A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands
Wetlands are a critical component of the landscape for climate mitigation, adaptation, biodiversity, and human health and prosperity. Keeping an eye on wetland vegetation is crucial due to it playing a major role in the planet’s carbon cycle and ecosystem management. By measuring the chlorophyll flu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/9/2392 |
| _version_ | 1797601769089400832 |
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| author | Maciej Bartold Marcin Kluczek |
| author_facet | Maciej Bartold Marcin Kluczek |
| author_sort | Maciej Bartold |
| collection | DOAJ |
| description | Wetlands are a critical component of the landscape for climate mitigation, adaptation, biodiversity, and human health and prosperity. Keeping an eye on wetland vegetation is crucial due to it playing a major role in the planet’s carbon cycle and ecosystem management. By measuring the chlorophyll fluorescence (ChF) emitted by plants, we can get a precise understanding of the current state and photosynthetic activity. In this study, we applied the Extreme Gradient Boost (XGBoost) algorithm to map ChF in the Biebrza Valley, which has a unique ecosystem in Europe for peatlands, as well as highly diversified flora and fauna. Our results revealed the advantages of using a set of classifiers derived from EO Sentinel-2 (S-2) satellite image mosaics to accurately map the spatio-temporal distribution of ChF in a terrestrial landscape. The validation proved that the XGBoost algorithm is quite accurate in estimating ChF with a good determination of 0.71 and least bias of 0.012. The precision of chlorophyll fluorescence measurements is reliant upon determining the optimal S-2 satellite overpass time, which is influenced by the developmental stage of the plants at various points during the growing season. Finally, the model performance results indicated that biophysical factors are characterized by greenness- and leaf-pigment-related spectral indices. However, utilizing vegetation indices based on extended periods of remote sensing data that better capture land phenology features can improve the accuracy of mapping chlorophyll fluorescence. |
| first_indexed | 2024-03-11T04:08:22Z |
| format | Article |
| id | doaj.art-d4fc8f8f09254121a23a90d6d7d6009d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T04:08:22Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d4fc8f8f09254121a23a90d6d7d6009d2023-11-17T23:39:34ZengMDPI AGRemote Sensing2072-42922023-05-01159239210.3390/rs15092392A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland WetlandsMaciej Bartold0Marcin Kluczek1Remote Sensing Centre, Institute of Geodesy and Cartography, 27 Modzelewskiego St., 02-679 Warszawa, PolandRemote Sensing Centre, Institute of Geodesy and Cartography, 27 Modzelewskiego St., 02-679 Warszawa, PolandWetlands are a critical component of the landscape for climate mitigation, adaptation, biodiversity, and human health and prosperity. Keeping an eye on wetland vegetation is crucial due to it playing a major role in the planet’s carbon cycle and ecosystem management. By measuring the chlorophyll fluorescence (ChF) emitted by plants, we can get a precise understanding of the current state and photosynthetic activity. In this study, we applied the Extreme Gradient Boost (XGBoost) algorithm to map ChF in the Biebrza Valley, which has a unique ecosystem in Europe for peatlands, as well as highly diversified flora and fauna. Our results revealed the advantages of using a set of classifiers derived from EO Sentinel-2 (S-2) satellite image mosaics to accurately map the spatio-temporal distribution of ChF in a terrestrial landscape. The validation proved that the XGBoost algorithm is quite accurate in estimating ChF with a good determination of 0.71 and least bias of 0.012. The precision of chlorophyll fluorescence measurements is reliant upon determining the optimal S-2 satellite overpass time, which is influenced by the developmental stage of the plants at various points during the growing season. Finally, the model performance results indicated that biophysical factors are characterized by greenness- and leaf-pigment-related spectral indices. However, utilizing vegetation indices based on extended periods of remote sensing data that better capture land phenology features can improve the accuracy of mapping chlorophyll fluorescence.https://www.mdpi.com/2072-4292/15/9/2392chlorophyll fluorescencewetlandsvegetation monitoringmachine learningbiodiversitySentinel-2 |
| spellingShingle | Maciej Bartold Marcin Kluczek A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands Remote Sensing chlorophyll fluorescence wetlands vegetation monitoring machine learning biodiversity Sentinel-2 |
| title | A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands |
| title_full | A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands |
| title_fullStr | A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands |
| title_full_unstemmed | A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands |
| title_short | A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands |
| title_sort | machine learning approach for mapping chlorophyll fluorescence at inland wetlands |
| topic | chlorophyll fluorescence wetlands vegetation monitoring machine learning biodiversity Sentinel-2 |
| url | https://www.mdpi.com/2072-4292/15/9/2392 |
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