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

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region? / Schumacher, Paul; Mislimshoeva, Bunafsha; Brenning, Alexander; Zandler, Harald; Brandt, Martin Stefan; Samimi, Cyrus; Koellner, Thomas.

In: Remote Sensing, Vol. 8, No. 7, 540, 2016.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Schumacher, P, Mislimshoeva, B, Brenning, A, Zandler, H, Brandt, MS, Samimi, C & Koellner, T 2016, 'Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?', Remote Sensing, vol. 8, no. 7, 540. https://doi.org/10.3390/rs8070540

APA

Schumacher, P., Mislimshoeva, B., Brenning, A., Zandler, H., Brandt, M. S., Samimi, C., & Koellner, T. (2016). Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region? Remote Sensing, 8(7), [540]. https://doi.org/10.3390/rs8070540

Vancouver

Schumacher P, Mislimshoeva B, Brenning A, Zandler H, Brandt MS, Samimi C et al. Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region? Remote Sensing. 2016;8(7). 540. https://doi.org/10.3390/rs8070540

Author

Schumacher, Paul ; Mislimshoeva, Bunafsha ; Brenning, Alexander ; Zandler, Harald ; Brandt, Martin Stefan ; Samimi, Cyrus ; Koellner, Thomas. / Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?. In: Remote Sensing. 2016 ; Vol. 8, No. 7.

Bibtex

@article{6d52f08761b3493786b2ff1cc253d288,
title = "Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?",
abstract = "Remote sensing-based woody biomass quantification in sparsely-vegetated areas is oftenlimited when using only common broadband vegetation indices as input data for correlation withground-based measured biomass information. Red edge indices and texture attributes are oftensuggested as a means to overcome this issue. However, clear recommendations on the suitability ofspecific proxies to provide accurate biomass information in semi-arid to arid environments are stilllacking. This study contributes to the understanding of using multispectral high-resolution satellitedata (RapidEye), specifically red edge and texture attributes, to estimate wood volume in semi-aridecosystems characterized by scarce vegetation. LASSO (Least Absolute Shrinkage and SelectionOperator) and random forest were used as predictive models relating in situ-measured abovegroundstanding wood volume to satellite data. Model performance was evaluated based on cross-validationbias, standard deviation and Root Mean Square Error (RMSE) at the logarithmic and non-logarithmicscales. Both models achieved rather limited performances in wood volume prediction. Nonetheless,model performance increased with red edge indices and texture attributes, which shows that theyplay an important role in semi-arid regions with sparse vegetation.",
author = "Paul Schumacher and Bunafsha Mislimshoeva and Alexander Brenning and Harald Zandler and Brandt, {Martin Stefan} and Cyrus Samimi and Thomas Koellner",
year = "2016",
doi = "10.3390/rs8070540",
language = "English",
volume = "8",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "M D P I AG",
number = "7",

}

RIS

TY - JOUR

T1 - Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?

AU - Schumacher, Paul

AU - Mislimshoeva, Bunafsha

AU - Brenning, Alexander

AU - Zandler, Harald

AU - Brandt, Martin Stefan

AU - Samimi, Cyrus

AU - Koellner, Thomas

PY - 2016

Y1 - 2016

N2 - Remote sensing-based woody biomass quantification in sparsely-vegetated areas is oftenlimited when using only common broadband vegetation indices as input data for correlation withground-based measured biomass information. Red edge indices and texture attributes are oftensuggested as a means to overcome this issue. However, clear recommendations on the suitability ofspecific proxies to provide accurate biomass information in semi-arid to arid environments are stilllacking. This study contributes to the understanding of using multispectral high-resolution satellitedata (RapidEye), specifically red edge and texture attributes, to estimate wood volume in semi-aridecosystems characterized by scarce vegetation. LASSO (Least Absolute Shrinkage and SelectionOperator) and random forest were used as predictive models relating in situ-measured abovegroundstanding wood volume to satellite data. Model performance was evaluated based on cross-validationbias, standard deviation and Root Mean Square Error (RMSE) at the logarithmic and non-logarithmicscales. Both models achieved rather limited performances in wood volume prediction. Nonetheless,model performance increased with red edge indices and texture attributes, which shows that theyplay an important role in semi-arid regions with sparse vegetation.

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

U2 - 10.3390/rs8070540

DO - 10.3390/rs8070540

M3 - Journal article

VL - 8

JO - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

IS - 7

M1 - 540

ER -

ID: 165842624