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Browsing by Author "Cohen, Warren B."

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    Current status of Landsat program, science, and applications
    Wulder, Michael A.; Loveland, Thomas R.; Roy, David P.; Crawford, Christopher J.; Masek, Jeffrey G.; Woodcock, Curtis E.; Allen, Richard G.; Anderson, Martha C.; Belward, Alan S.; Cohen, Warren B.; Dwyer, John; Erb, Angela; Gao, Feng; Griffiths, Patrick; Helder, Dennis; Hermosilla, Txomin; Hipple, James D.; Hostert, Patrick; Hughes, M. Joseph; Huntington, Justin; Johnson, David M.; Kennedy, Robert E.; Kilic, Ayse; Li, Zhan; Lymburner, Leo; McCorkel, Joel; Pahlevan, Nima; Scambos, Theodore A.; Schaaf, Crystal; Schott, John R.; Sheng, Yongwei; Storey, James; Vermote, Eric; Vogelmann, James E.; White, Joanne C.; Wynne, Randolph H.; Zhu, Zhe (Elsevier Inc., 2019-03-11)
    Formal planning and development of what became the first Landsat satellite commenced over 50 years ago in 1967. Now, having collected earth observation data for well over four decades since the 1972 launch of Landsat-1, the Landsat program is increasingly complex and vibrant. Critical programmatic elements are ensuring the continuity of high quality measurements for scientific and operational investigations, including ground systems, acquisition planning, data archiving and management, and provision of analysis ready data products. Free and open access to archival and new imagery has resulted in a myriad of innovative applications and novel scientific insights. The planning of future compatible satellites in the Landsat series, which maintain continuity while incorporating technological advancements, has resulted in an increased operational use of Landsat data. Governments and international agencies, among others, can now build an expectation of Landsat data into a given operational data stream. International programs and conventions (e.g., deforestation monitoring, climate change mitigation) are empowered by access to systematically collected and calibrated data with expected future continuity further contributing to the existing multi-decadal record. The increased breadth and depth of Landsat science and applications have accelerated following the launch of Landsat-8, with significant improvements in data quality. Herein, we describe the programmatic developments and institutional context for the Landsat program and the unique ability of Landsat to meet the needs of national and international programs. We then present the key trends in Landsat science that underpin many of the recent scientific and application developments and follow-up with more detailed thematically organized summaries. The historical context offered by archival imagery combined with new imagery allows for the development of time series algorithms that can produce information on trends and dynamics. Landsat-8 has figured prominently in these recent developments, as has the improved understanding and calibration of historical data. Following the communication of the state of Landsat science, an outlook for future launches and envisioned programmatic developments are presented. Increased linkages between satellite programs are also made possible through an expectation of future mission continuity, such as developing a virtual constellation with Sentinel-2. Successful science and applications developments create a positive feedback loop—justifying and encouraging current and future programmatic support for Landsat. © 2019
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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.