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Using Sentinel-2 and canopy height models to derive a landscape-level biomass map covering multiple vegetation types

Fassnacht, Fabian Ewald; Poblete-Olivares, Javiera; Rivero, Lucas; Lopatin, Javier; Ceballos-Comisso, Andrés; Galleguillos, Mauricio

Abstract (englisch):
Vegetation biomass is a globally important climate-relevant terrestrial carbon pool and also drives local hydrological
systems via evapotranspiration. Vegetation biomass of individual vegetation types has been successfully
estimated from active and passive remote sensing data. However, for many tasks, landscape-level biomass
maps across several vegetation types are more suitable than biomass maps of individual vegetation types. For
example, the validation of ecohydrological models and carbon budgeting typically requires spatially continuous
biomass estimates, independent from vegetation type. Studies that derive biomass estimates across multiple
vegetation or land-cover types to merge them into a single landscape-level biomass map are still scarce, and
corresponding workflows must be developed. Here, we present a workflow to derive biomass estimates on
landscape-level for a large watershed in central Chile. Our workflow has three steps: First, we combine field plotbased
biomass estimates with spectral and structural information collected from Sentinel-2, TanDEM-X and
airborne LiDAR data to map grassland, shrubland, native forests and pine plantation biomass using random forest
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Verlagsausgabe §
DOI: 10.5445/IR/1000124330
Veröffentlicht am 07.10.2020
Originalveröffentlichung
DOI: 10.1016/j.jag.2020.102236
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Geographie und Geoökologie (IFGG)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 02.2021
Sprache Englisch
Identifikator ISSN: 0303-2434
KITopen-ID: 1000124330
Erschienen in International journal of applied earth observation and geoinformation
Band 94
Seiten Art.-Nr.: 102236
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