The Performance of Random Forests in an Operational Setting for Large Area Sclerophyll Forest Classification

Andrew Mellor,Andrew Haywood,Christine Stone,Simon Jones

doi:10.3390/rs5062838

Abstract

Mapping and monitoring forest extent is a common requirement of regional forest inventories and public land natural resource management, including in Australia. The state of Victoria, Australia, has approximately 7.2 million hectares of mostly forested public land, comprising ecosystems that present a diverse range of forest structures, composition and condition. In this paper, we evaluate the performance of the Random Forest (RF) classifier, an ensemble learning algorithm that has recently shown promise using multi-spectral satellite sensor imagery for large area feature classification. The RF algorithm was applied using selected Landsat Thematic Mapper (TM) imagery metrics and auxiliary terrain and climatic variables, while the reference data was manually extracted from systematically distributed plots of sample aerial photography and used for training (75%) and accuracy (25%) assessment. The RF algorithm yielded an overall accuracy of 96% and a Kappa statistic of 0.91 (confidence interval (CI) 0.909–0.919) for the forest/non-forest classification model, given a Kappa maximised binary threshold value of 0.5. The area under the receiver operating characteristic plot produced a score of 0.91, also indicating high model performance. The framework described in this study contributes to the operational deployment of a robust, but affordable, program, able to collate and process large volumes of multi-sourced data using open-source software for the production of consistent and accurate forest cover maps across the full spectrum of Victorian sclerophyll forest types.

Highlights

Forest extent is a measure commonly assessed in national forest inventories (NFI) [1] and, under the Montreal process [2], is a specific indicator used for monitoring and reporting sustainable forest management
We evaluate the operational performance and utility of Random Forests (RF) for classifying forest extent across Victoria, Australia, using remote sensing, topographic and climate predictor variables
While studies on the production of forest and land cover maps derived from RF classification techniques using multi-source remote sensing and ancillary data are published routinely in the academic literature, a secondary objective of this paper is to describe a framework for operational implementation of the RF algorithm using open-source software

Summary

Introduction

Forest extent is a measure commonly assessed in national forest inventories (NFI) [1] and, under the Montreal process [2], is a specific indicator used for monitoring and reporting sustainable forest management. In Australia, under the Australian National Forest Inventory, forest is defined as “A land area, incorporating all living and non-living components, dominated by trees having usually a single stem and a mature or potentially mature stand height exceeding two metres and with existing or potential crown cover of overstory strata about equal to or greater than 20 percent. This definition includes native forests and plantations and areas of trees that are sometimes described as woodlands” [4]. The structural components in this definition encompass a wide range of forest types, from open low sparse canopy woodland to tall dense canopy forests (as illustrated by Figure 1, [5])

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