Volume III-8
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-8, 101-108, 2016
https://doi.org/10.5194/isprs-annals-III-8-101-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-8, 101-108, 2016
https://doi.org/10.5194/isprs-annals-III-8-101-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  07 Jun 2016

07 Jun 2016

OPTIMAL WAVELENGTH SELECTION ON HYPERSPECTRAL DATA WITH FUSED LASSO FOR BIOMASS ESTIMATION OF TROPICAL RAIN FOREST

T. Takayama1,2 and A. Iwasaki1 T. Takayama and A. Iwasaki
  • 1Dept. of Advanced Interdisciplinary Studies, University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo, Japan
  • 2Mitsubishi Research Institute, Inc., 2-10-3, Nagatacho, Chiyoda-ku, Tokyo, Japan

Keywords: Biomass, Hyperspectral, Forest Management, Fused Lasso, GLCM

Abstract. Above-ground biomass prediction of tropical rain forest using remote sensing data is of paramount importance to continuous large-area forest monitoring. Hyperspectral data can provide rich spectral information for the biomass prediction; however, the prediction accuracy is affected by a small-sample-size problem, which widely exists as overfitting in using high dimensional data where the number of training samples is smaller than the dimensionality of the samples due to limitation of require time, cost, and human resources for field surveys. A common approach to addressing this problem is reducing the dimensionality of dataset. Also, acquired hyperspectral data usually have low signal-to-noise ratio due to a narrow bandwidth and local or global shifts of peaks due to instrumental instability or small differences in considering practical measurement conditions. In this work, we propose a methodology based on fused lasso regression that select optimal bands for the biomass prediction model with encouraging sparsity and grouping, which solves the small-sample-size problem by the dimensionality reduction from the sparsity and the noise and peak shift problem by the grouping. The prediction model provided higher accuracy with root-mean-square error (RMSE) of 66.16 t/ha in the cross-validation than other methods; multiple linear analysis, partial least squares regression, and lasso regression. Furthermore, fusion of spectral and spatial information derived from texture index increased the prediction accuracy with RMSE of 62.62 t/ha. This analysis proves efficiency of fused lasso and image texture in biomass estimation of tropical forests.