Sparse regression for analyzing the development of foliar nutrient concentrations in coniferous trees

Abstract

Analyzing and predicting the development of foliar nutrient concentrations are important and challenging tasks in environmental monitoring. This article presents how linear sparse regression models can be used to represent the relations between different foliar nutrient concentration measurements of coniferous trees in consecutive years. In the experiments the models proved to be capable of providing relatively good and reliable predictions of the development of foliage with a considerably small number of regressors. Two methods for estimating sparse models were compared to more conventional linear regression models. Differences in the prediction accuracies between the sparse and full models were minor, but the sparse models were found to highlight important dependencies between the nutrient measurements better than the other regression models. The use of sparse models is, therefore, advantageous in the analysis and interpretation of the development of foliar nutrient concentrations.