Abstract

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 plot-based 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 regressions with an automatic feature selection. Second, we predict all models to the entire landscape. Third, we derive a land-cover map including the four considered vegetation types. We then use this land-cover map to assign the correct vegetation type-specific biomass estimate to each pixel according to one of the four considered vegetation types. Using a single repeatable workflow, we obtained biomass predictions comparable to earlier studies focusing on only one of the four vegetation types (Spearman correlation between 0.80 and 0.84; normalized-RMSE below 16 % for all vegetation types). For all woody vegetation types, height metrics were amongst the selected predictors, while for grasslands, only Sentinel-2 bands were selected. The land-cover was also mapped with high accuracy (OA = 83.1 %). The final landscape-level biomass map spatially agrees well with the known biomass distribution patterns in the watershed. Progressing from vegetation-type specific maps towards landscape-level biomass maps is an essential step towards integrating remote-sensing based biomass estimates into models for water and carbon management.

Highlights

  • The regular assessment of vegetation biomass is important for quantifying carbon stocks, which contributes to an improved under­ standing of carbon cycling (Houghton, 2005) and the sustainable use of biomass as energy-source and for forest inventory tasks (Koch, 2010)

  • 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 light detection and ranging (LiDAR) data to map grassland, shrubland, native forests and pine plantation biomass using random forest regressions with an automatic feature selection

  • Shrublands, pine plantations, and native forests

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Summary

Introduction

The regular assessment of vegetation biomass is important for quantifying carbon stocks, which contributes to an improved under­ standing of carbon cycling (Houghton, 2005) and the sustainable use of biomass as energy-source and for forest inventory tasks (Koch, 2010). Information on vegeta­ tion structure obtained by optical photogrammetric approaches using structure-from-motion algorithms with spaceborne (e.g., Fassnacht et al, 2017; Persson et al, 2013) and airborne data (e.g., Messinger et al, 2016; Ota et al, 2015) has been successfully applied to estimate forest biomass at local to regional scales. These studies often reported similar accuracies as studies based on more cost-intensive LiDAR data. For grassland and shrubland ecosystems, spectral information from spaceborne sensors alone can deliver reasonable biomass estimates (Yang et al, 2018a; Viana et al, 2012)

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