Mapping Aboveground Forest Biomass from Ikonos Satellite Image and Multi-Source Geospatial Data using Neural Networks and a Kriging Interpolation

Abstract

The present study develops a method for aboveground forest biomass mapping from Ikonos imagery and geospatial data. Reference biomass values by group of species were estimated using Ker's equations and inventory data from permanent sample plots (PEP) of 400 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . A supervised classification of the Ikonos image, based on the maximum likelihood method presenting the five species groups inventoried in the field study, was carried out. Thereafter, various vegetation indices and texture parameters were extracted from the Ikonos image. The extracted Ikonos data were then combined with geospatial data at the same 1 m spatial resolution. Inventory plots biomass values estimated by group of species were used for the neural networks model (Multi-layer Perceptron) training with the backpropagation algorithm. Thereafter, biomass values for sample pixels generated randomly by group of species were predicted with the Multi-layer Perceptron. Then, sample pixels biomass values of each group were used to derive biomass values of other pixels of the same species group by interpolation with the ordinary kriging method using five different variogram models. The Gaussian variogram model yielded the best biomass estimates by comparison with reference biomass values, with percentages of residual errors ranging between 2,6 and 9,8% (absolute value) and percentages of RMSE (root mean square error) ranging between 17.2 and 61.1%.