Above-ground biomass estimates based on active and passive microwave sensor imagery in low-biomass savanna ecosystems

Andreas Braun; Julia Wagner; Volker Hochschild

doi:10.1117/1.JRS.12.046027

11 December 2018 Above-ground biomass estimates based on active and passive microwave sensor imagery in low-biomass savanna ecosystems

Andreas Braun, Julia Wagner, Volker Hochschild

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Journal of Applied Remote Sensing, Vol. 12, Issue 4, 046027 (December 2018). https://doi.org/10.1117/1.JRS.12.046027

Abstract

Although many studies exist on the estimation and monitoring of above-ground biomass (AGB) of forest ecosystems by methods of remote sensing, very little research has been carried out for ecosystems of low primary production, such as grasslands, steppes, or savannas. Our study intends to approach this gap and investigates the correlation between space-borne radar information and AGB at the scale of 10 tons per hectare and below. Additionally, we introduce the integration of passive brightness temperature as an additional covariate for biomass estimation, based on the hypothesis that it contains information complementary to microwave backscatter of the active sensors. Our findings show that large-scale estimates of AGB can be conducted for grasslands and savannas at high accuracy (R² up to 0.52). Additionally, we found that the integration of passive radar can increase the quality of AGB estimates in terms of explained variance for selected cases. We hope that these indications are a starting point for more integrated approaches toward biomass estimations based on Earth observation methods.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Andreas Braun, Julia Wagner, and Volker Hochschild "Above-ground biomass estimates based on active and passive microwave sensor imagery in low-biomass savanna ecosystems," Journal of Applied Remote Sensing 12(4), 046027 (11 December 2018). https://doi.org/10.1117/1.JRS.12.046027

Received: 21 June 2018; Accepted: 13 November 2018; Published: 11 December 2018

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