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http://dx.doi.org/10.7780/kjrs.2020.36.3.1

Detection of Decay Leaf Using High-Resolution Satellite Data  

Sim, Suyoung (Major of Spatial Information Engineering, Division of Earth Environmental Science, Pukyong National University)
Jin, Donghyun (Major of Spatial Information Engineering, Division of Earth Environmental Science, Pukyong National University)
Seong, Noh-hun (Major of Spatial Information Engineering, Division of Earth Environmental Science, Pukyong National University)
Lee, Kyeong-sang (Major of Spatial Information Engineering, Division of Earth Environmental Science, Pukyong National University)
Seo, Minji (Major of Spatial Information Engineering, Division of Earth Environmental Science, Pukyong National University)
Choi, Sungwon (Major of Spatial Information Engineering, Division of Earth Environmental Science, Pukyong National University)
Jung, Daeseong (Major of Spatial Information Engineering, Division of Earth Environmental Science, Pukyong National University)
Han, Kyung-soo (Major of Spatial Information Engineering, Division of Earth Environmental Science, Pukyong National University)
Publication Information
Korean Journal of Remote Sensing / v.36, no.3, 2020 , pp. 401-410 More about this Journal
Abstract
Recently, many studies have been conducted on the changing phenology on the Korean Peninsula due to global warming. However, because of the geographical characteristics, research on plant season in autumn, which is difficult to measure compared to spring season, is insufficient. In this study, all leaves that maple and fallen leaves were defined as 'Decay leaves' and decay leaf detection was performed based on the Landsat-8 satellite image. The first threshold value of decay leaves was calculated by using NDVI and the secondary threshold value of decay leaves was calculated using by NDWI and the difference of spectral characteristics with green leaves. POD, FAR values were used to verify accuracy of the dry leaf detection algorithm in this study, and the results showed high accuracy with POD of 98.619 and FAR of 1.203.
Keywords
Phenology; Landsat-8; NDVI; Climate change;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
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