Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?

Paul Schumacher,Martin Brandt,Thomas Koellner,Cyrus Samimi,Harald Zandler,Bunafsha Mislimshoeva,Alexander Brenning

doi:10.3390/rs8070540

Paul Schumacher, Martin Brandt + Show 5 more

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https://doi.org/10.3390/rs8070540

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Abstract

Remote sensing-based woody biomass quantification in sparsely-vegetated areas is often limited when using only common broadband vegetation indices as input data for correlation with ground-based measured biomass information. Red edge indices and texture attributes are often suggested as a means to overcome this issue. However, clear recommendations on the suitability of specific proxies to provide accurate biomass information in semi-arid to arid environments are still lacking. This study contributes to the understanding of using multispectral high-resolution satellite data (RapidEye), specifically red edge and texture attributes, to estimate wood volume in semi-arid ecosystems characterized by scarce vegetation. LASSO (Least Absolute Shrinkage and Selection Operator) and random forest were used as predictive models relating in situ-measured aboveground standing wood volume to satellite data. Model performance was evaluated based on cross-validation bias, standard deviation and Root Mean Square Error (RMSE) at the logarithmic and non-logarithmic scales. Both models achieved rather limited performances in wood volume prediction. Nonetheless, model performance increased with red edge indices and texture attributes, which shows that they play an important role in semi-arid regions with sparse vegetation.

Highlights

Standing biomass in semi-arid to arid regions plays a significant role in preventing soil erosion and degradation and can be considered as an important carbon pool due to the vast extent of drylands over the Earth’s land surface
This study uses high-resolution red edge and texture attributes retrieved from RapidEye satellite images to tackle the challenge of remote sensing-based wood volume estimation in semi-arid regions
We demonstrate that red edge indices and texture attributes improve the predictive performance in comparison to conventional methods limited to broadband vegetation indices (VIs)

Summary

Introduction

Standing biomass in semi-arid to arid regions plays a significant role in preventing soil erosion and degradation and can be considered as an important carbon pool due to the vast extent of drylands over the Earth’s land surface. As optical EO data alone cannot directly generate reliable quantitative biomass information [6], a common approach correlates satellite-derived parameters—primarily vegetation indices (VIs) measuring photosynthetic vigor—with ground-based measured biomass information, e.g., [7,8,9,10]. This allows an indirect prediction of quantitative biomass information

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