A fusion approach to forest disturbance mapping using time-series ensemble techniques

Hislop, S, Jones, S, Soto-Berelov, M, Skidmore, A, Haywood, A and Nguyen, T 2019, 'A fusion approach to forest disturbance mapping using time-series ensemble techniques', Remote Sensing of Environment, vol. 221, pp. 188-197.

Document type: Journal Article
Collection: Journal Articles

Title A fusion approach to forest disturbance mapping using time-series ensemble techniques
Author(s) Hislop, S
Jones, S
Soto-Berelov, M
Skidmore, A
Haywood, A
Nguyen, T
Year 2019
Journal name Remote Sensing of Environment
Volume number 221
Start page 188
End page 197
Total pages 10
Publisher Elsevier Inc.
Abstract Time series analysis of Landsat data is widely used for assessing forest change at the large-area scale. Various change detection algorithms have been proposed, each employing different techniques to characterise abrupt disturbance events and longer term trends. However, results can vary significantly, depending on the algorithm, parameters and the spectral index (or indices) chosen. This mismatch in results has led to researchers hypothesizing that an ensemble based approach may increase accuracy. In this study we assess two change detection algorithms (LandTrendr and the R package strucchange), each with three indices (the Normalized Difference Vegetation Index or NDVI, the Normalized Burn Ratio or NBR, and Tasseled Cap Wetness or TCW). We test their ability to detect abrupt disturbances in sclerophyll forests over a 29 year time period, and subsequently evaluate a number of ensembles, using simple fusion rules and Random Forests models. A total of 4087 manually interpreted reference pixels, sampled from 9 million ha of forest, were used for training and validation. In addition, we assess the effects of priming the Random Forests classifier with confusing cases (commission errors from the time series algorithms). Our results clearly show that ensembles combining multiple change detection techniques out-perform any one method. Our most accurate Random Forests model, using an ensemble of all 6 algorithm outputs, along with 3 bi-temporal change rasters (change in NBR, NDVI and TCW), had an overall error rate of 7%, compared with the most accurate single algorithm/index approach (LandTrendr with NBR), which had an overall error of 21%. Our findings also indicate that acceptable results (14% error) can be achieved without the use of traditional change detection algorithms, by using robust reference data and Random Forests classification. However, by priming the classifier with confusing cases informed by the change detection algorithms, commission errors decreased subs
Subject Photogrammetry and Remote Sensing
Forestry Management and Environment
Keyword(s) Landsat time series
Forest disturbance
Random Forests
DOI - identifier 10.1016/J.RSE.2018.11.025
Copyright notice © 2018 Elsevier Inc. All rights reserved.
ISSN 0034-4257
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