Abstract
Accurate remote sensing of mountainous forest cover change is important for myriad social and ecological reasons, but is challenged by topographic and illumination conditions that can affect detection of forests. Several topographic illumination correction (TIC) approaches have been developed to mitigate these effects, but existing research has focused mostly on whether TIC improves forest cover classification accuracy and has usually found only marginal gains. However, the beneficial effects of TIC may go well beyond accuracy since TIC promises to improve detection of low illuminated forest cover and thereby normalize measurements of the amount, geographic distribution, and rate of forest cover change regardless of illumination. To assess the effects of TIC on the extent and geographic distribution of forest cover change, in addition to classification accuracy, we mapped forest cover across mountainous Nepal using a 25-year (1992–2016) gap-filled Landsat time series in two ways—with and without TIC (i.e., nonTIC)—and classified annual forest cover using a Random Forest classifier. We found that TIC modestly increased classifier accuracy and produced more conservative estimates of net forest cover change across Nepal (−5.2% from 1992–2016). TIC also resulted in a more even distribution of forest cover gain across Nepal with 3–5% more net gain and 4–6% more regenerated forest in the least illuminated regions. These results show that TIC helped to normalize forest cover change across varying illumination conditions with particular benefits for detecting mountainous forest cover gain. We encourage the use of TIC for satellite remote sensing detection of long-term mountainous forest cover change.
Highlights
Changes in global temperature and precipitation patterns are affecting the world’s mountains at an unprecedented rate [1,2,3,4]
topographic illumination correction (TIC) captured more net forest cover gain in IL 1–6 with the fastest gains occurring in the 1990s, which likely resulted from forest planting and expansion that started in the 1980s, while differences in IL 7–10 steadily decreased over the course of the study period as nonTIC captured ever more net forest cover gain in these strata. These results show the varying effects of TIC on forest cover measurement by year and by IL stratum, which are in part driven by specific kinds of forest cover change processes and patterns that vary by place and time
The temporally divergent effects of TIC on forest cover area measurements were only detectable through using a multi-decadal annual time series and are especially important to consider for accurate documentation of long-term forest cover change in mountainous or otherwise topographically complex regions
Summary
Changes in global temperature and precipitation patterns are affecting the world’s mountains at an unprecedented rate [1,2,3,4]. These changes have unfolding consequences for the estimated 23–28% of global forests in mountainous areas as well as the over 700 million people who depend on mountainous forests for timber and non-timber forest products for their livelihoods, food security, and sustainable development [5,6,7]. Most forest remote sensing studies to date (Table 1) have focused on evaluating the effect of TIC on classification accuracy using single-date satellite imagery (e.g., [17,26,27]) and have generally found that TIC improved overall forest cover classification accuracy by 1–3%
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