Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.5194/bg-23-4927-2026 |
Licence  |
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| Title (Primary) | Assessing forest biomass and productivity with data-driven vegetation indices: insights from 900 000 simulated forest stands |
| Author | Fischer, S.M.
; Fischer, R.; Huth, A. |
| Source Titel | Biogeosciences |
| Year | 2026 |
| Department | OESA; iDiv |
| Volume | 23 |
| Issue | 14 |
| Page From | 4927 |
| Page To | 4942 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Data and Software links | https://doi.org/10.5281/zenodo.16748241 |
| Supplements | Supplement 1 |
| Abstract | Vegetation indices (VIs) are widely used in remote sensing, but deriving novel VIs for estimating specific forest attributes remains challenging. Here, “data-driven” VIs, yielding information based on correlations identified in large datasets of forest and reflectance data, could help. In this study, we systematically consider simulated reflectances of temperate forests (400–2400 nm range) and evaluate their correlations to above-ground biomass, leaf area index (LAI), yearly gross primary production (GPP), and yearly net primary production (NPP) production (NPP). Considering 900 000 forest stands simulated via a classical forest model in combination with a radiative transfer model, we found that data-driven VIs could provide highly accurate estimates for the four analyzed forest attributes. Particularly VIs combining near infrared with shortwave infrared reflectances yielded good estimates. The wavelength combinations best suited for estimating above-ground biomass, LAI, and GPP showed considerable overlap. For less dense and structurally heterogeneous forests, reflectances from the visible band gained importance. We introduced a new class of “non-parametric” vegetation indices and compared them with linear indices in scenarios of different environmental and physiological variability. Both the functional form of the VIs as well as the variability did not primarily affect the achievable accuracy of the model estimates, rather than the range of wavelengths from which good indices could be constructed. This suggests that data-driven vegetation indices can yield valuable results if the wavelength choice is optimized. These findings open new pathways for utilizing recent hyperspectral satellite missions such as EnMAP or CHIME. |
| Fischer, S.M., Fischer, R., Huth, A. (2026): Assessing forest biomass and productivity with data-driven vegetation indices: insights from 900 000 simulated forest stands Biogeosciences 23 (14), 4927 - 4942 10.5194/bg-23-4927-2026 |
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