An Evaluation of Different Training Sample Allocation Schemes for Discrete and Continuous Land Cover Classification Using Decision Tree-Based Algorithms

René Colditz

doi:10.3390/rs70809655

Abstract

Land cover mapping for large regions often employs satellite images of medium to coarse spatial resolution, which complicates mapping of discrete classes. Class memberships, which estimate the proportion of each class for every pixel, have been suggested as an alternative. This paper compares different strategies of training data allocation for discrete and continuous land cover mapping using classification and regression tree algorithms. In addition to measures of discrete and continuous map accuracy the correct estimation of the area is another important criteria. A subset of the 30 m national land cover dataset of 2006 (NLCD2006) of the United States was used as reference set to classify NADIR BRDF-adjusted surface reflectance time series of MODIS at 900 m spatial resolution. Results show that sampling of heterogeneous pixels and sample allocation according to the expected area of each class is best for classification trees. Regression trees for continuous land cover mapping should be trained with random allocation, and predictions should be normalized with a linear scaling function to correctly estimate the total area. From the tested algorithms random forest classification yields lower errors than boosted trees of C5.0, and Cubist shows higher accuracies than random forest regression.

Highlights

Land cover classification from satellite images is one of the primary fields in remote sensing.Finer spatial resolution data (10–30 m), in particular from Landsat, has been widely used for regional studies of land cover and change, and very fine spatial resolution imagery (
Wall-to-wall mapping of large areas with 10–30 m data is expensive in terms of financial and computational resources, and there are only a few efforts for large areas, such as the National Land Cover Dataset (NLCD) of the United States [1], the National Land Cover of South
The coefficient values itself are all positive and indicate a sufficiently high correlation, i.e., the spatial patterns in Landsat and MODIS NDVI are closely related to each other. This finding is an important prerequisite for the following analysis as it permits a direct relation between Landsat-based NLCD2006 maps and MODIS

Summary

Introduction

Finer spatial resolution data (10–30 m), in particular from Landsat, has been widely used for regional studies of land cover and change, and very fine spatial resolution imagery (

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Conclusion