
You have already added 0 works in your ORCID record related to the merged Research product.
You have already added 0 works in your ORCID record related to the merged Research product.
<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>
Ground Data are Essential for Biomass Remote Sensing Missions

Several remote sensing missions will soon produce detailed carbon maps over all terrestrial ecosystems. These missions are dependent on accurate and representative in situ datasets for the training of their algorithms and product validation. However, long-term ground-based forest-monitoring systems are limited, especially in the tropics, and to be useful for validation, such ground-based observation systems need to be regularly revisited and maintained at least over the lifetime of the planned missions. Here we propose a strategy for a coordinated and global network of in situ data that would benefit biomass remote sensing missions. We propose to build upon existing networks of long-term tropical forest monitoring. To produce accurate ground-based biomass estimates, strict data quality must be guaranteed to users. It is more rewarding to invest ground resources at sites where there currently is assurance of a long-term commitment locally and where a core set of data is already available. We call these ‘supersites’. Long-term funding for such an inter-agency endeavour remains an important challenge, and we here provide costing estimates to facilitate dialogue among stakeholders. One critical requirement is to ensure in situ data availability over the lifetime of remote sensing missions. To this end, consistent guidelines for supersite selection and management are proposed within the Forest Observation System, long-term funding should be assured, and principal investigators of the sites should be actively involved.
- Laboratoire Parole et Langage France
- University of Leeds United Kingdom
- University of Maryland, College Park United States
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement France
- University of Maryland, Department of Geographical Sciences United States
[SDE] Environmental Sciences, 550, Télédétection, [SDE.MCG]Environmental Sciences/Global Changes, in situ data, 333, forest, Écologie forestière, K01 - Foresterie - Considérations générales, Biomasse, Validation, Biomass, Forest, validation, biomass, Analyse de données, calibration, F40 - Ecologie végétale, cartographie des fonctions de la forêt, [SDE.BE] Environmental Sciences/Biodiversity and Ecology, séquestration du carbone, [SDE.MCG] Environmental Sciences/Global Changes, In situ data, [SDE]Environmental Sciences, Calibration, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, U40 - Méthodes de relevé, agrovoc: agrovoc:c_331583, agrovoc: agrovoc:c_1374847637217, agrovoc: agrovoc:c_6498, agrovoc: agrovoc:c_3044, agrovoc: agrovoc:c_926, agrovoc: agrovoc:c_15962
[SDE] Environmental Sciences, 550, Télédétection, [SDE.MCG]Environmental Sciences/Global Changes, in situ data, 333, forest, Écologie forestière, K01 - Foresterie - Considérations générales, Biomasse, Validation, Biomass, Forest, validation, biomass, Analyse de données, calibration, F40 - Ecologie végétale, cartographie des fonctions de la forêt, [SDE.BE] Environmental Sciences/Biodiversity and Ecology, séquestration du carbone, [SDE.MCG] Environmental Sciences/Global Changes, In situ data, [SDE]Environmental Sciences, Calibration, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, U40 - Méthodes de relevé, agrovoc: agrovoc:c_331583, agrovoc: agrovoc:c_1374847637217, agrovoc: agrovoc:c_6498, agrovoc: agrovoc:c_3044, agrovoc: agrovoc:c_926, agrovoc: agrovoc:c_15962
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).115 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
