Fractional Snow Cover Mapping by Artificial Neural Networks and Support Vector Machines

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Date

2017-10-15

Author

ÇİFTÇİ, BORA BERKAY
KUTER, SEMİH
Akyürek, Sevda Zuhal
WEBER, GERHARD WİEHELM

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Snow is an important land cover whose distribution over space and time plays a significant role in various environmental processes. Hence, snow cover mapping with high accuracy is necessary to have a real understanding for present and future climate, water cycle, and ecological changes. This study aims to investigate and compare the design and use of artificial neural networks (ANNs) and support vector machines (SVMs) algorithms for fractional snow cover (FSC) mapping from satellite data. ANN and SVM models with different model building settings are trained by using Moderate Resolution Imaging Spectroradiometer surface reflectance values of bands 1-7, normalized difference snow index and normalized difference vegetation index as predictor variables. Reference FSC maps are generated from higher spatial resolution Landsat ETM+ binary snow cover maps. Results on the independent test data set indicate that the developed ANN model with hyperbolic tangent transfer function in the output layer and the SVM model with radial basis function kernel produce high FSC mapping accuracies with the corresponding values of R = 0.93 and R = 0.92, respectively.

Subject Keywords

Fractional Snow Cover Mapping, MODIS, Landsat ETM, Artificial Neural Networks, Multilayer Perceptron, Support Vector Machines, Support Vector Regression

URI

https://hdl.handle.net/11511/39893

DOI

https://doi.org/10.5194/isprs-annals-iv-4-w4-179-2017

Collections

Department of Civil Engineering, Conference / Seminar

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Citation Formats

B. B. ÇİFTÇİ, S. KUTER, S. Z. Akyürek, and G. W. WEBER, “Fractional Snow Cover Mapping by Artificial Neural Networks and Support Vector Machines,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39893.