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Remote Sens. 2014, 6(4), 3427-3445; doi:10.3390/rs6043427

Estimating Fractional Shrub Cover Using Simulated EnMAP Data: A Comparison of Three Machine Learning Regression Techniques

Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, D-10099 Berlin, Germany
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Received: 16 January 2014 / Revised: 11 April 2014 / Accepted: 14 April 2014 / Published: 22 April 2014
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
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Abstract

Anthropogenic interventions in natural and semi-natural ecosystems often lead to substantial changes in their functioning and may ultimately threaten ecosystem service provision. It is, therefore, necessary to monitor these changes in order to understand their impacts and to support management decisions that help ensuring sustainability. Remote sensing has proven to be a valuable tool for these purposes, and especially hyperspectral sensors are expected to provide valuable data for quantitative characterization of land change processes. In this study, simulated EnMAP data were used for mapping shrub cover fractions along a gradient of shrub encroachment, in a study region in southern Portugal. We compared three machine learning regression techniques: Support Vector Regression (SVR); Random Forest Regression (RF); and Partial Least Squares Regression (PLSR). Additionally, we compared the influence of training sample size on the prediction performance. All techniques showed reasonably good results when trained with large samples, while SVR always outperformed the other algorithms. The best model was applied to produce a fractional shrub cover map for the whole study area. The predicted patterns revealed a gradient of shrub cover between regions affected by special agricultural management schemes for nature protection and areas without land use incentives. Our results highlight the value of EnMAP data in combination with machine learning regression techniques for monitoring gradual land change processes. View Full-Text
Keywords: EnMAP; hyperspectral; land cover; partial least squares regression; random forest regression; shrub encroachment; Portugal; sub-pixel mapping; support vector regression EnMAP; hyperspectral; land cover; partial least squares regression; random forest regression; shrub encroachment; Portugal; sub-pixel mapping; support vector regression
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Schwieder, M.; Leitão, P.J.; Suess, S.; Senf, C.; Hostert, P. Estimating Fractional Shrub Cover Using Simulated EnMAP Data: A Comparison of Three Machine Learning Regression Techniques. Remote Sens. 2014, 6, 3427-3445.

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