P19: Vegetation height estimation | |
Maturity score | |
Mean: 2.5 | STD: 0.65 |
Constraints and limitations · Cloud presence · High cost of VHR satellite imagery · The machine learning models are limited to regions with similar vegetation characteristics where it was trained. · Uncertainty related to machine learning models | |
Relevant user needs UN37: Projection of risk to portfolio assets into the future. | |
R&D gaps · Lack of time series ground truth data (Light Detection and Ranging (LIDAR)) | |
Potential improvements drivers · Provide training datasets for different vegetation types over different regions in the world | |
Utilisation level review | |
Utilisation score | |
Mean: 2.40 | STD: 0.49 |
No utilisation Low utilisation · The use of the Global Ecosystem Dynamics Investigation (GEDI) sensor to assess carbon capture in standing/planted forests which are part of an offset mechanism. · Unawareness of the existence of commercial EO products with better specifications Medium utilisation Higher cost of using the best available commercial EO product High utilisation | |
Critical gaps related to relevant user needs | |
Guideline gap UN37: Projection of risk to portfolio assets into the future | |
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