant community distinction. We estimated plant phenology with ground-reference points and mapped the study area using Random Forest (RF) with both Sentinel and PlanetScope imagery. Most plant communities showed distinct phenological variations between April–June (growing season) and September–October (transitional season). Merging phenologically similar communities improved classification accuracy, with April and September imagery yielding better results than the peak summer months. Combining both seasons achieved the highest classification accuracy (~77%), with key RF predictors including digital elevation, and near-infrared and tasseled cap indices. Despite its higher spatial resolution, PlanetScope underperformed compared to Sentinel, as spectral similarities between plant communities limited classification accuracy. While Sentinel provides valuable data, higher spectral resolution is needed for distinguishing similar plant communities. Integrating phenology into mapping frameworks can improve the detection of rare and ephemeral vegetation, aiding conservation efforts.https://www.mdpi.com/2072-4292/17/10/1778plant phenologySentinelPlanetScopewetland ecosystem complexecosystem classification scheme |
| spellingShingle | Thilina D. Surasinghe Kunwar K. Singh Lindsey S. Smart Leveraging Phenology to Assess Seasonal Variations of Plant Communities for Mapping Dynamic Ecosystems Remote Sensing plant phenology Sentinel PlanetScope wetland ecosystem complex ecosystem classification scheme |
| title | Leveraging Phenology to Assess Seasonal Variations of Plant Communities for Mapping Dynamic Ecosystems |
| title_full | Leveraging Phenology to Assess Seasonal Variations of Plant Communities for Mapping Dynamic Ecosystems |
| title_fullStr | Leveraging Phenology to Assess Seasonal Variations of Plant Communities for Mapping Dynamic Ecosystems |
| title_full_unstemmed | Leveraging Phenology to Assess Seasonal Variations of Plant Communities for Mapping Dynamic Ecosystems |
| title_short | Leveraging Phenology to Assess Seasonal Variations of Plant Communities for Mapping Dynamic Ecosystems |
| title_sort | leveraging phenology to assess seasonal variations of plant communities for mapping dynamic ecosystems |
| topic | plant phenology Sentinel PlanetScope wetland ecosystem complex ecosystem classification scheme |
| url | https://www.mdpi.com/2072-4292/17/10/1778 |
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