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Using GEDI as training data for an ongoing mapping of landscape-scale dynamics of the plant area index
Leaf or plant area index (LAI, PAI) information is frequently used to describe vegetation structure in environmental science. While field measurements are time-consuming and do not scale to landscapes, model-based air- or space-borne remote-sensing methods have been used for many years for area-w...
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| Autoren: | , , , , , |
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| Format: | Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
Philipps-Universität Marburg
2023
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| Schlagworte: | |
| Online Zugang: | PDF-Volltext |
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| Zusammenfassung: | Leaf or plant area index (LAI, PAI) information is frequently used to describe vegetation structure
in environmental science. While field measurements are time-consuming and do not scale to
landscapes, model-based air- or space-borne remote-sensing methods have been used for many
years for area-wide monitoring. As of 2019, NASA’s Global Ecosystem Dynamics Investigation
(GEDI) mission delivers a point-based LAI product with 25 m footprints and periodical repetition.
This opens up new possibilities in integrating GEDI as frequently generated training samples with
high resolution (spectral) sensors. However, the foreseeable duration of the system installed on the
ISS is limited. In this study we want to test the potential of GEDI for regional comprehensive LAI
estimations throughout the year with a focus on its usability beyond the lifespan of the GEDI
mission. We study the landscape of Hesse, Germany, with its pronounced seasonal changes.
Assuming a relationship between GEDI’s PAI and Sentinel-1 and -2 data, we used a Random Forest
approach together with spatial variable selection to make predictions for new Sentinel scenes. The
model was trained with two years of GEDI PAI data and validated against a third year to provide a
robust and temporally independent model validation. This ensures the applicability of the
validation for years outside the training period, reaching a total RMSE of 1.12. Predictions for the
test year showed the expected seasonal and spatial patterns indicated by RMSE values ranging
between 0.75 and 1.44, depending on the land cover class. The overall prediction performance
shows good agreement with the test data set of the independent year which supports our
assumption that the usage of GEDI’s PAI beyond the mission lifespan is feasible for regional studies. |
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| Beschreibung: | Gefördert durch den Open-Access-Publikationsfonds der UB Marburg. |
| DOI: | 10.1088/1748-9326/acde8f |