Estimating forest crown closure using Hyperion hyperspectral data

In this paper,the capability of EO-1 Hyperion data in the estimation of forest crown closure(FCC) was assessed.A comparison of two feature extraction methods was made for estimating FCC.The methods are individual band selection(BS) and principal component analysis(PCA),which were used to calculate plot image signature:nearest pixel value(NP) and mean value of pixels of 3 by 3 windows(W33).Therefore,four methods were used to estimate FCC,which were BS-NP,BS-W33,PCA-NP and PCA-W33.Hyperion data were acquired on July 14,2001.A total of 200 FCC field sample plots were selected from the forest resources dynamic map,of which 130 for developing model and 70 for validation.The analysis procedure consists of:1) preprocessing of hyperspectral data,including vertical stripes removing,smile effect reduction and atmospheric correction;2) extracting features with the two methods:BS and PCA using stepwise regression technique;3)establishing multivariate regression prediction models and predicting forest crown closure;4) validating the FCC estimating results with the sample plot data.The accuracy for BS-NP,BS-W33,PCA-NP and PCA-W33 were 83.17%,84.21%,85.62% and 86.34% respectively.The primary results indicate that the features extracted with PCA method are more effective for estimating FCC than those with BS method.Results also prove that in predicting and mapping FCC,W33 method has higher precision than the nearest pixel method.