Abstract
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.
Highlights
As scientists shift toward viewing the earth as a single interconnected system [1], understanding forest dynamics and the complex relationships with human societies becomes more and more pertinent
It is worth noting that TC Angle (TCA), which we have classed as a greenness index, appears to be more capable than the other greenness indices in capturing fire disturbances one year after the event
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Summary
As scientists shift toward viewing the earth as a single interconnected system [1], understanding forest dynamics and the complex relationships with human societies becomes more and more pertinent. This type of analysis requires a multidisciplinary approach integrating local, regional, and global knowledge [2]. The opening of the image archive in 2008, along with the advances in computer processing, has led to a plethora of new and novel applications exploiting Landsat time-series [3]. Using a time-series, rather than image pairs, allows for change to be differentiated from background noise, whilst capturing longer-term ecological trends [4]
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