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Remote Sens. 2016, 8(7), 540; doi:10.3390/rs8070540

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

1
Professorship of Ecological Services, Faculty of Biology, Chemistry and Geosciences, University of Bayreuth, 95440 Bayreuth, Germany
2
Department of Geography, Friedrich Schiller University, 07743 Jena, Germany
3
Institute of Geography, University of Bayreuth, 95440 Bayreuth, Germany
4
Department of Geosciences and Natural Resource Management, University of Copenhagen, 1350 Copenhagen, Denmark
5
Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, 95440 Bayreuth, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Sangram Ganguly, Compton Tucker, Clement Atzberger and Prasad S. Thenkabail
Received: 22 March 2016 / Revised: 13 June 2016 / Accepted: 14 June 2016 / Published: 24 June 2016
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
View Full-Text   |   Download PDF [10673 KB, uploaded 29 June 2016]   |  

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. View Full-Text
Keywords: woody biomass; wood volume estimation; semi-arid; RapidEye; red edge; texture woody biomass; wood volume estimation; semi-arid; RapidEye; red edge; texture
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Schumacher, P.; Mislimshoeva, B.; Brenning, A.; Zandler, H.; Brandt, M.; Samimi, C.; Koellner, T. Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region? Remote Sens. 2016, 8, 540.

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