Browsing by Author "Yang, Zhiqiang"

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    Edyn: Dynamic Signaling of Changes to Forests Using Exponentially Weighted Moving Average Charts
    Brooks, Evan B.; Yang, Zhiqiang; Thomas, Valerie A.; Wynne, Randolph H. (MDPI, 2017-08-24)
    Remote detection of forest disturbance remains a key area of interest for scientists and land managers. Subtle disturbances such as drought, disease, insect activity, and thinning harvests have a significant impact on carbon budgeting and forest productivity, but current change detection algorithms struggle to accurately identify them, especially over decadal timeframes. We introduce an algorithm called Edyn, which inputs a time series of residuals from harmonic regression into a control chart to signal low-magnitude, consistent deviations from the curve as disturbances. After signaling, Edyn retrains a new baseline curve. We compared Edyn with its parent algorithm (EWMACD—Exponentially Weighted Moving Average Change Detection) on over 3500 visually interpreted Landsat pixels from across the contiguous USA, with reference data for timing and type of disturbance. For disturbed forested pixels, Edyn had a mean per-pixel commission error of 31.1% and omission error of 70.0%, while commission and omission errors for EWMACD were 39.9% and 65.2%, respectively. Edyn had significantly less overall error than EWMACD (F1 = 0.19 versus F1 = 0.13). These patterns generally held for all of the reference data, including a direct comparison to other contemporary change detection algorithms, wherein Edyn and EWMACD were found to have lower omission error rates for a category of subtle changes over long periods.
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    Growth, Removals, and Management Intensity
    Wynne, Randolph H.; Thomas, Valerie A.; Bender, Stacie; Brooks, Evan B.; Coulston, John W.; Derwin, Jill M.; Gopalakrishnan, Ranjith; Green, Patrick; Harding, David; Sumnall, Matthew; Joshi, Pratik; Ranson, Jon; Schleeweis, Karen; Thomas, R. Quinn; Yang, Zhiqiang (2019-05-01)
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    How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms?
    Cohen, Warren B.; Healey, Sean P.; Yang, Zhiqiang; Stehman, Stephen V.; Brewer, C. Kenneth; Brooks, Evan B.; Gorelick, Noel; Huang, Chengqaun; Hughes, M. Joseph; Kennedy, Robert E.; Loveland, Thomas R.; Moisen, Gretchen G.; Schroeder, Todd A.; Vogelmann, James E.; Woodcock, Curtis E.; Yang, Limin; Zhu, Zhe (MDPI, 2017-03-26)
    Disturbance is a critical ecological process in forested systems, and disturbance maps are important for understanding forest dynamics. Landsat data are a key remote sensing dataset for monitoring forest disturbance and there recently has been major growth in the development of disturbance mapping algorithms. Many of these algorithms take advantage of the high temporal data volume to mine subtle signals in Landsat time series, but as those signals become subtler, they are more likely to be mixed with noise in Landsat data. This study examines the similarity among seven different algorithms in their ability to map the full range of magnitudes of forest disturbance over six different Landsat scenes distributed across the conterminous US. The maps agreed very well in terms of the amount of undisturbed forest over time; however, for the ~30% of forest mapped as disturbed in a given year by at least one algorithm, there was little agreement about which pixels were affected. Algorithms that targeted higher-magnitude disturbances exhibited higher omission errors but lower commission errors than those targeting a broader range of disturbance magnitudes. These results suggest that a user of any given forest disturbance map should understand the map’s strengths and weaknesses (in terms of omission and commission error rates), with respect to the disturbance targets of interest.