Confirmation of post-harvest spectral recovery from Landsat time series using measures of forest cover and height derived from airborne laser scanning data

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White , J C , Saarinen , N , Kankare , V , Wulder , M A , Hermosilla , T , Coops , N C , Pickell , P D , Holopainen , M , Hyyppä , J & Vastaranta , M 2018 , ' Confirmation of post-harvest spectral recovery from Landsat time series using measures of forest cover and height derived from airborne laser scanning data ' , Remote Sensing of Environment , vol. 216 , pp. 262-275 . https://doi.org/10.1016/j.rse.2018.07.004

Title: Confirmation of post-harvest spectral recovery from Landsat time series using measures of forest cover and height derived from airborne laser scanning data
Author: White, Joanne C.; Saarinen, Ninni; Kankare, Ville; Wulder, Michael A.; Hermosilla, Txomin; Coops, Nicholas C.; Pickell, Paul D.; Holopainen, Markus; Hyyppä, Juha; Vastaranta, Mikko
Contributor: University of Helsinki, Department of Forest Sciences
University of Helsinki, Department of Forest Sciences
University of Helsinki, Department of Forest Sciences
University of Helsinki, University of Eastern Finland
Date: 2018-10
Language: eng
Number of pages: 14
Belongs to series: Remote Sensing of Environment
ISSN: 0034-4257
URI: http://hdl.handle.net/10138/253485
Abstract: Landsat time series (LTS) enable the characterization of forest recovery post-disturbance over large areas; however, there is a gap in our current knowledge concerning the linkage between spectral measures of recovery derived from LTS and actual manifestations of forest structure in regenerating stands. Airborne laser scanning (ALS) data provide useful measures of forest structure that can be used to corroborate spectral measures of forest recovery. The objective of this study was to evaluate the utility of a spectral index of recovery based on the Normalized Burn Ratio (NBR): the years to recovery, or Y2R metric, as an indicator of the return of forest vegetation following forest harvest (clearcutting). The Y2R metric has previously been defined as the number of years required for a pixel to return to 80% of its pre-disturbance NBR (NBRpre) value. In this study, the Composite2Change (C2C) algorithm was used to generate a time series of gap-free, cloud-free Landsat surface reflectance composites (1985–2012), associated change metrics, and a spatially-explicit dataset of detected changes for an actively managed forest area in southern Finland (5.3 Mha). The overall accuracy of change detection, determined using independent validation data, was 89%. Areas of forest harvesting in 1991 were then used to evaluate the Y2R metric. Four alternative recovery scenarios were evaluated, representing variations in the spectral threshold used to define Y2R: 60%, 80%, and 100% of NBRpre, and a critical value of z (i.e. the year in which the pixel's NBR value is no longer significantly different from NBRpre). The Y2R for each scenario were classified into five groups: recovery within 17 years, and not recovered. Measures of forest structure (canopy height and cover) were obtained from ALS data. Benchmarks for height (>5 m) and canopy cover (>10%) were applied to each recovery scenario, and the percentage of pixels that attained both of these benchmarks for each recovery group, was determined for each Y2R scenario. Our results indicated that the Y2R metric using the 80% threshold provided the most realistic assessment of forest recovery: all pixels considered in our analysis were spectrally recovered within the analysis period, with 88.88% of recovered pixels attaining the benchmarks for both cover and height. Moreover, false positives (pixels that had recovered spectrally, but not structurally) and false negatives (pixels that had recovered structurally, but not spectrally) were minimized with the 80% threshold. This research demonstrates the efficacy of LTS-derived assessments of recovery, which can be spatially exhaustive and retrospective, providing important baseline data for forest monitoring.
Subject: Landsat
Time series
Recovery
Regeneration
Harvest
ALS
Lidar
Boreal
BOREAL FORESTS
DISTURBANCE DETECTION
DETECTING TRENDS
IMAGERY
DYNAMICS
LIDAR
REFLECTANCE
WILDFIRE
ACCURACY
REGROWTH
1172 Environmental sciences
218 Environmental engineering
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