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Open AccessArticle

Using Landsat Spectral Indices in Time-Series to Assess Wildfire Disturbance and Recovery

1
School of Science, RMIT University, Melbourne, VIC 3000, Australia
2
Faculty for Geo-Information Science and Earth Observation (ITC), University of Twente, 7522 NB Enschede, The Netherlands
3
Cooperative Research Centre for Spatial Information (CRCSI), Carlton, VIC 3053, Australia
4
Department of Environmental Science, Macquarie University, Sydney, NSW 2109, Australia
5
European Forest Institute, Barcelona 08025, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(3), 460; https://doi.org/10.3390/rs10030460
Received: 24 January 2018 / Revised: 4 March 2018 / Accepted: 13 March 2018 / Published: 15 March 2018
(This article belongs to the Section Forest Remote Sensing)
Satellite earth observation is being increasingly used to monitor forests across the world. Freely available Landsat data stretching back four decades, coupled with advances in computer processing capabilities, has enabled new time-series techniques for analyzing forest change. Typically, these methods track individual pixel values over time, through the use of various spectral indices. This study examines the utility of eight spectral indices for characterizing fire disturbance and recovery in sclerophyll forests, in order to determine their relative merits in the context of Landsat time-series. Although existing research into Landsat indices is comprehensive, this study presents a new approach, by comparing the distributions of pre and post-fire pixels using Glass’s delta, for evaluating indices without the need of detailed field information. Our results show that in the sclerophyll forests of southeast Australia, common indices, such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), both accurately capture wildfire disturbance in a pixel-based time-series approach, especially if images from soon after the disturbance are available. However, for tracking forest regrowth and recovery, indices, such as NDVI, which typically capture chlorophyll concentration or canopy ‘greenness’, are not as reliable, with values returning to pre-fire levels in 3–5 years. In comparison, indices that are more sensitive to forest moisture and structure, such as NBR, indicate much longer (8–10 years) recovery timeframes. This finding is consistent with studies that were conducted in other forest types. We also demonstrate that additional information regarding forest condition, particularly in relation to recovery, can be extracted from less well known indices, such as NBR2, as well as textural indices incorporating spatial variance. With Landsat time-series gaining in popularity in recent years, it is critical to understand the advantages and limitations of the various indices that these methods rely on. View Full-Text
Keywords: Landsat; time-series; forest disturbance and recovery; spectral indices; wildfire; sclerophyll forests Landsat; time-series; forest disturbance and recovery; spectral indices; wildfire; sclerophyll forests
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MDPI and ACS Style

Hislop, S.; Jones, S.; Soto-Berelov, M.; Skidmore, A.; Haywood, A.; Nguyen, T.H. Using Landsat Spectral Indices in Time-Series to Assess Wildfire Disturbance and Recovery. Remote Sens. 2018, 10, 460.

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