Predicting plant water content in Eucalyptus grandis forest stands in KwaZulu-Natal, South Africa using field spectra resampled to the Sumbandila Satellite Sensor

摘要

The measurement of plant water content is essential to assess stress and disturbance in forest plantations. Traditional techniques to assess plant water content are costly, time consuming and spatially restrictive. Remote sensing techniques offer the alternative of a non-destructive and instantaneous method of assessing plant water content over large spatial scales where ground measurements would be impossible on a regular basis. In the context of South Africa, due to the cost and availability of imagery, studies focusing on the estimation of plant water content using remote sensing data have been limited. With the scheduled launch of the South African satellite SumbandilaSat evident in 2009, it is imperative to test the utility of this satellite in estimating plant water content. This study resamples field spectral data measured from a field spectrometer to the band settings of the SumbandilaSat in order to test its potential in estimating plant water content in a Eucalyptus plantation. The resampled SumbandilaSat wavebands were input into a neural network due to its ability to model non-linearity in a dataset and its inherent ability to perform better than conventional linear models. The integrated approach involving neural networks and the resampled field spectral data successfully predicted plant water content with a correlation coefficient of 0.74 and a root mean square error (RMSE) of 1.41% on an independent test dataset outperforming the traditional multiple regression method of estimation. The best-trained neural network algorithm that was chosen for assessing the relationship between plant water content and the SumbandilaSat bands was based on a few points only and more research is required to test the robustness and effectiveness of this sensor in estimating plant water content across different species and seasons. This is critical for monitoring plantation health in South Africa using a cheaply available local sensor containing key vegetation wavelengths.