Sensitivity analysis for predicting continuous fields of tree cover and fractional land cover distributions in cloud prone areas

Abstract

The use of multi-temporal data sets, such as vegetation index time series or phenological metrics, for improved classification and regression performance is well established in the remote sensing science community. However, the usefulness of such information is less apparent for areas with distinct wet season periods and heavily concentrated cloud cover. In view of this, this study examines the potential of multi-temporal data sets for the estimation of sub-pixel land cover fractions and tree density in an area having distinct wet and dry seasons. Prediction is based on a regression tree algorithm in combination with linear least squares regression planes, that relate multi-spectral and multi- temporal satellite data from the MODIS sensor to sub-pixel land cover proportions and tree cover densities, derived from high resolution land cover maps. Furthermore, several versions of the latter were produced by using different classification approaches in order to evaluate the sensitivity of the response variable on overall prediction accuracy. The results were evaluated according to absolute accuracy levels and according to their long-term inter-annual robustness by applying the regression models to MODIS data over a period of 11 years. The best regression model based on dry season information only, estimated continuous fields of tree density with a prediction error of less than 7% and an inter-annual variability of less than 4% over a time period of 11- years. The inclusion of intra-annual information did not contribute to any improvements in model accuracy compared to information from the dry season alone, and furthermore, deteriorated inter-annual robustness of model predictions. Aside from that, it has been shown that the quality of the response variable in the training data had significant effects on overall accuracy.